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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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DOE OSTI2024

Geospatial characterization of low-temperature heating and cooling demand in residential, commercial, manufacturing, agricultural, and data center sectors for potential geothermal applications in the United States

Thermal demand for heating and cooling has been predominantly supplied by fossil fuel combustion in the United States, although low-carbon alternatives are extensively available including geothermal, solar thermal, and waste heat. Here, this study analyzed end-use energy consumption, fuel expenditure, and data center commissioned power data to geospatially characterize the U.S. low-temperature heating and cooling demand at the county level in residential, commercial, manufacturing, agricultural, and data center sectors and understand potential opportunities for geothermal applications. In the analysis, the regional-scale energy consumption data was incorporated with system efficiencies to address actual demand and was then disaggregated with weighting factors to the county level. The results indicated that total low-temperature heating and cooling demand is 16.7 EJ, combining heating demand of 10.8 EJ and cooling demand of 5.9 EJ. Overall, 59.9 % (10 EJ) of the low-temperature heating and cooling demand occurred in the residential sector. The heating and cooling demand visualized in maps represented that the geospatial distribution of heating and cooling demand in the residential and commercial sectors is governed by the number of housing units and climate zone designations, while heating and cooling demand in the manufacturing, agricultural, and data center sectors is dependent on the number and location of facilities. The results also demonstrated that geothermal heat pumps are broadly used in the residential and commercial sectors for heating and cooling in the U.S. Midwest, South, and Northeast regions but are limited in the West, implying great decarbonization potential in the future.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Economic Evaluation of Modernization Expenditures for Electric Utility Distribution Systems: A Guide for Utility Regulators

Cost-effectiveness evaluation of potential grid modernization solutions is integral to integrated distribution system planning (IDSP). This report synthesizes and updates prior cost-effectiveness approaches identified by the U.S. Department of Energy for grid modernization investments, incorporating multi-objective decision-making. The structure of the report follows the IDSP process, from identifying objectives and priorities to prioritizing grid solutions. The report details two methods for conducting initial cost-effectiveness screening of individual and interdependent grid components: (1) Lowest Reasonable Cost and (2) Benefit-Cost Analysis. Drawing on solutions that passed the initial cost-effectiveness screen, the utility prioritizes grid solutions based on policy objectives and priorities, regulatory compliance, operational efficiencies, and other factors to develop a portfolio of distribution solutions. The utility uses multi-objective decision-making to assess each proposed expenditure against each objective and respective metric. The utility applies a weighting factor, reflecting the priority ranking of the objective, to the total numerical “score” based on the contribution of the proposed solution to addressing each objective. Ultimately, the utility ranks each expenditure from highest to lowest priority using the final score for each solution as well as its cost. The report includes examples of emerging best practices for states and utilities and a cost-effectiveness process checklist.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Moment extraction using an unfolding protocol without binning

Deconvolving (“unfolding”) detector distortions is a critical step in the comparison of cross-section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our moment unfolding technique uses machine learning and is inspired by Boltzmann weight factors and generative adversarial networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our moment unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

On MCNP Stochastic Volume Estimation Normalization

This paper resolves a perennial point of confusion regarding the source-weighting normalization factor recommended in the MCNP manual (𝜋⁢𝑟 2 ) to stochastically estimate the volume of a region within an enclosing inward-directed spherical surface source with radius 𝑟. The normalization factor arises from the relationship between a sphere’ s mean chord length, its volume, and the values estimated by MCNP track-length tallies. A brief derivation is given that relates these quantities and results in the stated normalization. The correctness of this factor is demonstrated by estimating the volume of a variety of convex and nonconvex volumes. A heuristic demonstration of how biasing the inward-directed source reduces the statistical uncertainty of the stochastic volume estimate is also given, but a rigorous analysis of this improvement is left as future work.

42 ENGINEERING↗

DOE OSTI2024

Geometry-aware training of factorized layers in tensor Tucker format

Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during train and inference of large networks. Given its easy implementation and flexibility, one promising approach is layer factorization, which reshapes weight tensors into a matrix format and parameterizes it as the product of two rank-r matrices. However, this family of approaches often requires an initial full-model warm-up phase, prior knowledge of a feasible rank, and it is sensitive to parameter initialization.In this work, we introduce a novel approach to train the factors of a Tucker decomposition of the weight tensors. Our training proposal proves to be optimal in locally approximating the original unfactorized dynamics and stable for the initialization. Furthermore, the rank of each mode is dynamically updated during training.We provide a theoretical analysis of the algorithm, showing convergence, approximation and local descent guarantees. The method's performance is further illustrated through a variety of experiments, showing remarkable training compression rates and comparable or even better performance than the full baseline and alternative layer factorization strategies.

