A hypersonic vehicle shape controllable generation method based on a CVAE-GAN model

Abstract

The application provides a hypersonic vehicle shape controllable generation method based on a CVAE-GAN model, and the method comprises the following steps: constructing a training data set; constructing a CVAE-GAN model and training; the CVAE-GAN model comprises an encoder, a decoder and a discriminator; a target hypersonic vehicle geometric feature control condition is given, and controllable generation of a target hypersonic vehicle shape is realized. The application discloses a hypersonic vehicle shape controllable generation method based on a CVAE-GAN model, which can realize generalization representation and controllable generation of a hypersonic vehicle shape, a plurality of target hypersonic vehicle shapes satisfying a target hypersonic vehicle geometric feature control condition can be obtained through multiple random samplings in a latent code space, and therefore the design efficiency of hypersonic vehicle shape layout optimization can be significantly improved; the method directly outputs a shape in a common digital model format, and is easy to use and practical in engineering practice.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of aircraft aerodynamic optimization design, and particularly relates to a controllable wave-rider shape generation method based on a CVAE-GAN model. BACKGROUND

[0002] As a key design element of hypersonic vehicles, wave-rider is widely used in aerospace, missiles and high-speed vehicles due to its unique streamlined shape and superior aerodynamic characteristics. The design goal of wave-rider is to optimize its lift-to-drag ratio, smoothness and stability to ensure optimal performance in supersonic flight conditions. The optimization method of wave-rider aerodynamic shape directly determines the capacity of the design space and the efficiency of the optimization design, and has great significance for the performance improvement and rapid iterative design of wave-rider.

[0003] In the optimization of wave-rider aerodynamic shape, the optimization control parameters of wave-rider are often obtained through optimization algorithm, and then a plurality of specific wave-rider shapes are generated according to the optimization control parameters of wave-rider, and aerodynamic test is performed on each obtained wave-rider shape to test its aerodynamic performance and obtain the best wave-rider shape.

[0004] It can be seen that in the optimization of wave-rider aerodynamic shape, how to quickly and efficiently generate a plurality of wave-rider shapes meeting the optimization control parameters of wave-rider directly affects the optimization efficiency of wave-rider aerodynamic shape.

[0005] In the prior art, the following problems exist in generating wave-rider shape: the "diversity" of the wave-rider shape generated by the design space defined by the traditional method is limited. For example, the most commonly used Class-Shape-Transformation (CST) method or Free-Form Deformation (FFD) method actually performs perturbation modification on the basis of the reference shape, and the final generated wave-rider shape result is largely dependent on the initial scheme, which is difficult to realize the change of aerodynamic layout topology, thereby limiting the potential of optimization design. SUMMARY

[0006] In view of the defects in the prior art, the application provides a controllable wave-rider shape generation method based on a CVAE-GAN model, which can effectively solve the above problems.

[0007] In order to achieve the above purpose, the application adopts the following technical scheme:

[0008] The application provides a controllable wave-rider shape generation method based on a CVAE-GAN model, comprising the following steps:

[0009] Step 1: Given a set of waverider reference shapes, including L waverider reference shapes, they are expressed as: waverider reference shapes x1, x2, ..., x L ; Among them, each waverider body reference shape is represented by the waverider body shape surface point cloud data;

[0010] Step 2: Build a training dataset:

[0011] For each waverider reference shape x k , k=1,2,...,L, use parameterization method to perturb it, and get k The waverider training shape set formed by waverider training shapes is expressed as:

[0012] Therefore, for L waverider reference shapes x1, x2, ..., x L , corresponding to the L waverider training shape sets, which are then integrated to form the training data set X=[X1,X2,...,X L ];

[0013] Step 3: Build a CVAE-GAN model; the CVAE-GAN model includes an encoder, a decoder, and a discriminator;

[0014] Step 4: Use the training data set to train the CVAE-GAN model, and use the gradient descent algorithm to optimize the model parameters of the CVAE-GAN model to obtain the trained CVAE-GAN model. The specific training and optimization methods are as follows:

[0015] Step 4.1, dividing the training data set into multiple batches of training data subsets; selecting multiple waverider training shapes contained in each batch of training data subsets to form a waverider training shape subset x i , and at the same time determine the geometric characteristics control condition c of the waverider;

