Digital Availability of Product Information for Collaborative Engineering of Spacecraft

In this paper, we introduce a system to collect product information from manufacturers and make it available in tools that are used for concurrent design of spacecraft. The planning of a spacecraft needs experts from different disciplines, like propulsion, power, and thermal. Since these different disciplines rely on each other there is a high need for communication between them, which is often realized by a Model-Based Systems Engineering (MBSE) process and corresponding tools. We show by comparison that the product information provided by manufacturers often does not match the information needed by MBSE tools on a syntactic or semantic level. The information from manufacturers is also currently not available in machine-readable formats. Afterwards, we present a prototype of a system that makes product information from manufacturers directly available in MBSE tools, in a machine-readable way.Comment: accepted at CDVE201

Applying model-based systems engineering in search of quality by design

2022 Spring.Includes bibliographical references.Model-Based System Engineering (MBSE) and Model-Based Engineering (MBE) techniques have been successfully introduced into the design process of many different types of systems. The application of these techniques can be reflected in the modeling of requirements, functions, behavior, and many other aspects. The modeled design provides a digital representation of a system and the supporting development data architecture and functional requirements associated with that architecture through modeling system aspects. Various levels of the system and the corresponding data architecture fidelity can be represented within MBSE environment tools. Typically, the level of fidelity is driven by crucial systems engineering constraints such as cost, schedule, performance, and quality. Systems engineering uses many methods to develop system and data architecture to provide a representative system that meets costs within schedule with sufficient quality while maintaining the customer performance needs. The most complex and elusive constraints on systems engineering are defining system requirements focusing on quality, given a certain set of system level requirements, which is the likelihood that those requirements will be correctly and accurately found in the final system design. The focus of this research will investigate specifically the Department of Defense Architecture Framework (DoDAF) in use today to establish and then assess the relationship between the system, data architecture, and requirements in terms of Quality By Design (QbD). QbD was first coined in 1992, Quality by Design: The New Steps for Planning Quality into Goods and Services [1]. This research investigates and proposes a means to: contextualize high-level quality terms within the MBSE functional area, provide an outline for a conceptual but functional quality framework as it pertains to the MBSE DoDAF, provides tailored quality metrics with improved definitions, and then tests this improved quality framework by assessing two corresponding case studies analysis evaluations within the MBSE functional area to interrogate model architectures and assess quality of system design. Developed in the early 2000s, the Department of Defense Architecture Framework (DoDAF) is still in use today, and its system description methodologies continue to impact subsequent system description approaches [2]. Two case studies were analyzed to show proposed QbD evaluation to analyze DoDAF CONOP architecture quality. The first case study addresses the analysis of DoDAF CONOP of the National Aeronautics and Space Administration (NASA) Joint Polar Satellite System (JPSS) ground system for National Oceanic and Atmospheric Administration (NOAA) satellite system with particular focus on the Stored Mission Data (SMD) mission thread. The second case study addresses the analysis of DoDAF CONOP of the Search and Rescue (SAR) navel rescue operation network System of Systems (SoS) with particular focus on the Command and Control signaling mission thread. The case studies help to demonstrate a new DoDAF Quality Conceptual Framework (DQCF) as a means to investigate quality of DoDAF architecture in depth to include the application of DoDAF standard, the UML/SysML standards, requirement architecture instantiation, as well as modularity to understand architecture reusability and complexity. By providing a renewed focus on a quality-based systems engineering process when applying the DoDAF, improved trust in the system and data architecture of the completed models can be achieved. The results of the case study analyses reveal how a quality-focused systems engineering process can be used during development to provide a product design that better meets the customer's intent and ultimately provides the potential for the best quality product

Industrial Adoption of Model-Based Systems Engineering: Challenges and Strategies

As design teams are becoming more globally integrated, one of the biggest challenges is to efficiently communicate across the team. The increasing complexity and multi-disciplinary nature of the products are also making it difficult to keep track of all the information generated during the design process by these global team members. System engineers have identified Model-based Systems Engineering (MBSE) as a possible solution where the emphasis is placed on the application of visual modeling methods and best practices to systems engineering (SE) activities right from the beginning of the conceptual design phases through to the end of the product lifecycle. Despite several advantages, there are multiple challenges restricting the adoption of MBSE by industry. We mainly consider the following two challenges: a) Industry perceives MBSE just as a diagramming tool and does not see too much value in MBSE; b) Industrial adopters are skeptical if the products developed using MBSE approach will be accepted by the regulatory bodies. To provide counter evidence to the former challenge, we developed a generic framework for translation from an MBSE tool (Systems Modeling Language, SysML) to an analysis tool (Agent-Based Modeling, ABM). The translation is demonstrated using a simplified air traffic management problem and provides an example of a potential quite significant value: the ability to use MBSE representations directly in an analysis setting. For the latter challenge, we are developing a reference model that uses SysML to represent a generic infusion pump and SE process for planning, developing, and obtaining regulatory approval of a medical device. This reference model demonstrates how regulatory requirements can be captured effectively through model-based representations. We will present another case study at the end where we will apply the knowledge gained from both case studies to a UAV design problem

