Simulation optimization method and system for heat exchange module

Abstract

The invention provides a simulation optimization method and system for a heat exchange module, and the method comprises the steps: building a three-dimensional steady-state model according to the design parameters of the heat exchange module, and obtaining the flow field distribution data, temperature field distribution data and pressure field distribution data of the heat exchange module based on the three-dimensional steady-state model; constructing a first thermal fluid model according to the flow field distribution data, the temperature field distribution data and the pressure field distribution data, and performing parameter calibration on the thermal fluid network model by using the flow field distribution data and the temperature field distribution data to obtain a second thermal fluid model; transient working condition simulation is conducted in the second thermal fluid model, and a heat exchange effect picture is generated; and target parameters are selected from a preset channel adjustment parameter set according to the heat exchange effect picture, and a channel adjustment report is generated according to the design parameters, the heat exchange effect picture and the target parameters.

Description

Technical Field

[0001] This application relates to the field of artificial vision technology, and in particular to a simulation optimization method and system for heat exchange modules. Background Technology

[0002] In the design of heat exchange modules for air conditioners, uneven fluid distribution within the microchannel structure can easily lead to local overheating and reduced heat exchange efficiency. Traditional design methods often rely on single-dimensional simulations or experimental attempts, making it difficult to accurately predict temperature distribution and flow characteristics under transient conditions at the system level, resulting in poor thermal management performance and a long design cycle. Summary of the Invention

[0003] This application provides a simulation optimization method and system for heat exchange modules, which can improve the design efficiency and accuracy of heat exchange modules.

[0004] In a first aspect, embodiments of this application provide a simulation optimization method for a heat exchange module, the method comprising: A three-dimensional steady-state model is constructed based on the design parameters of the heat exchange module, and the flow field distribution data, temperature field distribution data, and pressure field distribution data of the heat exchange module are obtained based on the three-dimensional steady-state model. A first thermofluid model is constructed based on the flow field distribution data, the temperature field distribution data, and the pressure field distribution data. The parameters of the thermofluid network model are calibrated using the flow field distribution data and the temperature field distribution data to obtain a second thermofluid model. Transient operating condition simulation is performed in the second thermal fluid model to generate heat transfer effect diagrams; Based on the heat exchange effect diagram, a target parameter is selected from the preset channel adjustment parameter set, and a channel adjustment report is generated based on the design parameters, the heat exchange effect diagram, and the target parameter.

[0005] Secondly, embodiments of this application provide a simulation optimization system for a heat exchange module, the simulation optimization system for the heat exchange module comprising: The parameter conversion module is used to construct a three-dimensional steady-state model based on the design parameters of the heat exchange module, and to obtain the flow field distribution data, temperature field distribution data and pressure field distribution data of the heat exchange module based on the three-dimensional steady-state model; The fluid analysis module is used to construct a first thermal fluid model based on the flow field distribution data, the temperature field distribution data, and the pressure field distribution data, and to calibrate the parameters of the thermal fluid network model using the flow field distribution data and the temperature field distribution data to obtain a second thermal fluid model. The operating condition simulation module is used to perform transient operating condition simulation in the second heat fluid model and generate heat transfer effect diagrams. The report generation module is used to select target parameters from a preset set of channel adjustment parameters based on the heat exchange effect diagram, and generate a channel adjustment report based on the design parameters, the heat exchange effect diagram, and the target parameters.

