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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Vertically Resolved Canopy Improves Chemical Transport Model Predictions of Ozone Deposition to North Temperate Forests

Abstract Dry deposition is the second largest tropospheric ozone (O 3 ) sink and occurs through stomatal and nonstomatal pathways. Current O 3 uptake predictions are limited by the simplistic big‐leaf schemes commonly used in chemical transport models (CTMs) to parameterize deposition. Such schemes fail to reproduce observed O 3 fluxes over terrestrial ecosystems, highlighting the need for more realistic treatment of surface‐atmosphere exchange in CTMs. We address this need by linking a resolved canopy model (1D Multi‐Layer Canopy CHemistry and Exchange Model, MLC‐CHEM) to the GEOS‐Chem CTM and use this new framework to simulate O 3 fluxes over three north temperate forests. We compare results with in situ measurements from four field studies and with standalone, observationally constrained MLC‐CHEM runs to test current knowledge of O 3 deposition and its drivers. We show that GEOS‐Chem overpredicts observed O 3 fluxes across all four studies by up to 2×, whereas the resolved‐canopy models capture observed diel profiles of O 3 deposition and in‐canopy concentrations to within 10%. Relative humidity and solar irradiance are strong O 3 flux drivers over these forests, and uncertainties in those fields provide the largest remaining source of model deposition biases. Flux partitioning analysis shows that: (a) nonstomatal loss accounts for 60% of O 3 deposition on average; (b) in‐canopy chemistry makes only a small contribution to total O 3 fluxes; and (c) the CTM big‐leaf treatment overestimates O 3 ‐driven stomatal loss and plant phytotoxicity in these temperate forests by up to 7×. Results motivate the application of fully online vertically explicit canopy schemes in CTMs for improved O 3 predictions.

Vermeuel, Michael P. [Department of Soil, Water, a↗
DOE OSTI2021

A database of global storm surge reconstructions

Storm surges are among the deadliest coastal hazards and understanding how they have been affected by climate change and variability in the past is crucial to prepare for the future. However, tide gauge records are often too short to assess trends and perform robust statistical analyses. Here we use a data-driven modeling framework to simulate daily maximum surge values at 882 tide gauge locations across the globe. We use five different atmospheric reanalysis products for the storm surge reconstruction, the longest one going as far back as 1836. The data that we generate can be used, for example, for long-term trend analyses of the storm surge climate and identification of regions where changes in the intensity and/or frequency of storms surges have occurred in the past. It also provides a better basis for robust extreme value analysis, especially for tide gauges where observational records are short. The data are made available for public use through an interactive web-map as well as a public data repository.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Floating Wind Turbine Control Optimization

We present a framework for optimizing the control parameters of floating offshore wind turbines (FOWTs). The framework combines aeroelastic simulations with a systems engineering model and control software. In an example of the optimization framework, we minimize tower damage equivalent loading with generator speed constraints. We also study the effect of thrust-limiting control and quantify the trade off between fatigue loading and energy capture using a set of optimal controller designs. Finally, we optimize the controller of four different FOWT models and compare their dynamic responses. Additional details and other use cases for the framework are presented, which can optimize different control problems and evaluate FOWT designs.

controls↗
DOE OSTI2020

Thermal extremes in regulated river systems under climate change: an application to the southeastern U.S. rivers

High river temperatures, or “thermal extremes”, can cause fish mortality and thermoelectric powerplant derating. Under climate change, projected higher air temperature and stronger surface energy fluxes will lead to increased water temperatures, exacerbating thermal extremes. However, cold hypolimnetic releases from thermally stratified reservoirs can depress tailwater temperatures and therefore alleviate thermal extremes. Thermal extremes are more harmful when they coincide with low flows, which we refer to as “hydrologic hot-dry events”. To assess multisectoral impacts of climate change over large regions, we evaluate thermal events according to three impact attributes: duration (D), intensity (I), and severity (S). We apply an established model framework to simulate streamflow and stream temperature over the southeastern US regulated river system. We quantify climate change impacts (by the 2080s under RCP8.5) by comparing historical and future periods and quantify regulation impacts by comparing unregulated and regulated scenarios. We find that climate change will exacerbate thermal extremes (all three metrics) in both unregulated and regulated scenarios, albeit less in the regulated scenario. Thermal mitigation from reservoir regulation will be stronger under climate change, decreasing the three metrics compared to the unregulated case. Even so, thermal extremes in the regulated scenario will still be more severe under climate change, and only 12.2%, 19.7%, and 26.0% of D, I, and S can be mitigated by reservoirs. Despite stronger reservoir stratification, the number of regulated river segments that experience simultaneous high temperature and low flow events (hydrologic hot-dry events) will increase by 21.4% by the 2080s under RCP8.5. These events will have a median annual duration of 10.3 days/year, over 10 times the historical value.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations

Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter’s surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Further, our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Topological Phenomena in Artificial Quantum Materials Revealed by Local Chern Markers

A striking example of frustration in physics is Hofstadter’s butterfly, a fractal structure that emerges from the competition between a crystal’s lattice periodicity and the magnetic length of an applied field. Current methods for predicting the topological invariants associated with Hofstadter’s butterfly are challenging or impossible to apply to a range of materials, including those that are disordered or lack a bulk spectral gap. Here, in this work, we demonstrate a framework for predicting a material’s local Chern markers using its position-space description and validate it against experimental observations of quantum transport in artificial graphene in a semiconductor heterostructure, inherently accounting for fabrication disorder strong enough to close the bulk spectral gap. By resolving local changes in the system’s topology, we reveal the topological origins of antidot-localized states that appear in artificial graphene in the presence of a magnetic field. Moreover, we show the breadth of this framework by simulating how Hofstadter’s butterfly emerges from an initially unpatterned 2D electron gas as the system’s potential strength is increased and predict that artificial graphene becomes a topological insulator at the critical magnetic field. Overall, we anticipate that a position-space approach to determine a material’s Chern invariant without requiring prior knowledge of its occupied states or bulk spectral gaps will enable a broad array of fundamental inquiries and provide a novel route to material discovery, especially in metallic, aperiodic, and disordered systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A Risk-Driven Probabilistic Approach to Quantify Resilience in Power Distribution Systems

It is of growing concern to ensure resilience in power distribution systems to extreme weather events. However, there are no clear methodologies or metrics available for resilience assessment that allows system planners to assess the impact of appropriate planning measures and new operational procedures for resilience enhancement. In this paper, we propose a resilience metric using parameters that define system attributes and performance. To represent extreme events (tail probability), the conditional value-at-risk of each of the parameters are combined using Choquet Integral to evaluate the overall resilience. The effectiveness of the proposed resilience metric is studied within the simulation-based framework under extreme weather scenarios with the help of a modified IEEE 123-bus system. With the proposed framework, system operators will have additional flexibility to prioritize one investment over the others to enhance the resilience of the grid.

Poudyal, Abodh↗
DOE OSTI2022

ML-PSA

The computer code uses a parallel simulated annealing framework with embedded machine learning components to solve multi-constrained optimization problems. The software automatically balances the execution of low and high fidelity physics models within the optimization procedure. The low fidelity model is used to rapidly explore the design space while the high fidelity physics model is executed sparingly to account for complex design constraints that are not resolved by the quickly executing low fidelity model.

Gurecky, William↗
DOE OSTI2025

PUMA:POWDER UTILIZATION MODELING APPLICATION

SF-25-084 PUMA a high performance modeling framework to simulate powder processing. It provides a scalable tool for manufacturers to simulate powder pre- and post-processing. The tool can predict the distortion, residual stress, and (for reactive processes) reaction completion fraction of complex parts after curing/debinding, sintering, and infiltration processes. These predictions are key metrics industry uses to optimize these processes to produce dense, defect-free, stable components.

