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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Evaluation of Precision Landing Performance Using A Generalized Aerospace Simulation in Simulink Framework

NASA’s science and exploration goals to return to the Moon and beyond will need to perform precision landings to place humans and cargo supplies near places of scientific interest, surface resources, or pre-established basecamps. With the maturation of new navigation technology, such as terrain relative navigation, precision landing is now feasible, enabling new exploration sites, such as the lunar poles. However, verification of precision landing performance becomes crucial since not reaching the designated landing site would have a high risk of loss of mission. Therefore, having a high-fidelity simulation platform to evaluate six degrees of freedom vehicle performance during high-risk phases of flight such landing is a fundamental part of the system verification and risk reduction. The NASA Marshall Space Flight Center has developed the GeneraLized Aerospace Simulation in Simulink® (GLASS) tool which incorporates guidance, navigation, and control algorithms, as well as vehicle and environmental models, such as gravity, vehicle mass properties, navigation sensors, propulsion, and terrain models. GLASS uses the MathWorks® Simulink® environment which provides a model-based design framework that allows the incorporation of vehicle models in a modular architecture. The Simulink® environment provides seamless integration with all the MathWorks® capabilities and toolboxes, such as control design toolboxes and Simscape™ Multibody™ dynamics toolbox. The MathWorks® environment also allows for guidance, navigation, and control algorithms to be auto coded in C language, enabling quick software and hardware in the loop testing. This paper provides an overview of GLASS capabilities for analyzing precision landing performance, including navigation trades applied to a NASA human lander reference design architecture.

Guidance↗
NASA NTRS1999

Detection in fixed and random noise in foveal and parafoveal vision explained by template learning

Foveal and parafoveal contrast detection thresholds for Gabor and checkerboard targets were measured in white noise by means of a two-interval forced-choice paradigm. Two white-noise conditions were used: fixed and twin. In the fixed noise condition a single noise sample was presented in both intervals of all the trials. In the twin noise condition the same noise sample was used in the two intervals of a trial, but a new sample was generated for each trial. Fixed noise conditions usually resulted in lower thresholds than twin noise. Template learning models are presented that attribute this advantage of fixed over twin noise either to fixed memory templates' reducing uncertainty by incorporation of the noise or to the introduction, by the learning process itself, of more variability in the twin noise condition. Quantitative predictions of the template learning process show that it contributes to the accelerating nonlinear increase in performance with signal amplitude at low signal-to-noise ratios.

NASA Center ARC↗
NASA NTRSDate not supplied

Temporal Changes in Astronauts’ Muscle and Cardiorespiratory Physiology Before, During, and After Spaceflight

Background: NASA’s planned space exploration missions will require astronauts to safely perform extravehicular activity (EVA) and to safely egress vehicles in a variety of landing scenarios. Prolonged exposure to spaceflight can diminish tolerance for physical activity, decrease cardiovascular and sensorimotor function, cause loss of bone mineral density, as well as reduced muscle mass and strength. Although exercise can mitigate these spaceflight-induced physiological decrements, little is known regarding the time-course of changes in muscle and aerobic performance during spaceflight. Furthermore, these exercise countermeasures are not fully protective. For example, maximal aerobic capacity (VO2pk), lower body muscle cross-sectional area, and strength decrease by about 10% to 15% after short- (≈14 days) and long-duration (≈6 months) missions on the International Space Station (ISS). Future space missions longer in duration and further from Earth will employ exploration vehicles that will have exercise hardware with less robust and more constrained exercise capabilities than of those available on the ISS. Thus, countermeasures will need to be optimized to protect crew health and performance on exploration-class missions lasting up to 3 years. This requires a more detailed understanding of the dynamic effects of spaceflight on human health and performance, the ability of exercise to protect against this deconditioning, and the interaction of exercise with interrelated factors like nutrition, sleep, and environmental conditions. Methods: We will use standardized research and medical testing protocols previously validated in 1g and microgravity to quantify the time course and the inter-individual variability of changes in physical performance, including cardiorespiratory fitness and muscle strength and endurance, before, during, and after spaceflight missions lasting 2 months, 6 months, and 1 year. Additionally, we will use an extrapolation model to predict changes associated with multi-year exploration missions. Additionally, we will monitor in-flight exercise, nutrition, and sleep using in-flight assessment tools. Significance: Our testing protocols will provide valuable information for determining time course of change and the interindividual variability of spaceflight-induced deconditioning of aerobic capacity and muscle strength and endurance over the course of spaceflight missions up to and beyond 1 year. This information will be vital to assess whether humans can be physically ready for deep space exploration, such as on a mission to Mars, using current technology, or if additional mitigation strategies are necessary.

