Gas oven shell heat dissipation structure topology optimization method and system

Abstract

This application provides a method and system for optimizing the topology of the heat dissipation structure of a gas oven shell. The method involves obtaining the thermal state parameters of the gas oven shell under multiple typical baking conditions, establishing a heat dissipation simulation model with material distribution as the design variable, solving for the temperature gradient and heat flux density vector field, and locating the heat dissipation bottleneck path. A convection weight matrix reflecting the heat dissipation robustness of each region is constructed. With the objectives of minimizing the maximum surface temperature, the bottleneck path temperature gradient, and the amount of material used, and with the constraint that the surface temperature does not exceed a safe threshold, the convection weight matrix is ​​used as a weighting factor to form the convection heat transfer boundary condition. Multi-objective iterative optimization is then performed to generate the shell's heat dissipation topology configuration. This application achieves accurate identification of heat dissipation bottlenecks and quantification of convection heat dissipation robustness based on multi-physics co-simulation. Through multi-objective driven topology optimization, the heat dissipation stability and overall heat dissipation efficiency of the shell under varying external flow fields are effectively improved while controlling material usage.

Description

Technical Field

[0001] This application relates to the field of gas oven technology, and more specifically, to a method and system for optimizing the heat dissipation structure of a gas oven shell. Background Technology

[0002] As a kitchen appliance that combines high-temperature baking with user safety, the design of the heat dissipation structure of a gas oven's outer shell directly determines whether the surface temperature meets safety standards. Early shell heat dissipation designs relied heavily on empirical formulas and repeated prototype trials, enhancing convective heat transfer by adding evenly distributed heat dissipation holes or fins to the shell surface. With the development of computational fluid dynamics and the finite element method, parametric optimization based on steady-state thermal-fluid coupling simulation has gradually become mainstream. This method can optimize the dimensions and spacing of heat dissipation features using single or multi-objective methods. In recent years, topology optimization technology has been introduced into the field of thin-walled structure heat dissipation design. It can automatically find the optimal material distribution path within a given design space, eliminating the dependence of traditional parametric design on the initial configuration and providing a new approach for generating innovative configurations of the shell heat dissipation structure.

[0003] However, existing topology-optimized heat dissipation structure design methods generally adopt fixed uniform convective heat transfer boundary conditions, failing to consider the influence of variable external flow fields faced by gas ovens in actual installation environments. In actual use, the airflow direction and velocity around the oven are affected by factors such as kitchen ventilation conditions, nearby obstacles, and indoor temperature differences, resulting in a significant spatial non-uniform distribution of the convective heat transfer coefficient on the outer shell surface. Moreover, the convective heat transfer capacity of different regions shows significant differences with changes in the environmental flow field. Existing methods optimize the entire outer shell surface as an equivalent uniform convective boundary, failing to identify and utilize surface areas that maintain high heat transfer robustness under multiple scenarios for priority heat dissipation, and also failing to effectively avoid weak areas where the convective heat transfer capacity fluctuates drastically with the flow field. At the same time, existing methods mostly only use surface temperature as the optimization target, lacking accurate identification and directional guidance of high heat transfer resistance areas inside the shell, making it difficult to improve heat dissipation efficiency from the heat transfer path level. The oversimplification of boundary conditions and the singular focus of optimization targets result in optimized heat dissipation topologies that only perform well under the assumed single flow field. Once placed in a real, variable installation environment, localized hot spots easily appear on the casing surface, leading to a decrease in safety compliance. Therefore, how to accurately identify internal heat dissipation bottlenecks and quantify external convection robustness based on multi-physics co-simulation, and improve the casing's heat dissipation performance under varying operating conditions through multi-objective driven topology optimization, has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method and system for optimizing the topology of the heat dissipation structure of a gas oven shell. Based on multi-physics field co-simulation, it achieves accurate identification of internal heat dissipation bottlenecks and quantification of the robustness of external convection heat dissipation. Through multi-objective driven iterative optimization of topology configuration, it improves the heat dissipation stability and overall heat dissipation efficiency of the shell under varying external flow fields while controlling the amount of material used.

[0005] In a first aspect, this application provides a method for optimizing the topology of the heat dissipation structure of a gas oven shell, including: The thermal state parameters of the oven shell under multiple typical baking conditions were obtained, and a heat dissipation performance simulation model coupling the internal combustion heat flow and the external environmental flow field was established, with material distribution parameters as design variables. Load the thermal state parameters of each typical baking condition, solve the heat dissipation performance simulation model, and locate the heat dissipation bottleneck path of the oven shell based on the obtained temperature gradient and heat flux density vector field. Multiple typical external flow field scenarios are loaded onto the heat dissipation performance simulation model. The spatial distribution of the convective heat transfer coefficient on the shell surface under each scenario is extracted, and then a convective weight matrix characterizing the heat dissipation robustness of each region is constructed. The optimization objectives are to minimize the highest surface temperature of the outer shell, minimize the temperature gradient at the heat dissipation bottleneck path, and minimize the amount of material used. The constraint is that the surface temperature does not exceed the safety threshold. The convection weight matrix is ​​used as a weighting factor to construct the convection heat transfer boundary conditions. Multi-objective iterative optimization is performed on the design variables to generate the heat dissipation topology configuration of the outer shell.

[0006] Preferably, establishing a simulation model for heat dissipation performance that couples internal combustion heat flow with external environmental flow field specifically includes: Construct a three-dimensional geometric model of the oven shell based on the initial structural parameters of the oven shell; The heat flow of the internal combustion wall is determined based on each typical baking condition, and the external ambient temperature is extracted from the thermal state parameters as the initial boundary value for multiphysics coupling simulation. The three-dimensional geometric model of the outer shell is spatially discretized using the finite volume method, and then a heat dissipation performance simulation model coupling the internal combustion heat flow and the external environmental flow field is constructed by combining the heat conduction equation, the convection heat transfer equation and the radiation heat transfer equation.

[0007] Preferably, the simulation model for solving the heat dissipation performance by loading the thermal state parameters of each typical baking condition specifically includes: For each typical baking condition, the heat flow of the internal combustion wall and the external ambient temperature corresponding to the condition are loaded into the heat dissipation performance simulation model as thermal boundary conditions. The temperature field and heat flux density vector field within the shell design domain under the current operating condition are calculated iteratively by a steady-state solver until convergence, thus obtaining the solution result for the current operating condition. The solutions for all typical baking conditions are solved sequentially, and the solution results for each condition are obtained. The solution results include the temperature field and the heat flux density vector field.

[0008] Preferably, based on the solved temperature gradient and heat flux density vector field, the specific path to locate the heat dissipation bottleneck of the oven shell includes: For the solution results of any typical baking condition, calculate the temperature gradient vector and heat flux density vector at each spatial location within the shell design domain; Calculate the magnitude of the temperature gradient vector at each location, use the temperature gradient magnitude to characterize the local heat transfer load intensity, and mark the locations where the temperature gradient magnitude exceeds a preset threshold as high resistance regions. The high-resistance regions marked under each operating condition are spatially superimposed, and the continuously distributed high-resistance strips after superposition are extracted as the heat dissipation bottleneck path.

[0009] Preferably, multiple typical external flow field scenarios are loaded onto the heat dissipation performance simulation model, and the spatial distribution of the convective heat transfer coefficient on the shell surface under each scenario is extracted. Then, a convective weight matrix characterizing the heat dissipation robustness of each region is constructed, specifically including: Load the incoming flow velocity and direction parameters for each typical external flow field scenario, and solve the convective heat transfer coefficient field on the shell surface for each scenario; The convective heat transfer coefficient field under each scenario is normalized to obtain the relative heat transfer intensity distribution of each region on the shell surface under each scenario. The relative heat transfer intensity of the same area under different scenarios is compared one by one, and the corresponding weight value is determined according to the degree to which the area maintains high heat transfer under each scenario. The weight values ​​of each region are integrated according to their spatial location to construct the convection weight matrix.

