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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A data-driven multiscale model for reactive wetting simulations

Here, we describe a data-driven, multiscale technique to model reactive wetting of a silver–aluminum alloy on a Kovar™ (Fe-Ni-Co alloy) surface. We employ molecular dynamics simulations to elucidate the dependence of surface tension and wetting angle on the drop’s composition and temperature. A design of computational experiments is used to efficiently generate training data of surface tension and wetting angle from a limited number of molecular dynamics simulations. The simulation results are used to parameterize models of the material’s wetting properties and compute the uncertainty in the models due to limited data. The data-driven models are incorporated into an engineering-scale (continuum) model of a silver–aluminum sessile drop on a Kovar™ substrate. Model predictions of the wetting angle are compared with experiments of pure silver spreading on Kovar™ to quantify the model-form errors introduced by the limited training data versus the simplifications inherent in the molecular dynamics simulations. The paper presents innovations in the determination of “convergence” of noisy MD simulations before they are used to extract the wetting angle and surface tension, and the construction of their models which approximate physio-chemical processes that are left unresolved by the engineering-scale model. Together, these constitute a multiscale approach that integrates molecular-scale information into continuum scale models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Open-Source Data for MAC-POSTS: Mobility Data Analytics Center - Prediction, Optimization, and Simulation Toolkit for Transportation Systems

MAC-POSTS (Mobility Data Analytics Center - Prediction, Optimization, and Simulation toolkit for Transportation Systems) is a toolkit for dynamic transportation network modeling. Developed by the Mobility Data Analytics Center (MAC) at Carnegie Mellon University, this package implements many classic dynamic transportation network models, as well as new models proposed by MAC members. It has served as one building block for many other models and research projects. As such, this package used to be treated as an internal research project of the MAC lab, and admittedly, the code base is messy, and the interface is hard to use. However, we are working hard to make it a generally usable and useful toolkit for dynamic transportation network modeling. We would really appreciate any feedback, comments, suggestions, or criticisms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

IER-517: Molybdenum Optimized Benchmark System Demonstrating Integral Correlations (MOBY DICK)

Nuclear criticality experiments are essential to the validation of nuclear data used in simulation software. The quality of nuclear data becomes paramount as simulation software becomes more relied upon for criticality safety studies and designs of nuclear systems. To improve the quality of nuclear data, experimenters can design critical experiments that are sensitive to isotope reaction pairs in materials of interest. The efforts conducted by the Organisation for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA) Working Party on Nuclear Criticality Safety (WPNCS) Subgroup 8: Preservation of Expert Knowledge and Judgement Applied to Criticality Benchmarks (SG8) to categorize benchmarks according to their usefulness for nuclear data validation have been of great importance. Based on the OECD studies benchmark experiments included in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook are concisely used by nuclear data evaluators, criticality safety engineers and others to validate nuclear data and simulation results. A lack of benchmarks sensitive to molybdenum in the (ICSBEP), particularly in the intermediate range, was noted by Los Alamos National Laboratory (LANL), the French Institut de Radioprotection et de Sûreté Nucléaire (IRSN), and Y-12 National Security Site prompting them to submit a joint integral experiment request to the Nuclear Criticality Safety Program (NCSP) in 2019. The request included both HEU and Plutonium systems in order to validate differential nuclear data focusing on the intermediate energy range but also includes thermal and fast configurations. This document represents the preliminary design work for a series of molybdenum integral experiments known as Molybdenum Optimized Benchmark System Demonstrating Integral Correlations (MOBY DICK).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Chicago microclimate and building energy use data

These data comprise three elements: - High resolution, 90 m simulated weather data for 1 year at 15 min. intervals (with known gaps toward the end of each month). These files are in .csv format. - A mapping of individual buildings with individual IDs, their latitude/longitude location, and height. (Excel file) - Energy simulation output of these individual buildings, at 15 min. intervals for a whole year. (.json and other files)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Chicago microclimate and building energy use data

These data comprise three elements: - High resolution, 90 m simulated weather data for 1 year at 15 min. intervals (with known gaps toward the end of each month). These files are in .csv format. - A mapping of individual buildings with individual IDs, their latitude/longitude location, and height. (Excel file) - Energy simulation output of these individual buildings, at 15 min. intervals for a whole year. (.json and other files)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Novel data interpretation method for DIII-D divertor retarding field energy analyzer with 3-D particle-in-cell simulations

