CN115481488A - Multi-objective optimization method for crashworthiness of square cone energy-absorbing structures based on machine learning - Google Patents
Multi-objective optimization method for crashworthiness of square cone energy-absorbing structures based on machine learning Download PDFInfo
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Abstract
本发明提供了一种基于机器学习的方锥式吸能结构耐撞性能多目标优化方法,包括以下步骤:建立地铁列车的方锥式吸能结构的有限元仿真模型;基于建立的有限元仿真模型与实验设计相结合的方法,提取地铁列车吸能结构的结构参数和吸能特性曲线;根据拉丁超立方法进行采样,通过实验设计(DOE),确定地铁列车方锥式吸能结构的最优吸能特性曲线预测模型,以及优化变量和优化目标;根据最优吸能特性曲线预测模型、优化变量和优化目标,建立优化理论模型;采用哈默斯雷采样法对优化理论模型进行重新采样,利用最优吸能特性曲线预测模型计算对应的吸能量和峰值力,生成新的DOE;采用全局响应面法(GRSM)进行多目标优化,得到优化结果;基于优化结果,得到优化目标的帕累托解集,采用最小距离法,对优化得到的帕累托解集进行最优决策,得到最优解。
The invention provides a multi-objective optimization method for the crashworthiness of a square cone energy-absorbing structure based on machine learning, comprising the following steps: establishing a finite element simulation model of the square cone energy-absorbing structure of a subway train; based on the established finite element simulation The method of combining the model with the experimental design is used to extract the structural parameters and energy-absorbing characteristic curves of the energy-absorbing structure of the subway train; sampling is carried out according to the Latin super-cubic method, and the optimal design of the square-cone energy-absorbing structure of the subway train is determined through the Design of Experiments (DOE). Optimal energy absorption characteristic curve prediction model, as well as optimization variables and optimization objectives; according to the optimal energy absorption characteristic curve prediction model, optimization variables and optimization objectives, an optimization theoretical model is established; the optimization theoretical model is re-sampled by Hamersley sampling method , use the optimal energy absorption characteristic curve prediction model to calculate the corresponding energy absorption and peak force, and generate a new DOE; use the global response surface method (GRSM) for multi-objective optimization, and obtain the optimization result; based on the optimization result, get the Pa of the optimization target The reciprocal solution set uses the minimum distance method to make an optimal decision on the optimized Pareto solution set to obtain the optimal solution.
Description
技术领域technical field
本发明涉及列车耐撞性能检测技术领域,特别地,涉及一种基于机器学习的方锥式吸能结构耐撞性能多目标优化方法。The invention relates to the technical field of train crashworthiness detection, in particular to a machine learning-based multi-objective optimization method for the crashworthiness of a square-cone energy-absorbing structure.
背景技术Background technique
城轨列车的碰撞过程中,通过车钩装置和方锥式吸能结构吸能结构进行能量吸收。其中,防爬吸能结构为列车被动安全防护的最后一道防线,其耐撞性对车体安全保障具有重要的意义,一旦防爬吸能结构失效造成碰撞事故,将会导致重大的人员伤亡。因此,针对地铁列车方锥式吸能结构的耐撞特性研究具有重要的意义。During the collision process of the urban rail train, the energy is absorbed through the coupler device and the square cone energy-absorbing structure. Among them, the anti-climbing energy-absorbing structure is the last line of defense for train passive safety protection, and its crashworthiness is of great significance to the safety of the car body. Once the anti-climbing energy-absorbing structure fails and causes a collision accident, it will cause heavy casualties. Therefore, it is of great significance to study the crashworthiness characteristics of square cone energy-absorbing structures for subway trains.
传统研究吸能结构的方式主要是采用了有限元法、实验法和多体动力学的方法对列车耐撞性能进行了研究,实验法研究需要耗费较多的物力和财力,并且实验具有较大的不确定性,采用有限元法和多体动力学的方法进行研究,对计算机的性能要求较高,计算的时间较长。The traditional way to study the energy-absorbing structure is mainly to use the finite element method, experimental method and multi-body dynamics method to study the crashworthiness of the train. The experimental method requires more material and financial resources, and the experiment has a large Uncertainty, the method of finite element method and multi-body dynamics is used for research, which requires high performance of the computer and takes a long time for calculation.
方锥式吸能结构作为地铁车辆的主要吸能元件,其吸能特性(力-位移) 曲线对地铁车辆的运行的安全性存在较大的影响,因此,探寻一种合理的方式得到吸能结构的力-位移曲线,对地铁列车的耐撞性优化具有重要的意义。目前,大部分都是采用有限元模型与试验相结合的方法,得到方锥式吸能结构的力-位移曲线,但是采用这种方式需要较长的时间和昂贵的经费。因此,需要采用更为有效的方法,对方锥式吸能结构的耐撞性进行多目标优化。The square cone energy-absorbing structure is the main energy-absorbing element of the subway vehicle, and its energy-absorbing characteristic (force-displacement) curve has a great influence on the safety of the subway vehicle operation. Therefore, it is necessary to find a reasonable way to obtain the energy-absorbing The force-displacement curve of the structure is of great significance to the optimization of the crashworthiness of subway trains. At present, most of them use the method of combining finite element model and test to obtain the force-displacement curve of the square cone energy-absorbing structure, but this method requires a long time and expensive funds. Therefore, it is necessary to adopt a more effective method for multi-objective optimization of the crashworthiness of the square-cone energy-absorbing structure.
