Design method and device for heat exchanger of air conditioning system of electric vertical take-off and landing aircraft and storage medium

Abstract

The invention discloses a design method and device for an air conditioner heat exchanger of an electric vertical take-off and landing aircraft and a storage medium, and the method comprises the steps: firstly determining multiple types of geometric design parameters and parameter ranges of the heat exchanger, and building a heat exchanger weight calculation model and a heat exchanger heat exchange amount calculation model; sampling parameter ranges of various geometric design parameters to obtain N sample point sets; then, a multi-objective evaluation function of a genetic algorithm is constructed based on the heat exchanger weight calculation model and the heat exchanger heat exchange calculation model, and the genetic algorithm is adopted to carry out multi-time iterative optimization solution based on the multi-objective evaluation function by taking the N sample point sets as populations to obtain multiple groups of heat exchanger geometric design parameter optimization solution sets; and finally, verifying the thermodynamic properties under each group of heat exchanger geometric design parameter optimal solution set, so as to determine the heat exchanger geometric design parameter optimal solution set. The device and the storage medium are used for implementing the method. According to the method, the rapid iteration and design type selection requirements of the thermal management system can be met.

Description

Technical Field

[0001] This invention relates to the field of heat exchanger design methods, specifically a heat exchanger design method, equipment, and storage medium for an air conditioning system of an electric vertical takeoff and landing aircraft. Background Technology

[0002] When an electric vertical takeoff and landing (eVTOL) aircraft is hovering, the mass of the air conditioning system used for thermal management will cause rotor load on the aircraft. However, reducing the mass of the air conditioning system will inevitably affect its performance. Therefore, it is necessary to study the impact of the mass of the air conditioning system on the total power consumption of the air conditioning system during the hovering process of an eVTOL aircraft based on the theory of compensatory loss, and to optimize and adjust the size and mass of the air conditioning system accordingly.

[0003] eVTOL aircraft place extremely stringent requirements on their air conditioning systems used for thermal management, as the weight of these systems directly impacts the aircraft's payload and range. Within these systems, the weight of the condenser and evaporator, acting as heat exchangers, significantly influences the overall weight. Traditional air conditioning systems for thermal management employ empirical formulas under fixed operating conditions, resulting in high weight redundancy and long development cycles, which cannot meet the rapid iteration and design selection requirements of eVTOL thermal management systems. Existing technologies also employ gradient descent algorithms to optimize heat exchangers in thermal management air conditioning systems, but these are limited to single-objective parameter adjustments. None of these methods can meet the stringent triple constraints of weight, efficiency, and reliability imposed by eVTOL air conditioning systems. Summary of the Invention

[0004] This invention provides a design method, equipment, and storage medium for the heat exchanger of the air conditioning system of an electric vertical takeoff and landing (eVTOL) aircraft, in order to solve the problem that the existing heat exchanger optimization methods for the air conditioning system in eVTOL aircraft cannot meet the design performance requirements.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A design method for a heat exchanger in the air conditioning system of an electric vertical takeoff and landing aircraft, the process of which is as follows: Step 1: Determine the various geometric design parameters of the heat exchangers in the air conditioning system used for thermal management in the electric vertical takeoff and landing aircraft, as well as the parameter range of each type of geometric design parameter; A heat exchanger simulation heat exchange model is established, which includes a heat exchanger weight calculation model and a heat exchanger heat exchange calculation model. Step 2: Using the improved Latin supersampling method, sample the parameter ranges of various geometric design parameters obtained in Step 1 to obtain N sample point sets. Each sample point set contains parameter value sample points of various geometric design parameters. Step 3: Construct a multi-objective evaluation function for the genetic algorithm based on the heat exchanger weight calculation model and the heat exchanger heat transfer calculation model; A genetic algorithm is used, based on a multi-objective evaluation function, and the N sample point set obtained in step 2 is used as a population to perform multiple iterations of optimization. This results in multiple sets of optimized solutions for heat exchanger geometric design parameters. Each set of optimized solutions for heat exchanger geometric design parameters contains optimized solutions for various geometric design parameters of the heat exchanger. Step 4: Verify the thermodynamic performance of the heat exchanger under the optimized solution set of each set of heat exchanger geometric design parameters, thereby determining the optimal solution set of heat exchanger geometric design parameters. The optimal solution set of heat exchanger geometric design parameters contains the optimal solutions of various geometric design parameters.

[0006] Furthermore, the various geometric design parameters in step 1 are heat exchanger width, heat exchanger thickness, number of flat tubes in the heat exchanger, number of flat tubes in each flat tube process, and spacing between adjacent flat tubes in each flat tube process, totaling five types of geometric design parameters.

[0007] Furthermore, in step 1, the heat exchanger weight calculation model calculates the heat exchanger weight based on various geometric design parameters, the proportion of heat exchanger material per unit volume, and the density of heat exchanger material.

