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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Distribution Development for Residual Inventory at the New York West Valley Site - 20400

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351 ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Operations led to contamination in a number of facilities and locations. Some of that contamination has migrated from waste disposal zones to other layers, formations, and features on and off the WNYNSC. Phase I decommissioning activities are ongoing and involve the removal of a number of areas and structures that have been associated with contamination. The purpose of this work is to outline the approach for characterizing contamination not associated with disposed wastes, contaminated structures, or specific releases. In this work, the term, residual radiological activity, is used to describe environmental contamination that exists subsequent to the completion of Phase I decommissioning activities, that is not associated with disposed wastes, contaminated structures, or specific releases. Contamination from the Site was quantified relative to data that characterize the concentrations of radionuclides that exist in background. Background concentrations are those present in the area but having no influence from Site related activities. The existence of residual radiological activity that is elevated relative to background has the potential to contribute to future risks to human health and the environment. As a consequence, the residual inventory information is used to inform the West Valley Probabilistic Performance Assessment (PPA) model to characterize potential future risks to human health and the environment. The centralized West Valley Data Management System (DMS) was the source of information for the data assembled in this analysis. The DMS is a fairly large compilation consisting of thousands of records from investigation studies, with sample dates ranging from 1990 to present. Samples from monitoring wells, boreholes, geoprobe studies, surface water, surface soils, storm water outfalls, ventilation stack filters, plant and animal tissues, and more are included in the DMS. Results are typically reported in units of activity per unit volume. For the purpose of the analyses presented here, all results were converted into consistent units of pCi per unit volume. Since 1990, data have been collected from various locations across the WNYNSC at different times with varying frequency over the course of several decades. As a consequence, a number of potential issues can arise with respect to the assembly of a dataset that is deemed adequate for the characterization of residual radiological activity. These issues were assessed and resolved to the extent possible through careful consideration of the properties of the distributions. The intent was to use data which characterize the current state of the Site. Radionuclides can be designated to one of several groups depending on their origin. In this work the groups considered were 1) Naturally Occurring Radioactive Material (NORM), 2) fallout, and 3) Other (including power plant, medical research, etc). This grouping is a useful construct with respect to the interpretation of fixed laboratory results. For example, NORM radionuclides that exist within a decay chain should have approximately equivalent distributions of concentrations if they are representative of background conditions. Insights such as these can be used as a check to identify sample results that need to be further investigated or omitted due to issues associated with reported results from fixed laboratory analyses. This type of analysis provided a foundation for the assessment of the adequacy of sample results for use in subsequent components of an assessment. The general process for the assessment of residual radiological contamination at the Site consists of a sequence of several steps. First, for each analyte, several statistical tests were performed to assess the weight of evidence against the null hypothesis that the mean of the distribution of concentrations was equal to zero. If the mean of the distribution of concentrations for a given radionuclide was not found to be greater than zero, then it was removed from consideration as a component of the residual radiological contamination. If there was significant evidence to reject the hypothesis of the mean being equal to zero, the second step was to compare the distribution of the data from the Site to that of the corresponding background. A suite of tests was used to compare the distributions of the site and background data. The results of these tests were collectively used to determine if site data are elevated relative to background. The third step was to develop distributions using a Bayesian framework to characterize the distribution of mean of the increment present above background for each of the radionuclides. The Bayesian model implemented allowed for the comparison of site-specific records to background concentrations to better approximate contamination attributed to the Site. A final screening step was employed for radionuclides that exceed background. This screening step compared 95% upper confidence limits (UCLs) from the increment distribution developed in the previous step to the risk screening levels. This approach yields a list of analytes that were determined to be elevated relative to background.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2024