[0006] This application provides a simulation optimization method for a heat exchange module. The method includes: constructing a three-dimensional steady-state model based on the design parameters of the heat exchange module; obtaining flow field distribution data, temperature field distribution data, and pressure field distribution data of the heat exchange module based on the three-dimensional steady-state model; constructing a first thermofluid model based on the flow field distribution data, temperature field distribution data, and pressure field distribution data, and calibrating the parameters of the thermofluid network model using the flow field distribution data and temperature field distribution data to obtain a second thermofluid model; performing transient operating condition simulation in the second thermofluid model to generate a heat exchange effect diagram; selecting target parameters from a preset set of channel adjustment parameters based on the heat exchange effect diagram; and generating a channel adjustment report based on the design parameters, the heat exchange effect diagram, and the target parameters. In the above method, detailed multi-field data is obtained by constructing a three-dimensional steady-state model. Based on this, a thermofluid model is constructed and calibrated, realizing multi-scale coupled simulation from local to system. It can efficiently predict the heat exchange effect under transient conditions and accurately locate defects during the design stage. Furthermore, it can generate concrete adjustment suggestions by combining preset parameter sets, which significantly improves the flow distribution uniformity and thermal management performance of the heat exchange module, effectively avoids local overheating, shortens the design cycle, and reduces trial and error costs. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic flowchart illustrating a simulation optimization method for a heat exchange module provided in an embodiment of this application; Figure 2 This is a schematic block diagram of a simulation optimization system for a heat exchange module provided in an embodiment of this application. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described below with reference to the accompanying drawings.

[0010] The terms "first" and "second," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0011] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0012] It should be understood that in this application, "at least one (item)" means one or more, "more than one" means two or more, "at least two (items)" means two or three or more, and "and / or" is used to describe the relationship between related objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0013] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating a simulation optimization method for a heat exchange module provided in an embodiment of this application. Figure 1 As shown, the specific steps of the simulation optimization method for this heat exchange module include: S101-S104.

[0014] S101. Construct a three-dimensional steady-state model based on the design parameters of the heat exchange module, and obtain the flow field distribution data, temperature field distribution data and pressure field distribution data of the heat exchange module based on the three-dimensional steady-state model.

[0015] For example, the process of constructing a three-dimensional steady-state model based on the design parameters of the heat exchange module begins by reading the geometric information of the heat exchange module from the product data management system or design files. This information specifically includes the layout structure of the microchannels, fin spacing, flow channel cross-sectional shape and size, manifold distributor configuration, and the overall outline of the module. Based on the read geometric information, a three-dimensional solid model is created in computational fluid dynamics software. The three-dimensional solid model needs to appropriately simplify complex fillets and small welds while retaining all key features affecting flow and heat transfer. Subsequently, a physical continuum is defined for the model to distinguish between the solid domain and the fluid domain. Material properties such as aluminum alloy or copper are specified for the solid domain, and working fluid properties such as refrigerant or ethylene glycol aqueous solution are specified for the fluid domain. After the physical continuum definition is completed, the computational domain is discretized. Polyhedral meshes or cut-body meshes are used to divide the fluid domain, and prism layer meshes are generated in the near-wall region to accurately analyze the flow and heat transfer within the boundary layer. At the same time, the solid domain is meshed, and it is ensured that the solid domain mesh and the fluid domain mesh share nodes or are mapped to surface meshes at the interface. After mesh generation, the solver is set to a pressure-based steady-state solver, and the k-ε turbulence model and energy equation are coupled for calculation. When setting boundary conditions, the inlet of the cooling medium is specified as a mass flow rate inlet with a design flow rate and inlet temperature, and the outlet is specified as a pressure outlet with a static pressure value. To simulate the actual heat dissipation environment, convective heat transfer boundary conditions are set on the outer surface of the heat exchange module, and the heat transfer coefficient and ambient temperature are specified. A volumetric heat source is set for the heat-generating components in the solid domain to simulate the heat generated by the chip or battery. After completing the settings, the solution calculation is started, and the residual curve and the inlet and outlet flow rates and temperature balance are monitored to determine convergence. After the solution is completed, the velocity vector field and pressure contour map of the entire computational domain are extracted from the post-processing module as flow field distribution data and pressure field distribution data, respectively. At the same time, the temperature contour maps of the solid and fluid domains are extracted as temperature field distribution data.

[0016] S102. Construct a first thermofluid model based on the flow field distribution data, temperature field distribution data, and pressure field distribution data, and calibrate the parameters of the thermofluid network model using the flow field distribution data and temperature field distribution data to obtain a second thermofluid model.