[0008] Furthermore, in step 1, the heat exchanger heat transfer calculation model includes an air-side heat transfer calculation model, a refrigerant-side heat transfer calculation model, a pipe wall heat conduction calculation model, and a weighted average model, wherein: The air-side heat transfer calculation model is used to calculate the air-side heat transfer based on five types of geometric design parameters and air-side thermodynamic parameters, including inlet air temperature, inlet air volume, or air velocity. The wall temperature of the flat tubes in the heat exchanger Air-side convective heat transfer coefficient ; The refrigerant-side heat exchange calculation model is used to calculate the refrigerant-side heat exchange based on five types of geometric design parameters and refrigerant-side thermodynamic parameters, including refrigerant inlet pressure, inlet enthalpy, and refrigerant flow rate. The wall temperature of the flat tubes in the heat exchanger convective heat transfer coefficient on the refrigerant side ; The tube wall heat conduction calculation model is used to calculate the wall temperature of the flat tube based on the air-side heat transfer calculation model. Air-side convective heat transfer coefficient The wall temperature of the flat tube in the heat exchanger, calculated by the refrigerant-side heat transfer calculation model. convective heat transfer coefficient on the refrigerant side The heat conduction of the pipe wall was calculated. Heat conduction of pipe wall ; Furthermore, the air-side heat transfer calculation model, the refrigerant-side heat transfer calculation model, and the pipe wall heat conduction calculation model are coupled and iterated multiple times over time. Through multiple iterations, the air-side heat transfer calculation model is calculated to obtain the required air-side heat transfer rate. The refrigerant-side heat exchange calculation model yields the refrigerant-side heat exchange. The heat conduction of the pipe wall is calculated by the pipe wall heat conduction calculation model. The difference is less than the set iterative convergence criterion value, and the air-side heat exchange at the time of convergence is used as the criterion. As the final air-side heat exchange and refrigerant-side heat exchange As the final refrigerant-side heat exchanger; The weighted average model calculates the final air-side heat exchange and the final refrigerant-side heat exchange by weighting the average to obtain the heat exchanger's heat exchange capacity.

[0009] Furthermore, the improved Latin oversampling method in step 2 is as follows: First, the parameter range of each type of geometric design parameter is divided into N non-overlapping sub-intervals.

[0010] Next, the N sub-intervals of each type of geometric design parameter are randomly arranged, and the N sub-intervals of the five types of geometric design parameters are randomly selected N times. Each sub-interval of each type of geometric design parameter is selected only once. Thus, each random selection yields the parameter values ​​of multiple types of geometric design parameters. Each random selection of multiple geometric design parameters is used as a sample point to form a set of sample points for the corresponding random selection, resulting in a total of N sample point sets. Each sample point set includes sample points of parameter values ​​obtained from the random selection of multiple geometric design parameters.

[0011] Furthermore, when the geometric design parameters are continuous variables, they are divided into N non-overlapping sub-intervals using an equal probability stratification method; when the geometric design parameters are discrete variables, they are divided into N non-overlapping sub-intervals using an integer grid partitioning method.

[0012] Furthermore, in step 3, based on the heat exchanger weight calculation model and heat exchange heat calculation model in the heat exchanger simulation heat exchange model in step 1, a multi-objective evaluation function of the genetic algorithm is constructed with the goal of minimizing the heat exchange heat and weight of the heat exchanger.

[0013] Furthermore, the iterative optimization process of the genetic algorithm in step 3 is as follows: Step (3.1): Using N sample point sets as the initial population, each sample point set in the initial population is used as a parent individual, and the fitness value of each parent individual is calculated through the multi-objective evaluation function. Step (3.2): Select multiple parent individuals from the parent individuals as the best parent LG; Step (3.3): Select multiple parent individuals from the parent individuals to determine the boundary range of the subpopulation. The boundary range of the subpopulation includes the boundary range of five types of geometric parameters. Step (3.4): Using the same improved Latin oversampling method as in step 2, Ns offspring individuals are generated by sampling from the boundary range of the subpopulation; Step (3.5): Cross-mix the offspring individuals obtained in step (3.3) with the best parent population to generate a new population; Step (3.6) and repeat steps (3.1)-(3.5) for multiple iterations, with the new population obtained in the previous iteration used as the initial population in the current iteration; If the number of iterations reaches the set maximum, the iteration stops, and the new population obtained from the last iteration is output. The set of multiple sample points contained in the new population obtained from the last iteration is the solution set of multiple heat exchanger geometric design parameters.

[0014] Furthermore, in step 4, the optimized solution set of each set of heat exchanger geometric design parameters is substituted into the heat exchanger heat transfer calculation model for calculation, and the heat transfer calculation result of each set of optimized solution set of heat exchanger geometric design parameters is obtained. The heat exchanger ... From all qualified heat exchanger geometric design parameter optimization solution sets, the qualified heat exchanger geometric design parameter optimization solution set corresponding to the smallest weight result calculated by the heat exchanger weight calculation model is selected as the optimal solution set of heat exchanger geometric design parameters. This optimal solution set of heat exchanger geometric design parameters contains the optimal solutions of five types of geometric design parameters.

