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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Antarctic ice sheet model comparison with uncurated geological constraints shows that higher spatial resolution improves deglacial reconstructions

Accurately reconstructing past changes to the shape and volume of the Antarctic ice sheet relies on the use of physically based and thus internally consistent ice sheet modeling, benchmarked against spatially limited geologic data. The challenge in model benchmarking against geologic data is diagnosing whether model-data misfits are the result of an inadequate model, inherently noisy or biased geologic data, and/or incorrect association between modeled quantities and geologic observations. In this work we address this challenge by (i) the development and use of a new model-data evaluation framework applied to an uncurated data set of geologic constraints, and (ii) nested high-spatial-resolution modeling designed to test the hypothesis that model resolution is an important limitation in matching geologic data. While previous approaches to model benchmarking employed highly curated datasets, our approach applies an automated screening and quality control algorithm to an uncurated public dataset of geochronological observations (specifically, cosmogenic-nuclide exposure-age measurements from glacial deposits in ice-free areas). This optimizes data utilization by including more geological constraints, reduces potential interpretive bias, and allows unsupervised assimilation of new data as they are collected. We also incorporate a nested model framework in which high-resolution domains are downscaled from a continent-wide ice sheet model. We highlight the application of this framework by applying these methods to a small ensemble of deglacial ice-sheet model simulations, and demonstrate that the nested approach improves the ability of model simulations to match exposure age data collected from areas of complex topography and ice flow. We develop a range of diagnostic model-data comparison metrics to provide more insight into model performance than possible from a single-valued misfit statistic, showing that different metrics capture different aspects of ice sheet deflation.

Geosciences↗
DOE OSTI2024

FORESTR: Finding, Organizing, Representing, Explaining, Summarizing, and Thinning Random forests

Random forests have become popular models used for data driven predictions. As a result, random forests are currently used or being considered for high-consequence mission applications in national security, such as the prediction of yield from optical signals and malware detection. While random forests may provide accurate predictions, the complexity of the algorithm causes a lack of interpretability. Random forests are an ensemble of regression or decision trees. Individual regression and decision trees are interpretable, but ensembles are inherently difficult to interpret due to the compilation of many models. We aim to increase the interpretability of random forests by finding patterns in the ensemble of trees that can be used to “thin” (or remove) trees. As a starting point, in this report, we develop a new distance metric for quantifying the similarity between trees based on their topologies (i.e., shapes). We base the metric on a novel distance metric for graphs that is a proper mathematical distance, is invariant to transformations, has registration between graphs, and computes topological evolutions between graphs. We use the tree distance metric to compute tree statistics such as a “mean tree” and to identify clusters of trees. We apply the developed methodology to a toy dataset and a mission relevant product inspection dataset to demonstrate how the metric can provide insight into random forests. Furthermore, we discuss the limitations of the approach and ideas for future research into how the metric could be used as a thinning tool to develop less complex models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Changes in When and Where People are Spending Time in Response to COVID-19

The COVID-19 pandemic has resulted in a significant change in driving behavior as people respond to the new environment. However, existing methods for analyzing driver behavior such as travel surveys and travel demand models are not suited for incorporating abrupt environmental disruptions. To address this, we analyze a set of high-resolution trip data and introduce two new metrics for quantifying driving behavioral shifts as a function of time, allowing us to compare the time periods before and after pandemic began. We apply these metrics to the Denver, Colorado metropolitan statistical area (MSA) to demonstrate the utility of the metrics. Then, we present a case study for comparing two distinct MSAs, Louisville, Kentucky; and Des Moines, Iowa which exhibit significant differences in the makeup of their labor markets. The results indicate that although the regions of study exhibit certain unique driving behavioral shifts, emerging trends can be seen when comparing between seemingly distinct regions. For instance, drivers in all three MSAs are generally shown to have spent more time at residential locations and less time in workplaces in the time period after the pandemic started. In addition, workplaces that may be incompatible with remote working, such as hospitals and certain retail locations, generally retained much of their pre-pandemic travel activity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling

Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Software Development for Data Visualization and Analysis of PN-Diodes & MOSFET Devices

