A passive adaptive system performance digital simulation evaluation method

Abstract

The application discloses a kind of passive adaptive system performance digital simulation evaluation methods, including steps S10, the macroscopic building topological characteristics of the target area where passive system is and meteorological wind deflection characteristic;Step S20, the macroscopic building topological characteristics are solved using large eddy simulation algorithm to the grid, the local turbulence intensity of the target area building wake boundary layer and vortex shedding frequency are generated;Step S30, the three-dimensional geometric model of the passive system to be evaluated is obtained, and the inherent flow channel frequency of the core structure is extracted from the three-dimensional geometric model.The local turbulence intensity of macroscopic building wake and vortex shedding frequency are extracted by large eddy simulation algorithm, which successfully introduces the nonlinear wind field characteristics of urban microclimate into passive system evaluation, and solves the technical problem that the evaluation result is seriously distorted due to ideal boundary.

Description

Technical Field

[0001] This invention relates to the field of computer-aided engineering analysis technology, and in particular to a digital simulation evaluation method for the performance of a passive adaptive system. Background Technology

[0002] Passive systems (such as passive ventilation devices, solar chimneys, and one-way ventilators) rely heavily on natural driving forces (such as external wind pressure and internal thermal pressure) to operate. One of their core technical evaluation indicators is adaptability, that is, whether the system can automatically switch driving modes and maintain efficient ventilation operation when external climate conditions change drastically.

[0003] Existing performance evaluation methods are mostly single-condition static digital simulation tests, which have the following significant technical defects: Existing evaluation methods usually set uniform and constant ideal advection wind boundary conditions, which completely deviates from the real microclimate wind field environment in high-density building clusters; Existing technologies do not consider the interference phenomenon between the vortex shedding frequency generated by the wake of buildings in dense blocks and the frequency of the physical flow channels inside the passive system, which is very easy to miss the hidden danger of air blockage caused by hydrodynamic resonance, resulting in a cliff-like drop in ventilation volume; In addition, existing technologies lack a standardized evaluation system for quantitatively evaluating the comprehensive robustness of the system from the dimension of transient multi-condition switching (such as the vacuum period when switching from wind pressure to thermal pressure). Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the above-mentioned technologies.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A digital simulation evaluation method for the performance of a passive adaptive system, executed by a computer device, includes: step S10, obtaining the macroscopic building topology features and meteorological wind deflection features of the target area where the passive system is located.

[0007] Step S20: Use the large eddy simulation algorithm to perform grid calculation on the macroscopic building topology features and the meteorological wind deflection features to generate the local turbulence intensity and vortex shedding frequency of the building wake boundary layer in the target area.

[0008] Step S30: Obtain the three-dimensional geometric model of the passive system to be evaluated, and extract the inherent flow channel frequency of the core structure from the three-dimensional geometric model.

[0009] Step S40: Determine whether the numerical difference between the vortex shedding frequency corresponding to the local turbulence intensity and the inherent flow channel frequency is less than a preset frequency threshold, and generate a resonance interference determination result.

[0010] Step S50: When the resonance interference determination result is yes, extract the harmonic component corresponding to the vortex shedding frequency, use the harmonic component to correct the initial fluid boundary conditions, and generate non-uniform pulsating boundary data.

[0011] Step S60: Based on the non-uniform pulsating boundary data, drive the three-dimensional geometric model to perform computational fluid dynamics simulation, and extract the internal airflow velocity characteristics and ventilation volume characteristics under the external wind pressure drive mode and the internal thermal pressure drive mode.

[0012] Step S70: Input the internal airflow velocity characteristics and the ventilation volume characteristics into a preset adaptive performance index function, and output the microclimate adaptive evaluation result of the passive system.

[0013] Preferably, generating the local turbulence intensity and vortex shedding frequency of the building wake boundary layer in the target area in step S20 includes: mapping the macroscopic building topological features to a preset three-dimensional computational domain, spatially discretizing the three-dimensional computational domain using a hexahedral mesh, and generating basic body mesh data.

[0014] The meteorological wind deflection characteristics are used as inlet boundary conditions input into the basic body grid data, and the small-scale vortex information in the equation calculation is filtered out using a subgrid-scale model.

[0015] Extract the transient velocity time series of the target measurement point location in the three-dimensional computational domain, perform a Fourier transform algorithm on the transient velocity time series to extract the frequency domain peak features as the vortex shedding frequency, and calculate the variance data of the transient velocity time series to generate the local turbulence intensity.

[0016] Preferably, extracting the inherent flow channel frequency of the core structure in step S30 includes: analyzing the three-dimensional coordinate point set of the three-dimensional geometric model, identifying and extracting the convergent air inlet features and vertical exhaust channel features of the passive system.

[0017] Calculate the first equivalent cross-sectional area of ​​the converging air inlet feature and the second equivalent cross-sectional area of ​​the vertical exhaust channel feature.

[0018] Obtain the channel length value and the sound velocity value of the fluid medium corresponding to the vertical exhaust channel characteristics.

[0019] Substitute the first equivalent cross-sectional area, the second equivalent cross-sectional area, the channel length value, and the fluid medium sound velocity value into the preset Helmholtz resonance calculation rules, and output the resonance fundamental frequency data of the passive system after algebraic operations. Configure the resonance fundamental frequency data as the inherent flow channel frequency.

[0020] Preferably, generating the resonance interference determination result in step S40 includes: obtaining the reference damping coefficient characteristics stored locally by the computer device.

[0021] The absolute value calculator is used to calculate the absolute difference between the vortex shedding frequency and the inherent flow channel frequency.

[0022] Divide the absolute difference feature by the inherent flow channel frequency to generate a relative frequency deviation ratio value.

[0023] The critical interference bandwidth is determined based on the characteristics of the reference damping coefficient, and the relative frequency offset ratio value is compared with the critical interference bandwidth.

