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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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NASA NTRS1994

Toxic Substances Registry System. Index of Material Safety Data Sheets

The October 1994 revision of the KSC Toxic Substances Registry System (TSRS) Material Safety Data Sheets (MSD's) is presented. The listed MSD's which were submitted to the TSRS are maintained by the Base Operations Contractors of the Biomedical Operations and Research Office of KSC. The purpose of the index is to provide a means of accessing information on the hazards associated with the toxic and otherwise hazardous chemicals stored and used at KSC. Indices are provided for manufacturers, trademarks, and stock numbers.

Source record↗

NASA NTRS2003

Radio Frequency Telemetry System for Sensors and Actuators

The present invention discloses and teaches apparatus for combining Radio Frequency (RF) technology with novel micro-inductor antennas and signal processing circuits for RF telemetry of real time, measured data, from microelectromechanical system (MEMS) sensors, through electromagnetic coupling with a remote poweringheceiving device. Such technology has many applications, but is especially useful in the biomedical area.

Rainee N Simons↗

NASA NTRS2003

Radio frequency telemetry system for sensors and actuators

The present invention discloses and teaches apparatus for combining Radio Frequency (RF) technology with novel micro-inductor antennas and signal processing circuits for RF telemetry of real time, measured data, from microelectromechanical system (MEMS) sensors, through electromagnetic coupling with a remote powering/receiving device. Such technology has many applications, but is especially useful in the biomedical area.

Simons, Rainee N.↗

NASA NTRS1991

Use of telescience for biomedical research during space flight

When the U.S. first embarked on a manned space flight program, NASA's use of medical telescience was focused on crew health monitoring. In recent years, medical telescience use has been expanded to include support of basic research in space medicine. It enables ground support personnel to assist on-board crews in the performance of experiments and improves the quality and quantity of data return. NASA is continuing to develop its telescience capabilities. Future plans include telemedicine that will enable physicians on Earth to support crewmembers during flight and telescience that will enable investigators at their home institutions to support and conduct in-flight medical research. NASA's use of telescience for crew safety and biomedical research from Project Mercury to the present is described and NASA's plans for the future are presented.

Huntoon, Carolyn L.↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA NTRS1972

Biotelemetry and computer analysis of sleep processes on earth and in space.

Developments in biomedical engineering now permit study of states of sleep, wakefulness, and focused attention in man exposed to rigorous environments, including aerospace flight. These new sensing devices, data acquisition systems, and computational methods have also been extensively applied to clinical problems of disordered sleep. A 'library' of EEG data has been prepared for sleep in normal man, and characterized for its group features by computational analysis. Sleep in an astronaut in space flight has been examined for the first and second 'nights' of space flight. Normal 90-min cycles were detected during the second night. Sleep patterns in quadriplegic patients deprived of all sensory inputs below the neck have indicated major deviations.

Adey, W. R.↗

NASA NTRS2016

Expanding NASA and Roscosmos Scientific Collaboration on the International Space Station

The International Space Station (ISS) is a world-class laboratory orbiting in space. NASA and Roscosmos have developed a strong relationship through the ISS Program Partnership, working together and with the other ISS Partners for more than twenty years. Since 2013, based on a framework agreement between the Program Managers, NASA and Roscosmos are building a joint program of collaborative research on ISS. This international collaboration is developed and implemented in phases. Initially, members of the ISS Program Science Forum from NASA and TsNIIMash (representing Roscosmos) identified the first set of NASA experiments that could be implemented in the "near term". The experiments represented the research categories of Technology Demonstration, Microbiology, and Education. Through these experiments, the teams from the "program" and "operations" communities learned to work together to identify collaboration opportunities, establish agreements, and jointly plan and execute the experiments. The first joint scientific activity on ISS occurred in January 2014, and implementation of these joint experiments continues through present ISS operations. NASA and TsNIIMash have proceeded to develop "medium term" collaborations, where scientists join together to improve already-proposed experiments. A major success is the joint One-Year Mission on ISS, with astronaut Scott Kelly and cosmonaut Mikhail Kornienko, who returned from ISS in March, 2016. The teams from the NASA Human Research Program and the RAS Institute for Biomedical Problems built on their considerable experience to design joint experiments, learn to work with each other's protocols and processes, and share medical and research data. New collaborations are being developed between American and Russian scientists in complex fluids, robotics, rodent research and space biology, and additional human research. Collaborations are also being developed in Earth Remote Sensing, where scientists will share data from imaging systems mounted on ISS as well as other orbiting spacecraft to improve our understanding of the Earth and its climate. NASA and Roscosmos continue to encourage international scientific cooperation and expanded use of the ISS Laboratory. "Long-term", larger collaborations will achieve scientific objectives that no single national science team or agency can achieve on its own. The joint accomplishments achieved so far have paved the way for a stronger international scientific community and improved results and benefits from ISS.

