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SA-BESO joint topological optimization method

A topology optimization and algorithm technology, applied in design optimization/simulation, special data processing applications, etc., can solve the problems of reducing the global optimality of the solution results, without considering the influence of the genetic algorithm objective function, etc., and achieve the goal of improving the global optimality Effect

Active Publication Date: 2021-07-09
SUN YAT SEN UNIV
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  • Description
  • Claims
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Problems solved by technology

However, the genetic algorithm and discrete particle swarm optimization only perform volume constraints and convergence judgments on the random update solution, ignoring the impact of understanding on the objective function, which reduces the global optimality of the solution results in the process of structural topology optimization
[0005] To sum up, in the prior art, in the process of structural topology optimization combining BESO with genetic algorithm or discrete particle swarm optimization algorithm, the influence of genetic algorithm or discrete particle swarm optimization algorithm on the objective function is not considered, resulting in a reduction of Global optimality of solution results

Method used

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Embodiment 1

[0033] Please refer to figure 1 The embodiment of the present invention provided for the embodiment of the present invention provides a SA-BESO joint topology optimization method, including the following steps:

[0034] S101: Acquire the i-th structure model, and initialize the SA-BESO parameters; where, i∈N*.

[0035] Among them, it needs to be further explained that SA-BESO parameters include initial temperature, deletion rate, volume constraint, penalty factor, Markov chain length L, and cooling index.

[0036] S102: Divide the i-th structural model into a plurality of i-th structural units, analyze the i-th structural units, and obtain i-th structural data.

[0037] Among them, the way of division depends on the specific situation. For example, for a two-dimensional structure, triangular or quadrilateral division can be selected. The i-th structure data includes related structure data such as stiffness matrix, displacement matrix and node information matrix.

[0038] S10...

Embodiment 2

[0044] Please refer to figure 2 The embodiment of the present invention provided for the embodiment of the present invention provides a SA-BESO joint topology optimization method, including the following steps:

[0045] S201: Acquire the i-th structure model, and initialize the SA-BESO parameters; where, i∈N*.

[0046]Among them, it needs to be further explained that according to the optimization problem, the i-th structural model is obtained, and the i-th structural model contains the objective function and corresponding constraint conditions, and in structural topology optimization, the i-th structural model will have load conditions such as force . Wherein, the i-th structure model generally takes the minimum of the compliance of the structure as the objective function, and uses volume constraints as constraint conditions, but it is not limited thereto.

[0047] Among them, SA-BESO parameters include initial temperature, deletion rate, volume constraint, penalty factor, ...

Embodiment 3

[0062] In this embodiment, the i-th structural unit is randomly updated by using binary codes for cross mutation, and the specific process is as follows:

[0063] Give each i-th structural unit a binary code of 6-10 bits, that is, the physical unit is represented by the number "1", such as "111111", and the empty unit is composed of "1" and "0" not exceeding a certain proportion, such as " 010101".

[0064] The method for randomly generating a new solution by cross-variation is as follows: the sensitivity corresponding to each i-th structural unit is sorted in descending order, and the unit sensitivity is divided into two parts, the first part is the first N (1-ER) sensitivity groups, It is called the pre-reservation array, and the second part is the remaining N×ER sensitivity groups, which is called the pre-removal array. Perform crossover and mutation operations between two arrays to establish the relationship between old and new solutions. In the crossover phase, each ind...

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Abstract

The invention discloses an SA-BESO joint topological optimization method, which comprises the following steps: dividing a structural model into a plurality of structural units, solving an initial solution, updating the structural units according to sensitivity to obtain a new structural model, solving the new structural model to obtain a new calculation result, and solving the optimal solution according to the new calculation result, the initial solution and the SA algorithm. According to the embodiment of the invention, in the process of carrying out structure topological optimization on the structure model, the SA algorithm and the BESO algorithm are combined, the structure of the structure model is continuously updated through the BESO algorithm, and the SA algorithm is introduced in the process of solving the optimal solution, so that the global optimality of solution is improved.

Description

technical field [0001] The invention relates to the field of structure topology optimization, in particular to a SA-BESO joint topology optimization method. Background technique [0002] At present, structural topology optimization has been widely used in related technical fields, such as aerospace, automobile manufacturing, architectural design, and additive manufacturing, and has become a hot area of ​​current academic research. More and more structural topology optimization methods have been proposed one after another. Such as variable density method, level set topology optimization method, optimization criterion method, progressive structure optimization method, topological derivative method, proportional-integral-derivative control algorithm, feature-driven method and intelligent algorithm. [0003] Structural topology optimization design has the characteristics of many design variables, and the target performance and constraints are nonlinear and non-monotonic implicit...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/23
CPCG06F30/23
Inventor 张岐良张卉陈洪波
Owner SUN YAT SEN UNIV
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