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DOE OSTI2026

Responsible Adoption of Artificial Intelligence (AI) in Electric Grid Operations

The future of the grid will be powered by AI—or undermined by it. Artificial intelligence is rapidly reshaping grid operations, improving fault detection, forecasting accuracy, and real-time optimization. As AI systems move closer to operational decision loops, however, they introduce new consequence pathways: expanded attack surfaces, model integrity risks, regulatory exposure, and human-automation challenges. This talk presents a consequence-driven framework for deploying AI responsibly in the electric grid. Attendees will gain practical strategies to strengthen resilience, boost reliability, and deploy AI securely — ensuring the grid of the future is not only smarter but safer.

25 - ENERGY STORAGE↗

DOE OSTI2024

Endogenizing Probabilistic Resource Adequacy Risks in Deterministic Capacity Expansion Models

In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding system designs with multiple orders of magnitude more shortfall risk than stated adequacy targets. We develop a novel approximation approach to efficiently endogenize awareness of this risk in a deterministic, linear capacity expansion framework. We compare this approach to exogenous tuning of an energy reserve margin, the leading alternative method to compensate for unmodeled probabilistic shortfall risk. Empirical results from a test system show that the new endogenous method cost-effectively meets all regional reliability targets with a single optimization solve, and produces a near-identical system design as the incumbent method without the need for repeated re-optimizations to find an appropriate reserve level. The endogenous method may also use iterative re-optimizations to further improve solution quality, although these incremental benefits were modest in the system studied.

capacity expansion modeling↗

NASA NTRS1981

The Geothermal Probabilistic Cost Model with an Application to a Geothermal Reservoir at Heber, California

A financial accounting model that incorporates physical and institutional uncertainties was developed for geothermal projects. Among the uncertainties it can handle are well depth, flow rate, fluid temperature, and permit and construction times. The outputs of the model are cumulative probability distributions of financial measures such as capital cost, levelized cost, and profit. These outputs are well suited for use in an investment decision incorporating risk. The model has the powerful feature that conditional probability distribution can be used to account for correlations among any of the input variables. The model has been applied to a geothermal reservoir at Heber, California, for a 45-MW binary electric plant. Under the assumptions made, the reservoir appears to be economically viable.

Orren, L. H.↗

NASA NTRS2004

Modeling and Simulation of Shuttle Launch and Range Operations

The simulation and modeling test bed is based on a mockup of a space flight operations control suitable to experiment physical, procedural, software, hardware and psychological aspects of space flight operations. The test bed consists of a weather expert system to advise on the effect of weather to the launch operations. It also simulates toxic gas dispersion model, impact of human health risk, debris dispersion model in 3D visualization. Since all modeling and simulation is based on the internet, it could reduce the cost of operations of launch and range safety by conducting extensive research before a particular launch. Each model has an independent decision making module to derive the best decision for launch.

Bardina, Jorge↗

NASA NTRS2018

Probabilistic Assessment of Tunguska-Scale Asteroid Impacts

The Tunguska meteor airburst that felled trees across >2000 sq km of Siberian forest in 1908 has been extensively studied and modeled in attempts to deduce its size, properties, and impact characteristics. However, most of the existing modeling and simulation studies have investigated a small subset of cases based on assumptions of representative densities, velocities, or other properties. In this study, we use the Probabilistic Asteroid Impact Risk (PAIR) model to assess 50 million Tunguska-scale asteroid impacts, covering a full range of potential impactor properties. The impact cases are sampled from probabilistic distributions representing our current knowledge of asteroid properties, entry trajectories, and size frequencies, and the entry, airburst, and resulting ground damage are modeled for each case. The results provide a broader characterization of the range and relative likelihood of asteroid properties that could yield Tunguska-scale impacts. The full results of this study and a companion study on impact frequencies are pending publication in an upcoming Tunguska special edition of Icarus [1,2].

