Aircraft fuel oil thermal management system architecture optimization design method

Abstract

The invention belongs to the field of aircraft fuel oil thermal management system architecture design, and particularly relates to an aircraft fuel oil thermal management system architecture optimization design method, which comprises the following steps: step 1, generating all potential architectures of a fuel oil thermal management system: S1, initializing to generate a point set of connection nodes representing the thermal load of the fuel oil thermal management system; s2, generating a plurality of edges corresponding to the number of thermal loads of the fuel oil thermal management system, connecting each node in each point set, and generating an edgeless directed graph; s3, endowing the fuel oil thermal management system with thermal load significance for each edge in each edgeless digraph to obtain a corresponding fuel oil thermal management system architecture, deleting repeated architectures in the architecture to obtain all potential architectures of the fuel oil thermal management system; and 2, taking the fuel oil flow at the outlet of the fuel tank as an optimization variable, taking the minimum required fuel oil flow as an optimization target, and preferentially obtaining an optimal fuel oil heat management system architecture from all potential architectures of the fuel oil heat management system.

Description

Technical Field

[0001] This application belongs to the field of aircraft fuel thermal management system architecture design, and specifically relates to an optimization design method for aircraft fuel thermal management system architecture. Background Technology

[0002] With the use of high-power electrical equipment, the aircraft's mission efficiency has been improved, but the heat dissipation requirements of the aircraft's fuel thermal management system have also increased significantly. At the same time, in order to ensure the aircraft's shape, the fuel thermal management system's ability to introduce ram air from the outside for cooling is limited. That is, while the heat dissipation requirements increase, the available cold source decreases, and only fuel can be used as the cold source. The available margin of the fuel thermal management system architecture design is further compressed.

[0003] The previous design method of manually calculating the architecture of fuel thermal management system is difficult to find the optimal solution. In order to meet the heat dissipation requirements of fuel thermal management system, significant concessions need to be made in terms of heat dissipation efficiency and heat sink utilization.

[0004] Optimizing the architecture of an intelligent fuel thermal management system is a key technology for solving system architecture design under limited margin conditions and improving heat dissipation efficiency and heat sink utilization. Therefore, this application is proposed. Summary of the Invention

[0005] The purpose of this application is to provide an optimized design method for the architecture of an aircraft fuel thermal management system, in order to solve the architecture design problem of the fuel thermal management system under the conditions of high heat dissipation requirements and limited fuel cold source intervention.

[0006] The technical solution of this application is:

[0007] An optimization design method for an aircraft fuel thermal management system architecture includes:

[0008] Step 1: Generate all potential architectures for the fuel oil thermal management system:

[0009] S1. Initialize and generate a set of connection nodes representing the thermal load of the fuel thermal management system;

[0010] S2. Corresponding to the number of heat loads in the fuel thermal management system, generate multiple edges to connect the nodes in the point set and generate an edgeless directed graph.

[0011] S3. Assign the meaning of the thermal load of the fuel thermal management system to each edge in each unbounded directed graph to obtain the corresponding fuel thermal management system architecture. Delete duplicate architectures to obtain all potential architectures of the fuel thermal management system.

[0012] Step 2: Using the fuel flow rate at the fuel tank outlet as the optimization variable and the minimum required fuel flow rate as the optimization objective, select the optimal fuel thermal management system architecture from all potential architectures of the fuel thermal management system.

[0013] According to at least one embodiment of this application, in the above-described aircraft fuel thermal management system architecture optimization design method, in S1, the point set generated during initialization has N points, where the minimum point set has two nodes and the maximum point set has N+1 nodes, where N is the number of thermal loads of the fuel thermal management system.

[0014] According to at least one embodiment of this application, in the above-described aircraft fuel thermal management system architecture optimization design method, in step S2, a recursive algorithm is used to generate N edges to connect the nodes in each point set, following the following rules:

[0015] An edge can only connect one input node and one output node;

[0016] For any edge, the input node must be smaller than the output node;

[0017] A single node can connect to the inputs and outputs of multiple edges, which are connected in parallel.

