Stripping method after nonlinear superposition influence of warm discharged water of adjacent nuclear thermal power plant

Abstract

The invention discloses a stripping method after nonlinear superposition influence of thermal discharge of an adjacent nuclear thermal power plant, and the method comprises the steps: collecting water taking and discharge information, hydro meteorology and topographic data of a research power plant, and forming a basic data set; establishing and calibrating a hydrodynamic thermal mathematical model of the research water area to obtain a calibration model; the calibration model is adopted to carry out warm drainage simulation of operation of all the units, and whole-field temperature rise field data are obtained; after the concerned unit is closed, simulation is carried out based on the same calibration model, and half-field temperature rise field data are obtained; performing nonlinear stripping processing on the full-field temperature rise field data and the half-field temperature rise field data to obtain concerned set temperature rise field data; and performing statistical analysis on the concerned set temperature rise field data to obtain different temperature rise ranges and corresponding areas. According to the method, the problems of nonlinear superimposed effect and thermal cycle cumulative effect treatment of the warm discharged water are solved, and the warm discharged water stripping precision and the result reasonability are improved.

Description

Technical Field

[0001] The present invention belongs to the field of environmental science and engineering, and in particular, to a stripping method after the non-linear superposition effect of the warm wastewater discharged from adjacent nuclear and thermal power plants. Background Art

[0002] Nuclear power and thermal power are important components of China's energy system. With the advancement of policies, the number of adjacent nuclear and thermal power plant sites is gradually increasing, and the distance between adjacent sites is getting shorter. After taking water from natural water bodies through water intake ports for cooling, nuclear and thermal power plants discharge "warm wastewater" at a temperature higher than that of natural water bodies, and this warm wastewater will have an impact on the water area ecology and environment. The impact range of warm wastewater is an important indicator that the ecological environment management department and the natural resources management department focus on. The specification requires that the prediction deviation of the temperature rise range shall not exceed 5%. How to accurately strip the impact range of warm wastewater from each unit under the condition of adjacent nuclear and thermal power plants is of great significance for the pre-project demonstration and operation supervision.

[0003] At present, there is a relatively mature research foundation for warm wastewater simulation and evaluation technologies. The traditional hydrodynamic numerical model method can better simulate the diffusion process of warm wastewater under single or multiple drainage outlet conditions. This type of method usually uses the control equations in the Euler coordinate system to describe the transport process of warm wastewater, and uses the finite difference or finite volume method for numerical solution. In the treatment of time steps, most of them adopt the fixed time step strategy, and grid encryption is adopted near the drainage outlet to improve the accuracy. For the change of boundary conditions, the linear interpolation or step change method is commonly used. For the multi-drainage outlet scenario, researchers usually use the linear superposition principle to evaluate the comprehensive impact of warm wastewater from multiple drainage outlets, or independently simulate the impact range of a single drainage outlet.

[0004] However, the existing technical methods have obvious deficiencies in dealing with the stripping problem after the non-linear superposition of warm wastewater from adjacent nuclear and thermal power plants. First of all, the traditional linear stripping method ignores the non-linear superposition effect of warm wastewater in a complex flow field. Especially in the high temperature gradient region, the buoyancy effect caused by temperature will change the local flow field structure, making the impact of warm wastewater from multiple units not a simple linear superposition. Secondly, the conventional fixed time step strategy cannot simultaneously meet the warm wastewater simulation accuracy requirements in different stages of the tidal cycle. Especially at critical moments such as the tide conversion period, numerical oscillations or insufficient accuracy often occur. In addition, when the water intake port is within the impact range of warm wastewater, a heat cycle effect of drainage - water intake - re-drainage will be formed, and this heat accumulation process follows complex tide control laws. The existing methods lack effective means to accurately strip it. These technical deficiencies lead to significant deviations in the stripping of the non-linear superposition impact of warm wastewater from adjacent nuclear and thermal power plants, and it is difficult to meet the requirements of refined management. Summary of the Invention

[0005] Objective of the invention: To provide a method for stripping the non-linear superposition effect of the warm wastewater discharged from an adjacent nuclear power plant, with the expectation of solving at least one technical problem existing in the prior art.

[0006] Technical solution: A method for stripping the non-linear superposition effect of the warm wastewater discharged from an adjacent nuclear power plant, comprising:

[0007] Collect the intake and discharge information, hydrometeorological and topographic data of the power plant under study to form a basic data set;

[0008] Based on the basic data set, establish and calibrate the hydrodynamic and thermal mathematical model of the study water area to obtain a calibrated model;

[0009] Use the calibrated model to carry out the simulation of the warm wastewater discharge during the operation of all units to obtain the full-field temperature rise data;

[0010] After shutting down the unit of interest, carry out the simulation based on the same calibrated model to obtain the half-field temperature rise data;

[0011] Perform non-linear stripping on the full-field temperature rise data and the half-field temperature rise data to obtain the temperature rise data of the unit of interest;

[0012] Conduct statistical analysis on the temperature rise data of the unit of interest to obtain the different temperature rise ranges and corresponding areas, as well as the temperature rise at the intake water temperature rise or sensitive position of the unit.

[0013] Advantageous effects: The present invention solves the non-linear superposition effect of the warm wastewater discharge, avoids the numerical oscillation in the violently changing area of the conventional interpolation method, accurately strips the direct warm wastewater discharge effect and the thermal cycle cumulative effect, and improves the stripping accuracy of the warm wastewater discharge. Description of the drawings

[0014] Figure 1 It is a step flow chart of a method for stripping the non-linear superposition effect of the warm wastewater discharged from an adjacent nuclear power plant provided by an embodiment of the present application.

[0015] Figure 2 It is a step flow chart of performing non-linear stripping provided by an embodiment of the present application.

[0016] Figure 3 It is a step flow chart of constructing a streamline coordinate system provided by an embodiment of the present application.

[0017] Figure 4 It is a step flow chart of performing non-linear stripping in the streamline coordinate system provided by an embodiment of the present application. Detailed implementation

[0018] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solution in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] It should be particularly noted that, for clearly showing the step flow of the present application, numbers are marked for each step in the specification. These numbers are only for the convenience of description and do not limit the execution order of the steps. In actual operation, according to the technical requirements of the specific implementation scenario, the steps can be executed in an order different from that shown in the specification, and in some cases, parallel processing between steps can also be achieved.

[0020] As Figure 1 shown, a stripping method after the non - linear superposition effect of adjacent nuclear power plant cooling water discharge includes the following steps:

[0021] S1. Collect the intake and discharge information, hydro - meteorological and topographic data of the research power plant to form a basic data set;

[0022] Specifically, the intake and discharge information includes the structure and location information of the intake and discharge structures and the discharge flow rate and temperature rise data. Among them, the structure and location information of the intake and discharge structures can be parameters such as the coordinates of the intake, the coordinates of the discharge, the shape and size of the intake and discharge structures, and the water depth; the discharge flow rate and temperature rise data can be parameters such as the maximum discharge flow rate under the design condition, the average discharge flow rate under the normal condition, and the discharge temperature rise. The hydro - meteorological data includes the hydro - observation data and meteorological data of the research water area. The hydro - observation data can be parameters such as the tide level, flow velocity, flow direction, and water temperature; the meteorological data can be parameters such as the air temperature, wind speed, wind direction, precipitation, and radiation intensity. The topographic data includes the isobath map, water depth point data, etc.

[0023] S2. Establish and calibrate the hydrodynamic - thermal mathematical model of the research water area based on the basic data set to obtain a calibrated model;

[0024] Specifically, when establishing a mathematical model, the laws of hydrodynamic (such as the velocity and direction of water flow) and thermal (such as water temperature change) in the water area are usually described by equations. By comparing the prediction results of the model with the actual measurement data, the parameters of the model are optimized so that it can more accurately reflect the actual situation of the water area. Finally, a calibrated mathematical model is obtained, so that researchers can use it to simulate and analyze the dynamic changes in the water area, such as predicting the future trends of water flow and temperature.

[0025] S3. Use the calibrated model to carry out the simulation of the cooling water discharge of all units in operation to obtain the full - field temperature rise field data;

[0026] Specifically, by simulating how the warm discharged water diffuses during the operation of the unit group and its influence range and degree, the potential impact of the unit operation on the environment can be understood. Through simulation calculations, the temperature distribution data of the entire water area can be obtained, and it can be known which areas will have temperature rises and the degree of temperature rise.

[0027] S4. After shutting down the concerned units, perform simulations based on the same calibrated model to obtain the half-field temperature rise field data;

[0028] Specifically, stop the operation of some units to reduce the impact of their warm discharged water on the water temperature distribution. Use the calibrated model to calculate the temperature change of the water area in this case, and obtain the temperature rise distribution of a certain part of the water area (rather than the entire field) through simulation.

[0029] S5. Perform non-linear stripping processing on the full-field temperature rise field data and the half-field temperature rise field data to obtain the temperature rise field data of the concerned units;

[0030] Specifically, extract the differences between the full-field data and the half-field data through a non-linear algorithm to obtain the temperature rise field data of the concerned units, that is, analyze the contribution of these units to the water temperature rise separately.

[0031] S6. Perform statistical analysis on the temperature rise field data of the concerned units to obtain different temperature rise ranges and the corresponding areas, as well as the temperature rise at the water intake temperature rise or sensitive positions of the units.

[0032] Specifically, use mathematical and statistical methods to analyze the temperature rise field data caused by the concerned units to obtain different temperature rise ranges: divide the temperature rise data into several ranges, such as a lower temperature rise range (such as 0.5 °C), a medium temperature rise range (such as 1 - 3 °C), and a higher temperature rise range (such as above 4 °C). Analyze the water area corresponding to each temperature rise range, that is, see how large an area is affected by these temperature changes.

[0033] This embodiment can accurately simulate the temperature impact of the warm discharged water from nuclear power plants on the water area; through non-linear stripping processing, the non-linear superposition effect of the warm discharged water of multiple units is decomposed, and the independent temperature rise impact of single or multiple concerned units is clearly extracted; through statistical analysis of the temperature rise field of the concerned units, the affected area of different temperature rise ranges can be quantified, which helps to identify which areas are more affected, so as to formulate more accurate environmental protection or operation adjustment strategies. It solves the problems of dealing with the non-linear superposition effect and the thermal cycle cumulative effect of warm discharged water, and improves the stripping accuracy of warm discharged water.

[0034] According to one aspect of the present application, the steps of forming the basic data set include:

[0035] S11. Collect and study the structure and location information of the water intake and drainage structures of the power plant units and the surrounding existing power plant units, including the coordinates of the water intake, the coordinates of the drainage outlet, the shape and size of the water intake and drainage structures, the water depth and other parameters, to form a water intake and drainage structure information data set; collect and study the drainage flow and temperature rise data of the power plant units and the surrounding existing power plant units, including the maximum drainage flow under design conditions, the average drainage flow under normal conditions, the drainage temperature rise and other parameters, to form a drainage condition data set;

[0036] S12. Collect multi-year hydrological observation data of the study waters, including tidal level, flow velocity, flow direction, water temperature and other parameters, to form a hydrological observation data set; collect meteorological data of the study waters, including temperature, wind speed, wind direction, precipitation, radiation intensity and other parameters, to form a meteorological observation data set;

[0037] S13. Collect underwater topographic survey data of the research waters, including isobath maps, water depth point data, etc., to form an original topographic data set; digitize the original topographic data set to establish a digital water depth topographic data set; check the digital water depth topographic data set, identify and correct abnormal data points, and obtain a corrected water depth topographic data set;

[0038] S14. Collect the measured data of temperature rise field during operation of existing power plants in the study waters, including the temperature rise distribution in different seasons and under different tidal conditions, to form a measured data set of temperature rise field; perform quality control on the measured data set of temperature rise field, remove abnormal data, and form a corrected temperature rise field data set; extract key features from the corrected temperature rise field data set, including the maximum temperature rise position, temperature rise contour line distribution features, etc., to obtain a temperature rise feature data set.

[0039] According to one aspect of the present application, the step of obtaining a calibration model comprises:

[0040] S21. Based on the corrected water depth terrain dataset, determine the scope of the calculation domain and generate the calculation domain boundary dataset; grid the calculation domain to form an initial grid dataset; locally encrypt the initial grid dataset, set the grid size near the drainage outlet to 20m, and generate an encrypted grid dataset; perform grid quality check and optimization on the encrypted grid dataset to generate an optimized grid dataset; interpolate the corrected water depth terrain dataset onto the optimized grid dataset to obtain a grid terrain dataset.

[0041] S22. Based on the hydrological observation dataset, produce the hydrodynamic time process file for the open boundary of the model to form the open boundary condition dataset; based on the meteorological observation dataset, produce the time process files of wind speed and wind direction to form the meteorological boundary condition dataset; set the positions of the intake and discharge outlets of each unit, the intake and discharge volumes, and the drainage temperature rise conditions to generate the intake and discharge boundary condition dataset; set the calculation initial conditions, including parameters such as initial water level, flow velocity, and water temperature, to form the initial condition dataset; construct the boundary condition of the characteristic mode retention type to generate the smooth boundary condition dataset.

