Optimization Method for Foam Aluminum Sandwich Panel Structure of Composite Oil-Filled Main Equipment and Related Devices

Abstract

The present invention belongs to the technical field of non-electrical quantity protection of power system transformers, and discloses an optimization method for the foam aluminum sandwich panel structure of composite oil-filled main equipment and related devices. Aiming at the problem of insufficient anti-explosion performance of existing composite oil-filled main equipment under high-energy arc faults, the present invention first establishes a finite element model of the composite oil-filled main equipment containing a foam aluminum sandwich panel, divides the fluid domain and the solid domain in the finite element model, sets relevant material properties and parameters, determines sample points through random sampling, obtains the mechanical output response through finite element calculation, constructs a response surface surrogate model, and uses the multi-objective particle swarm optimization algorithm to solve the Pareto solution set with the maximum energy absorption of the foam aluminum sandwich panel and the minimum maximum deformation of the composite oil-filled main equipment housing as multi-objective functions. This method can effectively improve the anti-explosion performance of oil-filled main equipment, improve the optimization efficiency, and reduce the explosion risk of composite oil-filled main equipment under arc faults, and has important engineering application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of non-electrical quantity protection of power system transformers, and particularly relates to an optimization method for the structure of an aluminum foam sandwich panel of a composite oil-filled main equipment and related devices. Background Art

[0002] The power system is the pillar of modern society, providing necessary electric energy for all walks of life. Oil-filled equipment such as transformers, instrument transformers, and switchgear is widely used in power transmission and transformation, and mineral insulating oil is usually used as the cooling and insulating medium inside the equipment. When a short-circuit fault occurs inside the oil-filled power equipment, the high-energy arc vaporizes and cracks the surrounding insulating oil, generating a large amount of combustible short-chain hydrocarbon gases and forming rapidly expanding bubbles, which in turn cause the internal oil pressure of the equipment to rise. When the structural stress of the oil-filled power equipment exceeds its tensile limit, it will cause serious consequences such as equipment cracking, oil spraying, and even explosion. Although existing oil-filled power equipment is usually equipped with relay protection and pressure relief devices, in the face of high-energy arc faults, due to the delayed action of relay protection and the insufficient pressure relief capacity of pressure relief devices, the frequency of explosion accidents of oil-filled power equipment remains high.

[0003] Due to its advantages such as light weight and good energy absorption effect, aluminum foam sandwich panels have been widely used in fields such as aerospace and automobiles, and their application effects in the explosion protection of power equipment have also been verified. However, there is currently a lack of a scientific optimization method for designing the structure of aluminum foam sandwich panels in composite oil-filled main equipment, so that their energy absorption effect can be fully exerted under arc faults, and further improve their anti-explosion performance in composite oil-filled main equipment. Summary of the Invention

[0004] In order to overcome the shortcoming in the above-mentioned prior art that there is a lack of a scientific optimization method for designing the structure of aluminum foam sandwich panels in composite oil-filled main equipment, resulting in the inability to fully exert the energy absorption and anti-explosion performance of the aluminum foam sandwich panel structure during arc faults, the present invention provides an optimization method for the structure of an aluminum foam sandwich panel of a composite oil-filled main equipment and related devices, which can effectively improve the anti-explosion performance of the composite oil-filled main equipment under high-energy arc faults.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] The first object of the present invention is to propose an optimization method for the structure of an aluminum foam sandwich panel of a composite oil-filled main equipment, including the following steps:

[0007] S1. Establish a finite element model of the composite oil-filled main equipment including an aluminum foam sandwich panel, and determine the design variables of the composite oil-filled main equipment;

[0008] S2. Randomly sample the design variables to generate sample points;

[0009] S3. Import the finite element model of the composite oil-filled main equipment into the fluid and structural solution modules of ANSYS, and divide the fluid domain and solid domain in the finite element model of the composite oil-filled main equipment;

[0010] Select the fluid properties and k - ω turbulence model, use the bubble dynamics equation, set the calculation step size and calculation time, and obtain the pressure in the fluid domain during the arc fault of the composite oil-filled main equipment;

[0011] Transfer the pressure in the fluid domain to the solid domain through the fluid-structure coupling surface, set the material property parameters of the solid domain, perform finite element calculations on the sample points, and obtain the mechanical output responses of each sample point;

[0012] S4. Based on the mechanical output responses of each sample point, use the response surface method to construct a response surface surrogate model between the design variables and the mechanical output responses;

[0013] S5. Evaluate the accuracy of the response surface surrogate model. If the accuracy does not meet the requirements, return to step S2 to increase the sampling points until the response surface surrogate model meets the accuracy requirements;

[0014] S6. Define a multi-objective function based on the response surface surrogate model that meets the accuracy requirements, and use the multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain the Pareto optimal solution set.

[0015] Further, in the S1, the aluminum foam sandwich panel is installed on the inner wall of the composite oil-filled main equipment housing;

[0016] The aluminum foam sandwich panel includes: a front panel, an aluminum foam core layer, and a back panel stacked in sequence, and the back panel is attached to the inner wall of the composite oil-filled main equipment housing;

[0017] The design variables include the density and thickness of the aluminum foam core layer, the thickness of the front panel, and the thickness of the back panel.