发明内容Contents of the invention
为了能在保证计算精度的同时减少计算的时间,本发明提出一种基于机器学习的方锥式吸能结构耐撞性能多目标优化方法。采用本发明方法,不仅吸能特性预测的准确性高,而且能够大幅度降低计算的时间,将地铁车辆方锥式吸能结构的耐撞性多目标优化进行大数据赋能。In order to reduce calculation time while ensuring calculation accuracy, the present invention proposes a multi-objective optimization method for crashworthiness of square cone energy-absorbing structures based on machine learning. By adopting the method of the invention, not only the prediction accuracy of the energy absorption characteristics is high, but also the calculation time can be greatly reduced, and the multi-objective optimization of the crashworthiness of the square-cone energy-absorbing structure of the subway vehicle is empowered with big data.
为实现上述目的,本发明提供了基于机器学习的方锥式吸能结构耐撞性能多目标优化方法,包括以下步骤:In order to achieve the above object, the present invention provides a multi-objective optimization method for the crashworthiness of square cone energy-absorbing structures based on machine learning, including the following steps:
建立地铁列车的方锥式吸能结构的有限元仿真模型;Establish the finite element simulation model of the square cone energy-absorbing structure of the subway train;
基于建立的有限元仿真模型与实验设计相结合的方法,提取地铁列车吸能结构的结构参数和吸能特性曲线;根据采用拉丁超立方法采样,进行虚拟实验设计,确定地铁列车方锥式吸能结构的最优吸能特性曲线预测模型,以及优化变量和优化目标;Based on the method of combining the established finite element simulation model with experimental design, the structural parameters and energy-absorbing characteristic curves of the energy-absorbing structure of the subway train were extracted; according to the sampling method using the Latin superstructure, a virtual experimental design was carried out to determine the square-cone absorbing structure of the subway train. The prediction model of the optimal energy absorption characteristic curve of the energy structure, as well as the optimization variables and optimization objectives;
根据最优吸能特性曲线预测模型、优化变量和优化目标,建立优化理论模型;According to the optimal energy absorption characteristic curve prediction model, optimization variables and optimization objectives, an optimization theoretical model is established;
采用哈默斯雷采样法对优化理论模型进行重新采样,利用最优吸能特性曲线预测模型计算对应的吸能量和峰值力,生成新的DOE;采用全局响应面法进行多目标优化,得到优化结果;Using the Hamersley sampling method to resample the optimized theoretical model, use the optimal energy absorption characteristic curve prediction model to calculate the corresponding energy absorption and peak force, and generate a new DOE; use the global response surface method for multi-objective optimization, and get the optimization result;
基于优化结果,得到优化目标的帕累托解集,采用最小距离法,对优化得到的帕累托解集进行最优决策,得到最优解。Based on the optimization results, the Pareto solution set of the optimization target is obtained, and the optimal decision is made on the optimized Pareto solution set by using the minimum distance method to obtain the optimal solution.
优选的,基于有限元仿真相同的边界条件,进行全尺寸的方锥式吸能结构动态冲击实验,采用实验与仿真相结合的方式,通过对比实验与仿真之间的力-位移曲线、位移-能量曲线和变形序列模式,验证建立的有限元模型的准确性。Preferably, based on the same boundary conditions of the finite element simulation, the dynamic impact experiment of the full-scale square cone energy-absorbing structure is carried out, and the combination of experiment and simulation is adopted. By comparing the force-displacement curve and displacement- Energy curves and deformation sequence patterns verify the accuracy of the established finite element model.
优选的,基于建立的有限元仿真模型与实验设计相结合的方法,提取地铁列车吸能结构的结构参数和吸能特性曲线;根据采用拉丁超立方法采样,进行虚拟实验设计,确定地铁列车方锥式吸能结构的最优吸能特性曲线预测模型,以及优化变量和优化目标,具体为:Preferably, based on the method of combining the finite element simulation model established and the experimental design, the structural parameters and the energy-absorbing characteristic curve of the energy-absorbing structure of the subway train are extracted; The prediction model of the optimal energy-absorbing characteristic curve of the conical energy-absorbing structure, as well as the optimization variables and optimization objectives, specifically:
将DOE的五个输入变量作为机器学习中的网络训练的输入特征,将DOE的输出作为机器学习中的网络训练的实际输出;Use the five input variables of DOE as the input features of network training in machine learning, and use the output of DOE as the actual output of network training in machine learning;
将网络训练的输入和实际输出进行随机划分为训练集和测试集;Randomly divide the input and actual output of network training into training set and test set;
对输入数据和实际输出数据进行归一化处理;Normalize the input data and the actual output data;
将吸能结构的输入几何图形映射到所需要的输出响应的机器学习(ML)体系结构中,使用归一化后的训练集和测试集作为样本,分别使用MLP,RNN,LSTM和GRU 四种机器学习的网络模型对吸能结构的吸能特性曲线进行预测,采对比分析不同模型预测的准确性,将预测输出曲线的准确度最高的网络模型作为地铁列车方锥式吸能结构的最优吸能特性曲线预测模型。Map the input geometry of the energy-absorbing structure to the required output response machine learning (ML) architecture, use the normalized training set and test set as samples, and use MLP, RNN, LSTM and GRU respectively The network model of machine learning predicts the energy-absorbing characteristic curve of the energy-absorbing structure, and compares and analyzes the prediction accuracy of different models. The network model with the highest accuracy of the predicted output curve is used as the optimal model for the square-cone energy-absorbing structure of the subway train. Energy absorption characteristic curve prediction model.
