Composite oil-filled main equipment foamed aluminum sandwich panel structure optimization method and related device

Abstract

The invention belongs to the technical field of power system transformer non-electric quantity protection, and discloses a composite oil-filled main equipment foamed aluminum sandwich panel structure optimization method and a related device. Aiming at the problem of insufficient anti-explosion performance of the existing composite oil-filled main equipment under the high-energy arc fault, the method comprises the following steps: firstly, establishing a finite element model of the composite oil-filled main equipment containing the foamed aluminum sandwich panel, dividing a fluid domain and a solid domain in the finite element model, setting related material attributes and parameters, and determining sample points through random sampling; a mechanical output response is obtained through finite element calculation, a response surface proxy model is constructed, and a Pareto solution set is solved by using a multi-objective particle swarm optimization algorithm by taking maximization of energy absorption of a foamed aluminum sandwich panel and minimization of maximum deformation of a composite oil charge main equipment shell as a multi-objective function. The method can effectively improve the anti-explosion performance of the oil-filled main equipment, improves the optimization efficiency, reduces the arc fault explosion risk of the composite oil-filled main equipment, and has important engineering application value.

Description

Technical Field

[0001] The invention belongs to the technical field of non-electrical quantity protection for transformers in power systems, and in particular relates to a method for optimizing the structure of a foam aluminum sandwich panel of a composite oil-filled main equipment and a related device. 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, mutual inductors and switchgear are widely used in power transmission and conversion, and mineral insulating oil is usually used as a cooling and insulating medium inside the equipment. When a short circuit occurs inside an oil-filled power device, the high-energy arc vaporizes and cracks the surrounding insulating oil, producing a large amount of flammable short-chain hydrocarbon gas and forming rapidly expanding bubbles, which in turn causes the oil pressure inside the equipment to increase. 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, the frequency of explosion accidents in oil-filled power equipment remains high when facing high-energy arc faults due to the delayed action of the relay protection and the insufficient pressure relief capacity of the pressure relief device.

[0003] Aluminum foam sandwich panels have been widely used in aerospace, automotive and other fields due to their light weight and good energy absorption effect. Their application effect in explosion-proof of power equipment has also been verified. However, there is currently a lack of scientific optimization methods to design the aluminum foam sandwich panel structure in composite oil-filled main equipment, so that it can fully exert its energy absorption effect under arc faults and further improve its explosion-proof performance in composite oil-filled main equipment. Summary of the invention

[0004] In order to overcome the shortcomings of the above-mentioned prior art that lacks a scientific optimization method to design the foam aluminum sandwich panel structure in the composite oil-filled main equipment, resulting in the foam aluminum sandwich panel structure not being able to fully exert its energy absorption and explosion-proof performance during arc faults, the present invention provides a method for optimizing the foam aluminum sandwich panel structure of the composite oil-filled main equipment and related devices, which can effectively improve the explosion-proof performance of the composite oil-filled main equipment under high-energy arc faults.

[0005] To achieve the above object, the present invention adopts the following technical solutions: The first object of the present invention is to propose a method for optimizing the structure of a composite oil-filled main equipment foam aluminum sandwich panel, comprising the following steps: 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 - oh 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.

[0006] Further, in said S1, the foam aluminum sandwich panel is installed on the inner wall of the composite oil-filled main equipment shell; The foam aluminum sandwich panel comprises: a front panel, a foam aluminum 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 shell; The design variables include the density and thickness of the foam aluminum core layer, the thickness of the front panel and the thickness of the back panel.

[0007] Furthermore, the material property parameters of the solid domain in S3 include material property parameters of the foam aluminum core layer, and the material property parameters of the foam aluminum core layer 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, s is stress, s P is the platform stress, c is the hardening coefficient ，α 2 is the scale factor, β is the shape factor, e For strain, e d For dense strain.

[0008] Furthermore, in S3 k - oh The turbulence model is shown in the following equation: (2) (3) In the formula, k is the turbulent kinetic energy, oh is the specific dissipation rate, r m is the fluid mixture density, m m is the molecular viscosity of the fluid, u m is the fluid velocity, m t,m is the turbulent viscosity of the fluid, s k and s ω is the turbulent Prandtl number, G k,m Turbulent kinetic energy k The generated items, G ω,m is the specific dissipation rate oh The generated items, Y k,m and Y ω,m The turbulence caused k and oh dissipation, t For time.

