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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
NASA NTRS2015

Reliability and Probabilistic Risk Assessment - How They Play Together

PRA methodology is one of the probabilistic analysis methods that NASA brought from the nuclear industry to assess the risk of LOM, LOV and LOC for launch vehicles. PRA is a system scenario based risk assessment that uses a combination of fault trees, event trees, event sequence diagrams, and probability and statistical data to analyze the risk of a system, a process, or an activity. It is a process designed to answer three basic questions: What can go wrong? How likely is it? What is the severity of the degradation? Since 1986, NASA, along with industry partners, has conducted a number of PRA studies to predict the overall launch vehicles risks. Planning Research Corporation conducted the first of these studies in 1988. In 1995, Science Applications International Corporation (SAIC) conducted a comprehensive PRA study. In July 1996, NASA conducted a two-year study (October 1996 - September 1998) to develop a model that provided the overall Space Shuttle risk and estimates of risk changes due to proposed Space Shuttle upgrades. After the Columbia accident, NASA conducted a PRA on the Shuttle External Tank (ET) foam. This study was the most focused and extensive risk assessment that NASA has conducted in recent years. It used a dynamic, physics-based, integrated system analysis approach to understand the integrated system risk due to ET foam loss in flight. Most recently, a PRA for Ares I launch vehicle has been performed in support of the Constellation program. Reliability, on the other hand, addresses the loss of functions. In a broader sense, reliability engineering is a discipline that involves the application of engineering principles to the design and processing of products, both hardware and software, for meeting product reliability requirements or goals. It is a very broad design-support discipline. It has important interfaces with many other engineering disciplines. Reliability as a figure of merit (i.e. the metric) is the probability that an item will perform its intended function(s) for a specified mission profile. In general, the reliability metric can be calculated through the analyses using reliability demonstration and reliability prediction methodologies. Reliability analysis is very critical for understanding component failure mechanisms and in identifying reliability critical design and process drivers. The following sections discuss the PRA process and reliability engineering in detail and provide an application where reliability analysis and PRA were jointly used in a complementary manner to support a Space Shuttle flight risk assessment.

Safie, Fayssal M.↗
NASA NTRSDate not supplied

Space Radiation Technologies for Human Missions beyond Low-Earth-Orbit

Ionizing radiation exposure is a pervasive occupational risk to astronauts and remains a major health risk associated with human exploration beyond low-Earth-orbit. Sustained lunar and Mars exploration crew will be at increased risk of radiogenic cancers and cardiovascular disease, as well as, the potential for in-mission performance decrements, immune system decrements, and late neurodegenerative conditions impacting crew health. Optimizing astronaut space radiation protection will incorporate both physical and biological radiological aspects and will implement protection strategies that span pre-mission, in-flight, and post mission timelines. Major facets of physical risk mitigation include accuracy in predicting the space radiation solar environment, monitoring environmental and human exposures, and minimizing exposure through vehicle shielding and design. Biological mitigation facets include understanding and predicting biological response to the space environment, developing advanced medical countermeasures, and identifying appropriate surveillance strategies to preserve long-term health. Coordinated investments in these major capability areas will support development of an integrated radiation protection solution to minimize astronaut risk for moon to Mars exploration missions.

Lisa C Simonsen↗
DOE OSTI2022

Feedstock to Function (F2F) v1

The Feedstock to Function (F2F) tool was designed to help scientists and companies explore viable biofuels and bioproducts early in the R&D cycle to support more productive experimentation, while reducing early-stage exploration from months/years to days/weeks (feedstock-to-function.lbl.gov). The tool focuses on using machine learning to predict biomass-derived molecule properties, while evaluating the cost, benefits, and risks of promising molecules for sustainable aviation fuels. The tool successfully predicts (within 15% of experimental values) high-throughput aviation properties for over 10,000 molecules while enabling users to explore new possibilities and opportunities rapidly and effortlessly. It also links to lightweight life-cycle analysis and techno-economic tools for cost and emissions analyses. Predicted molecule properties include melting point, boiling point, flash point, yield sooting index, and heat of combustion. To date, F2F is more expansive and outperforms several other molecule property prediction models while enabling users (scientists, companies, and policy makers) to explore new possibilities and opportunities rapidly and effortlessly. F2F provides the foundation for developing an adaptive computational tool that predicts properties, cost, benefits, and risk of promising new and uncertified alternative jet fuel pathways and their blending effects.