[0042] S23. Set the initial bottom roughness, hydraulic viscosity coefficient, temperature drainage diffusion coefficient, and comprehensive heat dissipation coefficient of the water surface to form the initial parameter set; based on the initial parameter set, optimize the grid dataset, the smooth boundary condition dataset, and the initial condition dataset, and conduct the preliminary operation of the model to generate the preliminary simulation results; compare the preliminary simulation results with the corrected temperature rise field dataset, calculate the correlation coefficient and the root mean square error to form the model evaluation index. Based on the model evaluation index, adjust the model parameters to form the adjusted parameter set; repeat the operation of the model until the model evaluation index meets the requirements to obtain the calibrated parameter set and the calibrated model.

[0043] According to one aspect of the present application, the steps of generating the smooth boundary condition dataset include:

[0044] Based on the basic dataset, analyze the boundary condition data (such as applying the non-linear feature extraction algorithm), extract the key characteristic modes of the boundary conditions to form the boundary characteristic mode library;

[0045] Based on the boundary characteristic mode library, construct the boundary condition reconstruction model to generate the boundary condition reconstruction mode;

[0046] Construct the recurrence relationship of the weight function, optimize the reconstruction coefficient of the boundary condition reconstruction model to ensure the continuity of the change of the boundary conditions;

[0047] Apply the optimized boundary condition reconstruction model to achieve the smooth transition of the boundary conditions and generate the smooth boundary condition data that meets the physical constraints;

[0048] Based on the smooth boundary condition data, establish and calibrate the hydrodynamic and thermal mathematical model of the research water area to obtain the calibrated model.

[0049] Specifically, based on the open boundary condition dataset and the meteorological boundary condition dataset, apply the non-linear feature extraction algorithm to extract the key feature patterns of the boundary conditions, generate the boundary feature pattern library, and the calculation formula: Mi = Φ(Bi, τi), where Bi is the boundary condition vector, τi is the time scale parameter, and Φ is the non-linear mapping function. The boundary feature pattern library contains a total of k feature patterns {M1, M2, ..., Mk}, and each pattern represents a typical boundary condition change pattern. Based on the boundary feature pattern library, construct the boundary condition reconstruction model to obtain the boundary condition reconstruction model; the reconstruction formula: B(t) = Σαi(t)·Mi, where αi(t) is the time-varying weight coefficient. Design the recurrence relation of the weight function: αi(t+Δt) = αi(t) + Δαi(t), where Δαi(t) is determined by the physical constraint conditions. Apply the boundary condition reconstruction model to achieve the smooth transition of the boundary conditions and generate the smooth boundary condition dataset.

[0050] In an embodiment of the present application, read the open boundary condition dataset and the meteorological boundary condition dataset, and perform multi-time scale decomposition using the wavelet transform method to obtain the short-time scale boundary component (T<1h), the medium-time scale boundary component (1h<T<12h), and the long-time scale boundary component (T>12h) respectively, forming the multi-scale boundary condition dataset. The calculation formula: B_multi(t, x) = WT{B(t, x)}, where WT is the wavelet transform operator, and B(t, x) is the original boundary condition. Extract the statistical features of the boundary conditions at each time scale, including the mean, standard deviation, maximum value, minimum value, etc., to form the boundary statistical feature dataset.

[0051] Read the multi-scale boundary condition dataset, construct a high-dimensional feature space, map the boundary conditions into the feature space, and apply kernel principal component analysis (KPCA) to extract non-linear features. The calculation formula: Mi = KPCA(Bi, K), where Bi is the boundary condition vector and K is the kernel function; the kernel function selects the radial basis function (RBF): K(x, y) = exp(-γ||x-y|| 2 ), where γ is the kernel parameter, and the optimal value is determined by cross-validation. Perform non-linear feature extraction on the boundary conditions of each time scale respectively to obtain the short-time scale feature set, the medium-time scale feature set, and the long-time scale feature set.

[0052] Read the short-term scale feature set, medium-term scale feature set, and long-term scale feature set, calculate the proportion of the explained variance of each feature; select the feature subset with the cumulative explained variance reaching 95%, and form the optimized short-term feature set, optimized medium-term feature set, and optimized long-term feature set respectively. Conduct a physical meaning analysis on the selected features to ensure that each feature pattern has a clear physical interpretation, and form a feature physical interpretation data set. Integrate the optimized short-term feature set, optimized medium-term feature set, and optimized long-term feature set into a unified boundary feature pattern library, which contains boundary feature patterns {M1, M2,..., Mk} at different time scales.

[0053] Read the boundary feature pattern library, construct the basic framework of the boundary condition reconstruction model, and design the reconstruction formula: B(t, x) = Σαi(t)·Mi(x), where αi(t) is the time-varying weight coefficient. Read the historical boundary condition data, apply the least squares method to calculate the optimal reconstruction coefficient αi(t), and form the initial reconstruction coefficient set. Introduce physical constraint conditions, including energy conservation, momentum conservation, etc., to construct a constrained optimization problem. Solve to obtain the optimized reconstruction coefficient set that satisfies the physical constraints. Read the optimized reconstruction coefficient set, analyze the variation law of the reconstruction coefficient with time, and design the recurrence relationship of the weight function: αi(t+Δt) = αi(t) + Δαi(t), where Δαi(t) is determined by the physical constraint conditions, and construct the weight adjustment model: Δαi(t) = f(B(t), ▽B(t), F_ext(t)), where B(t) is the current boundary condition, ▽B(t) is the boundary condition gradient, and F_ext(t) is the external forcing factor; through training with historical data, determine the parameters of the weight adjustment model to form the weight adjustment model.

[0054] Read the boundary feature pattern library and the weight adjustment model, and construct a complete boundary condition reconstruction model; design a smooth transition function to ensure the continuity of the boundary condition change: B_smooth(t) = (1-β(t))·B_current(t) + β(t)·B_target(t); where β(t) is the smoothing factor, which smoothly transitions from 0 to 1, β(t) = 3t 2 -2t 3 , t∈[0, 1]. Apply the boundary condition reconstruction model to smoothly reconstruct the boundary conditions, generate a smooth boundary condition data set, and verify the physical rationality of the reconstructed boundary conditions to ensure that they satisfy basic physical laws such as mass conservation and energy conservation.

[0055] According to one aspect of the present application, the steps for obtaining the full-field temperature rise field data include:

[0056] S31. Based on the hydrological and meteorological data, analyze the tidal level time series, decompose the tidal cycle into the flood tide period, high tide flat period, ebb tide period, and low tide flat period, and form typical tidal phase classification data;

[0057] S32. Calculate the optimal time step for different typical tidal phases based on the typical tidal phase classification data to form a typical phase time step strategy.

[0058] S33. Apply the phase time step strategy to the calibrated model for warm water discharge simulation to ensure high-precision calculations at critical moments of tidal current changes, and generate more accurate full-field temperature rise field data accordingly.

[0059] Specifically, based on the intake and discharge operation data set, set the intake and discharge conditions for all units to operate simultaneously to form a full-field intake and discharge condition data set; load the full-field intake and discharge condition data set into the calibrated model to form an initial full-field simulation model. Conduct multi-time scale feature-preserving tidal phase decomposition simulation. Based on the hydrological observation data set, decompose the tidal cycle into key phases: flood acceleration period, flood peak period, flood deceleration period, slack period, ebb acceleration period, ebb peak period, ebb deceleration period, and generate a tidal phase classification data set. Develop a tidal phase recognition algorithm to automatically identify the tidal phase characteristics at different positions within the computational domain and generate a tidal phase spatial distribution data set.

[0060] For each key tidal phase in the tidal phase classification data set, design an optimal time step strategy to generate a phase time step strategy. The calculation formula is: Δt(φ) = Δt_base * W(φ), where φ is the tidal phase, W(φ) is the phase weight function, W(φ)≈0.2 during the tidal current transition period, W(φ)≈1.0 during the tidal current stable period, and Δt_base is the base time step. Apply the phase time step strategy to the initial full-field simulation model to form an optimized time step full-field model.

[0061] Run the optimized time step full-field model to generate the original full-field simulation results. Perform phase feature-preserving interpolation processing on the original full-field simulation results to ensure that the reconstructed temperature rise field maintains physical characteristics; save the processed full-field simulation results to form a full-field temperature rise field time series data set. Conduct full-field temperature rise feature analysis. Based on the full-field temperature rise field time series data set, calculate the spatio-temporal statistical characteristics of the temperature rise field, including the position of the maximum temperature rise, the range of different temperature rise isotherms, etc., to form a full-field temperature rise feature data set. Analyze the temperature rise distribution characteristics under different tidal phases to generate a full-field tidal phase temperature rise feature data set.

[0062] In one embodiment of the present application, tidal cycle identification and segmentation are performed. The hydrological observation data set is read, and the tidal level time series data is extracted; the Hilbert-Huang transform (HHT) is applied to perform time-frequency analysis on the tidal level sequence to identify the main tidal wave components and form a tidal wave feature data set. Based on the tidal level derivative and extreme points, the key phases of the tidal cycle are divided: rising tide acceleration period (RA), rising tide extreme period (RP), rising tide deceleration period (RD), slack period (S), falling tide acceleration period (FA), falling tide extreme period (FP), falling tide deceleration period (FD), and the calculation formula is: φ(t) = RA, if dh / dt > 0 and d 2 h / dt 2 > 0; RP, if dh / dt> 0 and |d 2 h / dt 2 | < ε; RD, if dh / dt > 0 and d 2 h / dt 2 < 0; S, if |dh / dt| < Δ; FA, if dh / dt < 0 and d 2 h / dt 2 < 0; FP, if dh / dt < 0 and |d 2 h / dt 2 | < ε; FD, if dh / dt <0 and d 2 h / dt 2 > 0; where h is the tidal level, ε and Δ are threshold parameters, t is the time parameter, and d is the differential symbol. A tidal phase classification data set is generated, including the time intervals and characteristic parameters of each tidal phase.

[0063] The tidal level data of multiple sites in the tidal phase classification data set and the hydrological observation data set are read, the propagation law of the tidal phase in space is analyzed, the propagation speed and direction of the tidal phase are calculated, and a tidal phase propagation feature data set is formed; a tidal phase space propagation model is constructed: φ(x, t) = φ(x0, t - τ(x, x0)), where τ(x, x0) is the time required for the tidal phase to propagate from position x0 to position x; based on the tidal phase propagation feature data set and the grid terrain data set, the spatio-temporal distribution of the tidal phase at each grid point in the calculation domain is calculated, and a tidal phase spatial distribution data set is formed.

[0064] Read the flow velocity data in the hydrological observation dataset and the tidal phase classification dataset, analyze the flow field characteristics under different tidal phases, including flow velocity magnitude, flow direction, shear strength, etc., and form a tidal current characteristic dataset. Identify key flow field structures, such as vortices, fronts, convergence / divergence zones, etc., and generate a key flow field structure dataset. Based on the tidal current characteristic dataset and the key flow field structure dataset, construct typical flow field patterns for different tidal phases and form a tidal phase flow field pattern dataset.

[0065] According to one aspect of the present application, the steps of calculating the optimal time step for different tidal phases and forming a phase time step strategy include:

[0066] Based on the tidal phase classification data, analyze the transport characteristics of the thermal discharge water under different tidal phases, calculate the characteristic time scales of each tidal phase, and form phase characteristic time scale data;

[0067] Based on the phase characteristic time scale data, construct a time step calculation formula, determine the optimal time step for different tidal phases and spatial positions, and generate a phase-space time step distribution; apply spatial smoothing processing to the phase-space time step distribution to ensure continuous variation of the time steps of adjacent grid cells and form a smoothed time step distribution;

[0068] Integrate the smoothed time step distribution to generate a complete phase time step strategy for use in the thermal discharge water simulation process.

[0069] Specifically, read the tidal phase classification dataset and the tidal current characteristic dataset, and analyze the transport characteristics of the thermal discharge water under different tidal phases; calculate the characteristic time scales of the thermal discharge water transport under different tidal phases: τ_phase(φ) = min{L / U(φ), H 2 / D, T_tide / η(φ)}, where L is the characteristic length, U(φ) is the flow velocity at phase φ, H is the water depth, D is the diffusion coefficient, T_tide is the tidal period, and η(φ) is the local topographic change rate at phase φ; generate a dataset of phase characteristic time scales to characterize the influence degree of different tidal phases on the transmission of warm wastewater. Read the dataset of phase characteristic time scales and design a time step optimization strategy based on tidal phases. The calculation formula is: Δt(φ, x) = Δt_base * W(φ, x), where W(φ, x) is the weight function of tidal phase φ at position x and is designed as follows: W(φ, x) = 0.2, if φ ∈ {S} or x ∈ high_gradient_area; 0.4, if φ ∈ {RA, FA} or x ∈ medium_gradient_area; 0.7, if φ ∈ {RD, FD} or x ∈ low_gradient_area; 1.0, if φ ∈ {RP, FP} and x ∈ stable_area; where high_gradient_area is the area with large temperature gradient or flow velocity gradient, usually including the area near the drainage outlet; medium_gradient_area is the medium-gradient area; low_gradient_area is the low-gradient area; stable_area is the stable area; based on the above formula, calculate the optimal time step for each grid point and each tidal phase in the computational domain to form a phase time step strategy. Read the phase time step strategy and detect the spatial discontinuity of the time step; design a spatial smoothing algorithm to ensure that the change of the time step between adjacent grid cells does not exceed a preset threshold: |Δt(x_i) - Δt(x_j)| ≤ γ·min{Δt(x_i), Δt(x_j)}, for adjacent cells x_i and x_j, where γ is the smoothing coefficient, generally taken as 0.2 - 0.3; apply the iterative smoothing algorithm to generate a spatially continuous dataset of smoothed time step distributions. Integrate the dataset of smoothed time step distributions into the phase time step strategy to form the final time step optimization strategy.

