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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Probabilistic Assessment of Radiation Risk for Astronauts in Space Missions

Accurate predictions of the health risks to astronauts from space radiation exposure are necessary for enabling future lunar and Mars missions. Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons, (less than 100 MeV); and galactic cosmic rays (GCR), which include protons and heavy ions of higher energies. While the expected frequency of SPEs is strongly influenced by the solar activity cycle, SPE occurrences themselves are random in nature. A solar modulation model has been developed for the temporal characterization of the GCR environment, which is represented by the deceleration potential, phi. The risk of radiation exposure from SPEs during extra-vehicular activities (EVAs) or in lightly shielded vehicles is a major concern for radiation protection, including determining the shielding and operational requirements for astronauts and hardware. To support the probabilistic risk assessment for EVAs, which would be up to 15% of crew time on lunar missions, we estimated the probability of SPE occurrence as a function of time within a solar cycle using a nonhomogeneous Poisson model to fit the historical database of measurements of protons with energy > 30 MeV, (phi)30. The resultant organ doses and dose equivalents, as well as effective whole body doses for acute and cancer risk estimations are analyzed for a conceptual habitat module and a lunar rover during defined space mission periods. This probabilistic approach to radiation risk assessment from SPE and GCR is in support of mission design and operational planning to manage radiation risks for space exploration.

Kim, Myung-Hee↗
NASA NTRSDate not supplied

Circulating miRNA Signature Predicts and Rescues Spaceflight Associated Health Risks

We have identified and validated a spaceflight-associated microRNA (miRNA) signature that is shared by rodents and humans in response to simulated and real spaceflight and regulates vascular damage by simulated deep space radiation. In previous studies, we identified miRNAs that are predicted to regulate rodent responses to spaceflight in low-Earth orbit. Here we have validated the expression of key spaceflight-associated miRNAs in rodents reacting to simulated spaceflight conditions (exposure to ionizing radiation combined with simulated microgravity) and in astronaut samples from the NASA Twin Study. We demonstrate that this circulating miRNA siganture is conserved between these different species and will serve as an ideal biomarker to monitor increase health risk due to exposure to the space environment. Lastly, to demonstrate their physiological relevance, we have utilized antagomirs to target a subset of miRNAs from the spaceflight associated signature to successfully rescue simulated deep space radiation-mediated damage in human 3D vascular constructs. These results provide strong evidence to further develop a miRNA based countermeasure in astronauts.

Afshin Beheshti↗
DOE OSTI2024

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Abstract Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2015

Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis

Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

Operations↗
NASA NTRS1995

Psychosocial correlates of immune responsiveness and illness episodes in US Air Force Academy cadets undergoing basic cadet training

This study examined psychosocial correlates of immune function and illness in 89 male first-year US Air Force Academy cadets. A psychosocial questionnaire was administered to cadets prior to their arrival at the academy and was readministered during cadet orientation and during the stressful environment of Basic Cadet Training (BCT). Immune responsiveness was analyzed by PHA-, PMA-, or anti-CD3-stimulated thymidine uptake in mononuclear leucocytes. Illness episodes were assessed via medical chart review and self-reported symptoms. There were significant increases in distress levels as cadets entered BCT. No psychosocial measure assessed prior to arrival at the academy predicted level of PHA-, PMA-, and anti-CD3-stimulated thymidine uptake or risk of illness. However, hostility levels reported during BCT predicted risk of illness in the four weeks following psychosocial assessment (odds ratio = 7.1; 95% confidence interval: 1.4-36.1). Elevated response to environmental stressors and lower well-being levels also predicted impending illness, but only in the cohort of cadets who had not contracted food poisoning prior to assessment during BCT (OR = 9.3, CI = 1.9-46.7; OR = 0.09, CI = 0.02-0.53). These results suggest that self-report measures of hostility, response to environmental stressors and well-being may be useful predictors of impending illness episodes in males encountering high stress environments.

