23 results about "Steady state" patented technology

The invention relates to the technical field of coal-fired unit energy flow analysis and prediction, in particular to a coal-fired unit boiler energy flow analysis and prediction method under rapid variable load, which specifically comprises the following steps of: firstly, combining a thermodynamic physical framework with a recurrent neural network, and representing a function relationship between a time-varying parameter and a state variable in a mechanism model through a neural unit; rapid fluctuation of energy in the boiler is accurately captured; secondly, a multi-mode switching strategy based on an attention mechanism is introduced, flexible switching is carried out between a strong coupling state and a weak coupling state, and the prediction precision under high-speed load change is guaranteed; and finally, introducing an error compensation model, and dynamically correcting a hybrid model prediction residual error. According to the method, physical constraints of mechanism modeling, multi-mode switching of an attention mechanism and an error compensation model are integrated and fused, so that the problems that most of hybrid modeling studies in the prior art mainly focus on steady-state or finite transient prediction, and studies on energy flow dynamic characteristics under rapid load change are still relatively deficient are solved.

The application relates to a coaxial heat exchange well heat exchange efficiency prediction method in oil exploitation, which comprises the following steps: a coaxial heat exchange well coupling heat transfer physical model is established; according to the engineering structure and the geological conditions of a target block, a standardized heat transfer model directly used for mathematical solution is established; according to the temperature control equation in the oil pipe and the annular space, the wellbore temperature field partial differential equation is calculated by using Laplace transformation, the fluid temperature at a position of the wellbore is calculated, and the wellhead outlet temperature is obtained; the analytical solution of the formation temperature changing with the radial distance and the time in the target block is obtained, the range of the formation temperature drop in the radial direction after the coaxial heat exchange well runs for a predetermined time is evaluated; and the coaxial heat exchange well heat exchange efficiency is calculated and predicted according to the obtained wellhead outlet temperature and the known well inlet temperature. The application can solve the problems of long solution time in numerical simulation and inaccurate calculation results in the analytical solution based on the steady-state assumption, improve the oil production efficiency and reduce the energy consumption.

The invention belongs to the technical field of engineering simulation data processing, and particularly discloses a multi-physics field iterative coupling prediction method and system, a terminal and a medium. Comprising the steps of performing state initialization on a to-be-predicted object based on prediction batch input, and constructing structured feature representation for multi-physics field prediction; constructing a temperature prediction model and a pressure prediction model which are mutually independent; performing multiple rounds of prediction updating on the temperature physical field by adopting the temperature prediction model, and dynamically adjusting related state parameters according to the prediction state of the temperature physical field in the prediction updating process to obtain a stable prediction state of the temperature physical field; and on the basis, performing prediction processing on the pressure physical field by adopting a pressure prediction model to obtain a pressure physical field prediction result corresponding to the temperature physical field prediction state, and outputting a multi-physical field steady state prediction result. By introducing a multi-physics field iterative coupling prediction mechanism, the physical consistency and stability of a multi-physics field prediction result are improved.

The invention provides a rub-impact fault nonlinear characteristic evolution law analysis method based on peak statistics in a period, and the method comprises the steps: firstly building a rub-impact fault model, and selecting two kinetic parameters in the model as analysis parameters; according to the selected dynamic parameter value interval range, a two-dimensional parameter space is established, grid division is carried out, a plurality of grid points are obtained, and each grid point corresponds to one parameter combination; performing numerical integration on each parameter combination to obtain a converged model output steady-state track; and continuously capturing a plurality of extreme points in the steady-state track to perform cycle similarity test, counting the number Nspike of spike pulses in a single cycle as a characteristic quantity, mapping the characteristic quantity to a two-parameter plane to generate an isoperidic map, and obtaining a distribution range of a chaos interval in the isoperidic map. According to the method, the calculation complexity of parameter sensitivity analysis can be remarkably reduced on the premise of keeping accurate identification of chaotic threshold parameters by counting the peak characteristics of the system in periodic oscillation.