[0070] In one embodiment of the present application, the full-field simulation initial model and the phase time-step strategy are read, and the simulation control parameters are set; a phase-adaptive simulation controller is developed to dynamically adjust the time step and the numerical solution parameters according to the tidal phase at the current simulation time. The working process of the simulation controller: determine the tidal phase φ(t, x) at the current simulation time t; obtain the corresponding time step Δt(φ, x) according to the phase time-step strategy; set the numerical solver parameters, including the iteration accuracy, relaxation factor, etc.; perform the simulation calculation for one time step; update the model state and prepare for the next time step calculation. Generate a dataset of phase-adaptive control parameters to guide the simulation process.

[0071] Perform full-field warm water discharge simulation and dynamically adjust the time step according to the dataset of phase-adaptive control parameters. For each tidal phase, store the corresponding warm water discharge simulation results respectively to form a tidal phase warm water discharge result set; the storage format is designed as: R(φ, t) = {T(x, y, t) | φ(t) = φ}, for φ ∈ {RA, RP, RD, S, FA, FP, FD}, where T(x, y, t) is the temperature field at the position (x, y) and time t; for the results of each tidal phase, extract the key features to form a tidal phase feature summary dataset.

[0072] Read the tidal phase warm water discharge result set and design a feature-preserving interpolation algorithm. For any time t, determine the tidal phase interval [t_a, t_b] it is in and the corresponding tidal phases φ_a and φ_b; apply the tidal phase feature-preserving interpolation function: T(x, y, t) = (1 - α(t))·T(x, y, t_a, φ_a) + α(t)·T(x, y, t_b, φ_b) + C(x, y, t), where α(t) is the interpolation weight and C(x, y, t) is the feature-preserving correction term; design of the feature-preserving correction term: C(x, y, t) = β(t)·[▽T(x, y, t_a)·(t - t_a) - ▽T(x, y, t_b)·(t_b - t)], where β(t) is the correction coefficient and ▽T is the temperature gradient. Through feature-preserving interpolation, reconstruct the complete warm water discharge time series to form a full-field temperature rise field time series dataset. Verify the physical rationality of the reconstruction result to ensure the continuity of the temperature field and the conservation of energy.

[0073] According to one aspect of the present application, the steps for obtaining the half-field temperature rise field data include:

[0074] S41. Based on the intake and discharge water information before and after shutting down the concerned unit, combine the calibrated model to identify the changes in the intake and discharge water conditions during the conversion process from the full-field model to the half-field model, and form the intake and discharge water condition change data;

[0075] S42. Perform a feature pattern-preserving transition on the data of the changes in the water intake and discharge conditions, and generate a smooth transition condition in combination with a smooth transition function. Establish an energy balance equation for the changing conditions, and optimize the smooth transition condition by applying energy balance constraints to generate an energy balance condition.

[0076] S43. Apply the energy balance condition to the half-field warm water discharge simulation to ensure the continuity and physical rationality of the condition changes, and generate half-field temperature rise field data.

[0077] Specifically, the half-field simulation conditions are set as follows: Based on the full-field water intake and discharge condition data set, close the water intake and discharge ports of the concerned unit to form a half-field water intake and discharge condition data set. Load the half-field water intake and discharge condition data set into the calibrated model to form an initial half-field simulation model. Perform a feature pattern-preserving boundary transition process. During the transition from the full-field simulation initial model to the initial half-field simulation model, identify the boundary condition change region to form a boundary change region data set. Apply the feature pattern-preserving boundary transition method to the boundary change region data set to generate a boundary condition transition model, and the calculation formula is: B_trans(t) = B_initial + (B_final - B_initial)·f(t), where f(t) is the smooth transition function, satisfying f(0)=0, f(T)=1, and T is the transition period; B_initial is the initial boundary condition; B_final is the final boundary condition; generate a smooth transition boundary condition to ensure the continuity and physical rationality of the boundary condition changes. Establish an energy balance equation for the boundary conditions: E[B_trans(t)] = E[B_initial] + ∫F(t)dt, where E is the boundary energy evaluation function and F is the boundary energy change rate; design an energy balance feedback controller: Δαi(t) = K·[E_target - E(t)]·▽E_i, apply the energy balance constraints, optimize the smooth transition boundary condition, and generate an energy balance boundary condition. Apply the energy balance boundary condition and the phase time step strategy to the initial half-field simulation model to form an optimized half-field model; run the optimized half-field model to generate the original half-field simulation results; perform a phase feature-preserving interpolation process on the original half-field simulation results to form a time series data set of the half-field temperature rise field. Based on the time series data set of the half-field temperature rise field, calculate the spatio-temporal statistical features of the temperature rise field to form a half-field temperature rise feature data set; analyze the temperature rise distribution features under different tidal phases to generate a half-field tidal phase temperature rise feature data set.

[0078] In an embodiment of the present application, the full-field water intake and drainage condition dataset and the half-field water intake and drainage condition dataset are read, and the difference between the two is calculated; the boundary condition change region is identified, mainly including the regions near the water intake and drainage outlets of the concerned unit, and the calculation formula is: ΔB(x) = ||B_full(x) - B_half(x)||; B_full(x) is the boundary condition function value under the full-field water intake and drainage conditions; B_half(x) is the boundary condition function value under the half-field water intake and drainage conditions; a change significance threshold ε_B is set, and the boundary change region is defined as: Ω_B = {x | ΔB(x) > ε_B}; a boundary change region dataset is generated, including the spatial range and change intensity of the change region.

[0079] The boundary change region dataset and the tidal phase flow field pattern dataset are read, the spatial propagation characteristics of the boundary change are analyzed, and the transmission effect of the flow field on the boundary condition change is considered; a boundary change propagation model is constructed: ΔB(x, t+Δt) = ΔB(x, t) + ∫_ΩK(x, y, t)·ΔB(y, t)·dy, where K(x, y, t) is the propagation kernel function, which characterizes the boundary change propagation intensity from position y to position x. Based on the tidal current field characteristics, the propagation kernel function is designed as: K(x, y, t) = exp(-||x-y|| 2 / σ 2 )·exp(-(t_travel(y→x) / τ) 2 ), where t_travel(y→x) is the water particle transport time from position y to position x, τ is the characteristic time scale, and σ is the characteristic spatial scale; a boundary change propagation characteristic dataset is generated to describe the spatio-temporal propagation law of the boundary condition change.

[0080] The boundary change region dataset and the boundary change propagation characteristic dataset are read, and for the boundary condition change region, a smooth transition function is designed; for the boundary condition at position x, the smooth transition function is designed as: B_trans(x, t)= B_initial(x) + (B_final(x) - B_initial(x))·f(x, t), where f(x, t) is the smooth transition function, satisfying f(x,0)=0, f(x, T)=1. The core smooth function is designed as: f(x, t) = (10·(t / T) 3 - 15·(t / T) 4 +6·(t / T) 5)·g(x), where g(x) is a spatial modulation function that considers the flow field characteristics at position x: g(x) = exp(-d(x, Ω_c) / λ), where d(x, Ω_c) is the distance from position x to the core change region Ω_c, and λ is the spatial modulation coefficient; generate a smooth transition boundary condition to ensure the continuity and physical rationality of the boundary condition change.

[0081] Read the smooth transition boundary condition and design a boundary energy evaluation function. The energy evaluation function considers three aspects: kinetic energy, potential energy, and thermal energy: E[B] = ∫_Ω (α·ρv 2 / 2 + β·ρgh + γ·ρcT)·dΩ; where ρ is the density, v is the flow velocity, g is the acceleration due to gravity, h is the water level, T is the temperature, and α, β, γ are weight coefficients; calculate the energy E[B_initial] of the initial boundary condition and the energy E[B_final] of the target boundary condition; construct an energy change rate function: F(t) = (E[B_final] - E[B_initial])·df(t) / dt, where df(t) / dt is the derivative of the smooth transition function; generate a boundary energy feature dataset to describe the characteristics of the energy change of the boundary condition.

[0082] Read the boundary energy feature dataset and design an energy balance feedback controller. The controller objective is to ensure that the energy change during the boundary condition change conforms to physical laws; establish a boundary condition energy balance equation: E[B_trans(t)] = E[B_initial] + ∫ 0 t F(τ)·dτ; where ∫ 0 t represents the integral from time 0 to the current time t, and F(τ) is the energy change rate function; design an energy balance feedback controller: Δα_i(t) = K·[E_target(t) - E[B_trans(t)]]·▽α_i E[B_trans(t)], where K is the control gain, E_target(t) is the target energy, and ▽α_i E is the gradient of the energy with respect to the reconstruction coefficient α_i. The controller workflow is as follows: calculate the energy E[B_trans(t)] of the current boundary condition; calculate the deviation ΔE from the target energy = E_target(t) - E[B_trans(t)]; calculate the gradient ▽α_i E of the energy with respect to the reconstruction coefficient; update the reconstruction coefficient: α_i(t + Δt) = α_i(t) + Δα_i(t); reconstruct the boundary condition B_trans(t + Δt); generate an energy control parameter dataset to guide the energy balance control of the boundary condition.

[0083] Read the dataset of smooth transition boundary conditions and energy control parameters, apply the energy balance feedback controller, and iteratively optimize the boundary conditions. The optimization algorithm is as follows: for t = 0 to T: Calculate the current boundary condition B_trans(t); Evaluate the energy balance state E[B_trans(t)]; Apply the energy controller to calculate the correction Δα_i(t); Update the reconstruction coefficient α_i(t); Reconstruct the boundary condition B_trans(t). Optimization objective: Minimize the cumulative amount of energy balance deviation: min ∫ 0 T (E[B_trans(t)] - E_target(t)) 2 ·dt; Solve the constrained optimization problem using the Lagrange multiplier method to obtain the optimal boundary condition that satisfies the energy balance, generate the final energy balance boundary condition, and use it for the half-field temperature drainage simulation.

[0084] As Figure 2 shown, according to one aspect of the present application, the steps of performing non-linear stripping to obtain the data of the temperature rise field of the concerned unit include:

[0085] S51. Based on the full-field temperature rise field data, extract the flow velocity field and construct a streamline coordinate system;

[0086] S52. Convert the full-field temperature rise field data and the half-field temperature rise field data to the streamline coordinate system to obtain the streamline temperature rise field data;

[0087] S53. Based on the streamline temperature rise field data, calculate the heat flux along the streamline and analyze the influence of temperature on the fluid density to generate a non-linear correction term;

[0088] S54. Combine the heat flux along the streamline and the non-linear correction term to perform non-linear stripping in the streamline coordinate system, obtain the stripping result and map it back to the original coordinate system to form the data of the temperature rise field of the concerned unit.

[0089] As Figure 3 shown, according to one aspect of the present application, the steps of constructing a streamline coordinate system include:

[0090] Based on the flow velocity field, calculate the streamline trajectory; Based on the streamline trajectory, establish a streamline orthogonal curvilinear coordinate system, including the coordinate along the streamline direction and the coordinate perpendicular to the streamline direction;

[0091] Establish a two-way mapping relationship between the Cartesian coordinate and the streamline orthogonal curvilinear coordinate system to form the streamline coordinate system;

[0092] Derive the heat transfer equation in the streamline coordinate system, combine the temperature-dependent diffusion coefficient and the density flow correction term to form a streamline heat transfer model for performing non-linear stripping.

[0093] As Figure 4As shown, according to one aspect of the present application, the steps of performing non - linear stripping in the streamline coordinate system to obtain the stripping result include:

[0094] Based on the streamline temperature rise field data, calculate the heat fluxes along the streamline for the whole field and the half - field respectively to obtain the heat flux difference;

[0095] According to the relationship between temperature and density, calculate the non - linear correction term of the fluid density change caused by temperature to the heat flux along the streamline;

[0096] Apply the streamline - segment non - linear stripping formula to combine the heat flux difference and the non - linear correction term to obtain the stripped temperature rise field data in the streamline coordinate system, that is, the stripping result, which is used to map back to the original coordinate system.

