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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery

Passive permeation of cellular membranes is a key feature of many therapeutics. The relevance of passive permeability spans all biological systems as they all employ biomembranes for compartmentalization. A variety of computational techniques are currently utilized and under active development to facilitate the characterization of passive permeability. These methods include lipophilicity relations, molecular dynamics simulations, and machine learning, which vary in accuracy, complexity, and computational cost. This review briefly introduces the underlying theories, such as the prominent inhomogeneous solubility diffusion model, and covers a number of recent applications. Various machine-learning applications, which have demonstrated good potential for high-volume, data-driven permeability predictions, are also discussed. Due to the confluence of novel computational methods and next-generation exascale computers, we anticipate an exciting future for computationally driven permeability predictions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1986

Division of attention as a function of the number of steps, visual shifts, and memory load

The effects on divided attention of visual shifts and long-term memory retrieval during a monitoring task are considered. A concurrent vigilance task was standardized under all experimental conditions. The results show that subjects can perform nearly perfectly on all of the time-shared tasks if long-term memory retrieval is not required for monitoring. With the requirement of memory retrieval, however, there was a large decrease in accuracy for all of the time-shared activities. It was concluded that the attentional demand of longterm memory retrieval is appreciable (even for a well-learned motor sequence), and thus memory retrieval results in a sizable reduction in the capability of subjects to divide their attention. A selected bibliography on the divided attention literature is provided.

Chechile, R. A.↗
NASA NTRS1993

Engineering the future with America's high school students

The number of students enrolled in engineering is declining while the need for engineers is increasing. One contributing factor is that most high school students have little or no knowledge about what engineering is, or what engineers do. To teach young students about engineering, engineers need good tools. This paper presents a course of study developed and used by the authors in a junior college course for high school students. Students learned about engineering through independent student projects, in-class problem solving, and use of career information resources. Selected activities from the course can be adapted to teach students about engineering in other settings. Among the most successful techniques were the student research paper assignments, working out a solution to an engineering problem as a class exercise, and the use of technical materials to illustrate engineering concepts and demonstrate 'tools of the trade'.

Farrance, M. A.↗
NASA NTRS1995

Robust integrated neurocontroller for complex dynamic systems

The goal of this research effort is to develop an integrated control software environment for the purpose of creating an intelligent neurocontrol system. The system will be capable of estimating states, identifying parameters, diagnosing conditions, planning control strategies, and producing intelligent control actions. The distinct features of such control system are adaptability and on-line learning capability. The proposed system will be flexible to allow structure adaptability to account for changes in the dynamic system such as sensory failures and/or component degradations. The developed system should learn system uncertainties and changes, as they occur, while maintaining minimal control level on the dynamic system. The research activities set to achieve the research objective are summarized by the following general items: (1) Development of a system identifier or diagnostic system; (2) Development of a robust neurocontroller system, and; (3) Integration of above systems to create a robust Integration Control system (RIC-system). Two contrary approaches are investigated in this research: classical (traditional) design approach, and the simultaneous design approach. However, in both approaches neural network is the base for the development of different functions of the system. The two resulting designs will be tested and simulation results will be compared for better possible implementation.

Zein-Sabbato, S.↗
NASA NTRS1996

Robust Integrated Neurocontroller for Complex Dynamic Systems

The goal of this research effort is to develop an integrated control software environment for the purpose of creating an intelligent neurocontrol system. The system will be capable of estimating states, identifying parameters, diagnosing conditions, planning control strategies, and producing intelligent control actions. The distinct features of such control system are: adaptability and on-line learning capability. The proposed system will be flexible to allow structure adaptability to account for changes in the dynamic system such as: sensory failures and/or component degradations. The developed system should learn system uncertainties and changes, as they occur, while maintaining minimal control level on the dynamic system. The research activities set to achieve the research objective are summarized by the following general items: (1) Development of a system identifier or diagnostic system, (2) Development of a robust neurocontroller system, and 3. Integration of above systems to create a Robust Integrated Control system (RIC-system). Two contrary approaches are investigated in this research: classical (traditional) design approach, and the simultaneous design approach. However, in both approaches neural network is the base for the development of different functions of the system. The two resulting designs will be tested and simulation results will be compared for better possible implementation.

