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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Familial Associations of Prevalence and Cause-Specific Mortality for Thoracic Aortic Disease and Bicuspid Aortic Valve in a Large-Population Database

Thoracic aortic disease and bicuspid aortic valve (BAV) likely have a heritable component, but large population-based studies are lacking. This study characterizes familial associations of thoracic aortic disease and BAV, as well as cardiovascular and aortic-specific mortality, among relatives of these individuals in a large-population database. In this observational case-control study of the Utah Population Database, we identified probands with a diagnosis of BAV, thoracic aortic aneurysm, or thoracic aortic dissection. Age- and sex-matched controls (10:1 ratio) were identified for each proband. First-degree relatives, second-degree relatives, and first cousins of probands and controls were identified through linked genealogical information. Cox proportional hazard models were used to quantify the familial associations for each diagnosis. We used a competing-risk model to determine the risk of cardiovascular-specific and aortic-specific mortality for relatives of probands. The study population included 3,812,588 unique individuals. Familial hazard risk of a concordant diagnosis was elevated in the following populations compared with controls: first-degree relatives of patients with BAV (hazard ratio [HR], 6.88 [95% CI, 5.62–8.43]); first-degree relatives of patients with thoracic aortic aneurysm (HR, 5.09 [95% CI, 3.80–6.82]); and first-degree relatives of patients with thoracic aortic dissection (HR, 4.15 [95% CI, 3.25–5.31]). In addition, the risk of aortic dissection was higher in first-degree relatives of patients with BAV (HR, 3.63 [95% CI, 2.68–4.91]) and in first-degree relatives of patients with thoracic aneurysm (HR, 3.89 [95% CI, 2.93–5.18]) compared with controls. Dissection risk was highest in first-degree relatives of patients who carried a diagnosis of both BAV and aneurysm (HR, 6.13 [95% CI, 2.82–13.33]). First-degree relatives of patients with BAV, thoracic aneurysm, or aortic dissection had a higher risk of aortic-specific mortality (HR, 2.83 [95% CI, 2.44–3.29]) compared with controls. Our results indicate that BAV and thoracic aortic disease carry a significant familial association for concordant disease and aortic dissection. The pattern of familiality is consistent with a genetic cause of disease. Furthermore, we observed higher risk of aortic-specific mortality in relatives of individuals with these diagnoses. In conclusion, this study provides supportive evidence for screening in relatives of patients with BAV, thoracic aneurysm, or dissection.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2020

A Statistical Model to Predict the Extratropical Transition of Tropical Cyclones

This paper introduces a logistic regression model for the extratropical transition(ET) of tropical cyclones in the North Atlantic and the Western North Pacific, using elastic net regularization to select predictors and estimate coefficients.Predictors are chosen from the 1979-2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to two days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the Western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic, and 92% of those in the Western North Pacific. 90% of transition time errors are less than 24 h. Select examples of the model's performance on individual storms illustrate its strengths and weaknesses.Two versions of the model are presented: an "operational model" that may provide baseline guidance for operational forecasts, and a "hazard model"that can be integrated into statistical TC risk models. As instantaneous diagnostics for tropical/extratropical status, both models' zero lead time predictions perform about as well as the widely used Cyclone Phase Space (CPS) in the Western North Pacific and better than the CPS in the North Atlantic, and predict the timings of the transitions better than CPS in both basins.

Melanie Bieli↗
DOE OSTI2022

Modeling Predictive Maintenance for NuScale’s Condensate and Feedwater System Using EMRALD

Event Modeling Risk Assessment using Linked Diagrams (EMRALD) is used to model of NuScale’s feedwater and condenser system to capture component degradation and repairing process. To model the reliability of the components, a three-stage failure rate is used contrary to a constant rate leading to component failure. Results from EMRALD will be used to compare different maintenance strategies to reduce system downtime. The outcome of the project will assist in maximizing remaining useful life of the overall system and increasing the availability and revenue of the plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Understanding the Spatial Organization of Simultaneous Heavy Precipitation Events Over the Conterminous United States

