With technological advances in the 21st century and the rise of the commercial space industry, crewed Mars missions to land humans on our red neighbor have become increasingly feasible. One of the key challenges in accomplishing this goal is determining whether model-based systems engineering training is necessary for space systems engineers to adopt digital engineering. This paper reviews model-based systems engineering benefits and limitations, as well as historically significant crewed Mars missions that planned to land on the surface. Models are presented for a Mars transit habitat developed in the modeling language SysML using Cameo Systems Modeler. These models leverage Cameo’s simulation capabilities to execute MATLAB scripts integrating digital engineering software Systems Tool Kit into the modeling process, demonstrating a reusable interoperability methodology for Mars mission planning not yet existing in this domain’s literature. It can be concluded that integrating model-based systems engineering with digital engineering for crewed Mars landing mission planning can lead to multiple benefits for space systems digital engineering, including 1) greater system simulation capabilities, 2) enhanced model fidelity, and 3) improved system understanding. Future research to further enhance these benefits includes adding cybersecurity considerations and greater system detail to the models.

随着 21 世纪的技术进步和商业航天工业的兴起，载人火星任务将人类降落在我们的红色邻居上变得越来越可行。实现这一目标的关键挑战之一是确定基于模型的系统工程培训对于空间系统工程师采用数字工程是否必要。本文回顾了基于模型的系统工程的优点和局限性，以及计划登陆火星表面的具有历史意义的载人火星任务。展示了使用 Cameo Systems Modeler 以建模语言 SysML 开发的火星运输栖息地模型。这些模型利用 Cameo 的仿真功能来执行 MATLAB 脚本，将数字工程软件系统工具包集成到建模过程中，展示了该领域文献中尚不存在的用于火星任务规划的可重用互操作性方法。可以得出结论，将基于模型的系统工程与数字工程相结合，用于载人火星着陆任务规划可以为空间系统数字工程带来多重好处，包括 1) 更强的系统仿真能力，2) 增强模型保真度，以及 3) 改进系统理解。进一步增强这些好处的未来研究包括在模型中添加网络安全考虑和更多系统细节。可以得出结论，将基于模型的系统工程与数字工程相结合，用于载人火星着陆任务规划可以为空间系统数字工程带来多重好处，包括 1) 更强的系统仿真能力，2) 增强模型保真度，以及 3) 改进系统理解。进一步增强这些好处的未来研究包括在模型中添加网络安全考虑和更多系统细节。可以得出结论，将基于模型的系统工程与数字工程相结合，用于载人火星着陆任务规划可以为空间系统数字工程带来多重好处，包括 1) 更强的系统仿真能力，2) 增强模型保真度，以及 3) 改进系统理解。进一步增强这些好处的未来研究包括在模型中添加网络安全考虑和更多系统细节。