Rapp, Vi↗
NASA NTRSDate not supplied

Risk Assessment of Obstacle Collision for UAVs under off-nominal conditions

Enabling operations of unmanned aerial vehicles (UAVs) in low-altitude airspace, demands the need of robust risk monitoring framework for assessing the safety of airspace, ground-structures and people. As widespread applications emerge, the need of risk assessment becomes increasingly important for UAV flights beyond visual line-of-sight, especially subjected to off-nominal conditions introduced by component failures, degraded controllability or environmental disturbances such as wind gusts in an urban canyon. From a safety perspective, collision with obstacles can be detrimental not only to the vehicle and payload, but also to the structure and people on ground. Although it is safe to assume that approved UAVs would be equipped with collision avoidance systems, risk of collision which can be predicted for a flight trajectory even before the UAV encounters any obstacle is beneficial to resolve contingencies in its decision-making under off-nominal conditions. In this paper, a framework is presented for computing the risk of collision with obstacle based on a UAV's predicted trajectory, proximity to static and dynamic obstacles, sub-system state-of-health and external wind conditions. The conditional probability of trajectory deviation is generated using a Bayesian Belief Network (BBN) based on on-board sensor measurements. Further, a kinematic 3-DOF model is implemented to compute deviation in UAV's trajectory subjected to one case study of off-nominal condition i.e. wind gusts. Finally, the integrated risk factor is demonstrated on real data from experimental flights of an octocopter at NASA Langley Research Center, in presence of simulated obstacles and wind conditions. The proposed approach would enable risk-informed decision making process for timely mitigation of current and future unsafe events.

Portia Banerjee↗
NASA NTRSDate not supplied

Risk Assessment of Obstacle Collision for UAVs under Off-nominal Conditions

Enabling operations of unmanned aerial vehicles (UAVs) in low-altitude airspace demands the need of robust risk monitoring framework for assessing the safety of airspace, ground structures and people. As widespread applications emerge, the need of risk assessment becomes increasingly important for UAV flights beyond visual line-of-sight, especially subjected to off-nominal conditions introduced by component failures, degraded controllability or environmental disturbances such as wind gusts in an urban canyon. From a safety perspective, collision with obstacles can be detrimental not only to the vehicle and payload, but also to the structure and people on ground. Although it is safe to assume that approved UAVs would be equipped with collision avoidance systems, risk of collision which can be predicted for a flight trajectory even before the UAV encounters any obstacle is beneficial to resolve contingencies in its decision-making under off-nominal conditions. In this paper, a framework is presented for computing the risk of collision with obstacle based on a UAV’s predicted trajectory, proximity to static and dynamic obstacles, sub-system state-of-health and external wind conditions. The conditional probability of trajectory deviation is generated using a Bayesian Belief Network (BBN) based on on-board sensor measurements. Further, a kinematic 3-DOF model is implemented to compute deviation in UAV’s trajectory subjected to one case study of off-nominal condition i.e. wind gusts. Finally, the integrated risk factor is demonstrated on real data from experimental flights of an octocopter at NASA Langley Research Center, in presence of simulated obstacles and wind conditions. The proposed approach would enable risk-informed decision making process for timely mitigation of current and future unsafe events.

Bayesian network↗
DOE OSTI2023

High levels of exfoliated fragments following glycocalyx destruction in hemorrhagic fever with the renal syndrome are associated with mortality risk

Background The glycocalyx is a gel-like structure that covers the luminal side of vascular endothelial cells. It plays an important role in maintaining the integrity of the vascular endothelial barrier structure. However, the presence or absence of glycocalyx destruction in hemorrhagic fever with renal syndrome (HFRS) and its specific mechanism and role is still unclear. Methods In this study, we detected the levels of exfoliated glycocalyx fragments, namely, heparan sulfate (HS), hyaluronic acid (HA), and chondroitin sulfate (CS), in HFRS patients and investigated their clinical application value on the evaluation of disease severity and prognosis prediction. Results The expression of exfoliated glycocalyx fragments in plasma was significantly increased during the acute stage of HFRS. The levels of HS, HA, and CS in HFRS patients during the acute stage were significantly higher than in healthy controls and convalescent stages of the same type. HS and CS during the acute stage gradually increased with the aggravation of HFRS, and both fragments showed a significant association with disease severity. In addition, exfoliated glycocalyx fragments (especially HS and CS) showed a significant correlation with conventional laboratory parameters and hospitalization days. High levels of HS and CS during the acute phase were significantly associated with patient mortality and demonstrated an obvious predictive value for the mortality risk of HFRS. Conclusion Glycocalyx destruction and shedding may be closely associated with endothelial hyperpermeability and microvascular leakage in HFRS. The dynamic detection of the exfoliated glycocalyx fragments may be beneficial for the evaluation of disease severity and prognosis prediction in HFRS.

