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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m–km, days–weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1979

Diagnostics of wear in aeronautical systems

Maintenance costs associated with the transmissions and drive train greatly increase the maintenance burden and failure risk. Detection measurements fall under two general categories of vibration and particle detectors. The latter are more amenable to tracking wear. Wear debris analysis can supply a great deal of information such as: particle concentration, rate of change in concentration, composition, particle size and shape, principal metals, etc. It is not economically feasible to monitor all variables. At least one role of the lubrication and wear specialist is to provide guidance in selecting the most appropriate variables to monitor.

Wedeven, L. D.↗
NASA NTRS2000

Bone Quest - A Space-Based Science and Health Education Unit

This proposal addresses the need for effective and innovative science and health education materials that focus on space bone biology and its implications for bone health on Earth. The focus of these materials, bone biology and health, will increase science knowledge as well as health awareness. Current investigations of the bone loss observed after long-duration space missions provide a link between studies of bone health in space, and studies of osteoporosis, a disease characterized by bone loss and progressive skeletal weakness. The overall goal of this project is to design and develop web-based and print-based materials for high school science students, that will address the following: a) knowledge of normal bone biology and bone biology in a microgravity environment; b) knowledge of osteoporosis; c) knowledge of treatment modalities for space- and Earth-based bone loss; and d} bone-related nutrition knowledge and behavior. To this end, we propose to design and develop a Bone Biology Tutorial which will instruct students about normal bone biology, bone biology in a microgravity environment, osteoporosis - its definition, detection, risk factors, and prevention, treatment modalities for space- and Earth-based bone loss, and the importance of nutrition in bone health. Particular emphasis will be placed on current trends in . adolescent nutrition, and their relationships to bone health. Additionally, we propose to design and develop two interactive nutrition/health ' education activities that will allow students to apply the information provided in the Bone Biology Tutorial. In the first, students will apply constructs provided in the Bone Biology Tutorial to design "Bone Health Plans" for space travelers.

Smith, Scott M.↗
NASA NTRS2013

Sequential Probability Ratio Test for Spacecraft Collision Avoidance Maneuver Decisions

A document discusses sequential probability ratio tests that explicitly allow decision-makers to incorporate false alarm and missed detection risks, and are potentially less sensitive to modeling errors than a procedure that relies solely on a probability of collision threshold. Recent work on constrained Kalman filtering has suggested an approach to formulating such a test for collision avoidance maneuver decisions: a filter bank with two norm-inequality-constrained epoch-state extended Kalman filters. One filter models the null hypotheses that the miss distance is inside the combined hard body radius at the predicted time of closest approach, and one filter models the alternative hypothesis. The epoch-state filter developed for this method explicitly accounts for any process noise present in the system. The method appears to work well using a realistic example based on an upcoming, highly elliptical orbit formation flying mission.

Carpenter, J. Russell↗
NASA NTRS2011

Sequential Probability Ratio Test for Collision Avoidance Maneuver Decisions Based on a Bank of Norm-Inequality-Constrained Epoch-State Filters

Sequential probability ratio tests explicitly allow decision makers to incorporate false alarm and missed detection risks, and are potentially less sensitive to modeling errors than a procedure that relies solely on a probability of collision threshold. Recent work on constrained Kalman filtering has suggested an approach to formulating such a test for collision avoidance maneuver decisions: a filter bank with two norm-inequality-constrained epoch-state extended Kalman filters. One filter models 1he null hypothesis 1ha1 the miss distance is inside the combined hard body radius at the predicted time of closest approach, and one filter models the alternative hypothesis. The epoch-state filter developed for this method explicitly accounts for any process noise present in the system. The method appears to work well using a realistic example based on an upcoming highly-elliptical orbit formation flying mission.

Carpenter, J. R.↗
NASA NTRSDate not supplied

NASA Omics Archive Project

The space environment consists of a complex set of hazards including altered gravity, radiation, psychological/physiological stress, isolation, and confinement leading to complex biological responses. Advances in biotechnology capabilities offer considerable potential to provide novel insight into those responses as well as innovative diagnostic, treatment, and countermeasure solutions for astronauts as NASA begins to travel beyond low Earth orbit. Omics data (genomics, transcriptomics, proteomics, etc.) is one example that can provide NASA with critical knowledge of how a crewmember’s genetics, environment, and lifestyle can be used to develop individualized approaches for disease prevention, advance diagnostics, and improve treatment strategies. NASA ventured into the field of omics on human subjects with the successful completion of the NASA Twins Study which was the first step in mapping the multi-omic profile of astronauts to understand and mitigate the health consequences of spaceflight. The Human Research Program aims to build upon the success of the Twins Study with the NASA Omics Archive flight study, establishing a longitudinal biospecimen archive and efficiently generating a comprehensive high-quality multi-omic dataset from astronauts for the purpose of studying molecular, metabolic, and microbial changes associated with longduration spaceflight missions. The goal is to facilitate scientific and medical research community efforts to characterize and mitigate spaceflight health and performance risks. In this presentation, we will review details regarding the biospecimen and data archive to be generated by the NASA Omics Archive flight study. Data generated as part of this project will be archived in the NASA Life Sciences Portal (NLSP) and be made available for future hypothesis-driven research efforts or occupational surveillance through Institutional Review Board-approved data sharing and retrospective data requests submitted to the Life Sciences Data Archive (LSDA) team. We will also present results of a ground study performed to evaluate in-house procedures, new sample collection hardware, and vendor capabilities. The data repository generated and the samples to be archived by this study will enable future research efforts to assess an astronauts’ unique molecular and genetic profile with respect to individual spaceflight responses. Results of which will be instrumental in enabling precision health capabilities to better assess and mitigate spaceflight risks, detect disease states earlier, and actively monitor countermeasure treatments, ultimately improving clinical outcomes during future exploration class missions.

