Efficient structure optimization method for improving energy storage density of flywheel rotor

Abstract

The invention discloses an efficient structure optimization method for improving the energy storage density of a flywheel rotor, and relates to the technical field of new energy, and the method comprises the steps: constructing a flywheel geometric model, building a parameterized finite element model of the flywheel rotor through an ANSYS parameterized design language, and creating and storing the parameterized finite element model as a text file; based on the established parameterized finite element model, designing a fitness function and constraint conditions according to performance index requirements of the flywheel rotor, and establishing a flywheel rotor optimization mathematical model; the method comprises the following steps: writing a flywheel shape parameter optimization main program based on an MATLAB numerical calculation platform, carrying out global optimization on the flywheel rotor section thickness by using an SSA algorithm and an optimized ISSA algorithm, carrying out modeling and statics analysis verification by using optimal control point parameters obtained by optimization, carrying out contrastive analysis on the flywheel performance, and carrying out static analysis on the flywheel rotor section thickness. And evaluating the change of the performance index of the optimized flywheel. The method can solve the problems that when an integrated algorithm in finite element software is used for optimization, the process is complex, GUI operation is tedious, and the solving precision is low.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor. Background Art

[0002] Flywheel energy storage is a typical mechanical energy storage method. It achieves the purpose of energy storage and conversion by storing kinetic energy under high-speed rotation and releasing this energy for use at low speed. Since the working mode is pure physical mechanical movement, it has the advantages of high efficiency, no pollution, wide application and long life. It is widely used in the automotive industry, power system, aerospace and other fields. An important indicator for evaluating the performance of energy storage flywheels is mass energy storage density, that is, the energy stored per unit mass of the flywheel rotor. The mass energy storage density is determined by the moment of inertia and speed of the flywheel rotor. The moment of inertia of the flywheel rotor is closely related to the shape and mass of the rotor, and the maximum speed of the flywheel is limited by the strength of the flywheel rotor material. Therefore, if you want to increase the energy storage density of the flywheel, you can increase the energy storage density by increasing the size of the outer radius of the rotor. However, the actual flywheel structure design has strict size requirements, resulting in a lower energy storage density of the flywheel rotor with equal thickness. But for rotors with equal thickness, the energy storage density is related to the change of thickness along the radial direction, that is, the cross-sectional shape. Therefore, the flywheel rotor with equal thickness can be optimized by variable thickness. On the one hand, the flywheel can be designed to be lightweight, and on the other hand, the change in thickness and shape can also increase the moment of inertia of the flywheel rotor.

[0003] The shape optimization of the flywheel must consider the moment of inertia, mass and stress distribution inside the flywheel, which cannot be described by a unified mathematical formula; traditional mathematical optimization methods are powerless, and only intelligent algorithms such as genetic algorithms, evolutionary programming, evolutionary strategies, particle swarm algorithms, and differential evolution algorithms can be used. It is necessary to comprehensively consider the constraints of structural stress and mass. Therefore, how to design the variable thickness shape of the flywheel rotor becomes extremely critical. At present, although there have been intelligent optimization algorithms used to calculate the variable thickness of flywheels, most of them are optimized using integrated algorithms in finite element software, which has complex processes, cumbersome GUI operations, and low solution accuracy. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an efficient structural optimization method for improving the energy storage density of a flywheel rotor, which can solve the problems of traditional optimization using integrated algorithms in finite element software, complex processes, cumbersome GUI operations, and low solution accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solution, a high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor, comprising:

[0007] Construct a flywheel geometric model, use the ANSYS parametric design language to establish a parametric finite element model of the flywheel rotor, create and save it as a text file; based on the established parametric finite element model and according to the flywheel rotor performance index requirements, design the fitness function and constraints, and establish a flywheel rotor optimization mathematical model; based on the MATLAB numerical calculation platform, write the flywheel shape parameter optimization main program, use the SSA sparrow search algorithm and the optimized ISSA sparrow search algorithm to perform global optimization on the flywheel rotor cross-sectional thickness, use the optimal control point parameters obtained by the optimization to build a model and static analysis verification, compare and analyze the flywheel performance before and after optimization, and evaluate the changes in the flywheel performance indicators after optimization.