We introduce the idea of simultaneous heavy precipitation events (SHPEs) to understand whether extreme precipitation has a spatial organization manifested as specified tracks or contiguous fields with inherent scaling relationships. For this purpose, we created a database of SHPEs using ground-based precipitation observations recorded by the daily Global Historical Climatology Network across the conterminous United States during 1900–2014. SHPEs are examined for their seasonality, spatial manifestation, orientation, and areal extent. We quantified the spatial distribution of the centroids and principal axes of SHPEs and their quasi-elliptical manifestations, azimuthal orientations, and areal extents on the ground. Four seasons, December-January-February (DJF), March-April-May (MAM),June-July-August (JJA), and September-October-November (SON) are considered to examine the spatial patterns and associated large-scale atmospheric circulations. Results indicate that there are 54, 58, 103, and204 SHPEs in DJF, MAM, JJA, and SON seasons, and their longest stretches range on average between 650and 1,600, between 850 and 1,500, between 950 and 1,550, and between 750 and 1,450 km, respectively. SHPEs in the DJF, MAM, and JJA seasons occur mostly over the Pacific coast and central and midwestern United States, respectively. The atmospheric circulation patterns and mechanisms of precipitable water vapor and moisture transport in the atmosphere are also discussed in relation to these SHPEs. Power laws explain SHPEs' underlying area scaling behavior in all the four seasons, with stronger evidence in DJF and MAM. A seasonal spatial risk model is developed to predict the likelihood of SHPEs. Quantifying the characteristics of SHPEs and modeling their footprints can improve the projections of flood risk and understanding of damages to interconnected infrastructure systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Methods and Techniques for Risk Prediction of Space Shuttle Upgrades

Since the Space Shuttle Accident in 1986, NASA has been trying to incorporate probabilistic risk assessment (PRA) in decisions concerning the Space Shuttle and other NASA projects. One major study NASA is currently conducting is in the PRA area in establishing an overall risk model for the Space Shuttle System. The model is intended to provide a tool to predict the Shuttle risk and to perform sensitivity analyses and trade studies including evaluation of upgrades. Marshall Space Flight Center (MSFC) and its prime contractors including Pratt and Whitney (P&W) are part of the NASA team conducting the PRA study. MSFC responsibility involves modeling the External Tank (ET), the Solid Rocket Booster (SRB), the Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME). A major challenge that faced the PRA team is modeling the shuttle upgrades. This mainly includes the P&W High Pressure Fuel Turbopump (HPFTP) and the High Pressure Oxidizer Turbopump (HPOTP). The purpose of this paper is to discuss the various methods and techniques used for predicting the risk of the P&W redesigned HPFTP and HPOTP.

Hoffman, Chad R.↗
NASA NTRS2010

Risk-Informed Safety Assurance and Probabilistic Assessment of Mission-Critical Software-Intensive Systems

This report validates and documents the detailed features and practical application of the framework for software intensive digital systems risk assessment and risk-informed safety assurance presented in the NASA PRA Procedures Guide for Managers and Practitioner. This framework, called herein the "Context-based Software Risk Model" (CSRM), enables the assessment of the contribution of software and software-intensive digital systems to overall system risk, in a manner which is entirely compatible and integrated with the format of a "standard" Probabilistic Risk Assessment (PRA), as currently documented and applied for NASA missions and applications. The CSRM also provides a risk-informed path and criteria for conducting organized and systematic digital system and software testing so that, within this risk-informed paradigm, the achievement of a quantitatively defined level of safety and mission success assurance may be targeted and demonstrated. The framework is based on the concept of context-dependent software risk scenarios and on the modeling of such scenarios via the use of traditional PRA techniques - i.e., event trees and fault trees - in combination with more advanced modeling devices such as the Dynamic Flowgraph Methodology (DFM) or other dynamic logic-modeling representations. The scenarios can be synthesized and quantified in a conditional logic and probabilistic formulation. The application of the CSRM method documented in this report refers to the MiniAERCam system designed and developed by the NASA Johnson Space Center.

Guarro, Sergio B.↗
NASA NTRS2011

Sensitivity Analysis of Median Lifetime on Radiation Risks Estimates for Cancer and Circulatory Disease amongst Never-Smokers

Radiation risks are estimated in a competing risk formalism where age or time after exposure estimates of increased risks for cancer and circulatory diseases are folded with a probability to survive to a given age. The survival function, also called the life-table, changes with calendar year, gender, smoking status and other demographic variables. An outstanding problem in risk estimation is the method of risk transfer between exposed populations and a second population where risks are to be estimated. Approaches used to transfer risks are based on: 1) Multiplicative risk transfer models -proportional to background disease rates. 2) Additive risk transfer model -risks independent of background rates. In addition, a Mixture model is often considered where the multiplicative and additive transfer assumptions are given weighted contributions. We studied the influence of the survival probability on the risk of exposure induced cancer and circulatory disease morbidity and mortality in the Multiplicative transfer model and the Mixture model. Risks for never-smokers (NS) compared to the average U.S. population are estimated to be reduced between 30% and 60% dependent on model assumptions. Lung cancer is the major contributor to the reduction for NS, with additional contributions from circulatory diseases and cancers of the stomach, liver, bladder, oral cavity, esophagus, colon, a portion of the solid cancer remainder, and leukemia. Greater improvements in risk estimates for NS s are possible, and would be dependent on improved understanding of risk transfer models, and elucidating the role of space radiation on the various stages of disease formation (e.g. initiation, promotion, and progression).

