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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.

Probability Theory↗
NASA NTRS2017

Medical Optimization Network for Space Telemedicine Resources

INTRODUCTION: Long-duration missions beyond low Earth orbit introduce new constraints to the space medical system such as the inability to evacuate to Earth, communication delays, and limitations in clinical skillsets. NASA recognizes the need to improve capabilities for autonomous care on such missions. As the medical system is developed, it is important to have an ability to evaluate the trade space of what resources will be most important. The Medical Optimization Network for Space Telemedicine Resources was developed for this reason, and is now a system to gauge the relative importance of medical resources in addressing medical conditions. METHODS: A list of medical conditions of potential concern for an exploration mission was referenced from the Integrated Medical Model, a probabilistic model designed to quantify in-flight medical risk. The diagnostic and treatment modalities required to address best and worst-case scenarios of each medical condition, at the terrestrial standard of care, were entered into a database. This list included tangible assets (e.g. medications) and intangible assets (e.g. clinical skills to perform a procedure). A team of physicians working within the Exploration Medical Capability Element of NASA's Human Research Program ranked each of the items listed according to its criticality. Data was then obtained from the IMM for the probability of occurrence of the medical conditions, including a breakdown of best case and worst case, during a Mars reference mission. The probability of occurrence information and criticality for each resource were taken into account during analytics performed using Tableau software. RESULTS: A database and weighting system to evaluate all the diagnostic and treatment modalities was created by combining the probability of condition occurrence data with the criticalities assigned by the physician team. DISCUSSION: Exploration Medical Capabilities research at NASA is focused on providing a medical system to support crew medical needs in the context of a Mars mission. MONSTR is a novel approach to performing a quantitative risk analysis that will assess the relative value of individual resources needed for the diagnosis and treatment of various medical conditions. It will provide the operational and research communities at NASA with information to support informed decisions regarding areas of research investment, future crew training, and medical supplies manifested as part of the exploration medical system.

Shah, R. V.↗
NASA NTRS2016

NASA Glenn Research Center Solar Cell Experiment Onboard the International Space Station

Accurate air mass zero (AM0) measurement is essential for the evaluation of new photovoltaic (PV) technology for space solar cells. The NASA Glenn Research Center (GRC) has flown an experiment designed to measure the electrical performance of several solar cells onboard NASA Goddard Space Flight Center's (GSFC) Robotic Refueling Missions (RRM) Task Board 4 (TB4) on the exterior of the International Space Station (ISS). Four industry and government partners provided advanced PV devices for measurement and orbital environment testing. The experiment was positioned on the exterior of the station for approximately eight months, and was completely self-contained, providing its own power and internal data storage. Several new cell technologies including four-junction (4J) Inverted Metamorphic Multi-junction (IMM) cells were evaluated and the results will be compared to ground-based measurement methods.

Solar Cell Calibration Facility↗
NASA NTRS2016

Further Analyses of the NASA Glenn Research Center Solar Cell and Photovoltaic Materials Experiment Onboard the International Space Station

Accurate air mass zero (AM0) measurement is essential for the evaluation of new photovoltaic (PV) technology for space solar cells. The NASA Glenn Research Center (GRC) has flown an experiment designed to measure the electrical performance of several solar cells onboard NASA Goddard Space Flight Center's (GSFC) Robotic Refueling Mission's (RRM) Task Board 4 (TB4) on the exterior of the International Space Station (ISS). Four industry and government partners provided advanced PV devices for measurement and orbital environment testing. The experiment was positioned on the exterior of the station for approximately eight months, and was completely self-contained, providing its own power and internal data storage. Several new cell technologies including four-junction (4J) Inverted Metamorphic Multi-Junction (IMM) cells were evaluated and the results will be compared to ground-based measurement methods.