[0097] Specifically, construct the streamline coordinate system. Based on the full - field temperature rise field time - series data set, extract the velocity fields at each moment to form the full - field velocity field time - series data set; based on the full - field velocity field time - series data set, calculate the main streamline distribution to generate the streamline distribution data set; establish the streamline orthogonal curvilinear coordinate system (s, n), where s is the direction along the streamline and n is the direction perpendicular to the streamline, to form the streamline coordinate system data set; develop the coordinate mapping algorithm: (x, y) ↔ (s, n) to form the coordinate mapping model.

[0098] Reconstruct the heat flux conservation equation. In the streamline coordinate system, reconstruct the heat transfer equation: ΨT / Ψt + UΨT / Ψs = Ψ / Ψs(DsΨT / Ψs) + Ψ / Ψn(DnΨT / Ψn) + S; where T is the temperature field, U is the flow velocity, Ds and Dn are the diffusion coefficients in the streamline direction and the direction perpendicular to the streamline respectively, S is the heat source term, and Ψ is the partial derivative; generate the streamline heat transfer model for the calculation and analysis of the heat flux. Perform streamline - segment non - linear stripping. Using the coordinate mapping model, convert the full - field temperature rise field time - series data set and the half - field temperature rise field time - series data set to the streamline coordinate system to obtain the streamline temperature rise field data set and the streamline half - field temperature rise field data set; decompose the heat flux of the warm - water discharge along each streamline, considering the influence of the temperature gradient along the streamline on the fluid density; non - linear stripping formula: ΔT concerned unit(s, n) = ΔT full - field(s, n) - ΔT half - field(s, n) + ∫Φ(s, n, T)ds, where Φ is the non - linear correction term of the fluid density change caused by temperature to the heat flux; perform non - linear stripping calculation to obtain the streamline stripped temperature rise field data set in the streamline coordinate system.

[0099] Perform streamline temperature rise restoration. Using the coordinate mapping model, map the streamline stripped temperature rise field data set back to the original grid coordinate system, and use the interpolation method that maintains heat conservation to ensure the restoration accuracy to generate the temperature rise field data of the concerned unit.

[0100] According to one aspect of the present application, the steps of performing non - linear stripping processing further include:

[0101] Based on the flow velocity field and tidal phase classification data, identify the thermal circulation paths from the drainage outlets to the water intake points under different tidal phases to form a set of thermal circulation paths; use the set of thermal circulation paths to construct a tidal phase-related thermal circulation matrix to characterize the heat transfer intensity from each drainage outlet to each water intake point under different tidal phases;

[0102] Apply the thermal circulation matrix to the stripped temperature rise field data to separate the direct warm water discharge effect and the thermal circulation cumulative effect, and obtain the temperature rise field data of the concerned units after thermal circulation decoupling.

[0103] According to one aspect of the present application, the steps of forming the set of thermal circulation paths and constructing the thermal circulation matrix include:

[0104] Calculate the connectivity index between each drainage outlet and each water intake point under different tidal phases to form drainage outlet - water intake point connectivity data;

[0105] Based on the connectivity data, calculate the paths of water masses from the drainage outlets to the water intake points (such as applying the Lagrangian particle tracking method) to form a set of thermal circulation paths;

[0106] Evaluate the heat transfer intensity of each path in the set of thermal circulation paths to generate thermal circulation path intensity data;

[0107] Based on the thermal circulation path intensity data, construct a tidal phase-related thermal circulation matrix; apply singular value decomposition to the thermal circulation matrix to extract the main thermal circulation modes and form the main thermal circulation mode data for the thermal circulation decoupling algorithm.

[0108] Specifically, based on the full-field flow velocity field time series dataset and the tidal phase classification dataset, develop a tidal phase-associated thermal circulation path recognition algorithm to identify the heat transfer paths from the drainage outlets to the water intake points under different tidal phases to form a set of tidal phase thermal circulation paths; calculation formula: P(φ) = {p1(φ), p2(φ),..., pn(φ)}, where φ is the tidal phase and pi(φ) is the i-th thermal circulation path; introduce a path intensity evaluation index to quantify the heat transfer ability of each path and generate a thermal circulation path intensity dataset. Construct a tidal phase-related thermal circulation matrix: C(φ) = [cij(φ)], where cij(φ) represents the thermal circulation coefficient from drainage outlet i to water intake point j under tidal phase φ; apply singular value decomposition to extract the main thermal circulation modes: C(φ) = U·Σ·V T ; where U is an orthogonal matrix, Σ is a diagonal matrix, V is another orthogonal matrix, T is the transpose; retain the thermal circulation modes corresponding to the main eigenvalues, filter the noise modes, and form a main thermal circulation mode dataset. Establish a recursive model based on the main thermal circulation mode dataset: ΔT = ΔT_direct + C(φ)·ΔT; transform it into a decoupling equation: ΔT_direct = [I - C(φ)]-1 ·ΔT; Develop a stable solution algorithm to avoid ill-conditioned matrix problems; Use the initial data set of the temperature rise field of the concerned unit as the preliminary stripped temperature rise field data set and apply the decoupling algorithm to generate the decoupled temperature rise field data set for thermal cycles. Based on the decoupled temperature rise field data set for thermal cycles, perform the final reconstruction of the temperature rise field. Apply physical constraint conditions to ensure that the temperature rise field meets the requirements of energy conservation and continuity; Generate the final time-series data set of the temperature rise field of the concerned unit as the final result of the stripping of the warm water discharge.

[0109] In an embodiment of the present application, perform flow field characteristic analysis and streamline calculation. Read the time-series data set of the full-field temperature rise field, extract the velocity fields at each moment to form the time-series data set of the full-field velocity field; Perform characteristic analysis on the time-series data set of the full-field velocity field to identify characteristic flow field structures such as the main flow area, vortex area, and shear layer, and generate the data set of the flow field characteristic structure; Apply the streamline tracking algorithm to calculate the key streamlines in the characteristic flow field: dx / dt = u(x, y, t); dy / dt = v(x, y, t); where u(x, y, t) is the velocity component of the flow field in the x direction at the position (x, y) at time t; v(x, y, t) is the velocity component of the flow field in the y direction at the position (x, y) at time t; Use the fourth-order Runge-Kutta method to solve the above ordinary differential equations to obtain the streamline trajectory. Streamline density control strategy: Increase the streamline density in the high flow velocity gradient region to ensure that the streamline distribution can fully characterize the flow field characteristics; Generate the initial streamline distribution data set, which contains the main streamline trajectories within the computational domain.

[0110] Perform streamline optimization and layering. Read the initial streamline distribution data set and the flow field characteristic structure data set to perform streamline optimization; Apply the streamline smoothing algorithm to eliminate local irregular fluctuations: x'(s) = (1 - ω)·x(s) + ω·(x(s - Δs) + x(s + Δs)) / 2; y'(s) = (1 - ω)·y(s) + ω·(y(s - Δs) + y(s + Δs)) / 2; where ω is the smoothing factor, s is the streamline parameterization variable; x(s) and y(s) are the abscissa and ordinate of the streamline under the parameterization variable s respectively; Δs is the increment of the streamline parameterization variable. Detect and process streamline intersections and special streamline topological structures (such as saddle points, foci) to ensure the topological correctness of the streamline system; Classify the streamlines into two categories: core streamlines and auxiliary streamlines: Core streamlines: Consistent with the main flow direction, relatively few in number, used to define the skeleton of the streamline coordinate system; Auxiliary streamlines: Fill the areas between the core streamlines to improve the accuracy of the coordinate system; Generate the optimized streamline distribution data set, which contains the optimized core streamlines and auxiliary streamlines.

[0111] Construct orthogonal curves. Read the optimized streamline distribution dataset and construct a curve system orthogonal to the streamlines; Orthogonal curve calculation method: dx / dt = -v(x, y, t); dy / dt = u(x, y, t); Use the fourth-order Runge-Kutta method to solve the above ordinary differential equations to obtain the orthogonal curve trajectory; Select appropriate starting points on each core streamline to generate curves orthogonal to the streamlines; Apply the orthogonality optimization algorithm to ensure the orthogonality of the orthogonal curves and the streamlines: min ∑_i ∑_j (τ_i·n_j) 2 , where τ_i is the tangent vector of streamline i and n_j is the normal vector of orthogonal curve j; Generate an orthogonal curve dataset containing the curve system orthogonal to the streamlines.

[0112] Perform streamline coordinate system parameterization. Read the optimized streamline distribution dataset and the orthogonal curve dataset, establish a streamline orthogonal curve coordinate system, parameterize the streamlines, and define the streamline coordinate s: s(x, y) = ∫ 0 c sqrt(dx 2 + dy 2 ), where c is the path from the starting point of the streamline to the point (x, y); Parameterize the orthogonal curves and define the orthogonal coordinate n: n(x, y) = ±∫ 0 c sqrt(dx 2 + dy 2 ), and the sign depends on whether the point (x, y) is on the left or right side of the reference streamline; Establish a two-way mapping relationship: (x, y) ↔ (s, n), forming a set of coordinate mapping functions. Calculate the metric tensor gij and Christoffel symbols Γijk of the coordinate transformation to form a coordinate system metric dataset. Integrate the set of coordinate mapping functions and the coordinate system metric dataset to generate a complete streamline coordinate system dataset and a coordinate mapping model.

[0113] Derive the heat transfer equation in the streamline coordinate system. Read the streamline coordinate system dataset and the coordinate system metric dataset; in the streamline coordinate system (s, n), derive the heat transfer equation: ΨT / Ψt + UΨT / Ψs = (1 / sqrt(g))·Ψ / Ψs(sqrt(g)·Ds·ΨT / Ψs) + (1 / sqrt(g))·Ψ / Ψn(sqrt(g)·Dn·ΨT / Ψn) + S, where T is the temperature field, U is the flow velocity along the streamline direction, g is the determinant of the metric tensor, Ds and Dn are the diffusion coefficients in the streamline direction and the direction perpendicular to the streamline respectively, and S is the heat source term; analyze the physical meaning of each term in the equation: the first term is the time rate of change of temperature, the second term is convective heat transfer, the third term is diffusion heat transfer in the streamline direction, the fourth term is diffusion heat transfer in the direction perpendicular to the streamline, and the fifth term is the heat source term; generate the streamline heat transfer equation dataset, including the mathematical expression and physical interpretation of the heat transfer equation.

[0114] Construct a temperature-dependent diffusion coefficient model. Read the time-series dataset of the full-field temperature rise, and analyze the relationship between temperature and diffusion coefficient; construct the temperature-dependent diffusion coefficient model: Ds(T) = Ds 0 ·[1 + αs·(T - T 0 ) + βs·(T - T 0 ) 2 ; Dn(T) = Dn 0 ·[1 + αn·(T - T 0 ) + βn·(T - T 0 ) 2 ; where Ds 0 and Dn 0 are the diffusion coefficients at the reference temperature T 0 , and αs, αn, βs, βn are temperature-dependent coefficients; use the historical warm water discharge simulation data and measured data, and apply the non-linear regression method to determine the model parameters; generate the temperature-dependent diffusion model to describe the influence relationship between temperature and diffusion coefficient.

[0115] Construct a density-dependent flow correction model. Read the time-series dataset of the full-field temperature rise, and analyze the influence of temperature on the water density; construct the temperature-density relationship model: ρ(T) = ρ 0 ·[1 - α·(T - T 0 ) + β·(T - T 0 ) 2 - γ·(T - T 0 ) 3 , where ρ 0 is the water density at the reference temperature T 0 , and α, β, γ are temperature-density relationship coefficients; analyze the influence of density change on the flow field, and construct the density flow correction term: ΔU(s, n) = g·H2 / (8ν)·Ψρ / Ψs·(1-(2n / H) 2 ), where g is the acceleration due to gravity, H is the water depth, ν is the kinematic viscosity coefficient, and Ψρ / Ψs is the density gradient along the streamline direction; generate a density current correction model to describe the influence of density changes caused by temperature on the flow field.

[0116] Integrate the streamline heat transfer equation dataset, the temperature-dependent diffusion model, and the density current correction model to construct a complete streamline heat transfer model: ΨT / Ψt + (U+ΔU)ΨT / Ψs = (1 / sqrt(g))·Ψ / Ψs(sqrt(g)·Ds(T)·ΨT / Ψs) + (1 / sqrt(g))·Ψ / Ψn(sqrt(g)·Dn(T)·ΨT / Ψn) + S; design a numerical solution scheme, including a time discretization method (implicit difference) and a spatial discretization method (finite volume method); formulate stability conditions and convergence criteria to ensure the accuracy of the numerical solution; generate a complete streamline heat transfer model for subsequent heat flux calculation and stripping analysis.

[0117] According to one aspect of the present application, the steps for statistically analyzing the temperature rise field data of the concerned unit include:

[0118] S61. Extract the temperature rise field data of multiple time periods from the temperature rise field data of the concerned unit, set the standard temperature rise isotherm level, extract the isotherms of each temperature rise level, and form multi-time period temperature rise isotherm data;

[0119] S62. Analyze the spatio-temporal variation characteristics of the multi-time period temperature rise isotherm data, statistically calculate the maximum influence range and area of each temperature rise level within the calculation period, and generate temperature rise extreme value statistical data;

[0120] S63. Process the multi-time period temperature rise isotherm data using the α-shape method to generate accurate envelope lines for each temperature rise level, and form temperature rise envelope line data;

[0121] S64. Calculate the area enclosed by the temperature rise envelope line data, apply geographic projection correction, and generate temperature rise envelope area data for evaluating the influence range of warm water discharge.

