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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Development of a Human Systems Integration Plan

NASA defines Human Systems Integration (HSI) as part of the overall systems engineering and acquisition strategy for space systems. The HSI Plan defines how HSI activities will be implemented across the lifecycle of the mission, as required by NPR 7123.1C, NASA Systems Engineering Processes and Requirements, and NPR 8705.2C Human-Rating Requirements for Space Systems. The goal of this presentation is to share with government and industry how an HSI Plan can be implemented. The presentation will cover HSI implementation for flight systems, vehicle processing, and interfaces. These are divided into six NASA HSI Domains: human factors engineering, operations resources, safety, training, maintainability and supportability, habitability and environment. HSI activities go across the mission’s lifecycle from pre-formulation and acquisition through design, development, operations, maintenance, and decommissioning. The HSI Plan includes a description of the HSI activities and products that are essential for human rating, operability, maintainability, supportability, and affordability of the mission systems. It also describes the role of the HSI Team required as part of the Human Rating process. The HSI Plan utilizes the operational expertise within NASA to ensure designs and testing are successful, leading to acceptable human spaceflight vehicles.

Jackelynne Silva-Martinez↗
DOE OSTI2024

Cyber-Informed Engineering Validation Methods and Guidance

Validation is an important step in any systems engineering process to ensure the correct system was made to fulfill stakeholders’ needs, goals, and expectations. In the context of Cyber-Informed Engineering (CIE), validation ensures cyber impact is reduced through implemented design choices and CIE requirements. This document details a process in validating CIE-based design choices relative to their effectiveness at mitigating high consequence events. The document includes a case study to illustrate the CIE validation process. The case study explores the implementation of CIE validation within the engineering lifecycle of a chemical mixing plant.

42 ENGINEERING↗
NASA NTRS2016

Achieving Fast Operational Intelligence in NASA's Deep Space Network Through Complex Event Processing

NASA’s Deep Space Network (DSN) is a complex, global project, in which the expertise of human operators remain crucial for its successful operation. To find ways to save costs in operations and to improve its services, a number of modernization efforts are underway in the DSN. One such effort is a research and technology development task at the Jet Propulsion Laboratory that is investigating the use of complex event processing (CEP) for intelligent assessment of situations, trend analysis, and advanced automation. The technology leverages the significant business intelligence (BI) and data science advancements made in the enterprise industries over the last several years. The open source big data processing engine Apache SparkTM and the high-throughput, distributed messaging system Apache Kafka form the core of the DSN Complex Event Processing (DCEP) framework. This paper discusses the system engineering perspective of why achieving efficient, lower-cost operations in the DSN is a challenging problem, how the DCEP system handles the use cases that help realize intelligent operations, and how this solution fits into the overall model of the planned DSN Follow-the- Sun Operations (FtSO).

Choi, Joshua S.↗
DOE OSTI2026

Polymer Deconstruction and Redesign Strategies for Plastics Recycling

Advancing plastics recycling requires both the selective deconstruction of existing polymers and the design of new materials that enable efficient reuse without loss of performance. This perspective highlights an integrated approach that is rooted in polymer chemistry, catalysis, and process engineering which can enable a circular plastics economy. Here, we outline recent advances in catalytic, solvolytic, and enzymatic pathways for plastic deconstruction, and examine the molecular design principles driving next-generation recyclable-by-design and bio-based polymers. Despite these advances, major knowledge gaps remain in understanding the evolution of polymer morphology and catalyst structure during deconstruction, assessing deconstruction processes with realistic polymers, and offering redesigned polymers with competitive cost and environmental advantage over conventional plastics. United States Department of Energy (U.S. DOE) national laboratories offer unique capabilities to address these challenges through in situ and operando characterization, high-throughput experimentation, environmental studies, technoeconomic and life cycle assessment, scale-up support, and collaboration networks. Advances made in understanding plastic deconstruction mechanisms and structure-property correlations of redesigned polymers inform emerging research directions including autonomous experimentation, real-time feedback-enabled process optimization, and protein engineering for enzymatic depolymerization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Comparison of modular analytical wake models to the Lillgrund wind plant

