The New Observing Strategies Testbed (NOS‐T) is a digital engineering environment for enabling distributed space mission (DSM) technology demonstrations. Its event‐driven architecture enables users to orchestrate DSM test campaigns by developing applications that communicate state changes via messages. NOS‐T is motivated by requirements such as geographical distribution, cross‐boundary participation, wide applicability, and usability that make it unique in this field. This article introduces NOS‐T and describes its architecture in the context of an example DSM test suite, FireSat+. The scalability of NOS‐T is demonstrated with a performance assessment of its capabilities under a stress test of high message frequency and payload size, which are both related to the complexity of potential user‐generated test cases. Results show that message periodicity has no significant effect on median delay time over the ranges sampled; however, the message payload size induces linear growth in median delay time of approximately 1.5 ms per kB. Future NOS‐T applications can adjust the execution time scaling factor and message payload size to match operational constraints on allowable delay.

This study introduces the New Observing Strategies Testbed (NOS-T); a digital engineering environment to conduct coordinated experiments between independent and potentially geographically distributed nodes. The testbed supports both real-time applications and scaled-time simulations. NOS-T is built on an event-driven architecture where applications communicate via message payloads mediated by an event-broker. This allows for collaboration across organizational boundaries while maintaining autonomy and control over the flow of information. Testbed capabilities are demonstrated for an example problem exploring data downlink opportunities for two distinct ground network systems. A twenty-four hour mission is simulated for two Earth observing satellites in sun-synchronous orbits, with various iterations of traditional single arctic ground stations with fixed cost contracts versus newer commercial ground stations with on-demand cost models. Results demonstrate the capability of the commercial ground stations to maintain significant data margins throughout the mission with considerable cost-savings from the on-demand pricing.

This chapter describes our application of the modeling methods to develop models for various publicly available use cases. We first provide some background about a global scan from industry, government and academia that was conducted to determine the most advance approaches for using modeling process, methods and tools. The results of the global scan initiated the NAVAIR Systems Engineering Transformation and a number of research use cases that are summarized as they relate to modeling methods. We also discuss how to develop technical models for mission, system and subsystem models for analysis and design, as well as how we leverage model modularization using enabling technologies within descriptive modeling tools to link models at different levels of abstraction and to establish a collaborative, cross-model authoritative source of truth (AST). We then discuss the tools used to establish the AST and explain how modeling methods enable Digital Signoffs using OpenMBEE, DocGen View and Viewpoints, Model Management System, and web-app View Editor. We use DocGen and Digital Signoffs to demonstrate how to transition away from traditional documents and milestone reviews, toward performing continuous reviews within the modeling framework as different parts of the system design mature at different rates. We also provide some guidelines on performing model management, a variation of configuration management used to curate and pedigree models. Next, we demonstrate how to develop a Digital Systems Engineering Management and Technical Plan, which is a type of Digital Engineering Systems Engineering Plan (DESEP) that is linked to the other technical and stakeholder models. Finally, we discuss how Digital Signoff can support the Source Selection process using an unmanned air vehicle developed as one of the publicly available use cases.

This chapter introduces the Digital Engineering Framework for Integration and Interoperability (DEFII) as a methodological foundation for the use of ontologies and graph data structures in a digital engineering context. DEFII leverages enabling technologies to support modeling and simulation in different engineering disciplines and provides an approach for integrating analysis models and simulations results into a coherent digital thread that crosses disciplines at different levels of abstraction. The chapter presents a simple catapult use case and a python-based implementation of the methodology to provide further details of how the methodology can work in a domain-specific example.

This study investigates how interactive dashboards influence decision making by exploring how specific dashboard features impact design task performance, efficiency, understanding, and confidence. An experiment was conducted in which undergraduate student participants were given a design activity and randomly assigned to one of five dashboards, each using the same underlying functions but varying in the visualization features employed. These features include different graphical representations of the design decision inputs and performance outputs. Participants were first asked to use their assigned dashboard to design a catapult system that maximizes launch distance while meeting requirements related to height, weight, and cost. Following the design task, they were asked a series of questions about their experiences with the dashboard and their understanding of the catapult model. A between-subjects analysis then evaluated how the dashboard design influenced various outcomes of interest. The results show that students who used the most feature-rich dashboard did not perform objectively better than those with the most feature-sparse dashboard, though their self-reported performance was higher. The performance of female versus male participants was also compared, with no significant differences found. The findings support the notion that dashboards should be designed with minimal features to convey the necessary information, and they also point out the disconnect between objective performance and user-assessed performance with interactive dashboards.

Technological advances have enabled new types of distributed space missions (DSMs) that can improve the data resolution along many dimensions over monolithic, "flagship" spacecraft. Future DSMs will fuse data from a wide variety of sensors including other spacecraft and various ground- and air-based in situ platforms. The New Observing Strategies Testbed (NOS-T) is a new digital engineering environment based on systems engineering principles for simulating DSMs using a loosely coupled, event-driven architecture that manages communication between logically and geographically distributed user-developed applications. This paper demonstrates how NOS-T can evaluate new operational modes for satellite constellations using real-time stream gauge data from the U.S. Geological Survey (USGS) National Water Information System (NWIS) to decrease the latency of targeted spacecraft observations of flooded areas. The test case uses real-time data from NWIS stream gauges in the U.S., artificially triggers a flooding event, subsequently tasks satellite observations, and downlinks data to a ground station. It demonstrates how NOS-T enables the transfer of information between in situ and space-based sensors in a digital engineering environment to aid conceptual design of future DSMs across organizational boundaries.