N Strock↗
NASA NTRS2000

A Simple Tool for the Design and Analysis of Multiple-Reflector Antennas in a Multi-Disciplinary Environment

The process of designing and analyzing a multiple-reflector system has traditionally been time-intensive, requiring large amounts of both computational and human time. At many frequencies, a discrete approximation of the radiation integral may be used to model the system. The code which implements this physical optics (PO) algorithm was developed at the Jet Propulsion Laboratory. It analyzes systems of antennas in pairs, and for each pair, the analysis can be computationally time-consuming. Additionally, the antennas must be described using a local coordinate system for each antenna, which makes it difficult to integrate the design into a multi-disciplinary framework in which there is traditionally one global coordinate system, even before considering deforming the antenna as prescribed by external structural and/or thermal factors. Finally, setting up the code to correctly analyze all the antenna pairs in the system can take a fair amount of time, and introduces possible human error. The use of parallel computing to reduce the computational time required for the analysis of a given pair of antennas has been previously discussed. This paper focuses on the other problems mentioned above. It will present a methodology and examples of use of an automated tool that performs the analysis of a complete multiple-reflector system in an integrated multi-disciplinary environment (including CAD modeling, and structural and thermal analysis) at the click of a button. This tool, named MOD Tool (Millimeter-wave Optics Design Tool), has been designed and implemented as a distributed tool, with a client that runs almost identically on Unix, Mac, and Windows platforms, and a server that runs primarily on a Unix workstation and can interact with parallel supercomputers with simple instruction from the user interacting with the client.

Katz, Daniel S.↗
NASA NTRS2017

Application of a Fully Numerical Guidance to Mars Aerocapture

An advanced guidance algorithm, Fully Numerical Predictor-corrector Aerocapture Guidance (FNPAG), has been developed to perform aerocapture maneuvers in an optimal manner. It is a model-based, numerical guidance that benefits from requiring few adjustments across a variety of different hypersonic vehicle lift-to-drag ratios, ballistic co-efficients, and atmospheric entry conditions. In this paper, FNPAG is first applied to the Mars Rigid Vehicle (MRV) mid lift-to-drag ratio concept. Then the study is generalized to a design map of potential Mars aerocapture missions and vehicles, ranging from the scale and requirements of recent robotic to potential human and precursor missions. The design map results show the versatility of FNPAG and provide insight for the design of Mars aerocapture vehicles and atmospheric entry conditions to achieve desired performance.