Efficient and accurate wake models are required for wind plant performance modeling and the suite of engineering processes that support wind plant layout, control, and monitoring. Although many analytical and engineering wake models with low computational costs have been proposed, their ability to represent the power production of large wind plants in a wide range of atmospheric conditions is not completely understood. The following validation study reviews the underlying theory for analytical wake models, outlines quality control procedures for observational data, and compares model results with observational data from the Lillgrund Wind Plant. Lillgrund makes a valuable case study for wake modeling because of its regular arrangement and the relatively close spacing of constituent wind turbines, which lead to regular and significant wake interactions within the wind plant and the development of deep array flow conditions. Formulations for the velocity deficit, wake-added turbulence, and wake superposition methods are considered in a modular sense, yielding many possible configurations to represent wind turbine wakes, of which seven are examined in detail. Velocity deficit models that account for flow conditions in the near wake are better able to reproduce power production for wind turbines in the transitional region of the wind plant, where wind turbines experience as many as five wakes from upstream turbines. Finally, in the deep array, where power production reaches asymptotic values and wake statistics become quasi-periodic, wake superposition schemes become the largest driver in error reduction; using the linear or maximum wake superposition methods can reduce the relative root mean square error by as much as 40% in the deep array.

17 WIND ENERGY↗
NASA NTRS1994

Improving cost efficiency in large programs

This paper examines the question of cost, from the birth of a program to its conclusion, particularly from the point of view of large multi-center programs, and suggests how to avoid some of the traps and pitfalls. Emphasis is given to cost in the systems engineering process, but there is an inevitable overlap with program management. (The terms systems engineering and program management have never been clearly defined.) In these days of vast Federal budget deficits and increasing overseas competition, it is imperative that we get more for each research and development dollar. This is the only way we will retain our leadership in high technology and, in the long run, our way of life.

Hodge, John D.↗
DOE OSTI2024

Developing Multiphysics, Integrated, High-Fidelity, Massively Parallel Computational Capabilities for Fusion Applications Using MOOSE

As the need for fusion as a clean, sustainable, and abundant energy source grows internationally, so does the need for multiphysics, computational tools to model, study, and predict the complex interactions between plasma, materials, and engineering processes. These tools have a crucial role to play in solving scientific and engineering challenges and accelerating fusion energy deployment. To address these needs, modeling capabilities should enable massively parallel, multiphysics, fully integrated high-fidelity simulations of fusion systems. Additional attributes, such as being open source and modular while maintaining high software quality assurance standards will maximize impact by ensuring accessibility for all and wide acceptance, rapid expansion and development, as well as reliability, efficiency, and robustness. In this paper, we describe how the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which has a track record of success in the fission space thanks to the attributes listed above, can be leveraged in the fusion energy field. We highlight key successes of the MOOSE application in the fission space and describe how MOOSE has been and is being applied to fusion applications in the United States---e.g., Tritium Migration Analysis Program, version 8 (TMAP8), MOOSE Fusion Module, Fusion ENergy Integrated multiphys-X (FENIX)---and the United Kingdom---e.g., AURORA, Achlys, Apollo. These efforts aim to establish a suite of tools that can be further extended to accelerate fusion energy deployment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A simple and practical process modeling methodology for pressure swing adsorption

Although many dynamic models exist for the design and simulation of pressure swing adsorption (PSA) processes, these models involve the solution of a complex system of coupled partial differential equations. Process engineers need a simple, practical, and yet robust short-cut model that helps decide whether to implement a PSA system in a process flowsheet. This work presents a “virtual” moving bed modeling methodology that considers only mass and energy balances and adsorption isotherms to describe the cyclic steady state behavior of PSA systems. Similar to tray efficiencies in distillation calculations, adsorption efficiencies are further introduced to account for system “non-ideality.” Finally, a lab-scale air separation system is used to illustrate the application of this modeling methodology.