[0018] Furthermore, in the S3, the material property parameters of the solid domain include the material property parameters of the aluminum foam core layer, and the material property parameters of the aluminum foam core layer are constructed based on the Hanssen aluminum foam hardening model with density parameters. The specific formula of the Hanssen aluminum foam hardening model with density parameters is:

[0019] (1)

[0020] In the formula, σ is the stress, σ P is the plateau stress, γ is the hardening coefficient ，α 2 is the scale factor, β is the shape factor,ε is strain, ε d is dense strain.

[0021] Furthermore, in the said S3 k - ω the turbulence model is as follows:

[0022] (2)

[0023] (3)

[0024] In the formula, k is the turbulent kinetic energy, ω is the specific dissipation rate, ρ m is the fluid mixture density, μ m is the fluid molecular viscosity, u m is the fluid velocity, μ t,m is the fluid turbulent viscosity, σ k and σ ω is the turbulent Prandtl number, G k,m is the generation term of the turbulent kinetic energy k , G ω,m is the generation term of the specific dissipation rate ω ; Y k,m and Y ω,m are respectively the dissipation of k and ω caused by turbulence, t is time.

[0025] Furthermore, the bubble dynamics equation in the said S3 is:

[0026] (4)

[0027] Among them, the discrete summation term F n is defined as:

[0028] (5)

[0029] In the formula, R is the bubble radius, is the normal velocity of the bubble surface, is the normal acceleration of the bubble surface, c is the distance between the bubble center and the fluid domain boundary,p c is the pressure at the fluid domain boundary, δ is the specific heat ratio, ρ is the fluid density, ξ is the fluid surface tension coefficient, W arc is the arc energy, α is the heat transfer coefficient, μ is the fluid viscosity, N is the total number of calculation steps, p c,n is the n pressure at the fluid domain boundary at the th calculation step, t n is the n time corresponding to the

[0030] Furthermore, in S6, the multi-objective function is defined as minimizing the maximum deformation of the composite oil-filled main equipment and maximizing the energy absorption of the sandwich panel.

[0031] Even further, in S6, the multi-objective particle swarm optimization algorithm is used to optimize and solve the multi-objective function to obtain the Pareto optimal solution set. The specific steps are as follows:

[0032] 1) Define the dimension of the particle search space as the number of independent variables of the multi-objective function, and initialize the particles and velocities using a random function;

[0033] 2) Update the particle velocities and positions. By comparing the multi-objective function values of each particle, evaluate the quality of the particle's position and decide whether to update the individual historical best position and the population global historical optimal position again;

[0034] 3) Determine whether the predetermined maximum number of iterations has been reached. If the predetermined maximum number of iterations has not been reached, return to step 2) to continue updating the particle velocities and positions; if the predetermined maximum number of iterations has been reached, terminate the calculation to obtain the Pareto optimal solution set;

[0035] In step 2), the particle velocities and positions are updated by the following formula:

[0036] (6)

[0037] (7)

[0038] In the formula, v i (t) is the velocity of the particle i at time t , x i (t) is the particle i at timet The position of ψ is the inertia factor, c 1 and c 2 are the learning factors from the individual best and the global best; r 1 and r 2 are random numbers distributed between 0 and 1, x pbest is the optimal position encountered in the particle's history, x gbest is the optimal position encountered in all particles' history.

[0039] The second object of the present invention is to propose a system for the structural optimization method of the foam aluminum sandwich panel of the composite oil-filled main equipment, including:

[0040] A fluid-structure interaction multi-domain modeling module, which is used to establish a finite element model of the composite oil-filled main equipment including the foam aluminum sandwich panel and determine the design variables of the composite oil-filled main equipment;

[0041] A design variable random sampling module, which is used to randomly sample the design variables to generate sample points;

[0042] A fluid-structure interaction mechanics analysis module, which is used to import the finite element model of the composite oil-filled main equipment into the fluid and structure solution modules of ANSYS, divide the fluid domain and the solid domain in the finite element model of the composite oil-filled main equipment;

[0043] Select the fluid properties and k - ω turbulence model, use the bubble dynamics equation, set the calculation step size and calculation time, and obtain the pressure in the fluid domain during the arc fault of the composite oil-filled main equipment;

[0044] Transfer the pressure in the fluid domain to the solid domain through the fluid-structure interaction surface, set the material property parameters of the solid domain, perform finite element calculations on the sample points, and obtain the mechanical output responses of each sample point;

[0045] A surrogate model construction module, which is used to construct a response surface surrogate model between the design variables and the mechanical output responses by using the response surface method based on the mechanical output responses of each sample point;

[0046] A surrogate model accuracy iteration module, which is used to evaluate the accuracy of the response surface surrogate model. If the accuracy does not meet the requirements, return to the design variable random sampling module to increase the sampling points until the response surface surrogate model meets the accuracy requirements;

[0047] A target function solving module, which is used to define a multi-objective function based on the response surface surrogate model that meets the accuracy requirements, and use the multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain the Pareto optimal solution set.

[0048] The third object of the present invention is to propose an electronic device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, and when the processor executes the computer program, the steps of the optimization method for the foam aluminum sandwich panel structure of the composite oil-filled main equipment are realized.