Hasbrook, Pete↗

NASA NTRS2015

Credibility Assessment of Deterministic Computational Models and Simulations for Space Biomedical Research and Operations

Human missions beyond low earth orbit to destinations, such as to Mars and asteroids will expose astronauts to novel operational conditions that may pose health risks that are currently not well understood and perhaps unanticipated. In addition, there are limited clinical and research data to inform development and implementation of health risk countermeasures for these missions. Consequently, NASA's Digital Astronaut Project (DAP) is working to develop and implement computational models and simulations (M&S) to help predict and assess spaceflight health and performance risks, and enhance countermeasure development. In order to effectively accomplish these goals, the DAP evaluates its models and simulations via a rigorous verification, validation and credibility assessment process to ensure that the computational tools are sufficiently reliable to both inform research intended to mitigate potential risk as well as guide countermeasure development. In doing so, DAP works closely with end-users, such as space life science researchers, to establish appropriate M&S credibility thresholds. We will present and demonstrate the process the DAP uses to vet computational M&S for space biomedical analysis using real M&S examples. We will also provide recommendations on how the larger space biomedical community can employ these concepts to enhance the credibility of their M&S codes.

Mulugeta, Lealem↗

NASA NTRS2020

Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective

The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the devel-opment and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder com-munity survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing imple-mentations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.

Credibility↗

DOE OSTI2024

Deep learning-based temporal deconvolution for photon time-of-flight distribution retrieval

The acquisition of the time of flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions. Herein, we propose a deep learning model specifically to perform the deconvolution task in fluorescence lifetime imaging (FLI). The model is trained and validated with representative simulated FLI data with the goal of retrieving the true photon ToF distribution. Its performance and robustness are validated with well-controlled in vitro experiments using three time-resolved imaging modalities with markedly different temporal IRFs. The model aptitude is further established with in vivo preclinical investigation. Overall, these in vitro and in vivo validations demonstrate the flexibility and accuracy of deep learning model-based deconvolution in time-resolved FLI and diffuse optical imaging.

Pandey, Vikas (ORCID:0000000154771095)↗

NASA NTRS1976

Life sciences payloads analyses and technical program planning studies

Contractural requirements, project planning, equipment specifications, and technical data for space shuttle biological experiment payloads are presented. Topics discussed are: (1) urine collection and processing on the space shuttle, (2) space processing of biochemical and biomedical materials, (3) mission simulations, and (4) biomedical equipment.

Source record↗

NASA NTRS1991

Man/Machine Interaction Dynamics And Performance (MMIDAP) capability

The creation of an ability to study interaction dynamics between a machine and its human operator can be approached from a myriad of directions. The Man/Machine Interaction Dynamics and Performance (MMIDAP) project seeks to create an ability to study the consequences of machine design alternatives relative to the performance of both machine and operator. The class of machines to which this study is directed includes those that require the intelligent physical exertions of a human operator. While Goddard's Flight Telerobotic's program was expected to be a major user, basic engineering design and biomedical applications reach far beyond telerobotics. Ongoing efforts are outlined of the GSFC and its University and small business collaborators to integrate both human performance and musculoskeletal data bases with analysis capabilities necessary to enable the study of dynamic actions, reactions, and performance of coupled machine/operator systems.

Frisch, Harold P.↗

NASA NTRS2001

Model-based parameter estimation using cardiovascular response to orthostatic stress

This paper presents a cardiovascular model that is capable of simulating the short-term (< or approximately equal to 3 min) transient hemodynamic response to gravitational stress and a gradient-based optimization method that allows for the automated estimation of model parameters from simulated or experimental data. We perform a sensitivity analysis of the transient heart rate response to determine which parameters of the model impact the heart rate dynamics significantly. We subsequently include only those parameters in the estimation routine that impact the transient heart rate dynamics substantially. We apply the estimation algorithm to both simulated and real data and showed that restriction to the 20 most important parameters does not impair our ability to match the data.

