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Tag: Systems Engineering
  • Neural Ordinary Differential Equations for Rotorcraft Aerodynamics

    Abstract: High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential Equation (NODE) approach as a machine learning alternative to reduced order models in rotorcraft applications—specifically to predict the pitching moment on a rotor blade section from an initial condition, mach number, chord velocity and normal velocity. The results indicate that NODEs cannot outperform traditional reduced order models, but in some cases they can outperform simple multilayer perceptron networks. Additionally, the mathematical structure provided by NODEs seems to favor time-dependent predictions. We demonstrate how this mathematical structure can be easily modified to tackle more complex problems. The work presented in this report is intended to establish an initial evaluation of the usability of the modified NODE approach for time-dependent modeling of complex dynamics over seen and unseen domains.
  • Early Life-Cycle Prediction of Reliability

    Abstract: The intent of this project is to investigate a variety of approaches for the development of a basic model for the early life-cycle prediction of reliability (pre-Milestone A). The United States Department of Defense (DoD) currently utilizes an acquisition framework in which system development advances through a series of checkpoints known as milestones. Each milestone represents a decision point, with Milestone A being the earliest in the life cycle. At Milestone A, also known as the risk-reduction decision, the DoD evaluates design concepts while also committing funds to the maturation of technologies in an effort to mitigate future risks. Typically, little is known about the particular system to be developed at this point in the acquisition life cycle, but DoD regulations require program man-agers to submit system reliability information (OUSD[A&S] 2015). Traditional reliability predictions, however, require extensive knowledge of the system of interest to produce accurate results. This level of knowledge is unavailable at or before Milestone A, there-fore, there is a need to create models and methodologies for the prediction of system reliability. This report provides an overview of a variety of methods investigated to improve the prediction of early life cycle reliability.
  • An Ontology for an Epigenetics Approach to Prognostics and Health Management

    Abstract: Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowledge representation for engineered system state that encompasses components and environmental factors. Epigenetics is concerned with explaining how environmental factors affect the expression of an organism’s genetic material. The field has derived important insights into the development and progression of disease states based on how environmental factors impact genetic material, causing variations in how a gene is expressed. The health of an engineered system is similarly influenced by its environment. A foundation for a new approach to prognostics based on epigenetics must begin by representing the entities and relationships of an engineered system from the perspective of epigenetics. This paper presents an ontology for an epigenetics-inspired representation of an engineered system. An ontology describing the epigenetics of an engineered system will enable the composition of a formal model and the incremental development of a more robust, causal reasoning system.
  • STE Environmental Manager (STEEM) Demonstration Web Application

    Abstract: This report provides a summary of the development of the Synthetic Training Environment (STE) Environmental Manager (STEEM) demonstration web application. The purpose of this web application is twofold: (1) demonstrate a web application that enables non-technical users to prepare, run, and manage the physics-based models used by the STE to simulate realistic environmental conditions and (2) show how technologies developed by the Engineered Resilient Systems (ERS) Research and Development Area can be used to rapidly create applications to support U.S. Army Engineer Research and Development Center (ERDC) programs like the STE. A full build-out of STEEM would leverage the following ERS-developed technologies: data services, model development environment tools, model coupling/interface API, simulation workflow manager, and scenario generation tools.
  • PUBLICATION NOTICE: Applications of value modeling to USACE Civil Works and beyond

    Abstract: The US Army Corps of Engineers (USACE) Civil Works (CW) portfolio includes $250 billion worth of capital assets. As infrastructure ages and budgets change, new asset management (AM) investment strategies are required to support the maintenance, repair, and replacement (MR&R) of these assets while also providing the greatest value to the USACE and to the Nation. Shrinking budgets and increased scrutiny of government expenditures drive efforts to determine how best to optimize government funds for infrastructure improvement. As a result, USACE-CW AM seeks to create a value model capable of calculating the benefit of MR&R project alternatives regardless of business line. Furthermore, USACE-CW seeks to explore whether such a value model could be used for the generation of defensible budgets that consistently bring high value to the USACE and to the Nation. Thus, this special report reviews past USACE CW efforts to develop a value model for decision analytics. This report also provides an introduction to value modeling while covering applications of value modeling in multiple areas, including AM and portfolio decision analytics.