[0049] The fourth object of the present invention is to propose a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the optimization method for the foam aluminum sandwich panel structure of the composite oil-filled main equipment are realized.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention discloses an optimization method for the foam aluminum sandwich panel structure of a composite oil-filled main equipment. First, a finite element model of the composite oil-filled main equipment is established, and the mechanical output responses of the arc fault and the foam aluminum sandwich panel are analyzed by the bubble-fluid-structure coupling numerical method. Based on the simulation results, a response surface surrogate model is used to fit the functional relationship between the design variables and the mechanical output responses of the composite oil-filled main equipment. Finally, a multi-objective particle swarm optimization algorithm is used to solve the multi-objective function defined based on the response surface surrogate model to obtain the Pareto optimal solution set. The present invention proposes an optimization method for the foam aluminum sandwich panel structure of a composite oil-filled main equipment based on a response surface surrogate model to improve the explosion-proof performance of the aluminum foam sandwich panel under high-energy arc faults, which upgrades the improvement method of the foam aluminum sandwich panel structure installed in the composite oil-filled main equipment based on experience and intuition to a new stage of theoretical analysis, significantly reduces the arc explosion risk of oil-immersed power equipment, and has great engineering application value. Based on the response surface surrogate model, the optimization efficiency of the foam aluminum sandwich panel structure of the composite oil-filled main equipment is greatly improved, and it has stronger engineering practicability. At the same time, the multi-particle swarm optimization algorithm has a fast convergence speed. By optimizing the Pareto solution set of the multi-objective function, while the composite oil-filled main equipment maintains good structural strength, it can absorb the arc impact energy to the maximum extent, and the maximum stress and deformation of the composite oil-filled main equipment are significantly reduced, improving the overall safety of the composite oil-filled main equipment. Description of the Drawings

[0052] Figure 1 It is a schematic diagram of the foam aluminum sandwich panel structure of an embodiment of the present invention.

[0053] Figure 2 It is a schematic diagram of the optimization process of an embodiment of the present invention.

[0054] Figure 3 It is a schematic diagram of the system of an embodiment of the present invention.

[0055] Figure 4 It is a schematic diagram of the optimization process of a preferred embodiment of the present invention.

[0056] Figure 5 This is the finite element model diagram of the composite oil-filled main equipment of the preferred embodiment of the present invention.

[0057] Figure 6 This is the mesh division diagram of the finite element model of the composite oil-filled main equipment of the preferred embodiment of the present invention.

[0058] Figure 7 This is the Pareto front diagram obtained by optimization of the preferred embodiment of the present invention.

[0059] Among them, 1 - the first aluminum foam core layer, 2 - the second aluminum foam core layer, 3 - the third aluminum foam core layer, 4 - the front panel, 5 - the back panel, 6 - the inner wall of the composite oil-filled main equipment housing. Detailed implementation manners

[0060] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0061] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a method, system, device, or storage medium including a series of steps does not necessarily have to be limited to those steps clearly listed, but may include other steps or units not clearly listed or inherent to these methods, systems, devices, or storage media.

[0062] The composite oil-filled main equipment described in this application refers to a new type of composite oil-filled main equipment that is based on the body structure of the oil-filled main equipment and installs an aluminum foam sandwich panel inside it, and is structurally composed of the body structure of the oil-filled main equipment and the aluminum foam sandwich panel. This composite oil-filled main equipment is generally used to improve the explosion-proof performance of the body structure of the oil-filled main equipment under internal arc short-circuit faults.

[0063] The following further describes the present invention in detail with reference to the accompanying drawings:

[0064] Embodiment 1

[0065] Please refer to Figure 2 , which is the schematic flow diagram of the aluminum foam sandwich panel structure optimization method for the composite oil-filled main equipment in the present invention, and specifically includes the following steps:

[0066] S1. Establish a finite element model of the composite oil-filled main equipment including an aluminum foam sandwich panel, and determine the design variables of the composite oil-filled main equipment;

[0067] S2. Randomly sample the design variables to generate sample points;

[0068] S3. Import the finite element model of the composite oil-filled main equipment into the fluid and structure solution modules of ANSYS (a large general finite element analysis software developed by ANSYS), and divide the fluid domain and the solid domain in the finite element model of the composite oil-filled main equipment;

[0069] Select fluid properties and k - ω the turbulence model, use the bubble dynamics equation, set the calculation step size and calculation time, and obtain the pressure in the fluid domain during the arc fault of the composite oil-filled main equipment;

[0070] Transfer the pressure in the fluid domain to the solid domain through the fluid-structure coupling surface, set the material property parameters of the solid domain, perform finite element calculations on the sample points, and obtain the mechanical output responses of each sample point;

[0071] S4. Based on the mechanical output responses of each sample point, use the response surface method to construct a response surface surrogate model between the design variables and the mechanical output responses;

[0072] S5. Evaluate the accuracy of the response surface surrogate model. If the accuracy does not meet the requirements, return to step S2 to increase the sampling points until the response surface surrogate model meets the accuracy requirements;

[0073] S6. Define a multi-objective function based on the response surface surrogate model that meets the accuracy requirements, and use the multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain the Pareto optimal solution set.

[0074] This embodiment also proposes a system for the structural optimization method of the foam aluminum sandwich panel of the composite oil-filled main equipment. Refer to Figure 3 , including the following modules:

[0075] A fluid-structure coupling multi-domain modeling module for establishing a finite element model of the composite oil-filled main equipment including a foam aluminum sandwich panel and determining the design variables of the composite oil-filled main equipment;

[0076] A design variable random sampling module for randomly sampling the design variables to generate sample points;

[0077] A fluid-structure coupling mechanical analysis module for importing the finite element model of the composite oil-filled main equipment into the fluid and structure solution modules of ANSYS, and dividing the fluid domain and the solid domain in the finite element model of the composite oil-filled main equipment;

[0078] Select fluid properties and k - ω the turbulence model, use the bubble dynamics equation, set the calculation step size and calculation time, and obtain the pressure in the fluid domain during the arc fault of the composite oil-filled main equipment;

[0079] Transfer the pressure of the fluid domain to the solid domain through the fluid-structure interaction surface, set the material property parameters of the solid domain, perform finite element calculations on the sample points, and obtain the mechanical output responses of each sample point.

