车辆转弯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≥325，则应力强度等级为优秀；如果275≤最大应力<325,则应力强度等级为良好；如果225≤最大应力<275，则应力强度等级为合格；如果最大应力<225，则应力强度等级为不合格。

With the gradual improvement of combat, rescue and other functions of special-purpose unmanned ground vehicles (UGVs), their application scope is increasingly expanding. The diversification of future battlefields imposes higher requirements on the environmental adaptability of these special-purpose UGVs. To meet the requirements of operating condition applications, high maneuverability and low-cost development for special-purpose UGVs, the truss-type body structure of unmanned vehicles is optimized using the ideas of operating condition-based design and lightweight optimization, so as to achieve the coexistence of lightweight vehicle structure and functional requirements, and enhance the practicality of unmanned vehicles.
1. Construction of initial design space: Construct a multi-dimensional design space based on the reasonable ranges of structural variable indicators of the vehicle.
2. Construction of simulation dataset: Adopt the Latin Hypercube Sampling (LHS) method for design space exploration. Each variable dimension is divided into 500 layers, and a total of 500 sample points are collected. The operating condition simulation dataset is obtained through finite element analysis (FEA) calculations.
3. Construction of Gaussian Process: Construct a surrogate model $f(x) sim GP(mu(x), Sigma(x, x))$. Thus, the performance evaluation of the operating condition body structure is essentially a regression model. The design parameters of the body structure scheme (including length, width, height, and key reinforcing structures) are taken as the input parameters of the Gaussian process, and the maximum stress value, a key performance indicator of the body structure, is taken as the output parameter. The operating condition surrogate model can be obtained through training on the simulation data. Due to the confidentiality of the algorithm model, only the theoretical operational knowledge is presented here.
4. Performance prediction: Through the prediction of the Gaussian surrogate model, we can determine whether the performance indicators of the body structure meet the performance requirements. The specific discrimination criteria are as follows:
- If the maximum stress ≥ 325, the stress strength level is Excellent;
- If 275 ≤ maximum stress < 325, the stress strength level is Good;
- If 225 ≤ maximum stress < 275, the stress strength level is Qualified;
- If maximum stress < 225, the stress strength level is Unqualified.

搜集汇总

背景与挑战

背景概述

该数据集包含501条车辆结构设计参数和性能指标的记录，用于特种无人车辆的轻量化优化设计，应用高斯过程代理模型进行性能预测。