[0018] Node 1 must be connected to an input edge, but cannot be output as any edge.

[0019] The last node must be connected to at least one edge output, but cannot be used as any edge input;

[0020] A node must have both input and output simultaneously; if there is no input or output, the node is invalid.

[0021] According to at least one embodiment of this application, in the above-described aircraft fuel thermal management system architecture optimization design method, in S2, all potential architectures of the fuel thermal management system are represented in the form of codes, and two numbers are used to represent the input node and the output node for any edge, respectively, as the architecture code.

[0022] According to at least one embodiment of this application, in the above-described aircraft fuel thermal management system architecture optimization design method, in step two, a double-nested genetic algorithm is used to search for the optimal fuel thermal management system architecture from all potential architectures of the fuel thermal management system.

[0023] According to at least one embodiment of this application, in the above-mentioned aircraft fuel thermal management system architecture optimization design method, in step two, the inner layer genetic algorithm is the basic function called by the outer layer algorithm, which is used to find the minimum fuel flow rate that can satisfy the temperature constraints of each subsystem and the inlet temperature constraints of the engine system under the determination of the aircraft operating point and all potential architectures of the fuel thermal management system.

[0024] The outer genetic algorithm is used to call the inner genetic algorithm to obtain its objective function value. The minimum objective function value is used as the optimization objective to obtain the fuel thermal management system architecture with the minimum required fuel flow.

[0025] According to at least one embodiment of this application, in the above-described aircraft fuel thermal management system architecture optimization design method, the fitness function of the inner layer genetic algorithm is defined as:

[0026]

[0027] Among them, ObjF 内 T represents the fitness function value of the inner layer genetic algorithm. etl T represents the highest value of the engine inlet fuel temperature. outAS This refers to the actual value of the fuel inlet temperature at the engine, i.e., the fuel outlet temperature value at the fuel thermal management system outlet; T i Let T be the temperature of the fuel after it flows through the i-th heat load. i_Limit This represents the highest permissible temperature value after the fuel flows through the i-th heat load.

[0028] According to at least one embodiment of this application, in the above-described aircraft fuel thermal management system architecture optimization design method, the fitness function of the outer layer genetic algorithm is defined as:

[0029] ObjF 外 =Sum(Minq,condition);

[0030] Among them, ObjF 外 is the fitness function value of the outer genetic algorithm, and is the sum of fuel flow under different aircraft operating conditions; Minq is the minimum flow value calculated by the inner genetic algorithm under a single aircraft operating condition; condition is the number of aircraft operating conditions initially set by the outer genetic algorithm. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the aircraft fuel thermal management system architecture optimization design method provided in the embodiments of this application;

[0032] Figure 2 This is a schematic diagram of the equivalent mapping process of the topology connection method of the typical fuel thermal management system architecture provided in the embodiments of this application;

[0033] Figure 3 This is a schematic diagram of the topology connection method encoding of the fuel oil thermal management system architecture provided in the embodiments of this application;

[0034] Figure 4 This is a schematic diagram of all potential architectures for generating a fuel oil thermal management system provided in the embodiments of this application;

[0035] Figure 5 This is a schematic diagram of the inner-layer genetic algorithm provided in the embodiments of this application;

[0036] Figure 6 This is a schematic diagram of the outer genetic algorithm provided in the embodiments of this application;

[0037] Figure 7 This is a diagram showing the calculation results for four aircraft operating conditions, based on the aircraft fuel thermal management system architecture optimization design method provided in this application embodiment.

[0038] Figure 8 This is a schematic diagram of the fuel heat dissipation variance when fuel passes through each system architecture at a determined flow rate, as provided in the embodiments of this application. Detailed Implementation

[0039] To make the technical solution and advantages of this application clearer, the technical solution of this application will be described in a clearer and more complete manner below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only some embodiments of this application, and are only used to explain this application, not to limit this application. It should be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, and other related parts can be referred to the general design.