优选的,地铁列车方锥式吸能结构的最优吸能特性曲线预测模型为门控循环神经网络模型,将外壁厚度TA、外壁厚度TB、隔板厚度Tgb、铝蜂窝A的强度δA和铝蜂窝B的强度δB作为优化变量,方锥式吸能结构的吸能量和峰值力为优化目标。Preferably, the optimal energy-absorbing characteristic curve prediction model of the square-cone energy-absorbing structure of a subway train is a gated cyclic neural network model, and the outer wall thickness T A , outer wall thickness T B , partition thickness T gb , and strength δ A and the strength δ B of the aluminum honeycomb B are used as optimization variables, and the energy absorption and peak force of the square cone energy-absorbing structure are the optimization objectives.
优选的,建立的优化理论模型如下所示:Preferably, the established optimization theoretical model is as follows:
PCF=max(F(s))PCF=max(F(s))
其中,S为方锥式吸能结构的压缩位移;F(s)为方锥式吸能结构的轴向力;TA和 TB为方锥式吸能结构的外壁厚度;Tgb为方锥式吸能结构的隔板厚度;δA和δB分别为铝蜂窝A和铝蜂窝B的强度;EA为方锥式吸能结构的吸能量;PCF为方锥式吸能结构的峰值力。Among them, S is the compression displacement of the square cone energy-absorbing structure; F(s) is the axial force of the square cone energy-absorbing structure; T A and T B are the outer wall thicknesses of the square cone energy-absorbing structure; T gb is the square The diaphragm thickness of the cone energy-absorbing structure; δ A and δ B are the strengths of aluminum honeycomb A and aluminum honeycomb B respectively; EA is the energy absorption of the square cone energy-absorbing structure; PCF is the peak force of the square cone energy-absorbing structure .
优选的,基于GRSM的优化结果,得到了优化目标的帕累托解集,采用最小距离法,对优化得到的帕累托解集进行最优决策,得到最优解,具体为:Preferably, based on the optimization result of GRSM, the Pareto solution set of the optimization target is obtained, and the minimum distance method is used to make an optimal decision on the optimized Pareto solution set to obtain the optimal solution, specifically:
将吸能量EA、比吸能SEA、平均力Fmean和吸能效率IFE作为吸能评价指标,并采用雷达图对比最优解与实验结果,验证该优化方法的可行性:The energy absorption EA, specific energy absorption SEA, average force F mean , and energy absorption efficiency IFE are used as energy absorption evaluation indicators, and the optimal solution is compared with the experimental results using radar charts to verify the feasibility of the optimization method:
其中,D为膝关节点与帕累托解集点的距离;m为优化目标的个数,fi k为第i个优化目标的最优点k;Among them, D is the distance between the knee joint point and the Pareto solution set point; m is the number of optimization objectives, and f i k is the optimal point k of the i-th optimization objective;
其中,m为方锥式吸能结构的质量;s为方锥式吸能结构的压缩位移;Fmean为方锥式吸能结构的平均力,PCF为方锥式吸能结构的峰值力;IFE为方锥式吸能结构的吸能效率。Among them, m is the mass of the square cone energy-absorbing structure; s is the compression displacement of the square cone energy-absorbing structure; F mean is the average force of the square cone energy-absorbing structure, and PCF is the peak force of the square cone energy-absorbing structure; IFE is the energy absorption efficiency of the square cone energy-absorbing structure.
本发明具有以下有益效果:The present invention has the following beneficial effects:
1、本发明提出一种基于机器学习的方锥式吸能结构耐撞性能多目标优化方法,基于实验设计与有限元仿真相结合的方法,提取了地铁列车吸能结构的结构参数和吸能特性曲线。同时,通过使用多层感知机(MLP),循环神经网络(RNN),长短时记忆法(LSTM)和门控循环神经网络(GRU)四种神经网络模型,分别对吸能结构的吸能特性曲线(力-位移曲线)进行预测,GRU 模型预测输出曲线的准确度高。故本发明采用门控循环神经网络模型(GRU)作为地铁列车方锥式吸能结构的最优吸能特性曲线预测模型,使得吸能特性预测的准确性高,保证了预测特征曲线的可靠性。在保证计算效率的前提下,还可以提高预测的精度,保证了多目标优化结果的可信度。1. The present invention proposes a multi-objective optimization method for the crashworthiness of square cone energy-absorbing structures based on machine learning. Based on the method of combining experimental design and finite element simulation, the structural parameters and energy-absorbing structures of subway trains are extracted. characteristic curve. At the same time, by using four neural network models of multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory method (LSTM) and gated recurrent neural network (GRU), the energy-absorbing characteristics of the energy-absorbing structure are respectively analyzed. The curve (force-displacement curve) is predicted, and the accuracy of the output curve predicted by the GRU model is high. Therefore, the present invention adopts the gated cyclic neural network model (GRU) as the optimal energy-absorbing characteristic curve prediction model of the square cone type energy-absorbing structure of the subway train, so that the accuracy of energy-absorbing characteristic prediction is high, and the reliability of the predicted characteristic curve is guaranteed . On the premise of ensuring the computational efficiency, it can also improve the prediction accuracy and ensure the credibility of the multi-objective optimization results.
2、本发明提出一种基于机器学习的方锥式吸能结构耐撞性能多目标优化方法,采用了全局自适应响应面(GRSM)和机器学习相结合的方法,对地铁列车吸能结构的耐撞性能进行多目标优化。当需要计算的数据较大时,能够大幅度降低计算的时间,将地铁车辆方锥式吸能结构的耐撞性多目标优化进行大数据赋能。2. The present invention proposes a multi-objective optimization method for the crashworthiness of square cone energy-absorbing structures based on machine learning, which adopts the method of combining global adaptive response surface (GRSM) and machine learning, and improves the energy-absorbing structure of subway trains. Multi-objective optimization for crashworthiness. When the data to be calculated is large, the calculation time can be greatly reduced, and the multi-objective optimization of the crashworthiness of the square cone energy-absorbing structure of the subway vehicle can be empowered by big data.