[0017] For example, when constructing the first thermofluid model based on the flow field distribution data, temperature field distribution data, and pressure field distribution data obtained in the aforementioned steps, an equivalent network model reflecting the flow and heat transfer characteristics of the heat transfer module needs to be built. The equivalent network model consists of a series of resistive elements representing microchannel flow paths, capacitive elements representing manifold capacity, thermal capacitive elements representing solid thermal mass, and thermal resistance elements representing convective heat transfer, connected by nodes. The flow rate values ​​of each parallel microchannel branch are obtained by analyzing the flow field distribution data, and their distribution ratio relative to the total flow rate is calculated. The temperature difference between the solid and fluid in different regions is obtained by analyzing the temperature field distribution data, and the equivalent convective heat transfer coefficient and thermal resistance parameters are calculated in conjunction with the heat flux density data. The pressure drop data along the flow path is obtained by analyzing the pressure field distribution data, and the relationship curve between flow rate and pressure drop is fitted to determine the flow resistance characteristic parameters. The calculated flow distribution ratio, thermal resistance parameters, and flow resistance characteristic parameters are assigned to the corresponding elements in the equivalent network model to complete the initial construction of the first thermofluid model. Subsequently, the parameters of the first thermofluid model were calibrated using the original flow field distribution data and temperature field distribution data. The flow rate and temperature distribution of the simulation output were obtained by running the first thermofluid model. The simulation output was compared with the benchmark data provided by the three-dimensional steady-state model, and the relative error of flow rate and the absolute error of temperature were calculated. The flow resistance coefficient and thermal resistance coefficient in the equivalent network model were automatically adjusted by optimization algorithms such as gradient descent or genetic algorithm until the flow rate error and temperature error were both lower than the set convergence threshold. The parameterized model that meets the accuracy requirements was determined as the second thermofluid model.

[0018] S103. Perform transient operating condition simulation in the second heat fluid model to generate heat exchange effect diagram.

[0019] For example, when performing transient simulations in a second thermofluid model with calibrated parameters, dynamic boundary conditions reflecting actual operating conditions need to be defined in the equivalent network model. These dynamic boundary conditions include curves simulating the inlet mass flow rate over time to simulate air conditioning start-up and shutdown and load changes, curves simulating the inlet temperature over time to simulate ambient temperature fluctuations, and curves simulating the solid heat source over time to simulate heating power changes. These time-varying curve data are input into the corresponding boundary positions of the second thermofluid model in tabular or functional form to drive the transient simulation. The total simulation duration and output step size are set to balance the requirements of computational efficiency and result resolution. After initializing all state variables of the model, the transient solver is started for integration. During the solution process, the changes in state parameters at key locations in the model over time are monitored and recorded in real time, including the instantaneous flow rate values ​​of each flow path branch, the temperature values ​​of different solid nodes, and the overall pressure drop of the heat exchanger. After completing the transient calculation, the data of all time steps are post-processed. Through interpolation calculation, an animated cloud map of temperature distribution and flow distribution curve of the entire heat exchange module in the spatiotemporal dimension are generated. The cloud map is marked with a striking color to mark the local high temperature area exceeding the temperature threshold and the uneven distribution area where the flow is lower than the average value. All visualization elements are integrated to generate a heat exchange effect diagram that comprehensively reflects the transient heat exchange performance.

[0020] S104. Select the target parameter from the preset channel adjustment parameter set according to the heat exchange effect diagram, and generate a channel adjustment report based on the design parameters, heat exchange effect diagram and target parameter.

