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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 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.↗
NASA NTRS2001

Risk estimation based on chromosomal aberrations induced by radiation

The presence of a causal association between the frequency of chromosomal aberrations in peripheral blood lymphocytes and the risk of cancer has been substantiated recently by epidemiological studies. Cytogenetic analyses of crew members of the Mir Space Station have shown that a significant increase in the frequency of chromosomal aberrations can be detected after flight, and that such an increase is likely to be attributed to the radiation exposure. The risk of cancer can be estimated directly from the yields of chromosomal aberrations, taking into account some aspects of individual susceptibility and other factors unrelated to radiation. However, the use of an appropriate technique for the collection and analysis of chromosomes and the choice of the structural aberrations to be measured are crucial in providing sound results. Based on the fraction of aberrant lymphocytes detected before and after flight, the relative risk after a long-term Mir mission is estimated to be about 1.2-1.3. The new technique of mFISH can provide useful insights into the quantification of risk on an individual basis.

STS Shuttle Project↗
DOE OSTI2026

Probability of Hydrogen Ignition: A Landscape Review and Gaps Assessment

The primary hazard of a leak from a hydrogen system is due to the immediate or delayed ignition of the fuel leading to a jet flame or explosion. Therefore, understanding the hydrogen ignition probability is critical for analyzing the risk of hydrogen systems. This report reviews the current understanding of hydrogen ignition mechanisms and methods for modeling their probability. The stoichiometry, ignition strength, and ignition source temperature are all important characteristics that can affect both the probability of ignition and the outcome of the subsequent combustion event. A brief review of diffusion ignition demonstrates that ignition probability models must account for seemingly spontaneous ignition of hydrogen in addition to scenarios where the ignition source is readily identified. State-of-the art models for both immediate and delayed ignition probabilities are presented, including different physical aspects of the scenarios (e.g., flow rate, ignition source characteristics) that are considered in the different modeling approaches. Current models often fail to account for the unique properties of hydrogen compared to other fuels, and most lack rigorous validation with hydrogen as a fuel. A fault tree framework is proposed to systematically evaluate the probability of ignition by integrating various ignition mechanisms and their uncertainties. Furthermore, this type of framework could enable additional insights into the most important mechanisms and would enable uncertainty quantification in risk assessment modeling. Recommendations for future research include the need for experimental validation of ignition models and the development of comprehensive methodologies that incorporate the specifics of hydrogen behavior in real-world scenarios.

hydrogen↗
DOE OSTI2024

Aviation security screening optimizer for risk and throughput (ASSORT)

The increasing number of air travelers each year presents a challenge as many airports are near their capacity in terms of resources and space for passenger screening. Fortunately, advancements in technologies like next-generation millimeter wave scanning offer solutions to ease this strain. The focus remains on managing risk while enhancing the passenger experience for the traveling public. The risk model presented in this paper known as the Aviation Security Screening Optimizer for Risk and Throughput (ASSORT) is designed to assess risk-based approaches for passenger screening and checkpoint operations. Additionally, ASSORT is exploring various traveler categories — general, trusted, and trusted-plus — along with different checkpoint screening Concept of Operations tailored to each traveler type. For instance, travelers with a higher trust level may experience fewer screening technologies, resulting in quicker processing times at the checkpoint. The output of ASSORT provides a risk score for predefined threat scenarios, as well as the overall risk to the checkpoint, aircraft, and airport by traveler type. In conclusion, benefits of using this tool include assessing the trade-offs between the overall risk associated with checkpoints and the throughput rate of passengers screened. We show for example the impact that different passenger volumes at the checkpoint can have on risk.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Software For Advanced Large-scale Analysis Of Magnetic Confinement For Numerical Design, Engineering & Research (salamander)

As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems

Simon, Pierre-Clement [Idaho National Laboratory (↗
NASA NTRS1967

Mission risk appraisal.