Non-NASA Center↗

NASA NTRS2008

Structure Computation of Quiet Spike[Trademark] Flight-Test Data During Envelope Expansion

System identification or mathematical modeling is used in the aerospace community for development of simulation models for robust control law design. These models are often described as linear time-invariant processes. Nevertheless, it is well known that the underlying process is often nonlinear. The reason for using a linear approach has been due to the lack of a proper set of tools for the identification of nonlinear systems. Over the past several decades, the controls and biomedical communities have made great advances in developing tools for the identification of nonlinear systems. These approaches are robust and readily applicable to aerospace systems. In this paper, we show the application of one such nonlinear system identification technique, structure detection, for the analysis of F-15B Quiet Spike(TradeMark) aeroservoelastic flight-test data. Structure detection is concerned with the selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description that may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance for the development of robust parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion, which may save significant development time and costs. The objectives of this study are to demonstrate via analysis of F-15B Quiet Spike aeroservoelastic flight-test data for several flight conditions that 1) linear models are inefficient for modeling aeroservoelastic data, 2) nonlinear identification provides a parsimonious model description while providing a high percent fit for cross-validated data, and 3) the model structure and parameters vary as the flight condition is altered.

Kukreja, Sunil L.↗

DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

36 MATERIALS SCIENCE↗

NASA NTRS1974

Skylab mission report, third visit

An evaluation is presented of the operational and engineering aspects of the third Skylab visit, including information on the performance of the command and service module and the experiment hardware, the crew's evaluation of the visit, and other visit-related areas of interest such as biomedical observations. The specific areas discussed are contained in the following: (1) solar physics and astrophysics investigations; (2) Comet Kohoutek experiments; (3) medical experiments; (4) earth observations, including data for the multispectral photographic facility, the earth terrain camera, and the microwave radiometer/scattermometer and altimeter; (5) engineering and technology experiments; (6) food and medical operational equipment; (7) hardware and experiment anomalies; and (8) mission support, mission objectives, flight planning, and launch phase summary. Conclusions discussed as a result of the third visit to Skylab involve the advancement of the sciences, practical applications, the durability of man and systems in space, and spaceflight effectiveness and economy.

Source record↗

NASA NTRS2015

NASA's Rodent Research Project: Validation of Capabilities for Conducting Long Duration Experiments in Space

Research using rodents is an essential tool for advancing biomedical research on Earth and in space. Prior rodent experiments on the Shuttle were limited by the short flight duration. The International Space Station (ISS) provides a new platform for conducting rodent experiments under long duration conditions. Rodent Research (RR)-1 was conducted to validate flight hardware, operations, and science capabilities that were developed at the NASA Ames Research Center. Twenty C57BL6J adult female mice were launched on Sept 21, 2014 in a Dragon Capsule (SpaceX-4), then transferred to the ISS for a total time of 21-22 days (10 commercial mice) or 37 days (10 validation mice). Tissues collected on-orbit were either rapidly frozen or preserved in RNAlater at -80C (n2group) until their return to Earth. Remaining carcasses on-orbit were rapidly frozen for dissection post-flight. The three controls groups at Kennedy Space Center consisted of: Basal mice euthanized at the time of launch, Vivarium controls housed in standard cages, and Ground Controls (GC) housed in flight hardware within an environmental chamber. Upon return to Earth, there were no differences in body weights between Flight (FLT) and GC at the end of the 37 days in space. Liver enzyme activity levels of FLT mice and all control mice were similar in magnitude to those of the samples that were processed under optimal conditions in the laboratory. Liver samples dissected on-orbit yielded high quality RNA (RIN8.99+-0.59, n7). Liver samples dissected post-flight from the intact, frozen FLT carcasses yielded RIN of 7.27 +- 0.52 (n6). Additionally, wet weights of various tissues were measured. Adrenal glands and spleen showed no significant differences in FLT compared to GC although thymus and livers weights were significantly greater in FLT compared to GC. Over 3,000 tissue aliquots collected post-flight from the four groups of mice were deposited into the Ames Life Science Data Archives for future Biospecimen Sharing Program. Together, the RR validation flight successfully demonstrates the capability to support long-duration experimentation on the ISS to achieve both basic science and biomedical objectives.

spaceflight↗