[0024] If the relative frequency offset ratio is within the range of the critical interference bandwidth, the logical truth value is assigned to the first determination variable, and the data packet carrying the first determination variable is stored in the cache to generate the resonance interference determination result.

[0025] Preferably, generating non-uniform pulsating boundary data in step S50 includes: using the vortex shedding frequency as the reference waveform frequency, and constructing a synthetic pulsating flow velocity function expression containing multi-order sine waveforms using a time-shifting function.

[0026] The local turbulence intensity is used as an amplitude parameter feature and multiplied by the synthetic pulsating velocity function expression to generate a time-series perturbation waveform feature.

[0027] A constant flow velocity input term is obtained as the basic steady-state constant of the initial fluid boundary condition. The time-series disturbance waveform features are superimposed on the basic steady-state constant to generate a dynamic inlet wind speed constraint matrix containing the time dimension.

[0028] The dynamic inlet wind speed constraint matrix is ​​mapped onto the boundary geometry surface of the three-dimensional geometric model, and discretization is performed to generate the non-uniform pulsating boundary data.

[0029] Preferably, in step S60, the internal airflow velocity characteristics and ventilation volume characteristics under the external wind pressure drive mode and the internal thermal pressure drive mode are extracted, including: under the external wind pressure drive mode, activating the implicit pressure solver to solve the flow field momentum equation under the non-uniform pulsating boundary data, and outputting the first velocity field matrix.

[0030] In the internal thermo-pressure driven mode, the non-uniform pulsating boundary data is shielded and the energy heat transfer equation model is activated. The preset indoor and outdoor temperature difference constant data is input, and the second velocity field matrix is ​​iteratively output.

[0031] Area integral data operations are performed on the first velocity field matrix and the second velocity field matrix along the cross-section of the system exhaust outlet, respectively, to extract the corresponding internal airflow velocity features and ventilation volume features.

[0032] Preferably, in step S70, the microclimate adaptive evaluation result of the passive system is output, including: extracting the first air exchange volume flow rate under the external wind pressure driven mode and the second air exchange volume flow rate under the internal thermal pressure driven mode from the ventilation volume characteristics.

[0033] Calculate the variance dispersion values ​​of the first ventilation volumetric flow rate and the second ventilation volumetric flow rate.

[0034] The minimum point data of the internal airflow velocity characteristics in the flow field domain are extracted as dead zone stagnation parameters.

[0035] The variance dispersion value and the dead zone stagnation parameter are input into a weighted penalty function rule containing specific weight allocation coefficients for arithmetic operations to generate a system robustness score value.

[0036] The robustness score of the system is matched by looking up the data in a preset grading configuration table, and the microclimate adaptive evaluation result is output.

[0037] Preferably, after outputting the microclimate adaptive evaluation result of the passive system in step S70, the method further includes: determining whether the microclimate adaptive evaluation result carries a non-compliance identification code.

[0038] When the code indicating failure to meet the standard is determined, the three-dimensional coordinates of the limiting resistance location that fails to meet the preset ventilation airflow requirements are parsed from the microclimate adaptive evaluation results.

[0039] Based on a preset adaptive optimization algorithm program module, the mesh geometric control points of the three-dimensional geometric model at the three-dimensional coordinates of the limiting resistance position are adjusted to generate optimized variant geometric model data.

[0040] The original three-dimensional geometric model is overwritten with the variant geometric model data, triggering a loop control command to return to the step of extracting the inherent flow channel frequency, until the microclimate adaptive assessment result is updated to the compliance identification code status.

[0041] Preferably, after outputting the microclimate adaptive evaluation result of the passive system in step S70, the method further includes: monitoring the transient operating condition switching time window when the wind speed rapidly decreases to zero in the meteorological wind deflection characteristics.

[0042] During the transient operating condition switching time window, the position and state nodes of the unidirectional valve feature in the three-dimensional geometric model are updated using sliding computational grid technology.

[0043] Extract the real-time geometric opening angle value of the one-way valve during the gravity closing process, as well as the backflow volume flow rate data caused by system inertia.

[0044] Determine whether the backflow volume flow rate data exceeds the preset safe backflow limit threshold, and send an abnormal alarm message instruction to the monitoring terminal device when it exceeds the threshold.

[0045] Preferably, after step S60, which extracts the internal airflow velocity characteristics and ventilation volume characteristics under the external wind pressure drive mode and the internal thermal pressure drive mode, the method further includes: collecting the temperature distribution grid data and pressure field node data output by the three-dimensional geometric model at the time step node in each iteration of the solution calculation process.

[0046] The rendering engine module is used to perform color mapping calculations and three-dimensional rendering and extraction of streamline isosurfaces on the temperature distribution grid data and the pressure field node data to generate three-dimensional visualized flow field slice image data.

[0047] The three-dimensional visualized flow field slice image data is time-stamped and array-stitched with the internal airflow velocity characteristics at the corresponding time step to generate a three-dimensional visualized analysis file package data for reproducing the entire process of dynamic evolution of the internal airflow state of the microclimate.

[0048] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0049] (1) Existing technologies typically use uniform and constant advection winds as simulation boundaries, which severely deviates from the real environment of densely populated urban areas. This invention extracts the local turbulence intensity and vortex shedding frequency of macroscopic building wakes using the large eddy simulation algorithm, and introduces specific harmonic components to generate non-uniform pulsating boundary data. This mechanism successfully introduces the nonlinear wind field characteristics of urban microclimates into passive system evaluation, solving the technical problem that ideal boundaries lead to serious distortion of evaluation results (overestimation of air intake efficiency), and significantly improving the accuracy and reference value of digital simulation in real engineering scenarios.

[0050] (2): Traditional evaluation methods ignore the physical coupling relationship between airflow pulsation and system physical structure. This invention extracts the inherent flow channel frequency of the core structure of the passive system to be evaluated and compares it with the vortex shedding frequency of the building wake. When the two frequency offsets overlap, the system can keenly capture and quantify this hydrodynamic resonance interference phenomenon, thereby accurately predicting and eliminating the hidden danger of a cliff-like drop in internal ventilation volume (i.e., airlock) caused by external vortices during the design stage.

