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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improved biomass feedstock materials handling and feeding engineering data sets, design methods, and modeling/simulation tools

Forest Concepts led a project to develop a set of tools which enable feedstock handing equipment designers to better understand and model the flowability of bulk particulate biomass materials. The work products included improved flowability mathematical models, data sets used to populate the models, and two new laboratory devices – a biomass-scale true cubical triaxial tester and a biomass-scale gas pycnometer. This work was funded in part by the US Department of Energy under contact DE-EE0008254.

09 BIOMASS FUELS↗
DOE OSTI2025

Modular Hydronic Room Conditioning System (CRADA NFE-24-10120 Final Report)

This project presents the development and validation of high-fidelity numerical models for fin-tube heat exchangers to enable accurate performance prediction and informed design optimization. The modeling framework integrates detailed geometric specifications, thermophysical property data, and system-level constraints to simulate the heat exchanger’s behavior under a range of operating conditions. The model is calibrated using real-world product specifications and validated against experimental data collected from controlled cooling and heating tests. In cooling mode, the model captures the overall trends in capacity and outlet air temperature but tends to underpredict latent effects, especially at lower air flow rates. In heating mode, the simulation consistently overestimates both the thermal capacity and outlet air temperature, indicating the need for refinement in air-side heat transfer assumptions. Despite these deviations, the model provides a solid foundation for optimization, allowing key design variables to be tuned within physical and performance-based constraints. This research advances the ability to simulate, validate, and optimize fin-tube heat exchanger designs with greater confidence and efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Modular Hydronic Room Conditioning System

This project presents the development and validation of high-fidelity numerical models for fin-tube heat exchangers to enable accurate performance prediction and informed design optimization. The modeling framework integrates detailed geometric specifications, thermophysical property data, and system-level constraints to simulate the heat exchanger’s behavior under a range of operating conditions. The model is calibrated using real-world product specifications and validated against experimental data collected from controlled cooling and heating tests. In cooling mode, the model captures the overall trends in capacity and outlet air temperature but tends to underpredict latent effects, especially at lower air flow rates. In heating mode, the simulation consistently overestimates both the thermal capacity and outlet air temperature, indicating the need for refinement in air-side heat transfer assumptions. Despite these deviations, the model provides a solid foundation for optimization, allowing key design variables to be tuned within physical and performance-based constraints. This research advances the ability to simulate, validate, and optimize fin tube heat exchanger designs with greater confidence and efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Performance Study of Distance-Weighting Approach with Loopy Sum-Product Algorithm for Multi-Object Tracking in Clutter

In this paper, we explore the performance of the distance-weighting probabilistic data association (DWPDA) approach in conjunction with the loopy sum-product algorithm (LSPA) for tracking multiple objects in clutter. First, we discuss the problem of data association (DA), which is to infer the correspondence between targets and measurements. DA plays an important role when tracking multiple targets using measurements of uncertain origin. Second, we describe three methods of data association: probabilistic data association (PDA), joint probabilistic data association (JPDA), and LSPA. We then apply these three DA methods for tracking multiple crossing targets in cluttered environments, e.g., radar detection with false alarms and missed detections. We are interested in two performance metrics: tracking accuracy and computation time. LSPA is known to be superior to PDA in terms of the former and to dominate JPDA in terms of the latter. Last, we consider an additional DA method that is a modification of PDA by incorporating a weighting scheme based on distances between position estimates and measurements. This distance-weighting approach, when combined with PDA, has been shown to enhance the tracking accuracy of PDA without significant change in the computation burden. Since PDA constitutes a crucial building block of LSPA, we hypothesize that DWPDA, when integrated with LSPA, would perform better under the two performance metrics above. Contrary to expectations, the distance-weighting approach does not enhance the performance of LSPA, whether in terms of tracking accuracy or computation time.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. In this work, we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross-validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root-mean-square error of 0.034 m 3 /m 3 . It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Cross sections for the 54 Fe(n, n′) 54 Fe and 54 Fe(n, p′) 54 Mn reactions deduced from the detection of de-excitation γ rays

γ-ray production cross sections have been deduced for reactions with incident neutrons having energies from 1.5 - 4.7 MeV. Similar measurements were made on a natural Ti sample to establish an absolute normalization. The resulting γ-ray production cross sections are compared to TENDL and TALYS calculations, as well as data from previous measurements. The models are found to describe the production cross sections for most γ rays observed from 54Mn and 54Fe rather well.

