车辆冲击应力工况场景的结构轻量化设计数据

随着特种无人车辆作战、营救等功能的日益完善，其使用范围也越来越广泛。未来战场的多样化也对特种无人车辆的环境适应性提出了更高的要求。为满足特种无人车辆的工况使用、高机动能力与低成本研制等要求，采用工况设计与轻量化优化的思路对无人车桁架车身结构进行优化设计。从而达到轻量化车辆结构与功能并存，提高了无人车辆的实用性。1.初始设计空间的构建：根据车辆结构变量指标的合理区间，构建多维度的设计空间 2.仿真数据集的构建：采用拉丁超立方采样方法进行设计空间探索，每个变量维度分500层，共采集500样本点，通过有限元分析计算，获得工况仿真数据集。3.构建高斯过程：构建代理模型f（x）~GP（μ（x），σ（x，x）），因此对工况车身结构性能评估本质是一个回归问题的模型。将车身结构方案的设计参数（包括长、宽、高、重要加强结构）作为高斯过程的输入参数，车身结构性能指标最大应力值作为高斯过程的输出参数。通过仿真数据的训练，可得到工况的代理模型，由于算法模型的私密性，我们这里只是表述了运作的理论知识4.性能预测：通过高斯代理模型的预测，我们可以判别出该车身结构性能指标是否满足性能要求。具体的判别方式如下：如果最大应力≥200，则应力强度等级为优秀；如果150≤最大应力<200,则应力强度等级为良好；如果100≤最大应力<150，则应力强度等级为合格；如果最大应力<100，则应力强度等级为不合格。

As the functions of special unmanned ground vehicles (UGVs) such as combat and rescue continue to mature, their application scenarios have become increasingly widespread. The diversification of future battlefields also imposes higher requirements on the environmental adaptability of special UGVs. To meet the requirements of special UGVs including working condition adaptability, high maneuverability and low-cost development, the truss body structure of unmanned vehicles is optimized through the idea of working condition design and lightweight optimization, so as to achieve the coexistence of lightweight vehicle structure and functional requirements, and improve the practicability of unmanned vehicles. 1. Construction of initial design space: A multi-dimensional design space is constructed based on the reasonable intervals of structural variable indicators of the vehicle. 2. Construction of simulation dataset: Latin Hypercube Sampling (LHS) is adopted to explore the design space, with 500 layers per variable dimension, and a total of 500 sample points are collected. The working condition simulation dataset is obtained through finite element analysis (FEA) calculation. 3. Construction of Gaussian process: A surrogate model $f(x) sim GP(mu(x), Sigma(x, x'))$ is constructed. Therefore, the performance evaluation of the working condition body structure is essentially a regression model problem. The design parameters of the body structure scheme (including length, width, height and key reinforced structures) are taken as the input parameters of the Gaussian process, while the maximum stress value, which is the performance indicator of the body structure, is taken as the output parameter. The surrogate model for working conditions can be obtained through training with simulation data. Due to the confidentiality of the algorithm model, only the theoretical knowledge of its operation is described herein. 4. Performance prediction: Through the prediction of the Gaussian surrogate model, we can determine whether the performance indicators of the body structure meet the requirements. The specific determination methods are as follows: If the maximum stress ≥ 200, the stress strength level is rated as 'Excellent'; If 150 ≤ maximum stress < 200, the stress strength level is rated as 'Good'; If 100 ≤ maximum stress < 150, the stress strength level is rated as 'Qualified'; If the maximum stress < 100, the stress strength level is rated as 'Unqualified'.

搜集汇总

背景与挑战

背景概述

该数据集包含502条车辆结构轻量化设计数据，涵盖尺寸、材料、应力等关键参数，用于特种无人车辆的工况优化设计。数据格式为CSV，更新频次按需，适用于制造业中的结构轻量化研究。