[0016] Step 4.2: train the waverider shape subset x i and the waverider geometric feature control condition c to form an input sample x, which is input to the encoder. The encoder calculates the variance σ and mean μ of the latent code z of each waverider training shape in the input sample x, thereby obtaining a Gaussian distribution N(μ, σ) based on the variance σ and mean μ of the latent code z. The obtained Gaussian distribution N(μ, σ) is used as the latent code distribution q(z|x) of the latent code space;

[0017] Step 4.3: The decoder randomly samples the latent code distribution q(z|x) obtained by the encoder to obtain a latent code z;

[0018] Assume that the prior distribution p(z) of the latent code in the latent code space is a standard Gaussian distribution N(0, 1), sample the prior distribution p(z) to obtain the false latent code z′;

[0019] The decoder performs a decoding operation on the latent code z to obtain a true waverider shape x" corresponding to the waverider geometric characteristic control condition c;

[0020] The decoder performs a decoding operation on the false latent code z′ to obtain a false waverider shape x′;

[0021] Among them: the true waverider shape x" and the false waverider shape x' are both the surface point cloud data of the waverider shape;

[0022] Step 4.4: The input sample x, the true waverider shape x", and the false waverider shape x' are input to the discriminator; the discriminator respectively obtains the probability of judging the input sample x as true, the probability of judging the true waverider shape x' as true, and the probability of judging the false waverider shape x' as true.

[0023] Step 4.5, calculating the loss value of the loss function of the CVAE-GAN model;

[0024] Step 4.6, based on the loss value, respectively calculate the gradients of the model parameters of the encoder, the decoder, and the discriminator, and use the gradient descent algorithm to backpropagate and update the parameter values ​​of the model parameters;

[0025] Step 4.7: Return to step 4.1 and train the CVAE-GAN model in a loop until the model converges, thereby obtaining the trained CVAE-GAN model and latent space.

[0026] Step 5: According to the design requirements, a latent code is randomly sampled in the latent code space, which is represented by z * , and the geometric characteristics control condition c of the target waverider is given * ;

[0027] The potential code z * and the target waverider geometric characteristics control condition c * The decoder decodes the latent code z* to obtain the true waverider target shape corresponding to the target waverider geometric feature control condition c*, which is the surface point cloud data of the waverider target shape, thereby realizing controllable waverider shape generation.

[0028] Preferably, the encoder includes a three-layer convolutional network and a three-layer fully connected network connected in series; the decoder includes a three-layer fully connected network and a three-layer deconvolutional network connected in series; and the discriminator includes a three-layer fully connected network.

[0029] Preferably, in step 4.5, the loss function of the CVAE-GAN model is:

[0030] L Enc =L prior +L like

[0031] L Dnc =L like —L GAN

[0032] L Dis =L GAN

[0033] in:

[0034] L Enc represents the loss function of the encoder;

[0035] L prior Represents the KL divergence between the latent code distribution q(z|x) and the prior distribution p(z) in the encoder;

[0036] L like represents the reconstruction error of the decoder under the condition of the given encoder inference result, that is, the latent code distribution q(z|x);

[0037] L Dnc represents the loss function of the decoder;

[0038] L GAN represents the discriminative loss of the discriminator;

[0039] L Dis represents the loss function of the discriminator.

[0040] Preferably, the KL divergence between the latent distribution q(z|x) and the prior distribution p(z) is expressed as D KL , obtained by the following formula:

[0041]

[0042] in:

[0043] J represents the dimension of the latent code z;

[0044] represents the j-th dimension of the latent code z;

[0045] σ j and μ j , respectively represent the variance and mean of the j-th dimension of the latent code z inferred by the encoder.

[0046] Preferably, the discriminant loss L of the discriminator GAN Obtained by the following formula:

[0047] LGAN =log(Dis(x))+log(1-Dis(Dec(z')))++log(1-Dis(Dec(Enc(x))))where:

[0048] Dis(x) represents the probability that the discriminator judges the input sample x to be true;

[0049] Dis(Dwc(z′)) represents the probability that the discriminator identifies the false waverider shape x′ as true;

[0050] Dis)Dec)Emc)x))) represents the probability that the discriminator determines that the true waverider shape x" is true.

