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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Demonstration of a Safety Analysis on a Complex System

For the past 17 years, Professor Leveson and her graduate students have been developing a theoretical foundation for safety in complex systems and building a methodology upon that foundation. The methodology includes special management structures and procedures, system hazard analyses, software hazard analysis, requirements modeling and analysis for completeness and safety, special software design techniques including the design of human-machine interaction, verification, operational feedback, and change analysis. The Safeware methodology is based on system safety techniques that are extended to deal with software and human error. Automation is used to enhance our ability to cope with complex systems. Identification, classification, and evaluation of hazards is done using modeling and analysis. To be effective, the models and analysis tools must consider the hardware, software, and human components in these systems. They also need to include a variety of analysis techniques and orthogonal approaches: There exists no single safety analysis or evaluation technique that can handle all aspects of complex systems. Applying only one or two may make us feel satisfied, but will produce limited results. We report here on a demonstration, performed as part of a contract with NASA Langley Research Center, of the Safeware methodology on the Center-TRACON Automation System (CTAS) portion of the air traffic control (ATC) system and procedures currently employed at the Dallas/Fort Worth (DFW) TRACON (Terminal Radar Approach CONtrol). CTAS is an automated system to assist controllers in handling arrival traffic in the DFW area. Safety is a system property, not a component property, so our safety analysis considers the entire system and not simply the automated components. Because safety analysis of a complex system is an interdisciplinary effort, our team included system engineers, software engineers, human factors experts, and cognitive psychologists.

Leveson, Nancy↗
DOE OSTI2024

ATAT: Astronomical Transformer for time series and Tabular data

Context. The advent of next-generation survey instruments, such as theVera C. RubinObservatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Aims. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. Methods. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. Results. When trained on light curves and metadata, ATAT achieves a macro F1 score of 82.9 ± 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of 79.4 ± 0.1. Conclusions. The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as theVera C. RubinObservatory, in real-world brokering scenarios.

Astronomy & Astrophysics↗
DOE OSTI2023

Fully synthetic platform to rapidly generate tetravalent bispecific nanobody–based immunoglobulins

Nanobodies bind a target antigen with a kinetic profile similar to a conventional antibody, but exist as a single heavy chain domain that can be readily multimerized to engage antigen via multiple interactions. Presently, most nanobodies are produced by immunizing camelids; however, platforms for animal-free production are growing in popularity. Here, we describe the development of a fully synthetic nanobody library based on an engineered human V H 3-23 variable gene and a multispecific antibody-like format designed for biparatopic target engagement. To validate our library, we selected nanobodies against the SARS-CoV-2 receptor–binding domain and employed an on-yeast epitope binning strategy to rapidly map the specificities of the selected nanobodies. We then generated antibody-like molecules by replacing the V H and V L domains of a conventional antibody with two different nanobodies, designed as a molecular clamp to engage the receptor-binding domain biparatopically. The resulting bispecific tetra-nanobody immunoglobulins neutralized diverse SARS-CoV-2 variants with potencies similar to antibodies isolated from convalescent donors. Subsequent biochemical analyses confirmed the accuracy of the on-yeast epitope binning and structures of both individual nanobodies, and a tetra-nanobody immunoglobulin revealed that the intended mode of interaction had been achieved. This overall workflow is applicable to nearly any protein target and provides a blueprint for a modular workflow for the development of multispecific molecules.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?

In the last few years, we have seen the rise of deep learning applications in a broad range of computational chemistry research problems. Using human-engineered chemical features, such as molecular descriptors and fingerprints, deep learning models have shown similar, if not better performance that most traditional machine learning algorithms. Recently, we reported on the development of Chemception, a deep convolutional neural network (CNN) architecture for general-purpose small molecule property prediction. On average, Chemception matched the performance of expert-developed QSAR/QSPR models trained on chemical features (molecular fingerprints), despite that it was trained on just 2D images of molecular drawings with minimal chemical information. Here, we investigate the effects of systematically removing and adding basic chemical information to the image channels of the 2D images used to train Chemception. By augmenting our images with only 3 additional basic chemical information, we demonstrate the improvement of Chemception performance – that it is now more accurate than contemporary deep learning models trained on ECFP fingerprints for the prediction of toxicity, activity and solvation free energy, as well as physics-based free energy simulation methods for computing solvation properties. By altering the chemical information content in the image channels, and examining the resulting performance of Chemception, we also identify to two different “learning patterns” in toxicity/activity as compared to solvation free energy, and it parallels the fundamental differences in contemporary chemistry research for predicting toxicity/activity and solvation free energy.