HU, TIANCHEN (GARY)↗
DOE OSTI2026

Dynamic Disruption Resilience in Intermodal Transport Networks: Integrating Flow Weighting and Centrality Measures

Resilient intermodal freight networks are vital for sustaining supply chains amid increasing threats from natural hazards and cyberattacks. Transportation resilience has been widely studied; understanding how random and targeted disruptions affect structural connectivity and functional performance remains a key challenge. To address this, this study evaluates the robustness of the US intermodal freight network, which consists of rail and water modes, using a simulation-based framework that integrates graph-theoretic metrics with flow-weighted centrality measures. Disruption scenarios are examined, including random failures as well as targeted node and edge removals based on static and dynamically updated degree and betweenness centrality. To reflect more realistic conditions, flow-weighted degree centralities (WDC) and partial node degradation are considered. Two resilience indicators are used: (1) the size of the giant connected component to measure structural connectivity; and (2) flow-weighted network efficiency (NE) to assess freight mobility under disruption. The results show that progressively degrading nodes ranked by WDC to 60% of their original functionality causes a sharper decline in normalized NE, for up to approximately 45 affected nodes, than complete failure (100% loss of functionality) applied to nodes targeted by weighted betweenness centrality or selected at random. This highlights how partial degradation of high-tonnage hubs can produce disproportionately large functional losses. The findings emphasize the need for resilience strategies that go beyond network topology to incorporate freight flow dynamics.

42 ENGINEERING↗
DOE OSTI2023

Community Based Data of Potentiometric Titration of Iron Oxides: Ferrihydrite (HFO), Goethite, Hematite, Magnetite

This data release includes experimental data of potentiometric titration for iron oxides. The data in the provided .csv files is not our own experimental data but have been compiled from the multiple literature sources. The master database is L-SCIE (LLNL Surface Complexation/Ion Exchange) database, and the provided .csv files are extracted data from L-SCIE. The .csv files were obtained by using the Lawrence Livermore National Laboratory Surface Complexation Database Converter (SCDC) code written in the R programming language (free licensing available at https://ipo.llnl.gov/technologies/software/llnl-surface-complexation-database-converter-scdc).The released data was used for developing a comprehensive community data-driven surface complexation modeling (SCM) framework for simulating potentiometric titration of mineral surfaces. Compiled community data for ferrihydrite, goethite, hematite, and magnetite are fit to produce representative protolysis constants that can reproduce potentiometric titration data collected from multiple literature sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data Files for Runoff Evaluation in an Earth System Land Model for Permafrost Regions

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce.This data set includes all files that were produced and applied in the paper Runoff Evaluation in an Earth System Land Model for Permafrost Regions [Xiang et al. in review]. The paper is in review as of July 1 2025 in Geoscientific Model Development (GMD). In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-rich integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM’s parameterized representation of total runoff. This dataset contains 2 figure image files (*.png, *jpg) that describe the study site and methods, as well as folders (Figure*.zip) that contain the associated data files (*.csv, *.dat) and python code notebooks (*.ipynb) for figures 3-7 in the paper. Jupyter notebook (*.ipynb) files that produce the figure files using the associated data files will run within a python environment configured with Jupyter Lab or Notebook packages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data-Driven Energy Resilience Assessment and Enhancement in Urban Communities: A Case Study in Detroit

This paper presents a data-driven framework for assessing and enhancing energy resilience in urban communities. The resilience assessment is based on two datasets: 1) annual aggregated power outage data and 2) 15-minute interval outage data. High-impact, low-probability (HILP) events are identified within these datasets to evaluate community resilience under extreme conditions. To enhance resilience, an optimization framework utilizing mixed integer linear programming is developed to determine the optimal sizing and placement of solar photovoltaic (PV) systems and battery energy storage systems (BESS). This method offers a cost-effective and practical solution for improving energy resilience in vulnerable communities. Furthermore, a case study of the City of Detroit in Michigan demonstrates the effectiveness of the framework through simulation and validation.

Energy resilience assessment↗
DOE OSTI2020

Providing Ramping Service with Wind to Enhance Power System Operational Flexibility

This project develops an innovative, integrated, and transformative approach to mitigate the impact of wind ramping by providing flexible ramping products from wind power. The project will significantly contribute to the reduction of wind integration costs by making wind power dispatchable and allowing the efficient management of wind ramping characteristics. In addition, we collaborated with industry to test and validate the methodology, considering economic and reliability goals, by integrating the proposed methodology into the simulated market frameworks of two independent system operators: California Independent System Operator and the Electric Reliability Council of Texas.

