Manas Bajaj

Model based systems engineering (MBSE) applied to Radio Aurora Explorer (RAX) CubeSat mission operational scenarios

Small satellites are more highly resource-constrained by mass, power, volume, delivery timelines, and financial cost relative to their larger counterparts. Small satellites are operationally challenging because subsystem functions are coupled and constrained by the limited available commodities (e.g. data, energy, and access times to ground resources). Furthermore, additional operational complexities arise because small satellite components are physically integrated, which may yield thermal or radio frequency interference. In this paper, we extend our initial Model Based Systems Engineering (MBSE) framework developed for a small satellite mission by demonstrating the ability to model different behaviors and scenarios. We integrate several simulation tools to execute SysML-based behavior models, including subsystem functions and internal states of the spacecraft. We demonstrate utility of this approach to drive the system analysis and design process. We demonstrate applicability of the simulation environment to capture realistic satellite operational scenarios, which include energy collection, the data acquisition, and downloading to ground stations. The integrated modeling environment enables users to extract feasibility, performance, and robustness metrics. This enables visualization of both the physical states (e.g. position, attitude) and functional states (e.g. operating points of various subsystems) of the satellite for representative mission scenarios. The modeling approach presented in this paper offers satellite designers and operators the opportunity to assess the feasibility of vehicle and network parameters, as well as the feasibility of operational schedules. This will enable future missions to benefit from using these models throughout the full design, test, and fly cycle. In particular, vehicle and network parameters and schedules can be verified prior to being implemented, during mission operations, and can also be updated in near real-time with operational performance feedback.

SLIM: collaborative model-based systems engineering workspace for next-generation complex systems

Development of complex systems is a collaborative effort spanning disciplines, teams, processes, software tools, and modeling formalisms. It is the vision of model-based systems engineering (MBSE) to enable a consistent, coherent, interoperable, and evolving model of a system throughout its lifecycle. However, no currently available modeling language can represent all aspects of a system (including system-of-systems) at all levels of abstraction across the lifecycle.

4.3.1 Satellites to Supply Chains, Energy to Finance —SLIM for Model-Based Systems Engineering

Development of complex systems is a collaborative effort spanning disciplines, teams, processes, software tools, and modeling formalisms. Increasing system complexity, reduction in available resources, globalized and competitive supply chains, and volatile market forces necessitate that a unified model-based systems engineering environment replace ad-hoc, document-centric and point-to-point environments in organizations developing complex systems. To address this challenge, we envision SLIM—a collaborative, model-based systems engineering workspace for realizing next-generation complex systems. SLIM uses SysML to represent the front-end conceptual abstraction of a system that can “co-evolve” with the underlying fine-grained connections to models in discipline-specific tools and standards. With SLIM, system engineers can drive automated requirements verification, system simulations, trade studies and optimization, risk analyses, design reviews, system verification and validation, and other key systems engineering tasks from the earliest stages of development directly from the SysML-based system model. SLIM provides analysis tools that are independent of any systems engineering methodology, and integration tools that connect SysML with a wide variety of COTS and in-house design and simulation tools. We are presenting SLIM and its applications in two papers. In Part 1 (this paper), we present the motivation and challenges that led to SLIM. We describe the conceptual architecture (section 1) and use cases (section 2) of SLIM followed by tools available for production and evaluation usage (section 3). In Part 2 paper—SLIM Applications—we present the applications of SLIM tools to a variety of domains, both in traditional as well as non-traditional domains of systems engineering. Representative examples from space, energy, infrastructure, manufacturing and supply chain, military operations, and bank systems are presented. * Corresponding author: Manas Bajaj, manas.bajaj@intercax.com, phone: +1-404-592-6897. Preferred citation: Bajaj, M., Zwemer, D., Peak, R., Phung, A., Scott, A. and Wilson, M. (2011). Satellites to Supply Chains, Energy to Finance — SLIM for Model-Based Systems Engineering, Part 1: Motivation and Concept of SLIM. 21st Annual INCOSE International Symposium, Denver, CO, June 20-23, 2011.