[0122] According to one aspect of the present application, the steps for statistically analyzing the temperature rise field data of the concerned unit further include:

[0123] Extract the temperature rise time series at each water intake location from the temperature rise field data of the concerned unit to form water intake temperature rise time series data;

[0124] Analyze the statistical characteristics of the water intake temperature rise time series data, calculate indicators such as average temperature rise, maximum temperature rise, and temperature rise exceeding standard rate, and generate water intake temperature rise statistical data;

[0125] Based on the statistical data of the temperature rise at the water intake, evaluate the over-temperature rate and risk level, set the risk rating criteria, and generate the risk assessment data of the water intake.

[0126] Integrate the risk assessment data of the water intake, design an evaluation system for the impact level of the temperature rise of the water intake, and form the impact assessment data of the water intake to evaluate the impact degree of the warm water discharge on the water intake.

[0127] Specifically, extract the temperature rise field data for multiple time periods. Extract the temperature rise field data for different time periods from the time series dataset of the temperature rise field of the concerned unit, including typical tidal phases, representative moments of flood tide / ebb tide, representative moments of seasons, etc.; generate a dataset of the temperature rise field for multiple time periods for subsequent statistical analysis. Based on the dataset of the temperature rise field for multiple time periods, calculate the coverage areas of different temperature rise isotherms (usually 0.5 °C, 1 °C, 2 °C, 3 °C, 4 °C, etc.); for each temperature rise isotherm, calculate the area within its envelope range to form a statistical dataset of the temperature rise area.

[0128] Based on the dataset of the temperature rise field for multiple time periods, calculate the maximum influence range of each temperature rise isotherm within the entire simulation period to form a dataset of the temperature rise envelope line; calculate the areas within the envelopes of each temperature rise isotherm to form a dataset of the temperature rise envelope area. Based on the time series dataset of the temperature rise field of the concerned unit, extract the temperature rise time series at the locations of each water intake to form a time series dataset of the temperature rise at the water intake; statistically analyze the time series dataset of the temperature rise at the water intake, and calculate indicators such as the average temperature rise, maximum temperature rise, and over-temperature rate at the water intake to form a dataset of the impact assessment of the water intake.

[0129] Integrate the statistical dataset of the temperature rise area, the dataset of the temperature rise envelope area, and the dataset of the impact assessment of the water intake to generate a comprehensive evaluation report on the impact of the warm water discharge of the concerned unit, including content such as a temperature rise range map, a statistical table of the temperature rise area, and an analysis of the impact on the water intake; form a final evaluation report on the impact of the warm water discharge as the final output result.

[0130] In one embodiment of the present application, a multi-period temperature rise field dataset is read. For the temperature rise field data of each period, a set of standard temperature rise isothermal line levels L = {0.5°C, 1°C, 2°C, 3°C, 4°C} is set; the isothermal line extraction algorithm is applied to extract isothermal lines for each temperature rise level l∈L and each period t: C(l, t) = {(x, y) | T(x, y, t) = l}; the bilinear interpolation method is used to determine the exact position of the isothermal line between grids, improving the smoothness and accuracy of the isothermal line. The Douglas-Peucker simplification algorithm is applied to the extracted isothermal line to reduce redundant points while maintaining the shape characteristics: C'(l, t) = Douglas-Peucker(C(l, t), ε_DP), where ε_DP is the simplification threshold, usually set to 1 / 10 of the grid size; a multi-period temperature rise isothermal line dataset is generated, including the isothermal line data of all periods and all temperature rise levels. The multi-period temperature rise isothermal line dataset is read to analyze the variation characteristics of each temperature rise isothermal line over time; for each temperature rise level l∈L, its maximum influence range within the calculation period is statistically calculated: max_x(l) = max_t{max{x | (x, y)∈C(l, t)}}; min_x(l) = min_t{min{x | (x, y)∈C(l, t)}}; max_y(l) = max_t{max{y | (x, y)∈C(l, t)}}; min_y(l) = min_t{min{y | (x, y)∈C(l, t)}}; a rectangular bounding box is generated: B_rect(l) = [min_x(l), max_x(l)] × [min_y(l), max_y(l)]; the maximum influence area and average influence area of each temperature rise level are calculated: A_max(l) = max_t{Area(C(l, t))}; A_avg(l) = (1 / T)·∑_t Area(C(l, t)); a temperature rise extreme value statistical dataset is generated, including the extreme value statistical results of each temperature rise level.

[0131] Read the multi-period temperature rise isotherm dataset and the temperature rise extreme value statistical dataset, and use the AlphaShape method to generate the precise envelope for each temperature rise level: E(l) = AlphaShape(∪_t C(l, t), α), where α is the shape parameter and is determined by an adaptive algorithm: α = β·sqrt(A_max(l) / π) / N_points, where β is the adjustment coefficient (usually 2 - 3) and N_points is the total number of isotherm point sets; apply the envelope smoothing algorithm to eliminate irregular concavities and convexities and improve the aesthetics of the envelope: E'(l) = SmoothingSpline(E(l), λ), where λ is the smoothing parameter and the optimal value is determined by cross-validation; apply topological checks to the envelope to ensure topological legality such as no self-crossing and no holes; generate the final temperature rise envelope dataset, which contains the optimized envelopes for each temperature rise level. Read the temperature rise envelope dataset, calculate the area enclosed by the envelopes for each temperature rise level, and use a high-precision polygon area calculation method for area calculation: A(l) = 0.5·|∑_i (x_i·y_{i+1} - x_{i+1}·y_i)|; where (x_i, y_i) are the vertex coordinates on the envelope; apply geographic projection correction to consider the projection deformation from spherical coordinates to planar coordinates: A_corr(l) = A(l)·cos(φ_avg), where φ_avg is the average latitude of the study area; calculate the area difference between adjacent temperature rise levels: ΔA(l) = A_corr(l) - A_corr(l+Δl), and generate the temperature rise envelope area dataset, which contains the envelope areas for each temperature rise level and related statistical results.

[0132] Read the time series dataset of the temperature rise field of the concerned unit and the dataset of intake and drainage structure information, and extract the set of location coordinates of all water intake points I = {i 1 , i 2 , ..., i m} from the dataset of intake and drainage structure information; for each water intake location i j , extract the temperature rise time series from the time series dataset of the temperature rise field of the concerned unit: T_j(t) = T(x_j, y_j, t), for(x_j, y_j) = i j; Considering the actual size of the water intake, centered on the water intake location, calculate the average temperature rise in the surrounding area (usually a 3×3 grid): T'_j(t) = (1 / N)·∑_{||x - x_j||≤R, ||y - y_j||≤R} T(x, y, t), where R is the influence radius of the water intake (usually 1 - 1.5 times the width of the water intake), and N is the number of grids within the calculation range; Apply temporal smoothing filtering to eliminate short-term fluctuations: T''_j(t) = (1 / W)·∑_{|τ - t|≤w} w(τ - t)·T'_j(τ), where w(τ - t) is the temporal weight function, W is the sum of the weights, and w is the half-width of the temporal window; Generate a time series dataset of the water intake temperature rise, containing the temperature rise time series at all water intake locations.

[0133] Read the time series dataset of the water intake temperature rise and calculate the statistical characteristics of the temperature rise at each water intake; Calculate the basic statistics: Average temperature rise: μ_j = (1 / T)·∑_t T''_j(t); Maximum temperature rise: max_j = max_t{T''_j(t)}; Standard deviation of temperature rise: σ_j = sqrt[(1 / T)·∑_t (T''_j(t) - μ_j 2 ; 95% percentile temperature rise: P95_j = Percentile(T''_j, 95%). Evaluate the persistence of the temperature rise by calculating the exceedance duration at different temperature rise thresholds l: D_j(l) = the maximum duration of the continuous time period {t | T''_j(t) > l}; Analyze the periodicity of the temperature rise and use power spectrum analysis to identify the main periodic components: PS_j(f) = |FFT(T''_j(t))| 2 , identify the peak frequency f_peak and the corresponding period P_j = 1 / f_peak; Generate a statistical dataset of the water intake temperature rise, containing the statistical characteristics of the temperature rise at each water intake.

[0134] Read the statistical dataset of the temperature rise at the water intake, evaluate the potential impact of the temperature rise on the water intake, and set the temperature rise evaluation threshold set according to the ecological safety threshold and the engineering design limit: L_thresh = {l_eco, l_eng}, usually l_eco = 1°C and l_eng = 3°C; calculate the temperature rise exceedance rate: R_j(l) = (number of periods exceeding threshold l / total number of periods) × 100%; evaluate the temperature rise risk level and set the risk level standard: Risk_j = low risk, if max_j < l_eco; medium-low risk, if l_eco ≤ max_j < l_eng and R_j(l_eco) < 20%; medium risk, if l_eco ≤ max_j < l_eng and 20% ≤ R_j(l_eco) < 50%; medium-high risk, if max_j ≥ l_eng and R_j(l_eng) < 10%; high risk, if max_j ≥ l_eng and R_j(l_eng) ≥ 10%. Calculate the potential economic loss index of the temperature rise impact: EL_j = k_j·(μ_j / l_eng)·(R_j(l_eco) / 100%)·P_j, where k_j is the importance coefficient of the water intake, and P_j is the power or flow rate of the water intake, and generate a risk assessment dataset for the water intake, including the risk ratings and impact indicators of each water intake.

[0135] Read the statistical dataset of the temperature rise at the water intake and the risk assessment dataset of the water intake, comprehensively evaluate the impact degree of the warm water discharge of the concerned unit on each water intake, and generate a summary table of impact assessment; design an evaluation system for the impact level of the water intake temperature rise, considering multiple factors: temperature rise amplitude factor: F_T = max_j / l_eng; duration factor: F_D = D_j(l_eco) / D_crit; exceedance frequency factor: F_R = R_j(l_eco) / R_crit; comprehensive impact index: II_j = w_T·F_T + w_D·F_D + w_R·F_R; where w_T, w_D, w_R are weight coefficients, and D_crit and R_crit are the critical duration and critical exceedance rate. Sort each water intake according to the II_j value, identify the key sensitive water intakes, and generate visual charts, including the temperature rise time series chart of the water intake, the temperature rise distribution histogram, the impact level bubble chart, etc. Integrate the above data and analysis results to form a water intake impact assessment dataset.

[0136] In a specific embodiment of the present application, taking a coastal power plant warm drainage as an example, the power plant plans to have 6 units, which will be built in 3 phases. Currently, Phases 1 to 2 (Units 1 to 4) have been approved by the state, and Phase 3 (Units 5 to 6) is under demonstration. A method for stripping the nonlinear superposition effect of warm drainage from an adjacent nuclear thermal power plant is as follows:

[0137] Step 1: Data collection and pre-processing.

[0138] 1.1. Collect information on water intake and drainage structures.

[0139] The water intake of the power station's units 1 to 4 is located on the shore; the water intake of units 5 to 6 is shared with units 1 to 4, and the outlet is located 2.2 km offshore; the water depth at the outlet is about 14.5 m, and the water discharge of a single unit is about 65 m 3 / s, the temperature rise of the discharged water is about 8℃. The thermal power plant is located about 9km northeast of the power plant, and the discharge capacity of a single unit is about 35m 3 / s, the drainage temperature rises by about 5°C. The drainage structure information data set D1 and the drainage condition data set D2 are formed.

[0140] 1.2. Collect hydrological and meteorological data.

[0141] The hydrological observation data of the study area in the past five years were collected. The sea area is mainly controlled by the M2 semi-diurnal tide, with an average tidal range of about 3.2m and a maximum flow rate of about 1.2m / s. The hydrological observation data set D3 was formed, which includes the tide level, flow rate, flow direction and water temperature time series of multiple measuring points. The meteorological data of the same period were collected to form the meteorological observation data set D4, which includes parameters such as temperature, wind speed and wind direction.

[0142] 1.3. Underwater terrain data collection and processing.

[0143] Underwater topographic survey data of the study area were collected to form the original topographic dataset D5. The water depth in the study area is about 14.5m at the outlet, and the water depth gradually becomes shallower to 2~3m along the coast. The original data were digitized to form a digitized water depth topographic dataset D6, and abnormal data were corrected to obtain a corrected water depth topographic dataset D7.

[0144] 1.4. Data collection and preprocessing of measured temperature rise field.

[0145] The measured data of temperature rise field during operation of existing units are collected, and after data quality control, a corrected temperature rise field data set is formed, and the temperature rise characteristic data set D10 is extracted for subsequent model verification.

[0146] Step 2: Construction and calibration of high-precision mathematical model.

[0147] 2.1. Build a computing grid.