[0010] Preferably, the inflow velocity and direction parameters for each typical external flow field scenario are loaded, and the convective heat transfer coefficient field of the shell surface under each scenario is solved, specifically including: For each typical external flow field scenario, an external flow field computational domain including the oven shell is constructed, and a boundary layer mesh is generated. The inflow velocity and direction parameters corresponding to the scenario are applied at the inlet boundary of the external flow field calculation domain, the pressure outlet condition is applied at the outlet boundary, and the coupled heat exchange wall condition is applied on the shell surface. The velocity and temperature fields of the external flow field are solved iteratively by a steady-state solver until the residuals of the continuity equation, momentum equation, and energy equation converge to the preset criteria. Based on the wall heat flux density, wall temperature and far-field reference temperature obtained from the convergent solution, the convective heat transfer coefficient of each node on the shell surface is calculated, and the convective heat transfer coefficient field of the shell surface under this scenario is obtained.

[0011] Preferably, constructing an external flow field computational domain including the oven shell and dividing the boundary layer mesh specifically includes: Based on the geometry of the oven shell, establish a cuboid or cylindrical external air domain, and place the oven shell slightly below the geometric center of the external air domain; The air region near the surface of the oven shell is locally meshed to generate a body-fitting boundary layer mesh. The height of the first layer of the boundary layer mesh is determined according to the Reynolds number of the target working condition. A gradually sized unstructured mesh is generated between the boundary layer mesh and the far-field boundary of the external air domain to complete the mesh generation of the external flow field computational domain.

[0012] Secondly, this application provides a topology optimization system for the heat dissipation structure of a gas oven shell, comprising: The acquisition module is used to acquire the thermal state parameters of the oven shell under multiple typical baking conditions, and to establish a heat dissipation performance simulation model that couples the internal combustion heat flow with the external environmental flow field, using material distribution parameters as design variables. The processing module is used to load the thermal state parameters of each typical baking condition, solve the heat dissipation performance simulation model, and locate the heat dissipation bottleneck path of the oven shell based on the obtained temperature gradient and heat flux density vector field. The processing module is also used to load multiple typical external flow field scenarios onto the heat dissipation performance simulation model, extract the spatial distribution of convective heat transfer coefficients on the shell surface under each scenario, and then construct a convective weight matrix characterizing the heat dissipation robustness of each region. The optimization module is used to minimize the highest surface temperature of the shell, minimize the temperature gradient at the heat dissipation bottleneck path, and minimize the amount of material used as optimization objectives, with the surface temperature not exceeding a safety threshold as a constraint, and to construct convective heat transfer boundary conditions using the convection weight matrix as a weighting factor, and to perform multi-objective iterative optimization on the design variables to generate the shell heat dissipation topology configuration.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described gas oven shell heat dissipation structure topology optimization method.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the heat dissipation structure of a gas oven shell.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application constructs a mechanism for precise identification of heat dissipation bottlenecks and quantitative evaluation of convection robustness, combined with multi-physics field co-simulation and multi-objective topology optimization, ultimately achieving a robust improvement in the heat dissipation performance of the outer shell under various external flow fields. First, based on the solution results of the heat dissipation performance simulation model for various typical baking conditions, the temperature gradient vector and heat flux density vector field are extracted. By analyzing the spatial distribution of the temperature gradient amplitude, the heat dissipation bottleneck path is located. The continuous strips of heat transfer obstruction commonly present under multiple conditions are explicitly characterized as optimization intervention targets, thus extending the optimization focus from simply suppressing surface temperature to the targeted guidance of internal heat transfer paths. Second, multiple typical external flow field scenarios are loaded onto the heat dissipation performance simulation model. By comparing the distribution of convective heat transfer coefficients across scenarios, a characterization of heat dissipation in each region is constructed. The convection weight matrix for thermal robustness transforms the spatial non-uniformity and scenario differences of the external flow field into quantitative weighting factors that can be embedded in the optimization model. Then, the temperature gradient at the heat dissipation bottleneck path is elevated to an independent optimization objective, which, together with the maximum surface temperature of the shell and the amount of material, drives iterative evolution. The convection weight matrix is ​​used as a weighting factor to construct spatially distributed weighted convection heat transfer boundary conditions, so that the material distribution is simultaneously guided by both the improvement of internal thermal conductivity and the differentiation of external convection robustness. In summary, this application achieves joint optimization of internal heat transfer path guidance and external multi-scenario convection adaptation through the synergistic effect of heat dissipation bottleneck identification, convection robustness quantification and multi-objective topology optimization, thereby improving the working condition adaptability and design automation level of the shell heat dissipation topology configuration. Attached Figure Description

[0016] Figure 1 This is a schematic diagram illustrating an application scenario of the topology optimization method for the heat dissipation structure of a gas oven shell, as shown in some embodiments of this application. Figure 2 This is an exemplary flowchart of a method for optimizing the heat dissipation structure of a gas oven shell according to some embodiments of this application; Figure 3 This is a schematic diagram of the process for constructing a convection weight matrix according to some embodiments of this application; Figure 4 This is a schematic diagram of the topology optimization system for heat dissipation structure of a gas oven shell, according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device that implements a method for optimizing the heat dissipation structure of a gas oven shell, according to some embodiments of this application. Detailed Implementation

[0017] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] refer to Figure 1This figure is a schematic diagram of an application scenario for the topology optimization method of the heat dissipation structure of a gas oven shell according to some embodiments of this application. The figure mainly includes: sensors, a simulation platform, and a server. The sensors are connected to the simulation platform via data cables, and the simulation platform exchanges data with the server via a communication network. The server pre-constructs a heat dissipation performance simulation model and convection weight matrix that couples the internal combustion heat flow with the external environmental flow field, and receives in real time the thermal state parameters of the oven shell under multiple typical baking conditions collected by the sensors. The simulation platform loads the thermal state parameters to solve the heat dissipation performance simulation model, locates the heat dissipation bottleneck path based on the solved temperature gradient and heat flux density vector field, and simultaneously loads multiple parameters onto the model. In a typical external flow field scenario, the spatial distribution of the convective heat transfer coefficient on the shell surface is extracted to construct a convective weight matrix characterizing the heat dissipation robustness of each region. The server aims to minimize the highest temperature on the shell surface, the temperature gradient of the heat dissipation bottleneck path, and the amount of material used, with the constraint that the surface temperature does not exceed the safety threshold. The convective weight matrix is ​​used as a weighting factor to construct convective heat transfer boundary conditions. Multi-objective iterative optimization is performed on the material distribution design variables to generate the shell heat dissipation topology. The sensor can be a thermocouple or a heat flux sensor, the simulation platform can be a workstation that deploys finite element analysis and computational fluid dynamics software, and the server can be a local high-performance computing cluster or a cloud simulation optimization service platform.

[0019] refer to Figure 2 The figure is an exemplary flowchart of a method for optimizing the heat dissipation structure topology of a gas oven shell according to some embodiments of this application. The method mainly includes the following steps: Step 101: Obtain the thermal state parameters of the oven shell under multiple typical baking conditions, and establish a heat dissipation performance simulation model that couples the internal combustion heat flow with the external environmental flow field, using material distribution parameters as design variables.