Matz, Daniel A.↗
NASA NTRS2004

Special Purpose Crew Restraints for Teleoperation

With permanent human presence onboard the International Space Station (ISS), and long duration space missions being planned for the moon and Mars, humans will be living and working in microgravity over increasingly long periods of time. In addition to weightlessness, the confined nature of a spacecraft environment results in ergonomic challenges such as limited visibility, and access to the activity area. These challenges can result in prolonged periods of unnatural postures for the crew, ultimately causing pain, injury, and loss of productivity. Determining the right set of human factors requirements and providing an ergonomically designed environment is crucial to mission success. While a number of general purpose restraints have been used on ISS (handrails, foot loops), experience has shown that these general purpose restraints may not be optimal, or even acceptable for some tasks that have unique requirements. For example, some onboard activities require extreme stability (e.g., glovebox microsurgery), and others involve the use of arm, torso and foot movements in order to perform the task (e-g. robotic teleoperation); standard restraint systems will not work in these situations. The Usability Testing and Analysis Facility (WAF) at the NASA Johnson Space Center began evaluations of crew restraints for these special situations by looking at NASAs Robonaut. Developed by the Robot Systems Technology Branch, Robonaut is a humanoid robot that can be remotely operated through a tetepresence control system by an operator. It was designed to perform work in hazardous environments (e.g., Extra Vehicular Activities). A Robonaut restraint was designed, modeled for the population, and ultimately tested onboard the KC-135 microgravity aircraft. While in microgravity, participants were asked to get in and out of the restraint from different locations, perform maximum reach exercises, and finally to teleoperate Robonaut while in the restraint. The sessions were videotaped, and participants completed a questionnaire at the end of each flight day. Results from this evaluation are being used to develop the human factors design requirements for teleoperation tasks in microgravity.

Whitmore, Mihriban↗
DOE OSTI2018

Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the Ci PA Initiative

The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro‐arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (Ci PA ) was proposed that integrates multi‐ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on Ci PA training drugs. In this study, we report the application of the prespecified model and metric to independent Ci PA validation drugs. Over two validation datasets, the Ci PA model performance meets all pre‐specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current Ci PA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Screening green solvents for multilayer plastic film recycling processes

Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAPTM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. Here, this work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.

economics↗
DOE OSTI2020

Relevance of the Unfolded Protein Response to Spaceflight-Induced Transcriptional Reprogramming in Arabidopsis

Plants are primary producers of food and oxygen on Earth and will likewise be indispensable to the establishment of large-scale sustainable ecosystems and human survival in space. To contribute to the understanding of how plants respond to spaceflight stress, we examined the significance of the unfolded protein response (UPR), a conserved signaling cascade that responds to a number of unfavorable environmental stresses, in the model plant Arabidopsis thaliana. To do so, we performed a large-scale comparative transcriptome profiling in wild type and various UPR-defective mutants during the SpaceX-CRS12 mission to the International Space Station. We established that orbital culture substantially alters the expression of hundreds of stress-related genes compared with ground control conditions. Although expression of those genes varied in the UPR mutants on the ground, it was largely similar across the genotypes in the spaceflight condition. Furthermore, our results have yielded new information on how plants respond to growth in orbit and support the hypothesis that spaceflight induces the activation of signaling pathways that compensate for the loss of UPR regulators in the control of downstream transcriptional regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

C3F: Collaborative Container-based Model Coupling Framework

Solving complex real-world grand challenge problems requires in-depth collaboration of researchers from multiple disciplines. Such collaboration often involves harnessing multiscale and multi-dimensional data and combining models from different fields to simulate systems. However, the progress on this front has been limited mainly due to significant gaps in domain knowledge and tools that are typically employed in silos of the domains. Researchers from different fields face considerable barriers to understanding and reusing each other’s data/models in order to collaborate effectively. For example, in solving the global sustainability problems, researchers from hydrology, climate science, agriculture, and economics need to run their respective models to study different components of the global and local food, energy and water systems while, at the same time, need to interact with other researchers and integrate the results of one model with another. Developing this kind of model coupling workflow calls for (1) a large amount of data being processed and exchanged across domains and organizations, (2) identifying and processing the output of one model to make it ready for integration into another model, (3) controlling the workflow dynamically so that it runs until a certain convergence condition or other criteria is met, and (4) close collaboration among the modelers to explore, tune, and test the configuration and data transformation needed to link the models. We have developed C3F, a flexible collaborative model coupling framework to help researchers accelerate their model integration and linking efforts by leveraging advanced cyberinfrastructure such as high-performance computing and virtual containers. In this paper, we describe our experience and lessons learned in developing this cyberinfrastructure solution to support the linking of Water Balance Model (WBM) and SIMPLE-G agricultural economic model in an NSF funded INFEWS project and a DOE-funded Program on Coupled Human and Earth Systems (PCHES) to study the implications of groundwater scarcity for food-energy-water systems. The C3F model coupling framework can be extended to facilitate other model linkages as well.

