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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Comparison of Artemis 2 and Artemis 5 Model Outcomes Using the Impact Probabilistic Risk Assessment Tool

BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. This presentation will seek to compare IMPACT’s computational results upon potential complex space medical conditions (e.g., sprain/strain back or sleep disturbance) using IMPACT’s risk metrics and the associated optimized medical sets/kits between two Artemis missions: single segment Artemis 2 and multi-segmented Artemis 5. OVERVIEW By identifying potential medical conditions in space using input criteria such as crew quantity and composition, certain crew physical characteristics, mission duration and mission activities, IMPACT can produce analyses on the type of medical resources and capabilities needed to produce an optimized medical set/kit to address those medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. IMPACT’s risk metrics include loss of crew life (LOCL) – a measure of crew mortality due to medical conditions in space, return to definitive care (RTDC) – the need to perform crew evacuation, and task time lost (TTL) – a measure of the inability to perform activities due to crew disability. These risk metrics are applied to every medical condition identified by IMPACT’s computation analyses for every segment of the mission. Medical sets/kits are optimized to address these medical conditions but must fit within the stated Artemis Design Reference Mission (DRM) request for mass and volume physical size constraints. ANTICIPATED ANALYSIS AND CONCLUSION Using two Artemis missions, Artemis 2 and Artemis 5, IMPACT will provide the analyses for comparison of medical set/kit contents based upon mass and/or volume requirements and identify the at-risk medical conditions within both missions. This paper serves as an initial exploration of probabilistic risk assessment (PRA) medical risk calculations between two crewed Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.

probabilistic risk assessment↗
DOE OSTI2026

Integrated Transmission-Distribution Multi-Period Switching for Wildfire Risk Mitigation: Improving Speed and Scalability with Distributed Optimization: Preprint

With increasingly severe wildfire conditions driven by climate change, utilities must manage the risk of wildfire ignitions from electric power lines. During "public safety power shutoff'" events, utilities de-energize power lines to reduce wildfire ignition risk, which may result in load shedding. Distributed energy resources provide flexibility that can help support the system to reduce load shedding when lines are de-energized. We investigate a coordinated transmission-distribution optimization problem that balances wildfire risk mitigation and load shedding. We model distribution systems that include battery energy storage systems which may support loads when transmission lines are de-energized. This multi-period integrated transmission-distribution optimal switching problem jointly optimizes line switching decisions, the generators' setpoints, load shedding, and the batteries' states of charge, resulting in significant computational challenges. To improve scalability, we decompose the problem over both space and time and apply a distributed optimization algorithm. Using a large-scale synthetic California test case with realistic distribution models and real wildfire risk data, we show that distributed optimization can solve large-scale multi-period switching problems that are otherwise intractable for centralized solvers. We also discuss challenges and future directions for improving the distributed algorithm's convergence performance as the number of time periods increases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Probabilistic Evaluation of Geomechanical Risks in CO2 Storage: An Exploration of Caprock Integrity Metrics Using a Multilaminate Model

The probabilistic uncertainty assessment of geomechanical risk—specifically, caprock failure—attributable to CO2 injection, as presented in a simplified hypothetical geological model, was the focus of this study. Our approach amalgamates the implementation of a multilaminate model, the creation of a response surface model in conjunction with the Box–Behnken sampling design, the execution of associated numerical modeling experiments, and the utilization of Monte Carlo simulations. Probability distributions to encapsulate the inherent variability (elastic and mechanical properties of the caprock and reservoir) and uncertainty in prediction estimates (vertical displacement, total strain, and F value) were employed. Our findings reveal that the Young modulus of the caprock is a key factor controlling equivalent total strain but is insufficient as a stand-alone indicator of caprock integrity. It is confirmed that the caprock can accommodate significant deformation without failure, if it possesses a low Young’s modulus and high mechanical strength properties, such as the friction angle and uniaxial compressive strength. Similarly, vertical displacement was found to be an unreliable indicator for caprock integrity, as caprock failure can occur across a broad spectrum of vertical displacements, particularly when both the Young modulus and mechanical strength properties have wide ranges. This study introduces the F value as the most dependable indicator for caprock failure, although it is a theoretical attribute (the shortest distance between the Mohr circle and the nearest failure envelope used to measure the sensitivity to failure) and not physically measurable in the field. Deviatoric stress levels were found to vary based on stress regimes, with the maximum levels observed under extensive and compressive stress regimes. In conjunction with the use of the response surface method, this study demonstrates the efficacy of the multilaminate framework and the Mohr–Coulomb constitutive model in providing a simplified, yet effective, probabilistic model of the mechanical behavior of caprock failure, reducing mathematical and computational complexities.