42 ENGINEERING↗
NASA NTRS2002

Second Generation RLV Space Vehicle Concept

NASA has a long history of conducting development programs and projects in a consistent fashion. Systems Engineering within those programs and projects has also followed a given method outlined by such documents as the NASA Systems Engineering Handbook. The relatively new NASA Space Launch Initiative (SLI) is taking a new approach to developing a space vehicle, with innovative management methods as well as new Systems Engineering processes. With the program less than a year into its life cycle, the efficacy of these new processes has yet to be proven or disproven. At $776M for phase 1, SLI represents a major portion of the NASA focus; however, the new processes being incorporated are not reflected in the training provided by NASA to its engineers. The NASA Academy of Program and Project Leadership (APPL) offers core classes in program and project management and systems engineering to NASA employees with the purpose of creating a "knowledge community where ideas, skills, and experiences are exchanged to increase each other's capacity for strong leadership". The SLI program is, in one sense, a combination of a conceptual design program and a technology program. The program as a whole doesn't map into the generic systems engineering project cycle as currently, and for some time, taught. For example, the NASA APPL Systems Engineering training course teaches that the "first step in developing an architecture is to define the external boundaries of the system", which will require definition of the interfaces with other systems and the next step will be to "define all the components that make up the next lower level of the system hierarchy" where fundamental requirements are allocated to each component. Whereas, the SLI technology risk reduction approach develops architecture subsystem technologies prior to developing architectures. The higher level architecture requirements are not allowed to fully develop and undergo decomposition and allocation down to the subsystems before the subsystems must develop allocated requirements based on the highest level of requirements. In the vernacular of the project cycles prior to the mid 1990's, the architecture definition portion of the program appears to be at a generic Phase A stage, while the subsystems are operating at Phase B. Even the management structure of the SLI program is innovative in its approach to Systems Engineering and is not reflected in the APPL training modules. The SLI program has established a Systems Engineering office as an office separate from the architecture development or the subsystem technology development, while that office does have representatives within these other offices. The distributed resources of the Systems Engineering Office are co-located with the respective Project Offices. This template is intended to provide systems engineering as an integrated function at the Program Level. the program management of SLI and the MAT agree that "program/project managers and the systems engineering team must work closely together towards the single objective of delivering quality products that meet the customer needs". This paper will explore the differences between the methods being taught by NASA, which represent decades of ideas, and those currently in practice in SLI. Time will tell if the innovation employed by SLI will prove to be the model of the future. For now, it is suggested that the training of the present exercise the flexibility of recognizing the new processes employed by a major new NASA program.

Bailey, Michelle↗
NASA NTRS2012

Requirements Flowdown for Prognostics and Health Management

Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.

video acuity↗
DOE OSTI2024

Flux REaction TArget Prioritization (Flux RETAP) v1

Metabolic engineering is evolving rapidly as a result of new advances in synthetic biology and automation, as well as the irruption of machine learning (ML). ML has been shown to provide the predictive power synthetic biology lacked and needed, and to be able to effectively guide the metabolic engineering process. However, current technical limitations prevent the independent application of ML approaches to metabolic engineering without the use of previous biological knowledge in the form of a prioritized list of desirable engineering targets. Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale metabolic models (GSMs) for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing metabolite production. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production in the literature accessible to us, 50% of targets that experimentally improved taxadiene production in E. coli and ~60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets which can also be utilized in ML pipelines.