Nuclear Data, Gamma-ray Production Cross Sections↗
DOE OSTI2023

North American Lithium-Ion Battery Supply Chain Database Development - Phase II

Lithium-ion batteries (LIBs) are used in a wide range of applications, including cell phones, laptops, power tools, electric vehicles, and grid storage, and are essential for economic growth and addressing climate change. However, the significant demand for LIBs has led to supply chain issues for the United States, as China dominates the processing of battery materials and battery production. To address this concern, NAATBatt International, a trade association of North American battery companies, supported the National Renewable Energy Laboratory in developing a database of companies that mine, process, manufacture, reuse, and recycle batteries in North America. The purpose of this database was to identify strengths and gaps in the supply chain, so that private-government partnerships could develop strategies to create a competitive LIB supply chain in the US. NREL published the first version of this database in 2021 and the second version in 2022. The database includes companies that have a manufacturing facility in North America and are engaged in materials, cells, packs, end-of-life management, as well as those involved in LIB battery modeling, distribution, service and repair, and R&D. In this presentation, we will discuss our approach to collecting data and categorizing various segments and products. We will also provide a summary of the data and present various maps to illustrate the distribution of companies in the database.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2025

Review of Neutron Yield from (α, n) Reactions: Data, Methods, and Prospects

Understanding the radiogenic neutron production rate through the (α, n) reaction is crucial in many areas of physics, including dark matter searches, neutrino studies, and nuclear astrophysics. In addition to its relevance for fundamental research, the (α, n) reaction also plays a significant role in nuclear energy technologies, for example by contributing to neutron production in subcritical systems using UO2, as well as in applications such as medical physics. This review examines the current state of (α, n) yield calculations and neutron spectra, describes the computational tools used for their estimation, and discusses the available cross-section data. We discuss the uncertainties affecting (α, n) yield estimations and propose a strategy to enhance their accuracy. Furthermore, this paper discusses and emphasizes the need for new measurements of (α, n) cross-sections for a variety of relevant materials. Such measurements are essential for improving neutron flux predictions, which are crucial for reducing uncertainties in sensitivity estimates for next-generation physics experiments operating in the keV—MeV range.

(α, n)reactions↗
DOE OSTI2023

Data and code repository for "How do the weather regimes drive wind speed and power production at the sub-seasonal to seasonal timescales over the CONUS?"

There has been an increasing need for forecasting power generation at the sub-seasonal to seasonal (S2S) timescales to support the operation, management, and planning of the wind-energy system. At the S2S timescales, atmospheric variability is largely related to recurrent and persistent weather patterns, referred to as weather regimes (WRs). In the study "How do the weather regimes drive wind speed and power production at the sub-seasonal to seasonal timescales over the CONUS?", we identify four WRs that influence wind resources over North America using a self-organizing map (SOM) algorithm. These WRs are responsible for large-scale wind and power production anomalies over the CONUS at the S2S timescales. The WR-based reconstruction explains up to 50% of the monthly variance of power production over the western United States, and the explanatory power generally increases with the increase of timescales. The identified relationship between WRs and power production reveals the potential and limitations of the regional WR-based wind resource assessment over different regions of the CONUS across multiple timescales. This repository includes all the data and codes we use for analyses in this study. Users may use them to reproduce the results of this study on their end.