A novel data interpretation process that utilizes comprehensive particle-in-cell (PIC) simulations is developed for the new retarding field energy analyzer (RFEA) currently being constructed at DIII-D for the lower divertor using the Divertor Material Evaluation System. Furthermore, this probe is expected to survive a heat load of up to 100 MW/m 2 for up to 5 s and reliably measure the main ion temperature (T i ) on the divertor target ranging from 10 to 200 eV. These extreme conditions posed significant engineering limitations on the probe geometry, thus extensive validation work has been performed. The conventional fitting method for the RFEA I–V characteristics is based on a simplified 1-D model without considering the ion space charge inside the probe cavity and may not be sufficient for probes designed for the DIII-D divertor environment. In this article, a more realistic description of the particle propagation process within the RFEA cavity is achieved by including both 3-D geometric effects and ion space charge in the PIC simulations, and the capability to reconstruct the ion energy distribution functions is demonstrated with reasonable consistency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Improving vertical detail in simulated temperature and humidity data using machine learning

Atmospheric models used for weather forecasting and climate predictions discretise the atmosphere onto a vertical grid. There are however atmospheric phenomena that occur on scales smaller than the thickness of those model layers. The formation of low-level clouds due to temperature inversions is an example. This leads to atmospheric models underestimating, or even missing, these clouds and their radiative effects. Using radiosonde observations as training data, a machine learning model is used to improve the vertical detail of modelled profiles of temperature and specific humidity. In addition, a physics-informed machine learning model is developed and compared to the traditional approach; showing improvements in the cloud fraction profiles calculated from its predictions. The vertically enhanced profiles also improve the representation of layers of convective inhibition and anomalous refractivity gradients. This work facilitates targeted improvements to the representation of certain atmospheric processes without the burden of increased memory and computational cost from increasing vertical resolution throughout the whole model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Northern Hemisphere Snow Drought in Earth System Model Simulations and ERA5‐Land Data in 1980–2014

Abstract Low snow levels over the past few decades and predictions of a low‐to‐no snow future have spurred research into snow droughts, which pose a threat to water security and management. Systematic data‐model comparisons of snow drought have been lacking, hindering our understanding of the drivers of snow drought in the past. To address this gap, we analyzed snow drought events using standardized snow water equivalent index derived from monthly results of four numerical experiments using the E3SM Land Model (ELM) and ERA5‐Land data during the period of 1980–2014. Additionally, we compared snow drought duration calculated from models with those from the ERA5‐Land data during selected El Niño‐Southern Oscillation (ENSO) years. The numerical experiments were conducted with ELM driven by two prescribed atmospheric forcings, and with the coupled land‐atmosphere configuration of E3SM with and without plant hydraulics scheme feedback. Analysis reveals that 20%–30% of snow droughts occur due to factors other than above‐normal temperature and low snowfall, such as low soil moisture, warm soil temperature, and low relative humidity, etc., especially in high latitudes (50° North). Furthermore, our study highlights the exacerbating effect of ENSO events on snow drought conditions in various regions, despite some discrepancies between model and ERA5‐Land results. We also identified limitations of the coupled land‐atmosphere models in our current configuration in capturing the spatial patterns of snow droughts. This study underscores the challenge of predicting and mitigating snow drought and the need for a comprehensive understanding of the factors contributing to snow drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A path to intelligent watersheds: coordinating the data to decision pipeline

Operations of multi-reservoir systems are challenged in-part by the interplay of complex physical processes functioning within the watershed. The employment of intelligent systems can be of aid by linking environmental sensing, information technology, data analytics, simulation and decision support to achieve a data-to-decision flow of information. A further challenge is that watershed resources are managed for multiple purposes requiring some level of coordination among numerous resource managers, asset operators and users. System intelligence in this context relies on shared community platforms (data portals, community models), and coordinated communication between decision makers. Opportunities to enrich watershed intelligence has been the subject of a roadmapping exercise for the Department of Energy’s Water Power Technologies Office which has relied on broad stakeholder engagement. Initial phases of engagement involved personal interviews and a series of virtual group meetings, which focused on identifying opportunities to improve the intelligence of the physical infrastructure within our watersheds—examples of feedback include improved sensing of snowpack and runoff, data standards for facilitated data sharing, and better forecasting tools. The latter phase of engagement involved the conduct of a case study in the Upper Colorado River basin where key stakeholders were interviewed to map how their decisions are informed by intelligence from other basin stakeholders. Our presentation will highlight the interdisciplinary flow of information in complex watershed systems and identify physical and institutional opportunities toward the strategic operation of water infrastructure.