multi-junction↗
NASA NTRSDate not supplied

Developing A New Medical Condition List for Exploration Spaceflight

For the last decade, the National Aeronautics and Space Administration (NASA) has utilized a probabilistic risk analysis tool called the Integrated Medical Model (IMM), which contains 100 medical conditions, to conduct medical trade-space analyses and risk assessments for International Space Station (ISS) operations. Planning for future missions to the Moon and Mars will require updated tools and evidence that consider the unique features of exploration missions. To address this need, the Exploration Medical Capability (ExMC) Element of the NASA Human Research Program is developing an advanced trade-space analysis tool suite, IMPACT. As a part of this effort, ExMC has produced the IMPACT version 1 Condition List (ICL 1.0) which expands the scope and definition of medical conditions.

Amy Jean Kreykes↗
NASA NTRSDate not supplied

Extreme Environments Solar Power Project For Use Near Jupiter

The Extreme Environment Solar Power (EESP) Project goal was to develop solar cell and solar array design concept technologies for space power applications in high radiation and low solar intensity, low temperature (LILT) environments. NASA missions focused on outer planets, such as Jupiter, are subjected to intense radiation while experiencing less than ten percent of the solar flux relative to a mission in the general vicinity of Earth. Under these conditions, present solar array technology is not as efficient in converting the sun’s energy, and the solar array performance degrades quickly due to the additional radiation exposure. The EESP Project objectives were accomplished through four competitively selected contracts. These contracts were awarded with a Base and two option periods of performance. The Base requirements included System Design and Component Test & Analysis with a duration of nine and one half (9.5) months. The Option I requirements involved the fabrication of test hardware that NASA would evaluate following its delivery, and this period of performance had a duration of thirteen (13) months. The Option II requirements involved the fabrication of a scalable system prototype that can be integrated into a system level application and be demonstrated in a relevant environment. Option II had a duration of fifteen (15) months. The Transformational Array (TA) is an array concept developed by JHU-APL with Deployable Space Systems (DSS), SolAero Technologies and NewForge Technologies that proceeded through all three contract periods. The TA combines the DSS Roll Out Solar Array (ROSA) with Flexible Array Concentrator Technology (FACT) and SolAero’s Inverted Metamorphic Multijunction (IMM) solar cells for operation in the EESP Project environment. To achieve the EESP Project goals, the JHU-APL team conducted several investigations to improve performance of the solar cells and increase their efficiency, reduce outgassing contamination of the concentrators from the solar cell assembly adhesives, and to test the robustness of a sample blanket during deployment and retraction. Additionally, the team performed vibration, thermal cycling, and performance tests at a Standard Power Module (SPM) level. At the end of Option II, the JHU-APL team achieved the following performance metrics: • 39% beginning of life (BOL) cell efficiency at 5 AU and -125 C • 29% end of life (EOL) efficiency at the blanket level at 50 W/m2, -125 C and 4x1015 1 MeV e/cm2 • Increased specific power to 8 W/kg at EOL for the entire array including structure, deployment, and pointing mechanisms • Demonstrated the ability to survive numerous deploy-retract cycles without degradation • Reached a current System-Level Technology Readiness Level, TRL5 This presentation will describe the Transformational Array and component technologies and will discuss future potential uses for the system.

Anna Maria Pal↗
NASA NTRS2021

Comparison of Health and Performance Risk for Accelerated Mars Mission Scenarios

This document details the results for a quantitative estimate of the difference in human health and performance risk that crews would face for two hypothetical variations on Mars mission scenarios: an Accelerated Mars Mission (AMM) and a Standard Mars Mission(SMM).NASA goals for Mars mission concepts, duration, and tasks have varied over the years. While neither of the cases considered here are likely to accurately represent the initial mission to Mars, the exercise of evaluating significant differences in mission duration from an astronaut health perspective can provide bounding insight to the level of risk that is likely to be encountered in an eventual Mars mission. Medical Probabilistic Risk Assessment using the Integrated Medical Model (IMM)(1–3)and the NASA Space Radiation Cancer Risk Model (NSCR)(4,5) are used here to help mission planners gain the best insight currently available into the expected magnitude of impacts to astronaut health when undertaking a Mars mission. These impacts occur both in-mission as well as in the long-term health of the astronauts post-mission. These evaluations are currently the best available modeling estimates to characterize and bound the health risks in a proposed mission domain where humans have no experience.