Zangrando, Emanuele [Gran Sasso Science Institute ↗

DOE OSTI2024

MODAQ-BB (Modular Offshore Data Acquisition System - Blackbox) [SWR-24-72]

MODAQ-BB is a compact, rapid deployment data acquisition system that can withstand water depths up to 400m. Codenamed "BlackBox" (or simply BB), since its initial purpose was to track marine assets and record vital data streams that could later be recovered in the event of a mishap - much like a traditional black box used in aviation and shipping, the name has stuck. MODAQ BlackBox is a battery-operable microcontroller platform with internal inertial sensing, GPS, satellite communications, and additional I/O (input/output) support in a depth-rated pressure enclosure. Since BB is self-contained and relatively compact, it can be quickly deployed with minimal effort. The enclosure can be clamped to a tube (such as part of a railing) or mast in a location with unobstructed view of the sky using the available clamp accessory or a common hose clamp. While BB is designed to operate unattended, users can configure what data are uploaded and the frequency of satellite transmissions. Once data are uploaded, they can be relayed to an email distribution list, the MODAQ:Web operational dashboard, or a custom destination. BB has found utility in the National Laboratory of the Rockies' (NLR's) Waterpower projects beyond its original vision and has been configured and successfully deployed in more traditional data-gathering applications where a simple, battery-operated solution was indicated. As part of the MODAQ family, BB fills a space in the spectrum of missions that can be supported that were previously impractical using the traditional MODAQ hardware architecture due to factors such as weight, size, and cost.

Raye, Robert↗

DOE OSTI2025

SHOTEAM: Superalloy Heat exchangers Optimized for Temperature Extremes and Additive Manufacturability (Final Technical Report)

UCLA developed an extreme-condition heat exchanger technology targeted to ultra-high efficiency hybrid aviation power cycles. The heat exchanger targeted operation at 50 kW (thermal) at supercritical CO 2 pressures of 80 and 250 bar (1160 and 3626 psi) in hot and cold streams, respectively and at a hot-stream inlet temperature of 800°C (1472°F). A metallic superalloy capable of withstanding high temperature and pressure was used to fabricate a microtube shell-and-tube-based design supplemented with 3D-printed tube augmentations. The optimized design enhanced overall heat transfer while maintaining a small overall form factor and low weight. This class of heat exchanger could dramatically improve efficiency, power density, and cost effectiveness for new hybrid aviation power cycles, thus enabling economically feasible routes to air vehicle propulsion with substantially less CO 2 emissions to the environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Interfacial Inversion of Stealth Surfactants

Amphiphilic macromolecular surfactants segregate to liquid–liquid interfaces, thereby reducing the interfacial tension and free energy. Here, we investigated “stealth surfactants” in the form of core–shell bottlebrush polymers comprised of pH-responsive diblock copolymer side chains forming a hydrophilic core and a hydrophobic shell, enabling solubility in oil. At liquid–liquid interfaces, these polymers undergo a structural “inversion”, with hydrophilic blocks segregating into the aqueous phase and hydrophobic blocks residing in the oil phase. The reconfiguration kinetics and surfactant properties are influenced by multiple factors, including the molecular weights of the backbone and side chain components, the hydrophilic-to-hydrophobic balance of the side chains, and the pH of the aqueous phase. An observed nonmonotonic dependence of interfacial tension with time is attributed to a progressive structural inversion, where the projected area of the macromolecule onto the interface decreases. To validate this inversion hypothesis, interfacial properties were characterized by sum-frequency generation vibrational spectroscopy, which revealed configurational changes of the core–shell bottlebrush polymers at the fluid interface and revealed a pH-dependent interfacial coverage. Coarse-grained molecular dynamics simulations supported these experimental findings, showing that the pH-responsive core and hydrophobic shell assume a time-averaged configuration with orientations parallel and perpendicular to the plane of the interface, respectively. These findings open routes to design multistimuli-responsive polymeric surfactants and compatibilizers, expanding their potential applications in advanced interfacial systems.