当计算的数据量较大时,采用传统的有限元与优化算法相结合方法进行多目标优化,需要较长的时间产生数据集;采用机器学习的方法可以减小产生数据集的时间,将GRSM和机器学习方法相结合,可以将吸能结构的参数优化范围扩大,对吸能结构进行模式识别,大幅度提高计算效率,提高产品升级换代的能力,降低产品研发成本。When the amount of data to be calculated is large, it takes a long time to generate data sets by using the traditional finite element and optimization algorithm combined method for multi-objective optimization; using machine learning methods can reduce the time to generate data sets, and GRSM Combined with machine learning methods, the parameter optimization range of the energy-absorbing structure can be expanded, the pattern recognition of the energy-absorbing structure can be carried out, the calculation efficiency can be greatly improved, the ability of product upgrading can be improved, and the cost of product development can be reduced.
优化算法不能提高太多计算速度,主要是采用机器学习可以减少产生数据集的时间,优化过程是基于产生的数据集进行优化,因此笼统的解释了,采用机器学习与优化算法结合的方法,可以提高计算效率。The optimization algorithm cannot improve the calculation speed too much. The main reason is that machine learning can reduce the time to generate data sets. The optimization process is based on the generated data sets. Therefore, it is generally explained that the combination of machine learning and optimization algorithms can be used. Improve computational efficiency.
3、本发明提出一种基于机器学习的方锥式吸能结构耐撞性能多目标优化方法,为吸能结构的多目标优化提供一种新的思路。目前,方锥式吸能结构主要是采用有限元建模与优化算法相结合的方式,对吸能结构进行优化吸能结构,方法较为单一,采用本发明的方法,可以为吸能结构的优化提供一种新的思路。3. The present invention proposes a machine learning-based multi-objective optimization method for the crashworthiness of square cone energy-absorbing structures, providing a new idea for multi-objective optimization of energy-absorbing structures. At present, the square cone energy-absorbing structure mainly adopts the combination of finite element modeling and optimization algorithm to optimize the energy-absorbing structure. Provide a new way of thinking.
除了上面所描述的目的、特征和优点之外,本发明还有其它的目的、特征和优点。下面将参照图,对本发明作进一步详细的说明。In addition to the objects, features and advantages described above, the present invention has other objects, features and advantages. Hereinafter, the present invention will be described in further detail with reference to the drawings.
附图说明Description of drawings
构成本申请的一部分的附图用来提供对本发明的进一步理解,本发明的示意性实施例及其说明用于解释本发明,并不构成对本发明的不当限定。在附图中:The accompanying drawings constituting a part of this application are used to provide further understanding of the present invention, and the schematic embodiments and descriptions of the present invention are used to explain the present invention, and do not constitute an improper limitation of the present invention. In the attached picture:
图1是本发明的技术流程图;Fig. 1 is a technical flow chart of the present invention;
图2是本发明预测模型的ML(机器学习:Machine Learning)框架示意图;2 is a schematic diagram of the ML (Machine Learning: Machine Learning) framework of the prediction model of the present invention;
图3是不同深度学习模型的预测输出值曲线和真实输出曲线之间对比图;Figure 3 is a comparison between the predicted output curves of different deep learning models and the real output curves;
图4是本发明实验结果与优化结果雷达对比图;Fig. 4 is the comparison chart of experimental result of the present invention and optimized result radar;
图5是GRU和FE模型计算不同样本数量的DOE的时间对比图;Figure 5 is a time comparison diagram of DOE calculated by GRU and FE models with different sample sizes;
图6是本发明实验场景图;Fig. 6 is an experimental scene diagram of the present invention;
图7是本发明实验与仿真对比图:(a)力-位移曲线;(b)能量-位移曲线;Fig. 7 is the contrast diagram of experiment and simulation of the present invention: (a) force-displacement curve; (b) energy-displacement curve;
图8是本发明实验与仿真变形序列对比图。Fig. 8 is a comparison diagram of the experiment and simulation deformation sequences of the present invention.
具体实施方式detailed description
以下结合附图对本发明的实施例进行详细说明,但是本发明可以根据权利要求限定和覆盖的多种不同方式实施。The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in various ways defined and covered by the claims.
本发明的流程如图1所示:首先采用实验和有限元仿真结合的方式,验证有限元模型的准确性,然后利用基于有限元模型的实验设计(DOE),改变了方锥式吸能结构的结构参数,生成用于机器学习的训练集和测试集;引入多层感知机(MLP),循环神经网络(RNN),长短时记忆法(LSTM)和门控循环神经网络(GRU)四种机器模型,预测方锥式吸能结构的吸能特性,通过不同的精度指标,对比了四种模型的预测精度,机器学习模型对比如图2所示;最后,将门控循环神经网络(GRU)作为最适合预测方锥式吸能结构吸能特性的模型;最后,将门控循环神经网络(GRU)作为代理模型,采用自适应全局响应面(GRSM)的方法对方锥式吸能结构耐撞性能进行多目标优化,得到最优解。The flow process of the present invention is as shown in Figure 1: at first adopt the mode that experiment and finite element simulation combine, verify the accuracy of finite element model, then utilize the experimental design (DOE) based on finite element model, change square cone type energy-absorbing structure Structural parameters to generate training sets and test sets for machine learning; introduce four types of multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory method (LSTM) and gated recurrent neural network (GRU) The machine model predicts the energy absorption characteristics of the square cone energy-absorbing structure, and compares the prediction accuracy of the four models through different accuracy indicators. The comparison of the machine learning models is shown in Figure 2; finally, the Gated Recurrent Neural Network (GRU) As the most suitable model for predicting the energy-absorbing characteristics of square-cone energy-absorbing structures; finally, the gated recurrent neural network (GRU) is used as a proxy model, and the adaptive global response surface (GRSM) method is used for the crashworthiness of square-cone energy-absorbing structures Perform multi-objective optimization to obtain the optimal solution.