LTV-xEVA Applied Injury Biomechanics

Beginning in Artemis V, Lunar Terrain Vehicles (LTV) will be utilized to enable astronauts to explore the lunar south pole and conduct science farther from the landing site than during the Apollo program. However, LTV operation has the potential to cause injury to the suited crew member during their Extravehicular Activity (EVA). Injury risk caused by LTV acceleration and jerk combined with blunt loading from rigid suit components needs to be better understood. An effort began to create requirements for, model, and address the injury risk caused by the LTV combined with Exploration EVA (xEVA) suits. Mitigation of crew injury is a shared responsibility between LTV and the suit since neither can accomplish this independently. The modeling completed in Fiscal Year 2023 (FY23) helped to verify the fidelity of the requirements and parse out vendor responsibility (LTV, xEVAS, or NASA) for Artemis V and beyond. The scope of the modeling in FY23 used the LTV System Requirements Document (SRD) as worst-case inputs and modeled female 5th-, male 50th-, and male 95th-percentile subjects in hard-mounted seated and semi-standing postures. Soft-mounted (i.e., lap belt) and testing to validate the analysis was determined out of scope for FY23 work.

Spacesuit↗
NASA NTRSDate not supplied

LTV-xEVA Applied Injury Biomechanics

Beginning in Artemis V, Lunar Terrain Vehicles (LTV) will be utilized to enable astronauts to explore the lunar south pole and conduct science farther from the landing site than during the Apollo program. However, LTV operation has the potential to cause injury to the suited crew member during their Extravehicular Activity (EVA). Injury risk caused by LTV acceleration and jerk combined with blunt loading from rigid suit components needs to be better understood. An effort began to create requirements for, model, and address the injury risk caused by the LTV combined with Exploration EVA (xEVA) suits. Mitigation of crew injury is a shared responsibility between LTV and the suit since neither can accomplish this independently. The modeling completed in Fiscal Year 2023 (FY23) helped to verify the fidelity of the requirements and parse out vendor responsibility (LTV, xEVAS, or NASA) for Artemis V and beyond. The scope of the modeling in FY23 used the LTV System Requirements Document (SRD) as worst-case inputs and modeled female 5th-, male 50th-, and male 95th-percentile subjects in hard-mounted seated and semi-standing postures. Soft-mounted (i.e., lap belt) and testing to validate the analysis was determined out of scope for FY23 work.

Spacesuit↗
NASA NTRS2019

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has heightened local wildfire risk as canopy loss facilitates the conversion from bare to fire-prone grassland. We collected images from NASA satellite-based Earth observations to visualize land cover succession at roughly five-year intervals following a severe, mid-1990's beetle infestation to the present. We classified these data by vegetation cover type to quantify grassland encroachment patterns over time. Raster band math provided a change detection analysis on the land cover classifications. Results indicate the highest wildfire risk is linked to herbaceous and black spruce land cover types, The resulting land cover change image will give the Kenai National Wildlife Refuge (KENWR) ecologists a better understanding of where forests have converted to grassland since the 1990s. These classifications provided a foundation for us to integrate digital elevation models (DEMs), temperature, and historical fire data into a model using Python for assessing and mapping changes in wildfire risk. Spatial representations of this risk will contribute to a better understanding of ecological trajectories of beetle-affected landscapes, thereby informing management decisions at KENWR.

Risk modeling↗
DOE OSTI2025

Southwest Regional Partnership on Carbon Sequestration: Phase III (Final Scientific/Technical Report)