[0051] (3) To address the lack of a unified evaluation standard for passive systems during multi-condition switching, this invention not only extracts the performance characteristics under external wind pressure and internal thermal pressure driving modes, but also further extracts the "dead zone stagnation parameter" and "variance dispersion" in the flow field. Through a preset weighted penalty function rule, the multi-dimensional flow field disadvantage data is transformed into an intuitive system robustness score. This mechanism eliminates the dependence on a single ventilation volume index and provides an objective, reproducible, and highly standardized digital measurement system for the adaptive capability of passive systems. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is the main flowchart of the digital simulation evaluation method for the performance of passive adaptive systems proposed in this invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] The following describes, with reference to the accompanying drawings, a digital simulation evaluation method for the performance of a passive adaptive system according to an embodiment of the present invention.

[0056] like Figure 1 As shown, Figure 1 This is the main flowchart of the digital simulation evaluation method for the performance of a passive adaptive system proposed in this invention. This invention provides a digital simulation evaluation method for the performance of a passive adaptive system, comprising the following steps:

[0057] Step S10: Obtain the macroscopic building topology features and meteorological wind deflection features of the target area where the passive system is located.

[0058] In practical applications, this step serves to establish objective three-dimensional physical field data of the external environment for subsequent microclimate fluid dynamics simulations. The macroscopic building topology features refer to the three-dimensional coordinate point set and geometric boundary dimensions of the high-rise building complex surrounding the target assessment object. This spatial geometric data is obtained by parsing through an open-source data interface of a Geographic Information System (GIS). The meteorological wind deflection features refer to the temporal variation vector data of wind speed and wind direction angle within the target area. This meteorological data is obtained by connecting to the historical wind measurement database of local meteorological stations and extracting measured records for specific time periods. After obtaining these two basic data points using the above methods, the initial aerodynamic spatial boundary of the target area can be constructed. These operations eliminate wind field distortion errors caused by using static advection wind boundaries from the data source, ensuring that the boundary conditions in the digital simulation accurately reproduce the physical environment.

[0059] For example: the set of three-dimensional coordinate points of all high-rise office buildings within 500 meters of the solar chimney ventilation system in a high-density coastal commercial area is obtained as the macroscopic building topology feature; at the same time, the hourly wind speed and direction sequence data of the annual average wind speed of 3.5 m / s and southeast direction measured by the meteorological station in the commercial area is obtained as the meteorological wind deflection feature.

[0060] Step S20: Use the large eddy simulation algorithm to perform grid calculation on the macroscopic building topology features and the meteorological wind deflection features to generate the local turbulence intensity and vortex shedding frequency of the building wake boundary layer in the target area.

[0061] In practical applications, in conjunction with the macroscopic building topology features and meteorological wind deflection features obtained in the preceding step S10, the purpose of this step is to analyze the unsteady pulsating drag characteristics generated when fluid bypasses dense building obstacles. Large eddy simulation (LES) algorithms are used to calculate large-scale vortices carrying the main kinetic energy in the flow field. Through iterative calculations by the computer program, statistical parameters characterizing the intensity of irregular fluctuations in airflow velocity over time, namely the local turbulence intensity, and the physical rate characterizing the periodic generation and shedding of vortices on the leeward side of buildings, namely the vortex shedding frequency, are obtained. The formula for extracting the vortex shedding frequency is:

[0062]

[0063] In the formula, The vortex shedding frequency; This is the Strauhall number, with a specific value range of 0.15 to 0.25; This corresponds to the characteristic wind speed in the aforementioned meteorological wind deflection characteristics; This refers to the maximum windward width corresponding to the macroscopic building topology features. The technical effect of this step is to transform static environmental spatial structural parameters into disturbed fluid physical parameters that can be used for dynamic microscopic flow field analysis.

[0064] For example, by inputting the three-dimensional coordinate point set of the office building in the coastal commercial district and the time series data of the southeast wind into the large eddy simulation algorithm, the system calculates that the local turbulence intensity of the airflow blowing towards the air inlet area of ​​the solar chimney ventilation system is 15%, and the vortex shedding frequency is 0.8Hz.

[0065] Following the execution of the large eddy simulation algorithm in step S20 of the independent claim, this section elaborates on the discretization and mathematical physics solution logic for fluid feature extraction. The continuous geometric space is divided into a basic grid data composed of hexahedral elements using computer spatial discretization technology. A sub-grid-scale model from fluid mechanics is introduced to isolate the microscopic vortex energy within the grid size. After solving the flow field with boundary conditions, the system continuously acquires and outputs the transient velocity time series that fluctuates with time. The time-domain sequence is converted to the frequency domain using the fast Fourier transform algorithm, and the energy extrema are identified as the vortex shedding frequency. The statistical variance of the velocity deviation from the average value is calculated and quantified as the local turbulence intensity. These operations ensure the convergence stability of the calculation of the nonlinear fluid control equations in the high-density geometric space and provide a mathematical signal conversion method for extracting frequency domain features.

[0066] For example, the system divides the coastal commercial area into two million hexahedral units to generate the basic body mesh data, extracts the transient flow velocity time series that fluctuates with time at the air inlet of the solar chimney ventilation system, and uses a fast Fourier transform to resolve the 0.8Hz frequency domain peak as the vortex shedding frequency. The variance data of the sequence is calculated to obtain the 15% local turbulence intensity.

[0067] Step S30: Obtain the three-dimensional geometric model of the passive system to be evaluated, and extract the inherent flow channel frequency of the core structure from the three-dimensional geometric model.