[0020] It should be noted that the typical baking conditions in this application specifically include: top-heat baking mode, bottom-heat baking mode, and combined top and bottom heat baking mode. In specific implementation, the thermal state parameters of the oven shell under multiple typical baking conditions can be obtained in the following way: select several measuring points on the inner wall of the oven shell, arrange K-type thermocouples and thin-film heat flux sensors, and arrange K-type thermocouples at corresponding positions on the outer wall of the shell; sequentially start the top-heat baking mode, bottom-heat baking mode, and combined top and bottom heat baking mode of the oven, and after the internal temperature of the oven stabilizes in each mode, continuously collect data from each measuring point for no less than 10 minutes; take the average value of the data from each measuring point during the stable period, and use the inner wall temperature, inner wall heat flux density, and outer wall temperature as the thermal state parameters under the typical baking condition to form a multi-condition thermal state parameter set.

[0021] In some embodiments, a simulation model of heat dissipation performance coupling internal combustion heat flow and external environmental flow field can be established in the following manner: Construct a three-dimensional geometric model of the oven shell based on the initial structural parameters of the oven shell; The heat flow of the internal combustion wall is determined based on each typical baking condition, and the external ambient temperature is extracted from the thermal state parameters as the initial boundary value for multiphysics coupling simulation. The three-dimensional geometric model of the outer shell is spatially discretized using the finite volume method, and then a heat dissipation performance simulation model coupling the internal combustion heat flow and the external environmental flow field is constructed by combining the heat conduction equation, the convection heat transfer equation and the radiation heat transfer equation.

[0022] It should be noted that the heat dissipation performance simulation model refers to a numerical simulation model used to solve the temperature field and heat flux density vector field of the oven shell under the coupling effect of internal combustion heat flow and external environmental flow field.

[0023] In specific implementation, firstly, the structural parameters of the target oven shell are obtained. These structural parameters include the length, width, height, wall thickness, and the position and size of the reinforcing ribs or bending features of the shell. Based on the above structural parameters, a three-dimensional digital model with the same geometry as the target oven shell is constructed in a three-dimensional modeling environment. This three-dimensional digital model is used as the three-dimensional geometric model of the shell. The construction method of the above three-dimensional geometric model of the shell is a conventional modeling method mastered by those skilled in the art, and will not be described in detail here.

[0024] Secondly, for each typical baking condition, the rated power and flame coverage area parameters of the burner under that condition are obtained. The rated power is used as the total heat release of the burner per unit time, that is, the value of the rated power is directly used as the value of the total heat release per unit time. The flame length, flame angle, and vertical distance from the burner outlet to the inner wall of the outer shell are obtained from the flame coverage area parameters. Based on the flame length and flame angle, the radial expansion radius of the flame when it reaches the inner wall of the outer shell is calculated. Taking the vertical projection point of the burner outlet on the inner wall of the outer shell as the center, and the radial expansion radius as the center, the radial expansion radius is calculated. A circular region is drawn with the radius as the radius. This circular region is defined as the heated area on the inner wall of the outer shell that is directly exposed to flame radiation and high-temperature flue gas. The geometric area of ​​this circular region is calculated as the area of ​​the heated region. The total heat release is evenly distributed according to the area of ​​the heated region to obtain the average heat flux density value applied to the heated region on the inner wall of the outer shell. The heat flux density value of the inner wall region outside the heated region is set to zero. This forms the internal combustion wall heat flux of the inner wall of the outer shell under this typical baking condition. The internal combustion wall heat flux is the heat flux density value distributed according to spatial location.

[0025] Then, the spatial region occupied by the three-dimensional geometric model of the shell is spatially discretized using the finite volume method, dividing the three-dimensional geometric model of the shell into multiple interconnected hexahedral or tetrahedral control volume elements. The heat conduction equation is discretized on each control volume element to describe the heat conduction process inside the solid domain of the shell. A heat flow boundary is applied on the control volume element of the inner wall of the shell to couple the heat flow of the internal combustion wall. The convection heat transfer equation is discretized on the control volume element of the outer wall of the shell to describe the process of heat being carried away by the natural convection of the external air. At the same time, the radiation heat transfer equation is discretized on the control volume element of the outer wall of the shell to describe the process of heat radiation emitted by the outer wall of the shell to the external environment. Thus, a numerical calculation model is constructed that simultaneously includes discrete terms of the solid heat conduction equation, the convection heat transfer equation, and the radiation heat transfer equation, coupling the internal combustion heat flow with the external environment flow field. This numerical calculation model is used as a simulation model for heat dissipation performance.

[0026] It should also be noted that using material distribution parameters as design variables is beneficial for directly controlling the material retention state in each area of ​​the shell during the optimization process. This allows for simultaneous optimization of the heat dissipation path construction and material usage, avoiding the limitation of pre-set heat dissipation structure on the optimization results. As a result, a shell heat dissipation topology configuration that combines heat dissipation performance and material economy can be generated while meeting surface temperature safety constraints.

[0027] Step 102: Load the thermal state parameters of each typical baking condition, solve the heat dissipation performance simulation model, and locate the heat dissipation bottleneck path of the oven shell based on the obtained temperature gradient and heat flux density vector field.

[0028] In some embodiments, loading the thermal state parameters of each typical baking condition and solving the heat dissipation performance simulation model can be achieved by the following steps: For each typical baking condition, the heat flow of the internal combustion wall and the external ambient temperature corresponding to the condition are loaded into the heat dissipation performance simulation model as thermal boundary conditions. The temperature field and heat flux density vector field within the shell design domain under the current operating condition are calculated iteratively by a steady-state solver until convergence, thus obtaining the solution result for the current operating condition. The solutions for all typical baking conditions are solved sequentially, and the solution results for each condition are obtained. The solution results include the temperature field and the heat flux density vector field.

[0029] It should be noted that the thermal boundary conditions in this application are a set of input boundary parameters used to drive the numerical solution of the heat dissipation performance simulation model.

[0030] In specific implementation, firstly, a typical baking condition that has not yet been solved is selected as the current condition. The internal combustion wall heat flux and external ambient temperature corresponding to the current condition are retrieved from the acquired thermal state parameters. The internal combustion wall heat flux is applied to the inner wall nodes of the three-dimensional geometric model in the heat dissipation performance simulation model as the internal thermal boundary. The internal combustion wall heat flux is the heat flux density value distributed according to spatial location. When applying, each inner wall node is assigned a corresponding heat flux density value according to the spatial correspondence between the heated area and the inner wall node. The nodes within the heated area are assigned the average heat flux density value of the area, and the nodes outside the heated area are assigned a zero value. The external ambient temperature is applied to the far-field boundary nodes of the outer wall of the three-dimensional geometric model in the heat dissipation performance simulation model as the external ambient temperature boundary. The internal thermal boundary and the external ambient temperature boundary after application are used together as the thermal boundary condition.

[0031] Next, the steady-state solver is started, and the convergence tolerance value of the steady-state solver is set to 0.00001 and the maximum number of iteration steps is set. In each iteration step, the steady-state solver calculates the temperature distribution inside the solid domain of the shell, the convective heat transfer of the air near the outer wall, and the radiative heat transfer of the outer wall to the outside in sequence, and updates the temperature field value and the heat flux density vector field value. When the maximum relative change of the temperature field value between two consecutive iteration steps is less than the convergence tolerance value, it is determined to be converged, the steady-state solver stops iterating, and the temperature field value and heat flux density vector field value obtained in the last iteration are used as the solution result of the current working condition.