[0080] The surrogate model construction module is used to construct a response surface surrogate model between the design variables and the mechanical output responses based on the mechanical output responses of each sample point by using the response surface method.

[0081] The surrogate model accuracy iteration module is used to evaluate the accuracy of the response surface surrogate model. If the accuracy does not meet the requirements, return to the design variable random sampling module to increase the sampling points until the response surface surrogate model meets the accuracy requirements.

[0082] The objective function solving module is used to define a multi-objective function based on the response surface surrogate model and solve the multi-objective function by using the multi-objective particle swarm optimization algorithm to obtain the Pareto optimal solution set.

[0083] In addition, this embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the foregoing computer program of this embodiment, the steps of the optimization method for the foam aluminum sandwich panel structure of the composite oil-filled main equipment are implemented.

[0084] Finally, this embodiment proposes a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the optimization method for the foam aluminum sandwich panel structure of the composite oil-filled main equipment proposed in the foregoing of this embodiment are implemented.

[0085] Embodiment 2

[0086] This embodiment further limits the step S1 in Embodiment 1, specifically as follows:

[0087] According to the geometric parameters of the composite oil-filled main equipment including the foam aluminum sandwich panel, use Spaceclaim software to establish a 1:1 scale three-dimensional simulation model of the composite oil-filled main equipment. Appropriately simplify the components in the composite oil-filled main equipment structure that have little influence on the simulation results to reduce the computational complexity, and perform finite element meshing on the three-dimensional simulation model to obtain the finite element model of the composite oil-filled main equipment.

[0088] Further, refer to Figure 1 , the finite element model of the composite oil-filled main equipment includes the composite oil-filled main equipment housing and the foam aluminum sandwich panel installed on the inner wall 6 of the composite oil-filled main equipment housing. The foam aluminum sandwich panel can include a single-layer or multi-layer foam aluminum core layer.

[0089] Further, the aluminum foam sandwich panel includes a front panel 4, a back panel 5, and an aluminum foam core layer between the front panel 4 and the back panel 5.

[0090] Further, the back panel 5 of the aluminum foam sandwich panel is attached to the inner wall 6 of the composite oil-filled main equipment housing, and the front panel 4 is directly in contact with the insulating oil filled in the composite oil-filled main equipment.

[0091] Select the density and thickness of the foam core layer and the thicknesses of the front panel 4 and the back panel 5 as design variables, and determine the multi-dimensional variable design space according to the actual manufacturing process.

[0092] Example 3

[0093] This example further limits step S2 in Example 1, specifically:

[0094] Randomly sample the design variables within the design variable space and extract a certain number of sample points. The main steps of this method are:

[0095] 1) Determine the variable dimensions to be sampled, and divide the value range of each variable into several equally wide and non-overlapping sub-intervals;

[0096] 2) Independently and randomly permute the sub-intervals of each variable;

[0097] 3) Extract a sample point from each sub-interval, and the extraction position is a random point within the sub-interval;

[0098] 4) Combine the values extracted at the i th position of each variable to form the i th multi-dimensional sample point.

[0099] Example 4

[0100] This example further limits step S3 in Example 1, specifically:

[0101] 1. Import the finite element model of the composite oil-filled main equipment established in S1 into the fluid and structural solution modules of ANSYS, divide the fluid domain and the solid domain in the finite element model of the composite oil-filled main equipment, set the finite element mesh accuracy, and perform finite element mesh division;

[0102] 2. Solve the pressure in the fluid domain during the arc fault of the composite oil-filled main equipment

[0103] First, set the fluid properties to the material properties of the fluid filled in the composite oil-filled main equipment, such as setting them to the material properties of compressible hydraulic oil, including density, bulk modulus, kinematic viscosity coefficient, surface tension coefficient, etc.;

[0104] Then, select the k -ω Turbulence model k - ω The turbulence model is specifically as follows:

[0105] ,

[0106] ,

[0107] Among them, k is the turbulent kinetic energy, ω is the specific dissipation rate, ρ m is the fluid mixture density, μ m is the fluid molecular viscosity, u m is the fluid velocity, μ t,m is the fluid turbulent viscosity, σ k and σ ω are the turbulent Prandtl numbers, G k,m is the generation term of the turbulent kinetic energy k ; G ω,m is the generation term of the specific dissipation rate ω ; Y k,m and Y ω,m are respectively the dissipations of k and ω caused by turbulence, t is the time.