The Southwest Regional Partnership on Carbon Sequestration (SWP) is one of 7 regional partnerships formed in 2003 under the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnerships (RCSPs) initiative. The overall purpose of the initiative was to help determine and implement the technology, infrastructure, and regulations most appropriate to promote carbon storage in different regions of the country. Covering Arizona, Colorado, New Mexico, Oklahoma, Utah, and parts of Texas, Wyoming, and Kansas, the SWP evaluated regional carbon storage and utilization potential and focused on technologies and sites that could complement the region’s strong position in energy production. The project progressed through three phases: • Phase I (2003–2005): Characterized regional geologic formations and CO 2 sources, assessed sequestration potential, and identified pilot test sites. • Phase II (2005–2013): Conducted small-scale field tests to validate sequestration methods, including geologic and terrestrial projects. • Phase III (2008–2022): Demonstrated large-scale CO 2 injection at a commercial oil field to test monitoring, verification, and long-term storage strategies. This report covers Phase III. The final project site, the Farnsworth Unit (FWU) in Texas, provided real-world testing of reservoir characterization, monitoring, and risk evaluation tools and processes that could be used in any commercial scale carbon capture, utilization, and storage (CCUS) project. Extensive data collection and analysis helped refine best practices for reservoir characterization, injection monitoring, and storage verification. The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Key lessons learned include the importance of robust data management, strategic site selection, regulatory navigation, and effective industry collaboration. The project’s findings will inform ongoing and future carbon storage initiatives. Task 1 (Regional Characterization) • The SWP continued to participate in national outreach efforts and NATCARB. • The SWP evaluated multiple potential sites before selecting the FWU as the primary field test location. Task 2 (Public Outreach and Education) • The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Task 3 (Permitting and Regulatory Compliance) • The SWP ensured compliance with federal and state regulations, including National Environmental Policy Act (NEPA) requirements. • The SWP obtained all necessary permits for drilling, injection, and monitoring activities. Task 4 (Site Characterization and Planning) • The SWP developed work plans for four key activities: characterization, simulation, monitoring and verification, and risk evaluation. • The SWP collected and synthesized legacy data from multiple sources to build initial static geological models and dynamic reservoir models demonstrating project feasibility. • The SWP conducted an initial risk evaluation and developed mitigation plans. Task 5 (Field Operations and Data Collection) • The SWP drilled, logged, and cored three characterization wells to gather critical subsurface data. • The SWP conducted multiple geophysical surveys, including 3D seismic, crosswell seismic, and vertical seismic profiling, to improve reservoir characterization. Task 6 (Monitoring and Verification) • The SWP performed extensive geological characterization using data from characterization wells and seismic surveys. • The SWP established a surface monitoring network to track CO 2 flux in soil gas, groundwater chemistry, and near-surface atmospheric CO 2 levels. • The SWP built and refined reservoir models to study the effects of relative permeability on simulation behavior and improve calibration with experimental data. Task 7 (Risk Assessment and Model Refinement) • The SWP conducted multiple studies to evaluate reservoir integrity, predict CO 2 plume behavior and improve predictive modeling capabilities. • The SWP refined geological models and used them to enhance the accuracy of simulation models. • The SWP continued quantitative risk assessment of top-ranked risks and strengthened the link between qualitative and quantitative risk methodologies.

02 PETROLEUM↗
NASA NTRSDate not supplied

Design and Technology Maturation of the Stratospheric Projectile Experiment of Entry Dynamics