Comparison of Traditional Versus CubeSat Remote Sensing: A Model-Based Systems Engineering Approach

This thesis compares the ability of both traditional and CubeSat remote sensing architectures to fulfill a set of mission requirements for a remote sensing scenario. Mission requirements originating from a hurricane disaster response scenario are developed to derive a set of system requirements. Using a Model-based Systems Engineering approach, these system requirements are used to develop notional traditional and CubeSat architecture models. The technical performance of these architectures is analyzed using Systems Toolkit (STK); the results are compared against Measures of Effectiveness (MOEs) derived from the disaster response scenario. Additionally, systems engineering cost estimates are obtained for each satellite architecture using the Constructive Systems Engineering Cost Model (COSYSMO). The technical and cost comparisons between the traditional and CubeSat architectures are intended to inform future discussions relating to the benefits and limitations of using CubeSats to conduct operational missions

Developing Executable Digital Models with Model-Based Systems Engineering – An Unmanned Aerial Vehicle Surveillance Scenario Example

There is an increase in complexity in modern systems that causes inconsistencies in the iterative exchange loops of the system design process and in turn, demands greater quality of system organization and optimization techniques. A recent transition from document-centric systems engineering to Model-Based Systems Engineering (MBSE) is being documented in literature from various industries to address these issues. This study aims to investigate how MBSE can be used as a starting point in developing digital twins (DT). Specifically, the adoption of MBSE for realizing DT has been investigated, resulting in various literature reviews that indicate the most prevalent methodologies and tools used to enhance and validate existing and future systems. An MBSE-enabled template for virtual model development was executed for the creation of executable models, which can serve as a research testbed for DT and system and system-of-systems optimization. This study explores the feasibility of this MBSE-enabled template by creating and simulating a surveillance system that monitors and reports on the health status and performance of an armored fighting vehicle via an Unmanned Aerial Vehicle (UAV). The objective of this template is to demonstrate how executable SysML diagrams are used to establish a collaborative working environment between multiple platforms to better convey system behavior, modifications, and analytics for various system stakeholders

Simulation of multiangular remote sensing products using small satellite formations

To completely capture the multiangular reflectance of an opaque surface, one must estimate the bidirectional reflectance distribution function (BRDF), which seeks to represent variations in surface reflectance as a function of measurement and illumination angles at any time instant. The gap in angular sampling abilities of existing single satellites in Earth observation missions can be complemented by small satellites in formation flight. The formation would have intercalibrated spectrometer payloads making reflectance measurements, at many zenith and azimuthal angles simultaneously. We use a systems engineering tool coupled with a science evaluation tool to demonstrate the performance impact and mission feasibility. Formation designs are generated and compared to each other and multisensor single spacecraft, in terms of estimation error of BRDF and its dependent products such as albedo, light use efficiency (LUE), and normalized difference vegetation index (NDVI). Performance is benchmarked with respect to data from previous airborne campaigns (NASA's Cloud Absorption Radiometer), and tower measurements (AMSPEC II), and assuming known BRDF models. Simulations show that a formation of six small satellites produces lesser average error (21.82%) than larger single spacecraft (23.2%), purely in terms of angular sampling benefits. The average monolithic albedo error of 3.6% is outperformed by a formation of three satellites (1.86%), when arranged optimally and by a formation of seven to eight satellites when arranged in any way. An eight-satellite formation reduces albedo errors to 0.67% and LUE errors from 89.77% (monolithic) to 78.69%. The average NDVI for an eight satellite, nominally maintained formation is better than the monolithic 0.038

Improving System Design Through the Integration of Human Systems and Systems Engineering Models

The human is a critical aspect of many systems, but frequently there is a failure to properly account for human capabilities and involvement during system design. This inattention results in systems with higher lifecycle costs, decreased user compatibility, and the potential to produce disastrous consequences. This research presents an approach to integrating the human into system models by using two methods: static and dynamic modeling. The static method uses a user-centered design framework to create system- and human-centered models that deconstruct the system and user into their respective components. These models are integrated to create system models that include relevant information about the human and highlight potentially conflicting tasks. The dynamic method uses a human performance modeling tool to create a discrete event simulation (DES) of the system. This DES model is used to perform an analysis between system trades, by which constraints and assumptions placed on the human are verified. Data gained from the analysis are integrated back into system models in order to reflect true system performance. By applying these two integration methods early in the system’s lifecycle, system models can more effectively account for the human as a critical component of the system, thus improving system design