[0040] Furthermore, unless otherwise defined, the technical or scientific terms used in this application description shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The word "comprising" as used in this application description indicates that the concept preceding the word encompasses the concepts listed following the word and their equivalents, without excluding other related concepts.

[0041] An optimization design method for aircraft fuel thermal management system architecture, such as Figure 1 As shown.

[0042] Step 1: Generate all potential architectures for the fuel oil thermal management system.

[0043] The architecture design of a fuel oil thermal management system refers to generating a series-parallel arrangement that maximizes the utilization efficiency of the fuel oil heat sink, providing an architectural design basis for the design of the fuel oil thermal management system.

[0044] Typically, a fuel thermal management system contains six heat loads, including two fuel-oil radiators, two fuel-hydraulic oil radiators, one fuel-liquid radiator, and one fuel-air radiator. These heat loads can be connected in series and parallel to form a complex network. After the fuel flows out of the tank, it cools each heat load in sequence. The topology of the fuel thermal management system determines the fuel flow sequence and affects the utilization rate of the fuel heat sink.

[0045] The topology connectivity of a fuel oil thermal management system architecture can be described as follows:

[0046] Any thermal load has one and only one input and one output.

[0047] After the heat load is output, it can be connected to any heat load at its back end, but it cannot be connected to the heat load at the front end.

[0048] Parallel thermal loads need to be merged before they can enter the next system;

[0049] At least one heat load is connected to the oil tank as the system inlet.

[0050] This application establishes a mathematical expression for the topological connection method of a fuel thermal management system architecture based on graph theory. The fuel thermal management system architecture is viewed as a graph, where vertices represent nodes where fuel flows in or out, and edges represent heat loads. The topological connection method of a typical fuel thermal management system architecture is equivalent to the graph formation process, such as... Figure 2 As shown.

[0051] Since the edge set in the graph is relatively fixed, any edge can be represented by two numbers, one for the input node and one for the output node. For a fuel thermal management system with six heat loads, its architecture can be represented by a total of 12 numerical codes. For example... Figure 3 As shown in the diagram, the topology connection of the fuel thermal management system architecture can be encoded as 132323453412. Replacing the encoded numbers indicates a change in the series-parallel relationship of the heat load. Once a certain code is generated, the corresponding fuel thermal management system architecture can be decoded.

[0052] Based on the above, all potential architectures for the fuel thermal management system are generated, such as Figure 4 As shown below, please refer to the details:

[0053] S1. Initialize and generate a set of connection nodes representing the thermal load of the fuel thermal management system.

[0054] The initial generated point set has N nodes. The minimum point set has two nodes, corresponding to the case where all heat loads of the fuel thermal management system are connected in parallel. The maximum point set has N+1 nodes, where N is the number of heat loads of the fuel thermal management system, corresponding to the case where all heat loads of the fuel thermal management system are connected in series.

[0055] S2. Corresponding to the number of heat loads in the fuel thermal management system, generate multiple edges to connect the nodes in each point set, generating an edgeless directed graph.

[0056] A recursive algorithm is used to generate N edges to connect the nodes in each point set. The six heat loads in the fuel thermal management system correspond to six edges. For each edge, all feasible positions are recursively searched. After all edge positions are searched, an edgeless directed graph is generated, which serves as a basic architecture of the fuel thermal management system.

[0057] The rules for connecting nodes in the point set are defined as follows:

[0058] An edge can only connect one input node and one output node;

[0059] For any edge, the input node must be smaller than the output node;

[0060] A single node can connect to the inputs and outputs of multiple edges, which are connected in parallel.

[0061] Node 1 must be connected to an input edge, but cannot be output as any edge.