[0051] Preferably, step 4.6 is specifically as follows:

[0052] The following formula is used to calculate the gradient of the loss function obtained by inference with respect to the model parameters of the encoder, decoder, and discriminator respectively, and the parameter values ​​of the model parameters are updated by backpropagation using the gradient descent algorithm:

[0053]

[0054] in:

[0055] θ Enc ,θ Dec and θ Dis , are the model parameters of the encoder, decoder and discriminator respectively;

[0056] Represents the gradient operation.

[0057] Preferably, it also includes:

[0058] Step 6: Store the generated surface point cloud data of the true waverider target shape in the CPACS standard universal data format and perform efficient data management, and convert the surface point cloud data into IGS and STP digital-analog formats.

[0059] Preferably, it also includes: when the same target waverider geometric characteristic control condition c* is given, multiple random sampling is performed from the latent code space, and each time a latent code is randomly sampled, a true waverider target shape corresponding to the target waverider geometric characteristic control condition c* is obtained; therefore, by performing multiple random sampling from the latent code space, multiple true waverider target shapes that meet the target waverider geometric characteristic control condition c* are eventually obtained.

[0060] The present invention provides a controllable generation method of waverider shape based on the CVAE-GAN model, which has the following advantages:

[0061] The controllable generation method of waverider shape based on the CVAE-GAN model provided by the present invention can realize the generalized characterization and controllable generation of waverider shape. By performing multiple random samplings in the latent code space, a variety of target waverider shapes that meet the control conditions of the target waverider geometric characteristics can be obtained, thereby significantly improving the efficiency of the waverider shape layout optimization design; this method directly outputs the shape in a common digital-analog format, and is easy to use and practical in engineering practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic flow chart of a method for controllable generation of waverider shape based on a CVAE-GAN model provided by the present invention;

[0063] Figure 2 This is a schematic diagram of the principle of cone-guided waverider;

[0064] Figure 3 A diagram showing some samples of the Class A waverider shape training data set provided by an embodiment of the present invention;

[0065] Figure 4 A schematic diagram of the geometric shape of a Class B waverider provided in an embodiment of the present invention;

[0066] Figure 5 A diagram showing some samples of the training data set generated for the perturbation of the shape of a type B waverider provided in an embodiment of the present invention;

[0067] Figure 6 Schematic diagram of the CVAE-GAN model structure and training process provided by the present invention;

[0068] Figure 7 This is a diagram showing the appearance of some waveriders generated by generalization after the CVAE-GAN model provided by the present invention is trained;

[0069] Figure 8 A diagram showing the control conditions of a waverider provided in an embodiment of the present invention;

[0070] Figure 9 The basis provided by the embodiment of the present invention Figure 8 The waverider control conditions are used to generate a waverider target appearance display diagram. DETAILED DESCRIPTION

[0071] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0072] In response to the shortcomings of existing waverider shape optimization methods, the present invention proposes a controllable generation method for waverider shape based on the CVAE-GAN model. By combining the conditional variational autoencoder (CVAE) and the generative adversarial network (GAN) model to form a CVAE-GAN fusion model, the CVAE-GAN fusion model is applied to the controllable generation of waverider shape. The target waverider that meets the control conditions of the target waverider's geometric characteristics can be quickly generated, and the controllable generation of waverider shapes of different categories can be achieved. This provides a basis for the optimized design of the waverider shape and improves the efficiency of the optimized design of the waverider shape.

[0073] See Figure 1 The present invention provides a method for controllable generation of waverider shape based on the CVAE-GAN model, comprising the following steps:

[0074] Step 1: Given a set of waverider reference shapes, including L waverider reference shapes, they are expressed as: waverider reference shapes x1, x2, ..., x L ; Among them, each waverider body reference shape is represented by the waverider body shape surface point cloud data, and each surface point is specifically represented in the form of coordinates.

[0075] Step 2: Build a training dataset:

[0076] For each waverider reference shape x k , k=1,2,...,L, use CST, FFD and other parameterization methods to perturb it, and get k The waverider training shape set formed by waverider training shapes is expressed as:

[0077] Therefore, for L waverider reference shapes x1, x2, ..., x L , corresponding to the L waverider training shape sets, which are then integrated to form the training data set X=[X1,X2,...,X L ];

[0078] Step 3: Build a CVAE-GAN model; the CVAE-GAN model includes an encoder, a decoder, and a discriminator;

[0079] The CVAE-GAN model is a CVAE-GAN model built by combining the conditional variational autoencoder CVAE and the generative adversarial network GAN. As a specific structure, the encoder includes a three-layer convolutional network and a three-layer fully connected network in series; the decoder includes a three-layer fully connected network and a three-layer deconvolution network in series; and the discriminator includes a three-layer fully connected network.