Erik Antonsen↗
NASA NTRSDate not supplied

Developing Mission Segment-Specific Clinical Data for Impact

Space exploration missions represent a significant future objective for NASA’s human spaceflight operations for the near and long term. By operating missions during longer periods and over further distances, NASA’s mission planning models, hardware systems, and medical capabilities will be stretched beyond their original design constraints. The necessity of medical training and medical supply manifesting for such missions is likely to become incredibly intricate as trade-space analysis is performed over varying mission profiles. For this purpose, IMPACT was created to extend and improve the Integrated Medical Model (IMM) which is used for International Space Station (ISS) medical predications and consists of a probabilistic risk assessment tool that simulates missions to calculate risks given the constraints on crew complement, mission duration and activity (such as Extravehicular Activities), power, communication, mass, volume, and evacuation consideration as well as to predict mission outcomes. Currently, the medical evidence used to support IMPACT remains static, addressing risks of a medical condition and the impacts of the condition during spaceflight only, but is only calculated for a small number of mission parameters. Extended missions will consist of new and varied living environments, changing gravitational loads, and dynamic working requirements. Medical risks are not typically constant but will change depending upon the physical and physiological environment of the body during all phases of a mission. Risks are likely to shift during different mission segments, and understanding how components of a mission profile change risks will assist mission planners to increase chances for mission success.

Christopher Zahner↗
NASA NTRSDate not supplied

Derivation of the Most Influential Medical Conditions for An Extended Duration Artemis Mission

BACKGROUND: The risk of loss of mission due to medical conditions may be influenced by loss of crew life (LOCL), need for evacuation (RTDC; return to definitive care), and crew task time lost. Predicting what medical conditions are most likely to lead to crew morbidity and mortality may influence medical system design, clinical capability prioritization, and research strategies. NASA’s Informing Mission Planning via Analysis of Complex Tradespaces tool (IMPACT) applies Probabilistic Risk Assessment (PRA) methodology to assess these risks. OVERVIEW: A team of subject matter experts (SME) from a variety of medical disciplines developed a consensus-based process to determine 120 of the most clinically relevant medical conditions for long-duration exploration missions (LDEMs) . This IMPACT Condition List (ICL) expanded upon previous work done for Integrated Medical Model (IMM). For each condition a best-case and worst-case definition were derived. These definitions were used to identify probability of occurrence, proportion of cases that are best case vs. worst case, clinical phase duration, and risk of outcomes (task time loss [TTL], RTDC, and LOCL) for both treated and untreated states. These data were sources from existing spaceflight databases (e.g. Longitudinal Survey of Astronaut Health), relevant models (e.g. the ISS fire model), and/or terrestrial literature. Each condition was then tied to diagnostic and therapeutic resources and capabilities. IMPACT was then run for the LDLOLS DRM (see Abstract #2 for this panel). DISCUSSION: This abstract will present the process for generating the IMPACT condition list, the relevant data for each clinical condition, and present results for the ten most influential conditions impacting LOCL, RTDC, and TTL for a representative extended duration Artemis mission.

