A multidisciplinary optimization method for aircraft based on whale swarm algorithm

Abstract

The present invention discloses an overall multidisciplinary optimization method for an aircraft based on a whale swarm algorithm, which is applied to the overall conceptual design of an aircraft and can quickly iterate and optimize the best overall plan that meets the design indicators in the overall conceptual design stage of the aircraft. The present invention first constructs a whale swarm algorithm model, and then establishes an aircraft model expression according to the overall design requirements of the aircraft, selects a fitness function in combination with the optimization requirements, and uses the aircraft model expression to calculate the fitness as the optimization target of the whale swarm algorithm. The algorithm searches for the best within the constrained parameter range to obtain the best overall design plan for the aircraft. The present invention adopts an improved whale swarm algorithm with the characteristics of a wide search range and good convergence, which can solve the technical problems of low iteration efficiency, poor optimization ability, and reliance on the experience of overall professionals encountered in the overall optimization design of the aircraft.

Description

Technical Field

[0001] The invention belongs to the field of aircraft design, and in particular is an overall multidisciplinary optimization method for aircraft based on a whale group algorithm. Background Art

[0002] Aircraft have the advantages of low infrastructure requirements, fast moving speed, and high transportation efficiency. With the widespread application of various types of aircraft, their value in military and civilian fields has become increasingly prominent. However, with the continuous improvement of aircraft design performance indicators, more and more related professional disciplines are involved, and the coupling between various professional disciplines is becoming more and more complex. Therefore, how to coordinate the coupling relationship between various disciplines in the overall parameter design stage and obtain the optimal overall design plan has received more and more attention from relevant research institutions at home and abroad.

[0003] Currently, in the overall aircraft design process, each discipline such as layout, aerodynamics, structure, weight, and performance independently establishes a design analysis model, and after the design analysis results are formed, they are fed back to the overall discipline. The overall professionals then make further program optimization based on the evaluation results. There are problems such as low iteration efficiency, poor optimization ability, and reliance on the experience of overall professionals.

[0004] In summary, in view of the problems existing in the above-mentioned prior art such as low iteration efficiency of aircraft overall design optimization, poor optimization ability, and reliance on the experience of overall professionals, a new aircraft overall multidisciplinary optimization method is urgently needed.

[0005] The traditional whale swarm optimization algorithm is a bionic optimization algorithm based on whale swarm intelligence. This algorithm has been applied to many engineering problems and has the advantage of a wide search range, but it also has the problem of long convergence time. Summary of the invention

[0006] The purpose of the present invention is to provide an overall multidisciplinary optimization method for aircraft based on a whale swarm algorithm to solve one or more of the above-mentioned technical problems. The technical solution provided by the present invention adopts an improved whale swarm algorithm, which can enhance the convergence ability of the algorithm and solve the technical problem of the algorithm convergence time being too long. The technical solution provided by the present invention can solve the problems of low iteration efficiency, poor optimization ability, and extreme dependence on the experience of overall professionals in aircraft overall design optimization, and has good iteration efficiency and optimization ability.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] An overall multidisciplinary optimization method for an aircraft based on a whale swarm algorithm comprises the following steps:

[0009] Step 1: Construct an aircraft parametric model: Select a set of overall parameters that have a great impact on the overall aircraft plan as optimization parameters to construct an aircraft parametric model, add constraints to the optimization parameters, and construct a whale group algorithm model;

[0010] Step 2: Initialize the population using the whale group algorithm under the constraints, where each whale position in the population corresponds to a set of actual aircraft optimization parameters;

[0011] Step 3: For each whale position, calculate the corresponding aircraft flight time using the aircraft parameter model;

[0012] Step 4: Based on the flight time of the aircraft, a custom target fitness function is constructed, and the custom target fitness function value of each whale position is calculated;

[0013] Step 5: Determine the optimal flight time and corresponding optimization parameters based on the minimum value of the custom target fitness function:

[0014] Step 6: Determine whether the maximum number of iterations is reached. If not, proceed to step 7. If yes, proceed to step 10.

[0015] Step 7: Update the position coefficients used for iteration in the algorithm;

[0016] Step 8: Determine the method to update the location of the whale population;

[0017] Step 9: Update the whale population position, obtain the new population distribution, and return to step 3;

[0018] Step 10: Output the optimal aircraft design parameters and flight time.