Du, Hong↗
NASA NTRS2020

Reliability Analysis of Complex NASA Systems with Model Based Engineering

The emergence of model-based engineering, with Model- Based Systems Engineering (MBSE) leading the way, is transforming design and analysis methodologies. The recognized benefits to systems development include moving from document-centric information systems and document-centric project communication to a model-centric environment in which control of design changes in the life cycles is facilitated. In addition, a “single source of truth” about the system, that is up-to-date in all respects of the design, becomes the authoritative source of data and information about the system. This promotes consistency and efficiency in regard to integration of the system elements as the design emerges and thereby may further optimize the design. Therefore Reliability Engineers (REs) supporting NASA missions must be integrated into model-based engineering to ensure the outputs of their analyses are relevant and value-needed to the design, development, and operational processes for failure risks assessment and communication.

Reliability prediction↗
NASA NTRS2009

Characteristics of Whipple Shield Performance in the Shatter Regime

Between the onset of projectile fragmentation and the assumption of rear wall failure due to an impulsive load, multi-wall ballistic limit equations are linearly interpolated to provide reasonable yet conservative predictions of perforation thresholds with conveniently simple mathematics. Although low velocity and hypervelocity regime predictions are based on analytical expressions, there is no such scientific foundation for predictions in the intermediate (or shatter) regime. As the debris flux in low earth orbit (LEO) becomes increasingly dominated by manmade pollution, the profile of micrometeoroid and orbital debris (MMOD) risk shifts continually towards lower velocities. For the International Space Station (ISS), encounter velocities below 7 km/s now constitute approximately 50% of the penetration risk. Considering that the transition velocity from shatter to hypervelocity impact regimes described by common ballistic limit equations (e.g. new non-optimum Whipple shield equation [1]) occurs at 7 km/s, 50% of station risk is now calculated based on failure limit equations with little analytical foundation. To investigate projectile and shield behavior for impact conditions leading to projectile fragmentation and melt, a series of hypervelocity impact tests have been performed on aluminum Whipple shields. In the experiments projectile diameter, bumper thickness, and shield spacing were kept constant, while rear wall thickness was adjusted to determine spallation and perforation limits at various impact velocities and angles. The results, shown in Figure 1 for normal and 45 impacts, demonstrated behavior that was not sufficiently described by the simplified linear interpolation of the NNO equation (also shown in Figure 1). Hopkins et al. [2] investigated the performance of a nominally-identical aluminum Whipple shield, identifying the effects of phase change in the shatter regime. The results (conceptually represented in Figure 2) were found to agree well with those obtained in this study at normal incidence, suggesting that shielding performance in the shatter regime could be well described by considering more complex phase conditions than currently implemented in most BLEs. Furthermore, evidence of these phase effects were found in the oblique test results, providing the basis for an empirical description of these effects that can be applied in MMOD risk assessment software. In this paper, results of the impact experiments are presented, and characteristics of target damage are evaluated. A comparison of intermediate velocity impact failure mechanisms in current BLEs are discussed and compared to the findings of the experimental study. Risk assessment calculations have been made on a simplified structure using currently implemented penetration equations and predicted limits from the experimental program, and the variation in perceived mission risk is discussed. It was found that ballistic limit curves that explicitly incorporated phase change effects within the intermediate regime lead to a decrease in predicted MMOD risk for ISS-representative orbits. When considered for all Whipple-based shielding configurations onboard the ISS, intermediate phase change effects could lead to significant variations in predicted mission risk.