[0015] Furthermore, in step 4, when it is impossible to find a set of optimized solutions for the heat exchanger geometric design parameters whose calculated heat exchanger heat transfer result is greater than or equal to the heat exchanger target value, the heat exchanger heat transfer under the optimal fitness solution set obtained by the genetic algorithm is calculated. The parameter range of the sample points in the initial parent individuals is corrected according to the deviation direction between the calculated heat exchanger heat transfer result and the target value of the heat exchanger heat transfer. The corrected initial parent individuals are returned to the genetic algorithm to iterate and optimize the solution again.

[0016] An electronic device, including a processor and a memory, wherein program instructions in the memory are read and executed to perform the above-described design method for the heat exchanger of the air conditioning system of an electric vertical takeoff and landing aircraft.

[0017] A storage medium storing program instructions, which, when read and executed, perform the above-described design method for the heat exchanger of the air conditioning system of an electric vertical takeoff and landing aircraft.

[0018] Compared with existing technologies, this invention can quickly and accurately optimize the heat exchanger design parameters in an air conditioning system that meets thermodynamic performance requirements by utilizing multidimensional geometric design parameter variables. This satisfies the lightweight component requirements of eVTOL aircraft, achieves precise thermodynamic matching for thermal management, and meets the rapid iteration and design selection requirements of the air conditioning system for thermal management of eVTOL aircraft. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method according to an embodiment of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the embodiments will be described in detail below with reference to the accompanying drawings and examples. This will allow for a full understanding of how the present invention uses technical means to solve technical problems and achieve corresponding technical effects, and to facilitate its implementation. The embodiments of the present invention and the various features within them can be combined with each other without conflict, and all resulting technical solutions are within the protection scope of the present invention.

[0021] Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims, and accompanying drawings of this invention are intended to cover non-exclusive inclusion.

[0023] like Figure 1As shown in the figure, this embodiment discloses a design method for a heat exchanger in the air conditioning system of an electric vertical takeoff and landing aircraft. The process is as follows: Step 1: Determine the various geometric design parameters of the heat exchangers in the air conditioning system used for thermal management in the electric vertical takeoff and landing (EVTOL) aircraft, as well as the parameter range for each type of geometric design parameter. Then, establish a simulation heat transfer model of the heat exchangers using Amesim software.

[0024] In this embodiment, the heat exchanger refers to the condenser or evaporator in the air conditioning system. In this embodiment, the various geometric design parameters of the heat exchanger in the air conditioning system of the electric vertical takeoff and landing aircraft are determined as follows: heat exchanger width W, heat exchanger thickness D, number of flat tube passages Np in the heat exchanger, number of flat tubes Ntubes in each flat tube passage, and spacing L between adjacent flat tubes in each flat tube passage, for a total of five types of geometric design parameters.

[0025] Furthermore, based on the design requirements of electric vertical takeoff and landing aircraft, the parameter ranges for each type of geometric design parameter of the heat exchanger are determined, namely, the parameter ranges for the heat exchanger width W [W_min, W_max], the parameter ranges for the heat exchanger thickness D [D_min, D_max], the parameter ranges for the number of flat tube passages Np in the heat exchanger [Np_min, Np_max], the parameter ranges for the number of flat tubes Ntubes in each flat tube passage [Ntubes_min, Ntubes_max], and the parameter ranges for the distance L between adjacent flat tubes in each flat tube passage [L_min, L_max].

[0026] Wherein, W_min and W_max are the lower and upper bounds of the range of the heat exchanger width W, respectively. D_min and D_max are the lower and upper bounds of the range of the heat exchanger thickness D, respectively. Np_min and Np_max are the lower and upper bounds of the range of the range of the number of flat tubes Np in the heat exchanger, respectively. Ntubes_min and Ntubes_max are the lower and upper bounds of the range of the range of the number of flat tubes Ntubes in each flat tube flow, respectively. L_min and L_max are the lower and upper bounds of the range of the range of the range of the distance L between adjacent flat tubes in each flat tube flow, respectively.

[0027] Since the shape of the heat exchanger is continuous, the heat exchanger width W, heat exchanger thickness D, and the spacing L between adjacent flat tubes in each flat tube flow are considered continuous variables. Since each flat tube in the heat exchanger is a connected independent entity, the number of flat tube flows Np and the number of flat tubes in each flat tube flow Ntubes are considered discrete variables.

[0028] In the heat exchanger simulation heat exchange model constructed in this embodiment, a heat exchanger weight calculation model and a heat exchanger heat exchanger heat transfer calculation model are established.

[0029] The heat exchanger weight calculation model is based on various geometric design parameters, the material content per unit volume of the heat exchanger, and the density of the heat exchanger material. The heat exchanger weight calculation model is as follows: Flat tube weight

[0030] Fin weight

[0031] Where: W is the width of the heat exchanger; D is the thickness of the heat exchanger; Np is the number of flat tubes in the heat exchanger; Ntubes is the number of flat tubes in each flat tube flow; L is the distance between adjacent flat tubes. The volume ratio coefficient of flat tube material per unit outer contour volume; The volume ratio coefficient of fin material per unit outer contour volume; Density of the flat tube material; This represents the density of the fin material.