The supersonic and transonic dynamic stability of blunt-body reentry vehicles currently poses large risks in all of NASA’s ongoing entry missions (MSR SRL, MSR EES, and Dragonfly). These projects have allocated millions of dollars to testing and modeling efforts to buy down risk by using the current state-of-the-art (SoA) facilities at NASA’s disposal. While these facilities have heritage in supplying dynamics data to reentry missions, their availability is severely limited – particularly with the high number of concur-rent projects requesting simultaneous testing– and are costly when considering the science density per dollar. None of the current SoA facility methodologies allow the test model to have the dynamics fully develop through a flight relevant free-stream profile and as such require extrapolations with resultant high uncertainties in order to relate the test dynamics to flight expectations. SPEED is a NASA Ames Center Innovation Fund (CIF) project that is developing a highly tailorable and cost-effective test methodology to better assess the dynamic stability of blunt-body reentry vehicles via a stratospheric balloon flight. This is accomplished by dropping a suite of instrumented capsules from a stratospheric balloon to gain a statistically relevant dataset of scaled reentry vehicles in mission relevant free-flight conditions. This presentation will walk through how the test methodology is being implemented specifically for the Mars Sample Return (MSR) Earth Entry System (EES) geometry in an awarded Flight Opportunities Program (FOP) test flight in early CY24. SPEED Application to MSR: SPEED consists of three main mechanical systems: the Drop Platform, the Projectile, and the test Capsule. SPEED is being developed as a set of guidelines and recommendations for how to test with the proposed Concept of Operations (Conops) since the specific design parameters will vary depending on the specific project’s reference trajectory and entry vehicle design. As such, this presentation will walk through the development time-line as shown in Fig. 2. This is meant to serve as a blueprint for further missions as desired. Mechanical and Avionics Design. The SPEED test platform designed for the MSR-EES capsule geometry with nominal entry parameters has the ability to carry 10 Capsules to altitude instrumented with: 1. 3-Axis Accelerometer 2. IMU 3. Gyroscope 4. Magnetometer 5. Pressure Transducer cruciform 6. Uplook and Horizon Cameras To package the avionics/instrumentation suite, the capsule is approximately 1’ in diameter with the Outer Mold Line (OML) centroid-scaled from the full EES design. The internal volume is gutted and custom-shaped to fit the desired instrumentation suite, as well as to allow for the positioning of ballast mass such that the Center of Gravity is analogous to the flight vehicle. All structural components in the Capsule and Projectile are 3D printed, which significantly reduces the cost of each flight unit to around $1500 including all instrumentation, avionics, and structural components. Flight Conops. The test Capsule is accelerated to the desired altitude and Mach number while stowed in the Projectile, a missile-like vehicle consisting of steel ballast in the nose, a low-drag OML, and an Ejection Mechanism to reliably release the Capsule into the free-flow supersonic conditions. For the MSR-EES design, the capsule employs ~3kg of ballast mass at the nose to accelerate the 1.25kg test Capsule to ~Mach 1.7 at 23km altitude. This requires an initial release altitude of 40km, the quoted limit of a 80kg payload by the FOP-contracted balloon provider. Once the Ejection Mechanism avionics detect the proper conditions, the spring-loaded Ejection Mechanism will release and – guided by the sabot – expose the test Capsule to the desired test conditions for ~5 seconds of free-flight in the supersonic/transonic regimes. Dynamics in the subsonic regime will also be captured with the instrumentation suite with post-flight recovery operations aimed at recovering the high-G-load capable SD cards after the planned hard impact landings. Testing and Development: In the few months the SPEED project has worked the development of MSR-EES flight test, the team has performed lab and drone based testing which this presentation will overview. After the first design phase, the team fabricated Engineering Demonstration Units (EDUs) of all subsystems to perform validation testing shown in Fig. 5. After validation was completed on the subsystem level, a drone-drop test was performed at the recreational flight ceiling of 400ft altitude to assess the SPEED systems in a flight environment. Parameters such as in-flight stability, hard impact landing performance, and avionics performance were quantified and qualified. The FY23 CIF will culminate in a helicopter drop test aboard an Air National Guard Blackhawk. This will prepare the team for the CY24 FOP stratospheric balloon flight that should provide the final verification to begin offering the test platform for mission support. Focus of Presentation: This presentation will outline the technology maturation path of the SPEED implementation to the MSR-EES capsule baseline as well as the details regarding the mechanical system, avionics and instrumentation, and flight operations. Note that a complementary presentation is being submitted for a methodology overview of the SPEED test platform, introducing the testing technique and benefits as well as the full application space of the technology.

pitch damping coefficient↗
NASA NTRS1994

The NASA Space Radiation Health Program

The NASA Space Radiation Health Program is a part of the Life Sciences Division in the Office of Space Science and Applications (OSSA). The goal of the Space Radiation Health Program is development of scientific bases for assuring adequate radiation protection in space. A proposed research program will determine long-term health risks from exposure to cosmic rays and other radiation. Ground-based animal models will be used to predict risk of exposures at varying levels from various sources and the safe levels for manned space flight.