Mission risk appraisal technique for identification and quantification of high risk areas for unmanned interplanetary missions

MISSION PLANNING↗
NASA NTRS1993

The interaction of high voltage systems with the environments of the moon and Mars

An evaluation is made of the consequences of the interaction of high-voltage spacecraft systems designed for use in orbit or on the lunar and Martian surfaces with such environmental components as electrically charged dust, low pressure atmospheres, ionospheric plasmas and neutral gas species, and chemically reactive species. An account is given of plausible means for obtaining the requisite data and developing priorities for the study of each of the identified interactions. Attention is given to the computational tools whose development is required for the simulation and quantification of risk factors for the various likely interactions.

Hillard, G. B.↗
NASA NTRS2001

Quantitative risk assessment system (QRAS)

A quantitative risk assessment system (QRAS) builds a risk model of a system for which risk of failure is being assessed, then analyzes the risk of the system corresponding to the risk model. The QRAS performs sensitivity analysis of the risk model by altering fundamental components and quantifications built into the risk model, then re-analyzes the risk of the system using the modifications. More particularly, the risk model is built by building a hierarchy, creating a mission timeline, quantifying failure modes, and building/editing event sequence diagrams. Multiplicities, dependencies, and redundancies of the system are included in the risk model. For analysis runs, a fixed baseline is first constructed and stored. This baseline contains the lowest level scenarios, preserved in event tree structure. The analysis runs, at any level of the hierarchy and below, access this baseline for risk quantitative computation as well as ranking of particular risks. A standalone Tool Box capability exists, allowing the user to store application programs within QRAS.

Weinstock, Robert M↗
DOE OSTI2026

Envelope-driven comfort risk in residential demand response

Residential demand response (DR) is a valuable resource for grid reliability, but remains challenging because the highly heterogeneous residential building stock leads to widely varying and hard-to-predict load and comfort responses during DR events. Although prior research has estimated the technical potential of DR-capable technologies for achieving energy demand savings, little is known about how they affect thermal comfort. In particular, it remains unclear how indoor thermal conditions due to DR depend on the thermal envelope characteristics of the housing stock. To address this gap, this study provides a systematic, location-specific assessment of indoor thermal performance during DR-events across the US housing stock using both typical DR weather data and detailed building metadata. We evaluate how envelope characteristics influence indoor temperatures during realistic simulated summer and winter DR events across 37 US locations, applying both temperature threshold and rate of temperature change criteria to estimate region-level probabilities of discomfort. Additionally, we show the impact of distinct weather patterns that intensify or abate thermal stress on comfort outcomes. Results show a near-universal overheating risk in summer DR events, where comfort outcomes are strongly influenced by rapid risk of comfort violations. In contrast, overall winter DR discomfort risk is lower, risk escalation is more gradual and shows greater sensitivity to event duration. These findings offer a data-driven quantification of comfort risk across diverse climates and building envelopes, demonstrating the need for region-specific DR scheduling and discomfort mitigation strategies tailored to local weather patterns and the performance of existing residential buildings.

Demand response↗
DOE OSTI2025

Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers

Flooding events pose significant risk to human life, property, and infrastructure. Physically-consistent quantification of altered flood risks in global models requires hyper-resolution (~1 km) or fine flood simulations using two-dimensional (2D) physics schemes, both of which are unavailable in the current generation Earth System Models. Here, in this work, we have developed the River Dynamical Core (RDycore), which is an open-source, 2D shallow water equation (SWE) library for the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM). RDycore uses PETSc and libCEED libraries that allows it to run efficiently on CPUs and GPUs, as well as select a time-integration algorithm at runtime without requiring any code modifications. RDycore achieves spatial error convergence rates for problems with analytical and manufactured solutions similar to those reported previously in the literature, or consistent with the implemented first-order spatial discretization scheme. RDycore's accuracy in predicting flooding for a well-studied dam break problem is comparable to existing SWE models. For a problem with 471 million grid cells, RDycore achieves a speedup of 6.6x and 7.6x on GPUs compared to CPUs when using 320 compute nodes on DOE's Perlmutter and Frontier supercomputers, respectively. The one-way coupling of the RDycore library within E3SM is demonstrated by performing multiple 5-day flooding simulations during Hurricane Harvey driven by five precipitation datasets. The E3SM--RDycore simulations at 30 m spatial resolution accurately simulate maximum water height during the hurricane when benchmarked against a previously published study and achieve a speedup of 15x (Perlmutter) and 21x (Frontier) on GPUs relative to CPUs. The work presented here is the foundational step in providing hardware and algorithmic portability framework for simulating kilometer-scale river dynamics within E3SM.

