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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Impact of Defect Distributions on the Reliability of Structural Materials

The purpose of this study is to develop a probabilistic model to estimate the fatigue life of gas turbine disks made of powder superalloy metals. Such metals are known to have high strength and ductility under extremely high temperatures and known to have high fatigue lives. However, despite the high quality controls instituted during metal processing, sufficiently high numbers of ceramic inclusions materialize within the metal matrix creating weak links that lead to reduced fatigue lives. To study the effect of such ceramic inclusions on the fatigue life of gas turbine disks, an experimental program has been initiated at the NASA Glenn Research. In this phase of the study, a statistical analysis of the test results is undertaken which served as the basis of the probabilistic simulation model. The study is a part of the Crack Resistant Disk Materials task within the Ultra Safe Propulsion Project.

Ghosn, Michel J.↗
DOE OSTI2018

K2-231 b: A Sub-Neptune Exoplanet Transiting a Solar Twin in Ruprecht 147

We identify a sub-Neptune exoplanet (R {sub p} = 2.5 ± 0.2 R{sub ⊕}) transiting a solar twin in the Ruprecht 147 star cluster (3 Gyr, 300 pc, [Fe/H] = +0.1 dex). The ∼81 day light curve for EPIC 219800881 (V = 12.71) from K2 Campaign 7 shows six transits with a period of 13.84 days, a depth of ∼0.06%, and a duration of ∼4 hr. Based on our analysis of high-resolution MIKE spectra, broadband optical and NIR photometry, the cluster parallax and interstellar reddening, and isochrone models from PARSEC, Dartmouth, and MIST, we estimate the following properties for the host star: M {sub ⋆} = 1.01 ± 0.03 M{sub ⊙}, R {sub ⋆} = 0.95 ± 0.03 R{sub ⊙}, and T{sub eff} = 5695 ± 50 K. This star appears to be single based on our modeling of the photometry, the low radial velocity (RV) variability measured over nearly 10 yr, and Keck/NIRC2 adaptive optics imaging and aperture-masking interferometry. Applying a probabilistic mass–radius relation, we estimate that the mass of this planet is M {sub p} = 7 + 5 – 3 M{sub ⊕}, which would cause an RV semi-amplitude of K = 2 ± 1 m s{sup −1} that may be measurable with existing precise RV facilities. After statistically validating this planet with BLENDER, we now designate it K2-231b, making it the second substellar object to be discovered in Ruprecht 147 and the first planet; it joins the small but growing ranks of 22 other planets and three candidates found in open clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Re-evaluating probable maximum precipitation estimates: sensitivity to transposition domains and storm rotation using modern datasets

This study examines the sensitivity of Probable Maximum Precipitation (PMP) estimates to key methodological decisions embedded in the legacy approach adopted in the U.S. National Weather Service Hydrometeorological Reports No. 51 and No. 52. Although widely used for infrastructure design and risk regulation, fundamental aspects of PMP estimation—such as storm sample size, transposition domain, maximization procedures, and storm rotation—remain poorly constrained and lack formal guidance. Using the Red Rock watershed in Iowa as a case study, and leveraging the 2002–2023 NOAA Analysis of Record for Calibration (AORC) precipitation dataset, we systematically evaluate how each methodological choice, individually and in combination, influences PMP estimates. Our findings demonstrate that PMP is not a fixed physical upper bound but rather a modeling construct shaped heavily by user-defined assumptions. Notably, PMP values derived from modern gridded rainfall datasets can be substantially higher than the legacy estimate used in the original spillway design for Red Rock Dam. Decisions regarding storm sample size, domain extent, climatological window, and particularly storm rotation all contributed to higher PMP estimates. Storm rotation alone—a loosely constrained element in the current PMP practice—can amplify PMP by more than 25%. These results reveal the lack of standardized bounds in current PMP workflows and the need for systematic sensitivity and uncertainty analysis. As PMP estimation shifts toward probabilistic approaches, incorporating physically meaningful storm attributes will be key to developing more transparent, defensible methods for dam safety and climate-resilient infrastructure.

