有限元法
刚度
工程类
结构工程
踩
优化设计
汽车工程
计算机科学
机器学习
天然橡胶
有机化学
化学
作者
Priyankkumar Dhrangdhariya,Soumyadipta Maiti,Beena Rai
出处
期刊:SAE international journal of passenger vehicle systems
[SAE International]
日期:2023-07-18
卷期号:17 (1)
被引量:2
标识
DOI:10.4271/15-17-01-0001
摘要
<div>Non-pneumatic tires (NPTs) have been widely used due to their advantages of no occurrence of puncture-related problems, no need of air maintenance, low rolling resistance, and improvement of passenger comfort due to its better shock absorption. It has a variety of applications as in earthmovers, planetary rover, stair-climbing vehicles, and the like. Recently, the unique puncture-proof tire system (UPTIS) NPT has been introduced for passenger vehicles segment. The spoke design of NPT-UPTIS has a significant effect on the overall working performance of tire. Optimized tire performance is a crucial factor for consumers and original equipment manufacturers (OEMs). Hence to optimize the spoke design of NPT-UPTIS spoke, the top and bottom curve of spoke profile have been described in the form of analytical equations. A generative design concept has been introduced to create around 50,000 spoke profiles. Finite element model (FEM) model is developed to evaluate the stiffness and damage-resisting performance of NPT-UPTIS spoke. The FEM methodology has also been validated with average accuracy of more than 95% for experimental vertical stiffness for commercial NPT-Tweel. The stiffness and damage-resisting performance of generated designs have been predicted with the help of machine learning regression models, which were trained on the FEM results of 200 such designs. These 50,000 generated designs have been categorized in four different categories based on different level of stiffness and damage resistance performance. In this study, one optimized design from each category has been selected and their performance have been validated with 3D FEM simulation. It has been found that the suggested topology optimization approach is efficient to generate UPTIS spoke designs with having ±30% stiffness with 17%, 40%, and 56% more damage resistance performances with respect to the starting reference design.</div>
科研通智能强力驱动
Strongly Powered by AbleSci AI