David Kaslow

— Small satellite missions are becoming increasingly complex as scientists and engineers propose to utilize them to accomplish more ambitious science and technology goals. Small satellites such as CubeSats are challenging to design because they have limited resources, coupled subsystems, and must operate in dynamic environments. Model Based Systems Engineering (MBSE) is a key practice to advance systems engineering that can benefit CubeSat missions. MBSE creates a system model that helps integrate other discipline specific engineering models and simulations. The system level model is initiated at the start of a project and evolves throughout development. It provides a cohesive and consistent source of system requirements, design, analysis, and verification. This paper describes an integrated, executable MBSE

Model-Based Systems Engineering (MBSE) is a key practice to advance the systems engineering discipline, and the International Council on Systems Engineering (INCOSE) has established the MBSE Initiative to promote, advance, and institutionalize the practice of MBSE. As part of this effort, the INCOSE Space Systems Working Group (SSWG) Challenge Team has been investigating the applicability of MBSE for designing CubeSats since 2011. The goal of the team is to provide a sufficiently complete CubeSat Reference Model that can be adapted to any CubeSat project. At the core of MBSE is the development of the system model that helps integrate other discipline-specific engineering models and simulations. The team has been working to create this system model by capturing all aspects of a CubeSat project using the Systems Modeling Language (SysML), which is a graphical modeling language for systems engineering. In the past three phases of the project, the team has created the initial iteration ...

practice to advance systems engineering that can benefit CubeSat missions. MBSE creates a system model that helps integrate other discipline specific engineering models and simulations. The system level model is initiated at the start of a project and evolves throughout development. It provides a cohesive and consistent source of system requirements, design, analysis, and verification.

Model-Based Systems Engineering (MBSE) is the formalized application of modeling to support systems engineering. The International Council on Systems Engineering (INCOSE) Space Systems Working Group (SSWG) has been investigating the applicability of MBSE for designing CubeSats by developing a CubeSat Reference Model (CRM). The CRM is intended for instruction and for designing and building a mission-specific CubeSat. Additionally, we are collaborating with Object Management Group (OMG) Space Domain Task Force (SDTF) to develop the CRM as an OMG specification. Our application of MBSE utilizes the Systems Modeling Language (SysML) a graphical modeling language. This paper presents reports on the maturation of the CRM including: 1) CRM as an OMG specification, 2) expansion of architecture and requirement packages to component level, 3) population of architecture and requirement packages using table-based user interfaces, 4) incorporation of technical measures and use cases, and 5) CRM validation strategy.

— While much has been written about technical measurement and Model-Based Systems Engineering (MBSE), very little literature exists that ties the two together. What does exist treats the topic in a general manner and is void of details. Given the vital role that technical measurement plays in the systems engineering process, and the ever increasing adoption of the MBSE approach, there is a growing need to define how technical measurement would be implemented as part of a MBSE approach. The purpose of this paper is to address that need. Technical measurement is defined as the set of measurement activities used to provide insight into the progress made in the definition and development of the technical solution and the associated risks and issues [1]. Technical measures are used to: determine if the technical solution will meet stakeholder needs, provide early indications if the development effort is not progressing as needed to meet key milestones, predict the likelihood of the delivered solution to meet performance requirements, monitor high risk items, and assess the effectiveness of risk mitigation actions. MBSE is defined as the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases [2]. The benefits of using an MBSE approach over a traditional document-based systems engineering approach are: enhanced communications, reduced development risk, improved quality, and enhanced knowledge transfer. This paper defines a MBSE approach for technical measurement that begins with a set of mission objectives derived from stakeholder concerns. The objectives and concerns are represented as elements captured in the system model. Next, Measures of Effectiveness (MOEs) are derived from the mission objectives. Initially, these MOEs are captured in a special model element that allows for the MOEs to be described in a natural language format that stakeholders will understand. Those initial MOEs are then quantified and captured as value properties of the Enterprise block. The MOEs are traced back to their originating source in the mission objectives. Next, Measures of Performance (MOPs) are derived from the enterprise-level MOEs and captured as value properties of the System block. The derivation of the MOPs is captured through the development of constraint blocks and parametric diagrams. This provides for traceability between MOPs and MOEs and supports performance analysis of the MOPs to predict if the MOEs will be met. MOPs are also traced to system requirements captured in the system model. Next, the process steps at the system-level are repeated at the subsystem-level to derive Technical Performance Measures (TPMs). These TPMs are traced back to MOPs and subsystem requirements in the same manner as described for MOPs. Examples are provided throughout the paper which illustrate the application of this approach to a CubeSat. Using a CubeSat example is appropriate given the historically high failure rate and rapid growth of these missions and the role technical measurement could play in increasing their success.

As Model-Based Systems Engineering (MBSE) continues to mature and becomes part of space engineering practice, the concept of a Reference Model becomes increasingly important. The CubeSat Reference Model (CRM) is an example of a reference model that is being developed by the INCOSE Space Systems Working Group (SSWG). The intent of the model is to facilitate the design, verification and validation of CubeSat design. The CRM is being developed with sufficient flexibility to support customization for specific CubeSat missions by mission-specific CubeSat teams. This paper presents the key elements of the CRM developed using MBSE practices. It presents different views of the model along with a validation and verification approach. Further research is needed into how best to augment with other models to facilitate CubeSat test and evaluation.

The roles and interactions of activity planning and scheduling for Earth Observing Satellites are based on factors such as mission objective, system assets and resources, system and spacecraft constraints, planning criteria, scheduling strategies, timelines, and desired level of automation and operator interaction. Activities are generalized into four categories: accomplish the mission objective, support the mission objective, manage the system resources,

The phrase "planning and scheduling" represents a class of problems where a resource in limited supply is to be optimally applied. Planning and scheduling problems are accommodated by a branch of mathematics known as operations research. The goal of operations research technology is to find a solution to a known set of conditions such that some minimum "cost" or maximum

ABSTRACT Small satellite missions are becoming increasingly complex as scientists and engineers propose to utilize them to accomplish more ambitious science and technology goals. Small satellites such as CubeSats are challenging to design because they have limited resources, coupled subsystems, and must operate in dynamic environments. Model Based Systems Engineering (MBSE) is a key practice to advance systems engineering that can benefit CubeSat missions. MBSE creates a system model that helps integrate other discipline specific engineering models and simulations. The system level model is initiated at the start of a project and evolves throughout development. It provides a cohesive and consistent source of system requirements, design, analysis, and verification. This paper describes an integrated, executable MBSE representation of the Radio Aurora Explorer (RAX) CubeSat mission. The purpose of the RAX mission is to study the formation of magnetic field-aligned electron density irregularities in the Earth's ionosphere, which are known to disrupt tracking and communication between Earth and orbiting spacecraft. The RAX CubeSat model describes the configuration and properties for various systems and subsystems, and is capable of executing behavior and parametric models for analyzing subsystem functions and states of the spacecraft. It is comprised of a SysML model created with MagicDraw®, a set of analytical models developed in MATLAB®, and a high fidelity space system simulation model created in STK®. ModelCenter was used to integrate the analytical and simulation models. The integrated analyses were linked to the SysML model using MBSE Analyzer, a bridge between SysML tools and ModelCenter. Behavioral models were executed for a representative RAX mission to study energy state and data collection capabilities. This work was undertaken to demonstrate the power, scalability, and utility of MBSE tools and methods that a- e available to help meet the challenge of designing spacecraft missions of ever-increasing complexity. The RAX CubeSat model will be made available to the academic community for further study and potential extension for more complex missions.