Ryan, Shannon↗
DOE OSTI2025

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

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

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Too Good to Be True? Evaluation of Colonoscopy Sensitivity Assumptions Used in Policy Models

Background: Models can help guide colorectal cancer screening policy. Although models are carefully calibrated and validated, there is less scrutiny of assumptions about test performance. Methods: We examined the validity of the CRC-SPIN model and colonoscopy sensitivity assumptions. Standard sensitivity assumptions, consistent with published decision analyses, assume sensitivity equal to 0.75 for diminutive adenomas (<6 mm), 0.85 for small adenomas (6–10 mm), 0.95 for large adenomas (≥10 mm), and 0.95 for preclinical cancer. We also selected adenoma sensitivity that resulted in more accurate predictions. Targets were drawn from the Wheat Bran Fiber study. In the study, we examined how well the model predicted outcomes measured over a three-year follow-up period, including the number of adenomas detected, the size of the largest adenoma detected, and incident colorectal cancer. Results: Using standard sensitivity assumptions, the model predicted adenoma prevalence that was too low (42.5% versus 48.9% observed, with 95% confidence interval 45.3%–50.7%) and detection of too few large adenomas (5.1% versus 14.% observed, with 95% confidence interval 11.8%–17.4%). Predictions were close to targets when we set sensitivities to 0.20 for diminutive adenomas, 0.60 for small adenomas, 0.80 for 10- to 20-mm adenomas, and 0.98 for adenomas 20 mm and larger. Conclusions: Colonoscopy may be less accurate than currently assumed, especially for diminutive adenomas. Alternatively, the CRC-SPIN model may not accurately simulate onset and progression of adenomas in higher-risk populations. Impact: Misspecification of either colonoscopy sensitivity or disease progression in high-risk populations may affect the predicted effectiveness of colorectal cancer screening. When possible, decision analyses used to inform policy should address these uncertainties.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2012

Voluntary Consensus Organization Standards for Nondestructive Evaluation of Thin-Walled Metallic Liners and Composite Overwraps in Composite Overwrapped Pressure Vessels

NASA fracture control requirements outlined in NASA-STD-5009 and NASA-STD-5014 are predicated on the availability and use of sensitive nondestructive evaluation (NDE) methods that can detect and monitor defects, thereby providing data that can be used to predict failure or reduce the risk of failure in fracture critical components. However, in the case of composite materials and components, including composite overwrapped pressure vessels (COPVs), the effect of defects is poorly understood, the NDE methods used to evaluate locate and size defects are typically at lower technical readiness level than analogous NDE methods used for metals, and demonstration studies to verify the probability of detection (POD) are generally lacking or unavailable. These factors together make failure prediction of fracture critical composite materials and components based on size, quantity, or orientation of defects nearly impossible. Also, when inspecting metal liners in as-manufactured COPVs, sensitivity is lost and only the inner surface of the liner is accessible. Also, NDE of COPVs as applied during manufacturing varies significantly from manufacturer to manufacturer and has not yet been standardized. Although requirements exist to perform NDE immediately after manufacturing to establish initial integrity of the parts, procedural detail for NDE of composites is still nonexistent or under development. For example, in practice, only a visual inspection of COPVs is performed during manufacturing and service, leaving in question whether defects of concern, for example, bridging, overwrap winding anomalies, impact damage below visible threshold, out-of-family strain growth, and liner buckling have been adequately detected and monitored. To address these shortcomings, in 2005 the NASA Nondestructive Evaluation Working Group (NNWG) began funding work to develop and adopt standards for nondestructive evaluation of aerospace composites in collaboration with the American Society for Testing and Materials (ASTM) Committee E07 on Nondestructive Testing. Similarly, in 2006 the NASA Engineering and Safety Center (NESC) recommended that nondestructive evaluation methods that can predict composite failure in COPVs should be developed and verified, and integrated into the damage control plan for these vessels

