An aircraft thermal management system optimization method based on differential evolution method

Abstract

The present application relates to a kind of aircraft thermal management system optimization method based on differential evolution method, belong to aircraft thermal management system technical field, with the goal of reducing compensation loss and improving heat sink utilization, solve the unreasonable problem of key parameter setting in the prior art in aircraft thermal management system architecture design and flow.The steps of the aircraft thermal management system optimization method of the present application are as follows: step 1.establish the model of key components in aircraft environmental control system;Step 2. generate multiple thermal management system models satisfying the topological relationship of key component connection constraint by traversing combination;Step 3. determine the optimization objective function and constraint condition;Step 4. obtain the optimal topological architecture and corresponding operating condition.The present application supports efficient, lightweight, complex, low-compensation aircraft thermal management system design, and improves the working efficiency of the aircraft thermal management system obtained after optimization.

Description

Technical Field

[0001] This invention belongs to the technical field of aircraft thermal management systems, specifically relating to an optimization method for aircraft thermal management systems based on differential evolution. Background Technology

[0002] The function of an aircraft thermal management system is to maintain the temperature and pressure of the air in the pilot's cockpit and avionics bay within permissible or specified ranges under various flight conditions, playing a crucial role in modern aircraft. Early aircraft thermal management systems employed a simple method of using engine bleed air to generate cool air for heat dissipation through turbine expansion. However, with the increasing heat load on airborne equipment and rising passenger demands for environmental comfort, the number of components in aircraft thermal management systems has grown, and the system structure has become increasingly complex. Designers, based on experience, have proposed complex architectures such as two-wheeled and three-wheeled systems. However, an aircraft's thermal management system must not only meet the cabin's heat dissipation requirements but also comprehensively consider the impact of factors such as the system's weight, bleed air volume, and heat sink utilization efficiency on the overall aircraft performance. Designing such a complex system solely based on experience is insufficient to achieve globally optimal design. Summary of the Invention

[0003] To address the aforementioned problems, this application provides an optimization method for an aircraft thermal management system based on differential evolution, with the specific steps as follows:

[0004] Step 1: Establish models of key components in the aircraft's environmental control system;

[0005] Step 2: Based on the key component model, traverse and combine to generate multiple topological relationships that satisfy the connection constraints of the key components, and construct a candidate thermal management system model set;

[0006] Step 3: Determine the optimization objective function with the goal of maximizing heat sink utilization efficiency and minimizing compensatory losses, and set physical constraints.

[0007] Step 4: Based on the optimization objective function and constraints, the candidate thermal management system model set is optimized collaboratively using the differential evolution method to simultaneously solve for the optimal topology and its corresponding full flight profile operating parameters.

[0008] The operating parameters include flow rate, pressure, and temperature parameters during taxiing, climbing, level flight, and descent.

[0009] Preferably, the key component models include a ram air inlet model, a ram air outlet model, an engine bleed air inlet model, an engine bleed air outlet model, a compressor model, a turbine model, a three-way point model, and a pressure regulating valve model.

[0010] The three-way point model is constructed based on the principles of mass and energy conservation, and its expression is:

[0011]

[0012]

[0013] in, , and These are the flow rates of the first, second, and third pipe loops, respectively. , and These are the temperatures of the first, second, and third pipe circuits, respectively.

[0014] Preferably, the constraints described in step 3 include a maximum flow rate limit, a regulating valve opening limit, a compressor efficiency limit, a gas supply pressure limit, and a gas supply temperature limit.

[0015] The expression for the optimization objective function is:

[0016]

[0017] in, The objective function is... To improve the efficiency of heat sink utilization; To compensate for the losses; and As weight.