[0019] The technical effects of the present invention are as follows: The present invention proposes an overall multidisciplinary optimization method for aircraft based on a whale swarm algorithm, which can provide an efficient and convenient technical method for overall aircraft optimization design. The present invention first constructs an improved whale swarm algorithm, then constructs an aircraft model based on the main optimization parameters to obtain an expression of the flight time of the optimized target aircraft, selects a fitness function based on application requirements, and uses the flight time expression to calculate the fitness function as the optimization target of the whale swarm optimization algorithm. The algorithm searches for the best within the constrained parameter range, thereby obtaining an aircraft solution with optimized parameters.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention adopts the improved whale swarm algorithm, which can enhance the convergence ability of the algorithm and solve the technical problem of the traditional whale swarm algorithm having a long convergence time.

[0022] The present invention conducts overall multidisciplinary optimization design of aircraft based on the whale group algorithm, solves the problems of low iteration efficiency, poor optimization ability, and extreme dependence on the experience of overall professionals in traditional aircraft overall design optimization, and has better iteration efficiency and optimization ability.

[0023] The contents not described in detail in the specification of the present invention belong to the prior art known to the professional and technical personnel in the field. It is pointed out here that the above description helps those skilled in the art to understand the invention, but does not limit the protection scope of the invention. Any equivalent replacement, modification and / or simplification of the above description without departing from the essence of the invention falls within the protection scope of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art; obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 It is a schematic flow chart of an overall multidisciplinary optimization method for an aircraft based on a whale group optimization algorithm for implementing the present invention. Figure 1 The method includes step 1 between the start and the end, which is to construct a parametric model of the aircraft, select a group of overall parameters that have a greater impact on the overall plan of the aircraft as optimization parameters to construct the aircraft model, add constraints to the optimization parameters, and construct a whale group algorithm model; step 2, initialize the population using the whale group algorithm under the constraints, and each whale position in the population should have a group of actual aircraft optimization parameters; step 3, for each whale position, use the aircraft model to calculate the corresponding aircraft flight time; step 4, based on the flight time, construct a custom target fitness function, and calculate the custom target fitness function value of each whale position; step 5, determine the optimal flight time and the corresponding optimization parameters based on the minimum target fitness function value: step 6, determine whether the maximum number of iterations is reached, if not, continue to step 7, if yes, enter step 10; step 7, update the position coefficient used for iteration in the algorithm; step 8, determine what method to use to update the whale population position; step 9, update the whale population position, obtain a new population distribution, and return to step 3; step 10, output the optimal aircraft design parameters and flight time.

[0026] Figure 2 It is a schematic diagram of an airplane model in an example of the present invention;

[0027] Figure 3 It is a schematic diagram of the aircraft surface pressure calculated by the aircraft model using the vortex lattice method in the example of the present invention;

[0028] Figure 4 In the example of the present invention, the aircraft model uses the vortex lattice method to calculate the cruise drag ratio K schematic diagram; DETAILED DESCRIPTION

[0029] Below is the attached figure ( Figure 1-Figure 4 ) and Examples illustrate the present invention.

[0030] Figure 1 It is a schematic flow chart of an overall multidisciplinary optimization method for an aircraft based on a whale group algorithm for implementing the present invention. Figure 2 It is a schematic diagram of an airplane model in an example of the present invention; Figure 3 It is a schematic diagram of the aircraft surface pressure calculated by the aircraft model using the vortex lattice method in the example of the present invention; Figure 4 In the present invention, the aircraft model uses the vortex lattice method to calculate the cruise drag ratio K schematic diagram; Figure 1 As shown, a multidisciplinary optimization method for an aircraft based on a whale group algorithm includes the following steps: Step 1, constructing an aircraft parameterized model, selecting a group of overall parameters that have a greater impact on the overall aircraft plan as optimization parameters to construct an aircraft model, adding constraints to the optimization parameters, and constructing a whale group algorithm model; Step 2, using the whale group algorithm to initialize the population under the constraints, and each whale position in the population should have a group of actual aircraft optimization parameters; Step 3, for each whale position, using the aircraft model to calculate the corresponding aircraft flight time; Step 4, based on the flight time, constructing a custom target fitness function, and calculating the custom target fitness function value of each whale position; Step 5, determining the optimal flight time and the corresponding optimization parameters based on the minimum target fitness function value: Step 6, judging whether the maximum number of iterations is reached, if not, continuing to Step 7, if yes, entering Step 10; Step 7, updating the position coefficient used for iteration in the algorithm; Step 8, determining which method is used to update the whale population position; Step 9, updating the whale population position, obtaining a new population distribution, and returning to Step 3; Step 10, outputting the optimal aircraft design parameters and flight time.