Waller, Jess↗
NASA NTRS2025

Human Spaceflight Applications of Novel Miniature X-Ray Technologies

INTRODUCTION: Radiography (XR) has long been a cornerstone of terrestrial medical imaging, though it has not yet been used in the spaceflight environment. Medical systems for human spaceflight missions are constrained by mass, volume, and power, and until recently, XR systems have been considered too large and power-consuming for spaceflight diagnostic and therapeutic applications. However, the rise of commercial spaceflight and NASA’s refocused efforts on returning crews to the Moon for long-duration missions have introduced a higher degree of medical risk to human spaceflight and require a re-evaluation when optimizing medical system design. Over the last decade, XR devices have miniaturized while maintaining good diagnostic and therapeutic sensitivity and specificity, making new in-flight medical and non-medical XR applications a possibility. Initial research identified several medical conditions where miniature XR would be beneficial for the diagnosis and/or management of medical conditions arising in space, though a more in-depth analysis is required to identify whether XR may add value to the management of such conditions. With this presentation, we aim to introduce the potential utility of miniature XR, review prior work highlighting where XR may be beneficial, and evaluate how miniature XR may reduce medical risk in human spaceflight missions. METHODS: IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a risk assessment tool developed by NASA to advance exploration mission medical system design by quantitatively estimating mission medical risk. IMPACT v1.0 includes a novel evidence library baselined to exploration environments, an expanded list of 119 medical conditions, medical capabilities and resources critical for management of these medical conditions, and the ability for rapid and iterative analysis in the setting of modifiable design reference missions (DRMs). Our first analysis identified which of the 119 medical conditions XR had diagnostic or therapeutic utility for. Subject matter experts (SMEs) recorded which XR views would be performed under ideal terrestrial circumstances for diagnosis/management of each condition, as well as which views are pragmatic for spaceflight limitations. A second analysis utilized IMPACT to identify significant conditions that contribute greatest to medical risk during a notional long-duration Lunar orbit and Lunar surface DRM. Medical system risk estimates include loss of crew life (LOCL), need for return to definitive care (RTDC; medical evacuation), and an estimate of crew task time affected (TTA). Using a standardized semi-quantitative scoring methodology, a deeper evaluation of each of the most significant medical conditions was performed. Data from both of these separate analyses were used to hypothesize what ideal and pragmatic XR studies may impact clinical management of the most significant conditions predicted to lead to medical risk. RESULTS: Approximately 1/3 of the IMPACT conditions were identified as being more effectively or comprehensively assessed or treated with the addition of miniature XR technology. The resulting conditions benefitting diagnostically and therapeutically from XR are revealed, as well as the ideal and pragmatic XR views and medical procedures benefitting from XR. The conditions of clinical significance and those most contributing to risk are also displayed. Among the conditions that contribute greatest to LOCL, four conditions for which XR may improve the diagnosis and management of include: decompression sickness, traumatic shock, dental abscess, and respiratory failure. Among conditions that contributed to RTDC, the evaluation and management of wrist fracture is likely improved by XR. For conditions leading to crew TTA, evaluation and management of EVA shoulder injuries, upper and lower extremity strains, back strains, and EVA hand injuries are likely improved by XR. DISCUSSION: Miniature XR in spaceflight has the potential to improve the evaluation and management of a substantial portion of conditions that most contribute to medical risk. This presentation is an introduction to the possibilities miniature XR provides for future human spaceflight missions and subsequent presenters will expand on potential applications in more detail. LEARNING OBJECTIVES: 1) Understand the previous limitations of using radiography in the management of spaceflight medical conditions; 2) Evaluate the findings from the IMPACT tool analysis, which allows quantification of the benefit miniature XR could provide for managing high-risk medical conditions in long-duration lunar orbit and surface missions, focusing on improvements in crew health outcomes; 3) Analyze case studies where miniature XR technology could reduce the medical risks associated with spaceflight missions, specifically in diagnosing and managing conditions such as decompression sickness, traumatic shock, and EVA-related injuries.

A Anderson↗
DOE OSTI2020

Application and Interpretation of Acumen Risk ® Schedule and Cost Risk Analysis

Schedule risk and cost risk analyses use statistical techniques to predict a level of confidence in meeting a program’s completion date and cost. This paper describes how to interpret key Schedule Risk Analysis (SRA) and Cost Risk Analysis (CRA) results to determine the likelihood that the program completion dates are met, how much management reserve (MR) is needed to provide the acceptable certainty of completion by a specific date, and the risks and activities that are most likely to delay or add cost to a project. The combined process is often called a Schedule/Cost Uncertainty and Risk Analysis or “SCURA.”

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1997

Remote Imaging Applied to Schistosomiasis Control: The Anning River Project

The use of satellite imaging to remotely detect areas of high risk for transmission of infectious disease is an appealing prospect for large-scale monitoring of these diseases. The detection of large-scale environmental determinants of disease risk, often called landscape epidemiology, has been motivated by several authors (Pavlovsky 1966; Meade et al. 1988). The basic notion is that large-scale factors such as population density, air temperature, hydrological conditions, soil type, and vegetation can determine in a coarse fashion the local conditions contributing to disease vector abundance and human contact with disease agents. These large-scale factors can often be remotely detected by sensors or cameras mounted on satellite or aircraft platforms and can thus be used in a predictive model to mark high risk areas of transmission and to target control or monitoring efforts. A review of satellite technologies for this purpose was recently presented by Washino and Wood (1994) and Hay (1997) and Hay et al. (1997).