[0108] Next, the bubble dynamics equation is used to characterize the evolution process of the bubble radius induced by the internal fault arc of the oil-immersed power equipment, so as to simulate the internal fluid motion driven by the bubble. The calculation step size and calculation time are set, and the pressure of the fluid domain during the arc fault of the composite oil-filled main equipment is calculated. The bubble dynamics equation is specifically as follows:

[0109] ,

[0110] Among them, the discrete summation term F n is defined as:

[0111] ,

[0112] In the formula, R is the bubble radius, is the normal velocity of the bubble surface, is the normal acceleration of the bubble surface, c is the distance between the bubble center and the fluid domain boundary,p c is the pressure at the fluid domain boundary δ is the specific heat ratio ρ is the fluid density ξ is the fluid surface tension coefficient W arc is the arc energy α is the heat transfer coefficient μ is the fluid viscosity N is the total number of calculation steps p c,n For the n th calculation step, the pressure at the fluid domain boundary is the calculation step size t n For the n th calculation step, the corresponding time

[0113] 3. Solve the mechanical output responses of each sample point

[0114] The material property parameters of the solid domain include: the material parameters of the aluminum foam core layer and the material property parameters of other structures in the solid domain except the aluminum foam core layer. Among them, the material property parameters of other structures in the solid domain except the aluminum foam core layer are directly set in the ANSYS software, and the material parameters of the aluminum foam core layer are represented according to the Hanssen aluminum foam hardening model with density parameters

[0115] The specific solution process is as follows

[0116] 1) Based on the Hanssen aluminum foam hardening model with density parameters, fit the mathematical model of the mechanical behavior of the aluminum foam core layer under external force at a general density. The specific fitting method is as follows

[0117] First, select several aluminum foam core layer samples with different densities, and obtain the respective material parameters of the above-mentioned aluminum foam core layer samples with different densities through quasi-static compression tests σ P , γ, α 2 、β , where σ P is the plateau stress γ is the hardening coefficient α 2 is the scale factor β is the shape factor

[0118] Then, substitute the respective material parameters of the above-mentioned aluminum foam core layer samples with different densities into the following formula, and express the material parameter σ P 、 γ, α 2 、β as a power function of the relative density, as shown in the following formula

[0119] ,

[0120] In the formula, ρ f is the density of the foam aluminum core layer material; ρ f0 is the density of the matrix aluminum material in the foam aluminum core layer material; C 01 ~ C 04 , C 11 ~ C 14 are all fitting coefficients;

[0121] Finally, based on the Hanssen hardening model of foam aluminum with density parameters, the stress-strain curve of the foam aluminum core layer is described, and a mathematical model of the mechanical behavior of the foam aluminum core layer under external force at a general density is obtained by fitting. The specific formula is:

[0122] ,

[0123] In the formula, σ is the stress, ε is the strain, ε d is the densification strain.

[0124] 2) Set the material properties of the composite oil-filled main equipment housing, the front panel 4 and the back panel 5 of the foam aluminum sandwich panel in the composite oil-filled main equipment, such as the material quality, etc., and directly select the mathematical model of the mechanical behavior of the material under external force;

[0125] 3) In the fluid and structure solution modules of ANSYS, import the pressure in the fluid domain during the arc fault of the composite oil-filled main equipment into the explosion-proof device, that is, the front panel 4 of the foam aluminum sandwich panel, and simulate the mechanical responses of the foam aluminum sandwich panel and the composite oil-filled main equipment housing to determine the energy absorption of the foam aluminum sandwich panel EA and the maximum deformation Max of the composite oil-filled main equipment D results, and optimize the mesh quality using the dynamic mesh technology based on diffusion smoothing during the solution.

[0126] Example 5

[0127] This example further limits step S4 in Example 1, specifically:

[0128] The response surface method is used to construct a response surface surrogate model. The commonly used response surface mathematical model can be expressed as:

[0129] ,

[0130] ,

[0131] wherein, x is the input variable, n is the number of design variables, m is the order of the response surface, k is an integer from 1 to m . f r ( x ) is the output value of the response surface surrogate model, β i is the polynomial coefficient, is the polynomial basis function.

[0132] Example 6

[0133] This example further defines step S5 in Example 1, specifically:

[0134] After the response surface surrogate model is constructed, the accuracy of the established response surface surrogate model is evaluated using the statistical error method. If the response surface surrogate model does not meet the accuracy requirements, return to step S2 to increase the sampling points until the constructed response surface surrogate model meets the accuracy requirements. Common accuracy indicators include:

[0135] Coefficient of determination R 2 , and the calculation formula is:

[0136] ,

[0137] Root mean square error e RMS , and the calculation formula is:

[0138] ,

[0139] Relative error e i , and the calculation formula is:

[0140] ,

[0141] In the formula, y i and i are the surrogate model response and the finite element simulation response of the i th sampling point respectively; is the average value of the finite element simulation responses of the sample points, H is the total number of sampling points.

[0142] Example 7

[0143] This embodiment further defines step S6 in Embodiment 1, specifically as follows:

[0144] To maximize the energy absorption of the aluminum foam sandwich panel EA and minimize the maximum deformation Max of the composite oil-filled main equipment D as the multi-objective function, solve the multi-objective function to obtain the Pareto optimal solution set of the multi-objective function. Specifically, select the multi-objective particle swarm optimization algorithm to obtain the Pareto optimal solution set. The steps are as follows:

[0145] 1) Define the dimension of the particle search space as the number of independent variables of the multi-objective function, and initialize the particles and velocities using a random function;

[0146] 2) Update the particle velocities and positions according to the following formula. By comparing the multi-objective function values of each particle, evaluate the quality of the particle's position and decide whether to update the individual historical best position p best and the population global historical optimal position g best :

[0147] ,

[0148] ,

[0149] where v i v(t) is the velocity of the particle i at time t , x i x(t) is the position of the particle i at time t ,ω is the inertia factor, ψ c1 and c c2 are the learning factors from the individual best and the global best; c r r1 and r r2 are random numbers distributed between 0 and 1, x pbest pbest is the optimal position encountered by the particle in history, x gbest gbest is the optimal position encountered by all particles in history.