[0009] Furthermore, 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, d is the specific heat ratio, r is the fluid density, x is the surface tension coefficient of the fluid, W arc is the arc energy, α is the heat transfer coefficient, m 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, tn For the n The time corresponding to a calculation step.

[0010] Furthermore, 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 sandwich panel.

[0011] Furthermore, the S6 uses a multi-objective particle swarm optimization algorithm to optimize and solve the multi-objective function to obtain a Pareto optimal solution set, and 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 velocities; 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, x i (t) is the particle i In time t location, 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.

[0012] The second object of the present invention is to provide a system for optimizing the structure of a composite oil-filled main equipment foam aluminum sandwich panel, comprising: 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 - oh 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.

[0013] The third object of the present invention is to propose an electronic device, comprising 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 a method for optimizing the structure of a composite oil-filled main equipment foam aluminum sandwich panel are implemented.

[0014] The fourth object of the present invention is to provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the steps of the method for optimizing the structure of the foam aluminum sandwich panel of the composite oil-filled main equipment.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method for optimizing the structure of a foam aluminum sandwich panel of a composite oil-filled main equipment. First, a finite element model of the composite oil-filled main equipment is established, and the arc fault and the mechanical output response of the foam aluminum sandwich panel are analyzed by a bubble-fluid-structure coupling numerical method. Based on the simulation results, a response surface proxy model is used to fit the functional relationship between the design variables of the composite oil-filled main equipment and the mechanical output response. Finally, a multi-objective particle swarm optimization algorithm is used to solve the multi-objective function defined based on the response surface proxy model to obtain a Pareto optimal solution set. In order to improve the explosion-proof performance of the aluminum foam sandwich panel under high-energy arc faults, the present invention proposes a method for optimizing the structure of a foam aluminum sandwich panel of a composite oil-filled main equipment based on a response surface proxy model, which promotes 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 proxy model, the optimization efficiency of the foam aluminum sandwich panel structure of the composite oil-filled main equipment has been greatly improved, and it has stronger engineering practicality. At the same time, the multi-particle swarm optimization algorithm converges quickly. By optimizing the Pareto solution set of the multi-objective function, the composite oil-filled main equipment can maintain good structural strength while absorbing the arc impact energy to the maximum extent. The maximum stress and deformation of the composite oil-filled main equipment are significantly reduced, thereby improving the overall safety of the composite oil-filled main equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a schematic diagram of the structure of a foam aluminum sandwich panel according to an embodiment of the present invention.

[0017] Figure 2 The figure is a schematic diagram of an optimization process of an embodiment of the present invention.

[0018] Figure 3 A system schematic diagram of an embodiment of the present invention.

[0019] Figure 4 The figure is a schematic diagram of the optimization process of the preferred embodiment of the present invention.

[0020] Figure 5 This is a finite element model diagram of the composite oil-filled main equipment of the preferred embodiment of the present invention.

[0021] Figure 6 This is a mesh division diagram of the finite element model of the composite oil-filled main equipment of the preferred embodiment of the present invention.

[0022] Figure 7 This is a Pareto front diagram obtained by optimization of a preferred embodiment of the present invention.

[0023] Among them, 1-the first foam aluminum core layer, 2-the second foam aluminum core layer, 3-the third foam aluminum core layer, 4-the front panel, 5-the back panel, and 6-the inner wall of the composite oil-filled main equipment shell. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a method, system, device or storage medium comprising a series of steps is not necessarily limited to those steps explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these methods, systems, devices or storage media.

[0026] The composite oil-filled main equipment described in the present application refers to a new type of composite oil-filled main equipment which is based on the main structure of the oil-filled main equipment and has a foam aluminum sandwich panel installed inside. The main structure of the oil-filled main equipment and the foam aluminum sandwich panel are structurally composited. This type of composite oil-filled main equipment is generally used to improve the explosion-proof performance of the main structure of the oil-filled main equipment under internal arc short-circuit faults.