Seto, Edmund Y. W.↗
DOE OSTI2021

Wind Plant Performance Prediction Benchmark Phase 1 (Technical Report)

Financial risk resulting from the uncertainty associated with developing, owning, and operating wind power plants remains a barrier to reducing the levelized cost of energy (LCOE). On average, modern wind power plants in the U.S. underperform their expected annual energy output by 3.5-4.5% , with many underperforming by over 10%. To compensate for this uncertainty, investors require a larger return on investment (ROI) and apply "knock-down" factors that mask much of the underlying sources of uncertainty. Wind energy projects thus have reduced access to low-cost capital. Furthermore, operating wind plants often take a simple approach to estimating operations & maintenance (O&M) costs (e.g. straight-line estimates based on similar plants), which can eat into profits. To overcome these issues, the wind industry must improve the models they use for estimating wind plant performance and operations. An industry consortium (IC) requested that the National Renewable Energy Laboratory (NREL) lead a Department of Energy (DOE) working group to benchmark the accuracy of wind power plant energy predictions against real operational data. The IC was also motivated by DOE and NREL's potential to characterize systematic energy underperformance, identify sources of uncertainty, and explore root causes. The Wind Plant Performance Prediction (WP3) project was created out of this request, and this report represents the successful completion of Phase 1 of the WP3 project. During the project, wind plant owners provided both pre-construction and operational data to NREL. The pre-construction data was provided to wind resource assessment (WRA) consultants so they could conduct energy yield assessments (EYA). NREL took all of the completed EYAs, along with the operational data, and conducted an operational assessment to benchmark the EYA results against actual operational data. Given the large amounts of sensitive data required for this effort, as well as historical opposition to sharing data within industry, successful completion of Phase 1 represents an unprecedented milestone for industry data sharing. To improve the accuracy and confidence of pre-construction EYAs, wind plant owners and investors need better, more certain, energy yield predictions. The WP3 Benchmark Project is an industry-driven response to this reality. For the first time, industry has taken the important step of working together at scale, sharing valuable operational data with DOE and NREL in order to investigate the sources of bias and uncertainty in these energy estimates. This IC provides wind plant preconstruction and operational data to NREL in an organized and documented fashion and provides guidance and feedback as needed. The IC also provides introspection of the design of experiment, key metrics of success, data challenges, analysis best practices, and quality of results.

17 WIND ENERGY↗
NASA NTRS1998

T wave alternans as a predictor of recurrent ventricular tachyarrhythmias in ICD recipients: prospective comparison with conventional risk markers

INTRODUCTION: The current standard for arrhythmic risk stratification is electrophysiologic (EP) testing, which, due to its invasive nature, is limited to patients already known to be at high risk. A number of noninvasive tests, such as determination of left ventricular ejection fraction (LVEF) or heart rate variability, have been evaluated as additional risk stratifiers. Microvolt T wave alternans (TWA) is a promising new risk marker. Prospective evaluation of noninvasive risk markers in low- or moderate-risk populations requires studies involving very large numbers of patients, and in such studies, documentation of the occurrence of ventricular tachyarrhythmias is difficult. In the present study, we identified a high-risk population, recipients of an implantable cardioverter defibrillator (ICD), and prospectively compared microvolt TWA with invasive EP testing and other risk markers with respect to their ability to predict recurrence of ventricular tachyarrhythmias as documented by ICD electrograms. METHODS AND RESULTS: Ninety-five patients with a history of ventricular tachyarrhythmias undergoing implantation of an ICD underwent EP testing, assessment of TWA, as well as determination of LVEF, baroreflex sensitivity, signal-averaged ECG, analysis of 24-hour Holter monitoring, and QT dispersion from the 12-lead surface ECG. The endpoint of the study was first appropriate ICD therapy for electrogram-documented ventricular fibrillation or tachycardia during follow-up. Kaplan-Meier survival analysis revealed that TWA (P < 0.006) and LVEF (P < 0.04) were the only significant univariate risk stratifiers. EP testing was not statistically significant (P < 0.2). Multivariate Cox regression analysis revealed that TWA was the only statistically significant independent risk factor. CONCLUSIONS: Measurement of microvolt TWA compared favorably with both invasive EP testing and other currently used noninvasive risk assessment methods in predicting recurrence of ventricular tachyarrhythmias in ICD recipients. This study suggests that TWA might also be a powerful tool for risk stratification in low- or moderate-risk patients, and needs to be prospectively evaluated in such populations.

Non-NASA Center↗