Energy & Fuels↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
NASA NTRS2024

Big Bend Ecological Forecasting: Integrating Earth Observations Into Invasive Species Management Decisions in Big Bend National Park in Texas

Located in Brewster County Texas, U.S., along the Texas-Mexico border, Big Bend National Park is 3,243 square kilometers of desert, mountains, and rivers. NASA’s MSFC Spring 2024 DEVELOP Team partnered with the National Park Service (NPS), to address the environmental concern of perineal invasive grasses in Big Bend National Park. Buffelgrass (Cenchrus ciliaris), introduced to the park in the 1940’s, poses an ongoing threat, and causes habitat destruction for many of the park’s native ecosystems. Buffelgrass amplifies fire risk in the park, aids in the destruction of historic structures, and alters stream channels. To address the rising concern of Buffelgrass presence, unique advanced spatial technique applications were necessary to construct a habitat suitability model and perform a comprehensive fire risk assessment. A habitat suitability model was developed considering climate, vegetation, and phenological variables, in addition to physical and topographical variables. Subsequently, a fire risk model was developed, taking into account fire history data, accessibility factors, climate trends and predictions, along with developed areas. Multi-Source Land Surface Phenology (LSP) Sentinel-2 and Landsat 8 Operational Land Imager (OLI) imagery were used to predict Buffelgrass hotspot locations throughout the park. These analyses allowed the identification of optimal Buffelgrass habitat and hotspot locations, as well as park zones that reflect the greatest risk for future Buffelgrass invasion and fire risk. The collective results of the habitat suitability model, fire risk assessment, and Buffelgrass hot spot identification will allow the NPS to facilitate efficient mitigation measures and management strategies, and improved resource allocation where it’s most needed.

remote sensing↗
NASA NTRS1997

Biological Bases of Space Radiation Risk

In this session, Session JP4, the discussion focuses on the following topics: Hematopoiesis Dynamics in Irradiated Mammals, Mathematical Modeling; Estimating Health Risks in Space from Galactic Cosmic Rays; Failure of Heavy Ions to Affect Physiological Integrity of the Corneal Endothelial Monolayer; Application of an Unbiased Two-Gel CDNA Library Screening Method to Expression Monitoring of Genes in Irradiated Versus Control Cells; Detection of Radiation-Induced DNA Strand Breaks in Mammalian Cells By Enzymatic Post-Labeling; Evaluation of Bleomycin-Induced Chromosome Aberrations Under Microgravity Conditions in Human Lymphocytes, Using "Fish" Techniques; Technical Description of the Space Exposure Biology Assembly Seba on ISS; and Cytogenetic Research in Biological Dosimetry.

Source record↗
NASA NTRS2013

NASA Models of Space Radiation Induced Cancer, Circulatory Disease, and Central Nervous System Effects