[0027] The present invention is further described in detail below in conjunction with the accompanying drawings: Example 1 See also Figure 2 , is a schematic flow chart of the structural optimization method of the composite oil-filled main equipment foam aluminum sandwich panel in the present invention, which specifically includes the following steps: 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. Importing the finite element model of the composite oil-filled main equipment into the fluid and structure solving module of ANSYS (a large-scale general-purpose finite element analysis software developed by ANSYS), and dividing the fluid domain and the solid domain in the finite element model of the composite oil-filled main equipment; Select the fluid properties and k - oh 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.

[0028] This embodiment also proposes a system for optimizing the structure of the composite oil-filled main equipment foam aluminum sandwich panel, see Figure 3 , including the following modules: 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 - oh 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 the response surface surrogate model, and solve the multi-objective functions using the multi-objective particle swarm optimization algorithm to obtain the Pareto optimal solution set.

[0029] 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 aforementioned computer program of this embodiment, the steps of the method for optimizing the structure of the foam aluminum sandwich panel of the composite oil-filled main equipment are implemented.

[0030] Finally, this embodiment proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for optimizing the foam aluminum sandwich panel structure of the composite oil-filled main equipment proposed in the embodiment are implemented.

[0031] Example 2 This embodiment further defines step S1 in embodiment 1, specifically: According to the geometric parameters of the composite oil-filled main equipment containing foam aluminum sandwich panels, a 1:1 proportional three-dimensional simulation model of the composite oil-filled main equipment was established using Spaceclaim software. The components in the structure of the composite oil-filled main equipment that have little influence on the simulation results were appropriately simplified to reduce the calculation complexity. The three-dimensional simulation model was divided by finite element to obtain the finite element model of the composite oil-filled main equipment.

[0032] For further reference, Figure 1 The finite element model of the composite oil-filled main equipment includes a composite oil-filled main equipment shell and a foam aluminum sandwich panel installed on the inner wall 6 of the composite oil-filled main equipment shell. The foam aluminum sandwich panel may include a single layer or multiple layers of foam aluminum core layer.

[0033] Furthermore, the foam aluminum sandwich panel includes a front panel 4 , a back panel 5 , and a foam aluminum core layer between the front panel 4 and the back panel 5 .

[0034] Furthermore, the back panel 5 of the foam aluminum sandwich panel is attached to the inner wall 6 of the composite oil-filled main equipment shell, and the front panel 4 is directly in contact with the insulating oil poured into the composite oil-filled main equipment.

[0035] The density and thickness of the foam core layer and the thickness of the front panel 4 and the thickness of the back panel 5 are selected as design variables, and the multi-dimensional variable design space is determined according to the actual preparation process.

[0036] Example 3 This embodiment further defines step S2 in embodiment 1, specifically: Randomly sample the design variables in the design variable space and extract a certain number of sample points. The main steps of this method are: 1) Determine the dimension of the variable to be sampled, and divide the value interval of each variable into several equal-width and non-overlapping sub-intervals; 2) Perform independent random permutations on the subintervals of each variable; 3) Extract a sample point in each subinterval, and the sampling position is a random point in the subinterval; 4) For each variable in i The value combination extracted from the positions forms the i Multidimensional sample points.

[0037] Example 4 This embodiment further defines step S3 in embodiment 1, specifically: 1. Import the finite element model of the composite oil-filled main equipment established by S1 into the fluid and structure solution module of ANSYS, divide the fluid domain and 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; 2. Determine the pressure in the fluid domain during an arc fault in a composite oil-filled main device First, the fluid properties are set to the material properties of the fluid filled in the composite oil-filled main device, such as the material properties of the compressible hydraulic oil, including density, bulk modulus, kinematic viscosity coefficient, surface tension coefficient, etc.; Then, select the k - oh Turbulence models, k - oh The turbulence model is as follows: , , in, k is the turbulent kinetic energy, oh is the specific dissipation rate, r m is the fluid mixture density, m m is the molecular viscosity of the fluid, u m is the fluid velocity, m t,m is the turbulent viscosity of the fluid, s k and s ω is the turbulent Prandtl number, G k,m Turbulent kinetic energy k The generated items, G ω,m is the specific dissipation rate oh The generated items; Y k,m and Y ω,m The turbulence caused k and oh dissipation, t For time.