Flood Simulation↗
NASA NTRSDate not supplied

Soyuz Landing Risk Characterization

INTRODUCTION United States On-orbit Segment (USOS) astronauts have been returning to Earth from the International Space Station (ISS) aboard the Russian Soyuz vehicle since 2003. The energetics associated with Soyuz landings are comparable to our next generation of US space vehicles (Orion, Crew Dragon, and Starliner). Soyuz also lands crew in a similar state of spaceflight deconditioning that is anticipated using the future vehicles. A number of injuries have now been observed during these Soyuz landings. Currently it is unknown how Soyuz landing accelerations and the numbers and types of associated injuries relate to the new occupant protection requirements levied upon future space vehicles. Understanding this relationship will allow better quantification of the risk of injury for future spacecraft designs. METHODS An estimate of the occurrences of injury during Soyuz landings has been determined using data contained in NASA flight medicine databases and supplemented with data collected from crewmembers and flight surgeons. At the time of this abstract, 96 USOS astronauts have returned to Earth in the Soyuz (59 US, 31 International Partner, and 6 spaceflight participants). Of that total, data from 80 individual crew landings has been collected through this study. Injuries are classified as either contusions/abrasions, Class I (minor, no medical follow-up), Class II (moderate, medical follow-up necessary), Class III (severe), and Class IV (life threatening). Current NASA occupant protection standards are primarily based on the Multi-axial Dynamic Response Index (MDRI), which is a simple lumped-parameter spring, mass, damper model tuned to human impact responses in three orthogonal axes. RESULTS Using what is known about Soyuz landing dynamics from airborne and drop test data, the MDRI predicts essentially no Class IV or III injuries, < 1% Class II injuries, and < 5% Class I injuries. Approximately one-third of all USOS crewmembers for whom data has been gathered have suffered some sort of injury related to landing in the Soyuz vehicle. Most of these injuries fall into the contusion/abrasion category (21.3 %). Class I injuries have been sustained by 5.0% of all crew, and Class II injuries have been observed in 3.8%. No Class III or IV injuries have been sustained to date. Forty-three injuries have been documented, with some crewmembers sustaining more than one injury in a single landing. The knee was the most likely area of injury (n=9), followed by the back (n=8), and then the arm (n=7). CONCLUSION Soyuz landing dynamics are the most comparable analog environment for the anticipated loads associated with Orion and commercial crew vehicle landings. The MDRI under predicts the percentage of Class II injuries experienced during Soyuz landings. The MDRI is better at predicting Class I injury occurrences. However, if contusions/abrasions are included in the minor injury Class I category, then the MDRI also vastly under predicts those injuries as well. Given this data, it will be important to re-evaluate the expected injury rates for future vehicles, and work toward improving the predictive tools for spaceflight use. Spaceflight deconditioning may account for some of the under prediction, and work is ongoing to assess the relationship between landing injuries and impact tolerance changes due to spaceflight deconditioning.