A Nelson↗
NASA NTRSDate not supplied

Future Improvements to Impact for Long Duration Exploration Spaceflight

BACKGROUND: While NASA currently uses the Integrated Medical Model (IMM) to model spaceflight medical risk for Low Earth Orbit missions, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) was created to improve the fidelity of medical risk analysis for exploration missions involving the Lunar surface and beyond. OVERVIEW: While completion work continues on the initial version of the IMPACT tool, work has already begun to investigate ways to further enhance the fidelity of the model and its utility to stakeholders. DISCUSSION: Future additions leveraging the more robust and flexible IMPACT architecture include analyzing mission segments to account for segment-specific environments (ie. Lunar surface or microgravity), clinically tracible outcomes based on partial treatment of conditions, the ability to model mission outcomes based on provider Knowledge, Skills, and Abilities (KSAs), and the ability to affect the incidence of conditions based on the occurrence of related conditions (e.g. UTI progressing to sepsis). IMPACT also enables the addition of new conditions to its database as required to meet future risk assessment needs. Ultimately, the desire is to broaden IMPACT from a tool that models/trades medical risk to one that does so for all crew health and performance relevant systems (e.g., food, exercise, etc.).

E Stratton↗
NASA NTRSDate not supplied

Development of A Crew Health and Performance System Probabilistic Risk Assessment Tool: Proof-of-Concept Approach

The crew health and performance (CHP) system represents the span of technological interventions and tested processes and procedures that in combination address the human risk to space flight. The Human Research Program (HRP) mental model of the CHP system breaks the capabilities needed to meet NASA human flight systems standards into specific categories (i.e., countermeasures, behavioral health, medical intervention). These categories are further broken down into specific sub-groups generally associated with the human system risks that these capabilities seek to mitigate. Like the approach used to develop the Integrated Medical Model (IMM) and the Medical Extensible Dynamic Probabilistic Risk Analysis Tool (MEDPRAT), HRP tasked NASA GRC’s Cross-Cutting Computational Modeling Project with developing a CHP probabilistic risk assessment tool, the CHP-PRA. The CHP-PRA model seeks to quantify and relatively assess the human risk state within the crew health and performance domain, using a combination of knowledge about human system risks and technology and practices likely to be applied during space flight missions. This modeling system will incorporate customer and stakeholder feedback and be flexible enough to address multiple different questions about important low-level mission-specific parameters. This presentation will introduce the initial concept and development timeline for this tool and demonstrate proof-of-concept through an application addressing a specific human risk question posed within the Artemis program.

Risk analysis↗
NASA NTRS2022

Deep Interacting Multiple Model Filtering

In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.

Ghananeel Rotithor↗
NASA NTRSDate not supplied

The Future of Integrated Performance Modeling in the Crew Health and Performance – Probabilistic Risk Assessment Project

The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Human Research Program’s Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) was a significant step forward in efforts to robustly quantify the risk to crew health for exploration missions. However, there remains a significant gap in the ability to comprehensively assess and characterize risk across the disparate functionalities and capabilities which comprise the Crew Health and Performance (CHP) system. The Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA) project seeks to characterize CHP risks by expanding beyond the foundation established by its PRA predecessors like IMM and MEDPRAT, that simulate medical risk metrics like loss of crew life and evacuations. One of the new risk measures in the CHP-PRA system is embodied in our Performance Risk Model (PRisM). PRisM provides a novel way of assessing crew performance on mission tasks, using a generalized framework which relates back to NASA-STD-3001. This approach allows PRisM to capture and integrate data from a variety of different domains into a single, unified, reproducible representation of astronaut performance. In this presentation, we discuss the motivation for the CHP-PRA work and give a high level overview of the goals of the project, outline the forward work for PRisM, and discuss collaboration opportunities for the community who might explore if their domain knowledge and data could be represented, integrated, and quantified with these tools, whose outcomes are metrics useful for supporting operational mission planning and decision making.