Wheeler, Lorien↗

NASA NTRS2015

Radiant Heating Tests on Meteoritic Material

A new effort geared toward modeling the physics of meteor entry and break-up is underway at NASA Ames Research Center. This is part of a broader interdisciplinary effort on providing physics-based risk assessment models for potentially hazardous objects. As part of the entry modeling task we are seeking to improve our understanding of, among other things, the ablation of meteoric material during high speed entry into earths atmosphere. Meteoroid entry differs greatly in some key respects from spacecraft entry modeling. First, the aerothermal environment at these high velocities (18 km/s) is dominated by radiation. Second, meteoroids less than, say, 50m in size, will likely lose a significant portion of their mass during the high-speed atmospheric entry process due to vaporization as well as melting and spallation. The mass of the object, in turn, directly affects the amount of energy deposited in the atmosphere, and therefore the amount of damage done. Thus it is important for us to understand and be able to model this process in greater detail in order to assess the hazard posed by these objects.In this presentation, we first give an overview of the simple ablation models that are typically used in meteor entry calculations. Additionally, we will present a new model which utilizes a similar approach to what is typically done for spacecraft TPS response modeling. This uses an equilibrium assumption near the surface to compute the ablation rate, as is done in heritage material response codes. Next we describe the radiant heating experiment which uses a high-powered laser to emulate the radiation dominated heating environment experienced by meteoroids during atmospheric entry. The facility the Laser Hardened Material Evaluation Laboratory (LHMEL) permits us to expose samples of meteoritic material to heating rates in excess of 100kW/sq.cm. An overview of the experimental set-up and test plan for the initial exploratory campaign at this facility will be given. Then we present both qualitative and quantitative results from this initial test series. Comparisons between predicted and measured ablation rates suggest that there may be significant blockage of the incident beam by the ablation plume. Furthermore, melt is shown to be a significant ablation mechanics, even at high heating rates (16 kW/sq.cm). Finally, comparisons between the phenomenology of the ablation of terrestrial rocks namely, basalt -- to that of meteorites show very different behavior. This is shown to likely be due in part to the effect of composition on the melt viscosity.

Stern, Eric↗

NASA NTRSDate not supplied

Error Localization Examples: Looking for a Needle in a Haystack

Finite element models (FEM) are routinely developed and used during fabrication of high dollar-value hardware. NASA, as part of the pre-flight certification of launch vehicles, routinely conducts vibration and static tests to calibrate models used for flight-risk assessments. During model calibration, certain areas of the model are modified, using engineering judgment and sensitivity analysis, to match the test results. Unfortunately, tools to identify problem areas in the FEM using test data directly are scarce and infrequently applied. Over the years, error localization algorithms have been proposed with very limited success. Recently, the Analytical Dynamics Model Improvement (ADMI) algorithm, which computes closed-form mass and stiffness corrections to match the test data exactly, have been shown to be an effective Error Localization Algorithm (ELA). The paper discusses three examples where ELA is used with simulated test data to locate problem areas. To gain confidence in the approach, the exact answer is shown along with ELA results. Results show that ELA is able to identify general problem areas consistent with known problem areas. In all examples, the ELA identified area is larger than the exact problem area. Nonetheless, with proper optimization tools, calibration results using the ELA identified areas provide excellent results.

model calibration↗

NASA NTRS2013

Launch Vehicle Debris Models and Crew Vehicle Ascent Abort Risk

For manned space launch systems, a reliable abort system is required to reduce the risks associated with a launch vehicle failure during ascent. Understanding the risks associated with failure environments can be achieved through the use of physics-based models of these environments. Debris fields due to destruction of the launch vehicle is one such environment. To better analyze the risk posed by debris, a physics-based model for generating launch vehicle debris catalogs has been developed. The model predicts the mass distribution of the debris field based on formulae developed from analysis of explosions. Imparted velocity distributions are computed using a shock-physics code to model the explosions within the launch vehicle. A comparison of the debris catalog with an existing catalog for the Shuttle external tank show good comparison in the debris characteristics and the predicted debris strike probability. The model is used to analyze the effects of number of debris pieces and velocity distributions on the strike probability and risk.

Gee, Ken↗

DOE OSTI2025

Machine Learning Models for Mapping Groundwater Pollution Risk: Advancing Water Security and Sustainable Development Goals in Georgia, USA

The widespread use of pesticides, such as atrazine and malathion, in agricultural systems raises significant concerns regarding the contamination of groundwater, which serves as a critical resource for drinking water. This study applies machine learning techniques to predict the concentrations of atrazine and malathion in groundwater across Georgia, USA, using 2019 data. A Random Forest classifier was employed to integrate various environmental and demographic factors, including pesticide application rates, precipitation, lithology, and population density, to predict pesticide contamination in groundwater. The models demonstrated high training accuracies of 100% and moderate average testing accuracy of 55% for atrazine and 60% for malathion across five iterations. The low test accuracy of the model, ranging from 50% to 75%, is likely due to overfitting, which can be attributed to the small dataset size and the complex nature of pesticide-contamination patterns, making it challenging for the model to generalize to unseen data. Feature importance analysis revealed that average pesticide usage emerged as the most influential factor for atrazine, while aquifer lithology and precipitation played crucial roles in both models. These results provide valuable insights into the dynamics of pesticide contamination, highlighting areas at greater risk of contamination. The findings underscore the importance of integrating environmental, geological, and agricultural variables for more effective groundwater management and sustainable agricultural practices, contributing to the protection of water resources and public health.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Evaluating the Feasibility of Data Simulation for Radiation Research: Estimating RBE Values for HZE Radiation

