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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Surface and Buried Thermal, and RGB Unexploded Ordnance Data Collection

This document provides a description of a data collection campaign of unexploded ordnance (UXOI) set. The dataset captures a controlled UAV imaging campaign designed to support detection of UXO across varied environmental conditions. Data were collected during three campaigns in Norris and Northeast Knoxville, Tennessee, using RGB, and thermal sensors mounted on Parrot UKR. In total, the dataset contains 9925 images, 26 full-motion video, and approximately 81.99 GB of data, collected across late spring/summer conditions, every hour during sunlight, and multiple surface contexts, including tall grass, short grass, gravel, as well as buried in sand, and other gravel mixtures. The collection was designed to capture thermal and visual variability relevant to UXO detection in agricultural land, bare earth, and subsurface. Review of the imagery showed that ordnance was most detectable during periods of changing solar input, especially approximately 10-60 minutes after sunrise, approximately 20-60 minutes after sunset, and 2-3 min after cloud cover interrupted prolonged solar heating. These conditions increased thermal contrast because many ordnance items retained or released heat differently than the surrounding vegetation and ground surface. This dataset provides a useful resource for developing and evaluating airborne UXO detection methods under realistic field conditions. All ordnance used in the study was inert, and thermal behavior may differ from that of live ordnance. In addition, variation in ordnance type, composition, and placement introduced differences in thermal response that should be considered when interpreting results.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Thermal Reservoir Networks for Modularly Expandable Thermal Microgrids

The Department of Defense (DoD) faces the substantial challenge of cost-effectively retrofitting one to two installations per month, each comprising approximately 1,000 buildings, to improve resilience, reduce energy consumption, and enhance energy supply security. Achieving these objectives requires optimal system selection and effective risk mitigation during system integration. To address this need, we introduce Platform-Based Design (PBD), a structured, hierarchical methodology adapted from other industrial sectors to the domain of energy system retrofits. We demonstrate the effectiveness of PBD through a techno-economic feasibility study comparing geothermal-coupled thermal energy networks (TENs) with conventional energy systems for heating, cooling, and powering 17 buildings at Joint Base Andrews (JBA) in Maryland. Our analysis illustrates that the PBD approach enables rigorous, data-driven, sequential decision making, resulting in a family of Pareto-optimal systems, among which the TEN emerged as the most promising solution. The selected TEN design integrates geothermal borefields, heat recovery heat pumps, photovoltaic (PV) arrays, and battery storage. Compared to the baseline system – gas heating combined with air-source chillers – the proposed TEN reduces annual imported energy by 74% and peak electricity demand by 45%, achieves a levelized cost of energy of $\$0.210$/kWh, and substantially enhances resilience. Life-cycle costs increase by approximately 6%, and initial investment costs are about 2.5 times higher than the baseline. However, if central plant infrastructure, district loops, and utility-scale PV and battery systems are privately funded and operated, the initial investment would fall below the baseline system cost. Critical to achieving these significant performance improvements were detailed nonlinear dynamic simulations coupling geothermal heat transfer, energy system operation, and realistic feedback control logic. These simulations identified essential design modifications and control strategy refinements that substantially reduced energy use, peak demand, and compressor shortcycling, thereby improving durability and reliability—issues that would have been significantly more expensive to resolve during operation. Additionally, the verification step highlighted sensitivities to key design parameters that could reduce initial investment by approximately $\$2$ million and reduce annual life-cycle costs more than $\$300,000$. We recommend adopting the PBD methodology for future feasibility studies and TEN pilot projects to gain valuable operational experience. Furthermore, we recommend that DoD invest in transferring and scaling the PBD methodology to other installations. This entails developing standardized computational frameworks and component libraries as well as training industry in conducting PBD. Such investments would enable rapid, robust, reliable, and cost-effective retrofits, supporting DoD’s ambitious energy system modernization goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.