Zein-Sabatto, S.↗
NASA NTRS2006

Results and Lessons from MODIS Reflective Solar Bands Calibration: Pre-launch to On-orbit

MODIS is a major instrument for the NASA EOS Terra (1aunched in December 1999) and Aqua (launched in May 2002) missions. It was designed and built to enhance and extend its heritage sensors' measurements and data records with applications covering a wide range of studies of the Earth's land, oceans, and atmosphere. MODIS has 36 spectral bands (0.41 - 14.4 micrometers) located on four focal plane assemblies (FPAs). It makes measurements at three nadir spatial resolutions: 250m (bands 1-2), 500m (bands 3-7), and lkm (bands 8-36). Because of instrument design complexity and stringent calibration requirements, extensive calibration and characterization activities were conducted pre-launch by the sensor vendor (Raytheon / Santa Barbara Remote Sensing) for both Tesa and Aqua MODIS. For the 20 reflective solar bands (RSB), these activities include measurements for the detectors noise characterization and radiometric performance, system level response versus scan-angle (RVS), polarization sensitivity, and relative spectral response (RSR). Key radiometric performance was evaluated using thermal vacuum observations. On-orbit MODIS RSB calibration is performed using a solar diffuser (SD) and solar diffuser stability monitor (SDSM) system. The SD bi-directional reflectance factor (BRF) was characterized pre-launch by the sensor vendor with reference samples traceable to NIST reflectance standards. This paper provides a summary of Terra and Aqua MODIS RSB pre-launch and on-orbit calibration and characterization activities and results with focus on the detectors' noise characterization and radiometric performance. Challenging and concerning issues and lessons learned from RSB pre-launch calibration and their impact on post launch performance are also presented. A similar summary for MODIS thermal emissive bands (TEB) is reported in a separate paper in these proceedings.

Xiong, X.↗
NASA NTRS2007

A Parametric Geometry Computational Fluid Dynamics (CFD) Study Utilizing Design of Experiments (DOE)

Design of Experiments (DOE) was applied to the LAS geometric parameter study to efficiently identify and rank primary contributors to integrated drag over the vehicles ascent trajectory in an order of magnitude fewer CFD configurations thereby reducing computational resources and solution time. SME s were able to gain a better understanding on the underlying flowphysics of different geometric parameter configurations through the identification of interaction effects. An interaction effect, which describes how the effect of one factor changes with respect to the levels of other factors, is often the key to product optimization. A DOE approach emphasizes a sequential approach to learning through successive experimentation to continuously build on previous knowledge. These studies represent a starting point for expanded experimental activities that will eventually cover the entire design space of the vehicle and flight trajectory.

Rhew, Ray D.↗
NASA NTRS2008

Lessons Learned from Recent Failure and Incident Investigations of Composite Structures

During the past few decades, NASA Langley Research Center (LaRC) has supported several large-scale failure and incident investigations and numerous requests for engineering consultations. Although various extenuating circumstances contributed to each of these incidents, in all cases, the failure resulted from accumulation and/or propagation of damage that reduced the load carrying capability of the structure to a level below that which was needed to sustain structural loads. A brief overview of various failure and incident investigations supported by LaRC, including some of the computational and experimental methodologies that have been applied, is presented. An important outcome of many of these failure and incident investigations is the development of an improved understanding of not only the state-of-the-art in experimental and analytical methods but also the state-of-the-art in the design and manufacturing processes that may contribute to such failures. In order to provide insight into such large-scale investigations, a series of lessons learned were captured. Awareness of these lessons learned is highly beneficial to engineers involved in similar investigations. Therefore, it is prudent that the lessons learned are disseminated such that they can be built upon in other investigations and in ensuing research and development activities.

Ransom, J. B.↗
NASA NTRS2012

Slow Wave Sleep and Long Duration Spaceflight

While ground research has clearly shown that preserving adequate quantities of sleep is essential for optimal health and performance, changes in the progression, order and /or duration of specific stages of sleep is also associated with deleterious outcomes. As seen in Figure 1, in healthy individuals, REM and Non-REM sleep alternate cyclically, with stages of Non-REM sleep structured chronologically. In the early parts of the night, for instance, Non-REM stages 3 and 4 (Slow Wave Sleep, or SWS) last longer while REM sleep spans shorter; as night progresses, the length of SWS is reduced as REM sleep lengthens. This process allows for SWS to establish precedence , with increases in SWS seen when recovering from sleep deprivation. SWS is indeed regarded as the most restorative portion of sleep. During SWS, physiological activities such as hormone secretion, muscle recovery, and immune responses are underway, while neurological processes required for long term learning and memory consolidation, also occur. The structure and duration of specific sleep stages may vary independent of total sleep duration, and changes in the structure and duration have been shown to be associated with deleterious outcomes. Individuals with narcolepsy enter sleep through REM as opposed to stage 1 of NREM. Disrupting slow wave sleep for several consecutive nights without reducing total sleep duration or sleep efficiency is associated with decreased pain threshold, increased discomfort, fatigue, and the inflammatory flare response in skin. Depression has been shown to be associated with a reduction of slow wave sleep and increased REM sleep. Given research that shows deleterious outcomes are associated with changes in sleep structure, it is essential to characterize and mitigate not only total sleep duration, but also changes in sleep stages.

