Optimization method for ventilation system in supersonic cabin based on POD method and multi-target genetic algorithm

Abstract

The invention aims to provide an optimization design method for coupling a POD method and a multi-target genetic algorithm to carry out supersonic engine room optimization design. The method comprises the following steps: establishing a digital model including a cabin geometric structure and an air supply and exhaust device according to a cabin structure of an airplane; determining air supply and exhaust important boundary parameters including value ranges of air supply temperature, air supply speed and air supply angle, and performing uniform value selection; calculating flow field distribution in a cabin, a PMV numerical value of a microenvironment near a human body, an air age numerical value and a difference value of air supply and exhaust temperatures through numerical fluid mechanics, and forming a training data set of a rapid calculation model; constructing a rapid calculation model by using a POD interpolation method, and training POD model parameters by using a training data set; a multi-objective genetic algorithm is used for carrying out optimization calculation, and a Pareto optimal solution set is found through the multi-objective genetic algorithm. According to the method, the POD method and multi-target genetic algorithm coupled optimization design method is adopted to design the supersonic engine room ventilation environment, and the comfort experience of passengers in travel and the energy saving performance of engine room environment control are improved.

Description

Technical Field

[0001] The present invention belongs to the field of special environmental protection technology for optimizing the supersonic cabin ventilation system of large-scale transportation vehicles, and specifically involves using the POD method to quickly calculate the cabin environment, and then using a multi-objective genetic algorithm to optimize the design. Compared with the coupling calculation of the common genetic algorithm and the CFD method, the calculation speed is significantly improved. After the optimized design, the thermal comfort near the human body in the supersonic cabin and the energy saving of the cabin can be improved, and the comfort and satisfaction of passengers can be improved. Background Art

[0002] With the major special projects of large aircraft, it is conceivable that more and more people will travel by plane and high-speed rail in the future. Ensuring people's health and comfort in the cabin is a very important issue. For the narrow cabins of trains and airplanes, an excellent ventilation system is very important, especially for supersonic passenger aircraft. Because the cruising speed is much faster than that of ordinary passenger aircraft, the heat generated by friction will enter the cabin through heat conduction, making the environment in the cabin difficult to control. Therefore, it is necessary to optimize the design of the ventilation system of supersonic passenger aircraft.

[0003] A common optimization design is usually divided into two parts: fast calculation and optimization design. The present invention adopts the POD method as a fast calculation method and a multi-objective genetic algorithm as an optimization design method to optimize the design of the supersonic cabin.

[0004] The POD interpolation method reduces the dimension through spatial correlation, and then obtains the flow field through interpolation. This method can greatly reduce the computational cost. Xu et al. [1] adopted a uniform sampling method to reduce the number of samplings and the computational cost of the POD interpolation method. Xu and Wei et al. [2] proposed local density sampling in their research, that is, setting more sampling in places with complex physical environments, which can improve the accuracy of the POD interpolation method. Wei et al. [3] adopted the POD linear interpolation method in their research, reduced the dimension and interpolated the angle and temperature of the inlet gas, and could quickly obtain the flow field under unknown boundary conditions to facilitate subsequent optimization calculations. Lu et al. [4] used the POD method to study the flow field of high-speed trains. He used radial basis function (RBF) for interpolation and proved that the POD method using radial basis function interpolation saves 99% of the computational time compared with the CFD simulation method.

[0005] Genetic algorithm is a common optimization algorithm. It is often coupled with other computational methods to achieve optimization design. The most common method is to combine genetic algorithm with CFD. Zhai et al. [5] used a CFD method based on a multi-objective genetic algorithm to optimize the three design objectives of PMV (predicted mean vote), PD (dissatisfaction), and air age in a two-dimensional office and a three-dimensional cabin, thus improving the indoor environment. Pang et al. [6] used the GA-CFD method to optimize the cabin design. With the development of fast computational fluid dynamics, Xue et al. [7] combined fast computational fluid dynamics with genetic algorithm, which has certain computational advantages compared with the traditional GA-CFD method. Some people have also combined genetic algorithm with other fast computational methods. For example, Zhang et al. [8] combined genetic algorithm with neural network, which can optimize the ventilation system very well. Wei et al. [9] combined genetic algorithm, neural network and POD interpolation method, providing a good solution to the problem of optimizing the design of air supply parameters.

[0006] The present invention combines the POD method with the genetic algorithm, greatly improving the calculation speed, and is more superior than the common CFD method combined with the genetic algorithm.

[0007] References:

[0008] [1]XU YK,WEI XB,YU YL,et al.Inverse design of dynamic protective air supply based on proper orthogonal decomposition[J].Sustainable Cities andSociety,2023,95.