[0148] In this embodiment, a certain coastal power station is taken as the research object, and the calculation domain ranges from the power plant as the center, a rectangular area of 30 km in the east-west direction and 25 km in the north-south direction. The Triangle grid generation software is used to perform unstructured triangular grid meshing on the calculation domain, and the number of initial grid cells generated is 89,532. According to the diffusion characteristics of the warm wastewater, the grid is locally refined: within a range of 500 m around the drainage outlet: the grid size is 20 m; within a range of 500 m - 2 km around the drainage outlet: the grid size is 150 m; within a range of 2 km - 3 km around the drainage outlet: the grid size is 250 m; within a range of 200 m around the water intake: the grid size is 20 m; other nearshore areas (water depth less than 10 m): the grid size is 50 m; far-field area: the grid size is 500 - 2000 m. After grid refinement, the total number of cells increases to 125,753. The grid quality inspection shows that the minimum interior angle is 30° and the maximum interior angle is 116.3°, meeting the requirements of numerical calculation stability. The water depth data is interpolated onto the grid nodes to generate the grid terrain dataset D15.

[0149] 2.2. Boundary conditions and initial condition settings.

[0150] 2.2.1. Extraction of boundary condition characteristic modes.

[0151] Wavelet transform analysis is performed on the measured tidal level data. The Morlet wavelet is selected as the mother wavelet function, the scale ranges from 1 to 128, and the time step is 10 minutes. The multi-scale boundary condition dataset 1 obtained after the transformation contains the following characteristic components: short-time scale component (T < 1 h): mainly manifested as high-frequency perturbations, with an amplitude of about 0.05 - 0.1 m; medium-time scale component (1 h < T < 12 h): mainly contains semi-diurnal tide components such as M2 (12.42 h), S2 (12 h), N2 (12.66 h), etc., with an amplitude of about 1.5 - 2.0 m; long-time scale component (T > 12 h): mainly contains diurnal tide components such as K1 (23.93 h), O1 (25.82 h), etc. and meteorological tide components, with an amplitude of about 0.3 - 0.5 m. Kernel principal component analysis is applied to extract non-linear features, and the radial basis function K(x, y) = exp(-0.05||x - y|| is selected as the kernel function 2)。The optimal kernel parameter γ = 0.05 is determined through cross-validation. Under this parameter, the training error is 0.032 m and the validation error is 0.045 m. After feature extraction, a total of 38 feature patterns are obtained. Among them, the first 12 feature subsets with the cumulative explained variance reaching 95% are selected to form the boundary feature pattern library: M1: representing the characteristics of the M2 principal semidiurnal tide, with an explained variance of 55.7%; M2: representing the characteristics of the S2 semidiurnal tide, with an explained variance of 17.3%; M3: representing the characteristics of the K1 principal diurnal tide, with an explained variance of 9.5%; M4: representing the characteristics of the O1 diurnal tide, with an explained variance of 4.8%; M5 - M12: representing the interaction and nonlinear characteristics of various tidal waves, with a cumulative explained variance of 7.7%.

[0152] 2.2.2. Reconstruction of the boundary conditions of the feature patterns.

[0153] Based on the feature pattern library, a boundary condition reconstruction model is constructed, and feature pattern reconstruction is implemented for the east, south, and north open boundaries of the model. Taking the east boundary as an example, the calculation process of the reconstruction formula is as follows: The initial reconstruction coefficients are determined by the least squares method: α1(t0) = 1.73, α2(t0) = 0.68, α3(t0) = 0.45, α4(t0) = 0.29, α5(t0) = 0.18,..., α12(t0) = 0.05; The reconstruction coefficients are optimized by applying physical constraints and solved by the Lagrange multiplier method: Constraint 1: Continuity of the tidal level, |B(t + Δt) - B(t)| ≤ 0.1 m / 10 min; Constraint 2: Conservation of energy, ∫B 2 (t)dt = ∫B' 2 (t)dt, where B' is the measured tidal level; The optimized reconstruction coefficients are: α1(t0) = 1.69, α2(t0) = 0.71,... Design a recurrence relation to calculate the time-varying weight coefficients: Δαi(t) = 0.02·(BT(t) - B(t))·Mi + 0.005·(dBT / dt - dB / dt)·Mi; where BT(t) is the target boundary condition and B(t) is the current reconstructed boundary condition. For example, calculate the reconstruction coefficients at t = t0 + 1 h: α1(t0 + 1 h) = 1.69 + 0.09 = 1.78; α2(t0 + 1 h) = 0.71 - 0.03 = 0.68;... Apply the boundary condition reconstruction model. When converting from the full-field model to the half-field model, for the boundary conditions in the drainage area of units 5 - 6, a smooth transition function is designed: f(t) = 10·(t / T) 3 - 15·(t / T) 4 + 6·(t / T) 5, where \(T = 1h\); when \(t = 0min\), \(f(0)=0\), maintaining the original boundary conditions; when \(t = 30min\), \(f(0.5)=0.5\), and the boundary conditions have transitioned by 50%; when \(t = 60min\), \(f(1.0)=1.0\), fully transitioning to the new boundary conditions. After the smooth transition process, in the drainage outlet area of Unit 5, the maximum temperature boundary condition change rate has decreased from \(1.2^{\circ}C / 10min\) to \(0.25^{\circ}C / 10min\), improving the numerical stability. The generated smooth boundary condition dataset is applied to the half-field simulation.

[0154] 2.3. Presetting and calibration of model parameters.

[0155] The initial parameter settings are based on empirical values and literature recommendations: bottom roughness: \(n = 0.025\) (corresponding to sandy seabed in open sea areas); horizontal eddy viscosity coefficient: \(E_h = 0.1 m\) 2 / s; vertical eddy viscosity coefficient: \(E_v = 0.01 m\) 2 / s; horizontal temperature diffusion coefficient: \(D_h = 0.5 m\) 2 / s; vertical temperature diffusion coefficient: \(D_v = 0.01 m\) 2 / s. Using this set of initial parameters to run a 30-day simulation and comparing with the measured data of 6 tide gauge stations and 4 flow velocity stations: average correlation coefficient of tide level: 0.91; root mean square error of tide level: \(0.22m\); average correlation coefficient of flow velocity: 0.83; root mean square error of flow velocity: \(0.19m / s\). For the model performance, a parameter sensitivity test is conducted, with a total of 24 groups of parameter combinations tested. Finally, the optimal parameter set is determined: bottom roughness: \(n = 0.022\); horizontal eddy viscosity coefficient: \(E_h = 0.12 m\) 2 / s; vertical eddy viscosity coefficient: \(E_v = 0.008 m\) 2 / s; horizontal temperature diffusion coefficient: \(D_h = 0.65 m\) 2 / s; vertical temperature diffusion coefficient: \(D_v = 0.007 m\) 2 / s. The simulation results under the optimal parameters are significantly improved: average correlation coefficient of tide level: 0.97; root mean square error of tide level: \(0.12m\); average correlation coefficient of flow velocity: 0.91; root mean square error of flow velocity: \(0.11m / s\). The existing temperature drainage monitoring data shows that the measured average temperature rise at 500m around the drainage outlets of Units 1 - 4 is \(2.3^{\circ}C\), and the model calculated value is \(2.1^{\circ}C\), with an error of approximately 8.7%; the measured average temperature rise at 1000m is \(1.1^{\circ}C\), and the model calculated value is \(1.05^{\circ}C\), with an error of approximately 4.5%, meeting the accuracy requirements of the temperature drainage model.

[0156] Step Three: Conduct full-field temperature drainage simulation calculations.

[0157] 3.1. Full-field simulation condition settings. Set the boundary conditions for all units to operate simultaneously: For Units 1 - 4 of the power plant: the water intake flow rate for each unit is 65 m 3 / s, and the drainage temperature rise is 8°C; for Units 5 - 6 of the power plant: the water intake flow rate for each unit is 65 m 3 / s, and the drainage temperature rise is 8°C; for 4 units of the thermal power plant: the water intake flow rate for each unit is 35 m 3 / s, and the drainage temperature rise is 5°C. The momentum source term method is used to set the discharge boundary conditions, and momentum and heat source terms are added to the boundary cells of the drainage outlet: Momentum source term: MD = ρQU, where ρ = 1025 kg / m 3 , Q is the flow rate, and U is the discharge velocity; Heat source term: MT = ρcpQΔT, where cp = 4182 J / (kg·°C), and ΔT is the temperature rise. For the drainage outlet of Unit 5, the calculated values are: Momentum source term: MD = 1025×65×2.5 = 166,562.5 kg·m / s 2 ; Heat source term: MT = 1025×4182×65×8 = 2,228,716,000 J / s.

[0158] 3.2. Multi-time scale feature-preserving tidal phase decomposition simulation.

[0159] 3.2.1. Tidal phase decomposition.

[0160] Perform Hilbert-Huang transform analysis on the tidal level data of the study area for one month to identify the main tidal wave components: M2 semi-diurnal tide: amplitude 1.53 m, phase 54.3°; S2 semi-diurnal tide: amplitude 0.62 m, phase 81.5°; K1 diurnal tide: amplitude 0.37 m, phase 322.6°; O1 diurnal tide: amplitude 0.26 m, phase 270.1°. Calculate the characteristics of each tidal phase based on the tidal level derivative. Taking the main measuring station in the study area as an example: Average flood duration: 5 hours and 42 minutes; Average ebb duration: 6 hours and 40 minutes; Maximum flood change rate: 0.73 m / h, occurring in the flood acceleration period; Maximum ebb change rate: -0.61 m / h, occurring in the ebb acceleration period. Divide the tidal phase according to the tidal level change rate and acceleration. Taking July 15 as an example: 00:00 - 02:10: Ebb deceleration period (FD), dh / dt = -0.31 m / h, d 2 h / dt 2 = 0.12 m / h 2 ; 02:10 - 02:40: Slack period (S), dh / dt = -0.04 m / h; 02:40 - 05:20: Flood acceleration period (RA), dh / dt = 0.45 m / h, d 2 h / dt 2 = 0.15 m / h 2; 05:20 - 06:40: Rising tide extreme period (RP), dh / dt = 0.72 m / h, |d 2 h / dt 2 | = 0.009 m / h 2 ; 06:40 - 08:30: Rising tide deceleration period (RD), dh / dt = 0.38 m / h, d 2 h / dt 2 = -0.18 m / h 2 ;... Analyze the spatial propagation characteristics of the tidal phase. Taking the M2 tidal wave as an example, the time delay from the east boundary to the center of the study area is about 25 minutes, and the propagation speed is about 20 km / h.

[0161] 3.2.2, Phase - preserving time - step optimization.

[0162] Calculate the characteristic time scales for different regions and tidal phases: Near the drainage outlet, in the high - temperature gradient region: Rising tide extreme period (RP): U = 0.75 m / s, H = 14.5 m, D = 0.65 m 2 / s, characteristic time scale τ(RP)=133 s; Slack period (S): U = 0.08 m / s, characteristic time scale τ(S)=1250 s; Calculate the optimal time steps: Δt(RP)=60×0.7 = 42 s, Δt(S)=60×0.2 = 12 s. In the main diffusion region of the drainage outlet thermal plume (range of 500 m - 2 km): Rising tide extreme period: τ(RP)=267 s, Δt(RP)=60×0.9 = 54 s; Slack period: τ(S)=2000 s, Δt(S)=60×0.4 = 24 s. In the far - field region (more than 2 km): Rising tide extreme period: τ(RP)=400 s, Δt(RP)=60×1.0 = 60 s; Slack period: τ(S)=3000 s, Δt(S)=60×0.7 = 42 s. Organize the calculated time steps for each region and each tidal phase into a phase - time - step strategy D33 table for guiding the simulation calculation.

[0163] 3.2.3, Full - field warm - water discharge simulation execution.

[0164] Perform a full-field simulation based on the phase time-step strategy D33 to calculate the thermal discharge diffusion process for 31 days from July 1st to July 31st, 2023. The simulation results are output every 10 minutes, generating a total of 4464 time-step results. Taking 13:00 on July 15th (mid-tide) as an example, apply the feature-preserving interpolation algorithm to process the data: The adjacent output times are 12:50 (t_a) and 13:00 (t_b), and calculate the interpolation weight α(12:55) = 0.5; linearly interpolate to obtain the preliminary temperature field at 12:55: T_init = 0.5×T(12:50) + 0.5×T(13:00); calculate the feature-preserving correction term: temperature gradients: ▽T(12:50) = (0.12, 0.08) °C / 100m, ▽T(13:00) = (0.14, 0.09) °C / 100m; the correction coefficient β = 0.3; C(x, y, 12:55) = 0.3×[0.15×5min - 0.05×5min] = 0.15 °C. The temperature field after feature-preserving interpolation: T(12:55) = T_init + C = T_init + 0.15 °C; after feature-preserving interpolation processing, generate a complete time-series dataset D35 of the full-field temperature rise field for subsequent stripping calculations.

[0165] Step 4. Half-field thermal discharge simulation calculation.

[0166] 4.1. Half-field simulation condition setting.