Stealth surfactants↗

DOE OSTI2024

Mixing-Controlled Compression Ignition Combustion with Low-Lifecycle-CO 2 Fuels

Reducing lifecycle carbon-dioxide (CO 2 ) and toxic emissions via electrification or switching to carbon-free fuels is not currently feasible for many off-road, rail, and marine applications. This is due to factors including excessive cost, weight, or size of a battery of sufficient capacity to meet the application requirements, lack of infrastructure, insufficient time for recharging, demanding duty cycles, and severe ambient conditions. The guiding vision for the activities described herein is to enable rapid, cost-effective reductions of the environmental impacts of such applications by using improved, high efficiency engine combustion strategies with currently available and emerging low lifecycle-CO 2 fuels (LLCFs). This report summarizes progress toward achieving this vision in two project areas. The first is a Technology Commercialization Fund (TCF) project focused on facilitating the commercialization of ducted fuel injection (DFI) with LLCFs. The second is a more fundamental, Advanced Combustion Engines (ACE) research project focused on elucidating a new strategy called lean mixing-controlled combustion (LMCC) for use with emerging LLCFs.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics

Electrical power converters are critical to a wide range of applications ranging from renewable integration to transportation electrification, and can be a key factor determining the size, weight, and efficiency of energy conversion systems. Magnetic components are typically the largest and least efficient components in power electronics. While there have been major strides in the modeling and analysis of power semiconductor devices and circuit simulations, the necessary advances in the design of power magnetics have lagged. In this project, we have transformed the modeling and design of power magnetics with machine learning enabled methods and catalyze simultaneous disruptive improvements for ML-based power electronics design tools. A fully automated open-source machine learning based magnetics modeling platform – the MagNet project - with innovations in full stack have been developed to greatly accelerate the design process and provide new insights to magnetic material and geometry design. The ARPA-E funded MagNet platform contains three major building blocks: 1) a ML-Integrated Data Acquisition System (MIDAS): a highly automated data acquisition testbed which is capable of measuring a large number of magnetic cores with a wide range of electrical circuit excitations; 2) a ML-integrated Core Loss Model (MICLM): a machine-learning trained modeling method for modeling the core loss and saturation effects of magnetic materials for arbitrary excitation waveforms; 3) ML-guided Magnetics SPICE Simulation Tool (PMSPICE): a fully integrated CAD tool which can simulate the magnetics in SPICE. It can help the designers to quickly model the linear and non-linear characteristics of magnetic components and evaluate their behavior in SPICE simulations. The developed MagNet system has fully demonstrated the proposed performance target and has been open sourced to the entire power electronics community to advance the modeling and design of power magnetics from many different angles.

36 MATERIALS SCIENCE↗

DOE OSTI2025

An interregional optimization approach for time series aggregation in continent-scale electricity system models

Modeling electric power systems with high shares of weather-dependent resources requires tradeoffs between temporal, spatial, and operational resolution. Many studies perform time series aggregation using clustering algorithms to reduce the temporal dimension, but when modeling continent-scale electricity systems that are large enough to contain multiple independent weather systems, this approach requires large numbers of representative periods to minimize errors in regional wind and solar capacity factors. Here, a new optimization-based approach for representative period selection and weighting is introduced that minimizes regional errors in average renewable capacity factors and electricity demand. The method delivers higher regional fidelity with fewer representative periods than alternative clustering methods when applied to wind, solar, and demand profiles for the contiguous United States. When representative periods are selected from multiple weather years, the optimized method reproduces regional averages with lower error than a complete 365-day time series from any single weather year. The method identifies only representative (as opposed to outlying) periods but can be combined with an iterative "stress period" identification approach to guide efficient decision-making considering both average and high-risk weather conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Neural refinement of sample weights

Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be negative, and several post hoc methods have been developed to eliminate or mitigate the negative values. All of these methods share the common strategy of approximating the average weight as a function of phase space. We introduce an alternative approach, which, instead of reweighting to the average, refines the initial weights with a scaling transformation, utilizing a phase space-dependent factor. Since this new refinement method does not need to model the full weight distribution, it can be more accurate. High-dimensional and unbinned phase space is processed using neural networks for the refinement method. In addition to the refinement method, we introduce a new resampling protocol, which can be used in conjunction with any weight transformation to not only preserve the average weight but also the statistical uncertainties of the initial distribution. Using both realistic and synthetic examples, we show that the new neural refinement method is able to match or exceed the accuracy of similar weight transformations and that the new resampling protocol is simpler in implementation than previous methods while exhibiting equivalent statistical properties.