The risks of late effects from galactic cosmic rays (GCR) and solar particle events (SPE) are potentially a limitation to long-term space travel. The late effects of highest concern have significant lethality including cancer, effects to the central nervous system (CNS), and circulatory diseases (CD). For cancer and CD the use of age and gender specific models with uncertainty assessments based on human epidemiology data for low LET radiation combined with relative biological effectiveness factors (RBEs) and dose- and dose-rate reduction effectiveness factors (DDREF) to extrapolate these results to space radiation exposures is considered the current "state-of-the-art". The revised NASA Space Risk Model (NSRM-2014) is based on recent radio-epidemiology data for cancer and CD, however a key feature of the NSRM-2014 is the formulation of particle fluence and track structure based radiation quality factors for solid cancer and leukemia risk estimates, which are distinct from the ICRP quality factors, and shown to lead to smaller uncertainties in risk estimates. Many persons exposed to radiation on earth as well as astronauts are life-time never-smokers, which is estimated to significantly modify radiation cancer and CD risk estimates. A key feature of the NASA radiation protection model is the classification of radiation workers by smoking history in setting dose limits. Possible qualitative differences between GCR and low LET radiation increase uncertainties and are not included in previous risk estimates. Two important qualitative differences are emerging from research studies. The first is the increased lethality of tumors observed in animal models compared to low LET radiation or background tumors. The second are Non- Targeted Effects (NTE), which include bystander effects and genomic instability, which has been observed in cell and animal models of cancer risks. NTE's could lead to significant changes in RBE and DDREF estimates for GCR particles, and the potential effectiveness of radiation mitigator's. The NSRM- 2014 approaches to model radiation quality dependent lethality and NTE's will be described. CNS effects include both early changes that may occur during long space missions and late effects such as Alzheimer's disease (AD). AD effects 50% of the population above age 80-yr, is a degenerative disease that worsens with time after initial onset leading to death, and has no known cure. AD is difficult to detect at early stages and the small number of low LET epidemiology studies undertaken have not identified an association with low dose radiation. However experimental studies in mice suggest GCR may lead to early onset AD. We discuss modeling approaches to consider mechanisms whereby radiation would lead to earlier onset of occurrence of AD. Biomarkers of AD include amyloid beta (A(Beta)) plaques, and neurofibrillary tangles (NFT) made up of aggregates of the hyperphosphorylated form of the micro-tubule associated, tau protein. Related markers include synaptic degeneration, dentritic spine loss, and neuronal cell loss through apoptosis. Radiation may affect these processes by causing oxidative stress, aberrant signaling following DNA damage, and chronic neuroinflammation. Cell types to be considered in multi-scale models are neurons, astrocytes, and microglia. We developed biochemical and cell kinetics models of DNA damage signaling related to glycogen synthase kinase-3(Beta) (GSK3(Beta)) and neuroinflammation, and considered multi-scale modeling approaches to develop computer simulations of cell interactions and their relationships to A(Beta) plaques and NFTs. Comparison of model results to experimental data for the age specific development of A(Beta) plaques in transgenic mice will be discussed.

Cucinotta, Francis A.↗
NASA NTRS2007

Astronaut Risk Levels During Crew Module (CM) Land Landing

The NASA Engineering Safety Center (NESC) is investigating the merits of water and land landings for the crew exploration vehicle (CEV). The merits of these two options are being studied in terms of cost and risk to the astronauts, vehicle, support personnel, and general public. The objective of the present work is to determine the astronaut dynamic response index (DRI), which measures injury risks. Risks are determined for a range of vertical and horizontal landing velocities. A structural model of the crew module (CM) is developed and computational simulations are performed using a transient dynamic simulation analysis code (LS-DYNA) to determine acceleration profiles. Landing acceleration profiles are input in a human factors model that determines astronaut risk levels. Details of the modeling approach, the resulting accelerations, and astronaut risk levels are provided.

Lawrence, Charles↗
NASA NTRS1993

Developing interpretable models with optimized set reduction for identifying high risk software components

Applying equal testing and verification effort to all parts of a software system is not very efficient, especially when resources are limited and scheduling is tight. Therefore, one needs to be able to differentiate low/high fault frequency components so that testing/verification effort can be concentrated where needed. Such a strategy is expected to detect more faults and thus improve the resulting reliability of the overall system. This paper presents the Optimized Set Reduction approach for constructing such models, intended to fulfill specific software engineering needs. Our approach to classification is to measure the software system and build multivariate stochastic models for predicting high risk system components. We present experimental results obtained by classifying Ada components into two classes: is or is not likely to generate faults during system and acceptance test. Also, we evaluate the accuracy of the model and the insights it provides into the error making process.

Briand, Lionel C.↗
NASA NTRS2000

Microbial Impact on Success of Human Exploration Missions

The purpose of this study is to identify microbiological risks associated with space exploration and identify potential countermeasures available. Identification of microbial risks associated with space habitation requires knowledge of the sources and expected types of microbial agents. Crew data along with environmental data from water, surfaces, air, and free condensate are utilized in risk examination. Data from terrestrial models are also used. Microbial risks to crew health include bacteria, fungi, protozoa, and viruses. Adverse effects of microbes include: infections, allergic reactions, toxin production, release of volatiles, food spoilage, plant disease, material degradation, and environmental contamination. Risk is difficult to assess because of unknown potential changes in microbes (e.g., mutation) and the human host (e.g., immune changes). Prevention of adverse microbial impacts is preferred over remediation. Preventative measures include engineering measures (e.g., air filtration), crew microbial screening, acceptability standards, and active verification by onboard monitoring. Microbiological agents are important risks to human health and performance during space flight and risks increase with mission duration. Acceptable risk level must be defined. Prevention must be given high priority. Careful screening of crewmembers and payloads is an important element of any risk mitigation plan. Improved quantitation of microbiological risks is a high priority.