[0032] The heat exchanger heat transfer calculation model includes the air-side heat transfer calculation model, the refrigerant-side heat transfer calculation model, the pipe wall heat conduction calculation model, and the weighted average model. Among them:

[0033] The air-side heat transfer calculation model employs the Nusselt correlation heat transfer formula with fixed coefficients. This model is used to calculate the air-side heat transfer based on five types of geometric design parameters and air-side thermodynamic parameters, including inlet air temperature, inlet air volume, or air velocity. The wall temperature of the flat tubes in the heat exchanger Air-side convective heat transfer coefficient .

[0034] The refrigerant-side heat transfer calculation model uses the Shah correlation heat transfer correction formula provided in the Amesim software. Based on five types of geometric design parameters, as well as the refrigerant inlet pressure, inlet enthalpy, and refrigerant flow rate from the refrigerant-side thermodynamic parameters, the model calculates the refrigerant-side heat transfer. The wall temperature of the flat tubes in the heat exchanger convective heat transfer coefficient on the refrigerant side .

[0035] The tube wall heat conduction calculation model considers the heat conduction effect of the flat tube wall and is used to calculate the flat tube wall temperature based on the air-side heat transfer calculation model. Air-side convective heat transfer coefficient The wall temperature of the flat tube in the heat exchanger, calculated by the refrigerant-side heat transfer calculation model. convective heat transfer coefficient on the refrigerant side The heat conduction of the pipe wall was calculated. Heat conduction of pipe wall The calculation formula is as follows:

[0036] in: The air-side convective heat transfer coefficient; The refrigerant side convective heat transfer coefficient; For flat tubes with thick walls; The thermal conductivity is the inherent thermal conductivity of the flat tube material; The heat transfer area of ​​the tube wall is calculated using five types of geometric parameters; Furthermore, the air-side heat exchange calculation model, the refrigerant-side heat exchange calculation model, and the pipe wall heat conduction calculation model are coupled and iterated multiple times over time steps.

[0037] Specifically, the air-side heat transfer calculation model, the refrigerant-side heat transfer calculation model, and the pipe wall heat conduction calculation model are coupled and iterated multiple times over time. This time-step iterative calculation is the transient iteration inherent in the Amesim software, and the iteration process is as follows: 1. Divide the simulation time into small steps. Local steady-state calculations are performed at each time step. The pipe wall heat capacity effect: pipe wall temperature... Due to heat capacity As it changes over time, it is shown in the following formula:

[0038] in It is the amount of heat the refrigerant imparts to the pipe wall, i.e., the heat exchange on the refrigerant side; It is the heat released from the pipe wall to the air, that is, the heat exchange on the air side.

[0039] 2. In The iteration is performed within a single time step, as follows: S1, based on the current pipe wall temperature The heat exchange on the refrigerant side is calculated using the following formula:

[0040] in, This is the heat transfer coefficient between the refrigerant and the wall in the refrigerant-side heat transfer model, i.e., the refrigerant-side convective heat transfer coefficient. This refers to the heat exchange area between the refrigerant and the wall. This refers to the refrigerant temperature.

[0041] S2. The calculation of air-side heat exchange is shown in the following formula:

[0042] in, The air-to-wall heat transfer coefficient in the air-side heat transfer model, i.e., the air-side convective heat transfer coefficient; The area of ​​heat exchange between air and wall; This refers to the air temperature.

[0043] S3, The updated pipe wall temperature is shown in the following formula:

[0044] in, For the updated pipe wall temperature, This represents the iteration time step.

[0045] S4. If And the pipe wall temperature changes If so, then the current step converges. Otherwise, use... As the new initial value, repeat steps S1-S3 (i.e., perform sub-iterations) until the current step converges. , These are all set convergence criteria values.

[0046] 3. After the current step converges... The above process is repeated until the following condition is met, at which point the calculation is considered to have reached a global steady state and is terminated:

[0047] in, , , These are all set iterative convergence criteria values.

[0048] Therefore, through multiple iterative calculations, the air-side heat transfer calculation model was able to obtain the calculated air-side heat transfer volume. The refrigerant-side heat exchange calculation model yields the refrigerant-side heat exchange. The heat conduction of the pipe wall is calculated by the pipe wall heat conduction calculation model. The difference is less than the corresponding iteration convergence criterion value, and the air-side heat exchange at the time of convergence is used as the criterion. As the final air-side heat exchange and refrigerant-side heat exchange As the final refrigerant side heat exchanger.

[0049] The weighted average model calculates the final air-side heat exchange and the final refrigerant-side heat exchange by weighting the values ​​to obtain the heat exchanger's heat exchange capacity. The weighting values ​​are obtained from prior experiments.

[0050] Step 2: Using an improved Latin oversampling method, sample the parameter ranges of the five types of geometric design parameters obtained in Step 1 to obtain N sample point sets. Each sample point set contains parameter value sample points of the five types of geometric design parameters. The process is as follows: Step (2.1): First, divide the parameter range of each type of geometric design parameter into N non-overlapping sub-intervals.

[0051] Among them, for the geometric design parameters heat exchanger width W, heat exchanger thickness D, and spacing L between adjacent flat tubes in each flat tube process, which are regarded as continuous variables, the parameter ranges of the parameters heat exchanger width W, heat exchanger thickness D, and spacing L between adjacent flat tubes in each flat tube process are divided into N non-overlapping sub-intervals by using an equal probability stratification method.