[0021] For example, when selecting target parameters from a pre-defined set of channel adjustment parameters based on the generated heat transfer effect diagram, an image processing algorithm is invoked to automatically identify the pixel coordinates of the high-temperature regions and uneven flow distribution regions marked in the heat transfer effect diagram and map them back to the geometric space of the heat transfer module, thereby locating the specific microchannels that need optimization. The overtemperature amplitude quantification value and flow deviation quantification value corresponding to these target microchannels are extracted as input features and matched against a predefined set of channel adjustment parameters. This parameter set stores geometric parameters such as different channel widths, depths, fin spacing, and branch angles, along with their known flow and heat transfer characteristic influence coefficients, in the form of a database or knowledge graph. By executing a multi-objective optimization algorithm to balance pressure drop and the Pareto front of heat transfer performance, candidate parameter combinations that can simultaneously improve flow distribution and reduce temperature are selected from the parameter set. Furthermore, combined with the original channel dimensions defined in the initial design parameters, the structural adjustment amount corresponding to the candidate parameters and their expected impact on flow and temperature are calculated. All analysis results are structured and organized into a channel adjustment report, including: the suggested channel identifier, the specific structural dimension adjustment amount, the improvement magnitude of the predicted performance indicators after adjustment, and a recommended explanation of the modification priority.

[0022] This application provides a simulation optimization method for a heat exchange module. The method includes: constructing a three-dimensional steady-state model based on the design parameters of the heat exchange module; obtaining flow field distribution data, temperature field distribution data, and pressure field distribution data of the heat exchange module based on the three-dimensional steady-state model; constructing a first thermofluid model based on the flow field distribution data, temperature field distribution data, and pressure field distribution data, and calibrating the parameters of the thermofluid network model using the flow field distribution data and temperature field distribution data to obtain a second thermofluid model; performing transient operating condition simulation in the second thermofluid model to generate a heat exchange effect diagram; selecting target parameters from a preset set of channel adjustment parameters based on the heat exchange effect diagram; and generating a channel adjustment report based on the design parameters, the heat exchange effect diagram, and the target parameters. In the above method, detailed multi-field data is obtained by constructing a three-dimensional steady-state model. Based on this, a thermofluid model is constructed and calibrated, realizing multi-scale coupled simulation from local to system. It can efficiently predict the heat exchange effect under transient conditions and accurately locate defects during the design stage. Furthermore, it can generate concrete adjustment suggestions by combining preset parameter sets, which significantly improves the flow distribution uniformity and thermal management performance of the heat exchange module, effectively avoids local overheating, shortens the design cycle, and reduces trial and error costs.

[0023] To more clearly illustrate the technical solution of this application, the technical solution of this application will be described below through specific embodiments. It should be noted that the specific embodiments are used to expand the description of the technical solution of this application, and are not intended to limit this application.

[0024] In some embodiments, a first thermal fluid model is constructed based on flow field distribution data, temperature field distribution data, and pressure field distribution data, including: determining the flow distribution ratio of multiple parallel microchannels of the heat exchange module based on the flow field distribution data; determining the thermal resistance parameter of each region of the heat exchange module based on the temperature field distribution data; determining the friction coefficient of the parallel microchannels based on the pressure field distribution data; and constructing a first thermal fluid network model based on the flow distribution ratio, thermal resistance parameter, and friction coefficient.

[0025] For example, the flow distribution ratio of multiple parallel microchannels in the heat exchange module was determined based on flow field distribution data. Mass flow rates of 56 parallel microchannel outlet sections were extracted by analyzing three-dimensional flow field data, and the ratio of each channel's flow rate to the total flow rate was calculated to obtain the initial distribution ratio. The third channel accounted for 1.65%, and the 12th channel accounted for 1.92%, showing significant non-uniform distribution. The thermal resistance parameters of each region of the heat exchange module were determined based on temperature field distribution data. The equivalent thermal resistance values ​​of each region were calculated by analyzing the temperature gradient distribution between the solid and fluid and combining it with local heat flux density. The thermal resistance in the high-temperature region reached 0.0095 Kelvin per watt, while in the low-temperature region it was only 0.0078 Kelvin per watt. The friction factor of the parallel microchannels was determined based on pressure field distribution data. The resistance characteristic parameters were obtained by fitting the pressure drop versus flow rate curves of each channel. It was found that the friction factor of the third channel was 1.3 × 10⁻⁶. 5 Pascals per cubic meter, while the 12th channel is 9.8 × 10⁻⁶. 4 Pascals per cubic meter. Based on the flow distribution ratio, thermal resistance parameters, and friction factor, a first thermofluid network model was built in the MATLAB / Simulink environment, including 56 nonlinear flow resistance elements, 28 thermal resistance elements, and 14 heat capacity elements. The parameters of each element were strictly set according to the three-dimensional simulation data, forming an equivalent network system that can reflect the actual flow and heat transfer characteristics.