The U.S. Department of Energy has a broad mission to ensure the nation’s security by addressing ongoing environmental challenges, developing novel energy production technologies, and mitigating nuclear security concerns. These efforts benefit from developing more effective semiconductor materials and devices. The research discussed in this report aims to further the development of these new electronics by improving implementations of vertical device structures, which offer theoretically better performance compared to lateral structures. Specifically, it reviews the background and development of a new and easy-to-use program for data processing and analysis from experiments run on vertical gallium nitride power devices. Current vertical structures fall short in critical performance metrics, such as On-Resistance and Breakdown Voltage, due to poor management of the device’s electric field. Junction termination extensions, or JTEs, are a field management technique that increases a device’s resilience to expected failure modes by controlling its surface electric fields. Each JTE design requires significant experimental validation, consisting of hundreds of tested devices, each outputting an enormous set of data points. The new software includes multiple methods of filtering large sets of device testing data to identify and remove flawed devices, automate device analyses, and provide statistical breakdowns for four metrics used to define the effectiveness of the JTE and the current carrying capacity of the device. These results can then be related back to fabrication techniques and device design, and in the future, be combined with processing simulations to further improve our understanding of the JTE mechanisms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Modernizing GlideinWMS Factory Monitoring with Prometheus & Grafana

Large-scale scientific experiments like CMS and DUNE rely on the distributed workload management system GlideinWMS to efficiently utilize computing resources across heterogeneous computing environments. GlideinWMS currently records Factory statistics using Round Robin Databases (RRDBs), XML, and JSON files, and these statistics are displayed via custom monitoring Web pages, thereby limiting integration with modern observability platforms. This project investigates the use of Prometheus-based instrumentation to expose Factory metrics using OpenTelemetry principles. Factory statistics related to Glidein submission and job execution are exported as Prometheus metrics through the Prometheus Python Client Library and are served via an HTTP metrics endpoint. The collected metrics are inspected using the Prometheus web-based interface and are visualized through Grafana dashboards within the Landscape monitoring infrastructure at Fermilab. This project significantly streamlines the integration of modern monitoring technologies into GlideinWMS and establishes a framework for extending observability across additional system components.

Appiah, Gideon [Grambling State U.]↗
DOE OSTI2022

Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework

Building on the verification and validation work developed under the Second Wind Forecast Improvement Project, this work exhibits the value of a consistent procedure to evaluate wind power forecasts. We established an open-source Python code base tailored for wind speed and wind power forecast validation, WE-Validate. The code base can evaluate model forecasts with observations in a coherent manner. To demonstrate the systematic validation framework of WE-Validate, we designed and hosted a forecast evaluation benchmark exercise. We invited forecast providers in industry and academia to participate and submit forecasts for two case studies. We then evaluated the submissions with WE-Validate. Our findings suggest that ensemble means have reasonable skills in time series forecasting, whereas they are often inferior to single ensemble members in wind ramp forecasting. Adopting a voting scheme in ramp forecasting that allows ensemble members to detect ramps independently leads to satisfactory skill scores. Throughout this document, we also emphasize the importance of using statistically robust and resistant metrics as well as equitable skill scores in forecast evaluation.

17 WIND ENERGY↗
DOE OSTI2023

Reliable Devices Yield Stable Quantum Computations

Stable quantum computation requires noisy results to remain bounded even in the presence of noise fluctuations. Yet non-stationary noise processes lead to drift in the varying characteristics of a quantum device that can greatly influence the circuit outcomes. Here we address how temporal and spatial variations in noise relate device reliability to quantum computing stability. First, our approach quantifies the differences in statistical distributions of characterization metrics collected at different times and locations using Hellinger distance. We then validate an analytical bound that relates this distance directly to the stability of a computed expectation value. Our demonstration uses numerical simulations with models informed by the washington superconducting transmon device. We find that the stability metric is consistently bounded from above by the corresponding Hellinger distance, which can be cast as a specified tolerance level. These results underscore the significance of reliable quantum computing devices and the impact for stable quantum computation.

Dasgupta, Samudra↗
DOE OSTI2021

Quantifying the impacts of compound extremes on agriculture

Agricultural production and food prices are affected by hydroclimatic extremes. There has been a growing amount of literature measuring the impacts of individual extreme events (heat stress or water stress) on agricultural and human systems. Yet, we lack a comprehensive understanding of the significance and the magnitude of the impacts of compound extremes. This study combines a fine-scale weather product with outputs of a hydrological model to construct functional metrics of individual and compound hydroclimatic extremes for agriculture. Then, a yield response function is estimated with individual and compound metrics, focusing on corn in the United States during the 1981–2015 period. Supported by statistical evidence, the findings suggest that metrics of compound hydroclimatic extremes are better predictors of corn yield variations than metrics of individual extremes. The results also confirm that wet heat is more damaging than dry heat for corn. This study shows the average yield damage from heat stress has been up to four times more severe when combined with water stress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluating Synthetic Smart Meter Locations For Communication System Modeling