[0052] For the geometric design parameters Np (number of flat tubes in the heat exchanger) and Ntubes (number of flat tubes in each flat tube process), which are regarded as discrete variables, the parameter ranges of Np and Ntubes in each flat tube process are divided into N non-overlapping sub-intervals using an integer grid partitioning method.

[0053] Step (2.2): Next, the N sub-intervals of each type of geometric design parameter are randomly arranged to obtain the N sub-intervals of each type of geometric design parameter after random arrangement.

[0054] Step (2.3): Then, randomly select N values ​​for the N sub-intervals after randomly arranging a total of five types of geometric design parameters.

[0055] Each time a random value is selected, a sub-interval is chosen from the N sub-intervals randomly arranged for each type of geometric design parameter, and the parameter value corresponding to the geometric design parameter type is randomly selected from the selected sub-interval. During multiple random value selections, each sub-interval in the N sub-intervals randomly arranged for each type of geometric design parameter is selected only once, ensuring that each sub-interval of each type of geometric design parameter is selected only once and is not reused.

[0056] Therefore, each random selection yields parameter values ​​for five categories of geometric design parameters. These parameter values ​​are then used as sample points to form the corresponding set of sample points for each random selection. A total of N random selections result in N sets of sample points, each set containing sample points of the randomly selected parameter values ​​for the five categories of geometric design parameters.

[0057] Step 3: Based on the heat exchanger weight calculation model and heat exchanger heat transfer calculation model in the heat exchanger simulation heat transfer model in Step 1, construct a multi-objective evaluation function of the genetic algorithm with the minimum heat transfer and weight of the heat exchanger as the calculation objective.

[0058] A genetic algorithm is used, based on a multi-objective evaluation function, to perform multiple iterations of optimization using the set of N sample points obtained in step 2 as the population. This results in multiple sets of optimized solutions for heat exchanger geometric design parameters. Each set of optimized solutions contains optimized solutions for five types of geometric design parameters of the heat exchanger: heat exchanger width W, heat exchanger thickness D, number of flat tube passages Np in the heat exchanger, number of flat tubes Ntubes in each flat tube passage, and spacing L between adjacent flat tubes in each flat tube passage.

[0059] The iterative optimization solution process of the genetic algorithm in this embodiment is as follows: Step (3.1): Using the N sample point sets obtained in step 2 as the initial population, each sample point set in the initial population is used as a parent individual, and the fitness value of each parent individual is calculated through the multi-objective evaluation function.

[0060] Step (3.2): Sort each parent individual in descending order of fitness value to obtain the parent individual ranking. Select the top 20% of parent individuals from the parent individual ranking as the best parent LG.

[0061] Step (3.3): Select the top 50% of parent individuals from the parent individuals ranking, and determine the boundary range of the subpopulation based on the selected top 50% of parent individuals.

[0062] Specifically, based on the parameter values ​​of a certain type of geometric parameter in each of the top 50% of parent individuals in the parent generation sequence, the maximum and minimum parameter values ​​of that type of geometric parameter are found, thereby determining the boundary range of that type of geometric parameter. The subpopulation boundary range determined in this way includes the boundary ranges of five types of geometric parameters.

[0063] Step (3.4): Using the same improved Latin oversampling method as in step 2, Ns offspring individuals are sampled from the subpopulation boundary range, as follows: 3.4A) First, the parameter range of each type of geometric parameter in the subpopulation boundary range is divided into Ns non-overlapping sub-intervals.

[0064] For each type of geometric design parameter treated as a continuous variable, an equal-probability hierarchical method is used to divide it into Ns non-overlapping sub-intervals. For each type of geometric design parameter treated as a discrete variable, an integer grid partitioning method is used to divide it into Ns non-overlapping sub-intervals.

[0065] 3.4B) Next, the Ns sub-intervals of each type of geometric design parameter are randomly arranged to obtain the Ns sub-intervals of each type of geometric design parameter after random arrangement.

[0066] 3.4C) Then, Ns random values ​​are taken from the Ns sub-intervals after the five types of geometric design parameters are randomly arranged.

[0067] Each time a random value is selected, a sub-interval is chosen from the Ns sub-intervals after randomizing the geometric design parameters for each type, and the parameter value of the corresponding geometric design parameter is randomly selected from the selected sub-interval. In multiple random value selections, each sub-interval in the Ns sub-intervals after randomizing the geometric design parameters for each type is selected only once.

[0068] Therefore, each random selection yields parameter values ​​for five categories of geometric design parameters. These parameter values ​​are then used as sample points to form the offspring individuals for that specific random selection. A total of Ns random selections result in Ns offspring individuals, each containing the parameter values ​​obtained from the random selection of the five categories of geometric design parameters.

[0069] Step (3.5): Cross-mix the offspring individuals obtained in step (3.3) with the best parent population to generate a new population.

[0070] Step (3.6) Repeat steps (3.1)-(3.5) for multiple iterations, and use the new population obtained from the previous iteration as the initial population in the current iteration.