Towards next-generation design-for-manufacturability (DFM) frameworks for electronics product realization

This paper elucidates the process architecture of a pilot implementation of a DFM Framework (specifically the SFM DFM Framework or SDF), which consists of four key ingredients. The first ingredient is a Design Integrator that acquires product design information from an ECAD tool and in-house sources (each populating a subset of the design) and consolidates them into a STEP AP210 model. The second ingredient is a Rule-based Expert System (initiated at Boeing) that captures the manufacturability constraints as DFM rules and evaluates printed circuit assembly (PCA) designs against them. The third ingredient is a Design View Generator that extracts design information from the AP210 model (first ingredient) and library database and derives a Kappa design model for the expert system (second ingredient) to evaluate. The fourth ingredient is the Results Viewer that helps the user browse DFM analysis results and identify design improvement opportunities. This implementation of the SDF demonstrates the ability to extract PCA design information and build a higher fidelity standards-based design model. Additionally, it also shows the capability of Rule-based Expert Systems to emulate manufacturability checks on product (PCAs in this case) designs as well as increase analysis coverage and reduce human checking time via automation.

Enterprise modeling for CubeSats

Understanding the business aspect of a project or mission is of key importance in spacecraft systems engineering, including the mission cost, high level functions and objectives, workforce, hardware, and production of spacecraft. This is especially true for CubeSat missions, which typically deal with low costs, limited resources, low mass, low volume, and low power. Introducing enterprise modeling concepts to CubeSat missions allows for incorporation of analysis of cost, business processes, and requirements for the missions spacecraft and problem domain. The following describes an application of enterprise modeling to CubeSats.

4.3.3 Satellites to Supply Chains, Energy to Finance -SLIM for Model-Based Systems Engineering: Part 2: Applications of SLIM

Development of complex systems is a collaborative effort spanning disciplines, teams, processes, software tools, and modeling formalisms. Increasing system complexity, reduction in available resources, globalized and competitive supply chains, and volatile market forces necessitate that a unified model-based systems engineering environment replace ad-hoc, document-centric and point-to-point environments in organizations developing complex systems. To address this challenge, we envision SLIM—a collaborative, model-based systems engineering workspace for realizing next-generation complex systems. SLIM uses SysML to represent the front-end conceptual abstraction of a system that can “co-evolve” with the underlying fine-grained connections to models in discipline-specific tools and standards. With SLIM, system engineers can drive automated requirements verification, system simulations, trade studies and optimization, risk analyses, design reviews, system verification and validation, and other key systems engineering tasks from the earliest stages of development directly from the SysML-based system model. SLIM provides analysis tools that are independent of any systems engineering methodology, and integration tools that connect SysML with a wide variety of COTS and in-house design and simulation tools. We are presenting SLIM and its applications in two papers. In Part 1 paper—Motivation and Concept of SLIM—we presented the motivation and challenges that led to SLIM, the conceptual architecture of SLIM, and SLIM tools available for production and evaluation usage. In Part 2 (this paper), we present the applications of SLIM tools to a variety of domains, both in traditional as well as non-traditional domains of systems engineering. Representative SysML models and results of trade studies, risk analysis, and other system engineering tasks performed using SLIM tools are presented for the following domains—space systems, energy systems, infrastructure systems, manufacturing and supply chain systems, military operations, and bank systems. * Corresponding author: Manas Bajaj, manas.bajaj@intercax.com, phone: +1-404-592-6897. Preferred citation: Bajaj, M., Zwemer, D., Peak, R., Phung, A., Scott, A. and Wilson, M. (2011). Satellites to Supply Chains, Energy to Finance — SLIM for Model-Based Systems Engineering, Part 1: Motivation and Concept of SLIM. 21st Annual INCOSE International Symposium, Denver, CO, June 20-23, 2011.