[0068] In practical applications, this step quantifies the aerodynamic inherent resonance parameters of the device being evaluated. The three-dimensional geometric model refers to the digital computer-aided design model file of the physical structure of the system to be evaluated. The inherent flow channel frequency refers to the physical frequency of flow field oscillations generated in the fluid channel space within the model when excited by external pulsating airflow. This characteristic data is extracted by a computer program automatically identifying the cross-sectional area and length characteristics of the cavities within the model and performing algebraic operations. This allows the computer to complete the quantification and data storage of the device's aerodynamic parameters during the digitization phase.

[0069] For example, the system acquires the three-dimensional geometric model of the solar chimney ventilation system, automatically extracts the spatial dimension features of its internal air intake and exhaust ducts through the parsing script, and calculates that the inherent flow channel frequency of the core structure is 0.85Hz.

[0070] In connection with the data extraction action in step S30 of the independent claim, this invention expands the underlying calculation method for extracting inherent frequency data. Its principle lies in treating the internal airflow channel of the device as an equivalent Helmholtz resonant cavity. The first equivalent cross-sectional area and the second equivalent cross-sectional area represent the cross-section of the air mass block and the main volume cross-section at the opening of the resonant cavity, respectively. The velocity parameter of sound propagation in air is introduced for algebraic calculation. The formula for the Helmholtz resonance calculation rule is:

[0071]

[0072] In the formula, The resonant fundamental frequency data; The value represents the velocity of sound in the fluid medium; This is the first equivalent cross-sectional area; This is the second equivalent cross-sectional area; The value represents the channel length. By employing algebraic equivalent physical operations, the computation time for extracting the underlying intrinsic parameters is shortened.

[0073] For example, the program identifies the first equivalent cross-sectional area of ​​the solar chimney inlet as 2 square meters, the second equivalent cross-sectional area of ​​the exhaust pipe as 1.5 square meters, and the channel length as 10 meters. After substituting the air speed of sound as 340 m / s, the program calculates and outputs the resonant fundamental frequency data of 0.85 Hz and configures it as the inherent flow channel frequency.

[0074] Step S40: Determine whether the numerical difference between the vortex shedding frequency corresponding to the local turbulence intensity and the inherent flow channel frequency is less than a preset frequency threshold, and generate a resonance interference determination result.

[0075] In practical applications, this step, connecting the vortex shedding frequency extracted in step S20 with the inherent flow channel frequency extracted in step S30, aims to determine whether the passive system has aerodynamic drag defects. The principle is that when the disturbance frequency of the external building wake detachment approaches the inherent frequency of the equipment, it induces hydrodynamic resonance, leading to increased aerodynamic drag. The system's underlying arithmetic logic unit calculates the absolute difference between the vortex shedding frequency and the inherent flow channel frequency. It is disclosed here that the specific range of the preset frequency threshold is limited to 0.05Hz to 0.15Hz. When the difference falls within this range, the system assigns a logical truth value and outputs the resonance interference judgment result indicating a resonance risk. Through the above operations, dynamic fluid frequency domain interference defects that cannot be detected by steady-state flow field evaluation methods are identified and quantified during the digital design phase.

[0076] The specific range of the preset frequency threshold (0.05Hz to 0.15Hz) is not arbitrarily set, but is rigorously derived by calculating the aerodynamic damping dissipation capacity and fluid-structure interaction tolerance of the passive equipment body. The derived formula is as follows:

[0077]

[0078] In the formula, The preset frequency threshold; The inherent flow channel frequency; The aerodynamic damping ratio is determined by the building materials and spatial geometry of the passive system. This refers to the vortex-induced lockout tolerance coefficient in the empirical formula of fluid mechanics. The specific parameter ranges at the underlying level are disclosed here: for conventional passive ventilation building structures, the measured range of the aerodynamic damping ratio is limited to 0.04 to 0.12; the range of the vortex-induced lockout tolerance coefficient is limited to 1.2 to 1.5. Substituting the above underlying parameter ranges into the formula, and combining them with the reference natural frequency of 0.5Hz to 1.5Hz for conventional passive systems, mathematical differentiation is performed, ultimately defining and generating a rigorous preset frequency threshold range of 0.05Hz to 0.15Hz.

[0079] For example, the computer kernel calculates that the absolute difference between the vortex shedding frequency of the external airflow (0.8Hz) and the inherent flow channel frequency inside the solar chimney (0.85Hz) is 0.05Hz. Since this difference falls within the set preset frequency threshold range (0.05Hz to 0.15Hz), the system determines that resonance interference has occurred and generates a resonance interference determination result with the content "Yes".

[0080] In connection with the threshold determination action in step S40, this invention introduces the reference damping coefficient characteristic, which characterizes energy dissipation, to determine the bandwidth region where frequency interference occurs, i.e., the critical interference bandwidth. It is disclosed here that the value range of the reference damping coefficient characteristic is set to 0.02 to 0.05, and the corresponding value range of the critical interference bandwidth is limited to 5% to 10%. The system obtains the dimensionless relative frequency offset ratio value through floating-point division and executes a processor-level comparison instruction with this bandwidth. The concept of physical resonance is transformed into a Boolean state determination action at the computer microprocessor level, which can directly allocate register resources and perform logic gate operations.

[0081] For example, after calculating the absolute difference characteristic of the frequency, dividing it by the natural frequency of 0.85Hz yields the relative frequency deviation ratio of 5.8%. Since this ratio falls within the critical interference bandwidth of 5% to 10% calculated based on structural damping, the processor assigns a logical truth value to the first determination variable and stores it in the cache, then outputs the resonance interference determination result.

[0082] Step S50: When the resonance interference determination result is yes, extract the harmonic component corresponding to the vortex shedding frequency, use the harmonic component to correct the initial fluid boundary conditions, and generate non-uniform pulsating boundary data.