[0150] 3) Determine whether the termination condition is satisfied, that is, reach the predetermined maximum number of iterations. If not, continue to update the particle positions and velocities.

[0151] Embodiment 8

[0152] As Figure 1As shown, the composite oil-filled main equipment in this embodiment is a new type of composite oil-filled main equipment formed by structurally combining the body structure of an oil-immersed power transformer and a foam aluminum sandwich panel assembly. Among them, the oil-immersed power transformer belongs to one of the oil-filled main equipment.

[0153] The foam aluminum sandwich panel of this embodiment is installed on the inner wall 6 of the composite oil-filled main equipment housing. The foam aluminum sandwich panel includes three layers of foam aluminum core layers: the first foam aluminum core layer 1, the second foam aluminum core layer 2, and the third foam aluminum core layer 3. The front panel 4 is in contact with the insulating oil, and the back panel 5 is tightly connected to the inner wall 6 of the composite oil-filled main equipment housing. From the back panel 5 to the front panel 4, the third foam aluminum core layer 3, the second foam aluminum core layer 2, and the first foam aluminum core layer 1 are arranged in sequence in the middle.

[0154] As Figure 4 shown, the foam aluminum sandwich panel structure optimization method of the composite oil-filled main equipment based on the response surface surrogate model in this embodiment includes the following steps;

[0155] Step 1: Establish a finite element model of the composite oil-filled main equipment including the foam aluminum sandwich panel. In this embodiment, it is a 1m 3 cubic transformer oil tank, that is, a 1m 3 composite oil-filled main equipment. Ignore small components such as the mounting screws, nuts, and mounting counterbores of the riser that have little impact on the overall simulation results. Use Spaceclaim software to draw a reasonably simplified finite element three-dimensional model. As Figure 5 shown, the thicknesses of the front panel 4 and the back panel 5 of the foam aluminum sandwich panel are both 1mm, and the thicknesses of the three foam aluminum core layers are all 6mm respectively. The total thickness of the foam aluminum sandwich panel is 20mm.

[0156] Step 2: Determine the design variable sampling points through experimental design. Select the densities of the three layers of foam aluminum core layers as the design variables. Considering the actual preparation process of the foam aluminum core layer, the value ranges of the three foam aluminum core layer density design variables are 150 kg / m 3 ≤ ρ 1, ρ 2, ρ 3 ≤ 650 kg / m 3 , where ρ 1 is the density of the first foam aluminum core layer 1, ρ 2 is the density of the second foam aluminum core layer 2, ρ 3 is the density of the third foam aluminum core layer 3. Use the optimal Latin hypercube sampling method to randomly extract 50 sample points in this interval to fit the response surface surrogate model.

[0157] Step 3: Fit the foam aluminum hardening model. Based on the foam aluminum hardening model with density parameters by Hanssen, parameter fitting is carried out by the least squares method for the following formula:

[0158] ,

[0159] Fitting results C 01 , C 02 , C 03 , C 04 , C 11 , C 12 , C 13 , C 14 are 4.48, 24.48, -0.25, 0.72, 1908, 0.45, 6.25, 0.13 respectively; σ P , γ , α 2, β corresponding respectively n 1, n 2, n 3, n 4 are 3.77, -2.65, 0.74, -1.46 in turn.

[0160] Substitute the results into the Hanssen foam aluminum hardening model with density parameters to obtain the stress-strain curve of the foam aluminum core layer varying with density:

[0161] ;

[0162] Step 4: Import the finite element model of the composite oil-filled main equipment of the foam aluminum sandwich panel drawn in Step 1 into the fluid and structure solving modules of ANSYS. Divide the mesh. As Figure 6 shown, the average element masses of the solid domain and the fluid domain are 0.621 and 0.828 respectively, and it is considered that the calculation accuracy requirements have been met.

[0163] The fluid part is compressible insulating oil, and the relevant parameters are: density is 895 kg / m 3 , bulk modulus is 1.2×109 MPa, kinematic viscosity coefficient is 9.6×10 −6 m 2 / s, surface tension coefficient is 0.03 N / m, and the k - ω turbulence model is selected for simulation:

[0164] ;

[0165] ;

[0166] Step 5: Describe the change of the bubble boundary using the bubble dynamics equation:

[0167] ,

[0168] where the discrete summation term F n is defined as:

[0169] .

[0170] Step 6: Optimize the mesh quality using the dynamic mesh technology based on diffusion smoothing during the solution process. In this embodiment, the fault energy is set to 5 MJ, the calculation time is 80 ms, the calculation step size is 0.1 ms, and the oil pressure during the arc fault is calculated.

[0171] Step 7: Set the material properties of the solid domain of the device structure. The parameters of the aluminum foam core layer are obtained from Step 3 of this embodiment. The remaining materials, including the back panel 5, the front panel 4, and the composite oil-filled main device housing, are all set to steel. Select the bilinear isotropic hardening model, and the specific parameters are: density 7850 kg / m 3 , Young's modulus is 206 GPa, Poisson's ratio is 0.3, tangent modulus is 6450 MPa, and yield stress is 235 MPa. A bonded contact is adopted between the front panel 4 of the aluminum foam sandwich panel and the first aluminum foam core layer 1, and between the back panel 5 and the third aluminum foam core layer 3. An automatic face-to-face contact option is adopted between the first aluminum foam core layer 1 and the second aluminum foam core layer 2, and between the second aluminum foam core layer 2 and the third aluminum foam core layer 3. A bonded contact is adopted between the back panel 5 and the inner wall 6 of the composite oil-filled main device housing.