The New Observation Strategies Testbed (NOS-T) provides a computational platform to test, evaluate, and mature enabling technology for new Earth-observing mission concepts. NOS-T is built on an event-driven architecture where information can be shared among user-developed applications in real time via notifications of changes in state. This paper describes how a NOS-T test campaign can be developed to explore mission architectures for observing wildfires. The scenario requires four applications to model fire ignitions, a constellation of satellites for detecting fires, ground stations for receiving reports of detected fires, and a visual “scoreboard” to provide face validity and communicate observing scenarios. Test case execution results measure the distributions of fire detection and reporting time for alternative mission architectures.

The design of autonomous vessels is associated with unique opportunities and challenges. As naval architects, we rely on 10,000+ years of collective experience in the design of manned boats. However, with the emergence of unmanned surface vessels, there may be benefits in modifying early design phase procedures, as these vessels will have fundamentally different requirements. The aim of this study is to devise and test a system optimization approach particularly aimed at autonomous vessels. A small-scale autonomous sailing surface vessel (Maribot Vane) has been used as a case study. This paper details the process of applying a multidisciplinary, multi objective, reliability-based design optimization (RBDO) approach at the conceptual design stage of the Maribot Vane, in order to minimize the system cost and the probability failure under anticipated operational conditions. The results will inform the designers of the new platform about the trade-offs between cost and reliability, as well as the optimal selection of main particulars for the detailed design stage and finalization of the hull design. Additionally, future autonomous ship design will benefit from the multidisciplinary approach put forward in this paper, as it allows designers to rigorously explore optimal design concepts and tradeoffs and support key early-stage design decisions.

Mission engineering is a growing field with many practical opportunities and challenges. The goal of mission engineering is to increase system effectiveness, reduce life cycle costs, and aid in communicating system capabilities to key stakeholders. Optimizing system designs for their mission context is important to achieving these goals. However, system optimization is generally done using multiple key performance indicators (KPIs), which are not always directly representative of, nor easily translatable to, mission success. This paper introduces, motivates, and proposes a new approach for performing mission-level optimization (MLO), where the objective is to design systems that maximize the probability of mission success over the system life cycle. This builds on previous literature related to mission engineering, modeling, and analysis, as well as optimization under uncertainty. MLO problems are unique in their high levels of design, operational, and environmental uncertainty, as well as the single binary objective representing mission success or failure. By optimizing for mission success, designers can account for large numbers of KPIs and external factors when determining the best possible system design.

Multifidelity optimization leverages the fast run times of low-fidelity models with the accuracy of high-fidelity models (HFMs), in order to conserve computing resources while still reaching optimal solutions. This work focuses on the multifidelity multidisciplinary optimization of an aircraft system model with finite element analysis and computational fluid dynamics simulations in the loop. A two-step filtering method is used where a lower fidelity model is optimized, and then the solution is used as a starting point for a higher-fidelity optimization routine. By starting the high-fidelity routine at a nearly optimal region of the design space, the computing resources required for optimization are expected to decrease when using local algorithms. Results show that, when using surrogates for the lower fidelity models, the multifidelity workflows save statistically significant amounts of time over optimizing the original HFM alone. However, the impact on solution quality varies depending on the model behavior and optimization algorithm.

Multifidelity optimization leverages the fast run times of low-fidelity models with the accuracy of high-fidelity models, in order to conserve computing resources while still reaching optimal solutions. This work focuses on the multidisciplinary multifidelity optimization of an unmanned aerial system model with finite element analysis and computational fluid dynamics simulations in-the-loop. A two-step process is used where the lower fidelity models are optimized, and then the optimizer is used as a starting point for the higher-fidelity models. By starting the high fidelity optimization routine at a nearly optimal section of the design space, the computing resources required for optimization are expected to decrease when using gradient-based algorithms. Results show that, at least in some cases, the multifidelity work-flows save time over optimizing the original high fidelity model alone. However, the model management strategy did not find statistically significant differences between the differing optimization approaches when used on this test problem.

The Maribot Vane is an autonomous sailing drone project that has been in the prototype testing phase for two years, developing a fully capable platform to aid in long-term ocean research projects. ...

This research describes a comparison study of different ways to formulate and solve a Multi-Disciplinary Optimization (MDO) problem. Two MDO architectures, multidisciplinary feasible (MDF) and interdisciplinary feasible (IDF), were tested on a simulation-based aircraft model. The aircraft's aerodynamic performance is modeled with computational fluid dynamics, and its structure is modeled with finite element analysis. The results show that the MDF architecture finds better solutions when it comes to optimality, but it requires more computing resources, time, and has higher variability than IDF.