[0031] The optimization parameters in step 1 include Sw, AR, Λ, and λ, where Sw is the plane projection area of ​​the aircraft wing, AR is the aspect ratio of the aircraft wing, that is, the ratio of the average aerodynamic chord length to the aspect length, Λ is the sweep angle of the leading edge of the aircraft wing, and λ is the tip-to-heel ratio, that is, the ratio of the wing tip to the wing root. The constraints of each optimization parameter are as follows:

[0032] Sw min ≤Sw i ≤Sw max ,i∈N

[0033] AR min ≤AR i ≤ARmax ,i∈N

[0034] Λ min ≤Λ i ≤Λ max ,i∈N

[0035] λ min ≤λ i ≤λ max ,i∈N

[0036] Where i represents the i-th whale in the population, Sw i represents the plane projection area of ​​the aircraft wing corresponding to the i-th whale in the population, Sw min Indicates the lower limit of the aircraft wing plane projection area, Sw max Indicates the upper limit of the aircraft wing plane projection area, AR min Indicates the lower limit of the aircraft wing aspect ratio, AR i represents the aircraft wing aspect ratio corresponding to the i-th whale in the population, AR max represents the upper limit of the aircraft wing aspect ratio, Λ min Indicates the lower limit of the sweep angle of the leading edge of the aircraft wing, Λ i represents the sweep angle of the leading edge of the aircraft wing corresponding to the i-th whale in the population, Λ max Indicates the upper limit of the aircraft wing leading edge sweep angle, λ min represents the lower limit of the tip-to-heel ratio, λ i represents the tip-to-heel ratio corresponding to the i-th whale in the population, λ max Indicates the upper limit of the tip-to-heel ratio;

[0037] The whale group algorithm model in step 1 is as follows:

[0038]

[0039] Where X i Represents the position of a whale, that is, a set of solutions in the optimization algorithm, N is the size of the whale population, and D is the dimension of the search space. In the whale group algorithm, whales use a variety of methods to search for prey. The relevant models are as follows:

[0040]

[0041] A=2a*ra

[0042] C=2r

[0043]

[0044] D ′ =|X P (t)-X(t)|

[0045] In the above model, p is a random number between 0 and 1, which is used to determine which position update mode to use. X(t) represents the position space of the individual whale when the number of iterations is t, t is the current number of iterations, and t max is the maximum number of iterations, X P is the prey position, r is a random number between 0 and 1, l is a random number between -1 and 1, b is a constant, a i with a f are the initial value and the final value of a, μ>0 is the nonlinear adjustment coefficient, X r Represents the position vector of an individual whale randomly selected from the current population.

[0046] In step 2, each whale position is randomly generated within the parameter constraints through the whale group optimization algorithm, and each whale position corresponds to an aircraft optimization parameter, thereby completing the initialization of the population.

[0047] The aircraft parameter model expression in step 3 is as follows:

[0048]

[0049] b1=b / (1+λ)

[0050] b2=b1*λ

[0051] Where b is the average aerodynamic chord length of the wing, b1 is the root chord length of the wing, and b2 is the wingtip chord length. Combined with the leading edge sweep angle Λ of the aircraft wing, the plane shape of the aircraft wing can be constructed. Based on the plane shape of the wing and the original shapes of other components of the scheme (the optimization is only carried out for several key parameters of the aircraft, here refers to other parts that remain unchanged during the optimization process), refer to Figure 2 As shown in the figure, the cruise drag ratio K reference can be obtained by using the vortex grid method Figure 3 and Figure 4 shown.