An important part of good communication models for smart grid applications, particularly when wireless protocols are used, is the location information of the nodes. We’ve developed metrics to assist in evaluating whether a given model with such information is representative of feeders found in the world. These metrics are applied to a set of feeder models with known good location information for the smart meter locations and compared to a set of feeder models with similar location information that is not expected to be representative of real-world feeders. The comparison reveals that the suspect models do not pass statistical tests utilizing the developed metrics, providing initial validation of the analysis technique.

smart meter, feeder model, GridLAB-D↗
DOE OSTI2025

X-Ray Microtomography Measurements of Coevolving Particle Size and Shape

This study measures simultaneous changes in particle size and shape during grain crushing and proposes quantitative metrics to track their coevolving statistical distribution. Idealized granular materials with widely different initial particle shapes (i.e., spheres, quasi-ellipsoids, plates, and rods) are compressed oedometrically at vertical stress levels sufficient to induce particle breakage. X-ray microtomography and digital image analysis are then used to track changes in particle-scale characteristics. The observations indicate that more irregular particles tend to exhibit a higher degree of breakage compared to more spherical grains subjected to an equivalent stress level. Despite these differences, the measurements also indicate that all the tested particle sets approach a common attractor (expressed in terms of aspect ratio, flatness, and elongation), regardless of their initial morphology. A new shape evolution index is proposed to quantify such trends. With this new metric, it is shown that in all the tested materials, the particle shape evolves faster than size, especially for the more regular morphologies, thus implying that the shape tends to reach its attractor point before the grading reaches its ultimate particle size distribution. Furthermore, this finding underpins an ultimate stage of crushing-driven compression that occurs with particle size reduction but minimal shape changes (i.e., self-replicating shapes due to fractures).

Oedometric compression↗
DOE OSTI2023

Stochastic Framework for Optimal Control of Planetary Reentry Trajectories Under Multilevel Uncertainties

We present a novel stochastic optimal control framework that accounts for various types of uncertainties, with application to reentry trajectory planning. The formulation of the optimal trajectory control problem is presented in the context of an indirect method where a functional objective associated with the terminal vehicle speed is to be minimized. Uncertain input parameters in the optimal trajectory control model, including aerodynamic parameters and initial and terminal conditions, are modeled as aleatory random variables, while the statistical parameters of these aleatory distributions are themselves random variables. The parametric and model uncertainties are simultaneously propagated through an extended polynomial chaos expansion (EPCE) formalism. Several metrics are described to evaluate response statistics and presented as insightful tools for robust decision making. Specifically, the response probability density function (PDF) reflecting influence of both epistemic and aleatory uncertainties is obtained. By sampling over the random variables representing model error, an ensemble of response PDFs is generated and the associated failure probability is estimated as a random variable with its own polynomial chaos expansion. Besides, the sensitivity index functions of response PDF with respect to the statistical parameters are evaluated. Coupling parametric and model uncertainties within the EPCE framework leads to a robust and efficient paradigm for multilevel uncertainty propagation and PDF characterization in general optimal control problems.

Engineering↗
DOE OSTI2025

Relationships Between Mesoscale Convective System Properties and Midlevel Dynamic Perturbations

Abstract Past studies implicate dynamic anomalies operating on subsynoptic scales as a possible initiation source of summertime (July–August) mesoscale convective systems (MCSs) in the central United States during northwesterly flow regimes. To improve our understanding of warm season MCSs occurring over a variety of flow regimes, we track midlevel (600 hPa) vorticity perturbations (“MPs”) as 2D objects comprising wavelengths of 500–2,500 km over the central US from May–August of 2004–2021. We perform statistical analysis of relationships between metrics of MP objects (e.g., duration, size, intensity, and origin) and high‐resolution MCS precipitation characteristics (e.g., duration, total rainfall, rain coverage area, and motion) that occur while collocated with or in the absence of MPs to discern predictive capability of background dynamic features on storm precipitation potential. Although the majority of MPs collocated with MCS initiation occur during July–August, a significant number (40%) occur between May and June. Northwesterly flow MPs comprise a relative minority of our events, suggesting that MPs can affect MCSs across a variety of warm season flow regimes. MPs affecting MCSs initiated primarily over the high plains near the central Rockies. Only approximately 20% of tracked MCS initiation events were collocated with MPs, but these storms produced ∼25% greater lifetime rainfall and coverage area, and ∼29% more stratiform rain than non‐MP‐induced MCSs. In general, larger and more vigorous MPs resulted in more hydrologically impactful MCSs. The most directly attributable benefit to MCS initiation was from MP‐enhanced background vertical motion and thermodynamic instability (e.g., increased CAPE).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Extensive analysis of reconstruction algorithms for DESI 2024 baryon acoustic oscillations