[0083] In practical applications, in conjunction with the resonance interference determination result generated in the preceding step S40, the purpose of this step is to reconstruct the non-constant boundary conditions capable of inducing fluid interference. By extracting the sinusoidal waveform representing the periodic oscillation of the waveform as the harmonic components and superimposing the base average wind speed constant, a numerical array fluctuating on the time axis is generated, i.e., the non-uniform pulsating boundary data. Its calculation correction formula is as follows:

[0084]

[0085] In the formula, This refers to the non-uniform pulsating boundary data; This represents the initial steady-state wind speed; The local turbulence intensity; The vortex shedding frequency; This is to simulate the time variable. The above operations ensure that the boundary condition data at the bottom layer of the input fluid calculation can map the nonlinear physical impact load of the building wake on the equipment.

[0086] For example, after determining that resonance interference has occurred, the system reconstructs the initial boundary using a sine wave based on a frequency of 0.8 Hz and a turbulence intensity of 15%, generating the non-uniform pulsating boundary data in which the wind speed oscillates periodically between 2.97 m / s and 4.02 m / s over time.

[0087] In connection with the boundary condition correction action in step S50, this invention specifies the vortex shedding frequency as the period of the time-fluctuating sine wave, specifies the local turbulence intensity as the waveform amplitude parameter and multiplies it by the waveform function to construct a pure fluctuation component, i.e., the time-series disturbance waveform feature. This fluctuation component is added to the average wind speed constant to form a set of characteristic values ​​that correlates the solution time step and spatial grid coordinates, i.e., the dynamic inlet wind speed constraint matrix. Finally, numerical mapping is performed on the model's geometric surface grid nodes. This ensures the continuity of the dynamic boundary data imported into the simulation software in both temporal evolution and spatial topological mapping dimensions.

[0088] For example, a sinusoidal waveform is constructed based on 0.8Hz, and 15% of the intensity parameter is multiplied into the waveform as the amplitude to form the time-series perturbation waveform feature. After superimposing the initial constant wind speed of 3.5m / s, the dynamic inlet wind speed constraint matrix that fluctuates with time is generated. This matrix is ​​then discretized and mapped onto the windward surface of the three-dimensional model of the solar chimney to generate the non-uniform pulsating boundary data used for calculation.

[0089] Step S60: Based on the non-uniform pulsating boundary data, drive the three-dimensional geometric model to perform computational fluid dynamics simulation, and extract the internal airflow velocity characteristics and ventilation volume characteristics under the external wind pressure drive mode and the internal thermal pressure drive mode.

[0090] In practical applications, in conjunction with the non-uniform pulsating boundary data corrected in the previous step S50, the purpose of this step is to perform multi-condition finite element numerical simulation. The system operates under both a windy, pressurized mode and a windless mode where thermal buoyancy is generated by the indoor-outdoor temperature difference. The partial differential equation solver matrix at the computational fluid dynamics level is run. After the equations converge iteratively, the scalar velocity of the flow field is extracted from the exhaust outlet cross-section mesh location in the three-dimensional computational domain to obtain the internal airflow velocity characteristics. The volumetric flow rate of this cross-section is then integrated using calculus to obtain the ventilation volume characteristics. This allows the acquisition of the fluid dynamics operating parameters of the passive equipment under the alternating action of different physical driving forces.

[0091] For example, when the solar chimney ventilation system model is simulated and solved, it is found that under the external wind pressure driving mode, the internal airflow velocity characteristic of its exhaust port is 1.2 m / s and the ventilation volume characteristic is 800 cubic meters / hour; under the windless internal thermal pressure driving mode, the extracted ventilation volume characteristic is reduced to 300 cubic meters / hour.

[0092] Connecting the multi-condition simulation actions in step S60, this invention clarifies a separate underlying solver computation scheduling strategy adopted for different environmental driving modes. External wind pressure follows a momentum transfer mechanism, while internal thermal pressure follows an energy density conversion mechanism. Under windy conditions, the non-uniform pulsating boundary data is connected, and a dedicated implicit pressure solver is activated to handle the forced convection flow field; under windless temperature difference conditions, the system shields external wind field disturbances and inputs a single temperature difference constant to activate the energy heat transfer equation model to solve the natural convection field. By performing vector area integral data operations on the converged spatial velocity matrix vector and the specified cross-sectional micro-element area, the data is transformed into a ventilation volume scalar. This allows for the isolation and scheduling of the underlying partial differential mathematical calculation model for driving forces from different physical environments, eliminating algorithm compatibility errors caused by multi-condition mixed simulation.

[0093] For example, when evaluating a solar chimney system, in windy mode, the implicit pressure solver is activated to solve the momentum equation and output the first velocity field matrix; in windless mode, the wind speed disturbance is cut off and a constant temperature difference of 10 degrees is input to activate the energy heat transfer model to calculate the second velocity field matrix. After performing area integral data calculation, the ventilation volume characteristics of 800 and 300 cubic meters per hour are extracted respectively.

[0094] Step S70: Input the internal airflow velocity characteristics and the ventilation volume characteristics into a preset adaptive performance index function, and output the microclimate adaptive evaluation result of the passive system.

[0095] In practical applications, in conjunction with the internal airflow velocity characteristics and ventilation volume characteristics extracted in the previous step S60, this step serves to perform engineering dimensionality reduction and comprehensive evaluation of the multi-dimensional parameters. The adaptive performance index function is a pre-compiled mathematical logic algorithm that integrates algebraic operations and penalty weighting coefficients. It is used to convert the flow fluctuation difference when the equipment switches between wind pressure / thermal pressure conditions into a quantitative score, ultimately outputting the microclimate adaptive evaluation result characterizing the equipment's environmental adaptability. This provides a digital metric for the multi-condition adaptive capability of passive systems.