具体的,本发明提出一种基于机器学习的方锥式吸能结构耐撞性能多目标优化方法,包括以下步骤:Specifically, the present invention proposes a multi-objective optimization method for the crashworthiness of square cone energy-absorbing structures based on machine learning, including the following steps:
步骤一、建立地铁列车的方锥式吸能结构的有限元仿真模型。并基于有限元仿真相同的边界条件,进行全尺寸的方锥式吸能结构动态冲击实验,采用实验与仿真相结合的方式,通过对比实验与仿真之间的力-位移曲线、位移-能量曲线和变形序列模式,验证建立的有限元模型的准确性。
步骤二、将方锥式吸能结构的外壁厚度TA、外壁厚度TB、隔板厚度Tgb、铝蜂窝 A的强度δA和铝蜂窝B的强度δB作为设计变量,方锥式吸能结构的力-位移输出曲线作为输出,采用拉丁超立方法采样,进行虚拟实验设计(DOE),DOE的次数设置为 1000组。
步骤三、将DOE的五种输入变量作为机器学习中的网络训练的输入特征,将DOE 的输出作为机器学习中的网络训练的实际输出;然后,利用train_test_split()函数对网络训练的输入和实际输出进行随机划分为训练集和测试集,其中将数据的75%作为训练集,25%作为测试集;最后,采用Pytorch的MinMaxscale()函数分别对输入数据和实际输出数据进行归一化处理,归一化的计算方程如下所示:Step 3, use the five input variables of DOE as the input features of network training in machine learning, and use the output of DOE as the actual output of network training in machine learning; then, use the train_test_split() function to compare the input and actual network training The output is randomly divided into a training set and a test set, in which 75% of the data is used as a training set and 25% is used as a test set; finally, the input data and the actual output data are normalized using Pytorch's MinMaxscale() function, The normalized calculation equation is as follows:
其中,公式中i是第i个输入特征,i=1,…,5;d代表样本样本数,d=1,…,1000;t代表输出结果的维度,t=1,…,121;是第d个样本的第i个输入特征的正则化值;Xd,i第d个样本的第i个输入特征的值;是第d个样本的第i个输入特征对应输出的正则化值;Yd,i是第d个样本的第i个输入特征对应的真实输出;X和Y表示输入和输出样本;min(X)和min(Y)分别为样本输入和真实输出的最小值;max(X)和max(Y)分别为样本输入和真实输出的最大值。Among them, i in the formula is the i-th input feature, i=1,...,5; d represents the number of samples, d=1,...,1000; t represents the dimension of the output result, t=1,...,121; Is the regularization value of the i-th input feature of the d-th sample; X d, the value of the i-th input feature of the d-th sample; is the regularization value corresponding to the output of the i-th input feature of the d-th sample; Y d,i is the real output corresponding to the i-th input feature of the d-th sample; X and Y represent the input and output samples; min(X ) and min(Y) are the minimum values of sample input and real output, respectively; max(X) and max(Y) are the maximum values of sample input and real output, respectively.
步骤四、首先,将吸能结构的输入几何图形映射到所需要的输出响应的ML体系结构中,使用归一化后的训练集和测试集作为样本,将训练样本和测试样本进行批次划分,每个批次的样本数量为10;然后,利用均方误差(MSE)作为优化过程的损失函数MSE的计算方式,如公式3所示,使用Adam算法作为深度学习优化器,将平均绝对误(MAE)、均方根误差(RMSE)、回归系数(R2)和平均相对误差绝对值(RAAE)作为考核指标,分别使用多层感知机(MLP),循环神经网络(RNN),长短时记忆法 (LSTM)和门控循环神经网络(GRU)四种神经网络模型,分别对吸能结构的吸能特性曲线(力-位移曲线)进行预测,采对比分析不同模型预测的准确性,图2为预测模型的ML框架示意图。
从图3和图4可以看出GRU预测输出曲线与真实输出曲线拟合度较高,同时其损失函数也维持在一个较小的值,说明GRU模型预测输出曲线的准确度较高。四种模型在训练期间内的考核指标,当R2越大,并且MAE、RMSE和RAAE越小,说明训练的模型越准确,而GRU比其他三种模型更平稳,MAE、RMSE和RAAE一直保持在较小的值,R2一直保持在较大的值,说明GRU缺失比其他三种模型精度高。It can be seen from Figure 3 and Figure 4 that the GRU predicted output curve has a high degree of fitting with the real output curve, and its loss function is also maintained at a small value, indicating that the GRU model predicts the output curve with high accuracy. The evaluation indicators of the four models during the training period, when the R 2 is larger, and the MAE, RMSE and RAAE are smaller, it means that the trained model is more accurate, and the GRU is more stable than the other three models, and the MAE, RMSE and RAAE have been maintained At smaller values, R 2 has been maintained at larger values, indicating that GRU missing is more accurate than the other three models.
其中,n为样本的个数,为机器学习对应神经网络的预测输出,yi为样本的实际输出。Among them, n is the number of samples, is the predicted output of the neural network corresponding to machine learning, and yi is the actual output of the sample.