[0008] As a preferred solution of the efficient structural optimization method for improving the energy storage density of the flywheel rotor described in the present invention, the construction of the flywheel geometric model includes uniformly creating 10 control points along the radial direction of the flywheel rotor according to the size of the flywheel rotor, and using the spline curve fitting creation method in ANSYS to describe the thickness distribution of the rotor along the radial direction to generate the flywheel rotor geometric model.

[0009] As a preferred solution of the efficient structural optimization method for improving the energy storage density of the flywheel rotor described in the present invention, wherein: the establishment of a parametric finite element model of the flywheel rotor includes, through the constructed flywheel geometric model, defining the material properties of the rotor, selecting the unit type, and meshing the two-dimensional cross section of the flywheel rotor; applying loads and constraints to the flywheel, defining static solutions and adding post-processing commands, defining the mass m of the output model, the maximum Mises stress semax of the structure and the moment of inertia J around the center of the main axis, establishing a parametric finite element model of the flywheel rotor, and creating and saving it as a text file;

[0010] The material properties include elastic modulus, Poisson's ratio, and density.

[0011] As a preferred solution of the efficient structural optimization method for improving the energy storage density of a flywheel rotor according to the present invention, wherein: the establishment of a flywheel rotor optimization mathematical model includes, based on the established parameterized finite element model and according to the flywheel rotor performance index requirements, designing a fitness function and constraint conditions, and establishing a flywheel rotor optimization mathematical model;

[0012] The fitness function is expressed as:

[0013]

[0014]

[0015]

[0016]

[0017] Where m is the mass of the flywheel, ρ is the density of the flywheel material, and r a is the inner radius of the flywheel, r b is the outer radius of the flywheel, h(r) is the axial thickness of the flywheel, r is the radius of the flywheel, J is the moment of inertia of the flywheel, E is the kinetic energy of the flywheel, ω is the angular velocity at the maximum speed of the flywheel, and D is the energy storage density of the flywheel;

[0018] The constraints are expressed as:

[0019]

[0020] Among them, st is the optimization constraint, x1, x2, x4, x5, x6, x7, x8, x9, x 10 are 10 parameters of the flywheel, J is the moment of inertia of the flywheel, and SEMAX is the maximum equivalent force value received by the model.

[0021] As a preferred solution of the efficient structural optimization method for improving the energy storage density of the flywheel rotor described in the present invention, the flywheel shape parameter optimization main program includes, based on the MATLAB numerical calculation platform, writing the SSA sparrow search algorithm program and the optimized ISSA sparrow search algorithm program, and constructing the sparrow search algorithm mathematical model and the optimized sparrow search algorithm mathematical model.

[0022] As a preferred solution of the efficient structural optimization method for improving the energy storage density of the flywheel rotor described in the present invention, the SSA sparrow search algorithm includes determining the maximum number of iterations, population size, safety value, number of discoverers, and number of joiners of the SSA sparrow search algorithm, initializing the sparrow population, calculating the fitness of individuals according to the individual positions of the sparrow population, sorting the sparrow population from small to large according to the fitness, obtaining the finder with the smallest fitness, and updating the position of the finder during the iteration process. The finder sends foraging information to the joiner and updates the position of the joiner. During the foraging process, 20% of the sparrows in the group act as scouts to pay attention to whether there are threats in the foraging environment at all times. When a threat occurs, a signal is sent to let the group stay away, and the position of the scout is updated;

[0023] The initialized sparrow population is expressed as:

[0024] X i,j =lb+rand·(ub-lb)

[0025] Among them, X i,j is the position of the ith sparrow, j is the dimension, ub is the upper boundary position of the population, lb is the lower boundary position of the population, and rand is a function that generates uniformly distributed random numbers in the interval [0,1);

[0026] The fitness formula of the computing individual is as follows:

[0027] m / J = minf(x1, x2, x3, x4, x5, x6, x7, x8, x9, x 10 )

[0028] where x1, x2, x4, x5, x6, x7, x8, x9, x 10 are 10 parameters of the flywheel, m is the mass of the flywheel, J is the moment of inertia of the flywheel, and minf(x1, x2, x3, x4, x5, x6, x7, x8, x9, x 10 ) is the objective function;