[0018] Preferably, the specific steps of co-optimization using the differential evolution method in step 4 include:

[0019] Step 41: Initialize the population. The population encoding includes topology variables and operating condition variables. Set the population size NP and the maximum number of iterations g. max and stopping criteria ε;

[0020] Step 42: Enter the iteration loop and set the current algebra g=1;

[0021] Step 43: Perform mutation operation on the g-th generation population to obtain the g+1-th generation mutated population;

[0022] Step 44: Perform crossover on the mutant population to obtain the g+1th generation experimental population;

[0023] Step 45: Based on the optimization objective function and constraints, calculate the individual fitness value of each candidate model;

[0024] Step 46: Using a greedy selection strategy, compare the fitness values ​​of the experimental individuals with those of the original individuals, and retain the better individuals to form the g+1 generation population;

[0025] Step 47: Determine if the termination condition is met. If yes, output the optimal topology and the corresponding flow rate under the operating conditions. If no, set g = g + 1 and return to step 43.

[0026] Preferably, the initialization expression for the individual group in step 41 is:

[0027] +rand(0,1)( - );

[0028] rand(0,1) represents a random number in the interval [0,1]. and These are the upper and lower bounds of the dimension, respectively.

[0029] Preferably, the mutation operation in step 43 employs a difference strategy, expressed as:

[0030]

[0031] in, Indicates the first The individual Victor A population of variant individuals; , and It is the population size. The three random numbers in the range are: ; This is the scaling factor; Indicates the first The individual Victor The first generation Individual groups.

[0032] Preferably, the compressor model is constructed based on the gas state equation, the turbine model is constructed based on the adiabatic expansion efficiency and pressure loss, and the pressure regulating valve model is constructed based on the pressure reduction and stabilization logic.

[0033] This invention has at least the following beneficial effects: It automatically generates an effective architecture for the aircraft thermal management system using an enumeration method and optimizes the optimal operating conditions based on a differential algorithm, comprehensively considering the impact of multiple factors such as weight, engine bleed air volume, and heat sink utilization efficiency on aircraft performance. This method avoids the problems arising from traditional experience-based design methods, supports the design of efficient, lightweight, complex, and low-compensation aircraft thermal management systems, and improves the working efficiency of the optimized aircraft thermal management system. Attached Figure Description

[0034] Figure 1 This is a flowchart of the differential evolution method of the present invention;

[0035] Figure 2 The graph shows the convergence of the three optimization methods. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are only some embodiments of this application, not all embodiments. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without creative effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings. A specific embodiment of the present invention is as follows... Figures 1-2 A method for optimizing the thermal management system of an aircraft based on differential evolution is disclosed. The specific steps are as follows:

[0037] Step 1. Establish models of key components in the aircraft's environmental control system;

[0038] Specifically, the key component models include the ram air inlet (RSO) model, the ram air outlet (RSI) model, the engine bleed air inlet (BSO) model, the engine bleed air outlet (BSI) model, the compressor (ACP) model, the turbine (TUR) model, the three-way point (ND3) model, and the pressure regulating valve (PRV) model.

[0039] Furthermore, The expression for the (BSO) model is:

[0040]

[0041]

[0042]

[0043]

[0044] in, This refers to the compressor outlet pressure. The compressor boost ratio; For gas supply pressure; This refers to the compressor outlet temperature. For compressor efficiency; For gas supply temperature; This refers to the ram air inlet pressure; The boost ratio; Temperature ratio; and They are respectively Inlet temperature and pressure;

[0045] Furthermore, the expression for the ram air inlet (RSO) model is as follows:

[0046]

[0047]

[0048] in, Atmospheric temperature; Flight Mach; The air density ratio; Atmospheric pressure and These are the temperature and pressure at the ram air inlet, respectively.

[0049] Furthermore, the expression for the compressor (ACP) model is:

[0050]

[0051]

[0052] in, This refers to the intake pressure; This represents the volume of gas during intake. The intake air temperature; The temperature of the exhaust gas; The pressure of the exhaust gas; The volume of gas during exhaust; It's the compression ratio; It is the heat-to-melt ratio.

[0053] Furthermore, the expression for the turbine (TUR) model is:

[0054]

[0055]

[0056]

[0057] in, This refers to the turbine outlet pressure. This refers to the turbine inlet pressure. For cabin pressure; This refers to the pressure loss within the conduit; For water separator; The ratio of import and export pressure; Turbine outlet temperature; This refers to the turbine inlet temperature. For turbine efficiency.

