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XLDyn's Real-Time Management Visibility in Early Vehicle Requirements Development
技术
- 分析与建模 - 实时分析
- 机器人 - 自动导引车 (AGV)
适用行业
- 航天
- 汽车
适用功能
- 产品研发
用例
- 实时定位系统 (RTLS)
- 车辆性能监测
挑战
汽车、重型设备和航空航天行业在验证车辆要求、开发设计参数以及向管理层提供实时项目状态方面面临着重大挑战。在 Word 文档中捕获需求的传统方法既耗时又低效。此外,使用企业燃油经济性预测软件和电池方程验证这些要求的过程很复杂,并且需要高水平的专业知识。此外,设置车身弯曲和扭转模式固有频率目标以满足计划目标是一项需要精度和准确性的关键任务。该挑战还涉及使用粗略几何模型进行动态有限元分析,这需要将需求和工作请求传输给 CAE 团队。完成后,需要返回结果并自动更新状态,这个过程可能容易出现延迟和不准确。
关于客户
本案例研究中的客户是一家从事汽车、重型设备和航空航天行业的公司。他们参与车辆的开发,需要验证车辆要求、开发设计参数并向管理层提供实时项目状态。他们有需要满足的燃油经济性以及车身弯曲和扭转模式固有频率的具体目标。他们还使用粗几何模型进行动态有限元分析,并与 CAE 团队密切合作以实现他们的目标。
解决方案
XLDyn 的 MBSE 模型具有集成验证方法,为这些挑战提供了解决方案。最初在 Word 文档中捕获的需求可以在几分钟内导入 XLDyn 并转换为 SysML 图。这不仅节省了时间,还提高了流程的准确性和效率。然后使用企业燃油经济性预测软件和电池方程验证这些要求,确保车辆满足 26 MPG 的城市/公路综合燃油经济性目标。此外,还设置了车身弯曲和扭转模式固有频率目标来满足计划目标。粗几何模型用于动态有限元分析,并将需求和工作请求传输给 CAE 团队。完成后,返回结果并自动更新状态,提供项目状态的实时更新。这使得总工程师能够查看车辆程序的整体状态。
运营影响
数量效益
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