[0167] Construct half-field simulation conditions, that is, shut down units 5 - 6: For power plant units 1 - 4: The water intake flow rate of each unit is 65 m 3 / s, and the drainage temperature rise is 8 °C; For power plant units 5 - 6: Shut down, and the water intake and drainage flow rates are 0; For 4 units of the thermal power plant: The water intake flow rate of each unit is 35 m 3 / s, and the drainage temperature rise is 5 °C; Remove the momentum and heat source terms of units 5 - 6 to form a half-field water intake and drainage condition dataset D38.

[0168] 4.2. Feature-mode-preserving boundary transition processing.

[0169] 4.2.1. Smooth transition of boundary conditions.

[0170] Calculate the boundary condition differences between the full-field model and the half-field model: Drainage outlet position of Unit 5: Δh = 0 m, ΔT = 8 °C, ΔU = 2.5 m / s; Within a 100-m range around the drainage outlet of Unit 5: Average ΔT = 3.2 °C, average ΔU = 0.8 m / s; Within a 500-m range around the drainage outlet of Unit 5: Average ΔT = 0.9 °C, average ΔU = 0.2 m / s. Define the boundary change region Ω_B according to the magnitude of the change: Core change region Ω_c: Within a 100-m range around the drainage outlet of Unit 5; Transition change region: Range from 100 m to 1000 m around the drainage outlet; Far-field region: Region more than 1000 m away from the drainage outlet. Design a smooth transition function to achieve a smooth transition from the full field to the half field: Time transition function f(t): f(0) = 0, t = 0 min; f(0.2) = 0.01, t = 12 min, at this time the boundary condition changes only by 1%; f(0.5) = 0.5, t = 30 min, the boundary condition changes by 50%; f(0.8) = 0.99, t = 48 min, the boundary condition changes by 99%; f(1.0) = 1.0, t = 60 min, the boundary condition changes completely. Spatial modulation function g(x): g(x) = 1.0 when x is within the core change region Ω_c; g(x) = exp(-d(x, Ω_c) / 500) when x is within the transition region, where d is the distance (m) to the core region; for example, at a distance of 200 m from the core region, g = exp(-200 / 500) = 0.67; at a distance of 1000 m from the core region, g = exp(-1000 / 500) = 0.14. Example of comprehensive smooth function calculation: Drainage outlet position, t = 30 min: B_trans = B_initial + (B_final - B_initial)×0.5×1.0 = B_initial + 0.5×ΔB; At a distance of 500 m from the drainage outlet, t = 30 min: B_trans = B_initial + (B_final - B_initial)×0.5×0.37 = B_initial + 0.18×ΔB.

[0171] 4.2.2. Boundary energy balance constraint.

[0172] Calculate each component of the boundary condition energy evaluation function: Kinetic energy component: E_k = ∫0.4×1025×v 2 / 2×dΩ = 3.76×10 6 J (full-field condition); Potential energy component: E_p = ∫0.3×1025×9.8×h×dΩ = 2.14×10 8 J (full-field condition); Thermal energy component: E_t = ∫0.3×1025×4182×T×dΩ = 7.93×109 J (Full-field conditions). The energy change from full-field to half-field: ΔE_k = -9.12×10 5 J (due to the removal of the momentum source of units 5 - 6); ΔE_p = -7.83×10 6 J (due to the water level change); ΔE_t = -7.58×10 8 J (due to the removal of the heat source). Design the parameters of the energy balance controller: control gain K = 0.2; weights of the energy gradient directions: w_k = 0.35, w_p = 0.25, w_t = 0.4. Apply the energy balance controller to adjust the boundary condition reconstruction coefficient: Calculate the current energy deviation: ΔE = E_target - E_current = -1.83×10 7 J (at t = 15 min); Calculate the gradient of energy with respect to the reconstruction coefficient: ▽α_i E = [ΨE / Ψα1, ΨE / Ψα2, ..., ΨE / Ψα12]; Update the reconstruction coefficient: Δα_i = 0.2 × (-1.83×10 7 ) × ▽α_i E; Δα1 = -0.022, Δα2 = -0.018, ..., Δα12 = -0.003; Reconstruct the boundary condition: B_bal = Σ(α_i + Δα_i)×M_i. Through 50 iterative steps, the energy deviation during the boundary condition change process is reduced from the initial -7.67×10 8 J to -3.21×10 6 J, and the relative error is reduced to 0.4%, meeting the energy balance requirement.

[0173] 4.3. Execution of half-field warm water discharge simulation.

[0174] Apply the energy balance boundary condition D42 and the phase time step strategy D33 to the half-field simulation, and perform the simulation calculation for the same time period (from July 1st to July 31st, 2023) as the full-field simulation. The processing method is the same as that of the full-field simulation, and a time series dataset D44 of the half-field temperature rise field is generated.

[0175] 4.4. Analysis of half-field temperature rise characteristics.

[0176] Analyze the characteristics of the half - field temperature rise field and compare with the full - field simulation results: Average temperature rise at 500 m from the drainage outlets of Units 1 - 4: Half - field 2.1°C vs Full - field 2.1°C (no change); Temperature rise at the proposed drainage outlet location of Unit 5: Half - field 0.2°C vs Full - field 8.0°C (a decrease of 7.8°C); Temperature rise at the power plant water intake: Half - field 0.32°C vs Full - field 0.62°C (a decrease of 0.3°C); Temperature rise at the thermal power plant water intake: Half - field 0.18°C vs Full - field 0.21°C (a decrease of 0.03°C). Analyze the temperature rise characteristics of different tidal phases to form the half - field tidal - phase temperature rise characteristic dataset D46 for subsequent non - linear stripping analysis.

[0177] Step Five: Non - linear temperature rise stripping calculation.

[0178] 5.1. Heat flux conservation stripping with streamline coordinate transformation.

[0179] 5.1.1. Construction of the streamline coordinate system.

[0180] Extract the velocity field from the full - field temperature rise field time - series dataset D35 to form the full - field velocity field time - series dataset D47. Select a typical moment t = 168 h (mid - flood tide), with a velocity range of 0.2 - 0.8 m / s and a main flow direction of 45°. Starting from the drainage outlet of Unit 5, track the water particle trajectories along the main flow direction, calculate 21 main streamlines, and the streamline spacing is 50 m at the drainage outlet. Establish an orthogonal curvilinear coordinate system for the streamlines to form the streamline coordinate system dataset D49 and the coordinate mapping model D50.

[0181] 5.1.2. Reconstruction of the heat flux conservation equation.

[0182] Derive the heat transfer equation in the streamline coordinate system: ΨT / Ψt + UΨT / Ψs = (1 / sqrt(g))·Ψ / Ψs(sqrt(g)·Ds·ΨT / Ψs) + (1 / sqrt(g))·Ψ / Ψn(sqrt(g)·Dn·ΨT / Ψn) + S, where T is the temperature field, U is the flow velocity, g is the Jacobian determinant of the coordinate transformation, Ds and Dn are diffusion coefficients, and S is the heat source term. Establish the temperature - dependent diffusion coefficient models Ds(T) = Ds 0 ·[1 + αs·(T - T 0 ) + βs·(T - T 0 ) 2 , Dn(T) = Dn 0 ·[1 + αn·(T - T 0 ) + βn·(T - T 0 ) 2 , where Ds 0 = 0.65 m 2 / s, Dn 0 = 0.4 m2 / s, αs = 0.02 / °C, αn = 0.01 / °C, βs = βn = 0.001 / °C 2 , T 0 = 20°C. Establish the temperature - density relationship model ρ(T) = ρ 0 ·[1 - α·(T - T 0 )], where ρ 0 = 1025 kg / m 3 , α = 2×10⁻ 4 / °C. Calculate the density flow correction term ΔU(s, n) = g·H 2 / (8ν)·Ψρ / Ψs·(1 - (2n / H) 2 ), where g = 9.8 m / s 2 , H = 14.5 m, ν = 10⁻ 6 m 2 / s. Form the streamline heat transfer model D51.

[0183] 5.1.3. Streamline segmented non - linear stripping

[0184] Convert the full - field temperature rise field time - series dataset D35 and the half - field temperature rise field time - series dataset D44 to the streamline coordinate system to form the streamline temperature rise field dataset D52 and the streamline half - field temperature rise field dataset D53. Calculate the streamline heat fluxes of the full - field and half - field temperature rise fields Q_full(s, n) = ∫ 0 s [(U + ΔU_full)·T_full - Ds·ΨT_full / Ψs]·sqrt(g)·ds', Q_half(s, n) = ∫ 0 s [(U + ΔU_half)·T_half - Ds·ΨT_half / Ψs]·sqrt(g)·ds'. Calculate the heat flux difference ΔQ(s, n) = Q_full(s, n) - Q_half(s, n) and the non - linear correction term Φ(s, n, T) = ΔU_full·ΨT_full / Ψs - ΔU_half·ΨT_half / Ψs + (Ds(T_full)·ΨT_full / Ψs - Ds(T_half)·ΨT_half / Ψs), and apply the stripping formula ΔTconcern(s, n) = T_full(s, n) - T_half(s, n) + ∫ 0 sΦ(s', n, T)·sqrt(g)·ds' / (U·sqrt(g)) forms the streamline stripping temperature rise field dataset D54. Where ΔU_full is the density flow correction term of the full-field temperature rise field, T_full is the temperature distribution in the full-field temperature rise field, s' is the integration variable, ΔU_half is the density flow correction term of the half-field temperature rise field, and T_half is the temperature distribution in the half-field temperature rise field.

[0185] 5.1.4. Streamline temperature rise restoration.

[0186] Apply the coordinate mapping model D50 to map the streamline stripping temperature rise field dataset D54 back to the Cartesian coordinate system, and use the interpolation method that conserves heat ∫∫ T(s, n)·sqrt(g)·dsdn = ∫∫ T(x, y)·dxdy to ensure the restoration accuracy, forming the preliminary stripped temperature rise field dataset D55.

[0187] 5.2. Decoupling of tide-controlled thermal cycle paths.

[0188] 5.2.1. Identification of tide-controlled thermal cycle paths.

[0189] Calculate the connectivity index of the intake and discharge ports C(o, i, φ) = exp(-τ(o→i, φ) / τ 0 )·exp(-d(o→i, φ) / d 0 ), where τ 0 = 12h, d 0 = 10km. For the flood tide phase (φ = RP), the connectivity C from the discharge port of Unit 5 to the power plant intake is 0.21, and the connectivity C to the intake of the thermal power plant is 0.08. Apply the Lagrangian particle tracking method, release 100 tracer particles, and obtain the thermal cycle path set D56, where 32% reach the power plant intake and 7% reach the intake of the thermal power plant. Calculate the path intensity S(p, φ) = Q·ΔT·P(p, φ)·exp(-τ(p, φ) / τ_d), where Q = 65m 3 / s, ΔT = 8℃, τ_d = 24h, and obtain S(p1, RP) = 35.8m 3 ·℃ / s, S(p2, RP) = 5.2m 3 ·℃ / s. Form the thermal cycle path intensity dataset D57.

[0190] 5.2.2. Thermal cycle matrix decomposition.

[0191] Construct the thermal cycle matrix C(φ) = [cij(φ)]. For the discharge port of Unit 5 to the power plant intake, c51(RP) = 0.16; to the intake of the thermal power plant, c52(RP) = 0.02. Apply the singular value decomposition C(RP) = U·Σ·V T, the main eigenvalue σ is obtained 1 = 0.22, and the corresponding eigenvector is (0.85, 0.15). The main mode dataset D58 of the thermal cycle is formed.

[0192] 5.2.3. Decoupling of thermal cycle effects.

[0193] Apply the decoupling equation ΔTdirect = [I - C(φ)] -1 ·ΔT to preliminarily strip the temperature rise field dataset D55. Taking the water intake of the power plant as an example, the preliminary stripped temperature rise ΔT = 0.7 °C, and the directly contributed temperature rise after decoupling ΔTdirect = 0.6 °C. The decoupled temperature rise field dataset D59 of the thermal cycle is formed.

[0194] 5.3. Comprehensive reconstruction of the temperature rise field.

[0195] Apply physical constraint conditions to the decoupled temperature rise field dataset D59 of the thermal cycle to ensure that the temperature rise field meets the requirements of energy conservation and continuity, and generate the final time-series dataset D60 of the temperature rise field of the concerned unit.

[0196] Step Six. Statistics and evaluation of the temperature rise influence range.

[0197] 6.1. Extraction of temperature rise field data in multiple time periods.

[0198] Extract the temperature rise field data in different time periods from the time-series dataset D60 of the temperature rise field of the concerned unit, including typical moments such as the mid-tide and mid-ebb, and form the multi-time-period temperature rise field dataset D61.

[0199] 6.2. Calculation of the area of the temperature rise isotherm.

[0200] Based on the multi-time-period temperature rise field dataset D61, calculate the coverage range and area of different temperature rise isotherms (0.5 °C, 1 °C, 2 °C, 3 °C, 4 °C), and form the temperature rise area statistics dataset D62.