[0052] W TO =W BE +W pay +W fuel

[0053] W BE =W frame +W lg +W thrust +W sys

[0054] W frame =W wing +W body +W tail

[0055]

[0056] Wpj =W TO -0.5*W fuel

[0057]

[0058] Where W TO Aircraft takeoff weight, W BE is the empty weight of the aircraft, W pay is the aircraft load weight, W fuel is the aircraft fuel weight, W frame is the aircraft structure weight, W lg is the weight of the aircraft landing gear, W thrust is the weight of the aircraft propulsion system, W sys is the weight of the aircraft onboard system, W wing is the weight of the aircraft wing, W body is the weight of the aircraft, W tail is the weight of the aircraft tail, n z is the normal overload of the airfoil, y / c is the relative thickness of the airfoil, S csw is the control surface area, W pj is the average flight weight of the aircraft, c e is the aircraft fuel consumption rate, and T is the aircraft flight time.

[0059] The custom objective fitness function in step 4 is defined as follows:

[0060] Costfunc=1 / T

[0061] Since the fitness function Costfunc in the whale group algorithm seeks a minimum value, the optimization target T maximum flight time is treated as the inverse.

[0062] The position coefficients used for iteration in step 7 are A, C, a, D ′ .

[0063] The method of updating the position of the whale population involved in step 7 is to obtain the random numbers p and |A| through the corresponding model.

[0064] The whale group algorithm model in step 1 is an improved whale group algorithm, and the convergence factor a adopts a nonlinear design. In the early stage of the search, it increases with the number of iterations at a smaller value. When it increases to a larger value, it quickly decreases to a smaller value, and finally increases at a slower speed. This ensures that it has a strong global search capability and maintains a faster convergence speed in the early stage of the search, and has a faster convergence speed in the later stage of the search while avoiding the algorithm from falling into the local optimum.

[0065] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. An overall multidisciplinary optimization method for aircraft based on whale swarm algorithm, characterized in that: The following steps are involved: Step 1: Construct an aircraft parametric model: Select a set of overall parameters that have a great impact on the overall aircraft plan as optimization parameters to construct an aircraft parametric model, add constraints to the optimization parameters, and construct a whale group algorithm model; Step 2: Initialize the population using the whale group algorithm under the constraints, where each whale position in the population corresponds to a set of actual aircraft optimization parameters; Step 3: For each whale position, calculate the corresponding aircraft flight time using the aircraft parameterized model; The aircraft parameterized model expression in step 3 is as follows: b1=b / (1+λ) b2=b1*λ Where b is the average aerodynamic chord length of the wing, Sw is the plane projection area of ​​the aircraft wing, AR is the aspect ratio of the aircraft wing, that is, the ratio of the average aerodynamic chord length to the aspect length, b1 is the root chord length of the wing, λ is the tip-to-heel ratio, that is, the ratio of the wingtip to the wing root, b2 is the chord length of the wing tip, and combined with the aircraft wing leading edge sweep angle Λ, the plane shape of the aircraft wing can be constructed. Based on the plane shape of the wing and the original shapes of other components, the cruise drag ratio K can be obtained by using the vortex lattice method; IN TO =In BE +W pay +W fuel IN BE =In frame +W lg +W thrust +W sys IN frame =In wing +W body +W tail IN pj =In TO -0.5*W fuel Where W TO Aircraft takeoff weight, W BE is the empty weight of the aircraft, W pay is the aircraft load weight, W fuel is the aircraft fuel weight, W frame is the aircraft structure weight, W lg is the weight of the aircraft landing gear, W thrust is the weight of the aircraft propulsion system, W sys is the weight of the aircraft onboard system, W wing is the weight of the aircraft wing, W body is the weight of the aircraft, W tail is the weight of the aircraft tail, n z is the normal overload of the airfoil, y / c is the relative thickness of the airfoil, S csw is the control surface area, W pj is the average flight weight of the aircraft, c e is the aircraft fuel consumption rate, T is the aircraft flight time; Step 4: Based on the flight time of the aircraft, a custom target fitness function is constructed, and the custom target fitness function value of each whale position is calculated; Step 5: Determine the optimal flight time and corresponding optimization parameters based on the minimum value of the custom target fitness function: Step 6: Determine whether the maximum number of iterations is reached. If not, proceed to step 7. If yes, proceed to step 10. Step 7: Update the position coefficients used for iteration in the algorithm; Step 8: Determine the method to update the location of the whale population; Step 9: Update the whale population position, obtain the new population distribution, and return to step 3; Step 10: Output the optimal aircraft design parameters and flight time.