C. A. Theriot↗
DOE OSTI2020

System and methods for automated detection, reasoning and recommendations for resilient cyber systems

A method for securing an IT (information technology) system using a set of methods for knowledge extraction, event detection, risk estimation and explanation for ranking cyber-alerts which includes a method to explain the relationship (or an attack pathway) from an entity (user or host) and an event context to another entity (a high-value resource) and an event context (attack or service failure).

Choudhury, Sutanay↗
DOE OSTI2023

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because it can aid in both cybersecurity analysis and the evaluation of other cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

cyber-physical systems↗
DOE OSTI2024

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because they can aid in both cybersecurity analysis and the evaluation of cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

alfalfa↗
DOE OSTI2025

A Probabilistic Reasoner Based on Bayes Risk for Damage Detection in Structural Systems

Structural health monitoring (SHM) systems are used to inform operation of structural systems subject to loads and environments that may affect their integrity. SHM systems rely on continuous monitoring of the structure to determine its health state. These systems are often coupled with a model of the deployed structure to determine the consequences of changes in the system by forecasting the response to future states. These models, which may be thought of as digital twins, need to be updated to reflect the latest state of the structural system. This work makes use of an uncertainty-aware machine learning model that enforces distance preservation of the original input space to determine deviations from the training data input space distributions. This workflow enables domain shift detection to determine whether damage is present in the structure. The uncertainty metrics generated by this network are then used in a Bayes risk framework to design an optimal damage detector given cost and risk considerations. The approach is demonstrated on a computational example with simulated damage.

Najera-Flores, David [ATA Engineering, Inc.]↗
NASA NTRS2012

Remote Sensing and Modeling of Landslides: Detection, Monitoring and Risk Evaluation

Landslides are one of the most pervasive hazards in the world, resulting in more fatalities and economic damage than is generally recognized_ Occurring over an extensive range of lithologies, morphologies, hydrologies, and climates, mass movements can be triggered by intense or prolonged rainfall, seismicity, freeze/thaw processes, and antbropogertic activities, among other factors. The location, size, and timing of these processes are characteristically difficult to predict and assess because of their localized spatial scales, distribution, and complex interactions between rainfall infiltration, hydromechanical properties of the soil, and the underlying surface composition. However, the increased availability, accessibility, and resolution of remote sensing data offer a new opportunity to explore issues of landslide susceptibility, hazard, and risk over a variety of spatial scales. This special issue presents a series of papers that investigate the sources, behavior, and impacts of different mass movement types using a diverse set of data sources and evaluation methodologies.

Kirschbaum, Dalia↗
DOE OSTI2023

Hydrogen Equipment Enclosure Risk Reduction through Earlier Detection of Component Failures

Hydrogen component reliability and the hazard associated with failure rates is a critical area of research for the successful implementation and growth of hydrogen technology across the globe. The research team has partnered to quantify system risk reduction through earlier detection of hydrogen component failures. A model of hydrogen dispersion in a hydrogen equipment enclosure has been developed utilizing experimentally quantified hydrogen component leak rates as inputs. This model provides insight into the impact of hydrogen safety sensors and ventilation on the flammable mass within a hydrogen equipment enclosure. This model also demonstrates the change in safety sensor response time due to detector placement under various leak scenarios. The team looks to improve overall hydrogen system safety through an improved understanding of hydrogen component reliability and risk mitigation methods. This collaboration fits under the work program of IEA Hydrogen Task 43 Subtask E Hydrogen System Safety.

CFD↗
NASA NTRS2001

Risk reduction using DDP (Defect Detection and Prevention): Software support and software applications

Risk assessment and mitigation is the focus of the Defect Detection and Prevention (DDP) process, which has been applied to spacecraft technology assessments and planning, both hardware and software. DDP's major elements and their relevance to core requirement engineering concerns are summarized. The accompanying research demonstration illustrates DDP's tool support, and further customizations for application to software.