Artificial Intelligence, Cheminformatics, Computat↗
DOE OSTI2020

Post Conference Activities for the NCSE 2019 Annual Conference: Sustainable Infrastructure & Resilience

The National Council for Science and the Environment (NCSE) held its 19th Annual Conference from January 7-10, 2019. The NCSE 2019 Annual Conference: Sustainable Infrastructure & Resilience explored how systems thinking that includes tools, data, and synthesis effectively supports the increasingly urgent need to advance a more resilient, sustainable society. Nearly 600 participants from a range of scientific disciplines and sectors including natural and social science, humanities, engineering, government, business, and civil society attended the conference. Post-conference activities planned through this project were planned to extend the impact of the Annual Conference and were made possible through support from the Department of Energy. These efforts included: 1) developing and producing a research pilot on Energy Systems and Resilient Infrastructure, which advances research focused on the better alignment of education and workforce needs with respect to energy resilience; 2) facilitating dialogue and in-person convenings with the Energy Education Community of Practice across the broader NCSE research community; and 3) designing and hosting a webinar series focused on the role of science in infrastructure and resilience. This report summarizes the outcomes of the proposed activities and additional work made possible by support from the Department of Energy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Biomimicry in Clean Energy Futures: Workshop Report

On July 25, 2023, PNNL hosted a workshop to explore the potential for biomimicry – looking to nature and biology for solutions to human engineering challenges – to support clean energy futures and associated research. Participants represented a range of technical expertise ranging from ecological modeling to cyber-security. Workshop supporting material featured a literature review of biomimetic approaches in all renewable energy technologies and inspired from all kingdoms of life. Participants noted clear contrasts between natural design strategies and clean energy deployment and the electric grid. They also agreed that biomimicry offered a rich area of research potential but lacked definitive benefits or results in commercialization phases.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Artificial Intelligence for (AI) Nuclear Security: Expert Perspectives on AI Priorities for the Office of International Nuclear Security

Artificial intelligence (AI) has the potential to transform nuclear security operations, offering opportunities to enhance effectiveness while simultaneously introducing new challenges. As AI technologies rapidly evolve, agencies across the United States Government (USG) are researching, implementing, and evaluating various AI models and systems. Given the broad capabilities and applications of these technologies, it is essential for each agency to identify and articulate those areas where it can make meaningful contributions aligned with its mission and expertise. To address this need for strategic focus, in late Fiscal Year 2025 (FY2025), the Office of International Nuclear Security (INS) established an AI Task Force (AITF) to gather input from subject matter experts (SMEs) regarding the most appropriate role INS could serve in researching, evaluating, or implementing AI for nuclear security. The AITF engaged 15 experts from national laboratories with backgrounds in cyber security, physical security, transport security, insider threat mitigation, nuclear engineering, human-systems engineering, and AI/ML development. This white paper summarizes the insights gathered from these SMEs and presents a potential roadmap for INS engagement with AI technologies. The recommendations outlined here are intended to inform INS leadership as they make strategic decisions about resource allocation and program direction in this rapidly evolving technological domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Change Detection Applications in the Earth Sciences Using UAS-Based Sensing: A Review and Future Opportunities

Over the past decade, advancements in collection platforms such as unoccupied aerial systems (UAS), survey-grade GNSS, sensor packages, processing software, and spatial analytical tools have facilitated change detection analyses at an unprecedented resolution over broader spatial and temporal extents and in environments where such investigations present challenges. These technological improvements, coupled with the accessibility and versatility of UAS technology, have pushed the boundaries of spatial and temporal scales in geomorphic change detection. As a result, the cm-scale analysis of topographic signatures can detect and quantify surface anomalies during geomorphic evolution. This review focuses on the use of UAS photogrammetry for fine spatial (cm) and temporal (hours to days) scale geomorphic analyses, and it highlights analytical approaches to detect and quantify surface processes that were previously elusive. The review provides insight into topographic change characterization with precise spatial validations applied to landscape processes in various fields, such as the cryosphere and geosphere, as well as anthropogenic earth processes and national security applications. This work sheds light on previously unexplored aspects of both natural and human-engineered environments, demonstrating the potential of UAS observations in change detection. Our discussion examines the emerging horizons of UAS-based change detection, including machine learning and LIDAR systems. In addition, our meta-analysis of spatial and temporal UAS-based observations highlights the new fine-scale niche of UAS-photogrammetry. This scale advancement sets a new frontier in change detection, offering exciting possibilities for the future of land surface analysis and environmental monitoring in the field of Earth Science.