[0032] Then, it is determined whether there are any unsolved typical baking conditions. If so, the next unsolved typical baking condition is selected as the new current condition. The steps of loading the internal combustion wall heat flux and external ambient temperature as thermal boundary conditions and iteratively solving them through the steady-state solver are repeated until all typical baking conditions have been solved. The solution results corresponding to all typical baking conditions are summarized. The summarized solution results include the temperature field and heat flux density vector field under each typical baking condition. The summarized solution results are used as the solution results for each condition.

[0033] In some embodiments, the heat dissipation bottleneck path of the oven shell can be located based on the solved temperature gradient and heat flux density vector field using the following steps: For the solution results of any typical baking condition, calculate the temperature gradient vector and heat flux density vector at each spatial location within the shell design domain; Calculate the magnitude of the temperature gradient vector at each location, use the temperature gradient magnitude to characterize the local heat transfer load intensity, and mark the locations where the temperature gradient magnitude exceeds a preset threshold as high resistance regions. The high-resistance regions marked under each operating condition are spatially superimposed, and the continuously distributed high-resistance strips after superposition are extracted as the heat dissipation bottleneck path.

[0034] The temperature gradient vector and heat flux density vector in this application are a set of physical quantities used to characterize the driving force of heat transfer and the intensity of heat flow at various spatial locations within the shell design domain; the heat dissipation bottleneck path is a path curve used to characterize the continuous spatial direction of local thermal resistance concentration and heat transfer obstruction that coexist under multiple typical baking conditions within the shell design domain.

[0035] In practice, firstly, an unanalyzed typical baking condition is randomly selected from all typical baking conditions as the current analysis condition. The temperature field and heat flux density vector field in the solution results corresponding to the current analysis condition are retrieved. Each spatial grid node in the shell design domain is traversed, and the first-order partial derivatives of the temperature field along the three orthogonal coordinate directions are calculated at the spatial grid node. The three first-order partial derivatives are combined into a temperature gradient vector. At the same time, the components of the heat flux density along the three orthogonal coordinate directions at the spatial grid node are extracted from the heat flux density vector field, and the three components are combined into a heat flux density vector. In this way, the temperature gradient vector and heat flux density vector of each spatial grid node in the shell design domain under the current analysis condition are obtained. The temperature gradient vector and heat flux density vector of all spatial grid nodes are used together as the temperature gradient vector and heat flux density vector under the current analysis condition.

[0036] Secondly, for each spatial grid node under the current analysis condition, the amplitude of the temperature gradient vector of that node is calculated. The temperature gradient amplitude characterizes the local heat transfer load intensity of the node. The temperature gradient amplitude is compared with a preset heat transfer load threshold. If the temperature gradient amplitude of the node is greater than the preset heat transfer load threshold, it is determined that heat transfer at the node is blocked and the node is marked as a high-resistance node. If the temperature gradient amplitude of the node is less than or equal to the preset heat transfer load threshold, it is determined that heat transfer at the node is unobstructed and no marking is performed. After traversing all spatial grid nodes in the shell design domain and completing the above comparison and marking operations, the spatial locations of all nodes marked as high-resistance nodes are summarized as the high-resistance region under the current analysis condition. The preset heat transfer load threshold is set according to the statistical distribution of the temperature gradient amplitudes of all nodes in the shell design domain, and the temperature gradient amplitude corresponding to the top 20% percentile of the distribution is taken as the threshold.

[0037] Then, for each typical baking condition, the steps of obtaining the temperature gradient vector and heat flux density vector, calculating the temperature gradient amplitude, and marking the high resistance region are performed sequentially to obtain the high resistance region corresponding to each typical baking condition. The high resistance regions of all typical baking conditions are superimposed in the same shell design domain spatial coordinate system. The spatial positions that appear continuously in the high resistance regions of at least two typical baking conditions are extracted from the superposition results. The extracted continuous spatial positions are fitted into one or more spatial curves, and the one or more spatial curves are used as the heat dissipation bottleneck path.

[0038] Step 103: Load multiple typical external flow field scenarios onto the heat dissipation performance simulation model, extract the spatial distribution of convective heat transfer coefficients on the shell surface under each scenario, and then construct a convective weight matrix characterizing the heat dissipation robustness of each region.

[0039] In some embodiments, reference Figure 3 As shown in the figure, this is a schematic diagram of the process of constructing a convection weight matrix according to some embodiments of this application. In this embodiment, multiple typical external flow field scenarios are loaded onto the heat dissipation performance simulation model, and the spatial distribution of the convective heat transfer coefficient on the shell surface under each scenario is extracted. The convection weight matrix characterizing the heat dissipation robustness of each region can be constructed by the following steps: In step 1031, the incoming flow velocity and direction parameters of each typical external flow field scenario are loaded respectively, and the convective heat transfer coefficient field of the shell surface under each scenario is solved. In step 1032, the convective heat transfer coefficient field under each scenario is normalized to obtain the relative heat transfer intensity distribution of each region on the shell surface under each scenario. In step 1033, the relative heat transfer intensity of the same area under different scenarios is compared one by one, and the corresponding weight value is determined according to the degree to which the area maintains high heat transfer under each scenario. In step 1034, the weight values ​​of each region are integrated according to their spatial location to construct the convection weight matrix.

[0040] It should be noted that the relative heat transfer intensity distribution in this application reflects the relative strength of convective heat transfer capacity of different regions on the shell surface under the same external flow field scenario. The closer the relative heat transfer intensity value is to 1, the stronger the convective heat transfer capacity of that region under that scenario. The closer the relative heat transfer intensity value is to 0, the weaker the convective heat transfer capacity of that region under that scenario. The convective weight matrix in this application is a weighted data matrix used to quantify the spatial distribution characteristics of heat dissipation robustness of different regions on the shell surface under multiple external flow field scenarios. The larger the value of each element in the convective weight matrix, the smaller the influence of external flow field changes on the heat dissipation capacity at the corresponding spatial location, that is, the higher the heat dissipation robustness at that location.

[0041] In practice, the convective heat transfer coefficient field under each scenario is normalized to obtain the relative heat transfer intensity distribution of each region on the shell surface under each scenario. This can be achieved in the following way: For the convective heat transfer coefficient field corresponding to each typical external flow field scenario, the convective heat transfer coefficient values ​​of all nodes in the convective heat transfer coefficient field are traversed, and the largest convective heat transfer coefficient value is found as the normalization benchmark value for that scenario. The convective heat transfer coefficient value of each node in that scenario is divided by the normalization benchmark value, and the result is the relative heat transfer intensity value of that node in that scenario. After completing the normalization process of all nodes, the relative heat transfer intensity distribution of each region on the shell surface under that scenario is obtained. The relative heat transfer intensity distribution corresponding to each of all typical external flow field scenarios is obtained in this way.

[0042] In practice, the relative heat transfer intensity of the same region under different scenarios is compared one by one. The corresponding weight value is determined according to the degree to which the region maintains high heat transfer in each scenario. This can be achieved in the following way: Divide the shell surface into several spatial regions. For each spatial region, extract the relative heat transfer intensity value of the region under all typical external flow field scenarios. Count the number of scenarios in which the relative heat transfer intensity value of the region is greater than the preset high heat transfer threshold in all typical external flow field scenarios. Divide the counted number of scenarios by the total number of typical external flow field scenarios. The resulting ratio is the weight value corresponding to the region. The preset high heat transfer threshold can be set in the following way: Obtain the relative heat transfer intensity values ​​of all spatial regions under all typical external flow field scenarios. Sort all relative heat transfer intensity values ​​from largest to smallest. Take the relative heat transfer intensity values ​​in the top 30% after sorting as the preset high heat transfer threshold.