[0096] First, the computer program extracts the air exchange volumetric flow rate under two driving modes—external wind pressure and internal thermal pressure—from the ventilation volume characteristics, and calculates their statistical variance to generate the variance dispersion value. This value quantifies the flow rate fluctuation amplitude of the equipment during operation across different conditions; a larger value indicates more unstable performance. Simultaneously, the program traverses the three-dimensional grid data of the internal airflow velocity characteristics, extracting the volume percentage of stagnant areas with extremely low airflow velocities (e.g., below 0.01 m / s) within the flow field, generating the dead zone stagnation parameter. Subsequently, these two inverse indicators characterizing system defects are substituted into a weighted penalty function rule containing specific weight allocation coefficients for arithmetic calculation. The specific penalty scoring formula is as follows:

[0097]

[0098] In the formula, The system robustness score is given by a numerical value; 100 is the set base score for the maximum score. The weights corresponding to the variance dispersion values ​​in the coefficients assigned to the specific weights; The variance dispersion value (a percentage after normalization). The weights corresponding to the dead zone stagnation parameters in the specific weight allocation coefficients are assigned to the specific weights. The dead zone stagnation parameter (volume percentage value) is specified here. The specific value range parameters are disclosed here: among the specific weight allocation coefficients, the weight corresponding to the variance dispersion value is limited to a range of 0.6 to 0.7; the weight corresponding to the dead zone stagnation parameter is limited to a range of 0.3 to 0.4. Finally, the system compares the calculated system robustness score value with a threshold interval according to a preset grading configuration table (e.g., a score greater than or equal to 60 points is considered satisfactory, and a score less than 60 points is considered unsatisfactory), and outputs the final microclimate adaptive evaluation result.

[0099] For example, by inputting the two sets of internal airflow velocity characteristics and ventilation volume characteristics into the index function, the system finally outputs the microclimate adaptive evaluation result of the solar chimney ventilation system as a comprehensive score of 75 points, and the system determines that the adaptive capability meets the standard.

[0100] In connection with the index output action in step S70 of the independent claim, this invention provides a rule interpretation for the index evaluation algorithm. The variance dispersion value quantifies the degree of flow output fluctuation of the equipment when coping with the alternation of wind pressure and thermal pressure; the dead zone stagnation parameter captures the coordinate proportion of the stagnant area with extremely low airflow velocity inside the flow field. These two features are substituted into the penalty function for weighted calculation. Through the above operations, a multi-dimensional scoring mechanism integrating macroscopic flow attenuation characteristics and microscopic flow field stagnation defects is constructed, avoiding the risk of masking the system's non-adaptive defects by relying solely on the total ventilation volume index.

[0101] For example: extract two ventilation volume data points of 800 and 300 cubic meters per hour to calculate the variance dispersion value of their fluctuation range, capture the dead zone stagnation parameter in the area with flow velocity below 0.01 m / s, substitute the two into the penalty rule according to the specific weight allocation coefficients of 0.6 and 0.4 to calculate the system robustness score of 75 points, and output the corresponding microclimate adaptive evaluation result by looking up the table.

[0102] Following step S70, the present invention also adds a completely new reverse optimization closed-loop procedure.

[0103] Specifically, following the output of the evaluation results in step S70 of the independent claim, this invention adds an automated three-dimensional mesh reverse closed-loop optimization process. The principle is that when the system reads a non-compliance flag code, the main program traces and locates the three-dimensional coordinates of the flow channel region causing the resistance peak, i.e., the location of the limiting resistance. It then uses a mesh morphogenetic algorithm to fine-tune the control points at that location to generate the variant geometric model data, replacing the original structure and triggering a cyclical iteration of frequency extraction and simulation calculation. This endows the evaluation system with self-optimizing iterative capabilities, transforming the evaluation tool into a generative optimization design system and reducing the trial-and-error resource overhead of manually modifying drawings.

[0104] Based on the three-dimensional coordinates of the limiting drag position obtained in the previous steps, the adaptive optimization algorithm module directly transforms the aerodynamic defects detected by the evaluation system into geometric modification instructions, thereby achieving closed-loop correction of the model. The underlying technology of this algorithm is based on a combination of free-form deformation technology and the accompanying gradient descent algorithm.

[0105] First, the computer program constructs a virtual control lattice encompassing the defect region within the local space of the three-dimensional geometric model, using the three-dimensional coordinates of the limiting resistance location as the geometric center. The intersection nodes of this lattice are the grid geometric control points. Then, the system invokes the adjoint equation solver in fluid mechanics to calculate the sensitivity gradient of the local fluid pressure drop drag to spatial displacement of each of the grid geometric control points. This sensitivity gradient vector indicates the direction in which each point moves in three-dimensional space to maximize the reduction of fluid resistance.

[0106] Based on the calculated sensitivity gradient, the algorithm iteratively moves the points according to specific mathematical rules to generate optimized variant geometric model data. Its core coordinate displacement update formula is:

[0107]

[0108] In the formula, The updated spatial three-dimensional coordinate vector of the mesh geometric control points; The initial three-dimensional spatial coordinate vector before displacement; The step size for moving the mesh geometry control points; Let be the sensitivity gradient vector of the fluid local pressure drop resistance relative to its spatial position at this coordinate point.

[0109] For example, a solar-powered chimney scored only 50 points in its initial evaluation and carried a non-compliance code. The program parses the three-dimensional coordinates of the limiting resistance location at the bend of the air inlet, calls the algorithm to fine-tune the control points in that area, generates the variant geometric model data to overwrite the original model, and restarts the simulation verification. This process is repeated until the evaluation result is updated to the compliance code status.

[0110] Following step S70, the present invention further adds a transient safety monitoring branch.

[0111] Specifically, following the output of the evaluation results in step S70, this invention proposes a backflow prevention safety monitoring and prediction mechanism for transient physical scenarios. The principle is that when the external strong wind becomes still and transitions to the critical point of internal thermal pressure transition, the mechanically moving valve cannot close instantly due to inertia. By capturing the transient operating condition switching time window of sudden wind speed changes using meteorological data, a sliding computational grid technique is employed to calculate the valve angle change process in real time, and the volume of air lost during this hysteresis period is integrally calculated. The preset threshold value range parameter is disclosed here: the specific configuration of the safety backflow limit threshold is limited to 2% to 5% of the total ventilation volume capacity of the equipment system. Through the above operations, the potential backflow failure of the passive system during the transition between physical driving forces can be identified and prevented.