[0080] Step 4: Use the training data set to train the CVAE-GAN model, and use the gradient descent algorithm to optimize the model parameters of the CVAE-GAN model to obtain the trained CVAE-GAN model. The specific training and optimization methods are as follows:

[0081] Step 4.1, dividing the training data set into multiple batches of training data subsets; selecting multiple waverider training shapes contained in each batch of training data subsets to form a waverider training shape subset x i , and at the same time determine the geometric characteristics control condition c of the waverider;

[0082] Step 4.2: train the waverider shape subset x i and the waverider geometric feature control condition c to form an input sample x, which is input to the encoder. The encoder calculates the variance σ and mean μ of the latent code z of each waverider training shape in the input sample x, thereby obtaining a Gaussian distribution N(μ, σ) based on the variance σ and mean μ of the latent code z. The obtained Gaussian distribution N(μ, σ) is used as the latent code distribution q(z|x) of the latent code space;

[0083] Step 4.3: The decoder randomly samples the latent code distribution q(z|x) obtained by the encoder to obtain a latent code z;

[0084] Assume that the prior distribution p(z) of the latent code in the latent code space is a standard Gaussian distribution N(0, 1), sample the prior distribution p(z) to obtain the false latent code z′;

[0085] The decoder performs a decoding operation on the latent code z to obtain a true waverider shape x" corresponding to the waverider geometric characteristic control condition c;

[0086] The decoder performs a decoding operation on the false latent code z′ to obtain a false waverider shape x′;

[0087] Among them: the true waverider shape x" and the false waverider shape x' are both the surface point cloud data of the waverider shape;

[0088] Step 4.4: The input sample x, the true waverider shape x", and the false waverider shape x' are input to the discriminator; the discriminator respectively obtains the probability of judging the input sample x as true, the probability of judging the true waverider shape x' as true, and the probability of judging the false waverider shape x' as true.

[0089] In step 4.5, calculate the loss function for the CVAE-GAN model. Specifically, each input sample x is fed into the encoder, and the encoder output is fed into the decoder for forward inference. The following loss functions are used to calculate the loss values. In practical applications, the encoder, decoder, and discriminator can be represented as: encoder E, decoder G, and discriminator D, respectively.

[0090] In this invention, the loss function of the CVAE-GAN model consists of three parts:

[0091] L Enc =L prior +L like

[0092] L Dnc =L like —L GAN

[0093] L Dis =L GAN

[0094] in:

[0095] L Enc represents the loss function of the encoder;

[0096] L prior Represents the KL divergence between the latent code distribution q(z|x) and the prior distribution p(z) in the encoder;

[0097] L like It represents the reconstruction error of the decoder under the condition of the given encoder inference result, that is, the latent code distribution q(z|x), which can be the mean square error (MSE) or cross entropy loss.

[0098] L Dnc represents the loss function of the decoder;

[0099] L GAN represents the discriminative loss of the discriminator;

[0100] L Dis represents the loss function of the discriminator.

[0101] In the above formula description:

[0102] The KL divergence between the latent distribution q(z|x) and the prior distribution p(z), denoted as D KL , obtained by the following formula:

[0103]

[0104] in:

[0105] J represents the dimension of the latent code z;

[0106] j represents the jth dimension of the latent code z;

[0107] σ j and μ j , respectively represent the variance and mean of the j-th dimension of the latent code z inferred by the encoder.

[0108] In the above formula description:

[0109] The discriminant loss L of the discriminator GAN Obtained by the following formula:

[0110] L GAN =log(Dis(x))+log(1-Dis(Dec(z′)))+log(1-Dis(Dec(Enc(x))))

[0111] in:

[0112] Dis(x) represents the probability that the discriminator judges the input sample x to be true;

[0113] Dis(Dec(z′)) represents the probability that the discriminator determines that the false waverider shape x′ is true;

[0114] Dis(Dec(Enc(x))) represents the probability that the discriminator identifies the true waverider shape x" as true.