N Newby↗
NASA NTRSDate not supplied

Soyuz Landing Risk Characterization

INTRODUCTION United States On-orbit Segment (USOS) astronauts have been returning to Earth from the International Space Station (ISS) aboard the Russian Soyuz vehicle since 2003. The energetics associated with Soyuz landings are comparable to our next generation of US space vehicles (Orion, Crew Dragon, and Starliner). Soyuz also lands crew in a similar state of spaceflight deconditioning that is anticipated using the future vehicles. A number of injuries have now been observed during these Soyuz landings. Currently it is unknown how Soyuz landing accelerations and the numbers and types of associated injuries relate to the new occupant protection requirements levied upon future space vehicles. Understanding this relationship will allow better quantification of the risk of injury for future spacecraft designs. METHODS An estimate of injury occurrence during Soyuz landings has been determined using data contained in NASA flight medicine databases and supplemented with data collected from crewmembers and flight surgeons. At the time of this abstract, 96 USOS astronauts have returned to Earth in the Soyuz (59 US, 31 International Partner, and 6 spaceflight participants). Of that total, data from 80 individual crew landings has been collected through this study as of 2021. Injuries are classified either as contusions/abrasions, Class I (minor, no medical follow-up), Class II (moderate, medical follow-up necessary), Class III (severe), or Class IV (life threatening). Current NASA occupant protection standards are primarily based on the Multi-axial Dynamic Response Index (MDRI), which is a simple lumped-parameter spring, mass, damper model tuned to human impact responses in three orthogonal axes. RESULTS Using what is known about Soyuz landing dynamics from airborne and drop test data, the MDRI predicts essentially no Class IV or III injuries, < 1% Class II injuries, and < 5% Class I injuries. Approximately one-third of all USOS crewmembers for whom data has been gathered have suffered some sort of injury related to landing in the Soyuz vehicle. Most of these injuries fall into the contusion/abrasion category (21.3 %) (n.b. This is the percent for whom contusion/abrasion was the worst injury. Those with class I or II injuries typically also received contusions/abrasions). Class I injuries have been sustained by 5.0% of all crew, and Class II injuries have been observed in 3.8%. No Class III or IV injuries have been sustained to date. Forty-three injuries have been documented, with some crewmembers sustaining more than one injury in a single landing. The knee was the most likely area of injury (n=9), followed by the back (n=8), then the arm (n=7). CONCLUSION Soyuz landing dynamics are the most comparable analog environment for the anticipated loads associated with Orion and commercial crew vehicle landings. The MDRI under predicts the percentage of Class II injuries experienced during Soyuz landings, but it is better at predicting Class I injury occurrences. However, if contusions/abrasions are included in the minor injury Class I category, then the MDRI vastly under predicts those injuries as well. Given this data, it is important to re-evaluate the expected injury rates for future vehicles and work toward improving the predictive tools for spaceflight use. Spaceflight deconditioning may account for some of the under prediction, and work is ongoing to assess the relationship between landing injuries and impact tolerance changes due to spaceflight deconditioning. Future work includes expanding this study to involve private space missions on new vehicles to better characterize the effects of time in flight, suit and seat design, and vehicle dynamics.