Andresen, Christian G.↗
NASA NTRS1967

Power torque wrench concept for precision torque application

Precision electromechanical power wrench applies a given amount of torque to a series of fasteners. It uses a commercially available dc permanent magnet torque motor with a current-controllable torque output and torque value indicator designed to the principles of human engineering.

Peters, G. A.↗
NASA NTRS1972

GE underwater test facility studies in zero G simulation

The underwater test facility (UTF) is described as an indoor controlled environment test facility designed specifically for zero G simulation, hydrospace manned and unmanned equipment development, and personnel training for both space and underwater exploration. Programs conducted in the UTF include: human engineering criteria for maintenance and repairs of space stations, astronaut performance, helmet distortion, underwater telemetry, and blood transfusion.

Fry, R. H.↗
NASA NTRS1973

Integral aircraft passenger seat

Human-engineering approach was used to design integral seat which provides all the safety, comfort, and protective features that can possibly be afforded airline passengers. Results of dynamic impact testing indicated that seat can withstand and attenuate gravity loads of 21-g horizontal and 45-g vertical; by design, seat will withstand lateral g's as well.

Kubokawa, C. C.↗
NASA NTRS1973

Skylab experiments. Volume 7: Living and working in space

Experiments conducted on the Skylab vehicle that will measure and evaluate the ability of the crew to live and work effectively in space are discussed. The methods and techniques of human engineering as they relate to the design and evaluation of work spaces, requirements, and tools are described. The application of these methods and the Skylab measurements to the design of future spacecraft are analyzed.

Source record↗
NASA NTRS1975

Problems, questions and results in the use of the BBN-model

A hierarchical structured control model was used to describe pilot performance in a multivariable control problem. The model is based on the assumption that a well trained operator optimized a quadratic optimization criterion. The model considers some special characteristics of the human operator as time delay, neuromuscular time lag, observation and motor noise and the ability to extract the first derivative of a displaced value. Numerical results of a simple human engineering example using the BBN model demonstrate the influence of the variation of different parameters on covariance matrices of the state and observation vectors.

Dey, D.↗
NASA NTRS1978

Firefighters Integrated Response Equipment System

The Firefighters Integrated Response Equipment System (Project FIRES) is a joint National Fire Prevention and Control Administration (NFPCA)/National Aeronautics and Space Administration (NASA) program for the development of an 'ultimate' firefighter's protective ensemble. The overall aim of Project FIRES is to improve firefighter protection against hazards, such as heat, flame, smoke, toxic fumes, moisture, impact penetration, and electricity and, at the same time, improve firefighter performance by increasing maneuverability, lowering weight, and improving human engineering design of his protective ensemble.

Kaplan, H.↗
NASA NTRS1979

An autonomous payload controller for the Space Shuttle

The Autonomous Payload Control (APC) system discussed in the present paper was designed on the basis of such criteria as minimal cost of implementation, minimal space required in the flight-deck area, simple operation with verification of the results, minimal additional weight, minimal impact on Orbiter design, and minimal impact on Orbiter payload integration. In its present configuration, the APC provides a means for the Orbiter crew to control as many as 31 autononous payloads. The avionics and human engineering aspects of the system are discussed.

Hudgins, J. I.↗
NASA NTRS1980

Methods of sound simulation and applications in flight simulators

An overview of methods for electronically synthesizing sounds is presented. A given amount of hardware and computer capacity places an upper limit on the degree and fidelity of realism of sound simulation which is attainable. Good sound realism for aircraft simulators can be especially expensive because of the complexity of flight sounds and their changing patterns through time. Nevertheless, the flight simulator developed at the Research Institute for Human Engineering, West Germany, shows that it is possible to design an inexpensive sound simulator with the required acoustic properties using analog computer elements. The characteristics of the sub-sound elements produced by this sound simulator for take-off, cruise and approach are discussed.

Gaertner, K. P.↗
NASA NTRS1982

Geology and image processing

Digital image processing for geological applications will be integrated with geographic information systems and data base management systems. While multiband data sets from radar and multispectral scanners will make extreme demands on memory, bus and processor architectures, it is expected that array processors and VLSI/VHSIC dedicated function chips will allow the use of fast Fourier transform and classification algorithms. It is anticipted that, as processor power increases, the weakest link of a processing system will become the analyst who uses it. Human engineering of systems is therefore recommended for the most effective utilization of remotely sensed geologic data.

Daily, M.↗