Czajka, Jeffrey [Battelle Memorial Institute, Paci↗
NASA NTRS1985

The use of analogs in the innovation process - A software engineering protocol analysis

A human information processing account of how experienced practitioners utilize analogs in the innovation process is presented. The protocols of several practitioners are collected and analyzed. The problem behavior graphs, knowledge elements utilized, and operator elements applied are described, and a descriptive model of the innovation process is assembled. Implications for a production system, deviations from other models of the innovation process, and extensions to other models of the analogy procedure are explored.

Silverman, B. G.↗
NASA NTRS2022

Development of an Accepted Medical Condition List for Exploration Medical Capability Scoping

Future NASA human spaceflight programs are on the verge of moving beyond Low Earth Orbit (LEO) to implement missions in lunar space and ultimately Mars. The mission constraints for these types of missions are expected to be progressively challenging for integration of Human Systems requirements into the vehicle and mission architectures. Mass and volume allocations are expected to become increasingly restrictive at the same time that mission realities will drive an increasing need for crew self-sufficiency in the maintenance and repair of both vehicle systems and human systems. To meet these challenges, a systematic, traceable, and repeatable approach to identifying, defining, and prioritizing medical capabilities is required. To provide a systematic and repeatable approach to defining and prioritizing clinical capabilities for spaceflight medicine, a clear process is required for delivering a list of prioritized medical capabilities to the Systems Engineering process that will delineate the mass, power, volume, and similar needs and the trade space analysis for a given space vehicle and mission architecture.

Rebecca Blue↗
NASA NTRS1940

Resonance vibrations in intake and exhaust pipes of in-line engines III : the inlet process of a four-stroke-cycle engine

Using a previously developed method, the boundary process of four-stroke-cycle engines are set up. The results deviate considerably from those obtained under the assumption that the velocity fluctuation is proportional to the cylinder piston motion. The deviation is less at the position of resonance frequencies. By the method developed, the effect of the resonance vibrations on the volumetric efficiency can be demonstrated.

Lutz, O↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
NASA NTRS1995

NASA Systems Engineering Handbook

This handbook brings the fundamental concepts and techniques of systems engineering to NASA personnel in a way that recognizes the nature of NASA systems and environment. It is intended to accompany formal NASA training courses on systems engineering and project management when appropriate, and is designed to be a top-level overview. The concepts were drawn from NASA field center handbooks, NMI's/NHB's, the work of the NASA-wide Systems Engineering Working Group and the Systems Engineering Process Improvement Task team, several non-NASA textbooks and guides, and material from independent systems engineering courses taught to NASA personnel. Five core chapters cover systems engineering fundamentals, the NASA Project Cycle, management issues in systems engineering, systems analysis and modeling, and specialty engineering integration. It is not intended as a directive. Superseded by: NASA/SP-2007-6105 Rev 1 (20080008301).

Shishko, Robert↗
NASA NTRS2012

The Montana ALE (Autonomous Lunar Excavator) Systems Engineering Report

On May 2 1-26, 20 12, the third annual NASA Lunabotics Mining Competition will be held at the Kennedy Space Center in Florida. This event brings together student teams from universities around the world to compete in an engineering challenge. Each team must design, build and operate a robotic excavator that can collect artificial lunar soil and deposit it at a target location. Montana State University, Bozeman, is one of the institutions selected to field a team this year. This paper will summarize the goals of MSU's lunar excavator project, known as the Autonomous Lunar Explorer (ALE), along with the engineering process that the MSU team is using to fulfill these goals, according to NASA's systems engineering guidelines.

Lars Osborne↗
NASA NTRS2016

EDEN: A Novel Approach to Plant Growth in Space

The USU XHAB Eden Team has enjoyed the opportunity to participate in NASA’s X­HAB Challenge. Our project involved constructing a prototype to further investigate the complexities of growing plants in microgravity environments. Attached is the team’s report which covers the research and design of our prototype. Included is a summary of our design, drawings and schematics with an as built status, engineering process documents, and all other documentation necessary to satisfy the requirements of Utah State University’s Engineering Design Course (MAE 4810).

Timothy Taylor↗