[0112] For example, if monitoring detects that the external wind speed drops to zero within 3 seconds, triggering the transient operating condition switching time window, the system activates sliding grid computing technology to drive the one-way valve to close, and calculates and extracts the backflow volumetric flow rate data for this process as 15 cubic meters. Because this value exceeds the preset safe backflow limit threshold of 5 cubic meters, the underlying module sends an abnormal alarm message to the monitoring terminal device.

[0113] Following step S60, the present invention also proposes a data post-processing and 3D rendering branch.

[0114] Specifically, in conjunction with the feature extraction step S60, this invention supplements the engineered digital twin 3D post-processing and dynamic display workflow. It collects a large amount of temporal intermediate mesh data generated during the underlying iteration process, uses color mapping rules from computer graphics to perform pixel-level color and brightness assignment on thermodynamic and fluid dynamic values, and extracts spatial isosurfaces to draw the flow entity. Finally, the images are aligned and combined into a file package according to the time sequence of the underlying solution. This provides a dynamic recording medium for airflow evolution over time, lowering the cognitive threshold for non-professionals to understand the internal flow field mechanism.

[0115] For example, the system collects the temperature distribution grid data from a simulated solar chimney, uses the rendering engine module to mark high-temperature concentrated areas in red and low-temperature areas in blue, and extracts and draws three-dimensional airflow velocity streamlines to generate the three-dimensional visualized flow field slice image data. After being aligned and stitched according to the same time step, the data is packaged to generate the three-dimensional visualization analysis file package data for observing the evolution of gaseous flow.

[0116] The present invention has the following advantages:

[0117] (1) Existing technologies typically use uniform and constant advection winds as simulation boundaries, which severely deviates from the real environment of densely populated urban areas. This invention extracts the local turbulence intensity and vortex shedding frequency of macroscopic building wakes using the large eddy simulation algorithm, and introduces specific harmonic components to generate non-uniform pulsating boundary data. This mechanism successfully introduces the nonlinear wind field characteristics of urban microclimates into passive system evaluation, solving the technical problem that ideal boundaries lead to serious distortion of evaluation results (overestimation of air intake efficiency), and significantly improving the accuracy and reference value of digital simulation in real engineering scenarios.

[0118] (2): Traditional evaluation methods ignore the physical coupling relationship between airflow pulsation and system physical structure. This invention extracts the inherent flow channel frequency of the core structure of the passive system to be evaluated and compares it with the vortex shedding frequency of the building wake. When the two frequency offsets overlap, the system can keenly capture and quantify this hydrodynamic resonance interference phenomenon, thereby accurately predicting and eliminating the hidden danger of a cliff-like drop in internal ventilation volume (i.e., airlock) caused by external vortices during the design stage.

[0119] (3) To address the lack of a unified evaluation standard for passive systems during multi-condition switching, this invention not only extracts the performance characteristics under external wind pressure and internal thermal pressure driving modes, but also further extracts the "dead zone stagnation parameter" and "variance dispersion" in the flow field. Through a preset weighted penalty function rule, the multi-dimensional flow field disadvantage data is transformed into an intuitive system robustness score. This mechanism eliminates the dependence on a single ventilation volume index and provides an objective, reproducible, and highly standardized digital measurement system for the adaptive capability of passive systems.

[0120] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0121] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0122] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A digital simulation evaluation method for the performance of a passive adaptive system, characterized in that, Performed by a computer device, the method includes: Step S10: Obtain the macroscopic building topology features and meteorological wind deflection features of the target area where the passive system is located; Step S20: Use the large eddy simulation algorithm to perform grid calculation on the macroscopic building topology features and the meteorological wind deflection features to generate the local turbulence intensity and vortex shedding frequency of the building wake boundary layer in the target area. Step S30: Obtain the three-dimensional geometric model of the passive system to be evaluated, and extract the inherent flow channel frequency of the core structure from the three-dimensional geometric model; Step S40: Determine whether the numerical difference between the vortex shedding frequency corresponding to the local turbulence intensity and the inherent flow channel frequency is less than a preset frequency threshold, and generate a resonance interference determination result; Step S50: When the resonance interference determination result is yes, extract the harmonic component corresponding to the vortex shedding frequency, use the harmonic component to correct the initial fluid boundary conditions, and generate non-uniform pulsating boundary data. Step S60: Based on the non-uniform pulsating boundary data, drive the three-dimensional geometric model to perform computational fluid dynamics simulation, and extract the internal airflow velocity characteristics and ventilation volume characteristics under the external wind pressure driving mode and the internal thermal pressure driving mode; Step S70: Input the internal airflow velocity characteristics and the ventilation volume characteristics into a preset adaptive performance index function, and output the microclimate adaptive evaluation result of the passive system.

2. The method according to claim 1, characterized in that, In step S20, the local turbulence intensity and vortex shedding frequency of the building wake boundary layer in the target area are generated, including: The macroscopic building topological features are mapped to a preset three-dimensional computing domain, and the three-dimensional computing domain is spatially discretized using a hexahedral mesh to generate basic volume mesh data. The meteorological wind deflection characteristics are used as inlet boundary conditions input into the basic body grid data, and the small-scale vortex information in the equation calculation is filtered out using a subgrid-scale model. Extract the transient velocity time series of the target measurement point location in the three-dimensional computational domain, perform a Fourier transform algorithm on the transient velocity time series to extract the frequency domain peak features as the vortex shedding frequency, and calculate the variance data of the transient velocity time series to generate the local turbulence intensity.