[0058] Furthermore, the expression for the pressure regulating valve (PRV) model is as follows:

[0059]

[0060]

[0061]

[0062] in, This is due to export pressure on PRVs; For PRV inlet pressure; This is the preset pressure value.

[0063] Furthermore, the expression for the 3-way point (ND3) model is:

[0064]

[0065]

[0066] in, , and These are the flow rates of the first, second, and third pipe loops, respectively. , and These are the temperatures of the first, second, and third pipe circuits, respectively.

[0067] Step 2. Based on the key component models in the aircraft environmental control system of Step 1, traverse and combine them to generate multiple thermal management system models that satisfy the topological relationships of the connection constraints of the key components.

[0068] Step 3. Determine the objective function and constraints.

[0069] Specifically, the expression for the objective function is:

[0070]

[0071] in, The objective function is... To improve the efficiency of heat sink utilization; To compensate for the losses; and As weight.

[0072] It is understandable that the compensation loss is a comprehensive function of the overall system weight and engine bleed air volume; the optimization objective function is a function of factors such as overall weight, bleed air volume, heat sink utilization efficiency, and energy consumption, which can evaluate the system's advantages and disadvantages from multiple perspectives.

[0073] Specifically, the constraints include restrictions on the maximum flow rate, restrictions on the valve opening of the regulating valve, restrictions on the compressor efficiency, restrictions on the supply pressure, and restrictions on the supply temperature.

[0074] Step 4. Based on the optimization objective function and constraints determined in Step 3, the multiple thermal management system models from Step 2 are evolved using the differential evolution method to obtain the optimal topology and corresponding operating conditions.

[0075] Specifically, the operating conditions include the taxiing phase, climb phase, level flight phase, and descent phase.

[0076] Step 41: Initialize the individual population and the number of individuals in the population. Initial environment set, maximum number of iterations and stop the measurement ;

[0077] The environmental set includes the current generation's flow rate m, pressure P, and temperature T.

[0078] Based on the initial group of individuals, the expression for obtaining the initial environment set is:

[0079]

[0080] in, It is the first The individual The initial set of dimensions; Indicates the first The individual Initialize the individual population of the dimension; and They represent the first The individual The upper and lower bounds of a group of individuals.

[0081] Furthermore, traffic This includes the flow rate of gases such as engine bleed air and ram air.

[0082] Furthermore, the first The individual The expression for initializing the population of individuals in dimension is:

[0083] +rand(0,1)( - )

[0084] Where rand(0,1) represents the interval [0,1]. Random numbers on the array.

[0085] Step 42: Let =1, when When =1, it is the initial generation.

[0086] Step 43: For the first The individual Victor The first individual population was mutated to obtain the second The population of variant individuals is expressed as:

[0087]

[0088] in, Indicates the first The individual Victor A population of variant individuals; , and It is the population size. The three random numbers in the range are: ; This is the scaling factor; Indicates the first The individual Victor The first generation Individual groups.

[0089] Step 44: Use crossover to randomly select the first... The individual Victor The expression for selecting a group of individuals is:

[0090]

[0091] in, Indicates the first The individual Victor Select a group of individuals; The crossover probability; Indicates the first A population of variant individuals; Indicates the first The individual Victor Individual groups.

[0092] Step 45: Based on the optimization objective function and constraints in Step 3, obtain the individual fitness values ​​corresponding to the multiple thermal management system models in Step 2;

[0093] Step 46: Using a greedy selection strategy, select the optimal individual fitness value from the multiple individual fitness values ​​obtained in Step 45. Use the optimal individual fitness value as the judgment criterion to obtain the [missing value]. The individual Victor The expression for a group of individuals is:

[0094]

[0095] in, For the first The individual Victor Individual groups; Indicates the first The individual Victor Select a group of individuals; This represents the optimal individual fitness value.

[0096] Step 47: Determine if the maximum number of iterations has been reached. If the termination condition is met, the calculation ends, yielding the optimal topology of the thermal management system model and the corresponding flow rate under the operating conditions. Not satisfied, make Then return to step 43.

[0097] Specifically, the termination condition is the objective function. Is it less than the stop measurement? .