Colorado River↗
DOE OSTI2025

Minimizing CGYRO HPC Communication Costs in Ensembles with XGYRO by Sharing the Collisional Constant Tensor Structure

First-principles fusion plasma simulations are both compute and memory intensive, and CGYRO is no exception. The use of many HPC nodes to fit the problem in the available memory thus results in significant communication overhead, which is hard to avoid for any single simulation. That said, most fusion studies are composed of ensembles of simulations, so we developed a new tool, named XGYRO, that executes a whole ensemble of CGYRO simulations as a single HPC job. By treating the ensemble as a unit, XGYRO can alter the global buffer distribution logic and apply optimizations that are not feasible on any single simulation, but only on the ensemble as a whole. The main saving comes from the sharing of the collisional constant tensor structure, since its values are typically identical between parameter-sweep simulations. This data structure dominates the memory consumption of CGYRO simulations, so distributing it among the whole ensemble results in drastic memory savings for each simulation, which in turn results in overall lower communication overhead.

CGYRO↗
DOE OSTI2026

AIF for Vis (Active Inference for simulating human interpretation of data visualization) [SWR-26-084]

AIF for Vis contains the Active Inference models and analysis scripts used to study a simple visualization-interpretation task: estimating the average value of two bars in a bar chart. The work is a proof of concept for translating hypothesized cognitive strategies into executable, inspectable process models. We implement two idealized strategies inspired by dual-process accounts of visualization-aided decision making: *Fast model: a compressed, heuristic strategy that estimates the visual midpoint of the two bars and maintains a single belief over their average. *Slow model: a sequential, analytic strategy that estimates the two bar heights separately and maintains them in working memory before computing an average. Both models use a common Active-Inference-inspired framework for sequential perception, belief updating, action selection, and reporting. Their different internal representations produce distinct predicted vulnerabilities: *the Fast model is more susceptible to tick-salience bias; *the Slow model is more susceptible to working-memory decay. The repository includes the model implementations, scripts used for the experiments reported in the paper, precomputed trial-level results, and plotting scripts.

Goldwyn, Harrison [National Laboratory of the Rock↗
DOE OSTI2022

Foam Fracturing Study for Stimulation Development of Enhanced Geothermal Systems

This is a final technical report for the project: Foam Fracturing Study for Stimulation Development of Enhanced Geothermal Systems (EGS). The goal is to demonstrate the feasibility of foam fracturing in EGS applications. The project, led by Oak Ridge National Laboratory (ORNL), was conducted in collaboration with Temple University. The report describes the research activities with Task 1 at ORNL: foam fracturing testing system development and experimental study on foam fracturing, and Task 2 at Temple University: foam testing and foam characterization. Main findings are: 1. A foam fracturing test system has been developed at ORNL, which can be used to perform foam fracturing under pressure up to 6,000 psi. The system monitors foam density during fracturing online and is capable of testing materials in both monotonic and cyclic (up to 50 Hz) injections. 2. Foam fracturing tests were carried out on Charcoal black granite specimens with a blind borehole to the middle length. Two diameters of blind borehole were tested; G2 series: 9.53 mm and G3 series: 4.76 mm. N2-in-water foam was used with AOS as a surfactant. 3. There was a hole-size effect on fracture initiation pressure. The effect is smaller in the case of foam, which was influenced by the high penetrability of gas in foam. Breakdown pressure showed a behavior just as that of fracture pressure; namely an increased value for small hole samples, while the effect in water fracture was more impressive than in foam fracture. 4. Water mass was reduced in foam fracturing within similar range of breakdown pressures. In G2 series, it was decreased from 10.44 g for water fracturing to 5.17 g, representing more than 50% water reduction. Therefore, there is the potential to reduce water use in EGS stimulation through foam fracturing. 5. Use of cyclic injection has the potential to reduce the breakdown pressure and seismicity in EGS application. Experiments using 4-s cycle period found that specimens can be fractured with a low number of cycles. The fatigue pressure was approximately 64 - 77% of monotonic breakdown pressure for water fracturing and 58 - 94% of the breakdown pressure for foam fracturing. 6. A foam stability testing system has been developed that can test foam at 220 Deg C to 2,000 psi. Tested components of candidate foams included two gases: N2 and CO2; 4 surfactants: AOS, SDS, NP-40 and CTAC; 5 stabilizing agents: guar, bentonite clay, borate salt, silica NPs, and GO. 7. N2 and AOS provided the most stable performance over the tested ranges. Furthermore, the AOS foam with stabilizing agents of guar and borate salt (crosslinker) offered the highest half-life of 20 minutes at 200 Deg C and 1,000 psi. 8. Arrhenius equation and modified power law have been demonstrated to fit well the half-time vs. temperature and pressure data, respectively. These relations can be useful to provide the suggestion for future foam stability study. This submission contains the supporting data developed during the project: 1) A final technical report 2) Granite fracturing data in monotonic and cyclic injections with water and N2 foam Foam performance data in various temperatures and pressures, including half-time, is submitted separately.

15 GEOTHERMAL ENERGY↗