Artificial neural networks↗

DOE OSTI2026

Effect of polymer structure and material properties on mechanochemical reaction environments for polymer recycling

Plastic waste accumulation necessitates innovative recycling approaches to achieve sustainability goals. Mechanochemical depolymerization offers a solvent-free, energy-efficient route to convert polymers into valuable monomers. In addition to their chemical properties, the way that polymers absorb kinetic energy is a key parameter of any mechanochemical process. This perspective explores the principles underpinning mechanochemical recycling, emphasizing how deformation and localized transient heating mediate energy transfer between impacts and localized excitations. Key factors such as polymer crystallinity, molecular weight, viscoelasticity, and thermal effects are analyzed to elucidate their role in energy transfer mechanisms during ball milling. This work establishes a foundational framework for the design and optimization of mechanochemical recycling by connecting polymer response to mechanical energy with the intention to improve depolymerization efficiency. Future research opportunities are outlined to advance the integration of polymer science and mechanochemistry for scalable, sustainable plastic upcycling.

ball mill↗

DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Optimizing Energy For Delivery Drones - A Comprehensive Tool Set For Drone Energy Calculation And Drone Fleet Optimization

This tool is intended to be deployed for potential customers to compare the energy profiles across various drone types/classes. The primary factors considered were design of the drone, the weight of the drone, the weight of the payload, and how the drone is flown. It has energy comparison metrics like "Drone (A) vs Drone (B) ", "Drone vs Ground Vehicle", "Drone Energy from delivery via landing versus hovering". It also includes the ability to determine the number of drones and batteries needed to optimally deliver goods from a chosen location to a set of destinations.

Mendadhala, Rohit [Idaho National Laboratory (INL)↗

DOE OSTI2026

Estimated Hourly Electricity Demand Profiles for Each County in the Contiguous United States

In this data descriptor, we present hourly electricity demand estimates for each county in the contiguous United States from 2016 to 2023. The demand profiles represent the sum of two components for each county. The first is the weighted average of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. The second is the weighted average of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Hourly Electricity Demand Profiles for Each County in the Contiguous United States

This dataset provides estimated hourly electricity demand for each county in the contiguous United States from 2016-2023. The demand profiles represent the sum of two components: (1) Weighted averages of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. (2) Weighted averages of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector Annual county-level retail sales are estimated by aggregating utility-reported sales to the state level and allocating the results to counties according to each county's share of state population. Annual county-level direct use is calculated by aggregating power plant-reported direct use values. Annual county-level on-site consumption of DPV-generated electricity is estimated by aggregating utility-reported net metering data to determine the amount of DPV-generated electricity sold back to the grid for each state, subtracting those values from modeled state-level DPV generation estimates, and allocating the results to counties according to each county's share of statewide modeled DPV generation. The open-source Python code used to develop this dataset is available at "Historical Load Data Repository" link below.

14 SOLAR ENERGY↗

DOE OSTI2025

Intrinsic Kinetics of Polyethylene Terephthalate Pyrolysis via Micropyrolysis and Multivariate Chromatographic Analysis

This study provides an in-depth investigation of the primary decomposition of polyethylene terephthalate (PET) via pyrolysis, employing an experimental-analytic workflow that integrates design of experiments (DoE), micropyrolysis coupled with comprehensive two-dimensional gas chromatography (GC×GC), and multivariate data analysis to verify intrinsic kinetic conditions and elucidate evolving product distributions for mapping key reaction pathways. Peaks that could not be identified using commercial spectral libraries were assigned using Mass Frontier simulations, enabling the identification of divinyl terephthalate, ethyl vinyl terephthalate, and 2-(benzoyloxy)ethyl vinyl terephthalate. A polar×polar (non-orthogonal) column set tailored for the detection of carboxylic acids enhanced the quantification of benzoic acid, 4-vinylbenzoic acid, 4-ethylbenzoic acid, and methylbenzoic acid by up to 6-fold relative to an orthogonal column combination (non-polar×mid-polar). Moreover, pyrolysis variables were systematically evaluated using a Box- Behnken design (BBD), encompassing pyrolysis temperature (500−600 °C), sample weight (50−150 μg), and carrier gas flow rate (100−300 mL min −1 ). Among these, pyrolysis temperature was the only statistically significant factor influencing product yields, ranging from 58.78 to 84.26 wt %. In contrast, neither the sample weight nor the carrier gas flow rate had a significant effect on product yields within the evaluated experimental space. At 600 °C, the major pyrolysis products were benzoic acid (up to 20.20 ± 1.46 wt %) and CO 2 (up to 21.28 ± 1.46 wt %), which can be produced through decarboxylation reactions. These findings underscore the critical importance of selecting appropriate analytical columns for the accurate quantification of heteroatomcontaining products such as carboxylic acids, which may otherwise be underestimated or undetected due to their reactivity with the stationary phase of non-polar and mid-polar columns, as well as other GC components. They also highlight the importance of selecting pyrolysis conditions for investigating the primary decomposition of PET under an isothermal kinetically limited regime.

aromatic compounds↗