Probable maximum precipitation↗
DOE OSTI2018

Testing redMaPPer centring probabilities using galaxy clustering and galaxy–galaxy lensing

Here, galaxy cluster centring is a key issue for precision cosmology studies using galaxy surveys. Mis-identification of central galaxies causes systematics in various studies such as cluster lensing, satellite kinematics, and galaxy clustering. The red-sequence Matched-filter Probabilistic Percolation (redMaPPer) estimates the probability that each member galaxy is central from photometric information rather than specifying one central galaxy. The redMaPPer estimates can be used for calibrating the off-centring effect, however, the centring algorithm has not previously been well-tested. We test the centring probabilities of redMaPPer cluster catalogue using the projected cross-correlation between redMaPPer clusters with photometric red galaxies and galaxy–galaxy lensing. We focus on the subsample of redMaPPer clusters in which the redMaPPer central galaxies (RMCGs) are not the brightest member galaxies (BMEM) and both of them have spectroscopic redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Optimizing High Performance Markov Clustering for Pre-Exascale Architectures

HipMCL is a high-performance distributed memory implementation of the popular Markov Cluster Algorithm (MCL) and can cluster large-scale networks within hours using a few thousand CPU-equipped nodes. It relies on sparse matrix computations and heavily makes use of the sparse matrix-sparse matrix multiplication kernel (SpGEMM). The existing parallel algorithms in HipMCL are not scalable to Exascale architectures, both due to their communication costs dominating the runtime at large concurrencies and also due to their inability to take advantage of accelerators that are increasingly popular. In this work, we systematically remove scalability and performance bottlenecks of HipMCL. We enable GPUs by performing the expensive expansion phase of the MCL algorithm on GPU. Additionally, we propose a CPU-GPU joint distributed SpGEMM algorithm called pipelined Sparse SUMMA and integrate a probabilistic memory requirement estimator that is fast and accurate. Furthermore, we develop a new merging algorithm for the incremental processing of partial results produced by the GPUs, which improves the overlap efficiency and the peak memory usage. We also integrate a recent and faster algorithm for performing SpGEMM on CPUs. We validate our new algorithms and optimizations with extensive evaluations. With the enabling of the GPUs and integration of new algorithms, HipMCL is up to 12.4x faster, being able to cluster a network with 70 million proteins and 68 billion connections just under 15 minutes using 1024 nodes of ORNL's Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Effects of social structure and management on risk of disease establishment in wild pigs

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many 28 wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially-structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases 2 such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group, and spatial distribution (monthly home range overlap and distance) on wild pig contact. We also estimated the impacts of management-induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between-group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modeling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low-intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. Here, we demonstrate an approach for understanding the relative role of spatial, social, and individual-level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially- and socially-structured wildlife species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Stochastic Methods Applied to Power System Operations with Renewable Energy: A Review

Renewable energy resources have been rapidly integrated into power systems in many parts of the world, contributing to a cleaner and more sustainable supply of electricity. Wind and solar resources also introduce new challenges for system operations and planning in terms of economics and reliability because of their variability and uncertainty. Operational strategies based on stochastic optimization have been developed recently to address these challenges. In general terms, these stochastic strategies either embed uncertainties into the scheduling formulations (e.g., the unit commitment [UC] problem) in probabilistic forms or develop more appropriate operating reserve strategies to take advantage of advanced forecasting techniques. Other approaches to address uncertainty are also proposed, where operational feasibility is ensured within an uncertainty set of forecasting intervals. In this report, a comprehensive review is conducted to present the state of the art through Spring 2015 in the area of stochastic methods applied to power system operations with high penetration of renewable energy. Chapters 1 and 2 give a brief introduction and overview of power system and electricity market operations, as well as the impact of renewable energy and how this impact is typically considered in modeling tools. Chapter 3 reviews relevant literature on operating reserves and specifically probabilistic methods to estimate the need for system reserve requirements. Chapter 4 looks at stochastic programming formulations of the UC and economic dispatch (ED) problems, highlighting benefits reported in the literature as well as recent industry developments. Chapter 5 briefly introduces alternative formulations of UC under uncertainty, such as robust, chance-constrained, and interval programming. Finally, in Chapter 6, we conclude with the main observations from our review and important directions for future work.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

CIAE-ANL CRADA for SFR SAFETY ANALYSIS: Final CRADA Report

The purpose of this project is joint verification and validation of system analysis code capabilities for Sodium-cooled Fast Reactor (SFR) design and licensing. As the researchers and code developers at the Argonne National Laboratory (ANL) and China Institute of Atomic Energy (CIAE) independently build their own SFR system analysis capabilities, this collaboration facilitated exchange of information on, and evaluation of alternative approaches for, modeling the key phenomena (such as decay heat removal and natural circulation) and the safety-related components (such as pumps, heat exchangers, and outlet plenum) common in SFR systems. The analysis at ANL and CIAE also applied their respective computational capabilities and methods (in SAS4A/SASSYS-1 computer code at ANL and FR-Sdaso computer code at CIAE) on jointly identified benchmark problems for verification, and publicly available integral tests for validation, of their results. ANL team also shared the expertise on the established software quality assurance practices and the techniques for component reliability estimates to support probabilistic risk assessments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning

The GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to MeV-scale gamma rays over previous telescopes. The novel pixel-based charge readout allows for imaging of the tracks of electrons scattered by Compton interactions of incident gamma rays. The two primary contributors to the accuracy of a Compton telescope in reconstructing an incident gamma-ray’s original direction are its energy and position resolution. In this work, we focus on using deep learning to optimize the reconstruction of the initial position and direction of electrons scattered in Compton interactions, including using probabilistic models to estimate predictive uncertainty. We show that the deep-learning models are able to predict locations of Compton scatters of MeV-scale gamma rays from simulated 500 μm pixel-based data to better than 1 mm rms error and are sensitive to the initial direction of the scattered electron. We compare and contrast different deep-learning uncertainty estimation algorithms for reconstruction applications. Additionally, we show that event-by-event estimates of the uncertainty of the locations of the Compton scatters can be used to select those events that were reconstructed most accurately, leading to improvement in locating the origin of gamma-ray sources on the sky.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2014

Optimizing Medical Kits for Spaceflight

The Integrated Medical Model (IMM) is a probabilistic model that estimates medical event occurrences and mission outcomes for different mission profiles. IMM simulation outcomes describing the impact of medical events on the mission may be used to optimize the allocation of resources in medical kits. Efficient allocation of medical resources, subject to certain mass and volume constraints, is crucial to ensuring the best outcomes of in-flight medical events. We implement a new approach to this medical kit optimization problem. METHODS We frame medical kit optimization as a modified knapsack problem and implement an algorithm utilizing a dynamic programming technique. Using this algorithm, optimized medical kits were generated for 3 different mission scenarios with the goal of minimizing the probability of evacuation and maximizing the Crew Health Index (CHI) for each mission subject to mass and volume constraints. Simulation outcomes using these kits were also compared to outcomes using kits optimized..RESULTS The optimized medical kits generated by the algorithm described here resulted in predicted mission outcomes more closely approached the unlimited-resource scenario for Crew Health Index (CHI) than the implementation in under all optimization priorities. Furthermore, the approach described here improves upon in reducing evacuation when the optimization priority is minimizing the probability of evacuation. CONCLUSIONS This algorithm provides an efficient, effective means to objectively allocate medical resources for spaceflight missions using the Integrated Medical Model.

Optimization↗
NASA NTRS2014

Mass and Volume Optimization of Space Flight Medical Kits

Resource allocation is a critical aspect of space mission planning. All resources, including medical resources, are subject to a number of mission constraints such a maximum mass and volume. However, unlike many resources, there is often limited understanding in how to optimize medical resources for a mission. The Integrated Medical Model (IMM) is a probabilistic model that estimates medical event occurrences and mission outcomes for different mission profiles. IMM simulates outcomes and describes the impact of medical events in terms of lost crew time, medical resource usage, and the potential for medically required evacuation. Previously published work describes an approach that uses the IMM to generate optimized medical kits that maximize benefit to the crew subject to mass and volume constraints. We improve upon the results obtained previously and extend our approach to minimize mass and volume while meeting some benefit threshold. METHODS We frame the medical kit optimization problem as a modified knapsack problem and implement an algorithm utilizing dynamic programming. Using this algorithm, optimized medical kits were generated for 3 mission scenarios with the goal of minimizing the medical kit mass and volume for a specified likelihood of evacuation or Crew Health Index (CHI) threshold. The algorithm was expanded to generate medical kits that maximize likelihood of evacuation or CHI subject to mass and volume constraints. RESULTS AND CONCLUSIONS In maximizing benefit to crew health subject to certain constraints, our algorithm generates medical kits that more closely resemble the unlimited-resource scenario than previous approaches which leverage medical risk information generated by the IMM. Our work here demonstrates that this algorithm provides an efficient and effective means to objectively allocate medical resources for spaceflight missions and provides an effective means of addressing tradeoffs in medical resource allocations and crew mission success parameters.

Monte Carlo Method↗
NASA NTRS2016

Using Probabilistic Methods in Water Scarcity Assessments: A First Step Towards a Water Scarcity Risk Assessment Framework

Water scarcity -driven by climate change, climate variability, and socioeconomic developments- is recognized as one of the most important global risks, both in terms of likelihood and impact. Whilst a wide range of studies have assessed the role of long term climate change and socioeconomic trends on global water scarcity, the impact of variability is less well understood. Moreover, the interactions between different forcing mechanisms, and their combined effect on changes in water scarcity conditions, are often neglected. Therefore, we provide a first step towards a framework for global water scarcity risk assessments, applying probabilistic methods to estimate water scarcity risks for different return periods under current and future conditions while using multiple climate and socioeconomic scenarios.