[0115] Step 4.6, based on the loss value, respectively calculate the gradients of the model parameters of the encoder, the decoder, and the discriminator, and use the gradient descent algorithm to backpropagate and update the parameter values ​​of the model parameters;

[0116] Specifically, the following formula is used to calculate the gradient of the loss function obtained by inference on the model parameters of the encoder, decoder, and discriminator respectively, and the parameter values ​​of the model parameters are updated by backpropagation using the gradient descent algorithm:

[0117]

[0118] in:

[0119] θ Enc ,θ Dec and θ Dis , are the model parameters of the encoder, decoder and discriminator respectively;

[0120] Represents the gradient operation.

[0121] Step 4.7: Return to step 4.1 and train the CVAE-GAN model in a loop until the model converges, thereby obtaining the trained CVAE-GAN model and latent space.

[0122] Step 5: According to the design requirements, a latent code is randomly sampled in the latent code space, which is represented by z * , and the geometric characteristics control condition c of the target waverider is given * ;

[0123] The potential code z * and the target waverider geometric characteristics control condition c * Input to the decoder, the decoder performs * Perform decoding operation to obtain the geometric characteristics control condition c of the target waverider * The corresponding true waverider target shape is the surface point cloud data of the waverider target shape, thereby realizing controllable waverider shape generation.

[0124] In the present invention, since the prior distribution p(z) of the latent code in the latent code space is assumed to be the standard Gaussian distribution N(0, 1) when training the CVAE-GAN model, in the training loss function, the KL divergence measures the difference between the latent code distribution q(z|x) and the prior distribution p(z). Therefore, after training the CVAE-GAN model, the difference between the latent code distribution q(z|x) and the prior distribution p(z) becomes smaller and smaller. Therefore, the latent code distribution q(z|x) of the latent code space after training is very similar to the standard Gaussian distribution N(0, 1). Therefore, when sampling a latent code from the latent code space, in actual operation, it is also possible to directly sample from the latent code space that conforms to the standard Gaussian distribution N(0, 1).

[0125] In the present invention, given the same target waverider geometric characteristic control condition c * When , multiple random sampling is performed from the latent code space. Each time a latent code is randomly sampled, the control condition c of the geometric characteristics of the target waverider is obtained. * The corresponding true waverider target shape; therefore, by performing multiple random sampling from the latent code space, the target waverider geometric feature control condition c is finally obtained. * Therefore, the present invention can improve the efficiency of generating the target waverider shape that meets the control conditions of the target waverider geometric characteristics.

[0126] Also includes:

[0127] Step 6: Store the surface point cloud data of the generated true waverider target shape in the CPACS standard universal data format and perform efficient data management, convert the surface point cloud data into common digital and analog formats such as IGS and STP, and achieve rapid conversion of the generated true waverider target shape into common digital and analog formats such as IGS and STP.

[0128] The present invention provides a controllable generation method of waverider shape based on the CVAE-GAN model, which has the following advantages:

[0129] The generalized representation and controllable generation of waverider shapes can be achieved. By performing multiple random samplings in the latent code space, a variety of target waverider shapes that meet the control conditions of the target waverider's geometric characteristics can be obtained, thereby significantly improving the efficiency of the waverider shape layout optimization design. This method directly outputs the shape in a common digital-analog format, and is easy to use and practical in engineering practice.

[0130] An embodiment is listed below:

[0131] The embodiment of the present invention performs generalized characterization and controllable generation of two types of waverider reference shapes.

[0132] The reference shape of a type waverider is the analytically generated cone-guided waverider: Figure 2 As shown, a straight cone flow field is used as the reference flow field to generate a cone-guided waverider. For a given incoming flow Mach number and cone shock wave angle input, the corresponding cone flow field can be obtained by solving the Taylor-Maccoll flow control equation. After generating the cone flow field, first customize the bottom profile of the upper surface, project the profile in the opposite direction of the flow direction so that it intersects with the cone shock wave to form the leading edge line of the waverider, discretize the leading edge line into a point cloud, and start from each point cloud to perform streamline tracing in a two-dimensional plane to generate the lower surface of the waverider. Through the above steps, the point cloud contour of the waverider benchmark shape is obtained, and further shaping by CAD software can obtain the digital model of the waverider benchmark shape. 5000 shapes are generated by spatial sampling at the incoming flow Mach number of 5 to 30 and the shock wave angle of 5° to 40°, and a waverider training shape set corresponding to the type a waverider benchmark shape is obtained, as shown in FIG. Figure 3 , showing some sample shapes in the training shape set formed by the class A waverider benchmark shape.