[0026] In some embodiments, the first thermal fluid network model is calibrated using flow field distribution data and temperature field distribution data to obtain a second thermal fluid model, including: running the first thermal fluid network model to obtain first simulated flow distribution data and first simulated temperature distribution data; comparing the first simulated flow distribution data with reference flow distribution data extracted from the flow field distribution data to calculate a first error; comparing the first simulated temperature distribution data with reference temperature distribution data extracted from the temperature field distribution data to calculate a second error; adjusting the flow resistance characteristic parameters in the first thermal fluid network model until the first error is lower than a first threshold, and adjusting the thermal resistance parameters until the second error is lower than a second threshold, and using the adjusted model as the second thermal fluid model.

[0027] For example, a first thermal fluid network model is run to obtain first simulated flow distribution data and first simulated temperature distribution data. The first simulated flow distribution data is compared with the baseline flow distribution data extracted from the flow field distribution data to calculate the relative error of the flow rate for each channel (e.g., 0.15% error for channel 1 and 0.22% error for channel 2). The first simulated temperature distribution data is compared with the baseline temperature distribution data extracted from the temperature field distribution data to calculate the absolute temperature error (maximum error 0.38K). The flow resistance characteristic parameters in the first thermal fluid network model are adjusted until the flow rate error of all channels is lower than a first threshold of 0.2%, and the thermal resistance parameters are adjusted until the temperature error is lower than a second threshold of 0.3K. After 8 iterations, a second thermal fluid model with satisfactory accuracy is obtained, with the flow rate prediction error reduced to within 0.18% and the temperature prediction error controlled within 0.28K.

[0028] In some embodiments, transient operating condition simulation is performed in the second heat fluid model to generate a heat transfer effect diagram, including: setting transient boundary conditions, which include: the operating conditions of the heat transfer module, flow fluctuations, inlet temperature fluctuations, and heat load fluctuations; running the transient boundary conditions in the second heat fluid model to obtain transient response data; and generating a heat transfer effect diagram based on the transient response data, in which high-temperature regions and regions with uneven flow distribution are identified.

[0029] For example, the transient boundary conditions include: the operating conditions of the heat exchange module (rated flow rate 0.12 kg / s, inlet temperature 298 K), flow rate fluctuations (sinusoidal fluctuations with an amplitude of ±20%, period 180 s), inlet temperature fluctuations (random fluctuations with an amplitude of ±3 K), and heat load fluctuations (stepping from 100 kW / m² to 150 kW / m²). The transient boundary conditions are run in the second thermofluid model, with a transient simulation of 1000 s in 0.5 s increments, to obtain transient response data, including instantaneous flow rate, temperature, and pressure data from 56 channels. A heat exchange effect diagram is generated based on the transient response data, displaying the spatiotemporal temperature distribution through a temperature cloud map animation. High-temperature regions exceeding 325 K are marked in red, and unevenly distributed regions with flow rates below 15% of the average are marked in yellow.

[0030] In some embodiments, selecting a target parameter from a preset set of channel adjustment parameters based on a heat exchange effect diagram includes: extracting spatial location information and over-temperature amplitude value of the high-temperature region and flow deviation value of the region with uneven flow distribution from the heat exchange effect diagram; locating the microchannel to be optimized based on the spatial location information; matching the over-temperature amplitude value and flow deviation value with the preset set of channel adjustment parameters to filter out candidate parameters that can be used to adjust the microchannel to be optimized; and selecting the target parameter from the candidate parameters based on preset heat exchange optimization parameters.