[0062] The last node must be connected to at least one edge output, but cannot be used as any edge input;

[0063] A node must have both input and output simultaneously; if there is no input or output, the node is invalid.

[0064] S3. Assign the meaning of the thermal load of the fuel thermal management system to each edge in each unbounded directed graph to obtain the corresponding fuel thermal management system architecture. Delete duplicate architectures to obtain all potential architectures of the fuel thermal management system.

[0065] In the process of generating an unbounded directed graph, only the number of edges is determined, without specifying which specific heat load it is. For each valid unbounded directed graph, the meaning of each edge is assigned, the order of each edge is changed, and duplicate architectures are deleted to obtain all potential architectures of the fuel thermal management system.

[0066] The architecture of a fuel thermal management system can be represented in a coded form by using two numbers to represent the input and output nodes for any edge, respectively, as the architecture's code.

[0067] Fuel thermal management systems aim to optimize the architecture with the highest heat sink utilization rate. However, if fuel heat absorption efficiency is used directly as the optimization indicator, the optimized architecture will tend to have a high degree of series connection. This will result in excessive absorption of heat from certain subsystems to achieve the optimal indicator, rather than the architecture with the highest actual heat sink utilization rate.

[0068] In the design of the fuel thermal management system architecture, the fuel flow rate at the fuel tank outlet can be introduced as an optimization variable. The optimization index can be changed from maximizing the utilization rate of the heat sink to minimizing the required fuel flow rate under the temperature constraints of each system, i.e. minimizing the amount of fuel replenishment. The optimal architecture can accurately remove the heat generated by the heat load by absorbing the heat load with the minimum amount of fuel.

[0069] Step 2: Using the fuel flow rate at the fuel tank outlet as the optimization variable and the minimum required fuel flow rate as the optimization objective, select the optimal fuel thermal management system architecture from all potential architectures of the fuel thermal management system.

[0070] A double-nested genetic algorithm is used to search for the optimal fuel thermal management system architecture from all potential architectures.

[0071] Inner layer genetic algorithm:

[0072] The inner-layer genetic algorithm is the basic function called by the outer-layer algorithm. According to the definition of the aforementioned optimization index, the main function of the inner-layer genetic algorithm is to find the minimum fuel flow rate that can satisfy the temperature constraints of each subsystem and the inlet temperature constraints of the engine system, given the aircraft operating point and all potential architectures of the fuel thermal management system.

[0073] The principle of the inner layer genetic algorithm is as follows: Figure 5 As shown, its calculation includes initializing the population, calculating the fitness function, selection, crossover and recombination, mutation, re-insertion, and calculating the next cycle.

[0074] Population initialization: Population initialization requires defining the population size, maximum number of generations, generation gap definition, reproduction probability, and mutation probability.

[0075] Fitness function calculation: In the fuel thermal management system, each heat load has its temperature constraint. If, after the flow rate corresponding to a given individual is fed into the fuel thermal management system, any heat load exceeds its temperature limit, or the outlet temperature of the fuel thermal management system exceeds its temperature limit, the fitness function value for that individual is set to 0. After the flow rate corresponding to an individual is fed into the fuel thermal management system, if it satisfies the outlet temperature limits of any subsystem or aircraft system, then the objective function value is calculated.

[0076] The fitness function of the inner-layer genetic algorithm is defined as:

[0077]

[0078] Among them, ObjF 内 T represents the fitness function value of the inner layer genetic algorithm. etl T represents the highest value of the engine inlet fuel temperature. outAS This refers to the actual value of the fuel inlet temperature at the engine, i.e., the fuel outlet temperature value at the fuel thermal management system outlet; T i Let T be the temperature of the fuel after it flows through the i-th heat load. i_Limit This represents the highest permissible temperature value after the fuel flows through the i-th heat load.

[0079] Fitness calculation measures how well a fitness function value adapts to the environment; the larger the fitness function value, the greater the corresponding fitness.

