Comparative analysis of model‐based and traditional systems engineering approaches for simulating a robotic space system architecture through automatic knowledge processing

Abstract

Model-based Systems Engineering (MBSE) case studies in the literature assert that there are benefits to MBSE when applied to modeling and simulating space systems. This research evaluates the benefits of an MBSE modeling and simulation approach over a traditional, non-MBSE approach through modeling and simulation of an orbiting sample Capture and Orient Module (COM) architecture for potential Mars Sample Return (MSR). The COM architecture was modeled, simulated, and evaluated against nominal load power and output data rate requirements to compare the two approaches. A new modeling and simulation-centric V-model was synthesized from existing V-model and modeling and simulation process models to map out the modeling and simulation activities within the context of the system development lifecycle and used as a tool to compare the non-MBSE and MBSE approaches. A three-phase modeling and simulation process consisting of an analysis and modeling phase, computer programming and implementation phase, and experimentation phase was used to model and simulate the COM. The total number of manual and automatic knowledge processed was calculated for both approaches and used to quantify the benefits of the MBSE approach relative to the non-MBSE approach. In the non-MBSE approach, all knowledge elements were manually processed during the modeling and simulation process. In the MBSE approach, 49% of total knowledge processing was automated.The MBSE approach showed user experience benefits with modeling and simulating the COM robotic space system through providing a higher level of support for automation, reducing the burden of systems engineering tasks, and reducing effort.

Pragmatic verification and validation of industrial executable SysML models

“…The framework improves quality assurance by finding errors in the system behavior that could lead to failures in a worst-case scenario. Recognizing the difficulties in realizing an ideal solution, we define the long-term goals as (1) operating at an industrial scale (e.g., hundreds of actions in a state machine), (2) supporting frequently used modeling constructs (e.g., communicating state machines), and (3) being deployable in an enterprise environment as a common service used by teams of engineers.…”

“…(1) We limited the execution order of orthogonal regions to the same order as they are defined in the model. (2) We forbade do-activities completely as they are running concurrently with the state machines, and aborting them is not well-defined in the standard. (3) We forbade arbitrary source code, dynamic memory allocation, and loops in guards, actions, and effects because they are well-known difficult problems in software model checking.…”

“…Model‐Based Systems Engineering (MBSE) 1 is a widely used discipline to design and implement complex systems. MBSE promotes models from static documents to executable first‐class citizens, 2 connecting the steps of the whole engineering life cycle with explicit traceability information: starting from requirements through design, simulation and verification, until implementation and reporting 3 . A recent trend in MBSE is moving from standalone tools to integrated environments connecting various tools working on linked models.…”

Agile Processes Applied to Los Alamos National Laboratory SE approach: The Agile Processes and Technology (APT) Team

“…Agile-Lean implementation aims to deliver a decision-making space and to show where processes can be improved on future systems. SAFe have been widely applied in industry (Agile 2022;Ebert & Paasivaara 2017), not only on the software realm but in hardware and systems (Denning 05/27/2020;Knaster 2021), especially in the aerospace industry (Machado & Silva 1998;Taraila & Asundi 2022;Younse, Cameron & Bradley 2022;Younse, Cameron & Bradley 2021). The SAFe framework is an evolving collection of practices, knowledge and guidance founded on Agile-Lean principles.…”

Defense program quality‐cost‐delay optimization: architecture framework, a bridge between program management and system engineering

“…Others publications about return on investment present detailed studies on systems engineering versus Model-Based Systems Engineering (MBSE). 9,10 But to the best of our knowledge, none of those computations consider scheduling or project planning. Furthermore, they have in common return on experience on past projects, which does not imply that future projects would have the same scheduling hypothesis.…”