步骤五、通过对比分析MLP,RNN,LSTM和GRU四种机器学习网络模型的预测效果,将GRU作为地铁列车方锥式吸能结构的吸能特性曲线预测模型,将外壁厚度TA、外壁厚度TB、隔板厚度Tgb、铝蜂窝A的强度δA和铝蜂窝B的强度δB作为优化变量,方锥式吸能结构的吸能量(EA)和峰值力(PCF)作为优化目标,建立的优化理论模型如公式9所示;然后,采用哈默斯雷采样法(Hammersley)对优化进行重新采样,利用GRU网络模型进行计算对应的吸能量(EA)和峰值力(PCF),生成新的DOE;最后,采用全局响应面法(GRSM)进行多目标优化,GRSM优化算法的参数如表1所示。Step 5. By comparing and analyzing the prediction effects of four machine learning network models: MLP, RNN, LSTM and GRU, GRU is used as the prediction model of the energy absorption characteristic curve of the square cone energy-absorbing structure of the subway train, and the outer wall thickness T A , outer wall thickness T B , separator thickness T gb , strength δ A of aluminum honeycomb A and strength δ B of aluminum honeycomb B are used as optimization variables, and the energy absorption (EA) and peak force (PCF) of the square cone energy-absorbing structure are used as optimization targets. The established optimization theoretical model is shown in Equation 9; then, Hammersley sampling method (Hammersley) is used to resample the optimization, and the GRU network model is used to calculate the corresponding energy absorption (EA) and peak force (PCF), generating The new DOE; finally, the global response surface method (GRSM) is used for multi-objective optimization, and the parameters of the GRSM optimization algorithm are shown in Table 1.
PCF=max(F(s)) (8)PCF=max(F(s)) (8)
其中,s为方锥式吸能结构的压缩位移;F(s)为方锥式吸能结构的轴向力;TA和 TB为方锥式吸能结构的外壁厚度;Tgb为方锥式吸能结构的隔板厚度;δA和δB分别为铝蜂窝A和铝蜂窝B的强度。Among them, s is the compression displacement of the square cone energy-absorbing structure; F(s) is the axial force of the square cone energy-absorbing structure; T A and T B are the outer wall thicknesses of the square cone energy-absorbing structure; T gb is the square The diaphragm thickness of the conical energy-absorbing structure; δ A and δ B are the strengths of aluminum honeycomb A and aluminum honeycomb B, respectively.
其中,EA为方锥式吸能结构的吸能量;PCF为方锥式吸能结构的峰值力;Among them, EA is the energy absorption of the square cone energy-absorbing structure; PCF is the peak force of the square cone energy-absorbing structure;
表1 GRSM算法的参数Table 1 Parameters of GRSM algorithm
步骤6、基于GRSM的优化结果,得到了优化目标的帕累托解集,采用最小距离法(如下公式9所示),对优化得到的帕累托解集进行最优决策,得到最优解。将吸能量(EA)、比吸能(SEA)、平均力(Fmean)和吸能效率(IFE)作为吸能评价指标,并采用雷达图对比最优解与实验结果(如图4所示),验证该优化方法的可行性Step 6. Based on the optimization results of GRSM, the Pareto solution set of the optimization target is obtained, and the minimum distance method (as shown in the following formula 9) is used to make an optimal decision on the optimized Pareto solution set to obtain the optimal solution . The energy absorption (EA), specific energy absorption (SEA), mean force (F mean ) and energy absorption efficiency (IFE) were used as energy absorption evaluation indicators, and the optimal solution was compared with the experimental results using radar charts (as shown in Figure 4 ), to verify the feasibility of the optimization method
其中,D为膝关节点与帕累托解集点的距离;m为优化目标的个数,fi k为第i 个优化目标的最优点k。Among them, D is the distance between the knee joint point and the Pareto solution set point; m is the number of optimization objectives, and f i k is the optimal point k of the i-th optimization objective.
其中,m为方锥式吸能结构的质量;s为方锥式吸能结构的压缩位移;Fmean为方锥式吸能结构的平均力,PCF为方锥式吸能结构的峰值力;IFE为方锥式吸能结构的吸能效率。Among them, m is the mass of the square cone energy-absorbing structure; s is the compression displacement of the square cone energy-absorbing structure; F mean is the average force of the square cone energy-absorbing structure, and PCF is the peak force of the square cone energy-absorbing structure; IFE is the energy absorption efficiency of the square cone energy-absorbing structure.
为了进一步说明优化过程的效率,有必要分别使用GRU模型和有限元模型比较优化过程的计算时间。在模型优化过程中,GRU的训练时间为100.5小时,当模型训练完成时单个计算时间为0.5秒,有限元模型的单个计算时间是1小时,GRU和FE 模型计算不用样本数量的DOE所需的时间对比图如图5所示。GRU和FE模型的优化过程的计算时间如公式14-16所示。In order to further illustrate the efficiency of the optimization process, it is necessary to compare the calculation time of the optimization process using the GRU model and the finite element model, respectively. In the process of model optimization, the training time of GRU is 100.5 hours. When the model training is completed, the single calculation time is 0.5 seconds, and the single calculation time of the finite element model is 1 hour. The GRU and FE models are required to calculate the DOE without the number of samples. The time comparison chart is shown in Figure 5. The calculation time of the optimization process of the GRU and FE models is shown in Equations 14-16.
TML=ttrain+n×tml (14)T ML =t train +n×t ml (14)
其中,TML为机器学习GRU模型的计算DOE所需要的总的计算时间,ttrain为GRU 训练所需要的时间,tml为模型训练成功后,每计算一个样本所需要的时间,n为DOE 的样本个数Among them, T ML is the total calculation time required for calculating the DOE of the machine learning GRU model, t train is the time required for GRU training, t ml is the time required for each calculation of a sample after the model is successfully trained, and n is the DOE The number of samples
TFE=n×tFE (15)T FE =n×t FE (15)
其中,TFE为有限元模型的计算DOE所需要的总的计算时间,tFE为有限元模型每计算一个样本所需要的时间,n为DOE的样本个数。Among them, T FE is the total calculation time required for calculating the DOE of the finite element model, t FE is the time required for each calculation of a sample of the finite element model, and n is the number of samples of DOE.