NASA Discipline Regulatory Physiology↗
NASA NTRS2023

Numerical Investigation of Occupant Injury Risks During A Realistic Transport Aircraft Crash Conditions

Researchers at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) have conducted a full-scale crash test of a Fokker F28 MK1000 aircraft to investigate the performance of transport aircraft under realistic crash conditions. This crash test was computationally recreated using finite element (FE) human body models (HBMs) to further explore potential injury risks to occupants and analyze the utilization of HBMs in the aerospace crash environment. The Global Human Body Model Consortium (GHBMC) male 50th percentile occupant detailed model (v6.0) and the Toyota Human Model for Safety (THUMS) male 50th percentile occupant model (v6.1) were selected to be used in the crash simulations. The HBMs were simulated in conditions matching those of anthropomorphic test device (ATD) experiments included within the aircraft cabin during the crash test. Seven occupant locations within the cabin were simulated utilizing each of the models. The models were positioned in a neutral upright posture with hands resting on the legs and the feet contacting the floor. Head, brain, neck, and lumbar vertebra injury metrics were calculated for all trials. Both HBM models required minor modifications to stabilize these simulations. The GHBMC model required added erosion for six parts while the THUMS model only required one in order to complete the full simulation. The THUMS model, however, required a much smaller timestep for stability and therefore took significantly more computational time. In addition the GHBMC model includes integrated instrumentation while the THUMS model requires development and implementation of instrumentation. Both models predicted 100% injury risk for lumbar vertebra fracture in all test conditions. This prediction was in family with high lumbar load values measured by the ATDs during the crash test. The THUMS model consistently predicted lower injury risks than the GHBMC model in all three other metrics varying depending on the crash pulse. Overall, the THUMS model required less modifications to allow for this study. However, the GHBMC models significantly faster run time and integrated instrumentation make it a more intuitive model for this research.

Crashworthiness↗
NASA NTRS2020

Occupant Response Analysis of a Full-Scale Crash Test of a Fokker F28 Fellowship Aircraft

In this report, the capability of component fuselage testing and finite element models (FEMs) to predict occupant injury risk when applied to full-scale aircraft crash testing was evaluated. Component level and full-scale crash tests of a Fokker-F28 aircraft were performed in conjunction with representative FEM simulations. Lumbar spine injury risk, calculated through Anthropomorphic Test Device (ATD) outputs measured in the full-scale test, was compared with predictions made by component level testing and simulations. A quantitative assessment of FEM prediction accuracy was made using the International Organization for Standardization (ISO) Technical Report (TR) 16250 curve comparison methodology. Lumbar load injury risk measured in the full-scale test configuration was found to be significantly higher than in the component tests performed. The FEM provided a closer prediction of occupant injury risk than component testing but exhibited limitations in the multi-axis load environment. This work will help inform the application of component testing and FEM simulation when used to evaluate aircraft crashworthiness.

Jacob B Putnam↗
NASA NTRS2005

Uncertainty Considerations for Ballistic Limit Equations

The overall risk for any spacecraft system is typically determined using a Probabilistic Risk Assessment (PRA). A PRA determines the overall risk associated with a particular mission by factoring in all known risks to the spacecraft during its mission. The threat to mission and human life posed by the micro-meteoroid and orbital debris (MMOD) environment is one of the risks. NASA uses the BUMPER II program to provide point estimate predictions of MMOD risk for the Space Shuttle and the ISS. However, BUMPER II does not provide uncertainty bounds or confidence intervals for its predictions. In this paper, we present possible approaches through which uncertainty bounds can be developed for the various damage prediction and ballistic limit equations encoded within the Shuttle and Station versions of BUMPER II.