[0098] To demonstrate the effectiveness of the method proposed in this invention, the optimal topology and corresponding operating conditions of the thermal management system model were obtained using the Differential Evolutionary Algorithm (DEA), the Annealing Algorithm (SA), and the Particle Swarm Optimization (PSO) algorithm. See [link to relevant documentation]. Figure 2 The differential evolution algorithm is significantly better than the other two methods in terms of convergence speed. The value of the optimization objective function decreases rapidly with the iteration process. The final optimization result shows improvements in both weight and heat sink utilization compared to the traditional method design results, verifying that this method can effectively support the design of thermal management systems.

[0099] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present application should be determined by the scope of the claims.

Claims

1. An optimization method for an aircraft thermal management system based on differential evolution, characterized in that, The specific steps are as follows: Step 1: Establish models of key components in the aircraft's environmental control system; Step 2: Based on the key component model, traverse and combine to generate multiple topological relationships that satisfy the connection constraints of the key components, and construct a candidate thermal management system model set; Step 3: Determine the optimization objective function with the goal of maximizing heat sink utilization efficiency and minimizing compensatory losses, and set physical constraints. Step 4: Based on the optimization objective function and constraints, the candidate thermal management system model set is optimized collaboratively using the differential evolution method to simultaneously solve for the optimal topology and its corresponding full flight profile operating parameters. The operating parameters include flow rate, pressure, and temperature parameters during taxiing, climbing, level flight, and descent.

2. The aircraft thermal management system optimization method based on differential evolution as described in claim 1, characterized in that, The key component models include a ram air inlet model, a ram air outlet model, an engine bleed air inlet model, an engine bleed air outlet model, a compressor model, a turbine model, a three-way point model, and a pressure regulating valve model. The three-way point model is constructed based on the principles of mass and energy conservation, and its expression is: in, , and These are the flow rates of the first, second, and third pipe loops, respectively. , and These are the temperatures of the first, second, and third pipe circuits, respectively.

3. The aircraft thermal management system optimization method based on differential evolution as described in claim 1, characterized in that, The constraints mentioned in step 3 include maximum flow rate limit, regulating valve opening limit, compressor efficiency limit, supply pressure limit, and supply temperature limit; The expression for the optimization objective function is: in, The objective function is... To improve the efficiency of heat sink utilization; To compensate for the losses; and As weight.

4. The aircraft thermal management system optimization method based on differential evolution as described in claim 1, characterized in that, The specific steps for collaborative optimization using the differential evolution method in step 4 include: Step 41: Initialize the population. The population encoding includes topology variables and operating condition variables. Set the population size NP and the maximum number of iterations g. max and stopping criteria ε; Step 42: Enter the iteration loop and set the current algebra g=1; Step 43: Perform mutation operation on the g-th generation population to obtain the g+1-th generation mutated population; Step 44: Perform crossover on the mutant population to obtain the g+1th generation experimental population; Step 45: Based on the optimization objective function and constraints, calculate the individual fitness value of each candidate model; Step 46: Using a greedy selection strategy, compare the fitness values ​​of the experimental individuals with those of the original individuals, and retain the better individuals to form the g+1 generation population; Step 47: Determine if the termination condition is met. If yes, output the optimal topology and the corresponding flow rate under the operating conditions. If no, set g = g + 1 and return to step 43.

5. The aircraft thermal management system optimization method based on differential evolution as described in claim 1, characterized in that, The initialization expression for the individual group mentioned in step 41 is: +rand(0,1)( - ); rand(0,1) represents a random number in the interval [0,1]. and These are the upper and lower bounds of the dimension, respectively.

6. The optimization method for aircraft thermal management systems based on differential evolution as described in claim 1, characterized in that, The mutation operation in step 43 uses a difference strategy, expressed as follows: in, Indicates the first The individual Victor A population of variant individuals; , and It is the population size. The three random numbers in the range are: ; This is the scaling factor; Indicates the first The individual Victor The first generation Individual groups.

7. The aircraft thermal management system optimization method based on differential evolution as described in claim 1, characterized in that, The compressor model is based on the gas state equation, the turbine model is based on the adiabatic expansion efficiency and pressure loss, and the pressure regulating valve model is based on the pressure reduction and stabilization logic.

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