17 WIND ENERGY↗
DOE OSTI2020

Far-forward neutrinos at the Large Hadron Collider

We present a new calculation of the energy distribution of high-energy neutrinos from the decay of charm and bottom hadrons produced at the Large Hadron Collider (LHC). In the kinematical region of very forward rapidities, heavy-flavor production and decay is a source of tau neutrinos that leads to thousands of charged-current tau neutrino events in a 1 m long, 1 m radius lead neutrino detector at a distance of 480 m from the interaction region. In our computation, next-to-leading order QCD radiative corrections are accounted for in the production cross-sections. Non-perturbative intrinsic-kT effects are approximated by a simple phenomenological model introducing a Gaussian kT-smearing of the parton distribution functions, which might also mimic perturbative effects due to multiple initial-state soft-gluon emissions. The transition from partonic to hadronic states is described by phenomenological fragmentation functions. To study the effect of various input parameters, theoretical predictions for D± s production are compared with LHCb data on double-differential cross-sections in transverse momentum and rapidity. The uncertainties related to the choice of the input parameter values, ultimately affecting the predictions of the tau neutrino event distributions, are discussed. We consider a 3+1 neutrino mixing scenario to illustrate the potential for a neutrino experiment to constrain the 3+1 parameter space using tau neutrinos and antineutrinos. We find large theoretical uncertainties in the predictions of the neutrino fluxes in the far-forward region. Untangling the effects of tau neutrino oscillations into sterile neutrinos and distinguishing a 3+1 scenario from the standard scenario with three active neutrino flavours, will be challenging due to the large theoretical uncertainties from QCD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Current understanding of Oxidative Coupling of Methane (OCM) reaction over supported Mn-Na 2 WO 4 catalysts

This perspective reviews the current understanding of the Oxidative Coupling of Methane (OCM) reaction over the supported Mn-Na 2 WO 4 /SiO 2 catalyst, with a focus on recent insights gained from state-of-the-art in-situ and operando spectroscopic characterization and chemical probe experiments under controlled environments. The supported Mn-Na 2 WO 4 /SiO 2 catalyst exhibits dynamic structural changes during the OCM reaction, involving multiple reactive lattice and adsorbed oxygen species, each associated with different oxide phases. These oxygen species play distinct roles in various steps of the OCM mechanism. The catalytic active sites for activation of CH 4 are associated with isolated surface Na-WO x sites on the SiO 2 support and the role of surface MnO x sites on SiO 2 is to oxidatively dehydrogenate C 2 H 6 to C 2 H 4 . Furthermore, this paper provides a detailed discussion of these roles and also introduces new experimental data from Temporal Analysis of Products (TAP) studies to clarify the ongoing debate in the literature regarding the contributions of lattice versus adsorbed oxygen species in OCM reaction product formation. Additionally, recommendations are offered for optimizing the performance of supported Mn-Na 2 WO 4 /SiO 2 catalysts to enhance CH 4 activation and C 2 product selectivity.

03 - NATURAL GAS↗
DOE OSTI2021

Reweighting the Sivers function with jet data from STAR

The reweighting procedure that using Bayesian statistics incorporates the information contained in a new data set, without the need of re-fitting, is applied to the quark Sivers function extracted from Semi-Inclusive Deep Inelastic Scattering (SIDIS) data. We exploit the recently published single spin asymmetry data for the inclusive jet production in polarized pp collisions from the STAR Collaboration at RHIC, which cover a much wider x region compared to SIDIS measurements. The reweighting method is extended to the case of asymmetric errors and the results show a remarkable improvement of the knowledge of the quark Sivers function.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Electrocatalytic CO 2 Reduction to Fuels: Progress and Opportunities

The electrochemical reduction of CO 2 continues to be an appealing option for storing renewable energy in a chemical form. In this review, we assess progress in designing catalysts that convert CO 2 to high energy density products. We explain how reaction data can be reported to reflect the intrinsic properties of the catalyst. This analysis shows that limited advances have been made in improving the performance of Cu. We suggest that opportunities remain using bimetallic catalysts that are resistant to dealloying. While aqueous systems are instrumental to developing our understanding of this chemistry, gas-fed systems that operate at high current densities must be developed. Although obstacles remain for practical application of CO 2 reduction, advances in fundamental understanding made over the years give reason for optimism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

An Overview of Atmospheric Features Over the Western North Atlantic Ocean and North American East Coast - Part 1: Analysis of Aerosols, Gases, and Wet Deposition Chemistry