[0071] If the number of iterations reaches the set maximum, the iteration stops, and the new population obtained from the last iteration is output. The set of multiple sample points contained in the new population obtained from the last iteration is the solution set of multiple heat exchanger geometric design parameters.

[0072] The sample points of various geometric design parameters contained in the optimal solution set of each heat exchanger geometric design parameter set are the optimal solutions of the five types of geometric design parameters of the heat exchanger contained in that solution set.

[0073] Step 4: Verify the thermodynamic performance of the heat exchanger under the optimized solution set of each group of heat exchanger geometric design parameters, thereby determining the optimal solution set of heat exchanger geometric design parameters. The verification process is as follows: Given the air-side thermodynamic parameters, i.e., given the inlet air temperature, inlet air volume or air velocity; and given the refrigerant-side thermodynamic parameters, including the refrigerant inlet pressure, inlet enthalpy, and refrigerant flow rate.

[0074] The optimized solutions of the five types of geometric design parameters in the optimization solution set of each heat exchanger geometric design parameter are substituted into the heat exchanger heat transfer calculation model in the heat exchanger simulation heat transfer model in step 1 for calculation.

[0075] The air-side heat transfer calculation model in the heat exchanger heat transfer calculation model is used to calculate the air-side heat transfer capacity based on the optimized solutions of five types of geometric design parameters and the given air-side thermodynamic parameters, such as inlet air temperature, inlet air volume, or air velocity. The wall temperature of the flat tubes in the heat exchanger Air-side convective heat transfer coefficient .

[0076] The refrigerant-side heat transfer calculation model in the heat exchanger heat transfer calculation model, based on the optimized solution of five types of geometric design parameters, and the given refrigerant-side thermodynamic parameters including refrigerant inlet pressure, inlet enthalpy, and refrigerant flow rate, calculates the refrigerant-side heat transfer. The wall temperature of the flat tubes in the heat exchanger convective heat transfer coefficient on the refrigerant side .

[0077] The wall heat conduction calculation model is based on the air-side heat transfer calculation model, which calculates the wall temperature of the flat tube. Air-side convective heat transfer coefficient The wall temperature of the flat tube in the heat exchanger, calculated by the refrigerant-side heat transfer calculation model. convective heat transfer coefficient on the refrigerant side The heat conduction of the pipe wall was calculated. .

[0078] Furthermore, the air-side heat transfer calculation model, the refrigerant-side heat transfer calculation model, and the pipe wall heat conduction calculation model are coupled and iterated multiple times over time. Through multiple iterations, the air-side heat transfer calculation model is calculated to obtain the required air-side heat transfer rate. The refrigerant-side heat exchange calculation model yields the refrigerant-side heat exchange. The heat conduction of the pipe wall is calculated by the pipe wall heat conduction calculation model. The difference is less than the corresponding iteration convergence criterion value, and the air-side heat exchange at the time of convergence is used as the criterion. As the final air-side heat exchange and refrigerant-side heat exchange As the final refrigerant side heat exchanger.

[0079] The weighted average model calculates the final air-side heat exchange and the final refrigerant-side heat exchange by weighting the average, and obtains the heat exchange heat exchange based on the optimized solution set of geometric design parameters for each heat exchanger.

[0080] The heat exchanger heat transfer calculation model is used to calculate the heat exchanger heat transfer amount based on the optimized solution set of each group of heat exchanger geometric design parameters. The calculated heat exchanger heat transfer amount is compared with the target value of the heat exchanger heat transfer amount. Several optimized solution sets of heat exchanger geometric design parameters whose calculated heat exchanger heat transfer amount is close to the target value of the heat exchanger heat transfer amount are selected as qualified optimized solution sets of heat exchanger geometric design parameters.

[0081] All optimized solutions for qualified heat exchanger geometric design parameters are substituted into the heat exchanger weight calculation model for calculation. From all optimized solutions for qualified heat exchanger geometric design parameters, the set corresponding to the smallest weight calculated by the heat exchanger weight calculation model is selected as the optimal set of qualified heat exchanger geometric design parameters. This optimal set of heat exchanger geometric design parameters contains optimal solutions for five types of geometric design parameters.

[0082] Thus, in this embodiment, the optimal solutions for the five types of geometric design parameters of the heat exchanger are obtained, and the design of the basic geometric parameters of the heat exchanger is completed.

[0083] In this embodiment, a parameter range elastic adjustment mechanism is also established for the genetic algorithm. If the parameter range of each sample point in the current initial parent individual cannot select a heat exchanger geometric design parameter optimization solution set that meets the heat exchanger heat transfer performance requirements (i.e., the calculated heat exchanger heat transfer result is close to the heat exchanger target value), the genetic algorithm outputs the fitness-optimal solution set and calculates the heat exchanger heat transfer under this fitness-optimal solution set. Based on the deviation direction between the calculated heat exchanger heat transfer result and the heat exchanger heat transfer target value, the parameter range of the sample points in the initial parent individual is corrected. During correction, if the calculated heat exchanger heat transfer result is much smaller than the heat exchanger heat transfer target value, the upper bound of the parameter range of each sample point in the initial parent individual is increased; if the calculated heat exchanger heat transfer result is much larger than the heat exchanger heat transfer target value, the upper bound of the parameter range of each sample point in the initial parent individual is decreased. The corrected initial parent individual is returned to the genetic algorithm for iterative optimization.