containerization↗
DOE OSTI2018

Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages

This report is being submitted for the task “Final Report” of DOE NEUP Project 15-8121 “Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages.” Typical nuclear power plant (NPP) outages always involve thousands of maintenance and refueling activities and a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working in various workspaces across the NPP. High outage costs and expensive delays (approximately 1.5 million dollars of loss per day of delay) in NPP maintenance demand tight outage schedules. In packed workspaces, an automatic system that monitors human behaviors in real-time and provides insights about current and pending schedule deviations from the plan is critical for ensuring: 1) effective collaboration among workers and worker teams from different trades; 2) less waste of time and resources due to the lack of situational awareness; and 3) proactive outage project control. The overall goal of this project is to test the hypothesis that real-time imagery-based object tracking and spatial analysis, as well as human behavior modeling of outage participants, will significantly improve the efficiency of outage control while lowering the rates of accidents and incidents. Three objectives of this project are: 1) Establish real-time object tracking and spatiotemporal analysis methods that automatically assess the productivity of field activities and detect anomalous spatiotemporal relationships among activities that cause inefficiencies and risks; 2) Establish realtime human tracking and human factor modeling methods for automatically diagnosing unexpected actions of and interactions between outage participants, those which cause inefficient collaborations between Advanced Outage Control Center (AOCC), satellite outage centers, NPP workers, and maintenance service providers; and 3) Test the proposed automated object tracking, human behavior modeling, and spatiotemporal analysis methods in outage control case studies in order to characterize the effectiveness of automated imagery-data-driven methods in proactively improving the efficiency and safety of workflows in outage coordination and risk management. Recent studies of detailed human behavior monitoring on construction sites have examined the potential of applying advanced computer vision algorithms in detecting and tracking anomalous workers (i.e., workers who do not wear hard hats or safety vests) for ensuring job site safety. Some studies of human factor studies revealed the importance of modeling detailed interactions between individuals within and across teams in better understanding the impact of human in proactive project control. Other studies in the construction domain have developed computational simulation frameworks that formalize detailed spatiotemporal interactions between tasks in order to simulate the impacts of individual tasks on the performance of workflows. While integrated analyses that combine human factor assessment, as well as image processing and simulation, are in demand for effective decision-making, limited studies have examined the potential of such integrated analyses in NPP outage control. This research project has examined an automatic outage monitoring and control system that integrates human factor analysis, computer vision techniques, and simulation methods in order to enable engineers to better understand the interactions between humans, resources, and workflows during outage processes. The project aims at providing NPP maintenance agencies insights into more efficient use of limited resources in extending the life of a nuclear plant, as well as reducing waste while ensuring sufficient generation of electricity. This study is significant for the safety of nuclear plants, sustainable electricity generation for livable communities, and cost savings for maintaining electricity infrastructures in the United States.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Science and Technology Review (May 2021)

Lawrence Livermore has been on the forefront of cancer research for over 60 years. Early interest in cancer statistics stemmed from the nature of work, particularly how radiation affects humans. The Department of Energy funded research to investigate the effects of radiation on workers with long term exposure. This research quickly morphed into a wider breadth of cancer research topics, including the use of advanced computational models to investigate mutations in genes. Livermore is regarded as a leader in cancer research, from the Human Genome Center to its participation in the National Cancer Institute’s “Moonshot” project. The highly interdisciplinary Laboratory unites research in one more example: bringing together cancer biology, 3D printing, high-performance computing, big data, and materials science to address this pressing medical challenge.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

