An intercooler optimization design method based on porous media model

Abstract

The present invention discloses an intercooler optimization design method based on a porous medium model. The present invention is carried out according to the following steps: (1) parameterizing the local fin model according to the geometric parameters and periodic characteristics of the intercooler heat dissipation core fins; (2) analyzing the range of single parameter changes; (3) performing optimal Latin hypercube sampling in the design space; (4) performing numerical simulation using the steady Reynolds average method for each design scheme; (5) obtaining the porosity of the porous medium model according to the periodic local fin model; (6) performing numerical simulation using the steady Reynolds average method; (7) constructing a response surface model. The intercooler optimization design method based on a porous medium model of the present invention introduces the porous medium model into the optimization design of the intercooler, realizes the optimization design based on the overall model of the intercooler, and breaks the limitations of the traditional optimization design based on the local model of the intercooler.

Description

Technical Field

[0001] The invention relates to the technical field of vehicle intercooler optimization design, and more specifically, to an intercooler optimization design method based on a porous medium model. Background Art

[0002] As one of the conventional heat exchangers, intercoolers have been rapidly promoted in many fields. At present, ships, mobile power stations, large vehicles, etc. that use large and medium-sized diesel engines are basically equipped with intercoolers. With the rapid development of automobile engines, the power of the engine is improved by introducing more air into the combustion chamber. However, after the air is pressurized, although the increase in density will increase the intake volume, the gas temperature will rise rapidly after supercharging, which seriously restricts the further improvement of engine performance. Therefore, it is very important to study the improvement of intercooler heat transfer and flow resistance performance.

[0003] At present, most of the optimization design methods for intercoolers are based on the improvement of heat transfer and flow resistance performance of local fin models, while there are few studies on the overall flow resistance and heat transfer performance of intercoolers. This is because the number of fins on the heat dissipation core of the intercooler is huge, and the number of grids exceeds the computing performance of current computers. In the 1970s, researchers proposed to apply porous media models to the flow simulation process of heat exchangers. The introduction of porous media models reduced the number of computational grids, making it feasible to study the overall flow resistance and heat transfer performance of intercoolers. However, most of the current intercooler research based on porous media models only stays on the three-dimensional numerical simulation of the intercooler as a whole, and has not proposed an intercooler optimization design method based on the porous media model to improve the overall flow resistance and heat transfer performance of the intercooler. Summary of the invention

[0004] The present invention aims to utilize a porous medium model to replace the original heat dissipating fin core of the intercooler to realize the overall three-dimensional numerical simulation of the intercooler, and optimize the flow resistance and heat exchange performance of the intercooler based on the overall three-dimensional numerical simulation of the intercooler, and propose an intercooler optimization design method based on the porous medium model.

[0005] The present invention provides an intercooler optimization design method based on a porous medium model, which is performed according to the following steps:

[0006] (1) According to the geometric parameters and periodic characteristics of the intercooler heat dissipation core fins, the local fin model is parametrically modeled: a fin structure with a fixed period and the corresponding upper and lower partitions are selected to model the local fin model, and the fin geometric parameters such as fin thickness and pitch are changed to create different local fin models to achieve parameterization.

[0007] (2) Through single parameter variation range analysis, under the condition that the periodic local fin model does not suffer from serious distortion and deformation, the influence of a single design variable within the variation range on the flow resistance and heat transfer performance of the periodic local fin model is calculated, and the key design variables with the greatest influence of the fin geometric parameters on the flow resistance and heat transfer performance of the periodic local fin model are selected; the variation range in which the design variable always has a positive impact on the performance is taken as the design space;

[0008] (3) The optimal Latin hypercube sampling method is used to generate a sample set so that the sample points are evenly distributed in the entire design space. The number of initial samples in the sample set is generally related to the number of design variables, and the minimum number requirement is (n+1)*(n+2) / 2 times the number of design variables;

[0009] (4) For each design scheme in the initial sample set, the steady Reynolds average method is used for numerical simulation; the computational domain is discretized using an unstructured periodic grid, the NS equations and the energy equations are discretized based on the finite volume method, the RNG k-ε turbulence model is selected, the inlet conditions of the fluid domain are given as fixed temperature and velocity conditions, and the outlet conditions are pressure boundary conditions; the upper and lower boundaries of the solid domain are fixed temperature boundaries; the left and right boundaries of the fluid domain and the solid domain are both periodic boundary conditions, and the interface between the fluid domain and the solid domain is a thermal coupling surface, and the steady flow field of the periodic local fin model under different speed conditions is obtained;

[0010] (5) The inlet and outlet pressure drops are obtained from the steady flow field of the periodic local fin model, and the velocity-pressure drop quadratic polynomial relationship is constructed. According to the improved Darcy law, the inertial drag coefficient and viscous drag coefficient of the porous medium model are obtained. According to the periodic local fin model, the porosity of the porous medium model is obtained.