[0029] The formula for updating the position of the discoverer is expressed as:

[0030]

[0031] where iter max is the maximum number of iterations, i is the i-th sparrow, is the position information of the i-th sparrow in the j-th dimension in the t-th iteration, is the position information of the i-th sparrow in the j-th dimension in the (t + 1)-th iteration, α is a random number in (0, 1], R2 is the warning value, ST is the safety value, Q is a random number obeying the normal distribution, L is a 1×d matrix with elements all being 1, and exp is the natural exponential function; when R2 < ST, it means there is no predator around the foraging environment, and the discoverer performs a wide search operation; when R2 ≥ ST, it means the discoverer in the population has discovered a predator and sent an alarm to the remaining sparrows in the population, and all sparrows need to fly to a safe place to forage;

[0032] The formula for updating the position of the joiner is expressed as:

[0033]

[0034] where, is the current global worst position, is the current best position of the discoverer, is the position information of the c-th sparrow in the j-th dimension in the t-th iteration, is the position information of the c-th sparrow in the j-th dimension in the (t + 1)-th iteration, Q is a random number obeying the normal distribution, L is a 1×d matrix with elements all being 1, N is the population size, and exp is the natural exponential function; when c > N / 2, it means the c-th joiner with a low fitness value has not obtained food and is in a very hungry state, and needs to fly to a place outside the discovery area to forage and obtain energy; when c ≤ N / 2, it means the joiner continues to forage within the discovery area and has sufficient food;

[0035] The A + It is expressed as:

[0036] A + =A T (AA T ) -1

[0037] Where A is a 1×d matrix whose elements are randomly 1 or -1. T is the transposed matrix of A;

[0038] The formula for updating the scout position is expressed as:

[0039]

[0040] in, is the current global best position, is the current global worst position, β is the step size control parameter, K is a random number, f l is the fitness value of the current sparrow, f w is the fitness value of the current global worst individual, f g is the fitness value of the current global best individual, ε is the minimum value, is the position information of the lth sparrow in the jth dimension in the tth iteration, is the position information of the lth sparrow in the jth dimension in the t+1th iteration; when f l >f g When f l =f g , indicating that the sparrows in the middle of the flock are aware of the danger.

[0041] As a preferred solution of the efficient structural optimization method for improving the energy storage density of the flywheel rotor described in the present invention, wherein: the optimized ISSA sparrow search algorithm includes replacing the position update formula of the discoverer and the position update formula of the joiner in the original sparrow algorithm with the global exploration strategy in the flight, foraging and vigilance behavior of the BSA bird flock to obtain the optimized ISSA sparrow search algorithm, and using the optimized ISSA sparrow search algorithm to complete the main program of flywheel rotor flywheel shape parameter optimization;

[0042] The position update formula of the discoverer is:

[0043]

[0044] in, is the position information of the nth sparrow in the jth dimension in the tth iteration, is the position information of the nth sparrow in the jth dimension in the t+1th iteration, Q is a random number that follows a normal distribution, L is a 1×d matrix with 1 as its element, R2 is the warning value, ST is the safety value, and randn(0,1) is a Gaussian random distribution with a mean of 0 and a standard deviation of 1;

[0045] The position update formula of the joiner is:

[0046]

[0047] in, is the current global worst position, exp is the natural exponential function, Q is a random number that follows a normal distribution, is the position information of the oth sparrow in the jth dimension in the tth iteration, is the position information of the oth sparrow in the jth dimension in the t+1th iteration, FL is the probability that the joiner follows the producer to find food, randn(0,1) is a Gaussian random distribution with a mean of 0 and a standard deviation of 1, is the position information of the randomly selected discoverer s in the jth dimension.

[0048] As a preferred solution of the efficient structural optimization method for improving the energy storage density of the flywheel rotor described in the present invention, the flywheel performance comparison analysis includes substituting the optimal parameter values ​​obtained after optimization into ANSYS for modeling and static analysis, and obtaining the structural information of the optimized flywheel and the equivalent stress distribution cloud map of the two-dimensional cross-section of the flywheel rotor, calculating the performance indicators of the optimized flywheel according to the flywheel energy storage and mass energy storage density formula, and evaluating the changes in performance indicators of the optimized flywheel and the disc flywheel of the same mass.