[0133] The B-type waverider reference shape is a designed waverider shape, and its geometric shape is as follows Figure 4 As shown. Based on this shape, the FFD parameterization method is used to deform it and generate a large number of different layout shapes. Constrained-FFD is used in the deformation process to prevent the generation of unreasonable shapes. 5000 shapes are generated by perturbation. Figure 5 Some sample shapes in the waverider training shape set formed by the Class B waverider benchmark shape are shown.

[0134] Build a CVAE-GAN model; input a total of 10,000 waverider training shape data formed by the two types of waverider benchmark shapes a and b into the CVAE-GAN model in batches for training. The CVAE-GAN model structure and training process are as follows: Figure 6 shown.

[0135] After training, the CVAE-GAN model has the ability to generalize and generate. Figure 7 The figure shows the generalized shape of some waveriders after model training is completed. The present invention can quickly obtain the corresponding target waverider shape by changing the control conditions of the target waverider's geometric characteristics.

[0136] After training, the CVAE-GAN model has controllable generation capabilities. Figure 8 To control the geometric characteristics of the waverider, it is stipulated that the plane shape of the generated waverider should be similar to Figure 8 The parabolic family of shapes shown in Figure 8 In the figure, there are five parabolas, one parabola represents a control condition of the geometric characteristics of the waverider; Figure 9 Shown in Figure 8 The corresponding target waverider shape is generated under the control conditions, that is, for each waverider geometric feature control condition, a target waverider shape is generated accordingly.

[0137] In summary, the present invention discloses a controllable generation method of waverider shape based on the CVAE-GAN model, which can realize the generalized characterization and controllable generation of waverider shape. By performing multiple random samplings in the latent code space, a variety of target waverider shapes that meet the control conditions of the target waverider geometric characteristics can be obtained, thereby significantly improving the efficiency of the waverider shape layout optimization design; this method directly outputs the shape in a common digital-analog format, and is easy to use and practical in engineering practice.

[0138] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for controllable generation of waverider shape based on CVAE-GAN model, characterized in that: The following steps are involved: Step 1: Given a set of waverider reference shapes, including L waverider reference shapes, they are expressed as: waverider reference shapes x1, x2, ..., x L ; Among them, each waverider body reference shape is represented by the waverider body shape surface point cloud data; Step 2: Build a training dataset: For each waverider reference shape x k , k=1,2,...,L, use parameterization method to perturb it, and get k The waverider training shape set formed by waverider training shapes is expressed as: Therefore, for L waverider reference shapes x1, x2, ..., x L , corresponding to the L waverider training shape sets, which are then integrated to form the training data set X=[X1,X2,...,X L ]; Step 3: Build a CVAE-GAN model; the CVAE-GAN model includes an encoder, a decoder, and a discriminator; Step 4: Use the training data set to train the CVAE-GAN model, and use the gradient descent algorithm to optimize the model parameters of the CVAE-GAN model to obtain the trained CVAE-GAN model. The specific training and optimization methods are as follows: Step 4.1, dividing the training data set into multiple batches of training data subsets; selecting multiple waverider training shapes contained in each batch of training data subsets to form a waverider training shape subset x i , and at the same time determine the geometric characteristics control condition c of the waverider; Step 4.2: train the waverider shape subset x i and the waverider geometric feature control condition c to form an input sample x, which is input to the encoder. The encoder calculates the variance σ and mean μ of the latent code z of each waverider training shape in the input sample x, thereby obtaining a Gaussian distribution N(μ, σ) based on the variance σ and mean μ of the latent code z. The obtained Gaussian distribution N(μ, σ) is used as the latent code distribution q(z|x) of the latent code space; Step 4.3: The decoder randomly samples the latent code distribution q(z|x) obtained by the encoder to obtain a latent code z; Assume that the prior distribution p(z) of the latent code in the latent code space is a standard Gaussian distribution N(0, 1), sample the prior distribution p(z) to obtain the false latent code z′; The decoder performs a decoding operation on the latent code z to obtain a true waverider shape x" corresponding to the waverider geometric characteristic control condition c; The decoder performs a decoding operation on the false latent code z′ to obtain a false waverider shape x′; Among them: the true waverider shape x" and the false waverider shape x' are both the surface point cloud data of the waverider shape; Step 4.4: The input sample x, the true waverider shape x", and the false waverider shape x' are input to the discriminator; the discriminator respectively obtains the probability of judging the input sample x as true, the probability of judging the true waverider shape x' as true, and the probability of judging the false waverider shape x' as true. Step 4.5, calculating the loss value of the loss function of the CVAE-GAN model; Step 4.6, based on the loss value, respectively calculate the gradients of the model parameters of the encoder, the decoder, and the discriminator, and use the gradient descent algorithm to backpropagate and update the parameter values ​​of the model parameters; Step 4.7: Return to step 4.1 and train the CVAE-GAN model in a loop until the model converges, thereby obtaining the trained CVAE-GAN model and latent space. Step 5: According to the design requirements, a latent code is randomly sampled in the latent code space, which is represented by z * , and the geometric characteristics control condition c of the target waverider is given * ; The potential code z * and the target waverider geometric characteristics control condition c * Input to the decoder, the decoder performs * Perform decoding operation to obtain the geometric characteristics control condition c of the target waverider * The corresponding true waverider target shape is the surface point cloud data of the waverider target shape, thereby realizing controllable waverider shape generation.