The Gut Microbiome and Alcoholic Liver Disease: Ethanol Consumption Drives Consistent and Reproducible Alteration in Gut Microbiota in Mice

Phenotypic health effects, both positive and negative, have been well studied in association with the consumption of alcohol in humans as well as several other mammals including mice. Many studies have also associated these same health effects and phenotypes to specific members of gut microbiome communities. Here we utilized a chronic plus binge ethanol feed model (Gao-binge model) to explore microbiome community changes across three independent experiments performed in mice. We found significant and reproducible differences in microbiome community assemblies between ethanol-treated mice and control mice on the same diet absent of ethanol. We also identified significant differences in gut microbiota occurring temporally with ethanol treatment. Peak shift in communities was observed 4 days after the start of daily alcohol consumption. We quantitatively identified many of the bacterial genera indicative of these ethanol-induced shifts including 20 significant genera when comparing ethanol treatments with controls and 14 significant genera based on temporal investigation. Including overlap of treatment with temporal shifts, we identified 25 specific genera of interest in ethanol treatment microbiome shifts. Shifts coincide with observed presentation of fatty deposits in the liver tissue, i.e., Alcoholic Liver Disease-associated phenotype. The evidence presented herein, derived from three independent experiments, points to the existence of a common, reproducible, and characterizable “mouse ethanol gut microbiome”.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

An advanced teleoperator control system - Design and evaluation

The design goal of an advanced teleoperator control system is twofold: 1) to allow the operator's manual control to be robust to system nonlinearities such as time delays and operator's control errors, and 2) to support the high performance of teleoperation while reducing the operator's control burden by providing the master and slave arms with desirable dynamic properties and by allowing the slave arm to automatically perform such control tasks as compliance and force control in the form of task sharing. The authors present a novel teleoperator control system achieving the above design goal by taking the following into consideration: the human dynamics involved in generating control command based on visual and forced feedback is modeled and incorporated into the controller design and evaluation; the dynamic characteristics of slave and master arms are actively modified in such a way as to implement the desirable dynamic characteristics; and the force feedback is redefined in terms of the combination of opposition and force discrepancies in order to establish the required man/machine dynamic coordination under shared control. The proposed control system with human dynamics in the control loop is simulated and compared with a number of conventional methods in the presence of human control errors and time delays.

Lee, Sukhan↗
NASA NTRS1994

Probabilistic Simulation of the Human Factor in Structural Reliability

The formal approach described herein computationally simulates the probable ranges of uncertainties for the human factor in probabilistic assessments of structural reliability. Human factors such as marital status, professional status, home life, job satisfaction, work load, and health are studied by using a multifactor interaction equation (MFIE) model to demonstrate the approach. Parametric studies in conjunction with judgment are used to select reasonable values for the participating factors (primitive variables). Subsequently performed probabilistic sensitivity studies assess the suitability of the MFIE as well as the validity of the whole approach. Results show that uncertainties range from 5 to 30 percent for the most optimistic case, assuming 100 percent for no error (perfect performance).

Chamis, Christos C.↗
NASA NTRS2011

A New Business Model for Problem Solving-Infusing Open Collaboration and Innovation Health and Human Services

This slide presentation reviews the Space Life Sciences Directorate (SLSD) new business model for problem solving, with emphasis on open collaboration and innovation. The topics that are discussed are: an overview of the work of the Space Life Sciences Directorate and the strategic initiatives that arrived at the new business model. A new business model was required to infuse open collaboration/innovation tools into existing models for research, development and operations (research announcements, procurements, SBIR/STTR etc). This new model involves use of several open innovation partnerships: InnoCentive, Yet2.com, TopCoder and NASA@work. There is also a new organizational structure developed to facilitate the joint collaboration with other NASA centers, international partners, other U.S. Governmental organizations, Academia, Corporate, and Non-Profit organizations: the NASA Human Health and Performance Center (NHHPC).

Davis, Jeffrey R.↗