Chappell, Lori J.↗
NASA NTRS1998

An Overview of Quantitative Risk Assessment of Space Shuttle Propulsion Elements

Since the Space Shuttle Challenger accident in 1986, NASA has been working to incorporate quantitative risk assessment (QRA) in decisions concerning the Space Shuttle and other NASA projects. One current major NASA QRA study is the creation of a risk model for the overall Space Shuttle system. The model is intended to provide a tool to estimate Space Shuttle risk and to perform sensitivity analyses/trade studies, including the evaluation of upgrades. Marshall Space Flight Center (MSFC) is a part of the NASA team conducting the QRA study; MSFC responsibility involves modeling the propulsion elements of the Space Shuttle, namely: the External Tank (ET), the Solid Rocket Booster (SRB), the Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME). This paper discusses the approach that MSFC has used to model its Space Shuttle elements, including insights obtained from this experience in modeling large scale, highly complex systems with a varying availability of success/failure data. Insights, which are applicable to any QRA study, pertain to organizing the modeling effort, obtaining customer buy-in, preparing documentation, and using varied modeling methods and data sources. Also provided is an overall evaluation of the study results, including the strengths and the limitations of the MSFC QRA approach and of qRA technology in general.

Safie, Fayssal M.↗
NASA NTRS2018

Radar Measurements of the LEO Orbital Debris Environment

Access to space and the preservation of the near-Earth space environment is of critical significance. Increased interest in issues surrounding space traffic management and the continued assessment and discussion of orbital debris at the United Nations Committee on the Peaceful Uses of Outer Space (COPUOS) illustrates the significance of the topic of orbital debris. There are currently over 20,000 tracked objects in the publicly available satellite catalog on Space-Track.org. The catalog is maintained by the US Air Force Space Command using a network of optical and radar ground-based sensors and is believed to be complete for a characteristic size of 10 cm or larger in low Earth orbit (LEO). Based on the work of the NASA Orbital Debris Program Office (ODPO) over approximately the past 40 years it is understood that the small debris population (< 10 cm) increases by orders of magnitude as characteristic size decreases. With population estimates ranging from 500,000 to 1,000,000 small debris objects (> 5 mm) in orbit, it is currently not practical to track and maintain precision orbits on every object. Instead the NASA ODPO uses powerful ground-based radars to sample the low Earth Orbit (LEO) environment and assign approximate orbits to each detection. This poses an interesting signal processing challenge as we are trying to detect the smallest objects possible on the edge of the radar's sensitivity. For approximately the last 30 years, NASA ODPO has partnered with the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) to utilize the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR - formerly the Long-Range Imaging Radar or simply Haystack) and the Haystack Auxiliary (HAX) radar to collect orbital debris radar data. Additionally, the ODPO collaborates with the NASA Jet Propulsion Laboratory (JPL) to use the Goldstone Solar System Radar. The orbital debris detections from these radars serve as inputs for statistical risk models used by the human spaceflight and satellite communities to assess risk to spacecraft posed by orbital debris. In this paper, we will describe the history of orbital debris radar measurements conducted by NASA, provide an overview of current radar measurements techniques and facilities, discuss the signal processing software used for orbital debris measurements and the inference of debris size and orbital parameters from these measurements, and discuss how orbital debris radar measurements are validated for use in models that are used throughout the aerospace industry.

Blackwell, Chris↗
NASA NTRS2004

Malaria Modeling using Remote Sensing and GIS Technologies

Malaria has been with the human race since the ancient time. In spite of the advances of biomedical research and the completion of genomic mapping of Plasmodium falciparum, the exact mechanisms of how the various strains of parasites evade the human immune system and how they have adapted and become resistant to multiple drugs remain elusive. Perhaps because of these reasons, effective vaccines against malaria are still not available. Worldwide, approximately one to three millions deaths are attributed to malaria annually. With the increased availability of remotely sensed data, researchers in medical entomology, epidemiology and ecology have started to associate environmental and ecological variables with malaria transmission. In several studies, it has been shown that transmission correlates well with certain environmental and ecological parameters, and that remote sensing can be used to measure these determinants. In a NASA project, we have taken a holistic approach to examine how remote sensing and GIs can contribute to vector and malaria controls. To gain a better understanding of the interactions among the possible promoting factors, we have been developing a habitat model, a transmission model, and a risk prediction model, all using remote sensing data as input. Our objectives are: 1) To identify the potential breeding sites of major vector species and the locations for larvicide and insecticide applications in order to reduce costs, lessen the chance of developing pesticide resistance, and minimize the damage to the environment; 2) To develop a malaria transmission model characterizing the interactions among hosts, vectors, parasites, landcover and environment in order to identify the key factors that sustain or intensify malaria transmission, and 3) To develop a risk model to predict the occurrence of malaria and its transmission intensity using epidemiological data and satellite-derived or ground-measured environmental and meteorological data.

