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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Technology Infusion in U.S. Spacesuits - A Comparative System Analysis

The National Aeronautics and Space Administration (NASA) has evolved multiple spacesuit systems for performing extravehicular activity (EVA) or space walks. These spacesuit systems include the Apollo Extravehicular Mobility Unit (EMU), Space Shuttle and International Space Station (ISS) EMU, and Exploration EMU (xEMU). Each spacesuit system is like the other for functionality. However, each spacesuit system is different in configuration based on the technology infused into the system associated with the purpose of the mission. Each spacesuit system is made up of many components and the integrate environment targeted for operations leads to an integrated system that is complex. Since Apollo, NASA has invested in multiple technologies that make up these spacesuit systems in different iterations. The Apollo EMU was designed in the 1960’s with a focus to facilitate the first human to walk on the moon. The Space Shuttle EMU was designed in the 1970’s for reusable microgravity operations that began in the early 1980’s. The Space Shuttle EMU was enhanced to facilitate extended operation on the ISS. Over the last 15 years, NASA has been designing, developing, and testing a new spacesuit system, the xEMU which is considered a design, verification, and test unit. NASA is planning to land the first woman and first person of color on the Moon. NASA recently engaged industry through a new contractual arrangement to provide EVA services needed to return to the Moon and to continue operations on the ISS. Spacesuit systems are complex. Understanding the requirements, operational environment, the necessary technologies, and the integrated spacesuit system are paramount. In addition, understanding the technology infusion process to meet the mission objectives is critical. This paper will review the spacesuit systems for EVA and several component functions within the spacesuits, along with a system comparison of those technologies from Apollo to xEMU.

Extravehicular Activity (EVA)↗
NASA NTRS2005

Meeting the Challenges of Exploration Systems: Health Management Technologies for Aerospace Systems With Emphasis on Propulsion

The constraints of future Exploration Missions will require unique Integrated System Health Management (ISHM) capabilities throughout the mission. An ambitious launch schedule, human-rating requirements, long quiescent periods, limited human access for repair or replacement, and long communication delays all require an ISHM system that can span distinct yet interdependent vehicle subsystems, anticipate failure states, provide autonomous remediation, and support the Exploration Mission from beginning to end. NASA Glenn Research Center has developed and applied health management system technologies to aerospace propulsion systems for almost two decades. Lessons learned from past activities help define the approach to proper ISHM development: sensor selection- identifies sensor sets required for accurate health assessment; data qualification and validation-ensures the integrity of measurement data from sensor to data system; fault detection and isolation-uses measurements in a component/subsystem context to detect faults and identify their point of origin; information fusion and diagnostic decision criteria-aligns data from similar and disparate sources in time and use that data to perform higher-level system diagnosis; and verification and validation-uses data, real or simulated, to provide variable exposure to the diagnostic system for faults that may only manifest themselves in actual implementation, as well as faults that are detectable via hardware testing. This presentation describes a framework for developing health management systems and highlights the health management research activities performed by the Controls and Dynamics Branch at the NASA Glenn Research Center. It illustrates how those activities contribute to the development of solutions for Integrated System Health Management.

Melcher, Kevin J.↗
NASA NTRS2008

Organizational Considerations for Implementing Systems Engineering and Integration in the Ares Projects Office