[0038] Next, the bubble dynamics equation is used to characterize the evolution of the bubble radius induced by the internal fault arc of the oil-immersed power equipment, thereby simulating the internal fluid movement driven by bubbles. The calculation step size and calculation time are set to calculate the pressure of the fluid domain during the arc fault of the composite oil-filled main equipment. The bubble dynamics equation is specifically as follows: , Among them, the discrete summation term F n Defined as: , 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, d is the specific heat ratio, r is the fluid density, x is the surface tension coefficient of the fluid, W arc is the arc energy, α is the heat transfer coefficient, m 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.

[0039] 3. Solve the mechanical output response of each sample point The solid domain material property parameters include: material parameters of the foam aluminum core layer and property parameters of materials of other structures in the solid domain except the foam aluminum core layer: the property parameters of the materials of other structures in the solid domain except the foam aluminum core layer are directly set in the ANSYS software, and the material parameters of the foam aluminum core layer are represented according to the Hanssen foam aluminum hardening model containing density parameters.

[0040] The specific solution process is as follows: 1) Based on Hanssen's aluminum foam hardening model with density parameters, a mathematical model of the mechanical behavior of the aluminum foam core layer under external force at general density is fitted. The specific fitting method is as follows: First, several foam aluminum core layer samples with different densities are selected, and the material parameters of the foam aluminum core layer samples with different densities are obtained through quasi-static compression tests. s P 、c、a 2 、β ,in s P is the platform stress, c is the hardening coefficient, α2 is the scale factor, β is the shape factor; Next, the material parameters of the foam aluminum core samples with different densities are substituted into the following formula: s P 、 c、a 2 、β It is expressed as a power function of relative density, as shown below: , In the formula, r f is the density of the foam aluminum core material; r f0 It is the density of the matrix aluminum material in the foam aluminum core material; C 01 ~ C 04 , C 11 ~ C 14 All are fitting coefficients; Finally, based on Hanssen's foam aluminum hardening model with density parameters, the stress-strain curve of the foam aluminum core layer is described, and the mathematical model of the mechanical behavior of the foam aluminum core layer under external force at a general density is fitted. The specific formula is: , In the formula, s is stress, e For strain, e d For dense strain.

[0041] 2) Setting the material properties of the composite oil-filled main equipment shell, the front panel 4 and the back panel 5 of the foam aluminum sandwich panel in the composite oil-filled main equipment, such as material quality, etc., and directly selecting a mathematical model using the mechanical behavior of the material quality under the action of external force; 3) In the fluid and structure solving module of ANSYS, the pressure of the fluid domain during the arc fault of the composite oil-filled main equipment is introduced into the explosion-proof device, that is, the front panel 4 of the foam aluminum sandwich panel, and the mechanical response of the foam aluminum sandwich panel and the shell of the composite oil-filled main equipment is simulated to determine the energy absorption of the foam aluminum sandwich panel. EA And the maximum deformation of the composite oil-filled main equipment Max D As a result, the mesh quality is optimized during solution using a dynamic meshing technique based on diffusion smoothing.

[0042] Example 5 This embodiment further defines step S4 in embodiment 1, specifically: The response surface method is used to construct the response surface surrogate model. The commonly used response surface mathematical model can be expressed as: , , in, x is the input variable, n is the number of design variables, m is the response surface order, k From 1 to m integer, f r ( x ) is the output value of the response surface surrogate model, β i are the polynomial coefficients, is a polynomial basis function.

[0043] Example 6 This embodiment further defines step S5 in embodiment 1, specifically: After the response surface proxy model is constructed, the statistical error method is used to evaluate the accuracy of the established response surface proxy model. If the response surface proxy model does not meet the accuracy requirements, return to step S2 to increase sampling points until the constructed response surface proxy model meets the accuracy requirements. Commonly used accuracy indicators are: Coefficient of determination R 2 , the calculation formula is: , Root mean square error e RMS , the calculation formula is: , Relative error e i , the calculation formula is: , In the formula, y i and i Separate i The surrogate model response and finite element simulation response of each sampling point; is the average value of the finite element simulation response of the sample points, H is the total number of sampling points.