[0172] Step 8: Export the pressure obtained from the fluid calculation part to act on the front panel 4 of the aluminum foam sandwich panel, and use the explicit dynamics method to obtain the energy absorption of the aluminum foam sandwich panel and the maximum deformation of the inner wall 6 of the composite oil-filled main device housing.

[0173] Step 9: Repeat the finite element calculation according to the parameters given by the sample points.

[0174] Step 10: Select the response surface method to construct a surrogate model based on the energy absorption of the aluminum foam sandwich panel and the maximum deformation of the inner wall 6 of the composite oil-filled main device housing at the finite element calculation sample points. In this embodiment, a complete cubic polynomial is used to construct the surrogate model. Then, accuracy evaluation is carried out on the established surrogate model. Common indicators include:

[0175] Coefficient of determination R 2 , and the calculation formula is:

[0176] ,

[0177] Root Mean Square Error e RMS , the calculation formula is:

[0178] ,

[0179] Relative Error e i , the calculation formula is:

[0180] .

[0181] Step 11: The multi-objective function is to minimize the maximum deformation of the inner wall 6 of the composite oil-filled main equipment housing Max D and maximize the energy absorption of the aluminum foam sandwich panel EA.

[0182] The mathematical expression of the optimization problem is as follows:

[0183] ,

[0184] In the formula, ρ min is the minimum density that each aluminum foam core layer can take, ρ max is the maximum density that each aluminum foam core layer can take.

[0185] Using the multi-objective particle swarm optimization algorithm, first initialize the particles and velocities using a random function; then update the particle velocities and positions according to the following formula, and evaluate the quality of the positions of the particles by comparing the fitness function values of each particle to determine whether to update the individual historical best positions ( p best ) and the global historical optimal position of the population ( g best );

[0186] Update the particle velocities and positions according to the following formula:

[0187] ,

[0188] .

[0189] Judge whether the termination condition is satisfied, that is, the predetermined maximum number of iterations is reached. If not, return to update the particle positions.

[0190] The optimization solution obtains Max D and EA The Pareto front between is as Figure 7 shown.

[0191] Take the aluminum foam sandwich panel with a three-layer aluminum foam core layer of uniform density as the control group, and compare the protection performance of the aluminum foam sandwich panels of equal mass before and after optimization for the composite oil-filled main equipment:

[0192] Taking a 2MJ fault arc energy as an example, the density of the aluminum foam core layer before optimization is 320 kg / m 3 , and select the density of the aluminum foam core layer after optimization ρ 1, ρ 2, ρ 3 to be 329 kg / m 3 , 355 kg / m 3 , 277 kg / m 3 respectively. The maximum deformation of the inner wall 6 of the composite oil-filled main equipment housing with the optimized aluminum foam sandwich panel installed decreases from 9.8 mm to 8.2 mm, a decrease of 16.3%. The energy absorption of the aluminum foam sandwich panel increases from 58.9 kJ to 64.3 kJ, an increase of 9.2%. The safety performance of the composite oil-filled main equipment is improved.

[0193] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be understood that the specific embodiments of the present invention are limited thereto. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the premise of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the patent protection scope determined by the claims submitted for the present invention.

Claims

1. A method for optimizing the structure of a composite oil-filled main equipment foam aluminum sandwich panel, characterized in that: The following steps are involved: S1. Establish a finite element model of a composite oil-filled main device including a foam aluminum sandwich panel and determine the design variables of the composite oil-filled main device; S2. Randomly sample the design variables to generate sample points; S3. The composite oil-filled main device finite element model is imported into the fluid and structure solving module of ANSYS, and the fluid domain and the solid domain are divided in the composite oil-filled main device finite element model; Select the fluid properties and k - ω Turbulence model, using bubble dynamics equation, setting calculation step size and calculation time, to obtain the pressure of fluid domain during arc fault of composite oil-filled main equipment; The pressure of the fluid domain is transferred to the solid domain through the fluid-solid coupling surface, the material property parameters of the solid domain are set, and the finite element calculation is performed on the sample points to obtain the mechanical output response of each sample point; S4. Based on the mechanical output response of each sample point, a response surface proxy model between the design variables and the mechanical output response is constructed using the response surface methodology; S5. Evaluate the accuracy of the response surface proxy model. If the accuracy does not meet the requirements, return to step S2 to increase the sampling points until the response surface proxy model meets the accuracy requirements; S6. Based on the response surface surrogate model that meets the accuracy requirements, a multi-objective function is defined, and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective function to obtain the Pareto optimal solution set.

2. The structural optimization method of composite oil-filled main equipment foam aluminum sandwich panel according to claim 1 is characterized in that: The foam aluminum sandwich panel in S1 is installed on the inner wall (6) of the composite oil-filled main equipment shell; The foam aluminum sandwich panel comprises: a front panel (4), a foam aluminum core layer and a back panel (5) stacked in sequence, the back panel (5) being bonded to the inner wall (6) of the composite oil-filled main equipment shell; The design variables include the density and thickness of the foam aluminum core layer, the thickness of the front panel (4) and the thickness of the back panel (5).