aggregates↗
DOE OSTI2022

Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method

We study the problem of designing scalable algorithms to find effective intervention strategies for controlling stochastic epidemic processes on networks. This is a common problem arising in agent based models for epidemic spread. Previous approaches to this problem focus on either heuristics with no guarantees or approximation algorithms that scale only to networks corresponding to county-sized populations, typically, with less than a million nodes. In particular, the mathematical-programming based approaches need to solve the Linear Program (LP) relaxation of the problem using an LP solver, which restricts the scalability of this approach. In this work, we overcome this restriction by designing an algorithm that adapts the multiplicative weights update (MWU) framework, along with the sample average approximation (SAA) technique, to approximately solve the linear program (LP) relaxation for the problem. To scale this approach further, we provide a memory-efficient algorithm that enables scaling to large networks, corresponding to country-size populations, with over 300 million nodes and 30 billion edges. Furthermore, we show that this approach provides near-optimal solutions to the LP in practice.

Sambaturu, Prathyush↗
DOE OSTI2026

Integrative Modeling and Analysis of Fungal Central Carbon Metabolism

Over a thousand fungal genomes have been sequenced, yet manually curated genome-scale metabolic models (GEMs) are available for only a limited number of species. Moreover, these models have often been developed independently, leading to inconsistencies in namespaces, compartment definitions, and pathway representations that hinder comparative analysis, the systematic reuse of prior curation efforts, and the integration of consolidated metabolic knowledge. Here, we present the Consolidated Fungal Core Metabolism Model (CFCMM), constructed by integrating thirteen published fungal models spanning Ascomycota, Mucoromycota, and both Crabtree-positive and Crabtree-negative yeasts. We harmonized metabolites and reactions into a non-redundant shared ModelSEED ontological space, standardized compartmentalization, and refined gene–protein–reaction (GPR) rules. Using pathway-level visualization and systematic gap detection, we further improved the integrated network through literature-guided curation to correct stoichiometry, stereospecificity, and pathway architecture. Orthologous protein family reconstruction and functional annotation workflows were used to validate and inform GPR associations, with particular emphasis on ambiguous enzyme superfamilies and membrane-associated components. Using the resulting CFCMM, we built high-quality central carbon core models for each fungus and performed flux balance analysis to quantify ATP-yield variation under aerobic and anaerobic conditions, explicitly evaluating scenarios driven by differences in electron transport chain (ETC) composition. Simulations reproduced the expected fermentative yield of approximately 2 mmol ATP per mmol glucose under anaerobic conditions and separated the thirteen fungi into two bioenergetic groups under aerobic respiration based on Complex I status, with predicted yields of approximately 30 versus 22 mmol ATP per mmol glucose. Forcing flux through the alternative oxidase bypass further reduced ATP yields to approximately 12 and 4 mmol ATP per mmol glucose in Complex I-containing and Complex I-lacking fungi, respectively. Collectively, this work provides a manually curated, ModelSEED-consistent, and extensible fungal core metabolic template, deployed in DOE KBase as a resource for automated reconstruction of central carbon core models from any sequenced fungal genome. In addition, the CFCMM provides modular components for developing GEMs with more accurate energy predictions and enables robust comparative analyses of fungal bioenergetics and core metabolic diversity

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Advanced biorefinery feedstock from non-recyclable municipal solid waste by mechanical preprocessing