As the drive for deep space exploration intensifies, a comprehensive understanding of the health effects of radiation exposure becomes paramount to the future of human space flight. However, epidemiological data for radiation exposure, particularly to high-energy (HZE) ions, is limited, partially due to the financial and logistical costs of radiation studies. As an alternative, this study aims to assess the viability of data simulation strategies to accurately model potential study parameters prior to utilizing laboratory conditions. This study estimates a relative biological effectiveness (RBE) factor based on the solid tumor data for outbred mice provided by Edmundson et al. 2020. Excess relative risk (ERR) models for HZE-Fe ions and gamma radiation were estimated using Poisson regression with Weibull models to represent the background solid tumor hazard without radiation. RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope. The parameters from these models were then applied to simulate iterations of 300 datasets across HZE-Fe doses of 0.05, 0.2, 0.4, and 0.75 Gy; gamma radiation doses of 0.75, 2, and 3 Gy, as well as an unirradiated control group. Sample size per dose varied from 100-500 mice across simulations (800-4000 mice total per dataset). 1500 datasets were generated total (300 each for sample sizes 800, 1600, 3200, and 4000). For each dataset, ERR per radiation type and RBE for HZE-Fe were calculated. The RBE from the Edmondson data was calculated to be 5.55. The RBEs from the simulated data converged around this value across the increasing sample sizes. For n = 4000, the mean RBE across the 300 datasets was 5.71 (95% CI: 5.47-5.95). These results suggest that RBEs calculated from simulated data are credible. Based on this exploration, data simulation is a viable method of testing radiation studies. It provides a method of testing study assumptions and refining research questions prior to embarking on costly laboratory experiments.

Sneha Mehta↗

NASA NTRS1996

Estimate of Space Radiation-Induced Cancer Risks for International Space Station Orbits

Excess cancer risks from exposures to space radiation are estimated for various orbits of the International Space Station (ISS). Organ exposures are computed with the transport codes, BRYNTRN and HZETRN, and the computerized anatomical male and computerized anatomical female models. Cancer risk coefficients in the National Council on Radiation Protection and Measurements report No. 98 are used to generate lifetime excess cancer incidence and cancer mortality after a one-month mission to ISS. The generated data are tabulated to serve as a quick reference for assessment of radiation risk to astronauts on ISS missions.

Wu, Honglu↗

NASA NTRS2025

Assessment of Model Outcomes Between the Integrated Medical Model (IMM) and the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT)

The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is a computational model that provides human health and medical risk predictions for crewed spaceflight missions. MEDPRAT utilizes discrete event modeling and dynamic probabilistic simulation to predict critical mission outcomes (total medical events, crew health index, quality time lost, loss of crew life, removal to definitive care), condition occurrences, and resource consumption. Input parameters for MEDPRAT include crew attributes (e.g., sex), types of mission activities (e.g., whether and where crew members perform an extravehicular activity (EVA)), available resources, treatment information, and probability distributions for medical conditions. As an evolution of the Integrated Medical Model (IMM), MEDPRAT provides enhanced capabilities and higher fidelity, and incorporates more appropriate assumptions for long-duration spaceflight. IMM is the currently accepted standard for quantifying spaceflight mission medical risk in NASA operations that uses a probabilistic risk assessment (PRA) approach. MEDPRAT builds on the same logical foundation as IMM but implements the model architecture through highly optimized Monte Carlo sampling methods. An analysis is performed comparing the outputs from IMM with those from MEDPRAT V1.0 and V2.0 for the same reference missions in order to quantify similarities and differences in the model outcomes. The juxtaposition between IMM and MEDPRAT V1.0 and 2.0 shown in this report demonstrates that these two models generate very similar results; where differences in outcomes are shown, these are in accordance with the underlying assumptions and differences in the model architectures. This validation effort further establishes the credibility and reliability of the MEDPRAT software.

Matthew T Prelich↗

NASA NTRS2014

Data Used in Quantified Reliability Models

Data is the crux to developing quantitative risk and reliability models, without the data there is no quantification. The means to find and identify reliability data or failure numbers to quantify fault tree models during conceptual and design phases is often the quagmire that precludes early decision makers consideration of potential risk drivers that will influence design. The analyst tasked with addressing a system or product reliability depends on the availability of data. But, where is does that data come from and what does it really apply to? Commercial industries, government agencies, and other international sources might have available data similar to what you are looking for. In general, internal and external technical reports and data based on similar and dissimilar equipment is often the first and only place checked. A common philosophy is "I have a number - that is good enough". But, is it? Have you ever considered the difference in reported data from various federal datasets and technical reports when compared to similar sources from national and/or international datasets? Just how well does your data compare? Understanding how the reported data was derived, and interpreting the information and details associated with the data is as important as the data itself.