[0031] For example, spatial location information (coordinates X:125mm, Y:38mm; X:287mm, Y:42mm; X:412mm, Y:35mm) and overtemperature amplitude values ​​(4.2K, 3.8K, and 5.1K, respectively) of three high-temperature regions are extracted from the heat exchange effect diagram, along with flow deviation values ​​(-17.2% and -19.8%) of two regions with uneven flow distribution. Based on the spatial location information, microchannels 19, 37, and 42, which need optimization, are located. The overtemperature amplitude values ​​and flow deviation values ​​are matched with a preset set of channel adjustment parameters, including 128 combinations of parameters such as channel width (0.4-0.6mm), depth (1.6-2.2mm), and fin spacing (0.8-1.2mm), and their performance influence coefficients. Five sets of candidate parameters are selected through a multi-objective optimization algorithm. Based on the preset heat exchange optimization parameters (constrained by a pressure drop increase of no more than 10% and aiming for the maximum improvement in temperature uniformity), the target parameters selected from the candidate parameters are: increase the width of channel 19 by 0.12 mm, decrease the depth of channel 37 by 0.15 mm, and increase the fin spacing of channel 42 by 0.2 mm.

[0032] In some embodiments, generating a channel adjustment report based on design parameters, heat exchange effect diagrams, and target parameters includes: determining the baseline structural parameters of the microchannels in the heat exchange module based on the design parameters; comparing the target parameters with the baseline structural parameters to obtain the structural adjustment amount of the microchannels; determining the structural adjustment location of the structural adjustment amount based on the high-temperature region and the region with uneven flow distribution in the heat exchange effect diagram; analyzing the structural adjustment amount and the structural adjustment location to obtain a description of the expected effect; and generating a channel adjustment report based on the structural adjustment amount, the structural adjustment location, and the description of the expected effect.

[0033] For example, the baseline structural parameters of the microchannels in the heat exchange module are determined based on design parameters. Key parameters such as the standard geometric dimensions of the microchannels, including a width of 0.5 mm, a depth of 2.0 mm, a length of 500 mm, and a fin spacing of 1.2 mm, are obtained by parsing the product design document. The target parameters are compared with the baseline structural parameters, and the difference is calculated to determine the structural adjustment amount of the microchannels. For example, some channels may require an increase in width of 0.1 mm or a decrease in depth of 0.15 mm. Based on the distribution characteristics of high-temperature areas and areas with uneven flow distribution in the heat exchange effect diagram, the specific application location of the structural adjustment amount is determined through coordinate mapping, precisely mapping each adjustment amount to a specific microchannel number and its position along the flow path. Based on the structural adjustment amount and location analysis, the expected effect description is obtained, and quantitative indicators such as a predicted temperature reduction of 4.5 Kelvin, a 25% improvement in flow uniformity, and an 8% increase in system pressure drop are calculated using empirical formulas. A channel adjustment report is generated based on the structural adjustment amount, location, and expected effect description. The channel adjustment report contains detailed modification suggestions, expected performance improvement data, and implementation priority suggestions, providing clear technical guidance for designers.

[0034] In some embodiments, determining the flow distribution ratio of multiple parallel microchannels of a heat exchange module based on flow field distribution data includes: extracting mass flow rate or velocity data of the outlet section of each microchannel in the multiple parallel microchannels from the flow field distribution data; calculating the distribution ratio of the mass flow rate or velocity data of each parallel microchannel to the total mass flow rate or average velocity of all parallel microchannels; and determining the flow distribution ratio of each parallel microchannel according to a preset uniformity index and distribution ratio.