[0043] In specific implementation, the weight values ​​of each region are integrated according to their spatial location to construct the convection weight matrix. This can be achieved in the following way: a matrix data structure is established that corresponds one-to-one with the spatial discrete grid of the shell surface. The weight value of each spatial region is filled into the corresponding element position of the matrix data structure according to the actual spatial position of the region on the shell surface. After the weight values ​​of all spatial regions are filled, the completed matrix data structure is used as the convection weight matrix.

[0044] Preferably, in some embodiments, the inflow velocity and direction parameters for each typical external flow field scenario are loaded respectively, and the convective heat transfer coefficient field of the shell surface under each scenario can be solved by the following steps: For each typical external flow field scenario, an external flow field computational domain including the oven shell is constructed, and a boundary layer mesh is generated. The inflow velocity and direction parameters corresponding to the scenario are applied at the inlet boundary of the external flow field calculation domain, the pressure outlet condition is applied at the outlet boundary, and the coupled heat exchange wall condition is applied on the shell surface. The velocity and temperature fields of the external flow field are solved iteratively by a steady-state solver until the residuals of the continuity equation, momentum equation, and energy equation converge to the preset criteria. Based on the wall heat flux density, wall temperature and far-field reference temperature obtained from the convergent solution, the convective heat transfer coefficient of each node on the shell surface is calculated, and the convective heat transfer coefficient field of the shell surface under this scenario is obtained.

[0045] It should be noted that the convective heat transfer coefficient in this application is a physical quantity that measures the ability of a unit area of ​​the outer shell surface to transfer heat to the surrounding air through convection under a unit temperature difference. The larger the convective heat transfer coefficient value, the stronger the convective heat dissipation capacity at that surface location.

[0046] In specific implementation, the incoming flow velocity and direction parameters corresponding to the scenario are applied to the inlet boundary of the external flow field calculation domain, pressure outlet conditions are applied to the outlet boundary, and coupled heat transfer wall conditions are applied to the shell surface. This can be achieved in the following way: On the inlet boundary surface of the external flow field calculation domain, the incoming flow velocity vector and incoming flow direction angle corresponding to the typical external flow field scenario are applied. The velocity magnitude of the inlet boundary surface is set to the value of the incoming flow velocity parameter, and the velocity direction of the inlet boundary surface is set to the spatial direction pointed to by the incoming flow direction parameter. On the outlet boundary surface of the external flow field calculation domain, a pressure outlet condition with a relative static pressure of 0 is set, and a return turbulence parameter is set. On the outer wall surface of the oven shell, the wall velocity condition is set to no slip, and the wall thermal condition is set to coupled heat transfer. That is, the wall temperature and wall heat flux density are not directly given by humans, but are determined by the real-time iterative transfer of the heat conduction solution results of the solid domain inside the heat dissipation performance simulation model and the convection heat transfer solution results of the external air domain at the wall surface. The inlet boundary surface, outlet boundary surface, and shell wall surface after all the above settings are completed are used together as coupled heat transfer wall conditions.

[0047] In practical implementation, the velocity and temperature fields of the external flow field are iteratively solved using a steady-state solver until the residuals of the continuity, momentum, and energy equations converge to a preset standard. This can be achieved as follows: Start the steady-state solver, set the residual thresholds for the continuity, momentum, and energy equations to 0.0001, 0.0001, and 0.000001, and set the maximum number of iteration steps to 1000. Within each iteration step, the steady-state solver sequentially solves the continuity, momentum, and energy equations for the airflow in the external air domain. After each iteration step, the continuity equation, momentum equation, and energy equation are calculated. The current residuals of the continuity equation, momentum equation, and energy equation are compared with their corresponding residual thresholds. When all three current residuals are less than their respective residual thresholds, the solution is considered converged, the steady-state solver stops iterating, and the wall heat flux density field, wall temperature field, and far-field reference temperature values ​​obtained from the last iteration are output. If, when the maximum number of iterations is reached, one current residual still does not meet its corresponding residual threshold, the solution is considered unconverged, and the mesh generation or relaxation factor is adjusted before resolving. The combination of the above three residual thresholds is used as the preset standard.

[0048] In specific implementation, based on the wall heat flux density, wall temperature, and far-field reference temperature obtained from the converged solution, the convective heat transfer coefficient of each node on the shell surface is calculated to obtain the convective heat transfer coefficient field of the shell surface in this scenario. This can be achieved in the following way: extract the wall heat flux density and wall temperature values ​​at each node on the shell surface from the converged results output by the steady-state solver, and simultaneously extract the air temperature at the far-field boundary of the external air domain as the far-field reference temperature value. For each node on the shell surface, divide the wall heat flux density value at that node by the difference between the wall temperature value and the far-field reference temperature value at that node. The quotient obtained is the convective heat transfer coefficient at that node. Arrange the convective heat transfer coefficients of all nodes according to their spatial coordinates on the shell surface to form a spatial distribution of convective heat transfer coefficients that corresponds one-to-one with the spatial discrete grid of the shell surface. This spatial distribution of convective heat transfer coefficients is used as the convective heat transfer coefficient field.

[0049] Preferably, in some embodiments, constructing the external flow field computational domain including the oven shell and dividing the boundary layer mesh can be achieved by the following steps: Based on the geometry of the oven shell, establish a cuboid or cylindrical external air domain, and place the oven shell slightly below the geometric center of the external air domain; The air region near the surface of the oven shell is locally meshed to generate a body-fitting boundary layer mesh. The height of the first layer of the boundary layer mesh is determined according to the Reynolds number of the target working condition. A gradually sized unstructured mesh is generated between the boundary layer mesh and the far-field boundary of the external air domain to complete the mesh generation of the external flow field computational domain.

[0050] In practice, firstly, the length, width, and height dimensions of the oven shell along three orthogonal directions are obtained. The length dimension is multiplied by 3 to obtain the length dimension of the outer air domain, the width dimension is multiplied by 3 to obtain the width dimension of the outer air domain, and the height dimension is multiplied by 3 to obtain the height dimension of the outer air domain. Based on the length, width, and height dimensions of the outer air domain, a cuboid-shaped outer air domain is constructed. The three-dimensional geometric model of the oven shell is placed inside this outer air domain, with a bottom gap distance between the bottom surface of the oven shell and the bottom surface of the outer air domain. This bottom gap distance is 1 / 3 of the height dimension of the oven shell, and the geometric center of the oven shell in the horizontal direction coincides with the geometric center of the outer air domain in the horizontal direction. This forms a spatial layout in which the oven shell is located slightly below the geometric center of the outer air domain. The cuboid outer air domain after the oven shell is placed is taken as the outer air domain.

[0051] Secondly, the air region near the outer wall surface of the oven shell is locally meshed. Several layers of body-fitted mesh are generated outward along the normal direction of the outer wall surface. The thickness of the first mesh layer closest to the outer wall surface is determined as follows: the incoming flow velocity value of the target operating condition is obtained, the incoming flow velocity value is multiplied by the characteristic length of the oven shell and then divided by the kinematic viscosity of the air to obtain the Reynolds number of the target operating condition. The Reynolds number is then substituted into the empirical correlation formula for the wall friction coefficient to calculate the wall friction coefficient. The empirical correlation formula for the wall friction coefficient is that the wall friction coefficient is equal to 0.0576 divided by the Reynolds number to the power of 0.2. The friction velocity is calculated based on the wall friction coefficient. The friction velocity is equal to the incoming flow velocity multiplied by the wall friction coefficient divided by the square root of 2. The dimensionless wall distance target value is set to 1. The dimensionless wall distance target value is multiplied by the kinematic viscosity of air and then divided by the friction velocity to calculate the thickness of the first layer of mesh. From the second layer onwards, the thickness of each layer of mesh increases progressively, and the thickness growth rate between adjacent layers is controlled within the range of 1.2 times, until the total thickness of the body-fitted mesh layer covers the estimated thickness of the velocity boundary layer and temperature boundary layer on the shell surface. The completed body-fitted dense mesh layer is used as the body-fitted boundary layer mesh.