17 WIND ENERGY↗
DOE OSTI2020

A Bezier Curve Informed Melt Pool Geometry to Model Additive Manufacturing Microstructures Using SPPARKS

Additive manufacturing is a transformative technology with the potential to manufacture designs which traditional subtractive machining methods cannot. Additive manufacturing offers fast builds at near final desired geometry; however, material properties and variability from part to part remain a challenge for certification and qualification of metallic components. AM induced metallic microstructures are spatially heterogeneous and highly process dependent. Engineering properties such as strength and toughness are significantly affected by microstructure morphologies resulting from the manufacturing process Linking process parameters to microstructures and ultimately to the dynamic response of AM materials is critical to certifying and qualifying AM built parts and components and improving the performance of AM materials. The AM fabrication process is characterized by building parts layer by layer using a selective laser melt process guided by a computer. A laser selectively scans and melts metal according to a designated geometry. As the laser scans, metal melts, fuses, and solidifies forming the final geometry in a layerwise fashion. As the laser heat source moves away, the metal cools and solidifies forming metallic microstructures. This work describes a microstructure modeling application implemented in the SPPARKS kinetic Monte Carlo computational framework for simulating the resulting microstructures. The application uses Bzier curves and surfaces to model the melt pool surface and spatial temperature profile induced by moving the laser heat source; it simulates the melting and fusing of metal at the laser hot spot and microstructure formation and evolution when the laser moves away. The geometry of the melt pool is quite flexible and we explore effects of variances in model parameters on simulated microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Selecting and Implementing Resilience Metrics in Existing Energy Sector Models [Slides]

Resilience is a topic receiving much attention in relation to energy systems, with particular attention being paid to the supply of electricity. As a result of the growing interest in energy sector resilience, research communities have proposed a plethora of candidate resilience indicators and metrics, most of which remain immature at different scales and segments within the energy system. A necessary focus of the research community lies in implementing, testing, and validating resilience metrics and analysis approaches in energy sector models, which will be invaluable for informing resilience planning and investment decisions. Recognizing these challenges that need to be addressed, we explore how to effectively integrate resilience considerations into energy sector models and tools. The overarching goal of the effort was to evaluate the data needs, methodologies, and outcomes - including consequences and/or changes in investment or operational decisions due to avoided consequences - based on resilience analysis in a range of existing tools. In particular, we selected five models originally built at NREL to explore non-resilience energy research questions to implement and exercise resilience metrics and analysis approaches. To demonstrate the importance of perspective, we selected models that represent different segments of the energy sector, geographic scales, and modeling approaches. A second important aspect of our effort was the development of generalized power interruption scenarios. These scenarios were intended to help establish a framework for simulating the effects of real-world threats in terms of their impacts on system components and, in turn, power interruption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Demonstration of Advanced Experimental and Theoretical Characterization of Hydrogen Dynamics and Associated Behavior in Advanced Reactors

Advanced materials development, manufacturing, and modeling capabilities for innovative reactor designs support nuclear security and mission-focused science through enhanced technology for safer and more efficient and secure production of nuclear energy. The research in this project has established: 1) a state-of-the-art neutron-based hydrogen mapping and cross-section measurement capability as well as detailed crystallographic characterization of hydrogen atoms at LANSCE, and 2) a multi-physics framework for simulating behavior of moderator materials and other material performance in advanced nuclear reactors. Through the course of this project, we successfully developed and demonstrated measurement techniques for hydrogen distribution and atomistic-scale behavior of hydrogen atoms using pulsed neutron techniques. In parallel, advanced multi-physics simulation tools to predict the behavior of hydrogen atoms, e.g. in a moderator for a nuclear reactor, through materials performance, neutron transport, and thermal mechanical behavior were enhanced. Multi-discipline areas across the laboratory were involved in the project as the integration of improved experimental capabilities with enhanced modeling and simulation through MST, NEN, SIGMA, and XCP division subject matter experts.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