Systems Engineering and Integration (SE&I) is a critical discipline in developing new space systems. In 2005, NASA performed an internal study of 24 agency and Department of Defense (DoD) programs to evaluate methods of integrating SE&I practices and determine their effectiveness. The goal of the study was to determine the best SE&I implementation strategy for the Ares Projects Office. The study identified six SE&I organizational structures: 1. Lead systems integrator (LSI) with SE&I responsibility and government technical insight. 2a. Integration contractor with government SE&I responsibility (government insight). 2b. Integration contractor with government SE&I responsibility (government oversight). 3a. Prime contractor with SE&I responsibility (government insight). 3b. Prime contractor with SE&I responsibility (government oversight). 3c. Prime contractor with SE&I responsibility (government/industry partnership). 4a.Prime contractor with government SE&I responsibility (government insight). 4b. Prime contractor with government SE&I responsibility (government oversight). 4d.Prime contractors with total system performance responsibility (TSPR). 5. Prime contractor with government SE&I responsibility and integration products through a Federally Funded Research and Development Center (FFRDC). 6. Government/FFRDC in-house development with SE&I responsibility and function. The organizational structure used most often was number 4, using a prime contractor with government SE&I responsibility and government technical insight. However, data analyses did not establish a positive relationship between program development costs and specific SE&I organizational types, nor did it positively determine the relationship between successful programs or projects and their SE&I structure. The SE&I study reached the following conclusions: (1) Large, long-duration, technically complex programs or projects reach their technical goals, but rarely meet schedule or cost goals. NASA's recent successes have been smaller, short-duration development projects using heritage hardware/software, focused technology development, technical oversight and stable external factors. (2) Programs and projects have failed or been terminated due to lack of technical insight, relaxing of SE&I processes, and unstable external factors. (3) The study did not find a single, clear optimum SE&I organization type to fit all projects. However, while any organizational structure can be made to work, the fewer complexities in the program, the better the likelihood of success. (4) The most common successful SE&I organization structure type in the study was type 4b, where the government maintained integration responsibility, with the prime contractor providing SE&I products and the government providing technical oversight. This study was instrumental in helping the APO select organization structure 4, following the same SE&I and oversight process used during humanlund7s last voyages to the Moon.

Thomas, LeAnn↗
DOE OSTI2019

On One Integrable Discrete System

In this paper, we study a system of nonlinear equations on a square graph related to the affine algebra A{sup (1)}{sub 1} . This system is the simplest representative of the class of discrete systems corresponding to affine Lie algebras. We find the Lax representation and construct hierarchies of higher symmetries. In neighborhoods of singular points ⋋ = 0 and ⋋ = ∞, we construct formal asymptotic expansions of eigenfunctions of the Lax pair and, based on these expansions, find series of local conservation laws for the system considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

The energy performance of commercial buildings is greatly influenced by occupants which are highly variable and among the most unpredictable components of a building's operation. While most building control systems use fixed, predetermined occupancy schedules, these fixed occupancy levels can be quite different from actual occupancy. This can cause unnecessary energy consumption, particularly from heating, ventilation, and air conditioning (HVAC) and lighting systems which are responsible for approximately 60% of commercial buildings' energy use. The use of occupancy counting sensor systems integrated with building management system controls is one method that can be used to improve the energy-consuming performance of buildings. However, there is no standardized universal methodology and metrics to evaluate their reliability. The aim of this research is to develop a uniform evaluation methodology to assess the reliability of occupancy counting sensor systems in a controlled laboratory environment. The developed testing methodology includes both “typical” scenarios representing the occupancy scenarios of a typical commercial building, and “failure” testing scenarios which represent a range of potential scenarios that may impact a sensor system's reliability. These methods were then implemented in a case study to evaluate the performance of two novel occupancy counting sensor systems (i.e., door-centric, and camera-based). Results suggest that typical testing results can be used to compare the overall performance of the occupancy counting sensor systems; however, failure testing is also important to understand the weaknesses of the sensor system in order to select the suitable one for the intended use of the commercial building. In addition, the proposed methodology includes a modified confusion matrix which enables the ability to identify if failures are caused by over or under counting occupants and to what extent this occurs over the testing period.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators

Abstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations . These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.

Hossain, Raisa↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗
DOE OSTI2021

Framework for an adaptive integrated observation system using a hierarchy of machine learning approaches

Focal Area(s): 1. Data acquisition enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, and hardware-related efforts involving AI. 2. Insight gleaned from complex measurements using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI Science Challenge and Rationale: Atmospheric processes are stochastic, occur at scales from the micrometer to many kilometers, and are constantly changing over time. Characterizing these interactions and associated environmental conditions using traditional measurement techniques is difficult and can take years to build statistics on atmospheric phenomena that occurs episodically. Developing new innovative approaches to modify sampling strategies in real-time would enable the routine collection of targeted measurements focused on a specific set of science questions.

54 ENVIRONMENTAL SCIENCES↗