3. The structural optimization method of composite oil-filled main equipment foam aluminum sandwich panel according to claim 2, characterized in that: The solid domain material property parameters in S3 include material property parameters of the foam aluminum core layer, which are constructed based on the Hanssen foam aluminum hardening model containing density parameters. The specific formula of the Hanssen foam aluminum hardening model containing density parameters is: （1） In the formula, σ is stress, σ P is the platform stress, γ is the hardening coefficient ，α 2 is the scale factor, β is the shape factor, ε For strain, ε d For compaction strain.

4. The structural optimization method of composite oil-filled main equipment foam aluminum sandwich panel according to claim 1, characterized in that: As described in S3 k - ω The turbulence model is shown in the following equation: （2） （3） In the formula, k is the turbulent kinetic energy, ω is the specific dissipation rate, ρ m is the fluid mixture density, μ m is the molecular viscosity of the fluid, u m is the fluid velocity, μ t,m is the turbulent viscosity of the fluid, σ k and σ ω is the turbulent Prandtl number, G k,m Turbulent kinetic energy k The generated items, G ω,m is the specific dissipation rate ω The generated items; Y k,m and Y ω,m The turbulence caused k and ω The dissipation of t For time.

5. The structural optimization method of composite oil-filled main equipment foam aluminum sandwich panel according to claim 1 or claim 4, characterized in that: The bubble dynamics equation in S3 is: （4） Among them, the discrete summation term F n Defined as: （5） In the formula, R is the bubble radius, is the normal velocity of the bubble surface, is the normal acceleration of the bubble surface, c is the distance between the bubble center and the fluid domain boundary, p c is the pressure at the boundary of the fluid domain, δ is the specific heat ratio, ρ is the fluid density, ξ is the surface tension coefficient of the fluid, W arc is the arc energy, α is the heat transfer coefficient, μ is the fluid viscosity, N is the total number of calculation steps, p c,n For the n The boundary pressure of the fluid domain in each calculation step is: To calculate the step size, t n For the n The time corresponding to a calculation step.

6. The structural optimization method of composite oil-filled main equipment foam aluminum sandwich panel according to claim 1 is characterized in that: The multi-objective function in S6 is defined as minimizing the maximum deformation of the composite oil-filled main device and maximizing the energy absorption of the foam aluminum sandwich panel.

7. The method for optimizing the structure of the composite oil-filled main equipment foam aluminum sandwich panel according to claim 1 or claim 6, characterized in that: S6 uses a multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain a Pareto optimal solution set. The specific steps are: 1) Define the dimension of the particle search space as the number of independent variables of the multi-objective function, and use random functions to initialize particles and speeds; 2) Update the particle speed and position, evaluate the quality of the particle's position by comparing the multi-objective function value of each particle, and decide whether to re-update the individual's historical best position and the population's global historical best position; 3) Determine whether the predetermined maximum number of iterations has been reached. If not, return to step 2) to continue updating the particle speed and position. If the predetermined maximum number of iterations has been reached, terminate the calculation and obtain the Pareto optimal solution set. The particle velocity and position described in step 2) are updated by the following formula: （6） （7） In the formula, v i (t) is the particle i In time t speed, v i (t+1) is the particle i In time t +1 for speed, x i (t) is the particle i In time t location, x i (t+1) is the particle i In time t +1 position, ψ is the inertia factor, c 1 and c 2 is the learning factor from the individual best and the global best; r 1 and r 2 is a random number distributed between 0 and 1, x pbest is the optimal position the particle has ever encountered, x gbest is the optimal position ever encountered by all particles.

8. A system for optimizing the structure of composite oil-filled main equipment foam aluminum sandwich panels, characterized in that: include: Fluid-solid coupling multi-domain modeling module, used to establish a finite element model of the composite oil-filled main equipment including foam aluminum sandwich panels and determine the design variables of the composite oil-filled main equipment; Design variable random sampling module, used to randomly sample design variables and generate sample points; Fluid-solid coupling mechanical analysis module, used to import the finite element model of the composite oil-filled main equipment into the fluid and structure solution module of ANSYS, and divide the fluid domain and solid domain in the finite element model of the composite oil-filled main equipment; Select the fluid properties and k - ω Turbulence model, using bubble dynamics equation, setting calculation step size and calculation time, to obtain the pressure of fluid domain during arc fault of composite oil-filled main equipment; The pressure of the fluid domain is transferred to the solid domain through the fluid-solid coupling surface, the material property parameters of the solid domain are set, and the finite element calculation is performed on the sample points to obtain the mechanical output response of each sample point; A proxy model construction module is used to construct a response surface proxy model between design variables and mechanical output responses based on the mechanical output responses of each sample point using the response surface method; The proxy model accuracy iteration module is used to evaluate the accuracy of the response surface proxy model. If the accuracy does not meet the requirements, it returns to the design variable random sampling module to increase the sampling points until the response surface proxy model meets the accuracy requirements. The objective function solving module is used to define multi-objective functions based on a response surface proxy model that meets the accuracy requirements, and use a multi-objective particle swarm optimization algorithm to solve the multi-objective function to obtain a Pareto optimal solution set.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for optimizing the structure of the foam aluminum sandwich panel of the composite oil-filled main equipment according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the structure of the foam aluminum sandwich panel of the composite oil-filled main equipment according to any one of claims 1 to 7 are implemented.

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