water scarcity↗
NASA NTRS2017

Radar Detectability Studies of Slow and Small Zodiacal Dust Cloud Particles. III. The Role of Sodium and the Head Echo Size on the Probability of Detection

We present a path forward on a long-standing issue concerning the flux of small and slow meteoroids, which are believed to be the dominant portion of the incoming meteoric mass flux into the Earth's atmosphere. Such a flux, which is predicted by dynamical dust models of the Zodiacal Cloud, is not evident in ground-based radar observations. For decades this was attributed to the fact that the radars used for meteor observations lack the sensitivity to detect this population, due to the small amount of ionization produced by slow-velocity meteors. Such a hypothesis has been challenged by the introduction of meteor head echo (HE) observations with High Power and Large Aperture radars, in particular the Arecibo 430 MHz radar. Janches et al. developed a probabilistic approach to estimate the detectability of meteors by these radars and initially showed that, with the current knowledge of ablation and ionization, such particles should dominate the detected rates by one to two orders of magnitude compared to the actual observations. In this paper, we include results in our model from recently published laboratory measurements, which showed that (1) the ablation of Na is less intense covering a wider altitude range; and (2) the ionization probability, Beta ip, for Na atoms in the air is up to two orders of magnitude smaller for low speeds than originally believed. By applying these results and using a somewhat smaller size of the HE radar target we offer a solution that reconciles these observations with model predictions.

meteorites↗
DOE OSTI2017

Solar Thermoelectricity via Advanced Latent Heat Storage: A Cost-Effective Small-Scale CSP Application

We are developing a novel concentrating solar electricity-generating technology that is both modular and dispatchable. Solar ThermoElectricity via Advanced Latent heat Storage (STEALS) uses concentrated solar flux to generate high-temperature thermal energy, which directly converts to electricity via thermoelectric generators (TEGs), stored within a phase-change material (PCM) for electricity generation at a later time, or both allowing for simultaneous charging of the PCM and electricity generation. STEALS has inherent features that drive its cost-competitive scale to be much smaller than current commercial concentrating solar power (CSP) plants. Most obvious is modularity of the solid-state TEG, which favors smaller scales in the kilowatt range as compared to CSP steam turbines, which are minimally 50 MWe for commercial power plants. Here, we present techno-economic and market analyses that show STEALS can be a cost-effective electricity-generating technology with particular appeal to small-scale microgrid applications. We evaluated levelized cost of energy (LCOE) for STEALS and for a comparable photovoltaic (PV) system with battery storage. For STEALS, we estimated capital costs and the LCOE as functions of the type of PCM including the use of recycled aluminum alloys, and evaluated the cost tradeoffs between plasma spray coatings and solution-based boron coatings that are applied to the wetted surfaces of the PCM subsystem. We developed a probabilistic cost model that accounts for uncertainties in the cost and performance inputs to the LCOE estimation. Our probabilistic model estimated LCOE for a 100-kWe STEALS system that had 5 hours of thermal storage and 8-10 hours of total daily power generation. For these cases, the solar multiple for the heliostat field varied between 1.12 and 1.5. We identified microgrids as a likely market for the STEALS system. We characterized microgrid markets in terms of nominal power, dispatchability, geographic location, and customer type, and specified additional features for STEALS that are needed to meet the needs of this growing power market.

14 SOLAR ENERGY↗
DOE OSTI2020

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

bayesian structural time series↗
DOE OSTI2017

Risk Assessment and Management of Uranium Mill Tailings Legacies - 17508

exploitation activities took place at 61 sites. The great majority of these mining sites are located in central-east Portugal, including the Urgeirica mine that was the most important uranium deposit exploited in the country. The mining activities at this site generated 1.6 million m{sup 3} of radioactive wastes disposed in an open air area originating the largest radioactive tailings pile of the country. The environmental remediation of the Urgeirica site was one of the main priorities of the national plan for the rehabilitation of former uranium mining sites. Site remediation, that included the mine industrial area with several support infrastructures and a processing plant, was executed between 2006 and 2008 (1. phase) and between 2008 and 2014 (2. phase). Nevertheless, the environmental rehabilitation of the Portuguese uranium legacies sites is not yet concluded. One of the last largest mining complexes, the Quinta do Bispo open pit, is scheduled to be rehabilitated only in 2020. This work presents a study to evaluate the environmental impacts from the uranium mill tailings legacies before and after the remediation works, focusing on two different approaches: a deterministic methodology for dose assessment and a probabilistic methodology with the Monte Carlo method to estimate the incremental lifetime risk. Probabilistic distributions of the input parameters were used in the risk calculation approach. The methodology for dose and risk assessments may be applied to other sites that are still under intervention and the results may be used to prioritize the remediation stages. (authors)

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

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