Schimmerling, W.↗
NASA NTRSDate not supplied

Human System Risk Communication: Directed Acyclic Graphs

- The Human System Risk Board (HSRB) is responsible for the management of a portfolio of 30 human system risks that NASA tracks and configuration manages to mitigate for future crewed exploration missions. - The HSRB has been exploring the concept of causal diagrams (in the form of Directed Acyclic Graphs or DAGs) as an approach to creating knowledge graphs for each risk to enable shared mental models of causal flow from spaceflight hazards to mission outcomes among HSRB Stakeholders. - These diagrams are intended to improve insight and communication of risk across the myriad subject matter experts and management interested in human system risk reduction. This includes program managers, systems engineers, and operators in addition to the Human Health and Performance Directorate. - The DAG project was intended to create the foundation for composition of the 30 baselined DAGs into a single risk network and software is being developed in parallel to enable this forward work.

directed acrylic graph↗
NASA NTRS2011

The Analysis of Rush Orders Risk in Supply Chain: A Simulation Approach

Satisfying customers by delivering demands at agreed time, with competitive prices, and in satisfactory quality level are crucial requirements for supply chain survival. Incidence of risks in supply chain often causes sudden disruptions in the processes and consequently leads to customers losing their trust in a company's competence. Rush orders are considered to be one of the main types of supply chain risks due to their negative impact on the overall performance, Using integrated definition modeling approaches (i.e. IDEF0 & IDEF3) and simulation modeling technique, a comprehensive integrated model has been developed to assess rush order risks and examine two risk mitigation strategies. Detailed functions sequence and objects flow were conceptually modeled to reflect on macro and micro levels of the studied supply chain. Discrete event simulation models were then developed to assess and investigate the mitigation strategies of rush order risks, the objective of this is to minimize order cycle time and cost.

Mahfouz, Amr↗
NASA NTRS1976

Space-based solar power conversion and delivery systems study. Volume 3: Economic analysis of space-based solar power systems

A variety of economic and programmatic issues are discussed concerning the development and deployment of a fleet of space-based solar power satellites (SSPS). The costs, uncertainties and risks associated with the current photovoltaic SSPS configuration, and with issues affecting the development of an economically viable SSPS development program are analyzed. The desirability of a low earth orbit (LEO) demonstration satellite and a geosynchronous (GEO) pilot satellite is examined and critical technology areas are identified. In addition, a preliminary examination of utility interface issues is reported. The main focus of the effort reported is the development of SSPS unit production, and operation and maintenance cost models suitable for incorporation into a risk assessment (Monte Carlo) model (RAM). It is shown that the key technology area deals with the productivity of man in space, not, as might be expected, with some hardware component technology.

Hazelrigg, G. A., Jr.↗
DOE OSTI2023

Hanford Waste Treatment Plant Effluent Management Facility Stack Effluent Monitoring: Sampling Probe Location Qualification Evaluation