2. The overall multidisciplinary optimization method of an aircraft based on a whale swarm algorithm according to claim 1, characterized in that: The optimization parameters in step 1 include Sw, AR, Λ, and λ, where Λ is the sweep angle of the leading edge of the aircraft wing, and the constraints of each optimization parameter are as follows: Sw min ≤Sw i ≤Sw max ,i∈N WITH min ≤AR i ≤AR max ,i∈N L min ≤Λ i ≤Λ max ,i∈N l min ≤λ i ≤λ max ,i∈N Where i represents the i-th whale in the population, Sw i represents the plane projection area of ​​the aircraft wing corresponding to the i-th whale in the population, Sw min Indicates the lower limit of the aircraft wing plane projection area, Sw max Indicates the upper limit of the aircraft wing plane projection area, AR min Indicates the lower limit of the aircraft wing aspect ratio, AR i represents the aircraft wing aspect ratio corresponding to the i-th whale in the population, AR max represents the upper limit of the aircraft wing aspect ratio, Λ min Indicates the lower limit of the sweep angle of the leading edge of the aircraft wing, Λ i represents the sweep angle of the leading edge of the aircraft wing corresponding to the i-th whale in the population, Λ max Indicates the upper limit of the aircraft wing leading edge sweep angle, λ min represents the lower limit of the tip-to-heel ratio, λ i represents the tip-to-heel ratio corresponding to the i-th whale in the population, λ max Indicates the upper limit of the tip-to-heel ratio.

3. The overall multidisciplinary optimization method of an aircraft based on a whale group algorithm according to claim 1, characterized in that: The whale group algorithm model in step 1 is as follows: Where X i represents the position of a whale, that is, a set of solutions in the optimization algorithm, N is the size of the whale population, and D is the dimension of the search space; in the whale group algorithm, whales use a variety of methods to search for prey, and the relevant models are as follows: A=2a*ra C＝2r D′=|X P (t)-X(t)| Where p is a random number between 0 and 1, which is used to determine which position update mode to use. X(t) represents the position space of the individual whale when the number of iterations is t, t is the current number of iterations, and t max is the maximum number of iterations, X P is the prey position, r is a random number between 0 and 1, l is a random number between -1 and 1, b is a constant, a i with a f are the initial value and the final value of a, μ>0 is the nonlinear adjustment coefficient, X r Represents the position vector of an individual whale randomly selected from the current population.

4. The overall multidisciplinary optimization method of an aircraft based on a whale swarm algorithm according to claim 1, characterized in that: In step 2, each whale position is randomly generated within the parameter constraints through the whale group optimization algorithm, and each whale position corresponds to an aircraft optimization parameter, thereby completing the initialization of the population.

5. The overall multidisciplinary optimization method of an aircraft based on a whale swarm algorithm according to claim 1, characterized in that: The custom objective fitness function in step 4 is defined as follows: Costfunc=1 / T Since the fitness function Costfunc in the whale group algorithm seeks a minimum value, the optimization target T maximum flight time is treated as the inverse.

6. The overall multidisciplinary optimization method for aircraft based on whale swarm algorithm according to claim 1, characterized in that: The position coefficients used for iteration in step 7 are A, C, a, and D′.

7. The overall multidisciplinary optimization method of an aircraft based on a whale swarm algorithm according to claim 3, characterized in that: The method of updating the whale population position involved in step 7 is to obtain the position through the whale population algorithm model by combining the random number p and |A|.

8. The overall multidisciplinary optimization method for aircraft based on whale swarm algorithm according to claim 6, characterized in that: The whale swarm algorithm model in step 1 is an improved whale swarm algorithm, in which a adopts a nonlinear design, and increases with the number of iterations at a smaller value in the early stage of the search. After increasing to a larger value, it quickly decreases to a smaller value, and finally increases again at a slower rate; this ensures that the global search capability is strong in the early stage of the search while maintaining a faster convergence speed, and that the algorithm has a faster convergence speed in the later stage of the search while avoiding falling into a local optimum.

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