其中,TML为机器学习GRU模型的计算DOE所需要的总的计算时间;TFE为有限元模型的计算DOE所需要的总的计算时间;TIEF为机器学习GRU模型与有限元模型计算DOE所需的时间比值。Among them, T ML is the total calculation time required for calculating the DOE of the machine learning GRU model; T FE is the total calculation time required for calculating the DOE of the finite element model; TIEF is the time required for calculating the DOE of the machine learning GRU model and the finite element model required time ratio.
从图5中可以看出,当n=500,TML和TFE分别为600.57和500时,TIFE为120.12%,GRU所需时间大于有限元模型计算时间。当n=5000,TML和TFE分别为601.19和5000 时,TIFE为5.58%,GRU所需时间小于有限元模型计算时间。当n较小时,GRU的计算时间将比FE长,随着n个数的增加,GRU计算基本保持不变,而有限元模型计算时间的计算时间大幅增加,TIFE会呈现指数级下降。因此,可以发现,当优化过程中采样的样本数量较大时,使用机器学习GRU模型可以大大减少计算时间,机器学习GRU模型可以使优化过程实现大数据赋能,将方锥式吸能结构模型优化带入一个新领域。It can be seen from Figure 5 that when n=500, T ML and T FE are 600.57 and 500 respectively, TIFE is 120.12%, and the time required by GRU is greater than the calculation time of the finite element model. When n=5000, T ML and T FE are 601.19 and 5000 respectively, TIFE is 5.58%, and the time required by GRU is less than the calculation time of finite element model. When n is small, the calculation time of GRU will be longer than that of FE. As the number of n increases, the calculation time of GRU will basically remain unchanged, while the calculation time of the finite element model will increase significantly, and TIFE will show an exponential decline. Therefore, it can be found that when the number of samples sampled in the optimization process is large, the calculation time can be greatly reduced by using the machine learning GRU model. The machine learning GRU model can enable the optimization process to achieve big data empowerment, and the square cone energy-absorbing structure model Optimization brings into a new realm.
以下结合具体实施例对本发明进行解释和说明。The present invention is explained and illustrated below in conjunction with specific embodiments.
为观察端部吸能结构在高速碰撞过程中的吸能特性和行为机理,在标准轨道上对端部吸能结构结构进行了全尺寸碰撞试验。如图6所示,整个实验系统主要由一个提供一定速度的发射装置、一部台车、一个用于冲击的力均匀板、安装在刚性壁和力均匀板之间的动态力传感器、一个记录结构入射速度的速度计和一个用于捕捉冲击过程的高速摄像机组成。端部吸能结构固定在冲击小车的前端。将小车拖到撞击点的远端,通过电机驱动装置带动小车以17.9km/h的初速撞击试样。台车总重量为16.1t。In order to observe the energy-absorbing characteristics and behavior mechanism of the end energy-absorbing structure during a high-speed collision, a full-scale impact test was carried out on the end energy-absorbing structure on a standard track. As shown in Fig. 6, the whole experimental system is mainly composed of a launching device providing a certain speed, a trolley, a force uniform plate for impact, a dynamic force sensor installed between the rigid wall and the force uniform plate, a recording A velocimeter for the incident velocity of the structure and a high-speed camera for capturing the impact process. The end energy-absorbing structure is fixed on the front end of the impact trolley. Drag the trolley to the far end of the impact point, and drive the trolley to hit the sample at an initial velocity of 17.9km/h through the motor drive device. The total weight of the trolley is 16.1t.
为了验证方锥式吸能结构的耐撞性能,验证有限元建模方法的正确性,使用冲击试验台车系统进行了冲击试验。同时,数值和理论结果可以通过实验结果进一步验证。图7中(a)显示了实验和数值结果在力-位移曲线方面的比较。结果表明,在实验和模拟中,方锥式吸能结构与刚性壁接触后形成初始峰值力,然后迅速下降。考虑到实验条件比模拟条件复杂,力-位移曲线不能完全一致。尽管如此,可以发现两者的初始峰值力的数量是相同的,均形成了12个力波峰,并且初始峰值力的振幅基本上是一致的。如图7中(b)所示,实验和模拟结果在能量-位移曲线方面的比较。整个过程中实验和模拟的能量-位移曲线的变化趋势是一致的。此外,为了更好地比较,耐撞性指标和误差指标的值汇总在中。就EA、IPCF、d和MCF而言,实验和数值结果分别为220.38kJ和216.86kJ、549.69kN和526.67kN、702.09mm和711.92mm、313.89 kN和304.61kN。数据表明,模拟结果与实验值具有很好的一致性。In order to verify the crashworthiness of the square cone energy-absorbing structure and verify the correctness of the finite element modeling method, the impact test was carried out using the impact test bench system. Meanwhile, the numerical and theoretical results can be further verified by experimental results. Fig. 7(a) shows the comparison of experimental and numerical results in terms of force-displacement curves. The results show that, in both experiments and simulations, the square cone energy-absorbing structure forms an initial peak force after contacting the rigid wall, and then drops rapidly. Considering that the experimental conditions are more complex than the simulated conditions, the force-displacement curves cannot be completely consistent. Nevertheless, it can be found that the number of initial peak forces of the two is the same, both form 12 force peaks, and the amplitudes of the initial peak forces are basically the same. As shown in Fig. 7(b), the comparison of the experimental and simulation results in terms of energy-displacement curves. The changing trend of the energy-displacement curves of the experiment and the simulation is consistent throughout the process. In addition, for better comparison, the values of crashworthiness index and error index are summarized in . For EA, IPCF, d and MCF, the experimental and numerical results are 220.38 kJ and 216.86 kJ, 549.69 kN and 526.67 kN, 702.09 mm and 711.92 mm, 313.89 kN and 304.61 kN, respectively. The data show that the simulated results are in good agreement with the experimental values.
