Investigating the Flexibility of the MBSE Approach to the Biomass Mission

Abstraction

Model-based systems engineering (MBSE) represents a move away from the traditional approach of document-based systems engineering (DBSE), and is used to promote consistency, communication, clarity, and maintainability within systems engineering projects. MBSE offers approaches that can address issues associated with cost, complexity, and safety. One way that this can be achieved is by performing early functional validation of the high-level spacecraft functional avionics system. The use case discussed in this article focusses on the Biomass model, a systems modeling language-based representation of the Biomass Earth-observation mission. The MBSE approach is used to calculate the required size of the data handling unit onboard the Biomass spacecraft. The functional response of the system in terms of the onboard memory usage throughout the mission is simulated. Traditionally, this level of analysis would not be available at this early stage. The approach aims to replace ad hoc, spreadsheet-based calculations with a formal representation of the system that can be executed, interrogated and quantified. The flexibility of this MBSE approach is demonstrated by applying changes to the Biomass project and assessing the time required to implement these changes in the Biomass model and propagate them through to the results of the simulation. The changes have been made independently of each other and include: changes to the logical architecture, changes to the functional definition, changes to the mission profile, and changes to the requirements. Potential areas for improvement regarding the structure of the Biomass model are highlighted and discussed.

介绍

卫星1.5天的轨道

MBSE的用途：A core question that MBSE seeks to answer is how to integrate engineering models across tools and domain boundaries [6]. MBSE is the formalized application of modeling to support system requirements, design, analysis, optimization, verification, and validation [7]–[9]. By using interconnected models to store, represent, and relate this information and data, projects can expect improvements in consistency, communication, clarity, visibility, maintainability, etc.—thus addressing the growing issues associated with cost, complexity, and safety [10], [11].

本文的目的是展示MBSE方法的“灵活性”（也可以理解为灵敏度）。这是通过将可能的更改应用于生物质项目并评估在生物质模型中实施这些更改所需的时间和精力来完成的，并将它们传播到分析结果。这些更改彼此独立进行，包括：逻辑体系结构的更改，功能定义的更改，更改任务配置文件以及要求的更改。changes to the logical architecture, changes to the functional definition, changes to the mission profile, and changes to the requirements.

本文之所以重要，是因为“changes”是一个成功的系统工程的重要组成部分。

本文提出的两个问题：

  • 对于发生在系统规范system specification (functional definition and logical architecture)的变更，MBSE的灵活度如何？
  • 对于发生在系统需求和任务规范system requirements and the mission specification的变更，MBSE的灵活度如何？

本文的创新点还在于：介绍了MBSE方法和模型结构的定义，该定义可以用于航天器的电子学功能（high-level functional avionics of a spacecraft, in terms of both structure and behavior），还包括仿真。

MBSE

While there have been significant efforts to develop the MBSE approach to the simulation and analysis of spacecraft, the focus has remained on the description of system designs, and overlooks the importance of using this information, present in the model, to automatically analyze and validate the system itself.

本文使用的工具：Cameo System Modeler (18.5), produced by No Magic.

System modeling language

本文使用SysML，尽管有局限性，例如: some semantics have to be modified or used out of their originally intended context, and the notation does not use formal activity specifications.但是他是“最常用的”。

Biomass mission

生物量检测卫星：一个在25年5月发射的卫星。搭载了一个P波段合成孔径雷达（435MHz）。每年两次全球覆盖。sun-synchronous orbit，每三天基本重复。

SAR雷达的三种模式：OFF（发射前），Ready（休眠状态，海面上时），Measurement（记录科学数据，陆地上时）。

一个PDHU内存（960Gb），内存中有三个目录，一个housekeeping data（7.5kb/s保存），两个science data（分别是66.2Mb/s）。

每次飞行会经过ESA的Svalbard地面站，通过X波段下行天线（X-band downlink antenna）传输数据（467Mb/s），XDA有三个模式Off关闭，Ready准备和Downlink下传。

Biomass model

本节分析卫星的SAR-PDHU-XDA系统。分为四个层级：System Requierment, Mission definition, Functional Definition(described using structural block definition and internal block diagrams), Logical Architecture(described using behavioral activity and state machine diagrams)。

Mission definition：卫星的四种工作模态（海上关闭、陆地测量、地面站传输和陆地测量+地面站传输同时）。任务定义和系统设计是分离的，不能混淆。

本文实际介绍模式切换的过程是通过模型架构总视图推进的，根据轨道计算得到四种基本工作模态，然后把模态信息（对应mission definition）结合配置信息（functional definition）传输给执行单元进行计算，实际的数学计算仍然是由Matlab完成的。

Methodology

针对问题一和二的修改案例，来检验MBSE的灵活性。

  • A 更新内存限制：960G调整为400G
  • B 更新文件夹数量：添加了个文件夹4，内存机制有所改变
  • C 更新系统模式定义：SAR数据速率变为原来的四倍
  • D 添加behavior：不下传数据
  • E 更新任务配置文件：任务和系统是分离的，只更改任务（轨道）不改系统。
  • F 更新SAR最大速率：将最大下传速率调整为120Mb/s。

Results

对A-F的各个案例进行分析，介绍了更改配置所需时间和收集结果所需时间。

Discussion

好处：更改所需要的时间很短；直观；选取的案例有代表性；任务和系统分离；
局限：设计仿真完需要迭代优化；需要熟练使用者操作；SysML语法和实际场景不太匹配。

Future work

将这个案例扩展成一个模版；仿真时间需要减少；应用在别的工程案例上。

个人总结：本文围绕生物量监测卫星的测量和下传两个任务做了模拟分析，用SysML建模用Matlab仿真，整体上就是MBSE的一个数学建模，调整了几个配置参数进行分析，甚至可以让Matlab代替SysML的部分独立完成全部环节的仿真，没有实质上凸显SysML的作用。第五节模型介绍部分和第八节讨论部分介绍得略显散乱；前后几张图的串联关系不容易理解；实验略单薄。