Kiang, Richard↗
DOE OSTI2022

Instantiation of HCML Demonstrating Bayesian Predictive Modeling for Attentional Control

The research team developed models of Attentional Control (AC) that are unique to existing modeling approaches in the literature. The goal was to enable the research team to (1) make predictions about AC and human performance in real-world scenarios and (2) to make predictions about individual characteristics based on human data. First, the team developed a proof-of-concept approach for representing an experimental design and human subjects data in a Bayesian model, then demonstrated an ability to draw inferences about conditions of interest relevant to real-world scenarios. Ultimately, this effort was successful, and we were able to make reasonable (meaning supported by behavioral data) inferences about conditions of interest to develop a risk model for AC (where risk is defined as a mismatch between AC and attentional demand). The team additionally defined a path forward for a human-constrained machine learning (HCML) approach to make predictions about an individual's state based on performance data. The effort represents a successful first step in both modeling efforts and serves as a basis for future work activities. Numerous opportunities for future work have been defined.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Aeromedical Disposition and Waiver Consideration for ISS Crewmembers

Aeromedical certification of astronauts and cosmonauts traveling to the International Space Station is a multi?-tiered process that involv es standards agreed to by the partner agencies, and participation by the individual agency aeromedical boards and a multilateral space medi cine board. Medical standards are updated continually by a multilater al working group. The boards operate by consensus and strive to achie ve effective decision making through experience, medical judgment, medical evidence and risk modeling. The aim of the certification process is to minimize the risk to the ISS program of loss of mission object ives due to human health issues.

Taddeo, Terrance↗
NASA NTRS2015

Assessing the Effect of Spaceflight on the Propensity for Astronauts to Develop Disc Herniation

A previous study reported that the instantaneous risk of developing a Herniated Nucleus Pulposus (HNP) was higher in astronauts who had flown at least one mission, as compared with those in the corps who had not yet flown. However, the study only analyzed time to HNP after the first mission (if any) and did not account for the possible effects of multiple missions. While many HNPs occurred well into astronauts' careers or in somecases years after retirement, the higher incidence of HNPs relatively soon after completion of space missions appears to indicate that spaceflight may lead to an increased risk of HNP. In addition, when an HNP occurs after spaceflight, is it related to previous spaceflight exposure? The purpose of this study was to investigate whether multiple missions, sex, age, vehicle landing dynamics, and flight duration affect the risk of developing an HNP usinga competing risks model. The outcome of the study will inform the Human System Risk Board assessment of back pain, inform the risk of injury due to dynamic loads, and update the previous dataset, which contained events up to December 31, 2006.

Feiveson, A.↗
NASA NTRS2016

Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account risks attributable to manufacturing, assembly, and process controls. These sources often dominate component level reliability or risk of failure probability. While consequences of failure is often understood in assessing risk, using predicted values in a risk model to estimate the probability of occurrence will likely underestimate the risk. Managers and decision makers often use the probability of occurrence in determining whether to accept the risk or require a design modification. Due to the absence of system level test and operational data inherent in aerospace applications, the actual risk threshold for acceptance may not be appropriately characterized for decision making purposes. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

Hatfield, Glen S.↗
NASA NTRS2016

Pitfalls and Precautions When Using Predicted Failure Data for Quantitative Analysis of Safety Risk for Human Rated Launch Vehicles

Launch vehicle reliability analysis is largely dependent upon using predicted failure rates from data sources such as MIL-HDBK-217F. Reliability prediction methodologies based on component data do not take into account system integration risks such as those attributable to manufacturing and assembly. These sources often dominate component level risk. While consequence of failure is often understood, using predicted values in a risk model to estimate the probability of occurrence may underestimate the actual risk. Managers and decision makers use the probability of occurrence to influence the determination whether to accept the risk or require a design modification. The actual risk threshold for acceptance may not be fully understood due to the absence of system level test data or operational data. This paper will establish a method and approach to identify the pitfalls and precautions of accepting risk based solely upon predicted failure data. This approach will provide a set of guidelines that may be useful to arrive at a more realistic quantification of risk prior to acceptance by a program.