Because of accelerated urbanization and the development of a global economy, a large quantity of municipal solid waste (MSW) has been collected and disposed of by the municipalities. Due to this drastic increase in the disposal of MSW, the need for its management is a must to preserve the environment. Currently, approximately 50% of the total MSW generated in the United States has been utilized through various recycling, combustion, and composting technologies, which means the remaining 50% is sent to landfill; this is often known as non-recyclable MSW (nMSW). As this nMSW is physically and chemically heterogenous and contains very high amounts of inorganic material, processing is required prior to using it as a biorefinery feedstock. Thus, this study focused on how mechanical preprocessing advanced the physical and chemical properties of nMSW. The physical and chemical properties were investigated in terms of particle size distribution, bulk density, ultimate and proximate analysis, and the higher heating value (HHV). The combustion properties were examined in terms of ignition temperature, peak heat release rate, and combustion efficiency. Results showed that the variability of physical and chemical properties of nMSW can be reduced by mechanical preprocessing. For example, the variability of the bulk density of the as-received nMSW was approximately 17.3% while it reduced to 5.8% when the sample size was reduced to 2 mm. Similarly, the variability of ash and HHV reduced from 49.2% to 11.0% and 13.4%–4.2%, respectively. Combustion thermograms showed that the size reduction positively improved the combustion properties. For example, 2 mm of spec sample started to ignite approximately 4 times earlier and took 6.5 folds less time to reach the peak heating rate compared to as-received nMSW. Overall, the mechanical preprocessing reduced the variability of physical and chemical properties in addition to the improvement of combustion behavior of the nMSW which is one step forward toward the biorefinery feedstock.

09 BIOMASS FUELS↗
DOE OSTI2022

Vibrational Entropy of Crystalline Solids from Covariance of Atomic Displacements

The vibrational entropy of a solid at finite temperature is investigated from the perspective of information theory. Ab initio molecular dynamics (AIMD) simulations generate ensembles of atomic configurations at finite temperature from which we obtain the N-body distribution of atomic displacements, ρN. We calculate the information-theoretic entropy from the expectation value of lnρN. At a first level of approximation, treating individual atomic displacements independently, our method may be applied using Debye–Waller B-factors, allowing diffraction experiments to obtain an upper bound on the thermodynamic entropy. At the next level of approximation we correct the overestimation through inclusion of displacement covariances. We apply this approach to elemental body-centered cubic sodium and face-centered cubic aluminum, showing good agreement with experimental values above the Debye temperatures of the metals. Below the Debye temperatures, we extract an effective vibrational density of states from eigenvalues of the covariance matrix, and then evaluate the entropy quantum mechanically, again yielding good agreement with experiment down to low temperatures. Our method readily generalizes to complex solids, as we demonstrate for a high entropy alloy. Further, our method applies in cases where the quasiharmonic approximation fails, as we demonstrate by calculating the HCP/BCC transition in Ti.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Solid Wastes from Geothermal Energy Production and Implications for Direct Lithium Extraction

Direct lithium extraction (DLE) of brines after geothermal power production offers opportunities to produce environmentally benign “green” lithium; however, some environmental impact is inevitable. We examined solid waste production at geothermal power plants in southern California that are also locations for planned DLE facilities. Currently, the geothermal plants in this region produce approximately 79,800 metric tons (wet weight) per year of solid waste, which represents about 28 metric tons per GWh of net electricity production or approximately 500 mg solids per kg geothermal brine. Approximately 15% of this waste requires management as hazardous waste. Solids produced during power production represent about 0.2% of the total dissolved solids in the brine. Lithium production will require the removal of silica, iron, and other metals as part of the DLE process. Using a mass balance approach, we calculate that precipitation of silica and metals could produce up to an additional 6800 mg solids per kg brine. Calcium occurs at very high concentrations, and the amount of solids disposed in landfills will be dependent on the amount of calcium removed during lithium recovery. Our analysis shows that evaluation of brine chemistry in the context of the DLE process is useful for evaluating the potential solid waste impacts of producing lithium from brines.