[0035] For example, mass flow rate or velocity data for each microchannel outlet section in multiple parallel microchannels is extracted from the flow field distribution data. Post-processing software reads the mass flow rate values ​​of 56 channel outlet surfaces and records the average velocity for each section. The distribution ratio of the mass flow rate or velocity data of each parallel microchannel to the total mass flow rate or average velocity of all parallel microchannels is calculated. A formula is used to calculate the percentage of flow rate in each channel to the total flow rate, obtaining the initial distribution ratio. Based on preset uniformity indicators and distribution ratios, the flow distribution ratio of each parallel microchannel is determined. By calculating the flow distribution non-uniformity coefficient and comparing it with the standard value required by the design specifications, the formal flow distribution ratio of each channel is determined. These ratio data will serve as key input parameters for constructing a one-dimensional thermofluid network model, used for subsequent model calibration and optimization analysis.

[0036] Please see Figure 2 , Figure 2This is a schematic block diagram of a simulation optimization system for a heat exchange module provided in an embodiment of this application. The simulation optimization system 200 is used to execute the aforementioned simulation optimization method for the heat exchange module. The simulation optimization system 200 can be configured in a server.

[0037] The server can be a standalone server, a server cluster, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0038] like Figure 2 As shown, the simulation optimization system 200 for the heat exchange module includes: a parameter conversion module 201, a fluid analysis module 202, an operating condition simulation module 203, and a report generation module 204.

[0039] The parameter conversion module 201 is used to construct a three-dimensional steady-state model based on the design parameters of the heat exchange module, and to obtain the flow field distribution data, temperature field distribution data and pressure field distribution data of the heat exchange module based on the three-dimensional steady-state model.

[0040] The fluid analysis module 202 is used to construct a first thermofluid model based on flow field distribution data, temperature field distribution data, and pressure field distribution data, and to calibrate the parameters of the thermofluid network model using the flow field distribution data and temperature field distribution data to obtain a second thermofluid model.

[0041] The operating condition simulation module 203 is used to perform transient operating condition simulation in the second heat fluid model and generate heat transfer effect diagrams.

[0042] The report generation module 204 is used to select target parameters from a preset set of channel adjustment parameters based on the heat exchange effect diagram, and generate a channel adjustment report based on the design parameters, heat exchange effect diagram and target parameters.

[0043] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A simulation optimization method for a heat exchange module, characterized in that, The method includes: A three-dimensional steady-state model is constructed based on the design parameters of the heat exchange module, and the flow field distribution data, temperature field distribution data, and pressure field distribution data of the heat exchange module are obtained based on the three-dimensional steady-state model. A first thermofluid model is constructed based on the flow field distribution data, the temperature field distribution data, and the pressure field distribution data. The parameters of the thermofluid network model are calibrated using the flow field distribution data and the temperature field distribution data to obtain a second thermofluid model. Transient operating condition simulation is performed in the second thermal fluid model to generate heat transfer effect diagrams; Based on the heat exchange effect diagram, a target parameter is selected from the preset channel adjustment parameter set, and a channel adjustment report is generated based on the design parameters, the heat exchange effect diagram, and the target parameter.

2. The simulation optimization method for the heat exchange module as described in claim 1, characterized in that, The construction of the first thermofluid model based on the flow field distribution data, the temperature field distribution data, and the pressure field distribution data includes: The flow distribution ratio of the multiple parallel microchannels of the heat exchange module is determined based on the flow field distribution data. The thermal resistance parameters of each region of the heat exchange module are determined based on the temperature field distribution data. The friction coefficient of the parallel microchannels is determined based on the pressure field distribution data. A first thermal fluid network model is constructed based on the flow distribution ratio, the thermal resistance parameter, and the friction drag coefficient.