Pierson, Duane L.↗
DOE OSTI2020

Panel Session 129: Perspectives (US and Non-US) on the Use of Risk and Dose Assessment Tools (R9.3)

This panel focused on various dose and risk assessment tools for assessments, Deactivation and Decommissioning, remediation, and closure of sites of chemical and radioactive wastes. Representatives from the US government agencies, non-US countries and their representative regulatory bodies or authorities, as well as performance/risk assessment practitioners compared and contrasted the various guidance from regulatory agencies and authorities on how tools such as the Preliminary Remediation Goals (PRG) calculator, the Dose Compliance Concentrations (DCC) calculator, and Residual Radioactivity (RESRAD) should be used in support of analyses and decisions for environmental cleanup activities. Panelists with presentations: Cumulative Impact Evaluation: Innovative Tools for Evaluation of Groundwater Protection (Alaa Aly); Superfund Evaluation Process for Alternative Risk and Dose Models (with Focus on EPA and DOE Tools) (Stuart Walker); NRC Staff Perspective on Risk-Informed Approach and Reasonable Safety Assurance in D and D and LLW (Rateb (Boby) Abu Eid)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1995

Probabilistic risk assessment of the Space Shuttle. Phase 3: A study of the potential of losing the vehicle during nominal operation. Volume 2: Integrated loss of vehicle model

The application of the probabilistic risk assessment methodology to a Space Shuttle environment, particularly to the potential of losing the Shuttle during nominal operation is addressed. The different related concerns are identified and combined to determine overall program risks. A fault tree model is used to allocate system probabilities to the subsystem level. The loss of the vehicle due to failure to contain energetic gas and debris, to maintain proper propulsion and configuration is analyzed, along with the loss due to Orbiter, external tank failure, and landing failure or error.

Fragola, Joseph R.↗
DOE OSTI2024

Human–mouse comparison of the multistage nature of radiation carcinogenesis in a mathematical model

Abstract Mouse models are vital for assessing risk from environmental carcinogens, including ionizing radiation, yet the interspecies difference in the dose response precludes direct application of experimental evidence to humans. Herein, we take a mathematical approach to delineate the mechanism underlying the human–mouse difference in radiation‐related cancer risk. We used a multistage carcinogenesis model assuming a mutational action of radiation to analyze previous data on cancer mortality in the Japanese atomic bomb survivors and in lifespan mouse experiments. Theoretically, the model predicted that exposure will chronologically shift the age‐related increase in cancer risk forward by a period corresponding to the time in which the spontaneous mutational process generates the same mutational burden as that the exposure generates. This model appropriately fitted both human and mouse data and suggested a linear dose response for the time shift. The effect per dose decreased with increasing age at exposure similarly between humans and mice on a per‐lifespan basis (0.72‐ and 0.71‐fold, respectively, for every tenth lifetime). The time shift per dose was larger by two orders of magnitude in humans (7.8 and 0.046 years per Gy for humans and mice, respectively, when exposed at ~35% of their lifetime). The difference was mostly explained by the two orders of magnitude difference in spontaneous somatic mutation rates between the species plus the species‐independent radiation‐induced mutation rate. Thus, the findings delineate the mechanism underlying the interspecies difference in radiation‐associated cancer mortality and may lead to the use of experimental evidence for risk prediction in humans.