[0009] [2]XU Y,WEI

[0010] [3]WEI Y,ZHANG T F,JIN H B.Rapid prediction of airborne gaseouspollutant transport in aircraft cabins based on proper orthogonaldecomposition and the Markov chain method[J].Building and Environment,2023,228.

[0011] [4]LU Y B,WANG T T,ZHAO C L,et al.An efficient design method ofindoor ventilation parameters for high-speed trains using improved properorthogonal decomposition reconstruction[J].Journal of Building Engineering,2023,71.

[0012] [5]ZHAI Z Q,XUE Y,CHEN Q Y.Inverse design methods for indoorventilation systems using CFD-based multi-objective genetic algorithm[J].Building Simulation,2014,7(6):661-9.

[0013] [6]PANG L P,LIP,BAI L Z,et al.Optimization of air distribution modecoupled interior design for civil aircraft cabin[J].Building and Environment,2018,134:131-45.

[0014] [7]XUE Y, LIU W, WANG Q, et al. Development of an integrated approach for the inverse design of built environment by a fast fluid dynamics-basedgeneric algorithm[J]. Building and Environment, 2019,160.

[0015] [8]ZHANG TH,YOU XY.A simulation-based inverse design of presetaircraft cabin environment[J].Building and Environment,2014,82:20-6.

[0016] [9]WEI Y,LIU W,XUE Y,et al.Inverse design of aircraft cabinventilation by integrating three methods[J].Building and Environment,2019,150:33-43. Summary of the invention

[0017] To achieve the above object, the present invention provides the following solutions:

[0018] The optimization method of supersonic cabin ventilation system based on POD method and multi-objective genetic algorithm includes:

[0019] Establish a digital model based on the aircraft cabin structure, including cabin geometry, air supply and exhaust devices;

[0020] Determine the value ranges of important air supply and exhaust parameters, including air supply temperature, air supply speed, and air supply angle, and evenly select values ​​within their respective value ranges, for a total of 48 examples;

[0021] The flow field distribution in the cabin of 48 cases, the PMV value of the microenvironment near the human body, the air age value and the difference △T between the supply and exhaust air temperature were calculated by numerical fluid dynamics calculation software to form the training data set of the fast calculation model;

[0022] Use the POD interpolation method to build a fast calculation model and construct a training data set to train model parameters;

[0023] The constructed fast calculation model is used to perform optimization calculations using a multi-objective genetic algorithm, and the Pareto optimal solution set is found through the multi-objective genetic algorithm.

[0024] Optionally, the POD interpolation method in the step is as follows: first, the sampled flow fields are arranged in order (taking velocity as an example):

[0025]

[0026] Where n is the number of samples and m is the number of grids for a particular velocity field. Then the spatial model It can be determined by the following two formulas:

[0027]

[0028] Where λ is the eigenvalue, v is the eigenvector, and S is the autocorrelation matrix corresponding to U.

[0029]

[0030] In the formula, P is the number of the first P eigenvalues ​​arranged from large to small, which is also the number of spatial models obtained, N is the total number of eigenvalues, and E(P) is the equation describing the contribution rate. Generally, when E(P) reaches 99.99%, it can be considered that the POD method used can well construct the unknown flow field. That is, the first P eigenvectors corresponding to E(P) reaching 99.99%. Then the sampled speed is projected onto the spatial model obtained above, and the spatial parameter b corresponding to the spatial model can be obtained. i .

[0031]

[0032] Then we can use the spatial model With the space parameter b i Construct the position flow field. See the following formula for details. is the mean value of the matrix U with respect to the rows.

[0033]

[0034] In this way, the flow field of any unknown boundary condition within the sampling range can be constructed.

[0035] Beneficial Effects

[0036] This method uses the POD interpolation method to replace the commonly used CFD method in research, which can speed up the calculation speed of the optimization design and reduce the calculation cost. This method optimizes the design by adjusting the air supply parameters of the ventilation system, which is less costly than adjusting other parameters. For aircraft that have been built, this method can be used for regulation to increase passenger comfort and reduce cabin energy consumption. For aircraft that have not been built, this method can be used as a reference to give some design inspiration for the design of aircraft cabin ventilation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 This is a flow chart of a method for optimizing a supersonic cabin ventilation system according to an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of modeling a supersonic cabin according to an embodiment of the present invention;

[0040] Figure 3 Air age error histogram based on POD method;

[0041] Figure 4 PMV error histogram based on POD method;

[0042] Figure 5 Schematic diagram of the optimal solution Pareto front of an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The purpose of the present invention is to provide an optimization design method that couples a POD interpolation method with a multi-objective genetic algorithm, and optimizes three design objectives, namely, the |PMV| value, the air age value, and the inlet and outlet temperatures of the microenvironment near the human body in the cabin by adjusting different air supply parameters of the ventilation system, thereby improving the cabin environment, improving the passenger travel experience, and improving the energy efficiency of the cabin.