Schonberg, W. P.↗
DOE OSTI2021

Mutations that confer resistance to broadly-neutralizing antibodies define HIV-1 variants of transmitting mothers from that of non-transmitting mothers

Despite considerable reduction of mother-to-child transmission (MTCT) of HIV through use of maternal and infant antiretroviral therapy (ART), over 150,000 infants continue to become infected with HIV annually, falling far short of the World Health Organization goal of reaching <20,000 annual pediatric HIV cases worldwide by 2020. Prior to the widespread use of ART in the setting of pregnancy, over half of infants born to HIV-infected mothers were protected against HIV acquisition. Yet, the role of maternal immune factors in this protection against vertical transmission is still unclear, hampering the development of synergistic strategies to further reduce MTCT. It has been established that infant transmitted/founder (T/F) viruses are often resistant to maternal plasma, yet it is unknown if the neutralization resistance profile of circulating viruses predicts the maternal risk of transmission to her infant. In this study, we amplified HIV-1 envelope genes ( env ) by single genome amplification and produced representative Env variants from plasma of 19 non-transmitting mothers from the U.S. Women Infant Transmission Study (WITS), enrolled in the pre-ART era. Maternal HIV Env variants from non-transmitting mothers had similar sensitivity to autologous plasma as observed for non-transmitting variants from transmitting mothers. In contrast, infant variants were on average 30% less sensitive to paired plasma neutralization compared to non-transmitted maternal variants from both transmitting and non-transmitting mothers (p = 0.015). Importantly, a signature sequence analysis revealed that motifs enriched in env sequences from transmitting mothers were associated with broadly neutralizing antibody (bnAb) resistance. Altogether, our findings suggest that circulating maternal virus resistance to bnAb-mediated neutralization, but not autologous plasma neutralization, near the time of delivery, predicts increased MTCT risk. These results caution that enhancement of maternal plasma neutralization through passive or active vaccination during pregnancy may potentially drive the evolution of variants fit for vertical transmission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Expected individual benefit of prophylactic platelet transfusions in hemato‐oncology patients based on bleeding risks

Abstract Background Prophylactic platelet transfusions prevent bleeding in hemato‐oncology patients, but it is unclear how any benefit varies between patients. Our aim was to assess if patients with different baseline risks for bleeding benefit differently from a prophylactic platelet transfusion strategy. Study design and methods Using the data from the randomized controlled TOPPS trial (Trial of Platelet Prophylaxis), we developed a prediction model for World Health Organization grades 2, 3, and 4 bleeding risk (defined as at least one bleeding episode in a 30 days period) and grouped patients in four risk‐quartiles based on this predicted baseline risk. Predictors in the model were baseline platelet count, age, diagnosis, disease modifying treatment, disease status, previous stem cell transplantation, and the randomization arm. Results The model had a c‐statistic of 0.58 (95% confidence interval [CI] 0.54–0.64). There was little variation in predicted risks (quartiles 46%, 47%, and 51%), but prophylactic platelet transfusions gave a risk reduction in all risk quartiles. The absolute risk difference (ARD) was 3.4% (CI −12.2 to 18.9) in the lowest risk quartile (quartile 1), 7.4% (95% CI −8.4 to 23.3) in quartile 2, 6.8% (95% CI −9.1 to 22.9) in quartile 3, and 12.8% (CI −3.1 to 28.7) in the highest risk quartile (quartile 4). Conclusion In our study, generally accepted bleeding risk predictors had limited predictive power (expressed by the low c‐statistic), and, given the wide confidence intervals of predicted ARD, could not aid in identifying subgroups of patients who might benefit more (or less) from prophylactic platelet transfusion.

Cornelissen, Loes L.↗
DOE OSTI2020

Effects of social structure and management on risk of disease establishment in wild pigs

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many 28 wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially-structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases 2 such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group, and spatial distribution (monthly home range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modeling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. Here, we demonstrate an approach for understanding the relative role of spatial, social, and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially- and socially-structured wildlife species.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Estimating risk for earth-satellite attenuation prediction

Annual cumulative distributions of attenuation measurements and of rain-rate measurements were obtained from several locations in Europe and the United States. They were analyzed to estimate the year-to-year variability to be associated with a prediction of the expected cumulative distribution. Two models were constructed to estimate variability, an ad hoc model that summarized a number of observations when compared to model predictions and a probabilistic model that applied the ideas of order statistics to the prediction problem when the number of independent attenuation or rain-rate events in a sample year could be estimated. Based on these models, the statistical uncertainty in a model prediction may be estimated. The estimation procedure also provides an answer to the question of the number of years of observation needed to provide an estimate of the empirical distribution with a specified statistical uncertainty.