[0011] (6) The fluid domain of the overall intercooler model is defined by porous medium parameters, and the steady Reynolds average method is used for numerical simulation. The NS equations and energy equations are discretized based on the finite volume method. The RNG k-ε turbulence model is selected. The inlet boundary conditions are given as fixed temperature and velocity conditions, and the outlet conditions are pressure boundary conditions. The interface between the fluid domain and the solid domain is thermally coupled to obtain the steady flow field of the overall intercooler design condition.

[0012] (7) A response surface model was constructed to obtain the mapping relationship between the outlet temperature, the inlet and outlet pressure drop and the key design variables selected in step (2). The non-dominated sorting genetic algorithm (NSGA-Ⅱ) was used to implement the optimization. The objective function was: the minimum outlet temperature of the hot side, the maximum inlet and outlet pressure drop of the hot side. The Pareto frontier solution set was obtained, and the geometry corresponding to some optimal solutions was selected for numerical simulation calculations. Finally, the design scheme with the optimal flow resistance and heat transfer performance was obtained.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] 1. The intercooler optimization design method based on the porous medium model of the present invention introduces the porous medium model into the optimization design of the intercooler, realizes the optimization design based on the overall model of the intercooler, and breaks the limitation of the local model of the intercooler in the traditional optimization design. At the same time, the calculation results based on the porous medium model are combined with the non-dominated sorting genetic algorithm (NSGA-Ⅱ) to implement optimization, realizing multi-objective optimization under the overall model of the intercooler.

[0015] 2. An intercooler optimization design method based on a porous medium model of the present invention replaces the optimization target in the traditional method with the intercooler outlet temperature and the inlet and outlet pressure drop according to the calculation results of the intercooler overall model under the design conditions, and uses the response surface model to construct a mapping relationship between the design parameters and the outlet temperature and the inlet and outlet pressure drop. It can more clearly reflect the influence of the design parameters on the overall heat exchange and flow resistance changes of the intercooler, and provide new ideas and directions for the intercooler optimization design theory and method.

[0016] The intercooler optimization design method based on the porous medium model of the present invention introduces the porous medium model into the optimization design of the intercooler, realizes the optimization design based on the overall model of the intercooler, and breaks the limitation of the traditional optimization design based on the local model of the intercooler; on the other hand, based on the calculation results of the overall model of the intercooler under the design conditions, the optimization target in the traditional method is replaced by the outlet temperature and the inlet and outlet pressure drop of the intercooler, which more clearly reflects the influence of the design parameters on the overall heat exchange and flow resistance change of the intercooler, and provides new ideas and directions for the theory and method of intercooler optimization design. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a schematic diagram of the geometric structure of the hot side local fin period model used for optimization of a specific embodiment;

[0018] Figure 2 Shown is a schematic diagram of the geometric structure of the cold side local fin period model used in a specific embodiment;

[0019] Figure 3 Shown is a schematic diagram of the geometric structure of the integral intercooler model used in a specific embodiment;

[0020] Figure 4 Shown is the flow chart of non-dominated sorting genetic algorithm (NSGA-Ⅱ). Specific implementation methods

[0021] The intercooler optimization design method based on the porous medium model of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] The specific process of this method is as follows:

[0023] (1) According to the geometric parameters and periodic characteristics of the intercooler heat dissipation core fins, the local fin model is parametrically modeled: a fin structure with a fixed period and the corresponding upper and lower partitions are selected to model the local fin model, and the fin geometric parameters such as fin thickness and pitch are changed to create different local fin models to achieve parameterization.