Hatfield, Glen S.↗
DOE OSTI2020

A Tool for the Risk-Informed Management of Critical Mission Resilience

We describe a methodology and tool for the risk-informed management and planning of mission resilience. By mapping concepts of resilience onto the elements of a streamlined risk model we are able to tap the substantial portfolio of established risk concepts to provide rapid insights in the evaluation and high-level screening of prospective resilience enhancement measures. This provides a risk-informed, levelized basis for the comparison of disparate resilience solutions and the means of establishing preferences. The methodology begins with identification of critical missions met by a site, and establishment of the supporting physical assets. Scenarios that would result in failure of these assets are systematically identified. Each scenario comprises three elements: realization of a hazard or threat resulting in loss of resources (the current focus being on power, natural gas, and water) to the asset, failure of measures in place to protect the asset against those losses, and realization of the consequent impacts. These scenarios are quantified in terms of their probabilities of occurrence and the magnitude of the resultant consequences (mission outage time), allowing risk-prioritization to focus resilience enhancement considerations. What-If? analyses are conducted through adjusting elements of the risk calculation to reflect the deployment of prospective resilience measures, by which means the efficacies of each of those measures can be compared using common risk metrics across diverse resilience strategies. This paper will also describe the insights from example applications.

resilience, risk, risk assesment, energy, water↗
NASA NTRSDate not supplied

Advances in Entry Modeling for Impact Risk Assessment

A summary of recent advancements in the detailed modeling of asteroid atmospheric entry processes made through NASA’s Asteroid Threat Assessment Project (ATAP) is presented. Understanding, and accurately modeling these processes and their associated uncertainties is critical to predicting all downstream impact effects, such as blast wave and thermal damage footprints. Furthermore, there is (perhaps thankfully) a dearth of empirical data for large impactors of the kind that would pose a threat to human populations, on which to anchor and/or validate models used in risk assessments. Therefore, we must rely heavily on detailed theoretical and numerical modeling to develop robust assessments for decision makers. To that end, ATAP has made some significant progress in advancing the capabilities in this area. Two areas in particular are highlighted in the present work: meteoroid ablation mechanisms, and bolide luminosity. The first research area – meteoroid ablation mechanisms – has focused on performing novel high-enthalpy wind tunnel experiments on meteorites and meteorite analogs, and utilizing the resulting data to develop high-fidelity models for impactor mass loss at scale. These efforts have resulted in several insights. Of note, these data suggest a differential vaporization process where volatiles are liberated preferentially when the asteroidal material is subject to high heat, while refractory components remain in the molten layer on the surface. A numerical model has been developed which considers this phenomena, and its effect on the bulk impactors effective heat of ablation is examined. The second research thrust that is discussed is focused on accurate modeling of impactor radiation phenomena. While another submission to the conference will discuss the application of our approach to thermal ground damage modeling, here, we present an overview of our extensive efforts to utilize available ground- and space-based observations of large bolides (~1m diameter, and above) to inform and validate our detailed modeling approaches. These methods have been shown to accurately reconstruct the detailed spectra for the Benesov bolide, as well as approximate the burn footprint for the Tunguska event. Recent effort has focused on reconciling light curve data from multiple sources (all-sky camera networks, GLM, US government sensors), using our validated model, and providing a calibrated model for luminous efficiency which can then be utilized to infer impactor properties such as shape, mass, and composition. This work will be demonstrated through an exemplar case study focusing on a large bolide event with observational data (e.g. Chelyabinsk, Flensburg, etc.). Finally, our team’s assessment on the current maturity of atmospheric entry models, and priorities for future research will be provided.

ATAP↗
DOE OSTI2020

The Arctic LDRD project: What happened?

This presentation overviews results from the 2019 LDRD Reserve project “Diagnosing near-future changes in Arctic sea ice and ocean conditions” and a follow-up 2020 CSES Rapid Response project “Multi- Model Risk Assessment for Arctic Navigability.” We analyzed Arctic simulations from 4 CMIP6 model ensembles (E3SM, CESM-CAM, CESM-WACCM and CESM-DPLE), comparing them with observations. We then applied a machine-learning approach using copulas to produce joint probabilities of adverse conditions along potential shipping transit routes through the Arctic. Here we compare and contrast the different models and examine the utility of the copula approach applied to these data sets.

58 GEOSCIENCES↗