Whitmire, Alexandra↗
NASA NTRS2012

Design and Implementation of a Lunar Communications Satellite and Server for the 2012 SISO Smackdown

Last year, the Simulation Interoperability Standards Organization (SISO) inaugurated the now annual High Level Architecture (HLA) Smackdown at the Spring Simulation Interoperability Workshop (SIW). A primary objective of the Smackdown event is to provide college students with hands-on experience in the High Level Architecture (HLA). The University of Alabama in Huntsville (UAHuntsville) fielded teams in 2011 and 2012. Both the 2011 and 2012 smackdown scenarios were a lunar resupply mission. The 2012 UAHuntsville fielded four federates: a communications network Federate called Lunar Communications and Navigation Satellite Service (LCANServ) for sending and receiving messages, a Lunar Satellite Constellation (LCANSat) to put in place radios needed by the communications network for Line-Of-Sight communication calculations, and 3D graphical displays of the orbiting satellites and a 3D visualization of the lunar surface activities. This paper concentrates on the first two federates by describing the functions, algorithms, the modular FOM, experiences, lessons learned and recommendations for future Smackdown events.

Bulgatz, Dennis↗
NASA NTRS2013

Biomedical Results of the Space Shuttle Program

The Space Shuttle Program (SSP), which was triumphantly completed by the flight of STS-135 on July 21, 2011, holds a special place in the history of Human Space Exploration. For the first time, a vehicle was built that could perform multiple flights in space and return to Earth. Many important questions regarding the ability of humans to adapt and function in space have been answered during the 30 years since the SSP was launched and many lessons have been learned. The multidisciplinary studies conducted during this program allowed NASA to define the normal responses and potential pathologic changes of the major physiological systems to short-duration space flights. The program significantly contributed to the development of in-flight medical care capabilities and multiple countermeasures. A comprehensive system of preflight, in-flight, and post-flight monitoring of the astronauts was built. It is important to emphasize that the SSP was essential for building the International Space Station (ISS) that significantly expanded the boundaries of space research, allowing for planning of long-term near-terrestrial and extraterrestrial missions. As the program was coming to completion, a decision was made by the NASA Lyndon B. Johnson Space Center Human Research Program senior management to summarize the medico-biological results of the SSP in one comprehensive edition. This was quite an ambitious goal. It required selection of a group of experts in different areas to become the chapters lead authors. These experts were charged with the task of reviewing what was learned in the consequent fields to make the chapters a comprehensive source of information for planning of research, operational activities, and medical care for future missions. We hope that we were able to accomplish this goal and the book will serve as a reliable reference edition for the international space biomedicine community, for new generation of space researchers and Flight Surgeons, as well as for all those interested in space exploration. As space exploration continues, the major question of whether humans are capable of adapting to long-term presence and adequately functioning in space habitats will remain open for a long time. we expect that the comprehensive review of the medico-biological results of the SSP along with the data collected during the missions on the space stations (the Russian space station _Mir_ and ISS) provides a good starting point in seeking the answer to this question.

Diana Risin↗
DOE OSTI2024

NREL Marine Energy Desalination Research and Development Portfolio: Preprint

The National Renewable Energy Laboratory (NREL), in collaboration with the U.S. Department of Energy's (DOE's) Water Power Technologies Office (WPTO), has developed a unique R&D approach to advance marine energy desalination. Desalination is a foundational investment within WPTO's Powering the Blue Economy portfolio, and was the first investment within this portfolio. NREL's marine energy desalination spans techno-economic feasibility studies, numerical modeling, and laboratory testing at the component and subsystem level, as well as development of the Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This multilayered approach enables an innovative feedback loop where the data and lessons learned from laboratory and field experiments are used to refine modeling tools and analysis techniques, prioritize out-year activities, and refine strategic directions within NREL and across the WPTO portfolio. The primary objective of the NREL-led research is to identify key barriers associated with the commercialization of wave-powered desalination and develop solutions that can be adopted by the marine energy industry. In parallel, these R&D activities can help inform technical assistance and support of industry and academic technologies. These two tracks help build a common solver community approach, while also identifying key stakeholders, government agencies, and other organizations outside of the marine sector that will be necessary for developing a robust industry. For the Spanish version of this report, see NREL/CP-5700-88483 (https://www.nrel.gov/docs/fy24osti/88483.pdf).

analysis↗
DOE OSTI2024

Cartera de Investigacion y Desarrollo Sobre Desalinizacion de Energia Marina del NREL: Preprint (Spanish Translation)