Software-specific customizations↗
NASA NTRS1992

The effect of differences in time to detection of circulating microbubbles on the risk of decompression sickness

Circulating microbubbles (CMB) are frequently detected prior to the appearance of symptoms of Decompression Sickness (DCS). It is difficult to analyze the effect of CMB on symptoms due to differences in the time to detection of CMB. This paper uses survival analysis models to evaluate the risk of symptoms in the presence of CMB. Methods: Information on 81 exposures to an altitude of 6,400 m (6.5 psi) for a period of three hours, with simulated extravehicular activities, was examined. The presence or absence of CMB was included as a time dependent covariate of the Cox proportional hazards regression model. Using this technique, the subgroup of exposures with CMB was analyzed further. Mean (S.D.) time in minutes to onset of CMB and symptoms were 125 (63) and 165 (33) respectively, following the three hours exposure. The risk of symptoms (17/81) increased 14 times in the presence of CMB, after controlling for variations in time to detection of CMB. Further, the risk was lower when time to detection of CMB was greater than 60 minutes (risk ratio = 0.96; 95 percent confidence intervals = 0.94 - 0.99 0.99 P less than 0.01) compared to CMB before 60 minutes at altitude. Conclusions: Survival analysis showed that individual risk of DCS changes significantly due to variations in time to detection of CMB. This information is important in evaluating the risk of DCS in the presence of CMB.

Kumar, K. V.↗
DOE OSTI2020

Detection of Head and Neck Cancer Based on Longitudinal Changes in Serum Protein Abundance

Head and Neck Squamous Cell Carcinoma (HNSCC) is significantly represented in the military population with substantial impact on military operations. Two-thirds of new HNSCC cases are first diagnosed at advanced stage III or IV with lymph node metastases. Thus, there is an urgent need of biomarkers for early detection and risk stratification. Archived serum samples from the Department of Defense Serum Repository provide a unique resource for longitudinal studies of HNSCC in the military and the identification of early detection biomarkers. Methods: We identified 175 patients diagnosed with HNSCC with 175 matched healthy controls and retrieved a total of 978 serum samples drawn at the time of diagnosis, 2 and 4 years prior to diagnosis, and 2 years after diagnosis. Following immunoaffinity depletion, serum samples were analyzed by targeted proteomics assays for multiplexed quantification of a panel of 146 candidate protein biomarkers from the curated literature. Results: We derived a 13-protein signature that distinguishes case versus controls based on longitudinal changes in serum protein concentration. The abundances of each of the 13 proteins remain constant over time in control subjects. The area under the curve for the derived random forest classifier was 0.90. Conclusions: This 13-protein classifier is highly promising for detection of HNSCC prior to overt symptoms. Impact: Use of longitudinal samples has significant potential to identify biomarkers for detection and risk stratification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deep Cyber-Physical Situational Awareness for Energy Systems: A Secure Foundation for Next-Generation Energy Management

This document provides the final report for the CYPRES project. The purpose is (1) to highlight and summarize its major accomplishments and (2) to provide guidance on how its outcomes have informed and can inform important additional research and technology transfer. The goal of CYPRES was the research, development, and demonstration of a security-oriented next generation cyber-physical EMS for electric power systems that detects malicious and abnormal events through the fusion of cyber and physical data. To achieve this, the CYPRES project team researched, developed, and built a prototype of the solution, referred to as the CYPRES EMS. The CYPRES EMS is a proof-of-concept cyber-physical platform that demonstrates the management of the energy system, communications, security, and cyber-physical grid modeling and analytics. As part of the capabilities of the CYPRES EMS, the team designed and developed a suite of power system applications for monitoring, risk analyses, detection, and control that are inherently cyberaware. At its core, the project aimed to research, develop, and demonstrate a security-oriented next-generation cyber-physical Energy Management System (EMS) capable of detecting malicious and abnormal events through the innovative fusion of cyber and physical data. This approach represents a fundamental shift from traditional EMS, reimagining how critical infrastructure can be protected through unified cyber-aware and physics-aware secure data flow pipelines. The project’s cornerstone deliverable, the CYPRES EMS, serves as a proof-of-concept cyber-physical platform that revolutionizes the management of energy systems, communications, security, and cyber-physical grid modeling and analytics. This prototype implements a comprehensive suite of power system applications for monitoring, risk analyses, detection, and control, all designed with inherent cyber awareness. The system’s architecture extends from end-devices in the field through to control center applications, establishing a secure and resilient control framework that addresses the challenges posed by diverse devices of unknown trustworthiness connecting to modern power systems. Through this innovative approach to deep cyber-physical situational awareness, the CYPRES project not only advances the state-of-the-art in energy infrastructure protection but also establishes a new paradigm for how EMS can be designed, deployed, and operated in an increasingly complex threat landscape. The findings and developments from this project provide crucial insights for stakeholders across the energy sector, offering a blueprint for enhancing the reliability and resilience of our nation’s critical energy infrastructure in the face of evolving cyber threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separability of class distributions. Such challenges result in skewed or warped decision boundaries and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions within the feature space through partitioning. This enables the optimization of local models within the regions to overcome the impact of unobservability in different feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of unobservability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobservability in predictive models. Formal fusion methods, such as Dempster-Shafer theory, can better leverage the information from local regions to improve the performance of interpretable predictive models in the presence of unobservable variables.

Time Series Data↗