2. The method for controllable generation of waverider shape based on CVAE-GAN model according to claim 1, characterized in that: The encoder includes a three-layer convolutional network and a three-layer fully connected network connected in series; the decoder includes a three-layer fully connected network and a three-layer deconvolutional network connected in series; and the discriminator includes a three-layer fully connected network.

3. The method for controllable generation of waverider shape based on CVAE-GAN model according to claim 1, characterized in that: In step 4.5, the loss function of the CVAE-GAN model is: L Ene =L prior +L llike L Dne =L llike -L GAN L Dis =L GAN in: L Enc represents the loss function of the encoder; L prior Represents the KL divergence between the latent code distribution q(z|x) and the prior distribution p(z) in the encoder; L llike represents the reconstruction error of the decoder under the condition of the given encoder inference result, that is, the latent code distribution q(z|x); L Dnc represents the loss function of the decoder; L GAN represents the discriminative loss of the discriminator; L Dis represents the loss function of the discriminator.

4. The method for controllable generation of waverider shape based on CVAE-GAN model according to claim 3, characterized in that: The KL divergence between the latent distribution q(z|x) and the prior distribution p(z), also denoted as D KL , obtained by the following formula: in: J represents the dimension of the latent code z; j represents the jth dimension of the latent code z; σ j and μ j , respectively represent the variance and mean of the j-th dimension of the latent code z inferred by the encoder.

5. The method for controllable generation of waverider shape based on CVAE-GAN model according to claim 3, characterized in that: The discriminant loss L of the discriminator GAN Obtained by the following formula: L GAN =log(Dis(x))+log(1-Dis(Dec(z')))+log(1-Dis(Dec(Enc(x)))) in: Dis(x) represents the probability that the discriminator judges the input sample x to be true; Dis(Dec(z′)) represents the probability that the discriminator determines that the false waverider shape x′ is true; Dis(Dec)Emc)x))) represents the probability that the discriminator identifies the true waverider shape x" as true.

6. The method for controllable generation of waverider shape based on CVAE-GAN model according to claim 3, characterized in that: Step 4.6 is as follows: The following formula is used to calculate the gradient of the loss function obtained by inference with respect to the model parameters of the encoder, decoder, and discriminator respectively, and the parameter values ​​of the model parameters are updated by backpropagation using the gradient descent algorithm: in: θ Enc ,θ Dec and θ Dis , are the model parameters of the encoder, decoder and discriminator respectively; Represents the gradient operation.

7. The method for controllable generation of waverider shape based on CVAE-GAN model according to claim 3, characterized in that: Also includes: Step 6: Store the generated surface point cloud data of the true waverider target shape in the CPACS standard universal data format and perform efficient data management, and convert the surface point cloud data into IGS and STP digital-analog formats.

8. The method for controllable generation of waverider shape based on CVAE-GAN model according to claim 1, characterized in that: Also includes: When the same target waverider geometric characteristic control condition c* is given, multiple random samplings are performed from the latent code space. Each time a latent code is randomly sampled, the target waverider geometric characteristic control condition c* is obtained. * The corresponding true waverider target shape; therefore, by performing multiple random sampling from the latent code space, the target waverider geometric feature control condition c is finally obtained. * Multiple true waverider target shapes.

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