[0049] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, steps of an efficient structural optimization method for improving the energy storage density of a flywheel rotor are implemented.

[0050] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of an efficient structural optimization method for improving the energy storage density of a flywheel rotor are implemented.

[0051] Beneficial effects of the present invention: The method of the present invention realizes accurate simulation and efficient design of the flywheel rotor by constructing a flywheel geometric model and adopting the ANSYS parametric design language, thereby improving the convenience of modification and optimization. On this basis, the fitness function and constraints are designed, and an optimization mathematical model is established to ensure the rationality of the design and the clarity of the optimization target. By using the MATLAB platform and intelligent optimization algorithm, the optimal thickness of the flywheel rotor cross section is efficiently found, and modeling and static analysis verification are carried out through the optimal control point parameters, and finally the flywheel performance before and after optimization is compared and analyzed. This series of steps not only improves the design efficiency and quality, but also verifies the optimization effect, provides strong theoretical support for actual production, and significantly improves the performance indicators and competitiveness of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0053] Figure 1 A schematic flow chart of a highly efficient structural optimization method for improving the energy storage density of a flywheel rotor provided in accordance with an embodiment of the present invention.

[0054] Figure 2 A schematic diagram of APDL parametric 3D modeling of a flywheel rotor according to an efficient structural optimization method for improving the energy storage density of a flywheel rotor provided in one embodiment of the present invention.

[0055] Figure 3 A schematic diagram of a comparison between a novel swarm intelligence SSA algorithm and an optimized ISSA algorithm for a high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor provided in one embodiment of the present invention.

[0056] Figure 4 A cloud diagram of equivalent stress distribution of a two-dimensional cross section of a flywheel rotor before optimization according to an efficient structural optimization method for improving the energy storage density of a flywheel rotor provided by one embodiment of the present invention.

[0057] Figure 5 An optimized flywheel rotor two-dimensional cross-sectional equivalent stress distribution cloud diagram according to an efficient structural optimization method for improving the energy storage density of a flywheel rotor provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0061] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0062] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0063] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0064] Example 1, reference Figure 1-Figure 5 , which is the first embodiment of the present invention, provides an efficient structural optimization method for improving the energy storage density of a flywheel rotor, comprising:

[0065] S1: Construct the flywheel geometry model, use ANSYS parametric design language to establish the parametric finite element model of the flywheel rotor, create and save it as a text file;

[0066] The construction of the flywheel geometric model includes uniformly creating 10 control points along the radial direction of the flywheel rotor according to the size of the flywheel rotor, and using a spline curve fitting creation method in ANSYS to describe the thickness distribution of the rotor along the radial direction to generate the flywheel rotor geometric model.

[0067] It should be noted that if Figure 2 As shown, the geometric modeling of the pre-processing process is carried out in the finite element ANSYS software.

[0068] Furthermore, the establishment of the parametric finite element model of the flywheel rotor includes, through the constructed flywheel geometric model, defining the material properties of the rotor, selecting the unit type, and meshing the two-dimensional cross section of the flywheel rotor; applying loads and constraints to the flywheel, defining static solutions and adding post-processing commands, defining the mass m of the output model, the maximum Mises stress semax of the structure and the moment of inertia J around the main axis center, establishing the parametric finite element model of the flywheel rotor, and creating and saving it as a text file;

[0069] It should be noted that the material properties include elastic modulus, Poisson's ratio and density; the unit type is a high-precision three-dimensional 20-node solid unit SOLID186.

[0070] S2: Based on the established parametric finite element model and according to the flywheel rotor performance index requirements, the fitness function and constraint conditions are designed, and the flywheel rotor optimization mathematical model is established;

[0071] The establishment of the flywheel rotor optimization mathematical model includes, based on the established parameterized finite element model and according to the flywheel rotor performance index requirements, designing the fitness function and constraint conditions, and establishing the flywheel rotor optimization mathematical model;

[0072] The fitness function is expressed as:

[0073]

[0074]

[0075]

[0076]

[0077] Where m is the mass of the flywheel, ρ is the density of the flywheel material, and r a is the inner radius of the flywheel, r bis the outer radius of the flywheel, h(r) is the axial thickness of the flywheel, r is the radius of the flywheel, J is the moment of inertia of the flywheel, E is the kinetic energy of the flywheel, ω is the angular velocity at the maximum speed of the flywheel, and D is the energy storage density of the flywheel;

[0078] The constraints are expressed as:

[0079]

[0080] Among them, st is the optimization constraint, x1, x2, x4, x5, x6, x7, x8, x9, x 10 are 10 parameters of the flywheel, J is the moment of inertia of the flywheel, and SEMAX is the maximum equivalent force value received by the model.