Lauren McIntyre↗
DOE OSTI2018

Interactive Machine Learning at Scale with CHISSL

We demonstrate CHISSL, a scalable client-server system for real-time interactive machine learning. Our system is capa- ble of incorporating user feedback incrementally and imme- diately without a structured or pre-defined prediction task. Computation is partitioned between a lightweight web-client and a heavyweight server. The server relies on representation learning and agglomerative clustering to learn a dendrogram, a hierarchical approximation of a representation space. The client uses only this dendrogram to incorporate user feedback into the model via transduction. Distances and predictions for each unlabeled instance are updated incrementally and deter- ministically, with O(n) space and time complexity. Our al- gorithm is implemented in a functional prototype, designed to be easy to use by non-experts. The prototype organizes the large amounts of data into recommendations. This allows the user to interact with actual instances by dragging and drop- ping to provide feedback in an intuitive manner. We applied CHISSL to several domains including cyber, social media, and geo-temporal analysis.

Arendt, Dustin L.↗
DOE OSTI2016

The New International Project on Irradiated Graphite Processing Approaches (GRAPA) - 16559

Developing of strategies and methods for the management of spent nuclear (irradiated) graphite is one of the major task in decommissioning internationally. More than 250.000 Mg of i-graphite have been accumulated worldwide. Having completed in 2014 a comprehensive collaborative research programme on the treatment options for the disposal of i-graphite wastes to meet disposal criteria, the IAEA decided to maintain an active group of international specialists in this area as part of the International Decommissioning Network (IDN). To foster further development in R and D and steps towards industrial applications in i-graphite management this group was requested to propose the terms of reference and work programme for a new IAEA project. The basic concept for this future programme has been formulated by the German Forschungszentrum (Research Centre) Juelich, Institute IEK-6. The initiative has expanded to include representation from UK, USA, France, Italy, Russia, Ukraine and Lithuania. Additional Member States of the IAEA are expected to join the project. The concept presented by the IEK-6 was discussed among the experts and extended on an international meeting at IAEA in Vienna in February 2015. In addition to the development of the terms of reference a number of individual tasks have been identified. The project will combine a comprehensive state-of-the-art study with concerted actions as well as cross-cutting sub-projects. This will include a practical demonstration of the removal, treatment and disposal of highly-irradiated graphite from a decommissioned reactor. Prior to official implementation of this project preparatory work has been started on topic areas where weaknesses in the scientific knowledge or the technology have been identified. The scope of the international project ('Irradiated Graphite Processing Approaches, GRAPA') and its expected development as well as the possible impact of GRAPA on the future international i-graphite research activities will be described in this presentation. The project will be performed jointly by both the IAEA International Decommissioning Network and the new IAEA Pretreatment Network (IMM, under development). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Atmospheric Exposure Triggers Light-Induced Degradation in 2D Lead-Halide Perovskites

Quasi-2D perovskites have been pivotal in recent efforts to stabilize perovskite solar cells. Despite the stability boost provided when these materials are introduced in perovskite solar cells, little is known about the intrinsic light and environmental stability of quasi-2D perovskites. In this study, we characterize the photostability of exfoliated quasi-2D perovskite single crystals in air using photoluminescence, infrared, X-ray fluorescence, and energydispersive X-ray spectroscopy. Photoexcitation leads to severe material loss with oxygen as a prerequisite for material breakdown. The effect can be traced to the formation of reactive oxygen species, as demonstrated by increases in the photostability under oxygen-free conditions. We show the effect of combined passivation steps, showcasing the stability enhancement offered by 2D-capping layers in combination with an oxygen-free atmosphere. Our results reveal that the stability of illuminated quasi-2D perovskites depends critically on oxygen exposure, highlighting the importance of oxygen-blocking passivation strategies for stable 2D perovskite-based devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

Abstract Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backward optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network‐related geoscience research.

58 GEOSCIENCES↗
DOE OSTI2020

Indicator Patterns of Forced Change Learned by an Artificial Neural Network

Abstract Many problems in climate science require the identification of signals obscured by both the “noise” of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to predict the year of a given map of annual‐mean temperature (or precipitation) from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as “reliable indicators” of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal‐to‐noise ratios and multilinear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change.

54 ENVIRONMENTAL SCIENCES↗