[0052] Then, between the outermost boundary of the body-fitted boundary layer mesh and the far-field boundary of the external air domain, tetrahedral mesh elements are used to fill the remaining space. The size of the mesh elements smoothly transitions from the smaller size at the outermost boundary of the body-fitted boundary layer mesh to the larger size at the far-field boundary. The size growth ratio between two adjacent mesh elements is controlled within a range of no more than 1.3. After completing the mesh filling of all remaining space, the body-fitted boundary layer mesh, the unstructured mesh, and the boundary mesh of the external air domain are combined to form a complete spatial discrete mesh system. This complete spatial discrete mesh system is used as the mesh generation result after completing the mesh generation of the external flow field computational domain.

[0053] Step 104: With the optimization objectives of minimizing the highest surface temperature of the outer shell, minimizing the temperature gradient at the heat dissipation bottleneck path, and minimizing the amount of material used, and with the surface temperature not exceeding the safety threshold as a constraint, the convection weight matrix is ​​used as a weighting factor to construct the convection heat transfer boundary conditions. Multi-objective iterative optimization is performed on the design variables to generate the heat dissipation topology configuration of the outer shell.

[0054] It should be noted that the optimization objectives and constraints in this application are based on the following technical principles: the highest surface temperature of the outer shell is a direct evaluation indicator of the user safety of the gas oven, and using it as an optimization objective aims to ensure that the heat dissipation topology of the outer shell can meet the safety requirements under various operating conditions; the temperature gradient at the heat dissipation bottleneck path is a measure of the degree of local thermal resistance concentration, and the larger the temperature gradient, the more severe the heat transfer obstruction on that path, and using it as an optimization objective can guide the material distribution towards the bottleneck area to reduce local thermal resistance and improve the efficiency of heat conduction from the inside to the outer shell surface; the amount of material used is directly related to manufacturing costs and the need for lightweight structure, and using it as an optimization objective... The goal is to reduce unnecessary material consumption while ensuring thermal performance. The surface temperature safety threshold serves as a hard constraint, ensuring that candidate solutions in any iteration step of the optimization process meet safety specifications and avoiding the risk of surface temperature exceeding the limit when the objective function is weighted. Constructing convective heat transfer boundary conditions using the convection weight matrix as a weighting factor embeds the differentiated characteristics of convective heat dissipation robustness of different regions of the shell surface under multiple scenarios into the optimization model. This guides the material to be preferentially distributed in regions with high heat dissipation robustness, avoiding material waste in regions where convective heat dissipation capacity fluctuates drastically with changes in the external flow field, thereby improving the multi-scenario adaptability and engineering robustness of the final topology configuration.

[0055] In some embodiments, the optimization objectives are to minimize the highest surface temperature of the outer casing, minimize the temperature gradient at the heat dissipation bottleneck path, and minimize the material usage, with the constraint that the surface temperature does not exceed a safety threshold. The convection weight matrix is ​​used as a weighting factor to construct the convective heat transfer boundary conditions. Multi-objective iterative optimization of the design variables can be achieved through the following steps: Multiply the weight values ​​of each region in the convection weight matrix with the benchmark convection heat transfer coefficient to obtain the spatially weighted convection heat transfer boundary conditions, and apply them to the shell surface of the heat dissipation performance simulation model. The three optimization objectives—the highest surface temperature of the outer shell, the temperature gradient at the heat dissipation bottleneck path, and the amount of material used—are aggregated into a single objective function using a weighted summation method. With the constraints of surface temperature not exceeding a safety threshold and material volume fraction, the material distribution parameters are iteratively updated based on a gradient-based optimization algorithm. After each iteration, the heat dissipation performance simulation model with the convective heat transfer boundary conditions is re-solved until the single objective function converges, and the final material distribution parameters are output.

[0056] It should be noted that this application has the following advantages over the prior art: First, by incorporating the temperature gradient at the heat dissipation bottleneck path into the multi-objective optimization system, and synergistically driving the material distribution iteration with the highest surface temperature of the outer shell and the amount of material used, it accurately identifies and optimizes the internal high-resistance region based on Fourier's law of thermal conductivity. This solves the problem of excessive local thermal resistance and limited heat dissipation efficiency caused by existing methods that only focus on surface temperature and ignore the optimization of internal heat transfer paths, thus improving heat dissipation performance from the source of heat transfer. Second, by constructing spatially distributed weighted convection heat transfer boundary conditions using the convection weight matrix as a weighting factor, it enables the optimization process to achieve the desired results. The convective boundary conditions of the region are set differently according to its heat dissipation robustness. Compared with the existing methods that adopt the assumption of uniform convective boundary, the shell heat dissipation topology configuration optimized by this scheme can maintain stable heat dissipation performance under various external flow field scenarios, effectively improving the product's adaptability to actual working conditions. Third, the three optimization objectives are aggregated into a single objective function by weighted summation method and combined with gradient-type algorithm to iteratively optimize. The entire optimization process is automatically driven by simulation data, without relying on human experience to pre-set the layout of the heat dissipation structure. While shortening the design cycle, it can automatically explore the optimal balance configuration of heat dissipation performance and material usage that is difficult to obtain by manual design.

[0057] In specific implementation, the weight values ​​of each region in the convection weight matrix are multiplied by the baseline convective heat transfer coefficient to obtain spatially weighted convective heat transfer boundary conditions, which are then applied to the outer shell surface of the heat dissipation performance simulation model. This can be achieved in the following way: Without applying any convection weight correction, a standard natural convection environment external flow field scenario is loaded onto the heat dissipation performance simulation model, and the convective heat transfer coefficients of all nodes on the outer shell surface are solved. The arithmetic mean of the convective heat transfer coefficients of all nodes on the outer shell surface is calculated, and this arithmetic mean is used as the baseline convective heat transfer coefficient. Each element in the convection weight matrix is ​​traversed to obtain the corresponding element. The weight value and the spatial coordinates of the element on the shell surface are used to multiply the weight value by the reference convective heat transfer coefficient. The product is the weighted convective heat transfer coefficient value at that spatial location. The weighted convective heat transfer coefficient values ​​at all spatial locations on the shell surface are obtained in this way. All weighted convective heat transfer coefficient values ​​are arranged according to their respective spatial coordinates to form a spatially distributed weighted convective heat transfer coefficient field. The weighted convective heat transfer coefficient value at each spatial location in the spatially distributed weighted convective heat transfer coefficient field is assigned to the convective heat transfer coefficient parameters of the corresponding grid node on the shell surface of the heat dissipation performance simulation model to complete the application of convective heat transfer boundary conditions.

[0058] In specific implementation, with the surface temperature not exceeding a safety threshold and the material volume fraction as constraints, the iterative update of material distribution parameters based on gradient-based optimization algorithms can be achieved as follows: A preset safety threshold for the shell surface temperature is obtained, and this safety threshold is used as the upper limit of the temperature constraint, meaning that the temperature values ​​of all grid nodes on the shell surface must be less than or equal to this safety threshold; a preset upper limit for the material volume fraction is obtained, which is taken as 30% of the total volume of the design domain, meaning that the sum of all material distribution parameters divided by the total number of grid nodes in the design domain must be less than or equal to 30%; the single objective function constructed in the previous steps is used as the optimization objective, and the temperature constraint and material volume fraction constraint are used as constraints; using a gradient-based optimization algorithm, in each iteration, the sensitivity value of the single objective function to each material distribution parameter is calculated, and the sensitivity values ​​of the temperature constraint and material volume fraction constraint to each material distribution parameter are also calculated. These sensitivity values ​​are substituted into the update formula of the optimization algorithm to calculate the update amount of each material distribution parameter in the current iteration step. The material distribution parameters of the current iteration step are added to the update amount to obtain the material distribution parameters for the next iteration step, completing one iteration update.