[0081] The flywheel shape parameter optimization main program includes, based on the MATLAB numerical calculation platform, writing the SSA sparrow search algorithm program and the optimized ISSA sparrow search algorithm program, and constructing the sparrow search algorithm mathematical model and the optimized sparrow search algorithm mathematical model.

[0082] S3: Based on the MATLAB numerical computing platform, the main program for flywheel shape parameter optimization is written. The SSA sparrow search algorithm and the optimized ISSA sparrow search algorithm are used to perform global optimization on the flywheel rotor section thickness respectively. The optimal control point parameters obtained by the optimization are used for modeling and static analysis verification. The flywheel performance before and after optimization is compared and analyzed, and the changes in the flywheel performance indicators after optimization are evaluated.

[0083] The SSA sparrow search algorithm includes determining the maximum number of iterations, population size, safety value, number of discoverers, and number of joiners of the SSA sparrow search algorithm, initializing the sparrow population, calculating the fitness of individuals according to the individual positions of the sparrow population, sorting the sparrow population according to the fitness from small to large, obtaining the discoverer with the smallest fitness, and updating the position of the discoverer during the iteration process. The discoverer sends foraging information to the joiner and updates the position of the joiner. During the foraging process, 20% of the sparrows in the group act as scouts to pay attention to whether there are threats in the foraging environment at all times. When a threat occurs, a signal is sent to let the group stay away, and the position of the scout is updated;

[0084] The initialized sparrow population is expressed as:

[0085] X i,j =lb+rand·(ub-lb)

[0086] Among them, X i,j is the position of the i-th sparrow, j is the dimension, ub is the upper boundary position of the population, lb is the lower boundary position of the population, and rand is a function that generates uniformly distributed random numbers in the interval [0,1). i,jAlways located in [50, 200];

[0087] The fitness formula for calculating an individual is:

[0088] m / J = minf(x1, x2, x3, x4, x5, x6, x7, x8, x9, x 10 )

[0089] Where x1, x2, x4, x5, x6, x7, x8, x9, x 10 are 10 parameters of the flywheel, m is the mass of the flywheel, J is the moment of inertia of the flywheel, and minf(x1, x2, x3, x4, x5, x6, x7, x8, x9, x 10 ) is the objective function;

[0090] The formula for updating the position of the discoverer is expressed as:

[0091]

[0092] Where iter max is the maximum number of iterations, i is the i-th sparrow, is the position information of the i-th sparrow in the j-th dimension in the t-th iteration, is the position information of the i-th sparrow in the j-th dimension in the (t + 1)-th iteration, α is a random number in (0, 1], R2 is the warning value, ST is the safety value, Q is a random number subject to a normal distribution, L is a 1×d matrix with elements all being 1, and exp is the natural exponential function; when R2 < ST, it means there are no predators around the foraging environment, and the discoverer performs an extensive search operation; when R2 ≥ ST, it means the discoverers in the population have discovered predators and sent out alarms to the remaining sparrows in the population, and all sparrows need to fly to a safe place to forage;

[0093] The formula for updating the position of the joiner is expressed as:

[0094]

[0095] Where, is the current global worst position, is the current best position of the discoverer, is the position information of the c-th sparrow in the j-th dimension in the t-th iteration, is the position information of the cth sparrow in the jth dimension in the t+1th iteration, Q is a random number that follows a normal distribution, L is a 1×d matrix with 1 as its element, N is the population size, and exp is a natural exponential function; when c>N / 2, it means that the cth participant with a low fitness value has not obtained food and is in a very hungry state, and needs to fly outside the discovery site to forage for energy; when c≤N / 2, it means that the participant continues to forage within the discovery site and obtains sufficient food;

[0096] The A + It is expressed as:

[0097] A + =A T (AA T ) -1

[0098] Where A is a 1×d matrix whose elements are randomly 1 or -1. T is the transposed matrix of A;

[0099] The formula for updating the scout position is expressed as:

[0100]

[0101] in, is the current global best position, is the current global worst position, β is the step size control parameter, K is a random number, f l is the fitness value of the current sparrow, f w is the fitness value of the current global worst individual, f g is the fitness value of the current global best individual, ε is the minimum value, is the position information of the lth sparrow in the jth dimension in the tth iteration, is the position information of the lth sparrow in the jth dimension in the t+1th iteration; when f l >f g When f l =f g When , it means that the sparrows in the middle of the population are aware of the danger and need to move closer to the remaining sparrows to reduce the risk of being preyed upon.

[0102] The optimized ISSA sparrow search algorithm includes replacing the position update formula of the discoverer and the position update formula of the joiner in the original sparrow algorithm with the global exploration strategy in the flight, foraging and vigilance behavior of the BSA bird flock to obtain the optimized ISSA sparrow search algorithm, and using the optimized ISSA sparrow search algorithm to complete the main program of flywheel rotor flywheel shape parameter optimization;

[0103] The position update formula of the discoverer is:

[0104]

[0105] in, is the position information of the nth sparrow in the jth dimension in the tth iteration, is the position information of the nth sparrow in the jth dimension in the t+1th iteration, Q is a random number that follows a normal distribution, L is a 1×d matrix with 1 as its element, R2 is the warning value, ST is the safety value, and randn(0,1) is a Gaussian random distribution with a mean of 0 and a standard deviation of 1;

[0106] The position update formula of the joiner is:

[0107]

[0108] in, is the current global worst position, exp is the natural exponential function, Q is a random number that follows a normal distribution, is the position information of the oth sparrow in the jth dimension in the tth iteration, is the position information of the oth sparrow in the jth dimension in the t+1th iteration, FL is the probability that the joiner follows the producer to find food, randn(0,1) is a Gaussian random distribution with a mean of 0 and a standard deviation of 1, is the position information of the randomly selected discoverer s in the jth dimension.

[0109] The parameters set in both SSA and ISSA algorithms are: population size POP = 100, maximum number of iterations M = 100, and the fitness function is the maximum energy storage density. The ISSA algorithm performs global optimization on the 10 control point parameters in the optimization range of [50, 200]; Figure 3 The fitness function of the SSA algorithm and the ISSA algorithm changes with the number of iterations. It shows that the SSA algorithm converges at the 88th generation; the optimization result obtained is 21.9452, and the ISSA algorithm converges at the 15th generation, and the optimization result obtained is 21.2457, which verifies the superiority of the ISSA algorithm.

[0110] The optimal parameter values ​​obtained after optimization are substituted into ANSYS for modeling and static analysis, and the structural information of the optimized flywheel and the following are obtained: Figure 5 The equivalent stress distribution cloud diagram of the two-dimensional cross-section of the flywheel rotor is shown.

[0111] The performance indicators of the optimized flywheel are calculated according to the flywheel energy storage and mass energy storage density formulas, and the changes in various performance indicators of the optimized flywheel and the disc flywheel of equal mass are evaluated.

[0112] Example 2, reference Figure 4-5 , which is an embodiment of the present invention, provides an efficient structural optimization method for improving the energy storage density of a flywheel rotor. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0113] The initial model mass of the flywheel is m = 892.746 kg, and the moment of inertia is J = 39.96 kg·m 2 , the rated speed is 18000RPM, the energy storage is 19.71kWh, the energy storage density is 22.1Wh / kg, and the allowable stress of the flywheel material is 1000Mpa. From Table 1, it can be found that the energy storage density of the optimized flywheel is increased by 5.7% compared with the disc flywheel of the same mass, and the maximum equivalent stress is 993Mpa. Figure 4 , Figure 5 These are the equivalent stress distribution cloud diagrams of the two-dimensional cross-section of the flywheel rotor before and after optimization. It can be seen that the stress size and storage capacity meet the requirements, the stress distribution is relatively uniform, and meets the safety standards.