The Western North Atlantic Ocean (WNAO) and adjoining East Coast of North America are of great importance for atmospheric research and have been extensively studied for several decades. This broad region exhibits complex meteorological features, diverse cloud types, and a wide range of conditions associated with gas and particulate species that originate from many sources regionally and from other continents. As Part 1 of a 2-part paper series, this work provides a characterization of quantities associated with atmospheric chemistry, including gases, aerosols, and wet deposition, based on satellite observations, ground-based data, model simulations, and reanalysis products. Key results include spatial and seasonal differences in composition along the North American East Coast and over the WNAO associated with varying sources of smoke and dust and meteorological drivers such as temperature, moisture, and precipitation. Spatial and seasonal variations of tropospheric carbon monoxide and ozone highlight different pathways towards accumulation of these species in the troposphere. Spatial distributions of speciated aerosol optical depth and vertical profiles of aerosol mass mixing ratios show a clear seasonal cycle highlighting the influence of both natural and anthropogenic sources in addition to the impact of intercontinental transport on their vertical profiles. Analysis of long-term climate model simulations of aerosol species and satellite observations of carbon monoxide confirm that there has been a significant decline in recent decades among anthropogenic constituents owing to regulatory activities. The results can inform current and future lines of research for the region.

Corral, Andrea↗
DOE OSTI2022

Inverse size-dependent Stokes shift in strongly quantum confined CsPbBr 3 perovskite nanoplates

Colloidal semiconductor nanocrystals (NCs) are used as bright chromatic fluorophores for energy-efficient displays. Here we focus here on the size-dependent Stokes shift for CsPbBr 3 nanocrystals. The Stokes shift, i.e., the difference between the wavelengths of absorption and emission maxima, is crucial for display application, as it controls the degree to which light is reabsorbed by the emitting material reducing the energetic efficiency. One major impediment to the industrial adoption of NCs is that slight deviations in manufacturing conditions may result in a wide dispersion of the product's properties. A data-driven analysis of over 2000 reactions comparing two data sets, one produced via standard colloidal synthesis and the other via high-throughput automated synthesis is discussed. We show that differences in the reaction conditions of colloidal CsPbBr 3 nanocrystals yield nanocrystals with opposite Stokes shift size-dependent trends. These match the morphologies of two-dimensional nanoplatelets (NPLs) and nanocrystal cubes. The Stokes shift size dependence trend of NPLs and nanocubes is non-monotonic indicating different physics is at play for the two nanocrystal morphologies. For nanocrystals with cubic shape, with the increase of edge length, there is a significant decrease in Stokes shift values. However, for NPLs with the increase of thickness (1–4 ML), Stokes shift values will increase. The study emphasizes the transition from a spectroscopic point of view and relates the two Stokes shift trends to 2D and 0D exciton dimensionalities for the two morphologies. Our findings highlight the importance of CsPbBr 3 nanocrystal morphology for Stokes shift prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Study of the p-p-K + and p-p-K - dynamics using the femtoscopy technique

The interactions of kaons (K) and antikaons ($\bar{K}$ ) with few nucleons (N) were studied so far using kaonic atom data and measurements of kaon production and interaction yields in nuclei. Some details of the three-body $\bar{K}$NN and NN dynamics are still not well understood, mainly due to the overlap with multi-nucleon interactions in nuclei. An alternative method to probe the dynamics of three-body systems with kaons is to study the final state interaction within triplet of particles emitted in pp collisions at the Large Hadron Collider, which are free from effects due to the presence of bound nucleons. This Letter reports the first femtoscopic study of p–p–K + and p–p–K - correlations measured in high-multiplicity pp collisions at $\sqrt{s}$ = 13 TeV by the ALICE Collaboration. The analysis shows that the measured p–p–K + and p–p–K - correlation functions can be interpreted in terms of pairwise interactions in the triplets, indicating that the dynamics of such systems is dominated by the two-body interactions without significant contributions from three-body effects or bound states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Fleet Utilization

A key goal of NextGen Profiles' fleet utilization study was to conduct a comprehensive, strategic, and standardized assessment of the operational behavior and utilization patterns across EV and EVSE production-ready fleets. These data-driven insights were intended to inform current fleet management strategies and support future infrastructure planning, ensuring the effective adoption and adaptation of the growing EV fleet market. The study applied a series of metrics defined in NextGen Profiles to evaluate diverse fleet operations across various use cases, emphasizing trends in charging, routing, and other critical behaviors. The fleet utilization dataset includes these three sets of metrics from 17 EV fleets, each consisting of a wide range of vehicle types and operational categories, as well as two EVSE fleets. Data were collected from a variety of sources and reformatted into a unified structure before metric computation, ensuring consistency and comparability across all fleets. To protect confidentiality, all fleet metadata are anonymized, and the publicly released metric datasets are aggregated to an hourly cadence.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