此外,所提出的理论模型预测的端部吸能结构的理论动态MCF为296.22kN。理论预测的动态稳态力与冲击试验吻合较好,相对误差为5.63%,满足工程计算精度要求。因此,所提出的理论模型和构建的有限元模型具有足够的精度来研究端部吸能结构的能量吸收特性。Furthermore, the theoretical dynamic MCF of the end energy-absorbing structure predicted by the proposed theoretical model is 296.22 kN. The theoretically predicted dynamic steady-state force is in good agreement with the impact test, with a relative error of 5.63%, which meets the requirements of engineering calculation accuracy. Therefore, the proposed theoretical model and the constructed finite element model have sufficient precision to study the energy absorption characteristics of end energy-absorbing structures.
表格2实验与仿真结果对比Table 2 Comparison of experimental and simulation results
准确预测冲击响应并不是唯一的标准,还需要有效预测变形模式。方锥式吸能结构的实验和模拟变形过程如图8所示。可以看出,模拟中方锥式吸能结构的变形模式与碰撞过程中的实验结果基本一致。无论是实验还是模拟,从碰撞端到后端,变形都是稳定有序的,最终形成规则的形状。由于隔膜的存在,在模拟和实验变形过程中都出现了12次折叠。Accurately predicting the shock response is not the only criterion, effective prediction of deformation modes is also required. The experimental and simulated deformation process of the square cone energy-absorbing structure is shown in Fig. 8. It can be seen that the deformation mode of the square cone energy-absorbing structure in the simulation is basically consistent with the experimental results during the collision. Whether it is an experiment or a simulation, from the collision end to the rear end, the deformation is stable and orderly, and finally forms a regular shape. Due to the presence of the septum, 12 folds occurred during both simulated and experimental deformations.
综上所述,数值模拟中的力-位移、能量-位移和结构变形模式与冲击实验吻合较好,表明该仿真模型具有较好的精度,可用于后续研究。In summary, the force-displacement, energy-displacement and structural deformation modes in the numerical simulation are in good agreement with the impact experiments, indicating that the simulation model has good accuracy and can be used for subsequent research.
以上所述仅为本发明的优选实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明可以有各种更改和变化。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116306113A (en) * | 2023-02-20 | 2023-06-23 | 中南大学 | Rail vehicle composite structure geometric parameter and crashworthiness prediction method based on transfer learning and image recognition |
CN116911144A (en) * | 2023-09-11 | 2023-10-20 | 西南交通大学 | Train collision energy management optimization method based on machine learning |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106081126A (en) * | 2016-06-13 | 2016-11-09 | 王晨 | Bionical cellular active safety escape compartment embeds application and the design of aviation aircraft |
CN109697309A (en) * | 2018-12-05 | 2019-04-30 | 西北机电工程研究所 | A kind of bullet high speed impact gets into resistance fast acquiring method |
CN110020466A (en) * | 2019-03-19 | 2019-07-16 | 南京理工大学 | Negative poisson's ratio structure energy-absorption box optimization design method based on agent model |
CN110309601A (en) * | 2019-07-04 | 2019-10-08 | 中南大学 | An optimization method and system for collision energy management of long MUs |
WO2022007409A1 (en) * | 2020-07-08 | 2022-01-13 | 大连理工大学 | Curve reinforced structure layout intelligent design method based on image feature learning |
-
2022
- 2022-09-21 CN CN202211151523.7A patent/CN115481488B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106081126A (en) * | 2016-06-13 | 2016-11-09 | 王晨 | Bionical cellular active safety escape compartment embeds application and the design of aviation aircraft |
CN109697309A (en) * | 2018-12-05 | 2019-04-30 | 西北机电工程研究所 | A kind of bullet high speed impact gets into resistance fast acquiring method |
CN110020466A (en) * | 2019-03-19 | 2019-07-16 | 南京理工大学 | Negative poisson's ratio structure energy-absorption box optimization design method based on agent model |
CN110309601A (en) * | 2019-07-04 | 2019-10-08 | 中南大学 | An optimization method and system for collision energy management of long MUs |
WO2022007409A1 (en) * | 2020-07-08 | 2022-01-13 | 大连理工大学 | Curve reinforced structure layout intelligent design method based on image feature learning |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116306113A (en) * | 2023-02-20 | 2023-06-23 | 中南大学 | Rail vehicle composite structure geometric parameter and crashworthiness prediction method based on transfer learning and image recognition |
CN116306113B (en) * | 2023-02-20 | 2025-01-07 | 中南大学 | Rail vehicle composite structure geometric parameter and crashworthiness prediction method based on transfer learning and image recognition |
CN116911144A (en) * | 2023-09-11 | 2023-10-20 | 西南交通大学 | Train collision energy management optimization method based on machine learning |
CN116911144B (en) * | 2023-09-11 | 2023-11-21 | 西南交通大学 | Train collision energy management optimization method based on machine learning |
US20250086355A1 (en) * | 2023-09-11 | 2025-03-13 | Southwest Jiaotong University | Train crash energy management (cem) optimization method based on machine learning |
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