[0044] Example 7 This embodiment further defines step S6 in embodiment 1, specifically: To maximize the energy absorption of foam aluminum sandwich panels EA , minimize the maximum deformation of the composite oil-filled main equipment Max Dis a multi-objective function, and the multi-objective function is solved to obtain the Pareto optimal solution set of the multi-objective function. Specifically, a multi-objective particle swarm optimization algorithm is selected to obtain the Pareto optimal solution set. The steps include: 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 velocities; 2) Update the particle speed and position according to the following formula, 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 p best and the global historical optimal position of the population g best : , , in, v i (t) is the particle i In time t speed, x i (t) is the particle i In time t location, 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.

[0045] 3) Determine whether the termination condition is met, that is, whether the predetermined maximum number of iterations is reached. If not, continue to update the particle position and velocity.

[0046] Example 8 like Figure 1 As shown, the composite oil-filled main equipment in this embodiment is a new type of composite oil-filled main equipment composed of the main structure of an oil-immersed power transformer and a foam aluminum sandwich panel assembly through structural composite, wherein the oil-immersed power transformer is a type of oil-filled main equipment.

[0047] The foam aluminum sandwich panel of this embodiment is installed on the inner wall 6 of the composite oil-filled main equipment shell. The foam aluminum sandwich panel includes three foam aluminum core layers: a first foam aluminum core layer 1, a second foam aluminum core layer 2 and a 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 shell. 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.

[0048] like Figure 4 As shown, the structural optimization method of the composite oil-filled main equipment foam aluminum sandwich panel based on the response surface proxy model in this embodiment includes the following steps: Step 1: Establish a finite element model of the composite oil-filled main equipment including the foam aluminum sandwich panel. In this embodiment, the finite element model is 1m 3 cubic transformer tank, i.e. 1m 3 The composite oil-filled main equipment ignores the small parts such as the mounting screws, nuts and mounting countersunk holes of the riser that have little effect on the overall simulation results, and uses Spaceclaim software to draw a reasonably simplified finite element 3D model, such as Figure 5 As shown, the thickness of the front panel 4 and the back panel 5 of the foam aluminum sandwich panel are both 1 mm, the thickness of the three foam aluminum core layers are respectively 6 mm, and the total thickness of the foam aluminum sandwich panel is 20 mm.

[0049] Step 2: Experimental design Determine the sampling points of the design variables and select the density of the three-layer aluminum foam core layer as the design variable. Considering the actual preparation process of the aluminum foam core layer, the value range of the three aluminum foam core layer density design variables is 150 kg / m 3 ≤ r 1, r 2, r 3≤650 kg / m 3 ,in r 1 is the density of the first foam aluminum core layer 1, r 2 is the density of the second foam aluminum core layer 2, r 3 is the density of the third foam aluminum core layer 3, and 50 sample points are randomly selected in this interval by using the optimal Latin hypercube sampling method to fit the response surface proxy model.

[0050] Step 3: Fitting the foam aluminum hardening model, based on the Hanssen foam aluminum hardening model with density parameters, the following parameter fitting is performed by the least squares method: , Fitting results C 01 , C 02 , C 03 , C 04 ,C 11 , C 12 , C 13 , C 14 They are 4.48, 24.48, -0.25, 0.72, 1908, 0.45, 6.25, 0.13 respectively; s P , c , α 2, β Corresponding to n 1, n 2, n 3, n 4 are 3.77, -2.65, 0.74, and -1.46 respectively.

[0051] Substituting the results into Hanssen's aluminum foam hardening model with density parameters, the stress-strain curve of the aluminum foam core layer with density changes is obtained: ; 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 solution module of ANSYS. Divide the grid, such as Figure 6 As shown, the average unit masses of the solid domain and the fluid domain are 0.621 and 0.828 respectively, which are considered to have met the calculation accuracy requirements.

[0052] The fluid part is compressible insulating oil, and the relevant parameters are: density is 895 kg / m 3 , the bulk modulus is 1.2×109MPa, and the kinematic viscosity coefficient is 9.6×10 −6 m 2 / s, the surface tension coefficient is 0.03 N / m, and the k - oh Turbulence model for simulation: ; ; Step 5: Use the bubble dynamics equation to describe the bubble boundary changes: , Among them, the discrete summation term F n Defined as: .