[0114]

[0115] Table 1

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0117] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0118] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0119] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0122] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0123] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An efficient structural optimization method for improving the energy storage density of a flywheel rotor, characterized in that: include, Construct the flywheel geometry model, use ANSYS parametric design language to establish the parametric finite element model of the flywheel rotor, create and save it as a text file; Based on the established parametric finite element model and according to the performance index requirements of the flywheel rotor, the fitness function and constraint conditions are designed, and the flywheel rotor optimization mathematical model is established; Based on the MATLAB numerical computing platform, the main program for flywheel shape parameter optimization was written. The SSA sparrow search algorithm and the optimized ISSA sparrow search algorithm were used to perform global optimization on the flywheel rotor cross-section thickness. The optimal control point parameters obtained by the optimization were used for modeling and static analysis verification. The flywheel performance before and after optimization was compared and analyzed, and the changes in the flywheel performance indicators after optimization were evaluated.

2. A high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor as claimed in claim 1, characterized in that: The flywheel geometric model is constructed by uniformly creating 10 control points along the radial direction of the flywheel rotor according to the size of the flywheel rotor, and using a spline curve fitting creation method in ANSYS to describe the thickness distribution of the rotor along the radial direction to generate the flywheel rotor geometric model.

3. A high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor as claimed in claim 2, characterized in that: The establishment of the parametric finite element model of the flywheel rotor includes: defining the material properties of the rotor, selecting the unit type, and meshing the two-dimensional cross section of the flywheel rotor through the constructed flywheel geometric model; applying loads and constraints to the flywheel, defining static solutions and adding post-processing commands, defining the mass of the output model, the maximum Mises stress of the structure and the moment of inertia around the main axis center, establishing the parametric finite element model of the flywheel rotor, and creating and saving it as a text file; The material properties include elastic modulus, Poisson's ratio, and density.

4. A high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor as claimed in claim 3, characterized in that: The establishment of the flywheel rotor optimization mathematical model includes, based on the established parameterized finite element model, designing fitness functions and constraint conditions according to the flywheel rotor performance index requirements, and establishing the flywheel rotor optimization mathematical model; The fitness function is expressed as: Where m is the mass of the flywheel, ρ is the density of the flywheel material, and r a is the inner radius of the flywheel, r b is the outer radius of the flywheel, h(r) is the axial thickness of the flywheel, r is the radius of the flywheel, J is the moment of inertia of the flywheel, E is the kinetic energy of the flywheel, ω is the angular velocity at the maximum speed of the flywheel, and D is the energy storage density of the flywheel; The constraints are expressed as: Among them, st is the optimization constraint, x1, x2, x4, x5, x6, x7, x8, x9, x 10 are 10 parameters of the flywheel, J is the moment of inertia of the flywheel, and SEMAX is the maximum equivalent force value received by the model.

5. A high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor as claimed in claim 4, characterized in that: The flywheel shape parameter optimization main program includes, based on the MATLAB numerical calculation platform, writing the SSA sparrow search algorithm program and the optimized ISSA sparrow search algorithm program, and constructing the sparrow search algorithm mathematical model and the optimized sparrow search algorithm mathematical model.