N Newby↗
NASA NTRSDate not supplied

Soyuz Landing Risk Characterization

INTRODUCTION United States On-orbit Segment (USOS) astronauts have been returning to Earth from the International Space Station (ISS) aboard the Russian Soyuz vehicle since 2003. The energetics associated with Soyuz landings are comparable to our next generation of US space vehicles (Orion, Crew Dragon, and Starliner). Soyuz also lands crew in a similar state of spaceflight deconditioning that is anticipated using the future vehicles. A number of injuries have now been observed during these Soyuz landings. Currently it is unknown how Soyuz landing accelerations and the numbers and types of associated injuries relate to the new occupant protection requirements levied upon future space vehicles. Understanding this relationship will allow better quantification of the risk of injury for future spacecraft designs. METHODS An estimate of injury occurrence during Soyuz landings has been determined using data contained in NASA flight medicine databases and supplemented with data collected from crew members and flight surgeons. At the time of this abstract, 96 USOS astronauts have returned to Earth in the Soyuz (59 US, 31 International Partner, and 6 spaceflight participants). Of that total, data from 80individual crew landings has been collected through this study as of September 2023. Injuries are classified either as contusions/abrasions, Class I (minor, no medical follow-up), Class II (moderate, medical follow-up necessary), Class III (severe), or Class IV (life threatening). Current NASA occupant protection standards are primarily based on the Multi-axial Dynamic Response Index (MDRI), which is a simple lumped-parameter spring, mass, damper model tuned to human impact responses in three orthogonal axes. RESULTS Using what is known about Soyuz landing dynamics from airborne and drop test data, the MDRI predicts essentially no Class IV or III injuries, < 1% Class II injuries, and < 5% Class I injuries. Approximately one-third of all USOS crewmembers for whom data has been gathered have suffered some sort of injury related to landing in the Soyuz vehicle. Most of these injuries fall into the contusion/abrasion category (21.3%) (n.b. This is the percent for whom contusion/abrasion was the worst injury. Those with class I or II injuries typically also received contusions/abrasions). Class I injuries have been sustained by 5.0% of all crew, and Class II injuries have been observed in 3.8%. No Class III or IV injuries have been sustained to date. Forty-three injuries have been documented, with some crewmembers sustaining more than one injury in a single landing. The knee was the most likely area of injury (n=9), followed by the back (n=8), then the arm (n=7). CONCLUSION Soyuz landing dynamics are the most comparable analog environment for the anticipated loads associated with Orion and commercial crew vehicle landings. The MDRI under predicts the percentage of Class II injuries experienced during Soyuz landings, but it is better at predicting Class I injury occurrences. However, if contusions/abrasions are included in the minor injury Class I category, then the MDRI vastly under predicts those injuries as well. Given this data, it is important to re-evaluate the expected injury rates for future vehicles and work toward improving the predictive tools for spaceflight use. Spaceflight deconditioning may account for some of the under prediction, and work is ongoing to assess the relationship between landing injuries and impact tolerance changes due to spaceflight deconditioning. Future work includes expanding this study to involve private space missions on new vehicles to better characterize the effects of time in flight, suit and seat design, and vehicle dynamics.

N Newby↗
NASA NTRSDate not supplied

Soyuz Landing Risk Characterization

INTRODUCTION United States On-orbit Segment (USOS) astronauts have been returning to Earth from the International Space Station (ISS) aboard the Russian Soyuz vehicle since 2003. The energetics associated with Soyuz landings are comparable to our next generation of US space vehicles (Orion, Crew Dragon, and Starliner). Soyuz also lands crew in a similar state of spaceflight deconditioning that is anticipated using the future vehicles. A number of injuries have now been observed during these Soyuz landings. Currently it is unknown how Soyuz landing accelerations and the numbers and types of associated injuries relate to the new occupant protection requirements levied upon future space vehicles. Understanding this relationship will allow better quantification of the risk of injury for future spacecraft designs. METHODS An estimate of injury occurrence during Soyuz landings has been determined using data contained in NASA flight medicine databases and supplemented with data collected from crewmembers and flight surgeons. At the time of this abstract, 96 USOS astronauts have returned to Earth in the Soyuz (59 US, 31 International Partner, and 6 spaceflight participants). Of that total, data from 80 individual crew landings has been collected through this study as of September 2023. Injuries are classified either as contusions/abrasions, Class I (minor, no medical follow-up), Class II (moderate, medical follow-up necessary), Class III (severe), or Class IV (life threatening). Current NASA occupant protection standards are primarily based on the Multi-axial Dynamic Response Index (MDRI), which is a simple lumped-parameter spring, mass, damper model tuned to human impact responses in three orthogonal axes. RESULTS Using what is known about Soyuz landing dynamics from airborne and drop test data, the MDRI predicts essentially no Class IV or III injuries, < 1% Class II injuries, and < 5% Class I injuries. Approximately one-third of all USOS crewmembers for whom data has been gathered have suffered some sort of injury related to landing in the Soyuz vehicle. Most of these injuries fall into the contusion/abrasion category (21.3 %) (n.b. This is the percent for whom contusion/abrasion was the worst injury. Those with class I or II injuries typically also received contusions/abrasions). Class I injuries have been sustained by 5.0% of all crew, and Class II injuries have been observed in 3.8%. No Class III or IV injuries have been sustained to date. Forty-three injuries have been documented, with some crewmembers sustaining more than one injury in a single landing. The knee was the most likely area of injury (n=9), followed by the back (n=8), then the arm (n=7). CONCLUSION Soyuz landing dynamics are the most comparable analog environment for the anticipated loads associated with Orion and commercial crew vehicle landings. The MDRI under predicts the percentage of Class II injuries experienced during Soyuz landings, but it is better at predicting Class I injury occurrences. However, if contusions/abrasions are included in the minor injury Class I category, then the MDRI vastly under predicts those injuries as well. Given this data, it is important to re-evaluate the expected injury rates for future vehicles and work toward improving the predictive tools for spaceflight use. Spaceflight deconditioning may account for some of the under prediction, and work is ongoing to assess the relationship between landing injuries and impact tolerance changes due to spaceflight deconditioning. Future work includes expanding this study to involve private space missions on new vehicles to better characterize the effects of time in flight, suit and seat design, and vehicle dynamics.