The invention relates to the technical field of industrial control, in particular to a control method and system for an insulating material production line. The method comprises the steps of obtaining initial operation data of an insulating material production line in real time; calculating a transient specific energy index in combination with a standard plasticizing reference absolute temperature; performing smooth filtering on the machine head melt pressure to extract a time differential item to obtain a pressure change rate; deducing a dynamic thinning coefficient by combining the transient specific energy index, the previous sampling moment value, the pressure change rate and the machine head melt pressure; constructing a state matrix mismatch correction factor based on the dynamic thinning coefficient, a sliding data window historical maximum thinning limit value and a standard steady-state thinning reference value; and correcting the discrete state space model system matrix in a targeted manner by using the correction factor, and executing adaptive model prediction control to output an optimal control instruction. The method can effectively solve the mismatch problem of the static model under the nonlinear sudden change condition, improves the control precision and reliability, and guarantees the stable and efficient operation of a production line.

This application discloses a method and apparatus for predicting the electrical risk of oil-paper insulated equipment. The method includes: acquiring multiple oil and gas sample data with labels and confidence levels from the oil-paper insulated equipment at different time windows; generating a molecular model formulation of the mixture; constructing a molecular simulation mixture model of the mixture formulation at the current temperature; and performing geometric optimization, model annealing, and ensemble balancing to obtain a steady-state model. The method then calculates the total dipole moment of the molecular dynamics system of the steady-state model and the dielectric loss factor, using the planned quantitative value of the dielectric loss factor to assess the degree of electrical risk of the oil-paper insulated equipment. Therefore, this method provides a mechanistic and quantitative mapping from monitoring data to electrical risk, accurately calculates real-time dielectric parameters, considers individual equipment differences, quantifies risk values, and classifies them, improving the accuracy of early degradation identification and thus enhancing the accuracy of electrical risk monitoring for oil-paper insulated equipment.

The application relates to the technical field of industrial control, in particular to a control method and system of an insulating material production line. The method comprises the following steps: acquiring initial operation data of the insulating material production line in real time; calculating a transient specific energy index in combination with a standard plasticization reference absolute temperature; extracting a pressure change rate by performing smooth filtering on a time differential item of a head melt pressure; deducing a dynamic thinning coefficient in combination with the transient specific energy index, a value at a previous sampling moment, the pressure change rate and the head melt pressure; constructing a state matrix mismatch correction factor based on the dynamic thinning coefficient, a historical maximum thinning limit value of a sliding data window and a standard steady-state thinning reference value; and performing adaptive model predictive control to output an optimal control instruction by using the correction factor to target correct a discrete state space model system matrix. The application can effectively solve the mismatch problem of a static model under a nonlinear mutation condition, improve control precision and reliability, and guarantee stable and efficient operation of the production line.

The invention discloses a quantitative prediction method for an icing load of a power transmission line, and the method comprises the following steps: collecting and fusing multi-source meteorological data: employing a dual-polarization radar to invert the particle size distribution of liquid drops through differential reflectivity, calculating the content of supercooled water through a differential phase, and obtaining an atmosphere liquid water path and a vertical temperature profile; according to the measured wind speed, wind direction and wind shear index, an environment parameter field is constructed, and icing early warning is triggered; conducting wire state parameter real-time sensing: monitoring conducting wire three-axis amplitude and galloping frequency, obtaining conducting wire surface temperature gradient, quantifying damping effect of conducting wire galloping on collision efficiency and providing boundary conditions for a thermodynamic equation; mixed phase state dynamic discrimination: constructing a phase state discrimination matrix based on a combination rule of a temperature interval and droplet particle size distribution; dynamic collision efficiency correction: calculating the dynamic collision efficiency by adopting an unsteady state collision formula; thermodynamics-fluid mechanics coupling calculation is carried out; and ice coating load accumulation and output: calculating the ice coating thickness, mass and mechanical load.

The application discloses a numerical calculation method for a high-temperature and high-pressure flash evaporation process, and comprises the following steps: a Laval nozzle fluid domain model is established, the model is divided into a structured grid to obtain a grid model, the grid model is imported into simulation software, an energy equation is enabled, high-temperature and high-pressure inlet boundary conditions, pressure outlet boundary conditions and wall surface conditions are defined, the dependence of the physical property parameters of water and steam on temperature is written into the simulation software as a user-defined function, solver parameters are set, and a steady-state flow field result is obtained, the steady-state flow field result is taken as an initial condition, a ZGB model considering thermodynamic non-equilibrium effects is selected and activated, transient solution parameters are set, and a transient simulation result is obtained, and the transient simulation result is imported into CFD post-processing software to extract and obtain the physical quantity change curve of a key position. The method can predict a flash evaporation starting position and steam volume distribution, and realizes qualitative and quantitative prediction of flash evaporation under complex working conditions.