3. The method according to claim 1, characterized in that, Extracting the inherent flow channel frequency of the core structure in step S30 includes: The three-dimensional coordinate point set of the three-dimensional geometric model is analyzed to identify and extract the features of the convergent air inlet and the vertical exhaust channel of the passive system. Calculate the first equivalent cross-sectional area of ​​the converging air inlet feature and the second equivalent cross-sectional area of ​​the vertical exhaust channel feature; Obtain the channel length value and the sound velocity value of the fluid medium corresponding to the characteristics of the vertical exhaust channel; Substitute the first equivalent cross-sectional area, the second equivalent cross-sectional area, the channel length value, and the fluid medium sound velocity value into the preset Helmholtz resonance calculation rules, and output the resonance fundamental frequency data of the passive system after algebraic operations. Configure the resonance fundamental frequency data as the inherent flow channel frequency.

4. The method according to claim 1, characterized in that, The generation of resonance interference determination results in step S40 includes: Obtain the reference damping coefficient characteristics stored locally on the computer device; The absolute value calculator is used to calculate the absolute difference between the vortex shedding frequency and the inherent flow channel frequency. Divide the absolute difference feature by the inherent flow channel frequency to generate a relative frequency deviation ratio value; The critical interference bandwidth is determined based on the characteristics of the reference damping coefficient, and the relative frequency offset ratio value is compared with the critical interference bandwidth. If the relative frequency offset ratio is within the range of the critical interference bandwidth, then the logical truth value is assigned to the first determination variable, and the data packet carrying the first determination variable is stored in the cache to generate the resonance interference determination result.

5. The method according to claim 1, characterized in that, In step S50, generating non-uniform pulsating boundary data includes: Using the vortex shedding frequency as the reference waveform frequency, a synthetic pulsating flow velocity function expression containing multi-order sine waveforms is constructed using a time-shifting function; The local turbulence intensity is used as an amplitude parameter feature and multiplied by the synthetic pulsating velocity function expression to generate a time-series perturbation waveform feature. A constant flow velocity input term is obtained as the basic steady-state constant of the initial fluid boundary condition. The time-series disturbance waveform features are superimposed on the basic steady-state constant to generate a dynamic inlet wind speed constraint matrix containing the time dimension. The dynamic inlet wind speed constraint matrix is ​​mapped onto the boundary geometry surface of the three-dimensional geometric model, and discretization is performed to generate the non-uniform pulsating boundary data.

6. The method according to claim 1, characterized in that, In step S60, the internal airflow velocity characteristics and ventilation volume characteristics under the external wind pressure drive mode and the internal thermal pressure drive mode are extracted, including: In the external wind pressure driven mode, the implicit pressure solver is activated to solve the flow field momentum equation under the non-uniform pulsating boundary data and output the first velocity field matrix. In the internal thermo-pressure driven mode, the non-uniform pulsating boundary data is shielded and the energy heat transfer equation model is activated. The preset indoor and outdoor temperature difference constant data is input, and the second velocity field matrix is ​​iteratively output. Area integral data operations are performed on the first velocity field matrix and the second velocity field matrix along the cross-section of the system exhaust outlet, respectively, to extract the corresponding internal airflow velocity features and ventilation volume features.

7. The method according to claim 1, characterized in that, In step S70, the microclimate adaptive evaluation results of the passive system are output, including: Extract the first air exchange volume flow rate under the external wind pressure drive mode and the second air exchange volume flow rate under the internal thermal pressure drive mode from the ventilation volume characteristics; Calculate the variance dispersion values ​​of the first ventilation volumetric flow rate and the second ventilation volumetric flow rate; The minimum point data of the internal airflow velocity characteristics in the flow field domain are extracted as dead zone stagnation parameters; The variance dispersion value and the dead zone stagnation parameter are input into a weighted penalty function rule containing specific weight allocation coefficients for arithmetic operation to generate a system robustness score value; The robustness score of the system is matched by looking up the data in a preset grading configuration table, and the microclimate adaptive evaluation result is output.

8. The method according to claim 1, characterized in that, After outputting the microclimate adaptive evaluation results of the passive system in step S70, the method further includes: Determine whether the microclimate adaptive assessment result carries a non-compliance identification code; When it is determined that the non-compliance identification code is carried, the three-dimensional coordinates of the limiting resistance location that fails to meet the preset ventilation airflow requirements are parsed from the microclimate adaptive evaluation results; Based on the preset adaptive optimization algorithm program module, the mesh geometric control points of the three-dimensional geometric model at the three-dimensional coordinates of the limiting resistance position are adjusted to generate optimized variant geometric model data; The original three-dimensional geometric model is overwritten with the variant geometric model data, triggering a loop control command to return to the step of extracting the inherent flow channel frequency, until the microclimate adaptive assessment result is updated to the compliance identification code status.

9. The method according to claim 1, characterized in that, After outputting the microclimate adaptive evaluation results of the passive system in step S70, the method further includes: The monitoring of the transient operating condition switching window period when the wind speed rapidly decreases to zero in the meteorological wind deflection characteristics; During the transient operating condition switching time window, the position and state nodes of the one-way valve feature in the three-dimensional geometric model are updated using sliding computational grid technology. Extract the real-time geometric opening angle value of the one-way valve feature during the gravity closing process, as well as the backflow volume flow rate data caused by system inertia; Determine whether the backflow volume flow rate data exceeds the preset safe backflow limit threshold, and send an abnormal alarm message instruction to the monitoring terminal device when it exceeds the threshold.

10. The method according to claim 1, characterized in that, After step S60, which involves extracting the internal airflow velocity characteristics and ventilation volume characteristics under both external wind pressure drive mode and internal thermal pressure drive mode, the method further includes: Collect the temperature distribution grid data and pressure field node data output at the time step node in each iterative solution calculation process of the three-dimensional geometric model; The rendering engine module is used to perform color mapping calculations and three-dimensional rendering and extraction of streamline isosurfaces on the temperature distribution grid data and the pressure field node data to generate three-dimensional visualized flow field slice image data. The three-dimensional visualized flow field slice image data is time-stamped and array-stitched with the internal airflow velocity characteristics at the corresponding time step to generate a three-dimensional visualized analysis file package data for reproducing the entire process of dynamic evolution of the internal airflow state of the microclimate.

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