[0053] Step 6: During the solution, the dynamic mesh technology based on diffusion smoothing is used to optimize the mesh quality. In this embodiment, the fault energy is set to 5MJ, the calculation time is 80 ms, the calculation step is 0.1 ms, and the oil pressure during the arc fault is calculated.

[0054] Step 7: Set the material properties of the solid domain of the device structure. The parameters of the foam aluminum 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 shell are all set to steel. Select the bilinear isotropic hardening model. 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. The front panel 4 of the foam aluminum sandwich panel and the first foam aluminum core layer 1, the back panel 5 and the third foam aluminum core layer 3 adopt binding contact, the first foam aluminum core layer 1 and the second foam aluminum core layer 2, the second foam aluminum core layer 2 and the third foam aluminum core layer 3 adopt automatic face-to-face contact options, and the back panel 5 and the inner wall 6 of the composite oil-filled main equipment shell adopt binding contact.

[0055] Step 8: The pressure obtained by the fluid calculation part is exported to act on the front panel 4 of the foam aluminum sandwich panel, and the energy absorption of the foam aluminum sandwich panel and the maximum deformation of the inner wall 6 of the composite oil-filled main equipment shell are obtained by using the display dynamics method.

[0056] Step 9: Repeat the finite element calculation according to the parameters given by the sample points.

[0057] Step 10: Based on the finite element calculation of the energy absorption of the foam aluminum sandwich panel and the maximum deformation of the inner wall 6 of the composite oil-filled main equipment shell at the sample point, the response surface method is selected to build the proxy model. In this embodiment, a complete cubic polynomial is used to build the proxy model. After that, the accuracy of the established proxy model is evaluated. The commonly used indicators are: Coefficient of determination R 2 , the calculation formula is: , Root mean square error e RMS , the calculation formula is: , Relative error e i , the calculation formula is: .

[0058] Step 11: The multi-objective function is to minimize the maximum deformation of the inner wall 6 of the composite oil-filled main equipment shell Max D , maximize the energy absorption of foam aluminum sandwich panels EA.

[0059] The optimization problem is mathematically expressed as follows: , In the formula, r min is the minimum density of each foam aluminum core layer. r max It is the maximum density that each foam aluminum core layer can take.

[0060] Using the multi-objective particle swarm optimization algorithm, we first use a random function to initialize the particles and their speeds; then we update the particle speeds and positions according to the following formula, and evaluate the quality of the particle positions by comparing the fitness function values ​​of each particle, and decide whether to re-update the individual historical best position ( p best ) and the population's global historical optimal position ( g best ); Update particle velocity and position according to the following formula: , .

[0061] Determine whether the termination condition is met, that is, whether the predetermined maximum number of iterations is reached. If not, return to update the particle position.

[0062] Optimization solution Max D and EA The Pareto front between Figure 7 shown.

[0063] The foam aluminum sandwich panel with three layers of uniform density foam aluminum core was used as the control group, and the protective performance of the foam aluminum sandwich panels of equal mass before and after optimization on the composite oil-filled main equipment was compared: Taking 2MJ fault arc energy as an example, the density of the foam aluminum core layer before optimization is 320 kg / m 3 , select the optimized foam aluminum core density r 1, r 2, r 3329 kg / m 3 、355 kg / m 3 , 277 kg / m 3 The maximum deformation of the inner wall 6 of the composite oil-filled main equipment shell installed with the optimized foam aluminum sandwich panel was reduced from 9.8 mm to 8.2 mm, a decrease of 16.3%. The energy absorption of the foam aluminum sandwich panel increased from 58.9 kJ to 64.3 kJ, an increase of 9.2%. The safety performance of the composite oil-filled main equipment was improved.

[0064] The above content is a further detailed description of the present invention in combination with a specific preferred embodiment. It should not be understood that the specific embodiments of the present invention are limited thereto. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the present invention, which should be regarded as belonging to the scope of patent protection determined by the submitted claims of 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 dissipation, 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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