[0059] In specific implementation, after each iteration, the heat dissipation performance simulation model with the applied convective heat transfer boundary conditions is re-solved until the single objective function converges. The final material distribution parameters can be output in the following way: After each iteration update of the material distribution parameters, the spatial distribution of material properties in the shell solid domain of the heat dissipation performance simulation model is reconstructed based on the updated material distribution parameters. The spatially weighted convective heat transfer boundary conditions are re-applied to the shell surface of the heat dissipation performance simulation model, and the heat dissipation performance simulation model is solved again to obtain the updated temperature field. Based on the updated temperature field, the current value of the single objective function is recalculated. The current value of the single objective function obtained in this iteration is compared with the current value of the single objective function obtained in the previous iteration. The absolute value of the relative change between the two is calculated. When the absolute value of the relative change is less than a preset convergence tolerance value, it is determined that the single objective function has converged, the iteration process is terminated, and the material distribution parameters obtained in the last iteration update are output as the final material distribution parameters. The preset convergence tolerance value is 0.001.

[0060] Preferably, in some embodiments, the three optimization objectives—the highest surface temperature of the outer casing, the temperature gradient at the heat dissipation bottleneck path, and the amount of material used—can be aggregated into a single objective function using a weighted summation method, which can be achieved through the following steps: Obtain the temperature values ​​of all nodes on the shell surface in the current iteration step, extract the maximum temperature value among them as the current value of the highest temperature on the shell surface, and record it as the first current value; Extract the temperature gradient magnitude of each node on the heat dissipation bottleneck path, calculate the average value of the temperature gradient magnitude of each node as the current value of the temperature gradient at the heat dissipation bottleneck path, and denot it as the second current value. The volume percentage of solid material in the material distribution parameters at the current iteration step is used as the current value of material usage, and is denoted as the third current value. The first, second, and third current values ​​are respectively subjected to dimensionless normalization, and the normalized current values ​​are summed according to preset weight coefficients to obtain the single objective function.

[0061] In specific implementation, firstly, from the temperature field obtained after solving the heat dissipation performance simulation model in the current iteration step, the temperature values ​​at all grid nodes on the shell surface are extracted. All extracted temperature values ​​are sorted according to their numerical values, and the temperature value with the largest value is selected as the current value of the highest temperature on the shell surface. This current value of the highest temperature on the shell surface is recorded as the first current value.

[0062] Next, the heat dissipation bottleneck path located in the previous step is obtained, and the spatial coordinates of all grid nodes traversed by the heat dissipation bottleneck path are extracted. The temperature values ​​of all grid nodes are retrieved from the temperature field obtained in the current iteration step. The first-order partial derivative of the temperature is calculated for each grid node along the three orthogonal coordinate directions, and the three first-order partial derivatives are combined into the temperature gradient vector of the node. The magnitude of the temperature gradient vector is taken as the temperature gradient amplitude of the node. The arithmetic mean of the temperature gradient amplitudes of all grid nodes on the heat dissipation bottleneck path is calculated, and the average value is taken as the current value of the temperature gradient at the heat dissipation bottleneck path. The current value of the temperature gradient at the heat dissipation bottleneck path is recorded as the second current value.

[0063] Then, the material distribution parameters under the current iteration step are obtained. The material distribution parameters are the material density values ​​corresponding to each spatial discrete unit in the shell design domain. All spatial discrete units in the shell design domain are traversed, and the number of units with a material density value equal to 1 is counted. The counted number of units with a material density value equal to 1 is divided by the total number of all spatial discrete units in the shell design domain. The resulting ratio is the volume ratio of the solid material. The volume ratio of the solid material is used as the current value of the material usage, and the current value of the material usage is recorded as the third current value.

[0064] Finally, the highest surface temperature of the shell obtained from the initial solution of the heat dissipation performance simulation model using the initial material distribution parameters before the start of the optimization iteration is used as the first normalized benchmark value. The initial material distribution parameters are a uniform distribution where the material density of all spatial discrete units within the shell design domain is set to 0.5. The average temperature gradient at the heat dissipation bottleneck path obtained from the initial solution of the heat dissipation performance simulation model using the initial material distribution parameters before the start of the optimization iteration is used as the second normalized benchmark value. The first current value is divided by the first normalized benchmark value to obtain the first normalized value, the second current value is divided by the second normalized benchmark value to obtain the second normalized value, and the third current value is directly used as the third normalized value. The preset first weighting coefficient of 0.5 and the second weighting coefficient of 0 are obtained. The first weighting coefficient is set to 3 and the second weighting coefficient to 0.2. This setting is because the highest surface temperature of the outer casing is directly related to user safety and is the primary control objective of heat dissipation design, hence the first weighting coefficient is set to the largest value. The temperature gradient at the heat dissipation bottleneck path reflects the degree of local thermal resistance concentration. Reducing this temperature gradient helps to improve the thermal conductivity of the heat dissipation path, so its importance is secondary, hence the second weighting coefficient is set to a moderate value. The amount of material used affects manufacturing cost and lightweighting. Under the premise of satisfying the first two thermal performance objectives, economic optimization is carried out, hence the third weighting coefficient is set to the smallest value. Furthermore, the first normalized value is multiplied by the first weighting coefficient, the second normalized value is multiplied by the second weighting coefficient, and the third normalized value is multiplied by the third weighting coefficient, and then the sum is the current value of the single objective function.

[0065] On the other hand, in some embodiments, this application provides a topology optimization system for the heat dissipation structure of a gas oven shell, referring to... Figure 4 The figure is a schematic diagram of a gas oven shell heat dissipation structure topology optimization system according to some embodiments of this application. The gas oven shell heat dissipation structure topology optimization system 400 includes: an acquisition module 401, a processing module 402, and an optimization module 403, which are described below: The acquisition module 401 in this application is mainly used to acquire the thermal state parameters of the oven shell under multiple typical baking conditions, and to establish a heat dissipation performance simulation model that couples the internal combustion heat flow with the external environmental flow field, using material distribution parameters as design variables. Processing module 402, in this application, is used to load the thermal state parameters of each typical baking condition, solve the heat dissipation performance simulation model, and locate the heat dissipation bottleneck path of the oven shell based on the solved temperature gradient and heat flux density vector field. In this application, the processing module 402 is also used to load multiple typical external flow field scenarios onto the heat dissipation performance simulation model, extract the spatial distribution of the convective heat transfer coefficient on the shell surface under each scenario, and then construct a convective weight matrix characterizing the heat dissipation robustness of each region. The optimization module 403 in this application is mainly used to minimize the highest surface temperature of the shell, minimize the temperature gradient at the heat dissipation bottleneck path, and minimize the amount of material as optimization objectives, with the surface temperature not exceeding the safety threshold as a constraint, and to construct convective heat transfer boundary conditions by using the convection weight matrix as a weighting factor, and to perform multi-objective iterative optimization on the design variables to generate the shell heat dissipation topology configuration.

[0066] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described gas oven shell heat dissipation structure topology optimization method.