[0080] Selection: The purpose of selection is to simulate the process of natural selection. This application uses the roulette wheel selection method in the genetic algorithm. Individuals in the population are placed on a roulette wheel according to the proportion of their fitness to the total fitness of all individuals. By rotating the roulette wheel, the selected individual is determined according to the pointer. The higher the fitness, the higher the probability of being selected.

[0081] Crossover and recombination: During population initialization, a reproductive probability is defined. A random number is generated. If this random number is less than the reproductive probability, genes are exchanged between adjacent individuals at randomly generated locations. If the random number is greater than the reproductive probability, genes are not exchanged between adjacent individuals, and the original genes are passed on to the next generation of the population.

[0082] Mutation: During population initialization, a mutation probability is defined. A random number is generated. If this random number is less than the mutation probability, the mutation location is randomly generated. The mutation may occur by setting a binary number from 0 to 1 or from 1 to 0. If the random number is greater than the mutation probability, no mutation occurs.

[0083] Reinsertion: Reinsertion requires calculating the objective function value of the offspring. By sorting the objective function values ​​of the previous generation and the offspring separately, the individuals with the largest objective function values ​​in the previous generation are retained, and then the offspring are reinserted into the previous generation, thus completing the re-expansion of the offspring population and forming the next generation population.

[0084] Next cycle: After completing the above work, increment the generation number of the marked system by 1, record the system individual corresponding to the maximum value of the objective function, and enter the next cycle.

[0085] Outer layer genetic algorithm:

[0086] The main function of the outer genetic algorithm is to call the inner genetic algorithm to obtain its objective function value, and take the minimum objective function value as the optimization objective to obtain the fuel thermal management system architecture with the minimum required fuel flow.

[0087] The principle of outer layer genetic algorithm is as follows: Figure 6 As shown, the difference between the two lies in the definition of the fitness function, while the other steps remain the same.

[0088] The fitness function of the outer genetic algorithm is defined as:

[0089] ObjF 外 =Sum(Minq,condition);

[0090] Among them, ObjF 外is the fitness function value of the outer genetic algorithm, and is the sum of fuel flow under different aircraft operating conditions; Minq is the minimum flow value calculated by the inner genetic algorithm under a single aircraft operating condition; condition is the number of aircraft operating conditions initially set by the outer genetic algorithm.

[0091] The fitness function of the outer genetic algorithm represents the sum of fuel flow rates of all potential architectures of the fuel thermal management system under different aircraft operating conditions. The outer genetic algorithm seeks the minimum value of the fitness function, so a linear negative correlation fitness function is defined.

[0092] In a specific example, using the aircraft fuel thermal management system architecture optimization design method disclosed in the above embodiments, the calculation results for four aircraft operating conditions are as follows: Figure 7 As shown, the fuel heat dissipation variance occurs when fuel flows through each system architecture at a defined flow rate, such as... Figure 8 As shown, a smaller variance indicates a higher degree of average heat dissipation. Figure 8 The middle circle represents the optimal architecture obtained by the aircraft fuel thermal management system architecture optimization design method in the embodiments of this application. It can be seen from the comparison that the architecture has a smaller variance and the most uniform heat dissipation compared to other architectures.

[0093] The aircraft fuel thermal management system architecture optimization design method disclosed in the above embodiments uses a nested recursive algorithm to generate all potential architectures, takes the minimum fuel replenishment amount as the objective function, and combines heat dissipation uniformity and heat dissipation efficiency as evaluation criteria to design a system architecture optimization algorithm based on a two-layer genetic algorithm, which can effectively solve the problem of optimal architecture design of the fuel thermal management system under narrow redundancy conditions.

[0094] The aircraft fuel thermal management system architecture optimization design method disclosed in the above embodiments is based on an intelligent optimization algorithm. It can automatically design the system architecture, improve the heat dissipation performance of the fuel thermal management system, reduce human intervention, reduce labor costs, and quickly provide the latest architecture solution based on the iteration of the aircraft system solution.