The application discloses a kind of regularized constraint heat transfer coefficient dynamic determination method and system.The application introduces sine oscillation in jacket temperature, data is divided into sliding window;Linear model containing low-frequency term and simple harmonic term of the same frequency is constructed in window, and the energy balance equation is established by extracting the same frequency complex amplitude;Further, the least square target function with regularization term is constructed with the estimation value of the previous window as prior, and the non-negative constraint based on the interpolation of the boundary before and after reaction is applied, and the UA dynamic evaluation is realized by cyclic solution.The application effectively decouples non-steady-state drift and noise, suppresses numerical mutation, obtains smoother and more physical law heat transfer coefficient and heat release rate result, and significantly improves the robustness of complex reaction process calorimetry.

The application provides a micro-cantilever generalized parameter inversion method, system and device, relates to the micro-cantilever parameter inversion technical field, and the method comprises the following steps: obtaining the steady-state response data of the micro-cantilever system to be measured at a preset driving frequency as observation data for model training; constructing a PINN model, the input is time, and the output is corresponding predicted displacement; the geometric parameter to be inverted is used as a trainable variable to construct a composite loss function containing data residuals and physical residuals; the model is iteratively trained and inverted to solve; the weights of the model and the geometric parameter to be inverted are jointly optimized until the model converges; and the final learned geometric parameter value in the model is extracted as the actual physical size of the micro-cantilever system. The scheme not only greatly reduces the dependence on experimental data, but also has strong structure generalization ability, and can accurately identify micron-level size deviation caused by process errors and the like through single observation data.

The application belongs to the technical field of engineering simulation data processing, and specifically discloses a multi-physical field iterative coupling prediction method, system, terminal and medium. The method comprises the following steps: based on a prediction batch input, initializing the state of a to-be-predicted object, and constructing a structured feature representation for multi-physical field prediction; constructing a temperature prediction model and a pressure prediction model which are independent of each other; performing multi-round prediction update on the temperature physical field by using the temperature prediction model, and dynamically adjusting relevant state parameters according to the predicted state of the temperature physical field in the prediction update process to obtain a stable temperature physical field prediction state; on this basis, performing prediction processing on the pressure physical field by using the pressure prediction model to obtain a pressure physical field prediction result corresponding to the temperature physical field prediction state, and outputting a multi-physical field steady-state prediction result. By introducing the multi-physical field iterative coupling prediction mechanism, the physical consistency and stability of the multi-physical field prediction result are improved.

This invention proposes a method and system for obtaining the trajectory of a periodic time-varying system based on Chebyshev placement, belonging to the field of power electronics technology. The method includes: establishing a periodic boundary value problem model of the periodic time-varying system, determining the system period and the number of placement points; performing time-scale mapping on a single-period interval, selecting Chebyshev placement points and constructing a spectral differential matrix; applying system dynamic constraints at each placement point, discretizing the differential equations into a system of nonlinear implicit algebraic equations; introducing periodic closure constraints to form a closed nonlinear algebraic equation system; and using an internal and external iterative strategy to solve the closed equation system, outputting the steady-state periodic discrete trajectory. This invention, by placing Chebyshev points within a single-period interval and constructing a spectral differential matrix, directly transforms the differential equations into a system of algebraic equations, avoiding long time-domain integration, and possesses advantages such as high computational efficiency, fast convergence speed, good numerical stability, and strong engineering feasibility.