6. A high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor as claimed in claim 5, characterized in that: The SSA sparrow search algorithm includes determining the maximum number of iterations, population size, safety value, number of discoverers, and number of joiners of the SSA sparrow search algorithm, initializing the sparrow population, calculating the fitness of individuals according to the individual positions of the sparrow population, sorting the sparrow population according to the fitness from small to large, obtaining the discoverer with the smallest fitness, and updating the position of the discoverer during the iteration process. The discoverer sends foraging information to the joiner and updates the position of the joiner. During the foraging process, 20% of the sparrows in the group act as scouts to pay attention to whether there are threats in the foraging environment at all times. When a threat occurs, a signal is sent to let the group stay away, and the position of the scout is updated; The initialized sparrow population is expressed as: X i,j =lb+rand·(ub-lb) Among them, X i,j is the position of the ith sparrow, j is the dimension, ub is the upper boundary position of the population, lb is the lower boundary position of the population, and rand is a function that generates uniformly distributed random numbers in the interval [0,1); The fitness formula for calculating an individual is: m / J=minf(x1,x2,x3,x4,x5,x6,x7,x8,x9,x 10 ) Among them, x1, x2, x4, x5, x6, x7, x8, x9, x 10 are the 10 parameters of the flywheel, m is the mass of the flywheel, J is the moment of inertia of the flywheel, minf(x1,x2,x3,x4,x5,x6,x7,x8,x9,x 10 ) is the objective function; The update finder's position formula is expressed as: where iter max is the maximum number of iterations, i is the i-th sparrow, is the position information of the i-th sparrow in the j-th dimension at the t-th iteration, is the position information of the i-th sparrow in the j-th dimension at the (t + 1)-th iteration, α is a random number in (0, 1], R2 is the warning value, ST is the safety value, Q is a random number following a normal distribution, L is a 1×d matrix with all elements being 1, and exp is the natural exponential function; when R2 < ST, it means there are no predators around the foraging environment, and the discoverers perform extensive search operations; when R2 ≥ ST, it means the discoverers in the population have discovered predators and sent out alarms to the remaining sparrows in the population, and all sparrows need to fly to a safe place to forage; The formula for updating the position of the joiner is expressed as: in, is the current global worst position, is the best position of the current finder, is the position information of the cth sparrow in the jth dimension in the tth iteration, is the position information of the cth sparrow in the jth dimension in the t+1th iteration, Q is a random number that follows a normal distribution, L is a 1×d matrix with 1 as its element, N is the population size, and exp is a natural exponential function; when c>N / 2, it means that the cth participant with a low fitness value has not obtained food and is in a very hungry state, and needs to fly outside the discovery site to forage for energy; when c≤N / 2, it means that the participant continues to forage within the discovery site and obtains sufficient food; The A + It is expressed as: A + =A T (CHALLENGE ACCEPTED T ) -1 Where A is a 1×d matrix whose elements are randomly 1 or -1. T is the transposed matrix of A; The formula for updating the scout position is expressed as: in, is the current global best position, is the current global worst position, β is the step size control parameter, K is a random number, f l is the fitness value of the current sparrow, f w is the fitness value of the current global worst individual, f g is the fitness value of the current global best individual, ε is the minimum value, is the position information of the lth sparrow in the jth dimension in the tth iteration, is the position information of the lth sparrow in the jth dimension in the t+1th iteration; when f l >f g When f l =f g , indicating that the sparrows in the middle of the flock are aware of the danger.

7. A high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor as claimed in claim 6, characterized in that: The optimized ISSA sparrow search algorithm includes replacing the position update formula of the discoverer and the position update formula of the joiner in the original sparrow algorithm with the global exploration strategy in the flight, foraging and vigilance behavior of the BSA bird flock to obtain the optimized ISSA sparrow search algorithm, and using the optimized ISSA sparrow search algorithm to complete the main program of flywheel rotor flywheel shape parameter optimization; The position update formula of the discoverer is: in, is the position information of the nth sparrow in the jth dimension in the tth iteration, is the position information of the nth sparrow in the jth dimension in the t+1th iteration, Q is a random number that follows a normal distribution, L is a 1×d matrix with 1 as its element, R2 is the warning value, ST is the safety value, and randn(0,1) is a Gaussian random distribution with a mean of 0 and a standard deviation of 1; The position update formula of the joiner is: in, is the current global worst position, exp is the natural exponential function, Q is a random number that follows a normal distribution, is the position information of the oth sparrow in the jth dimension in the tth iteration, is the position information of the oth sparrow in the jth dimension in the t+1th iteration, FL is the probability that the joiner follows the producer to find food, randn(0,1) is a Gaussian random distribution with a mean of 0 and a standard deviation of 1, is the position information of the randomly selected discoverer s in the jth dimension.

8. A high-efficiency structural optimization method for improving the energy storage density of a flywheel rotor as claimed in claim 7, characterized in that: The flywheel performance comparison analysis includes substituting the optimal parameter values ​​obtained after optimization into ANSYS for modeling and static analysis, and obtaining the structural information of the optimized flywheel and the equivalent stress distribution cloud diagram of the two-dimensional cross-section of the flywheel rotor, calculating the performance indicators of the optimized flywheel according to the flywheel energy storage and mass energy storage density formula, and evaluating the changes in performance indicators of the optimized flywheel and the disc flywheel of the same mass.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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