[0067] In some embodiments, reference Figure 5 The figure is a schematic diagram of a computer device implementing a method for optimizing the heat dissipation structure of a gas oven shell, according to some embodiments of this application. The method for optimizing the heat dissipation structure of a gas oven shell in the above embodiments can... Figure 5 The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0068] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0069] The communication bus 502 can be used to transmit information between the aforementioned components.

[0070] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0071] The memory 503 stores program code for executing the solution of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. In the above embodiment, the method for optimizing the heat dissipation structure topology of the gas oven shell can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0072] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0073] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0074] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0075] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the heat dissipation structure of a gas oven shell.

[0076] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0077] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for topology optimization of a heat dissipation structure of a gas oven enclosure, characterized in that, Includes the following steps: The thermal state parameters of the oven shell under multiple typical baking conditions were obtained, and a heat dissipation performance simulation model coupling the internal combustion heat flow and the external environmental flow field was established, with material distribution parameters as design variables. Load the thermal state parameters of each typical baking condition, solve the heat dissipation performance simulation model, and locate the heat dissipation bottleneck path of the oven shell based on the obtained temperature gradient and heat flux density vector field. Multiple typical external flow field scenarios are loaded onto the heat dissipation performance simulation model. The spatial distribution of the convective heat transfer coefficient on the shell surface under each scenario is extracted, and then a convective weight matrix characterizing the heat dissipation robustness of each region is constructed. The optimization objectives are to minimize the highest surface temperature of the outer shell, minimize the temperature gradient at the heat dissipation bottleneck path, and minimize the amount of material used. The constraint is that the surface temperature does not exceed the safety threshold. The convection weight matrix is ​​used as a weighting factor to construct the convection heat transfer boundary conditions. Multi-objective iterative optimization is performed on the design variables to generate the heat dissipation topology configuration of the outer shell.

2. The method of claim 1, wherein, Establishing a simulation model for heat dissipation performance that couples internal combustion heat flow with external environmental flow field specifically includes: Construct a three-dimensional geometric model of the oven shell based on the initial structural parameters of the oven shell; The heat flow of the internal combustion wall is determined based on each typical baking condition, and the external ambient temperature is extracted from the thermal state parameters as the initial boundary value for multiphysics coupling simulation. The three-dimensional geometric model of the outer shell is spatially discretized using the finite volume method, and then a heat dissipation performance simulation model coupling the internal combustion heat flow and the external environmental flow field is constructed by combining the heat conduction equation, the convection heat transfer equation and the radiation heat transfer equation.

3. The method of claim 1, wherein, Loading the thermal state parameters for various typical baking conditions and solving the heat dissipation performance simulation model specifically includes: For each typical baking condition, the heat flow of the internal combustion wall and the external ambient temperature corresponding to the condition are loaded into the heat dissipation performance simulation model as thermal boundary conditions. The temperature field and heat flux density vector field within the shell design domain under the current operating condition are calculated iteratively by a steady-state solver until convergence, thus obtaining the solution result for the current operating condition. The solutions for all typical baking conditions are solved sequentially, and the solution results for each condition are obtained. The solution results include the temperature field and the heat flux density vector field.

4. The method of claim 1, wherein, Based on the obtained temperature gradient and heat flux density vector field, the specific heat dissipation bottleneck paths of the oven shell are located as follows: For the solution results of any typical baking condition, calculate the temperature gradient vector and heat flux density vector at each spatial location within the shell design domain; Calculate the magnitude of the temperature gradient vector at each location, use the temperature gradient magnitude to characterize the local heat transfer load intensity, and mark the locations where the temperature gradient magnitude exceeds a preset threshold as high resistance regions. The high-resistance regions marked under each operating condition are spatially superimposed, and the continuously distributed high-resistance strips after superposition are extracted as the heat dissipation bottleneck path.

5. The method of claim 1, wherein, Multiple typical external flow field scenarios are loaded onto the heat dissipation performance simulation model. The spatial distribution of the convective heat transfer coefficient on the shell surface under each scenario is extracted, and then a convective weight matrix characterizing the heat dissipation robustness of each region is constructed, specifically including: Load the incoming flow velocity and direction parameters for each typical external flow field scenario, and solve the convective heat transfer coefficient field on the shell surface for each scenario; The convective heat transfer coefficient field under each scenario is normalized to obtain the relative heat transfer intensity distribution of each region on the shell surface under each scenario. The relative heat transfer intensity of the same area under different scenarios is compared one by one, and the corresponding weight value is determined according to the degree to which the area maintains high heat transfer under each scenario. The weight values ​​of each region are integrated according to their spatial location to construct the convection weight matrix.

6. The method of claim 5, wherein, The inflow velocity and direction parameters for each typical external flow field scenario are loaded, and the convective heat transfer coefficient field of the shell surface under each scenario is solved, specifically including: For each typical external flow field scenario, an external flow field computational domain including the oven shell is constructed, and a boundary layer mesh is generated. The inflow velocity and direction parameters corresponding to the scenario are applied at the inlet boundary of the external flow field calculation domain, the pressure outlet condition is applied at the outlet boundary, and the coupled heat exchange wall condition is applied on the shell surface. The velocity and temperature fields of the external flow field are solved iteratively by a steady-state solver until the residuals of the continuity equation, momentum equation, and energy equation converge to the preset criteria. Based on the wall heat flux density, wall temperature and far-field reference temperature obtained from the convergent solution, the convective heat transfer coefficient of each node on the shell surface is calculated, and the convective heat transfer coefficient field of the shell surface under this scenario is obtained.

7. The method of claim 6, wherein, Constructing the external flow field computational domain, including the oven shell, and meshing the boundary layer specifically includes: Based on the geometry of the oven shell, establish a cuboid or cylindrical external air domain, and place the oven shell slightly below the geometric center of the external air domain; The air region near the surface of the oven shell is locally meshed to generate a body-fitting boundary layer mesh. The height of the first layer of the boundary layer mesh is determined according to the Reynolds number of the target working condition. A gradually sized unstructured mesh is generated between the boundary layer mesh and the far-field boundary of the external air domain to complete the mesh generation of the external flow field computational domain.

8. A gas oven enclosure heat dissipation structure topology optimization system, characterized in that, include: The acquisition module is used to acquire the thermal state parameters of the oven shell under multiple typical baking conditions, and to establish a heat dissipation performance simulation model that couples the internal combustion heat flow with the external environmental flow field, using material distribution parameters as design variables. The processing module is used to load the thermal state parameters of each typical baking condition, solve the heat dissipation performance simulation model, and locate the heat dissipation bottleneck path of the oven shell based on the obtained temperature gradient and heat flux density vector field. The processing module is also used to load multiple typical external flow field scenarios onto the heat dissipation performance simulation model, extract the spatial distribution of convective heat transfer coefficients on the shell surface under each scenario, and then construct a convective weight matrix characterizing the heat dissipation robustness of each region. The optimization module is used to minimize the highest surface temperature of the shell, minimize the temperature gradient at the heat dissipation bottleneck path, and minimize the amount of material used as optimization objectives, with the surface temperature not exceeding a safety threshold as a constraint, and to construct convective heat transfer boundary conditions using the convection weight matrix as a weighting factor, and to perform multi-objective iterative optimization on the design variables to generate the shell heat dissipation topology configuration. 9.A computer device, comprising a memory and a processor, wherein the memory stores code, and the code comprises the following steps: The processor is configured to acquire the code and execute the gas oven shell heat dissipation structure topology optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. When the computer program is executed by the processor, it implements the gas oven shell heat dissipation structure topology optimization method as described in any one of claims 1 to 7.

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