The invention provides a hot water force multi-field monitoring analysis method and system suitable for a TEHB test system, and the method comprises a model framework building step, a steady-state initial field solving step, a hot water force coupling solving step, and a multi-field response data collection and recording step. The invention further provides a hot water force multi-field monitoring and analyzing system suitable for the TEHB test system, a three-dimensional model containing the six-direction waveguide rod and the rock sample is established, a mechanical, seepage and temperature steady-state initial field before impact loading is obtained, hot water force coupling dynamic calculation is carried out under the action of triaxial six-direction synchronous impact, and the test result is obtained. And curves and statistical indexes of key results such as stress, fracture, pore pressure, temperature and the like are automatically output. According to the invention, the impact process simulation can be realized under the hot water-force coupling loading condition, and multi-field response data can be monitored and analyzed, so that the dynamic mechanical behavior of the rock under the hot water-force coupling effect can be revealed, and a basis is provided for the scientific design of deep engineering.

The application discloses a network-constructed energy storage capacity configuration optimization method and system, belongs to the technical field of power system planning and operation control, and substitutes initial capacity into a constructed multi-time scale nonlinear differential algebraic equation model to perform time domain simulation until each state variable in the network-constructed energy storage system reaches a periodic steady state, and the periodic steady state trajectory of each state variable is obtained; according to the periodic steady state trajectory, a state transition matrix in a fundamental period is obtained, and Floquet characteristic indexes are determined based on Floquet theory; the real part of the Floquet characteristic indexes is taken as a system electromagnetic stability index; the network-constructed energy storage capacity is gradually increased from the initial capacity through a search step, and when the index is greater than a preset safety threshold for the first time, the capacity value corresponding to the last search step is taken as the maximum allowed network-constructed energy storage capacity satisfying the electromagnetic dynamic stability safety threshold. The method can optimize the network-constructed energy storage capacity configuration under the premise of ensuring the system electromagnetic dynamic stability.

The invention discloses a power system equivalent damping identification method based on an improved grey wolf algorithm, and relates to the field of power system parameter identification, and the method comprises the steps: building an SFR improved model; six unknown parameters in the SFR improved model are classified, weighted summation is adopted for a system equivalent inertia time constant, a prime mover high-pressure cylinder output power proportion and a prime mover reheating time constant to obtain a fitting value, a parameter constraint relation of a final steady-state value based on power disturbance and frequency deviation is established to determine an equivalent damping coefficient, and the equivalent damping coefficient is calculated. According to the inertia time constant of the speed regulator and frequency response data and disturbance power measured based on a wide area measurement system, an improved grey wolf algorithm is adopted for optimizing and calculating an equivalent damping coefficient, model parameters are determined by means of equivalent calculation, steady-state data extraction, parameter identification and the like, the improved model parameters are reasonable in setting, and the stability is high. Frequency response can accurately represent key indexes of system frequency, and the model precision is remarkably improved compared with a classic model.

The invention provides a steady-state quantum turbulence generation method and a steady-state quantum turbulence generation system capable of guaranteeing particle number conservation. The method comprises the following steps: constructing an evolution model of the quantum fluid system, wherein the evolution model is obtained by introducing an external driving item and a particle number conservation dissipation item based on a Gross-Pitaevskii equation; the external driving item is an additional external potential of spatio-temporal variation and is used for injecting energy into the system; the particle number conservation dissipation item depends on the time change rate of the particle number density and is used for dissipating the energy of the system; the external driving item and the particle number conservation dissipation item are configured to keep the total particle number conservation of the system; and performing time evolution on the quantum fluid system based on the evolution model until the system reaches a steady-state quantum turbulence state of total kinetic energy dynamic balance. According to the method, the loss of chemical potential compensation particles does not need to be adjusted, quantum turbulence structures of different scales can be stably generated and regulated, and the physical self-consistency and long-term stability of the quantum fluid evolution process are guaranteed.

This invention belongs to the technical field of autoclave temperature control in composite material molding processes. Specifically, it discloses a method and system for smoothing the inflection point of an autoclave temperature control curve. The method includes: superimposing a test disturbance signal and acquiring response data before reaching the temperature control inflection point; constructing a mathematical model to identify the local time constant and steady-state process gain; converting this parameter into a hard constraint boundary and generating a dynamic time-series transition trajectory sequence by combining the slope calculations before and after the inflection point; generating a predicted future temperature value using a state predictor; calculating the predicted control deviation based on the trajectory sequence and the predicted value, and extracting the trajectory change rate; and calculating the proportional-integral adjustment component and the feedforward compensation component respectively, synthesizing them into an execution control quantity to drive the autoclave. This invention eliminates temperature overshoot and response lag, achieving a smooth inflection point transition.