Oncology↗
NASA NTRS2001

Prediction of coronary artery disease in patients undergoing operations for mitral valve degeneration

OBJECTIVES: We sought to develop and validate a model that estimates the risk of obstructive coronary artery disease in patients undergoing operations for mitral valve degeneration and to demonstrate its potential clinical utility. METHODS: A total of 722 patients (67% men; age, 61 +/- 12 years) without a history of myocardial infarction, ischemic electrocardiographic changes, or angina who underwent routine coronary angiography before mitral valve prolapse operations between 1989 and 1996 were analyzed. A bootstrap-validated logistic regression model on the basis of clinical risk factors was developed to identify low-risk (< or =5%) patients. Obstructive coronary atherosclerosis was defined as 50% or more luminal narrowing in one or more major epicardial vessels, as determined by means of coronary angiography. RESULTS: One hundred thirty-nine (19%) patients had obstructive coronary atherosclerosis. Independent predictors of coronary artery disease include age, male sex, hypertension, diabetes mellitus,and hyperlipidemia. Two hundred twenty patients were designated as low risk according to the logistic model. Of these patients, only 3 (1.3%) had single-vessel disease, and none had multivessel disease. The model showed good discrimination, with an area under the receiver-operating characteristic curve of 0.84. Cost analysis indicated that application of this model could safely eliminate 30% of coronary angiograms, corresponding to cost savings of $430,000 per 1000 patients without missing any case of high-risk coronary artery disease. CONCLUSION: A model with standard clinical predictors can reliably estimate the prevalence of obstructive coronary atherosclerosis in patients undergoing mitral valve prolapse operations. This model can identify low-risk patients in whom routine preoperative angiography may be safely avoided.

Non-NASA Center↗
NASA NTRS2016

Integrating Spaceflight Human System Risk Research

NASA is working to increase the likelihoods of human health and performance success during exploration missions, and subsequent crew long-term health. To manage the risks in achieving these goals, a system modeled after a Continuous Risk Management framework is in place. "Human System Risks" (Risks) have been identified, and approximately 30 are being actively addressed by NASA's Human Research Program (HRP). Research plans for each of HRP's Risks have been developed and are being executed. Ties between the research efforts supporting each Risk have been identified, however, this has been in an ad hoc fashion. There is growing recognition that solutions developed to address the full set of Risks covering medical, physiological, behavioral, vehicle, and organizational aspects of the exploration missions must be integrated across Risks and disciplines. We will discuss how a framework of factors influencing human health and performance in space is being applied as the backbone for bringing together sometimes disparate information relevant to the individual Risks. The resulting interrelated information is allowing us to identify and visualize connections between Risks and research efforts in a systematic and standardized way. We will discuss the applications of the visualizations and insights to research planning, solicitation, and decision-making processes.

Mindock, J.↗
NASA NTRS2016

Integrating Spaceflight Human System Risk Research

NASA is working to increase the likelihood of human health and performance success during exploration missions as well as to maintain the subsequent long-term health of the crew. To manage the risks in achieving these goals, a system modelled after a Continuous Risk Management framework is in place. "Human System Risks" (Risks) have been identified, and approximately 30 are being actively addressed by NASA's Human Research Program (HRP). Research plans for each of HRP's Risks have been developed and are being executed. Inter-disciplinary ties between the research efforts supporting each Risk have been identified; however, efforts to identify and benefit from these connections have been mostly ad hoc. There is growing recognition that solutions developed to address the full set of Risks covering medical, physiological, behavioural, vehicle, and organizational aspects of exploration missions must be integrated across Risks and disciplines. This paper discusses how a framework of factors influencing human health and performance in space is being applied as the backbone for bringing together sometimes disparate information relevant to the individual Risks. The resulting interrelated information enables identification and visualization of connections between Risks and research efforts in a systematic and standardized manner. This paper also discusses the applications of the visualizations and insights into research planning, solicitation, and decision-making processes.

Mindock, Jennifer↗
NASA NTRS2023

Modeling and Simulation Credibility Assessments of Whole-Body Finite Element Computational Models for Use in NASA Extravehicular Activity Applications

Computational finite element (FE) models are used in suited astronaut injury risk assessments; however, these models’ verification, validation, and credibility (VV&C) procedures for simulating injuries in altered gravity environments are limited. Our study conducts VV&C assessments of THUMS and Elemance whole-body FE models for predicting suited astronaut injury biomechanics using eight credibility factors, as per NASA-STD-7009A. Credibility factor ordinal scores are assigned by reviewing existing documentation describing VV&C practices, and credibility sufficiency thresholds are assigned based on input from subject matter experts. Our results show the FE models are credible for suited astronaut injury investigation in specific ranges of kinematic and kinetic conditions correlating to highway and contact sports events. Nevertheless, these models are deficient when applied outside these ranges. Several credibility elevation strategies are prescribed to improve models’ credibility for the NASA-centric application domain.

Finite Element↗