P Greenhalgh↗
NASA NTRS1998

Risk Management

This presentation focuses on the identification of risk management, risk management processes such as: quantification and prioritization; mitigation planning; implementation of risk reduction; and tracking process. It develops examples and answers questions about Risk Management.

Lloyd Thomas↗
NASA NTRS2023

EVA Task and 3D Pose Recognition from Video

Extravehicular Activity (EVA) has been known to involve potential risks of biomechanical stresses and injuries to crewmembers. Gathering of EVA motion patterns is necessary for risk analysis and mitigation. However, many existing techniques, such as motion capture systems, are not only cost-prohibitive but are impractical for retrospective analysis of past missions. In this work, a software tool was developed, which can estimate the 3D poses of a spacesuit from photographs or videos, without using special sensors or equipment. The tool is based on the state-of-the-art artificial intelligence and machine learning (AI/ML) system, which was trained by studying and capturing motion patterns of past and current spacesuit test data. The AI/ML tool was further enhanced using synthetically generated data, in which the suit postures, backgrounds, camera angles and illumination conditions were parametrically adjusted and rendered for training. The tool, incorporated the methodologies of Convolutional Neural Network (CNN), was trained, and tested in the cloud computing environment. The trained model was then applied on new imagery and video to extract estimated joint positions and suit outlines. The joint positions were further processed to capture activity (“digging”), pose labels (“bending”), and other useful downstream information. The model performance on new imagery and video was successfully assessed for accuracy and reliability. This AI/ML based posture recognition tool thus allows for the quantification of injury risk and task performance characterization for both current and past missions and training, which can immensely help to improve EVA task and suit design.

Kyung Han Kim↗
DOE OSTI2024

Accelerating uncertainty quantification in incremental dynamic analysis using dimension reduction-based surrogate modeling

We propose a surrogate modeling framework based on dimension reduction to facilitate the quantification of seismic risk of structural systems in performance-based earthquake engineering. The framework adopts incremental dynamic analysis (IDA) for addressing hazard variability, and promotes significant computational efficiency improvement for propagating epistemic uncertainties associated with the structural models. It utilizes both linear and nonlinear dimension reduction approaches, equipped with inverse mappings, to learn a functional between the input parameter space (e.g., the epistemic uncertainties of the structure) to the high-dimensional output space created through the IDA implementation across different ground motions and seismic intensity levels. Polynomial chaos expansion is adopted as the surrogate model to learn this functional in the reduced space. A nine-story steel moment-resisting frame with uncertain structural properties is used as a testbed. Furthermore, we select the seismic fragility curves as a measure of the structure’s seismic performance, since it provides an estimate of the probability of entering specified damage states for given levels of ground shaking.

42 ENGINEERING↗