[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] Implementation Examples

[0047] Next, a method for controlling longitudinal airflow in a cabin space provided by this embodiment is specifically described. Figure 2 , this embodiment includes:

[0048] Step (1): Establish a digital model based on the cabin structure of the aircraft, including the cabin geometry and air supply and exhaust devices;

[0049] Step (2): Determine the value ranges of important air supply and exhaust parameters, including air supply temperature, air supply speed, and air supply angle, and evenly select values ​​within their respective value ranges, for a total of 48 examples;

[0050] Step (3): Calculate the flow field distribution in the cabin of the 48 examples in step (2) by numerical fluid dynamics calculation software, the PMV value of the microenvironment near the human body, the air age value and the difference △T between the supply and exhaust air temperatures, and form a training data set for the fast calculation model;

[0051] Step (4): Use the POD interpolation method to construct a fast calculation model, and use the training data set in step (3) to train the model parameters;

[0052] Step (5): using the fast calculation model constructed in step (4), using a multi-objective genetic algorithm to perform optimization calculations, and finding the Pareto optimal solution set through the multi-objective genetic algorithm;

[0053] The POD interpolation method in step (4) is as follows: first, the sampled flow fields are arranged in order (taking velocity as an example):

[0054]

[0055] Where n is the number of samples and m is the number of grids for a particular velocity field. Then the spatial model It can be determined by the following two formulas:

[0056]

[0057] Where λ is the eigenvalue, v is the eigenvector, and S is the autocorrelation matrix corresponding to U.

[0058]

[0059] In the formula, P is the number of the first P eigenvalues ​​arranged from large to small, which is also the number of spatial models obtained, N is the total number of eigenvalues, and E(P) is the equation describing the contribution rate. Generally, when E(P) reaches 99.99%, it can be considered that the POD method used can well construct the unknown flow field. That is, the first P eigenvectors corresponding to E(P) reaching 99.99%. Then the sampled speed is projected onto the spatial model obtained above, and the spatial parameter b corresponding to the spatial model can be obtained. i .

[0060]

[0061] Then we can use the spatial model With the space parameter b i Construct the position flow field. See the following formula for details. is the mean value of the matrix U with respect to the rows.

[0062]

[0063] In this way, the flow field of any unknown boundary condition within the sampling range can be constructed.

[0064] The calculation of PMV in step (3) is performed as follows.

[0065] PMV=[0.303×exp(-0.036M)+0.0275]×{MW-3.05[5.733-0.007(MW)-P a ]-0.42(MW-58.15)-1.73×10 -2 ×M(5.867-P a )-0.0014M(34-t a )-3.96×10 -8 f cl [(t cl +273) 4 -(t r +273) 4 ]-f cl h c (t cl -t a )}

[0066] Where, M--metabolic volume (W / m 2 );

[0067] W——Human external work output (W / m 2 );

[0068] f cl - the ratio of clothed to naked body surface;

[0069] t a ——Local temperature (℃);

[0070] t r ——mean radiant temperature (℃);

[0071] t cl ——Temperature of the outer surface of the clothing (℃);

[0072] h c ——Surface heat transfer coefficient between clothing and air (W / m 2 ℃);

[0073] P a ——Water vapor partial pressure (Pa);

[0074] Where t cl ,h c and f cl It can be calculated according to the following formula.

[0075] t cl =35.7-0.028(MW)-I cl {3.96×10 -8 ×f cl [(t cl +273) 4 -(t r +273) 4 ]+f cl h c (t cl -t a )}

[0076]

[0077] Among them, I cl is the thermal resistance of the clothing (m 2 K / W),v ar is the air flow velocity (m / s).

[0078] The air age value in step (3) is calculated according to the following formula.

[0079]

[0080] in, is the average cabin air age, C e is the outlet pollutant concentration.

[0081] Take a cabin section in a certain large aircraft cabin as an example. Its length is 5.1m, width is 2.286m, height is 1.862m, there are 5 rows and 10 seats. Other cabins are similar, such as Figure 1 As shown,

[0082] According to step (1), a supersonic cabin is modeled using modeling software (such as space claim, etc.).

[0083] According to step (2), the air supply temperature t, air supply speed v, and air supply angle α (0° perpendicular to the air supply port) of the supersonic aircraft are selected as the design variables for this optimization. The optimal ranges of the three design variables are determined by consulting relevant standards and are shown in Table 1. Samples are uniformly sampled within their respective ranges. The number of samples is shown in the last column of Table 1. The total number of cases is 4×3×4=48, as shown in the following table.