The Hanford Tank Waste Treatment and Immobilization Plant Effluent Management Facility (EMF) stack monitor location was qualified using a combination of scale model stacks to mitigate the risk of identifying that the sampling location does not meet the qualification criteria on the full-scale stack. The LV-S1 scale model stack was used as a baseline, augmented by the LB-S1 and LV-S2 scale model stacks to address the Direct Feed Low Activity Waste Effluent Management Facility Vessel Vent Process (DVP) injection into the main Active Confinement Ventilation (ACV) system duct. As required by the American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1-1999 standard, the scale model and its sampling locations were geometrically similar to the actual stack, and the Reynolds numbers for both the actual and model stacks were >10,000. An additional criterion is that the product of the hydraulic diameter and mean velocity (DV) of the full-scale stack must be between 1/6 DV and 6 DV of the scale model stack tests. The LV-S1, LB-S1, and LV-S2 scale model stack tests have met the criteria of the ANSI/HPS N13.1-1999 standard to demonstrate the stack sampling locations are well mixed. Verification tests of the EMF stack were performed at normal operating conditions. The minimum 1/6 DV value and the maximum 6 DV value from the scale model testing determine the range of stack flow rates for which the full-scale stack may be operated while remaining in compliance with the stack verification criterion. A practical range for the full-scale stack qualification uses the average DV through 6 DV from the scale model tests to compute the corresponding flow rates. Table S1 lists the operating flow rate along with the average and maximum qualified stack flow rate based on the LV-S1 scale model DV values. The operating flow is below the maximum qualified stack flow, which means that the scale model test results are acceptable for stack qualification. The remaining criteria for the stack verification to be considered valid involve the flow angle and velocity uniformity results. First, the flow angle at the full-scale stack must be ≤20°. Second, the velocity uniformity at the full-scale stack must be ≤20% coefficient of variance (COV). Finally, the velocity uniformity results for the actual and scale model stack tests must agree within 5% COV. These criteria were met through the full-scale stack test at the EMF. Flow angle results were <5°; all flow angle results were within the ≤20° criterion. The velocity uniformity results for each test condition ranged between 2.2% COV and 4.3% COV, all of which were within the range of the target % COV values from the scale model tests on the LV-S1, LB-S1, and LV S2 scale models. Based on these stack verification test results, the EMF filtered exhaust stack sampling location meets the qualification criteria provided in the ANSI/HPS N13.1-1999 standard for all planned fan operating configurations. This includes each combination of ACV fans with DVP exhausters. Further changes to the system configuration or operating conditions that are outside the qualified flow rates described in this report may require additional tests or analyses to determine compliance with the standard.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach

Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.

25 ENERGY STORAGE↗
NASA NTRS1991

Surface knowledge and risks to landing and roving - The scale problem

The role of surface information in the performance of surface exploration missions is discussed. Accurate surface models based on direct measurements or inference are considered to be an important component in mission risk management. These models can be obtained using high resolution orbital photography or a combination of laser profiling, thermal inertia measurements, and/or radar. It is concluded that strategies for Martian exploration should use high confidence models to achieve maximum performance and low risk.

Bourke, Roger D.↗
NASA NTRS1999

Systems Engineering Design Via Experimental Operation Research: Complex Organizational Metric for Programmatic Risk Environments (COMPRE)

Unique and innovative graph theory, neural network, organizational modeling, and genetic algorithms are applied to the design and evolution of programmatic and organizational architectures. Graph theory representations of programs and organizations increase modeling capabilities and flexibility, while illuminating preferable programmatic/organizational design features. Treating programs and organizations as neural networks results in better system synthesis, and more robust data modeling. Organizational modeling using covariance structures enhances the determination of organizational risk factors. Genetic algorithms improve programmatic evolution characteristics, while shedding light on rulebase requirements for achieving specified technological readiness levels, given budget and schedule resources. This program of research improves the robustness and verifiability of systems synthesis tools, including the Complex Organizational Metric for Programmatic Risk Environments (COMPRE).

Mog, Robert A.↗
DOE OSTI2024

Envisioning U.S. Climate Predictions and Projections to Meet New Challenges

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways, and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

A Systems Model for System-Wide Safety Safety Demonstrator (SD-1): Wildfire Response Operations

The aim of this internship-based project was to contribute to the ongoing development of a systems model for System-Wide Safety’s first Technical Challenge 5 (TC5) series Safety Demonstrator (SD-1), which will be a demonstration of an In-Time Aviation Safety Management System (IASMS) in emerging wildfire response operations. Using Models-Based Systems Engineering (MBSE) principles to develop the model, I organized and traced previously collected stakeholder needs from the Spring 2022 NASA System-Wide Safety Wildland Firefighting Operations Virtual Workshop (https://nari.arc.nasa.gov/sws-wildfire) to system elements, creating connections which can be used in the future by the project engineers to identify and address requirements gaps throughout the system design process. I also identified and modeled preliminary use case scenarios for aerial assets in the demonstrator and, building on previously produced preliminary high-level models of the 8 SD-1 Services, Functions, and Capabilities (SFCs) and their IASMS data flows, worked to model the Real-Time Risk Assessment (RTRA) tool as an implementation of Risk Assessment and Management that can take in multiple sets of data monitored by SFCs. Project deliverables include stakeholder requirements tables and matrices and systems model diagrams produced with MagicDraw software in the SysML Systems Modeling Language, with eventual plans to connect model diagrams to a Department of Defense Operational Viewpoint (OV-1) graphic, a high-level operational concept graphic that will be used to visualize the SD-1 scenarioin a future phase. The system model serves to provide a common understanding of the scope of and activities necessary for the completion of SD-1,and traces how stakeholder needs are to be addressed.

