汽车动力学仿真:BMW M3和雪佛兰Cavalier

Zhenchun Dong, Jiale Xue
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引用次数: 1

摘要

汽车工业不同于一般产品,是一个巨大的社会经济系统工程。基于CAD软件,采用有限元法建立了雪佛兰和宝马汽车的数值模型和图像模型。我们用编程的方法构造相应的力学图像。通过教授对图像和数据的分析,我们了解了不同模型之间性能差异的主要原因。汽车产品是综合性很强的最终产品,需要组织专业化、合作化的社会化大批量生产及相关工业产品。学术研究不可能不遇到问题。当遇到问题时,我们可以先向我们的队友寻求帮助,看看他的新想法。如果解决不了,我们可以请教授帮忙,如实汇报自己的问题,不要掩盖自己的经验和经验不足,这样才能解决这个实际问题。但是我们不能长期依靠教授来帮助我们找到问题的根源。当我们遇到问题时,我们应该首先尝试使用我们的知识并尝试解决它们。当我们无能为力时,我们可以反复检查数据,观察模型,看看思维和解决方案是否存在问题。只有积累经验,我们才能更好地面对未来科学研究中的问题。例如,在比较雪佛兰和宝马车型的相关数据时,我们首先对车型信息进行分割。我们共同努力,找到了宝马和雪佛兰在不同州最准确的数据。在收集支持宝马车型的数据时,我们遇到了数据输入失败,Python无法运行的问题。然后我们咨询了教授,他给了我们最专业和详细的答案。当我们看到数据模型的正常运行时,我们很兴奋。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulations of Car Dynamics: BMW M3 and Chevrolet Cavalier
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.
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