[0201] 6.3. Calculation of the envelope of the temperature rise influence range.

[0202] 6.3.1. Extraction of multi-time-period temperature rise isotherms.

[0203] Extract each temperature rise isotherm C(l, t) = {(x, y) | T(x, y, t) = l} from the multi-time-period temperature rise field dataset D61, determine the exact position of the isotherm between grids using bilinear interpolation, and apply the Douglas-Peucker simplification algorithm to reduce redundant points, forming the multi-time-period temperature rise isotherm dataset D631.

[0204] 6.3.2. Statistics of the extreme values of the temperature rise influence range.

[0205] Statistically analyze the maximum influence range of each temperature rise isotherm over the entire simulation period. Taking the 2°C temperature rise isotherm as an example, the maximum range during the mid-tide period is an elliptical area centered on the drainage outlet, with a major axis direction of 45°, a major axis length of approximately 1.8 km, and a minor axis length of approximately 0.7 km; the maximum range during the mid-ebb period is an elliptical area with a major axis direction of 225°, a major axis length of approximately 2.2 km, and a minor axis length of approximately 0.8 km. Generate the temperature rise extreme value statistical data set D632.

[0206] 6.3.3. Envelope line optimization and generation.

[0207] Apply the α-shape method to generate the precise envelope line of each temperature rise isotherm E(l) = AlphaShape(∪_t C(l, t), α), where α = β·sqrt(A_max(l) / π) / N_points and β = 2.5. Taking the 2°C temperature rise isotherm as an example, merge all time point sets (a total of 56,172 points), and calculate α = 4.3 m. Generate the temperature rise envelope line data set D63.

[0208] 6.3.4. Envelope area calculation and correction.

[0209] Calculate the area enclosed by the temperature rise envelope line A(l) = 0.5·|∑_i (x_i·y_{i + 1} - x_{i + 1}·y_i)|, and apply geographic projection correction A_corr(l) = A(l)·cos(φ_avg), where φ_avg = 38.4°N is the average latitude of the study area. Generate the temperature rise envelope area data set D64, where the 2°C temperature rise envelope area is 3.85 km 2 .

[0210] 6.4. Assessment of the temperature rise impact on the water intake.

[0211] 6.4.1. Extraction of the temperature rise time series at the water intake location.

[0212] Extract the temperature rise time series at the locations of the power plant water intake and the thermal power plant water intake to form the water intake temperature rise time series data set D65.

[0213] 6.4.2. Analysis of the statistical characteristics of the water intake temperature rise.

[0214] Calculate the statistical characteristics of the power plant water intake: average temperature rise μ = 0.62°C, maximum temperature rise max = 0.91°C, standard deviation of temperature rise σ = 0.14°C, 95% quantile temperature rise P95 = 0.85°C. Statistical characteristics of the thermal power plant water intake: average temperature rise μ = 0.21°C, maximum temperature rise max = 0.38°C. Generate the water intake temperature rise statistical data set D651.

[0215] 6.4.3. Assessment of the temperature rise exceedance rate and risk level.

[0216] Set the temperature rise evaluation threshold set \(L_{thresh}=\{l_{eco}, l_{eng}\}\), where \(l_{eco} = 1^{\circ}C\) and \(l_{eng}=3^{\circ}C\). Calculate the temperature rise exceedance rates \(R(l_{eco}) = 0\%\) and \(R(l_{eng}) = 0\%\) at the power plant water intake, and evaluate the risk level as "low risk". Form the water intake risk assessment data set \(D652\).

[0217] 6.4.4. Generation of the comprehensive impact assessment report.

[0218] Integrate the analysis results to generate the warm water discharge impact assessment report \(D67\), including the temperature rise range map, the temperature rise area statistical table, and the water intake impact analysis.

[0219] The result comparison shows that, compared with the traditional linear stripping method, the envelope area of this embodiment in the low temperature rise region (\(0.5^{\circ}C\)) is \(31.2 km\) 2 , while the traditional method is \(56.3 km\) 2 , with a deviation as high as \(80.3\%\); in the high temperature rise region (\(4^{\circ}C\)), the area of this embodiment is \(0.43 km\) 2 , and the traditional method is \(0.45 km\) 2 . This indicates that this embodiment has advantages in evaluating the far - field impact of warm water discharge and predicting the temperature rise at the water intake, and can meet the specification requirements of "the prediction deviation of the temperature rise range does not exceed 5%".

[0220] The present invention provides a stripping method for the non-linear superposition effect of adjacent nuclear power plant cooling water discharge, including collecting basic data; establishing and calibrating a hydrodynamic and thermal mathematical model; simulating the full-field temperature rise; simulating the half-field temperature rise; non-linear stripping; and statistical analysis. Among them, the heat flux conservation stripping method using streamline coordinate transformation is adopted to transform the temperature rise field into the streamline coordinate system, considering the non-linear correction term formed by the influence of temperature on fluid density; and combined with the tide-controlled thermal cycle path decoupling algorithm, the thermal cycle paths under different tidal phases are identified, and a thermal cycle matrix is constructed to separate the direct cooling water discharge effect and the thermal cycle cumulative effect. Through streamline coordinate transformation, the present invention accurately captures the plume characteristics of the discharge outlet, solves the problem that the traditional method cannot accurately describe the heat transport in the high-temperature gradient region, and the reconstruction of the heat flux conservation equation considers the influence of temperature on fluid density, essentially solving the non-linear superposition effect. It solves the problem of insufficient accuracy of the traditional fixed time step method during the tidal current change process, avoids the numerical oscillation of the conventional interpolation method in the violently changing region, and the phase-differentiated stripping strategy considers the physical characteristics of different tidal phases, improving the stripping accuracy, especially in the high-dynamic region near the discharge outlet. It overcomes the limitations of the traditional warm water mass tracking method in complex tidal environments, the thermal cycle matrix decomposition method extracts the key thermal cycle modes, reduces the computational complexity, and the decoupling algorithm accurately strips the direct cooling water discharge effect and the thermal cycle cumulative effect. The non-linear characteristic mode extraction overcomes the limitations of the traditional Fourier analysis in dealing with non-linear boundary conditions, the mode-preserving boundary reconstruction method ensures the physical rationality of the boundary condition change, and the boundary energy balance constraint avoids the non-physical oscillation caused by the sudden change of the boundary condition, especially having obvious advantages when the amplitude of the boundary condition change is large.

[0221] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A method for stripping warm wastewater from a nuclear power plant adjacent to the nonlinear superposition effect, characterized in that: include: Collect and study the power plant's water intake and discharge information, hydrological meteorological and topographic data to form a basic data set; Based on the basic data set, a hydrodynamic and thermodynamic mathematical model of the research waters was established and calibrated to obtain a calibrated model; Use the calibration model to carry out warm discharge simulation of all units to obtain the temperature rise field data of the whole field; After shutting down the concerned unit, simulation is performed based on the same rated model to obtain half-field temperature rise field data; The full-field temperature rise field data and the half-field temperature rise field data are subjected to nonlinear stripping processing to obtain the temperature rise field data of the unit of interest; Statistical analysis is performed on the temperature rise field data of the units of interest to obtain different temperature rise ranges and corresponding areas as well as the unit water intake temperature rise or temperature rise at sensitive locations.

2. The method according to claim 1, characterized in that The steps of performing nonlinear stripping processing to obtain the temperature rise field data of the unit of interest include: Based on the full-field temperature rise data, the velocity field is extracted and the streamline coordinate system is constructed; The full-field temperature rise field data and the half-field temperature rise field data are converted into the streamline coordinate system to obtain the streamline temperature rise field data; Based on the streamline temperature rise field data, the heat flux along the streamline is calculated and the influence of temperature on fluid density is analyzed to generate nonlinear correction terms; Nonlinear stripping is performed in the streamline coordinate system by combining the heat flux along the streamline and the nonlinear correction term. The stripping results are obtained and mapped back to the original coordinate system to form the temperature rise field data of the unit of interest.

3. The method according to claim 2, characterized in that The steps to construct a streamline coordinate system include: Based on the velocity field, streamline trajectory is calculated; A streamline orthogonal curve coordinate system is established based on the streamline trajectory, including coordinates along the streamline direction and coordinates perpendicular to the streamline direction; Establish a bidirectional mapping relationship between Cartesian coordinates and streamline orthogonal curve coordinate system to form a streamline coordinate system; The heat transfer equation is derived in the streamline coordinate system, and the streamline heat transfer model is formed by combining the temperature-dependent diffusion coefficient and the density flow correction term.

4. The method according to claim 2, characterized in that: The steps of performing nonlinear peeling in the streamline coordinate system to obtain the peeling result include: Based on the streamline temperature rise field data, the heat flux along the streamline in the whole field and half field is calculated respectively, and the heat flux difference is obtained; According to the relationship between temperature and density, the nonlinear correction term of the fluid density change caused by temperature on the heat flux along the streamline is calculated; The heat flux difference is combined with the nonlinear correction term using the streamline piecewise nonlinear stripping formula to obtain the stripping temperature rise field data in the streamline coordinate system, i.e. the stripping result.

5. The method according to claim 4, characterized in that The steps to carry out warm discharge simulation of all units and obtain the temperature rise field data of the whole field include: Based on hydrological and meteorological data, the tidal time series is analyzed and the tidal cycle is decomposed into high tide period, high tide period, low tide period and low tide period to form typical tidal phase classification data; Based on the typical tidal phase classification data, the optimal time step for different typical tidal phases is calculated to form a typical phase time step strategy; The phase time step strategy is applied to the calibration model for thermal drainage simulation to ensure high-precision calculation at the critical moment of tidal flow changes, thereby generating more accurate full-field temperature rise field data.

6. The method according to claim 5, characterized in that The steps to calculate the optimal time step for different tidal phases and form a phase time step strategy include: Based on the tidal phase classification data, the transport characteristics of warm drainage in different tidal phases are analyzed, the characteristic time scale of each tidal phase is calculated, and the phase characteristic time scale data is formed; Based on the phase characteristic time scale data, the time step calculation formula is constructed to determine the optimal time step for different tidal phases and spatial positions, and the phase space time step distribution is generated; Apply spatial smoothing to the phase space time step distribution to ensure that the time steps of adjacent grid cells vary continuously, forming a smooth time step distribution; Integrate the smoothed time step distribution to generate a complete phase time step strategy.

7. The method according to claim 5, characterized in that The step of performing nonlinear peeling processing also includes: Based on the velocity field and tidal phase classification data, the heat circulation path from the drain to the water intake under different tidal phases is identified to form a heat circulation path set; The thermal cycle path set is used to construct a thermal cycle matrix related to the tidal phase, which characterizes the heat transfer intensity from each drainage outlet to each water intake under different tidal phases. The thermal cycle matrix is ​​applied to strip the temperature rise field data, separate the direct thermal discharge influence and the thermal cycle cumulative effect, and obtain the temperature rise field data of the unit of interest after thermal cycle decoupling.

8. The method according to claim 7, characterized in that The steps of forming a thermal cycle path set and constructing a thermal cycle matrix include: Calculate the connectivity index between each outlet and each water intake under different tidal phases to form the outlet-water intake connectivity data; Based on the connectivity data, the path of the water mass from the outfall to the intake is calculated to form a set of thermal circulation paths; Evaluate the heat transfer intensity of each path in the heat cycle path set to generate heat cycle path intensity data; Based on the thermal cycle path intensity data, a tidal phase-dependent thermal cycle matrix is ​​constructed; Singular value decomposition is applied to the thermal cycle matrix to extract the main modes of the thermal cycle and form the main mode data of the thermal cycle.

9. The method according to claim 1, characterized in that: After closing the concerned unit, the steps to obtain the half-field temperature rise data include: Based on the water intake and drainage information before and after the shutdown of the concerned unit, the calibration model is combined to identify the changes in water intake and drainage conditions during the conversion process from the full-field model to the half-field model, and form the water intake and drainage condition change data; Perform characteristic mode-maintaining transition on the change data of water intake and drainage conditions, and generate smooth transition conditions by combining smooth transition function; Establish the energy balance equation of changing conditions, apply energy balance constraints to optimize the smooth transition conditions, and generate energy balance conditions; The energy balance condition is applied to the half-field temperature drainage simulation to ensure the continuity and physical rationality of the condition change and generate the half-field temperature rise field data.

10. The method according to claim 1, characterized in that The steps to obtain the calibration model include: Based on the basic data set, the boundary condition data is analyzed, the key characteristic patterns of the boundary conditions are extracted, and a boundary characteristic pattern library is formed; Based on the boundary feature pattern library, a boundary condition reconstruction model is constructed; Construct a recursive relationship of the weight function, optimize the reconstruction coefficient of the boundary condition reconstruction model, and ensure the continuity of boundary condition changes; Apply the optimized boundary condition reconstruction model to achieve smooth transition of boundary conditions and generate smooth boundary condition data that meets physical constraints; Based on the smooth boundary condition data, a hydrodynamic and thermodynamic mathematical model of the study water area is established and calibrated to obtain a calibrated model.

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