{"title":"Simulations of Car Dynamics: BMW M3 and Chevrolet Cavalier","authors":"Zhenchun Dong, Jiale Xue","doi":"10.1109/ICID54526.2021.00071","DOIUrl":null,"url":null,"abstract":"Automobile industry is a huge socio-economic system engineering, which is different from general products. Based on CAD software, the numerical and image models of Chevrolet and BMW are established by finite element method. We use the programming method to construct the corresponding mechanical images. Through the professor's analysis of images and data, we understand the main reasons for the performance differences between different models. Automobile product is a highly comprehensive final product, which needs to organize professional and cooperative socialized mass production and related industrial products. It is impossible for academic research not to encounter problems. When encountering problems, we can first ask our teammates for help and see his new ideas. If it can't be solved, we can ask the help of professors, report our problems truthfully, and do not cover up our own experience and lack of experience, so as to solve this practical problem. But we can't rely on professors to help us find the source of problems for a long time. When we encounter problems, we should first try to use our knowledge and try to solve them. When there is nothing we can do, we can repeatedly check the data and observe the model to see if there is a problem in the thinking and solution. Only by accumulating experience can we better face the problems in scientific research in the future. For example, when comparing the relevant data of Chevrolet and BMW models, we first divided the model information. We worked together to find the most accurate data of BMW and Chevrolet in different states. When collecting data supporting BMW vehicle models, we had the problem that data entry failed and Python could not run. Then we consulted with the professor, who gave us the most professional and detailed answers. When we see the normal operation of the data model, we are excited.","PeriodicalId":266232,"journal":{"name":"2021 2nd International Conference on Intelligent Design (ICID)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Conference on Intelligent Design (ICID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICID54526.2021.00071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Automobile industry is a huge socio-economic system engineering, which is different from general products. Based on CAD software, the numerical and image models of Chevrolet and BMW are established by finite element method. We use the programming method to construct the corresponding mechanical images. Through the professor's analysis of images and data, we understand the main reasons for the performance differences between different models. Automobile product is a highly comprehensive final product, which needs to organize professional and cooperative socialized mass production and related industrial products. It is impossible for academic research not to encounter problems. When encountering problems, we can first ask our teammates for help and see his new ideas. If it can't be solved, we can ask the help of professors, report our problems truthfully, and do not cover up our own experience and lack of experience, so as to solve this practical problem. But we can't rely on professors to help us find the source of problems for a long time. When we encounter problems, we should first try to use our knowledge and try to solve them. When there is nothing we can do, we can repeatedly check the data and observe the model to see if there is a problem in the thinking and solution. Only by accumulating experience can we better face the problems in scientific research in the future. For example, when comparing the relevant data of Chevrolet and BMW models, we first divided the model information. We worked together to find the most accurate data of BMW and Chevrolet in different states. When collecting data supporting BMW vehicle models, we had the problem that data entry failed and Python could not run. Then we consulted with the professor, who gave us the most professional and detailed answers. When we see the normal operation of the data model, we are excited.