[0084] This embodiment also discloses an electronic device, which includes a processor and a memory. The memory stores program instructions that can be read and executed by the processor or other external processing devices. When the program instructions are read and executed, steps 1-4 of the above-described design method for the heat exchanger of the air conditioning system of an electric vertical take-off and landing aircraft are performed.

[0085] This embodiment also discloses a storage medium storing program instructions, which, when read and executed, perform steps 1-4 of the above-described design method for the heat exchanger of the air conditioning system of an electric vertical takeoff and landing aircraft.

[0086] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. These embodiments are merely descriptions of preferred embodiments and are not intended to limit the scope or concept of the invention. The specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. Such combinations, as long as they do not violate the spirit of the present invention, should also be considered as part of this disclosure. To avoid unnecessary repetition, the present invention will not further describe the various possible combinations.

[0087] This invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this invention and without departing from the design idea of ​​this invention, all modifications and improvements made by those skilled in the art to the technical solutions of this invention should fall within the protection scope of this invention. The technical content for which protection is sought in this invention has been fully described in the claims.

Claims

1. A design method for a heat exchanger in the air conditioning system of an electric vertical takeoff and landing aircraft, characterized in that, The process is as follows: Step 1: Determine the various geometric design parameters of the heat exchangers in the air conditioning system used for thermal management in the electric vertical takeoff and landing aircraft, as well as the parameter range of each type of geometric design parameter; A heat exchanger simulation heat exchange model is established, which includes a heat exchanger weight calculation model and a heat exchanger heat exchange calculation model. Step 2: Using the improved Latin supersampling method, sample the parameter ranges of various geometric design parameters obtained in Step 1 to obtain N sample point sets. Each sample point set contains parameter value sample points of various geometric design parameters. Step 3: Construct a multi-objective evaluation function for the genetic algorithm based on the heat exchanger weight calculation model and the heat exchanger heat transfer calculation model; A genetic algorithm is used, based on a multi-objective evaluation function, and the N sample point set obtained in step 2 is used as a population to perform multiple iterations of optimization. This results in multiple sets of optimized solutions for heat exchanger geometric design parameters. Each set of optimized solutions for heat exchanger geometric design parameters contains optimized solutions for various geometric design parameters of the heat exchanger. Step 4: Verify the thermodynamic performance of the heat exchanger under the optimized solution set of each set of heat exchanger geometric design parameters, thereby determining the optimal solution set of heat exchanger geometric design parameters, which contains the optimal solutions of various geometric design parameters.

2. The design method for a heat exchanger of an air conditioning system for an electric vertical takeoff and landing aircraft according to claim 1, characterized in that, The various geometric design parameters in step 1 are heat exchanger width, heat exchanger thickness, number of flat tube passages in the heat exchanger, number of flat tubes in each flat tube passage, and spacing between adjacent flat tubes in each flat tube passage, totaling five types of geometric design parameters.

3. The design method for a heat exchanger of an air conditioning system for an electric vertical takeoff and landing aircraft according to claim 1, characterized in that, In step 1, the heat exchanger weight calculation model calculates the heat exchanger weight based on various geometric design parameters, the proportion of heat exchanger material per unit volume, and the density of heat exchanger material.

4. The design method for a heat exchanger of an air conditioning system for an electric vertical takeoff and landing aircraft according to claim 1, characterized in that, In step 1, the heat exchanger heat transfer calculation model includes the air-side heat transfer calculation model, the refrigerant-side heat transfer calculation model, the pipe wall heat conduction calculation model, and the weighted average model, wherein: The air-side heat transfer calculation model is used to calculate the air-side heat transfer based on five types of geometric design parameters and air-side thermodynamic parameters, including inlet air temperature, inlet air volume, or air velocity. The wall temperature of the flat tubes in the heat exchanger Air-side convective heat transfer coefficient ; The refrigerant-side heat exchange calculation model is used to calculate the refrigerant-side heat exchange based on five types of geometric design parameters and refrigerant-side thermodynamic parameters, including refrigerant inlet pressure, inlet enthalpy, and refrigerant flow rate. The wall temperature of the flat tubes in the heat exchanger convective heat transfer coefficient on the refrigerant side ; The tube wall heat conduction calculation model is used to calculate the wall temperature of the flat tube based on the air-side heat transfer calculation model. Air-side convective heat transfer coefficient The wall temperature of the flat tube in the heat exchanger, calculated by the refrigerant-side heat transfer calculation model. convective heat transfer coefficient on the refrigerant side The heat conduction of the pipe wall was calculated. Heat conduction of pipe wall ; Furthermore, the air-side heat transfer calculation model, the refrigerant-side heat transfer calculation model, and the pipe wall heat conduction calculation model are coupled and iterated multiple times over time. Through multiple iterations, the air-side heat transfer calculation model is calculated to obtain the required air-side heat transfer rate. The refrigerant-side heat exchange calculation model yields the refrigerant-side heat exchange. The heat conduction of the pipe wall is calculated by the pipe wall heat conduction calculation model. The difference is less than the set iterative convergence criterion value, and the air-side heat exchange at the time of convergence is used as the criterion. As the final air-side heat exchange and refrigerant-side heat exchange As the final refrigerant-side heat exchanger; The weighted average model calculates the final air-side heat exchange and the final refrigerant-side heat exchange by weighting the average to obtain the heat exchanger's heat exchange capacity.