[0024] (2) Through single parameter variation range analysis, under the condition that the periodic local fin model does not suffer from serious distortion and deformation, the influence of a single design variable within the variation range on the flow resistance and heat transfer performance of the periodic local fin model is calculated, and the key design variables with the greatest influence of the fin geometric parameters on the flow resistance and heat transfer performance of the periodic local fin model are selected; the variation range in which the design variable always has a positive impact on the performance is taken as the design space;

[0025] (3) The optimal Latin hypercube sampling method is used to generate a sample set so that the sample points are evenly distributed in the entire design space. The number of initial samples in the sample set is generally related to the number of design variables, and the minimum number requirement is (n+1)*(n+2) / 2 times the number of design variables;

[0026] (4) For each design scheme in the initial sample set, the steady Reynolds average method is used for numerical simulation; the computational domain is discretized using an unstructured periodic grid, the NS equations and the energy equations are discretized based on the finite volume method, the RNG k-ε turbulence model is selected, the inlet conditions of the fluid domain are given as fixed temperature and velocity conditions, and the outlet conditions are pressure boundary conditions; the upper and lower boundaries of the solid domain are fixed temperature boundaries; the left and right boundaries of the fluid domain and the solid domain are both periodic boundary conditions, and the interface between the fluid domain and the solid domain is a thermal coupling surface, and the steady flow field of the periodic local fin model under different speed conditions is obtained;

[0027] (5) The inlet and outlet pressure drops are obtained from the steady flow field of the periodic local fin model, and the velocity-pressure drop quadratic polynomial relationship is constructed. According to the improved Darcy law, the inertial drag coefficient and viscous drag coefficient of the porous medium model are obtained. According to the periodic local fin model, the porosity of the porous medium model is obtained.

[0028] (6) The fluid domain of the overall intercooler model is defined by porous medium parameters, and the steady Reynolds average method is used for numerical simulation. The NS equations and energy equations are discretized based on the finite volume method. The RNG k-ε turbulence model is selected. The inlet boundary conditions are given as fixed temperature and velocity conditions, and the outlet conditions are pressure boundary conditions. The interface between the fluid domain and the solid domain is thermally coupled to obtain the steady flow field of the overall intercooler design condition.

[0029] (7) A response surface model was constructed to obtain the mapping relationship between the outlet temperature, the inlet and outlet pressure drop and the key design variables selected in step (2). The non-dominated sorting genetic algorithm (NSGA-Ⅱ) was used to implement the optimization. The objective function was: the minimum outlet temperature of the hot side, the maximum inlet and outlet pressure drop of the hot side. The Pareto frontier solution set was obtained, and the geometry corresponding to some optimal solutions was selected for numerical simulation calculations. Finally, the design scheme with the optimal flow resistance and heat transfer performance was obtained.

[0030] Example

[0031] (1) This example is an air-to-air intercooler for a vehicle. Figure 1 The geometric structure of the hot side local fin model selected in this optimization process is given. According to the process in step (2), the influence of a single design variable on the flow resistance and heat transfer performance of the intercooler within the variation range is calculated. Based on the experience of previous related studies, three key design variables that have an influence of more than 4% on the flow resistance and heat transfer performance of the intercooler in this example are selected in the fin geometry, including fin thickness, fin spacing, and fin pitch.

[0032] (2) Through univariate analysis, the specific range of parameter changes is determined (with the original parameters as the baseline value): the fin thickness range is 0.1mm-0.5mm; the fin spacing range is: 1mm-5mm; the fin pitch range is 2mm-10mm.

[0033] (3) Using the method introduced in step (3) to generate 20 samples in the design space, it can ensure that the sample points are evenly distributed in the design space. According to the grid generation method and boundary conditions introduced in step (4), these samples are subjected to steady numerical simulation to obtain steady flow field results.

[0034] (4) Figure 2 For the geometric structure of the local fin model on the cold side of the intercooler in the selected example, a steady numerical simulation is performed on the geometric structure model according to the grid generation method and boundary conditions introduced in step (4) to obtain the steady flow field results.

[0035] (5) The inertial resistance coefficient, viscous resistance coefficient and porosity of the cold side and hot side porous media models are obtained by using the method of step (5), and the porous media parameter definition is used. Figure 3The fluid domain of the overall intercooler model is given. The samples are subjected to steady-state numerical simulation according to the grid generation method and boundary conditions introduced in step (6). After completing the calculation of all samples, a response surface model is constructed to obtain the mapping relationship between the outlet temperature, the inlet and outlet pressure drop, and the selected key design variables. The cross-validation method is used to verify the accuracy of the proxy model. The predicted values ​​of the performance indicators are well compared with the calculated values, and the root mean square error (RMSE) is less than 0.1. It can be considered that the proxy model in this example has a high mapping accuracy and can be used for subsequent optimization.