3. The simulation optimization method for the heat exchange module as described in claim 2, characterized in that, The step of calibrating the parameters of the first thermal fluid network model using the flow field distribution data and the temperature field distribution data to obtain the second thermal fluid model includes: Run the first thermal fluid network model to obtain the first simulated flow distribution data and the first simulated temperature distribution data; The first simulated flow distribution data is compared with the baseline flow distribution data extracted from the flow field distribution data, and the first error is calculated. The first simulated temperature distribution data is compared with the reference temperature distribution data extracted from the temperature field distribution data to calculate the second error; Adjust the flow resistance characteristic parameters in the first thermal fluid network model until the first error is lower than the first threshold, and adjust the thermal resistance parameters until the second error is lower than the second threshold. Use the adjusted model as the second thermal fluid model.

4. The simulation optimization method for the heat exchange module as described in claim 3, characterized in that, The transient operating condition simulation in the second thermal fluid model, generating a heat transfer effect diagram, includes: Set transient boundary conditions, which include: the operating conditions of the heat exchange module, flow rate fluctuations, inlet temperature fluctuations, and heat load fluctuations; Run the transient boundary conditions in the second thermal fluid model to obtain transient response data; A heat exchange effect diagram is generated based on the transient response data, and the heat exchange effect diagram identifies high-temperature areas and areas with uneven flow distribution.

5. The simulation optimization method for the heat exchange module as described in claim 4, characterized in that, The step of selecting the target parameter from the preset channel adjustment parameter set based on the heat exchange effect diagram includes: Extract the spatial location information and over-temperature amplitude value of the high-temperature region, as well as the flow deviation value of the region with uneven flow distribution, from the heat exchange effect diagram; The microchannel to be optimized is located based on the spatial location information; The over-temperature amplitude value and the flow deviation value are matched with the preset channel adjustment parameter set to filter out candidate parameters that can be used to adjust the microchannel to be optimized; The target parameter is selected from the candidate parameters based on the preset heat exchange optimization parameters.

6. The simulation optimization method for the heat exchange module as described in claim 1, characterized in that, The process of generating a channel adjustment report based on the design parameters, the heat exchange effect diagram, and the target parameters includes: Based on the design parameters, the baseline structural parameters of the microchannels in the heat exchange module are determined; The target parameters are compared with the baseline structural parameters to obtain the structural adjustment amount of the microchannel; Based on the high-temperature region and the uneven flow distribution region shown in the heat exchange effect diagram, determine the structural adjustment position of the structural adjustment amount; The expected effect is described based on the structural adjustment amount and the structural adjustment location; A channel adjustment report is generated based on the structural adjustment amount, the structural adjustment location, and the expected effect description.

7. The simulation optimization method for the heat exchange module as described in claim 2, characterized in that, The determination of the flow distribution ratio of the multiple parallel microchannels of the heat exchange module based on the flow field distribution data includes: Mass flow rate or velocity data of the outlet section of each of the multiple parallel microchannels are extracted from the flow field distribution data; Calculate the distribution ratio of the mass flow rate or velocity data of each of the parallel microchannels to the total mass flow rate or average velocity of all the parallel microchannels; The flow distribution ratio of each parallel microchannel is determined based on the preset uniformity index and the distribution ratio.

8. A simulation optimization system for a heat exchange module, characterized in that, The simulation optimization system for the heat exchange module is used to execute the simulation optimization method for the heat exchange module as described in any one of claims 1-7, and the simulation optimization system for the heat exchange module includes: The parameter conversion module is used to construct a three-dimensional steady-state model based on the design parameters of the heat exchange module, and to obtain the flow field distribution data, temperature field distribution data and pressure field distribution data of the heat exchange module based on the three-dimensional steady-state model; The fluid analysis module is used to construct a first thermal fluid model based on the flow field distribution data, the temperature field distribution data, and the pressure field distribution data, and to calibrate the parameters of the thermal fluid network model using the flow field distribution data and the temperature field distribution data to obtain a second thermal fluid model. The operating condition simulation module is used to perform transient operating condition simulation in the second heat fluid model and generate heat transfer effect diagrams. The report generation module is used to select target parameters from a preset set of channel adjustment parameters based on the heat exchange effect diagram, and generate a channel adjustment report based on the design parameters, the heat exchange effect diagram, and the target parameters.

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