42 ENGINEERING↗
DOE OSTI2020

Structure and Behavior of the Ni End-Member Schreibersite Ni 3 P under Compression to 50 GPa

To better understand the potential presence of light element alloys of Fe and Ni in the Earth’s interior, the crystal structure and compressional behavior of the Ni-P binary compound, schreibersite (Ni 3 P), have been investigated using synchrotron X-ray diffraction experiments. Both powder and two single-crystal samples of synthetic Ni 3 P (in different orientations with respect to the loading axis of the diamond anvil cell) were compressed up to approximately 50 GPa at ambient temperature. The compressional data obtained for Ni3P were fitted with a 3rd order Birch–Murnaghan equation of state. All data indicated that the c/a ratio of unit cell parameters remained approximately constant up to about 30 GPa but then increased progressively with pressure, exhibiting a second slight discontinuity at approximately 40 GPa. The changes in unit cell parameters at ~30 GPa and ~40 GPa suggested discontinuous changes in magnetic ordering. Moreover, the threshold of these subtle discontinuities is sensitive to the stress state and orientation of the crystal in the diamond anvil cell. This study is the first report on the compressional behavior of both powder and single-crystal schreibersite at high-pressure (up to 50 GPa). It offers insights into the effects of Ni 3 P components on the compressional behavior of the Earth’s core.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Additive Manufacturing of Miniaturized Peak Temperature Monitors for In-Pile Applications

Passive monitoring techniques have been used for peak temperature measurements during irradiation tests by exploiting the melting point of well-characterized materials. Recent efforts to expand the capabilities of such peak temperature detection instrumentation include the development and testing of additively manufactured (AM) melt wires. In an effort to demonstrate and benchmark the performance and reliability of AM melt wires, we conducted a study to compare prototypical standard melt wires to an AM melt wire capsule, composed of printed aluminum, zinc, and tin melt wires. The lowest melting-point material used was Sn, with a melting point of approximately 230 °C, Zn melts at approximately 420 °C, and the high melting-point material was aluminum, with an approximate melting point of 660 °C. Through differential scanning calorimetry and furnace testing we show that the performance of our AM melt wire capsule was consistent with that of the standard melt-wire capsule, highlighting a path towards miniaturized peak-temperature sensors for in-pile sensor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The Vera C. Rubin Observatory Data Preview 1

We present Rubin Data Preview 1 (DP1), the first data from the National Science Foundation–Department of Energy Vera C. Rubin Observatory, comprising raw and calibrated single-epoch images, coadds, difference images, detection catalogs, and ancillary data products. DP1 is based on 1792 optical–near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera (LSSTComCam) on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile in late 2024. DP1 covers ∼15 deg 2 distributed across seven roughly equal-sized noncontiguous fields, each independently observed in six broad photometric bands, ugrizy. The median FWHM of the point-spread function across all bands is approximately 1"14, with the sharpest images reaching about 0." 58. The 5σ point-source depths for coadded images in the deepest field, the Extended Chandra Deep Field South, are u = 24.55, g = 26.18, r = 25.96, i = 25.71, z = 25.07, and y = 23.1. Other fields are no more than 2.2 mag shallower in any band, where they have nonzero coverage. DP1 contains approximately 2.3 million distinct astrophysical objects, of which 1.6 million are extended in at least one band in coadds, and 431 solar system objects, of which 93 are new discoveries. DP1 is approximately 3.5 TB in size and is available to Vera C. Rubin Observatory data rights holders via the Rubin Science Platform, a cloud-based environment for the analysis of petascale astronomical data. While small compared to future LSST releases, its high quality and diversity of data support a broad range of early science investigations ahead of full operations in 2026.

Ground-based astronomy↗
DOE OSTI2021

Circumbinary Disk Accretion into Spinning Black Hole Binaries

Supermassive black hole binaries are likely to accrete interstellar gas through a circumbinary disk. Shortly before merger, the inner portions of this circumbinary disk are subject to general relativistic effects. To study this regime, we approximate the spacetime metric of close orbiting black holes by superimposing two boosted Kerr–Schild terms. After demonstrating the quality of this approximation, we carry out very long-term general relativistic magnetohydrodynamic simulations of the circumbinary disk. We consider black holes with spin dimensionless parameters of magnitude 0.9, in one simulation parallel to the orbital angular momentum of the binary, but in another anti-parallel. These are contrasted with spinless simulations. We find that, for a fixed surface mass density in the inner circumbinary disk, aligned spins of this magnitude approximately reduce the mass accretion rate by 14% and counter-aligned spins increase it by 45%, leaving many other disk properties unchanged.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Implementation of Monte Carlo Transport in the General Relativistic SpEC Code