[0036] (6) Optimize using step (7) During the optimization process, the non-dominated sorting genetic algorithm (NSGA-Ⅱ) is used to implement the multi-objective optimization of the intercooler. The process is as shown in the attached figure. Figure 4 As shown in Figure 1, the minimization of the hot side outlet temperature and the minimization of the inlet and outlet pressure drop are taken as optimization objectives, the Pareto frontier solution set is obtained, and the geometry corresponding to some optimal solutions is screened for numerical simulation calculations. Finally, the optimal design scheme for flow resistance and heat transfer performance is obtained. It can be seen from Table 1 that the flow resistance and heat transfer performance of the intercooler after multi-objective optimization have been improved to a certain extent, among which the flow resistance performance has been improved more significantly.

[0037] Table 1 Comparison of outlet temperature and inlet and outlet pressure drop before and after intercooler optimization

[0038]

[0039] The above is only an introduction to the preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several modifications and substitutions can be made without departing from the principle of the present invention, and these modifications and substitutions should also be regarded as the protection scope of the present invention.

Claims

1. An intercooler optimization design method based on a porous medium model, characterized in that: The following steps are involved: (1) According to the geometric parameters and periodic characteristics of the intercooler heat dissipation core fins, the local fin model is parametrically modeled: a fixed period fin structure and the corresponding upper and lower baffles are selected to model the local fin model, and the key design variables that affect the intercooler flow resistance and heat transfer performance by more than 4% are changed to create different local fin models for parameterization; (2) Through single parameter variation range analysis, under the condition that the periodic local fin model does not suffer from serious distortion and deformation, the influence of a single design variable within the variation range on the flow resistance and heat transfer performance of the periodic local fin model is calculated, and the key design variables with the greatest influence of the fin geometric parameters on the flow resistance and heat transfer performance of the periodic local fin model are selected; the variation range in which the design variable always has a positive impact on the performance is taken as the design space; (3) Perform optimal Latin hypercube sampling in the design space to generate an initial sample set so that the sample points are evenly distributed in the entire design space; (4) For each design scheme in the initial sample set generated in step (3), a steady Reynolds average method is used for numerical simulation; the computational domain is discretized using an unstructured periodic grid, the NS equations and the energy equations are discretized based on the finite volume method, the RNG k-ε turbulence model is selected, the inlet conditions of the fluid domain are given as fixed temperature and velocity conditions, and the outlet conditions are pressure boundary conditions; the upper and lower boundaries of the solid domain are fixed temperature boundaries; the left and right boundaries of the fluid domain and the solid domain are both periodic boundary conditions, and the interface between the fluid domain and the solid domain is a thermal coupling surface, and the steady flow field of the periodic local fin model under different speed conditions is obtained; (5) The inlet and outlet pressure drops are obtained from the steady flow field of the periodic local fin model, and the velocity-pressure drop quadratic polynomial relationship is constructed. According to the improved Darcy's law, the inertial drag coefficient and the viscous drag coefficient of the porous medium model are obtained. According to the periodic local fin model, the porosity of the porous medium model is obtained; (6) The fluid domain of the overall intercooler model is defined by porous medium parameters, and the steady Reynolds average method is used for numerical simulation. The NS equations and energy equations are discretized based on the finite volume method. The turbulence model is RNG k-ε. The inlet boundary conditions are given as fixed temperature and velocity conditions, the outlet conditions are pressure boundary conditions, and the interface between the fluid domain and the solid domain is thermally coupled to obtain the steady flow field of the overall intercooler design condition. (7) A response surface model is constructed to obtain the mapping relationship between the outlet temperature, the inlet and outlet pressure drop and the key design variables selected in step (2). The non-dominated sorting genetic algorithm is used to implement the optimization. The objective function is: the minimum outlet temperature of the hot side and the maximum inlet and outlet pressure drop of the hot side. Finally, the optimal design scheme for flow resistance and heat transfer performance is obtained.

2. The intercooler optimization design method based on porous medium model according to claim 1, characterized in that: The key design variables that affect the flow resistance and heat transfer performance of the intercooler by more than 4% are fin thickness, fin spacing and fin pitch.

3. The intercooler optimization design method based on porous medium model according to claim 2, characterized in that: The single parameter variation range described in step 2 is specifically that the fin thickness variation range is 0.1mm-0.5mm; the fin spacing variation range is: 1mm-5mm; the fin pitch variation range is 2mm-10mm.

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