The invention provides a hydrological model parameter time-varying optimization method and system, and relates to the technical field of hydrological models and hydrological prediction.The method comprises the steps that hydrological meteorological data and runoff observation data are divided into time windows according to a sliding window, parameter optimization calibration is conducted through an optimization algorithm, and an optimal parameter set is obtained; splicing according to a time sequence, and generating a continuous curve of parameters changing along with time; performing score comparison on the continuous curves, and determining the window with the highest score as a steady-state window; the optimal parameters of the steady-state window, the climate statistics of the steady-state window and the underlying surface features are stored in a parameter database; and finally, determining the optimal parameter of the target time period based on the continuous curve or the parameter database. According to the method, dynamic adjustment of hydrological model parameters is realized through the sliding window, the optimization algorithm and the parameter database, the simulation precision of the model is improved, the stability of parameter optimization is enhanced, and the risk of local optimum is reduced.

The invention relates to a method for predicting the heat exchange efficiency of a coaxial heat exchange well in oil exploitation, which comprises the following steps of: establishing a coupling heat transfer physical model of the coaxial heat exchange well, and establishing a standardized heat transfer model directly used for mathematical solution according to an engineering structure and geological conditions of a target block; according to an oil pipe and annulus internal temperature control equation, the fluid temperature of a certain position of a shaft is calculated through a shaft temperature field partial differential equation by means of Laplace conversion, and the well mouth outlet temperature is obtained; obtaining an analytical solution of the formation temperature of the target block changing along with the radial distance and time, and evaluating the range of temperature drop of the formation in the radial direction after the coaxial heat exchange well runs for preset time; and calculating and predicting the heat exchange efficiency of the coaxial heat exchange well according to the solved wellhead outlet temperature and the known in-well inlet temperature. According to the method, the problems of long solving time in numerical simulation and inaccurate calculation result in analytic solution based on steady-state hypothesis can be solved, the oil extraction efficiency is improved, and the energy consumption is reduced.

The invention provides a self-adaptive source iteration acceleration method based on physics-data driving, and belongs to the field of reactor physical computation.The method comprises the steps that an initial threshold value and a convergence standard are set firstly, a steady-state neutron diffusion equation is discretized through a coarse mesh finite difference method, and an equation set is established; whether an acceleration strategy is executed or not is determined by judging the relation between the maximum flux relative error and a threshold value. When the conditions are met, acceleration parameters are calculated, a flux error snapshot matrix is constructed, a residual error ratio is calculated by adopting incremental orthogonalization analysis, and whether flux prediction correction is carried out or not is judged according to the residual error ratio; and after correction, continuously calculating an equation set twice, and adaptively updating a threshold value. And carrying out loop iteration until convergence conditions are met. According to the method, physical constraint and data driving advantages are fused, parameters and strategies are adjusted in a self-adaptive mode, the number of iterations is greatly reduced on the premise that the calculation precision is guaranteed, and the solving efficiency of the steady-state multi-dimensional multi-group neutron diffusion equation is remarkably improved.

The application relates to a robust noise covariance estimation method based on an iterative reweighted least square, which comprises the following steps: step 1, constructing a state space model of a linear time-invariant system, collecting measurement data in the system operation process, calculating an innovation sequence, and estimating an observation vector composed of self-covariance; step 2, based on the observation vector, using the state space model of the system and a steady-state Kalman gain, constructing a linear regression equation group of the innovation covariance and the noise covariance; step 3, using an iterative reweighted least square algorithm, obtaining a robust noise covariance estimation value from the linear regression equation group; and step 4, feeding back the robust noise covariance estimation value to a Kalman filter, performing Kalman filter parameter updating and circulation, and realizing online adaptive adjustment of noise statistical parameters. The application significantly improves the robustness to abnormal values in noise statistical parameter estimation, and can guarantee the accuracy and stability of Kalman filtering in an environment containing noise and abnormal data.

A method is provided for estimating time associated with steady-state conditions of a process. The method includes: obtaining time-domain parameter data of a continuous process that converges to steady-state conditions; fitting a function to the time-domain parameter data; and determining the intersection time of the function with the estimated steady-state parameter values.