[0084]

[0085] According to step (3), numerical simulation is performed based on the 48 cases determined using numerical fluid dynamics calculation software (such as FLUENT), and the values ​​of the three design objectives of the microenvironment area near the human body model (PMV value, air age value and the difference △T between the supply and exhaust air temperatures) are calculated, and the calculated values ​​constitute a data set for training relevant parameters of the POD method.

[0086] According to step (4), the data set calculated in step (3) is subjected to data dimension reduction using the POD method, and the spatial model and spatial parameters corresponding to the design target are calculated. Based on these parameters, the design target flow field at the position boundary can be quickly and accurately calculated by the interpolation method. In order to verify the accuracy of the fast calculation method, 15 random working conditions are selected within the optimal sampling range, as shown in the following table.

[0087]

[0088]

[0089] The 15 random working conditions were calculated and compared using the POD interpolation method and the CFD method to verify the accuracy of the POD interpolation method. We used PMV and air age as evaluation indicators, and regarded the simulated values ​​obtained by CFD simulation as true values, and compared them with the values ​​calculated by the POD interpolation method to make their relative errors. Figure 3 and Figure 4 shown.

[0090] Through the two error histograms, we can make a qualitative analysis. Although the POD interpolation method has some poor prediction values, if the error less than 20% is regarded as a relatively good prediction, most of these predictions are within the error range of less than 20%. Therefore, the calculation accuracy of the POD interpolation method is good. For the rigor of the study, we take the average of all the calculated relative errors and quantitatively analyze whether the POD interpolation method is qualified as a fast calculation method for optimization design, as shown in the following table.

[0091]

[0092] The table shows the average relative error for PMV and air age, which confirms that the POD interpolation method is a reliable and fast calculation method.

[0093] According to step (5), using the fast calculation model in step (4), a multi-objective genetic algorithm is used to optimize the design. The multi-objective genetic algorithm uses spread as the iterative convergence criterion, and the definition is as follows:

[0094]

[0095] Where M is the number of objective functions, n is the number of non-dominated solutions, and are the distances to the boundary solutions (extreme solutions in the target space), d ij is the distance between adjacent solutions in the target space, and All d ij Average value. Spread is an indicator to measure the distribution of solutions in space in multi-objective optimization algorithms. Generally speaking, the smaller the spread, the more evenly distributed the solution set is, and the larger the spread, the more concentrated the distribution is. When the spread does not change much, it can be considered that the algorithm has converged. The algorithm converged after 162 iterations. The Pareto frontier found by the algorithm can be seen in Figure 5 .

Claims

1. A supersonic cabin ventilation system optimization method based on the POD method and multi-objective genetic algorithm includes the following steps: Step (1): establishing a digital model according to the cabin structure of the aircraft, including the cabin geometry and the air supply and exhaust devices; Step (2): Determine the important boundary parameters of the supply and exhaust air, including the value ranges of the supply air temperature, supply air speed, and supply air angle, and evenly select values ​​within their respective value ranges, for a total of 48 examples; Step (3): Calculate the flow field distribution in the cabin of the 48 examples in step (2) by numerical fluid dynamics calculation software, the PMV value of the microenvironment near the human body, the air age value and the difference between the supply and exhaust air temperatures, and form a training data set for the fast calculation model; Step (4): Use the POD interpolation method to construct a fast calculation model, and use the training data set in step (3) to train the model parameters; Step (5): Utilize the fast calculation model constructed in step (4) and use a multi-objective genetic algorithm to perform optimization calculations, and use the multi-objective genetic algorithm to find the Pareto optimal solution set.

2. The method for optimizing the ventilation system in a supersonic aircraft cabin based on the POD method and the multi-objective genetic algorithm according to claim 1, characterized in that: The POD interpolation method in step (4) is as follows. Taking velocity as an example, the sampled flow fields are first arranged in order: Where n is the number of samples and m is the number of grids for a particular velocity field. Then the spatial model It can be determined by the following two formulas: Where λ is the eigenvalue, v is the eigenvector, and S is the autocorrelation matrix corresponding to U. In the formula, P is the number of the first P eigenvalues ​​arranged from large to small, which is also the number of spatial models obtained, N is the total number of eigenvalues, and E(P) is the equation describing the contribution rate. Generally, when E(P) reaches 99.99%, it can be considered that the POD method used can well construct the unknown flow field. That is, the first P eigenvectors corresponding to E(P) reaching 99.99%. Then the sampled speed is projected onto the spatial model obtained above, and the spatial parameter b corresponding to the spatial model can be obtained. i . Then we can use the spatial model With the space parameter b i Construct the position flow field. See the following formula for details. is the mean value of the matrix U with respect to the rows. In this way, the flow field of any unknown boundary condition within the sampling range can be constructed.

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