Knowledge Composition for Efficient Analysis Problem Formulation: Part 2 — Approach and Analysis Meta-Model

In Part 1 we presented technical background and a gap analysis leading to the identification of five requirements for a methodology for efficient formulation of analysis problems for VTMB design alternatives. These requirements are founded on (a) abstraction of analysis knowledge as modular, reusable, computer-interpretable, analyst-intelligible building blocks, and (b) automated creation, reconfiguration, and verification of analysis models. In this paper (Part 2), we present an example scenario to overview the Knowledge Composition Methodology (KCM) that is aimed at satisfying these requirements. The methodology is founded on analysis knowledge building blocks and a model transformation process based on graph transformations. With KCM an analyst may automatically compose an analysis model from a design model and these building blocks. In this paper, we focus on the analysis knowledge component of this methodology (illustrated for structural and thermal disciplines), and describe four dimensions of analysis knowledge. Using these dimensions, we develop a decision template for analysts to create specifications for analysis models. Analysis models can be automatically created from a given specification using model transformation techniques (not described in this paper). We leverage the notion of choices and decisions to (a) define primitive and complex building blocks of analysis knowledge, and (b) formalize an analysis meta-model that represents the structure of analysis models. We also relate this analysis meta-model to the NIST Core Product Model (CPM2). The envisioned methodology impact is a formal and systems-oriented foundational approach for analysis problem formulation that is time- and cost-effective.Copyright

Knowledge Composition for Efficient Analysis Problem Formulation –- Part 1: Motivation and Requirements

In simulation-based design a key challenge is to formulate and solve analysis problems efficiently to evaluate a variety of design alternatives. Numerically solving analysis problems has benefited from advancements in commercial off-the-shelf mathematical solvers and computational capabilities. However, the formulation of analysis problems for a given set of design alternatives is still typically a laborious and costly process. In the scope of design alternatives with variable topology multi-body (VTMB) characteristics, these papers (Part 1 and Part 2) present research that addresses the following primary question: How can we improve the efficiency of the analysis problem formulation process for VTMB design alternatives? The objective of this paper (Part 1) is to identify requirements for a methodology that answers this. The methodology is formulates analysis problems for VTMB design alternatives based on decisions taken by analysts and independent of the solution method (such as finite element analysis) and the solver. This paper presents a gap analysis using an example VTMB problem and identifies key inadequacies in existing approaches for analysis problem formulation. Based on the gap analysis and technical background, we present five main requirements relating to (a) key drivers for efficiently creating analysis models; (b) abstracting and formalizing analysis knowledge for composing analysis models; and (c) automatically creating, reconfiguring and verifying analysis models.Copyright

FEDERATED PRODUCT MODELS FOR ENABLING SIMULATION-BASED PRODUCT LIFECYCLE MANAGEMENT

Across product lifecycle processes, engineers continually analyze product behavior and refine product specifications. Owing to the collaborative and multi-disciplinary nature of product realization, engineers work on subsets of a product’s specification, also known as a product view, and use their expertise to analyze domain-specific (e.g., electrical, structural, thermal) product behavior. In this paper, we present the notion of a product view federation that embodies engineering processes related to the creation, enrichment and reuse of a particular product view. We make the first step towards answering the following question — Can one formalize the process of creating a product view federation from component federates to enable knowledge reuse? We describe and exemplify one particular graph-based inference approach for creating the product view federation.Copyright

Architecture To Geometry - Integrating System Models With Mechanical Design

Model-Based Systems Engineering is founded on the principle of a unified system model that can coordinate architecture, mechanical, electrical, software, verification, and other discipline-specific models across the system lifecycle. This vision of a Total System Model as the digital blueprint (or digital twin) of a system, federating models in multiple vendor tools and configuration-controlled repositories, has gained tremendous support from the practitioners. A software platform, Syndeia, developed by Intercax, provides capabilities for seamless model-based communication between systems engineering and X (where X = mechanical/electrical, simulation, PLM, ALM, project management, and other disciplines), replacing the existing document-centric approaches. This paper elaborates research and development performed by NASA JPL and Intercax for integrating system architecture models (SysML) and mechanical design models (CAD) with applications to the Europa Clipper Mission. Specifically, this paper demonstrates (1) seeding of mechanical design models from system specifications (SysML) as a starting point for mechanical design, (2) model-based connections between system and mechanical design parameters, including compare and bi-directional synchronization, (3) abstracting system architecture from mechanical assemblies for transitioning existing/old projects to a model-based systems approach, and (4) use of persistent, fine-grained connections between system architecture and mechanical design models for continuous verification and communication between the two disciplines. The paper also covers organizational, cultural, and technical challenges that need to be addressed for seamless integration between system architecture models and mechanical/electrical design models, as well as other disciplines.