The National Renewable Energy Laboratory (NREL), in collaboration with the U.S. Department of Energy's (DOE's) Water Power Technologies Office (WPTO), has developed a unique R&D approach to advance marine energy desalination. Desalination is a foundational investment within WPTO's Powering the Blue EconomyTM portfolio [1], and was the first investment within this portfolio. NREL's marine energy desalination spans techno-economic feasibility studies, numerical modeling, and laboratory testing at the component and subsystem level, as well as development of the Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This multilayered approach enables an innovative feedback loop where the data and lessons learned from laboratory and field experiments are used to refine modeling tools and analysis techniques, prioritize out-year activities, and refine strategic directions within NREL and across the WPTO portfolio. The primary objective of the NREL-led research is to identify key barriers associated with the commercialization of wave-powered desalination and develop solutions that can be adopted by the marine energy industry. In parallel, these R&D activities can help inform technical assistance and support of industry and academic technologies. These two tracks help build a common solver community approach, while also identifying key stakeholders, government agencies, and other organizations outside of the marine sector that will be necessary for developing a robust industry. For the English version of this report, see NREL/CP-5700-86724 (https://www.nrel.gov/docs/fy24osti/86724.pdf).

analysis↗
DOE OSTI2024

NREL Marine Energy Desalination R&D Portfolio

The National Renewable Energy Laboratory (NREL), in collaboration with the U.S. Department of Energy's (DOE's) Water Power Technologies Office (WPTO), has developed a unique R&D approach to advance marine energy desalination. Desalination is a foundational investment within WPTO's Powering the Blue EconomyTM portfolio [1], and was the first investment within this portfolio. NREL's marine energy desalination spans techno-economic feasibility studies, numerical modeling, and laboratory testing at the component and subsystem level, as well as development of the Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This multilayered approach enables an innovative feedback loop where the data and lessons learned from laboratory and field experiments are used to refine modeling tools and analysis techniques, prioritize out-year activities, and refine strategic directions within NREL and across the WPTO portfolio. The primary objective of the NREL-led research is to identify key barriers associated with the commercialization of wave-powered desalination and develop solutions that can be adopted by the marine energy industry. It should be noted, while WPTO's marine energy portfolio includes wave, tidal, ocean current, thermal gradients, and pressure gradients the bulk of the marine energy desalination work has been focused on wave powered desalination. In parallel, these R&D activities can help inform technical assistance and support of industry and academic technologies. These two tracks help build a common solver community approach, while also identifying key stakeholders, government agencies, and other organizations outside of the marine sector that will be necessary for developing a robust industry.

desalination↗
DOE OSTI2025

Ensuring Safe, Effective, and Reliable Use of Artificial Intelligence-Based Applications for Nuclear Electricity Generation through a Systems Approach

There is a significant potential to reduce operating and maintenance cost at nuclear power plants using artificial intelligence (AI) and machine learning (ML). For instance, AI/ML has the potential to significant improve work management processes, condition reporting, and plant surveillance activities. However, the nuclear industry has been slow in adopting AI/ML due to several multifaceted barriers discussed in this paper. This work presents such multidisciplinary approach intended to 1) accelerate industry adoption of AI/ML-based applications at nuclear power plants and 2) ensure their safe, reliable, and effective use. This approach is discussed further in this work and will be used to address overarching challenges associated with AI deployment at scale to provide industry guidance that support accelerated adoption of AI/ML technologies throughout the industry.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Ten questions concerning reinforcement learning for building energy management

As buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The increased integration of variable energy sources, such as renewables, introduces uncertainties and unprecedented flexibilities, necessitating buildings to adapt their energy demand to enhance grid resiliency. Consequently, buildings must transition from passive energy consumers to active grid assets, providing demand flexibility and energy elasticity while maintaining occupant comfort and health. This fundamental shift demands advanced optimal control methods to manage escalating energy demand and avert power outages. Reinforcement learning (RL) emerges as a promising method to address these challenges. Here, in this paper, we explore ten questions related to the application of RL in buildings, specifically targeting flexible energy management. We consider the growing availability of data, advancements in machine learning algorithms, open-source tools, and the practical deployment aspects associated with software and hardware requirements. Our objective is to deliver a comprehensive introduction to RL, present an overview of existing research and accomplishments, underscore the challenges and opportunities, and propose potential future research directions to expedite the adoption of RL for building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Machine learning for continuous quantum error correction on superconducting qubits

Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

From machine learning to chemical insight: Darwinian chance and the stability of charge carriers in flow batteries

In chemistry, rationalizing and predicting reaction behavior in complex environments is challenging, but this knowledge is required for practical applications and materials advancement. Here, we focus on one such example that uses organic molecules as redox-active materials in electrochemical energy storage. In flow batteries, these molecules (also known as redoxmers) serve as charge carriers, and exceptional chemical stability in all states of charge is required for long-term use. Here, we show how machine learning combined with chemist's knowledge can be used to reveal patterns in the reactivity of charged redoxmers by providing mechanistically tractable clues.

25 ENERGY STORAGE↗