5. The design method for a heat exchanger of an air conditioning system for an electric vertical takeoff and landing aircraft according to claim 1, characterized in that, In step 2, the improved Latin oversampling method proceeds as follows: First, the parameter range of each type of geometric design parameter is divided into N non-overlapping sub-intervals; Next, the N sub-intervals of each type of geometric design parameter are randomly arranged, and the N sub-intervals of the five types of geometric design parameters are randomly selected N times. Each sub-interval of each type of geometric design parameter is selected only once. Thus, each random selection yields the parameter values ​​of multiple types of geometric design parameters. Each random selection of multiple geometric design parameters is used as a sample point to form a set of sample points for the corresponding random selection, resulting in a total of N sample point sets. Each sample point set includes sample points of parameter values ​​obtained from the random selection of multiple geometric design parameters.

6. The design method for a heat exchanger of an air conditioning system for an electric vertical takeoff and landing aircraft according to claim 5, characterized in that, When the geometric design parameters are continuous variables, they are divided into N non-overlapping sub-intervals using an equal probability stratification method; when the geometric design parameters are discrete variables, they are divided into N non-overlapping sub-intervals using an integer grid partitioning method.

7. The design method for a heat exchanger of an air conditioning system for an electric vertical takeoff and landing aircraft according to claim 1, characterized in that, In step 3, based on the heat exchanger weight calculation model and heat exchanger heat transfer calculation model in the heat exchanger simulation heat transfer model in step 1, a multi-objective evaluation function of the genetic algorithm is constructed with the goal of minimizing the heat transfer and weight of the heat exchanger.

8. The design method for a heat exchanger of an air conditioning system for an electric vertical takeoff and landing aircraft according to claim 1, characterized in that, The iterative optimization process of the genetic algorithm in step 3 is as follows: Step (3.1): Using N sample point sets as the initial population, each sample point set in the initial population is used as a parent individual, and the fitness value of each parent individual is calculated through the multi-objective evaluation function. Step (3.2): Select multiple parent individuals from the parent individuals as the best parent LG; Step (3.3): Select multiple parent individuals from the parent individuals to determine the boundary range of the subpopulation. The boundary range of the subpopulation includes the boundary range of five types of geometric parameters. Step (3.4): Using the same improved Latin oversampling method as in step 2, Ns offspring individuals are generated by sampling from the boundary range of the subpopulation; Step (3.5): Cross-mix the offspring individuals obtained in step (3.3) with the best parent population to generate a new population; Step (3.6) and repeat steps (3.1)-(3.5) for multiple iterations, with the new population obtained in the previous iteration used as the initial population in the current iteration; If the number of iterations reaches the set maximum, the iteration stops, and the new population obtained from the last iteration is output. The set of multiple sample points contained in the new population obtained from the last iteration constitutes the multiple sets of optimized solutions for the heat exchanger geometric design parameters.

9. The design method for a heat exchanger of an air conditioning system for an electric vertical takeoff and landing aircraft according to claim 1, characterized in that, In step 4, the optimized solution set of each set of heat exchanger geometric design parameters is substituted into the heat exchanger heat transfer calculation model for calculation, and the heat transfer calculation result of each set of optimized solution set of heat exchanger geometric design parameters is obtained. The heat exchanger ... From all qualified heat exchanger geometric design parameter optimization solution sets, the qualified heat exchanger geometric design parameter optimization solution set corresponding to the smallest weight result calculated by the heat exchanger weight calculation model is selected as the optimal solution set of heat exchanger geometric design parameters. This optimal solution set of heat exchanger geometric design parameters contains the optimal solutions of five types of geometric design parameters.

10. The design method for a heat exchanger of an air conditioning system for an electric vertical takeoff and landing aircraft according to claim 9, characterized in that, In step 4, when it is impossible to find a heat exchanger geometric design parameter optimization solution set whose heat exchanger heat transfer calculation result is greater than or equal to the heat exchanger target value, the heat exchanger heat transfer under the fitness optimal solution set obtained by the genetic algorithm is calculated. The parameter range of the sample points in the initial parent individuals is corrected according to the deviation direction between the heat exchanger heat transfer calculation result and the heat exchanger heat transfer target value. The corrected initial parent individuals are returned to the genetic algorithm to iterate and optimize the solution again.

11. An electronic device, comprising a processor and a memory, characterized in that, When the program instructions in the memory are read and executed, the heat exchanger design method for the air conditioning system of an electric vertical takeoff and landing aircraft as described in any one of claims 1-10 is performed.

12. A storage medium storing program instructions, characterized in that, When the program instructions are read and executed, the heat exchanger design method for the air conditioning system of an electric vertical takeoff and landing aircraft as described in any one of claims 1-10 is performed.

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