[0095] The technical solution of this application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. Those skilled in the art should understand that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for optimizing the architecture of an aircraft fuel thermal management system, characterized in that, include: Step 1: Generate all potential architectures for the fuel oil thermal management system: S1. Initialize and generate a set of connection nodes representing the thermal load of the fuel thermal management system; S2. Corresponding to the number of heat loads in the fuel thermal management system, generate multiple edges to connect the nodes in the point set and generate an edgeless directed graph. S3. Assign the meaning of the thermal load of the fuel thermal management system to each edge in each unbounded directed graph to obtain the corresponding fuel thermal management system architecture. Delete duplicate architectures to obtain all potential architectures of the fuel thermal management system. Step 2: Using the fuel flow rate at the fuel tank outlet as the optimization variable and the minimum required fuel flow rate as the optimization objective, select the optimal fuel thermal management system architecture from all potential architectures of the fuel thermal management system.

2. The aircraft fuel thermal management system architecture optimization design method according to claim 1, characterized in that, In S1, there are N point sets generated during initialization. The minimum point set has two nodes, and the maximum point set has N+1 nodes, where N is the number of heat loads in the fuel thermal management system.

3. The aircraft fuel thermal management system architecture optimization design method according to claim 2, characterized in that, In S2, a recursive algorithm is used to generate N edges, connecting the nodes in each set of points, following the following rules: An edge can only connect one input node and one output node; For any edge, the input node must be smaller than the output node; A single node can connect to the inputs and outputs of multiple edges, which are connected in parallel. Node 1 must be connected to an input edge, but cannot be output as any edge. The last node must be connected to at least one edge output, but cannot be used as any edge input; A node must have both input and output simultaneously; if there is no input or output, the node is invalid.

4. The aircraft fuel thermal management system architecture optimization design method according to claim 3, characterized in that, In S2, all potential architectures of the fuel thermal management system are represented in coded form. For any edge, two numbers are used to represent the input node and the output node, respectively, as the architecture code.

5. The aircraft fuel thermal management system architecture optimization design method according to claim 4, characterized in that, In step two, a double-nested genetic algorithm is used to search for the optimal fuel thermal management system architecture from all potential architectures.

6. The aircraft fuel thermal management system architecture optimization design method according to claim 5, characterized in that, In step two, the inner genetic algorithm is the basic function called by the outer algorithm. It is used to find the minimum fuel flow rate that can satisfy the temperature constraints of each subsystem and the inlet temperature constraint of the engine system, given the aircraft operating point and all potential architectures of the fuel thermal management system. The outer genetic algorithm is used to call the inner genetic algorithm to obtain its objective function value. The minimum objective function value is used as the optimization objective to obtain the fuel thermal management system architecture with the minimum required fuel flow.

7. The aircraft fuel thermal management system architecture optimization design method according to claim 6, characterized in that, The fitness function of the inner-layer genetic algorithm is defined as: Among them, ObjF 内 T represents the fitness function value of the inner layer genetic algorithm. etl T represents the highest value of the engine inlet fuel temperature. outAS This refers to the actual value of the fuel inlet temperature at the engine, i.e., the fuel outlet temperature value at the fuel thermal management system outlet; T i Let T be the temperature of the fuel after it flows through the i-th heat load. i_Limit This represents the highest permissible temperature value after the fuel flows through the i-th heat load.

8. The aircraft fuel thermal management system architecture optimization design method according to claim 7, characterized in that, The fitness function of the outer genetic algorithm is defined as: ObjF 外 =Sum(Minq,condition); Among them, ObjF 外 is the fitness function value of the outer genetic algorithm, and is the sum of fuel flow under different aircraft operating conditions; Minq is the minimum flow value calculated by the inner genetic algorithm under a single aircraft operating condition; condition is the number of aircraft operating conditions initially set by the outer genetic algorithm.

Images