model-based systems engineering↗
NASA NTRS2011

Integration of Evidence Base into a Probabilistic Risk Assessment

INTRODUCTION: A probabilistic decision support model such as the Integrated Medical Model (IMM) utilizes an immense amount of input data that necessitates a systematic, integrated approach for data collection, and management. As a result of this approach, IMM is able to forecasts medical events, resource utilization and crew health during space flight. METHODS: Inflight data is the most desirable input for the Integrated Medical Model. Non-attributable inflight data is collected from the Lifetime Surveillance for Astronaut Health study as well as the engineers, flight surgeons, and astronauts themselves. When inflight data is unavailable cohort studies, other models and Bayesian analyses are used, in addition to subject matters experts input on occasion. To determine the quality of evidence of a medical condition, the data source is categorized and assigned a level of evidence from 1-5; the highest level is one. The collected data reside and are managed in a relational SQL database with a web-based interface for data entry and review. The database is also capable of interfacing with outside applications which expands capabilities within the database itself. Via the public interface, customers can access a formatted Clinical Findings Form (CLiFF) that outlines the model input and evidence base for each medical condition. Changes to the database are tracked using a documented Configuration Management process. DISSCUSSION: This strategic approach provides a comprehensive data management plan for IMM. The IMM Database s structure and architecture has proven to support additional usages. As seen by the resources utilization across medical conditions analysis. In addition, the IMM Database s web-based interface provides a user-friendly format for customers to browse and download the clinical information for medical conditions. It is this type of functionality that will provide Exploratory Medicine Capabilities the evidence base for their medical condition list. CONCLUSION: The IMM Database in junction with the IMM is helping NASA aerospace program improve the health care and reduce risk for the astronauts crew. Both the database and model will continue to expand to meet customer needs through its multi-disciplinary evidence based approach to managing data. Future expansion could serve as a platform for a Space Medicine Wiki of medical conditions.

Saile, Lyn↗
DOE OSTI2024

Utilizing digitized occurrence records of Midwestern feral Cannabis sativa to develop ecological niche models

Hemp (Cannabis sativa L.) has historically played a vital role in agriculture across the globe. Feral and wild populations have served as genetic resources for breeding, conservation, and adaptation to changing environmental conditions. However, feral populations of Cannabis, specifically in the Midwestern United States, remain poorly understood. This study aims to characterize the abiotic tolerances of these populations, estimate suitable areas, identify regions at risk of abiotic suitability change, and highlight the utility of ecological niche models (ENMs) in germplasm conservation. The Maxent algorithm was used to construct a series of ENMs. Validation metrics and MOP (Mobility-oriented Parity) analysis were used to assess extrapolation risk and model performance. We also projected the final projected under current and future climate scenarios (2021–2040 and 2061–2080) to assess how abiotic suitability changes with time. Climate change scenarios indicated an expansion of suitable habitat, with priority areas for germplasm collection in Indiana, Illinois, Kansas, Missouri, and Nebraska. This study demonstrates the application of ENMs for characterizing feral Cannabis populations and highlights their value in germplasm conservation and breeding efforts. Populations of feral C. sativa in the Midwest are of high interest, and future research should focus on utilizing tools to aid the collection of materials for the characterization of genetic diversity and adaptation to a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