DeMott, Diana↗

NASA NTRS2003

Risk of Skin Cancer from Space Radiation

We review the methods for estimating the probability of increased incidence of skin cancers from space radiation exposure, and describe some of the individual factors that may contribute to risk projection models, including skin pigment, and synergistic effects of combined ionizing and UV exposure. The steep dose gradients from trapped electrons, protons, and heavy ions radiation during EVA and limitations in EVA dosimetry are important factors for projecting skin cancer risk of astronauts. We estimate that the probability of increased skin cancer risk varies more than 10-fold for individual astronauts and that the risk of skin cancer could exceed 1 % for future lunar base operations for astronauts with light skin color and hair. Limitations in physical dosimetry in estimating the distribution of dose at the skin suggest that new biodosimetry methods be developed for responding to accidental overexposure of the skin during future space missions.

Cucinotta, Francis A.↗

NASA NTRS2000

Toxicological Risks During Human Space Exploration

The goal of toxicological risk assessment of human space flight is to identify and quantify significant risks to astronaut health from air pollution inside the vehicle or habitat, and to develop a strategy for control of those risks. The approach to completing a toxicological risk assessment involves data and experience on the frequency and severity of toxicological incidents that have occurred during space flight. Control of these incidents depends on being able to understand their cause from in-flight and ground-based analysis of air samples, crew reports of air quality, and known failures in containment of toxic chemicals. Toxicological risk assessment in exploration missions must be based on an evaluation of the unique toxic hazards presented by the habitat location. For example, lunar and Martian dust must be toxicologically evaluated to determine the appropriate control measures for exploration missions. Experience with near-earth flights has shown that the toxic products from fires present the highest risk to crew health from air pollution. Systems and payload leaks also present a significant hazard. The health risk from toxicity associated with materials offgassing or accumulation of human metabolites is generally well controlled. Early tests of lunar and Martian dust simulants have shown that each posses the potential to cause fibrosis in the lung in a murine model. Toxicological risks from air pollutants in space habitats originate from many sources. A number of risks have been identified through near-earth operations; however, the evaluation of additional new risks present during exploration missions will be a challenge.

James, John T.↗

NASA NTRS2020

Orbital Debris Quarterly News, April 2020

Inside - Second Fragmentation of Fregat Upper Stage Debris - DAS v3.1 Release - Development of Experimental Hypervelocity Impact Capabilities with Non-Spherical Projectiles - Short-Term Satellite Breakup Risk Assessment Model Process - Two Recent Breakup Events Updated - Space Missions and Satellite Box Score

Orbital Debris↗

NASA NTRS2022

Milwaukee Urban Development: Assessing the Drivers of Urban Flooding Vulnerability in Milwaukee Using NASA Earth Observations

Milwaukee County has experienced an increase in flooding due to climate change and urbanization. The frequency and severity of flooding vary spatially due to differences in land cover, surface permeability, and infrastructure. Marginalized communities tend to experience disproportionately high flooding and damage due to infrastructural inequalities and limited access to resources. To quantify these differences, we used the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model to calculate and create maps of runoff retention, nominal flood depth, and economic damage to buildings in Milwaukee. Our model inputs included land cover, surface permeability, and rainfall. To inform our precipitation inputs, we used NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) and National Weather Service (NWS) data. We assessed the relationship between flood risk and social and environmental spatial data including redlining, racial demographics, greenspace, and community resilience. The data demonstrate that flood risk is higher in historically redlined neighborhoods, majority Hispanic and Black census block groups, areas that lack parks and trees, and areas of low community resilience as measured by the Census Bureau’s Community Resilience Estimates (CRE). These findings will support our partners, Groundwork Milwaukee and Groundwork USA, in their efforts to promote the equitable distribution of resources and support environmental health in urban spaces. The end products of this project provide our partners with tools to assess urban flooding vulnerability, guide future intervention projects, quantify the effects of environmental injustice, and improve stakeholder access to data.

Madeleine Tango↗

DOE OSTI2026

System-Level Integration of Modular Language Models for Real-Time Risk Assessment in Third-Party Risk Management Systems

Large enterprises typically rely on dedicated teams to govern and implement security measures throughout their supply chains, ensuring compliance with enterprise security procedures. There is a significant reliance on Third-Party Risk Management (TPRM) platforms, which often require complete, highly structured information from potential vendors. The review and compliance assurance processes are time- and labor intensive, often requiring several rounds of review between the supply chain security risk management teams, business users, and potential vendors, leading to delays in the supply chain processing and consumer experience. Significant challenges in the risk management paradigm include handling unstructured data in various formats and providing real-time feedback to users to reduce the required review time. This paper presents a novel solution to these challenges. A modular multi-step system architecture is proposed using advances in language processing, specifically for unstructured responses and provides real-time feedback (i.e., 3 seconds) so that users can improve their responses before the TPSRM team review. This novel system architecture will increase information accuracy and significantly reduce time and labor during the review process.

99 - GENERAL AND MISCELLANEOUS↗