Crane, Robert K.↗
NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗
NASA NTRS2006

Aircraft Conceptual Design and Risk Analysis Using Physics-Based Noise Prediction

An approach was developed which allows for design studies of commercial aircraft using physics-based noise analysis methods while retaining the ability to perform the rapid trade-off and risk analysis studies needed at the conceptual design stage. A prototype integrated analysis process was created for computing the total aircraft EPNL at the Federal Aviation Regulations Part 36 certification measurement locations using physics-based methods for fan rotor-stator interaction tones and jet mixing noise. The methodology was then used in combination with design of experiments to create response surface equations (RSEs) for the engine and aircraft performance metrics, geometric constraints and take-off and landing noise levels. In addition, Monte Carlo analysis was used to assess the expected variability of the metrics under the influence of uncertainty, and to determine how the variability is affected by the choice of engine cycle. Finally, the RSEs were used to conduct a series of proof-of-concept conceptual-level design studies demonstrating the utility of the approach. The study found that a key advantage to using physics-based analysis during conceptual design lies in the ability to assess the benefits of new technologies as a function of the design to which they are applied. The greatest difficulty in implementing physics-based analysis proved to be the generation of design geometry at a sufficient level of detail for high-fidelity analysis.

Olson, Erik D.↗
NASA NTRS1986

Cost Risk Analysis Based on Perception of the Engineering Process

In most cost estimating applications at the NASA Langley Research Center (LaRC), it is desirable to present predicted cost as a range of possible costs rather than a single predicted cost. A cost risk analysis generates a range of cost for a project and assigns a probability level to each cost value in the range. Constructing a cost risk curve requires a good estimate of the expected cost of a project. It must also include a good estimate of expected variance of the cost. Many cost risk analyses are based upon an expert's knowledge of the cost of similar projects in the past. In a common scenario, a manager or engineer, asked to estimate the cost of a project in his area of expertise, will gather historical cost data from a similar completed project. The cost of the completed project is adjusted using the perceived technical and economic differences between the two projects. This allows errors from at least three sources. The historical cost data may be in error by some unknown amount. The managers' evaluation of the new project and its similarity to the old project may be in error. The factors used to adjust the cost of the old project may not correctly reflect the differences. Some risk analyses are based on untested hypotheses about the form of the statistical distribution that underlies the distribution of possible cost. The usual problem is not just to come up with an estimate of the cost of a project, but to predict the range of values into which the cost may fall and with what level of confidence the prediction is made. Risk analysis techniques that assume the shape of the underlying cost distribution and derive the risk curve from a single estimate plus and minus some amount usually fail to take into account the actual magnitude of the uncertainty in cost due to technical factors in the project itself. This paper addresses a cost risk method that is based on parametric estimates of the technical factors involved in the project being costed. The engineering process parameters are elicited from the engineer/expert on the project and are based on that expert's technical knowledge. These are converted by a parametric cost model into a cost estimate. The method discussed makes no assumptions about the distribution underlying the distribution of possible costs, and is not tied to the analysis of previous projects, except through the expert calibrations performed by the parametric cost analyst.

Dean, Edwin B.↗
NASA NTRS2017

Methods for Probabilistic Uncertainty Analysis and Bayesian Analysis with Examples of Statistically Analyzing Data to Revise MMOD Risk Estimates and Compare Models

Probabilistic methods are presented for characterizing and quantifying uncertainties in models and model predictions. Techniques are given for constructing specific uncertainty distributions based on available information. Alternative techniques are given for propagating uncertainties in model inputs to obtain the uncertainty in the model result or prediction. Bayesian techniques are also described for utilizing data and information to update and revise model results and predictions. The focus is on applications with numerous specific examples given. The use of data to revise Micrometeoroid and Orbital Debris (MMOD) risk prediction models are among the examples given.

Risk↗