Reconstruction of the baryon acoustic oscillation (BAO) signal has been a standard procedure in BAO analyses over the past decade and has helped to improve the BAO parameter precision by a factor of ∼2 on average. The Dark Energy Spectroscopic Instrument (DESI) BAO analysis for the first year (DR1) data uses the “standard” reconstruction framework, in which the displacement field is estimated from the observed density field by solving the linearized continuity equation in redshift space, and galaxy and random positions are shifted in order to partially remove non-linearities. There are several approaches to solving for the displacement field in real survey data, including the multigrid (MG), iterative Fast Fourier Transform (iFFT), and iterative Fast Fourier Transform particle (iFFTP) algorithms. In this work, we analyze these algorithms and compare them with various metrics including two-point statistics and the displacement itself using realistic DESI mocks. We focus on three representative DESI samples, the emission line galaxies (ELG), quasars (QSO), and the bright galaxy sample (BGS), which cover the extreme redshifts and number densities, and potential wide-angle effects. We conclude that the MG and iFFT algorithms agree within 0.4% in post-reconstruction power spectrum on BAO scales with the RecSym convention, which does not remove large-scale redshift space distortions (RSDs), in all three tracers. The RecSym convention appears to be less sensitive to displacement errors than the RecIso convention, which attempts to remove large-scale RSDs. However, iFFTP deviates from the first two; thus, we recommend against using iFFTP without further development. In addition, we provide the optimal settings for reconstruction for five years of DESI observation. The analyses presented in this work pave the way for DESI DR1 analysis as well as future BAO analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Quantitative differentiation of benign and misfolded glaucoma-causing myocilin variants on the basis of protein thermal stability

Accurate predictions of the pathogenicity of mutations associated with genetic diseases are key to the success of precision medicine. Inherited missense mutations in the myocilin (MYOC) gene, within its olfactomedin (OLF) domain, constitute the strongest genetic link to primary open-angle glaucoma via a toxic gain of function, and thus MYOC is an attractive precision-medicine target. However, not all mutations in MYOC cause glaucoma, and common variants are expected to be neutral polymorphisms. The Genome Aggregation Database (gnomAD) lists ~100 missense variants documented within OLF, all of which are relatively rare (allele frequency <0.001%) and nearly all are of unknown pathogenicity. To distinguish disease-causing OLF variants from benign OLF variants, we first characterized the most prevalent population-based variants using a suite of cellular and biophysical assays, and identified two variants with features of aggregation-prone familial disease variants. Next, we considered all available biochemical and clinical data to demonstrate that pathogenic and benign variants can be differentiated statistically based on a single metric: the thermal stability of OLF. Our results motivate genotyping MYOC in patients for clinical monitoring of this widespread, painless and irreversible ocular disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Challenge Problem 1: Preliminary Results of the Direct Numerical Simulation of Transient Flows

This report presents the first direct numerical simulations (DNS) of transient mixed convection in an idealized downcomer-like channel (Challenge Problem 1, Phase II). Using the GPU-accelerated NekRS solver, we modeled a sudden decay in driving pressure, mimicking loss-of-flow events, and tracked the resulting evolution of Reynolds number, boundary-layer structure, turbulence statistics, and heat-transfer metrics. Key findings include the systematic thickening and eventual asymmetry of velocity and thermal boundary layers under buoyant deceleration; minimal “memory” lag in Reynolds shear stress and TKE profiles when sampled at matching Re, yet clear shifts of peak locations toward the cooled wall; overshoots in transient eddy-viscosity and eddy-diffusivity (and corresponding sub-unity turbulent Prandtl numbers) on the cooled side; and a pronounced transient Nusselt-number enhancement driven by wall-temperature inertia and residual eddy mixing. These effects combined to offer a temporary cooling margin above steady-state predictions during reactor LOF transients. Future work will extend this work to a more complex “Case II” geometry (90° turn + lower plenum) and generate multi-Re/Pr datasets for data-driven turbulence closures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