Neutrino transport and neutrino-matter interactions are known to play an important role in the evolution of neutron star mergers and of their post-merger remnants. Neutrinos cool remnants, drive post-merger winds, and deposit energy in the low-density polar regions where relativistic jets may eventually form. Neutrinos also modify the composition of the ejected material, impacting the outcome of nucleosynthesis in merger outflows and the properties of the optical/infrared transients that they power (kilonovae). So far, merger simulations have largely relied on approximate treatments of the neutrinos (leakage, moments) that simplify the equations of radiation transport in a way that makes simulations more affordable but also introduces unquantifiable errors in the results. To improve on these methods, we recently published a first simulation of neutron star mergers using a low-cost Monte Carlo algorithm for neutrino radiation transport. Our transport code limits costs in optically thick regions by placing a hard ceiling on the value of the absorption opacity of the fluid, yet all approximations made within the code are designed to vanish in the limit of infinite numerical resolution. We provide here an in-depth description of this algorithm, of its implementation in the SpEC merger code, and of the expected impact of our approximations in optically thick regions. We argue that the last is a subdominant source of error at the accuracy reached by current simulations and for the interactions currently included in our code. We also provide tests of the most important features of this code.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The DESI Survey Validation: Results from Visual Inspection of Bright Galaxies, Luminous Red Galaxies, and Emission-line Galaxies

The Dark Energy Spectroscopic Instrument (DESI) Survey has obtained a set of spectroscopic measurements of galaxies to validate the final survey design and target selections. To assist in these tasks, we visually inspect DESI spectra of approximately 2500 bright galaxies, 3500 luminous red galaxies (LRGs), and 10,000 emission-line galaxies (ELGs) to obtain robust redshift identifications. We then utilize the visually inspected redshift information to characterize the performance of the DESI operation. Based on the visual inspection (VI) catalogs, our results show that the final survey design yields samples of bright galaxies, LRGs, and ELGs with purity greater than 99%. Moreover, we demonstrate that the precision of the redshift measurements is approximately 10 km s –1 for bright galaxies and ELGs and approximately 40 km s –1 for LRGs. The average redshift accuracy is within 10 km s –1 for the three types of galaxies. The VI process also helps improve the quality of the DESI data by identifying spurious spectral features introduced by the pipeline. Finally, we show examples of unexpected real astronomical objects, such as Ly α emitters and strong lensing candidates, identified by VI. These results demonstrate the importance and utility of visually inspecting data from incoming and upcoming surveys, especially during their early operation phases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Mercury’s Chaotic Secular Evolution as a Subdiffusive Process

Abstract Mercury’s orbit can destabilize, generally resulting in a collision with either Venus or the Sun. Chaotic evolution can causeg 1 to decrease to the approximately constant value ofg 5 and create a resonance. Previous work has approximated the variation ing 1 as stochastic diffusion, which leads to a phenomological model that can reproduce the Mercury instability statistics of secular andN-body models on timescales longer than 10 Gyr. Here we show that the diffusive model significantly underpredicts the Mercury instability probability on timescales less than 5 Gyr, the remaining lifespan of the solar system. This is becauseg 1 exhibits larger variations on short timescales than the diffusive model would suggest. To better model the variations on short timescales, we build a new subdiffusive phenomological model forg 1 . Subdiffusion is similar to diffusion but exhibits larger displacements on short timescales and smaller displacements on long timescales. We choose model parameters based on the behavior of theg 1 trajectories in theN-body simulations, leading to a tuned model that can reproduce Mercury instability statistics from 1–40 Gyr. This work motivates fundamental questions in solar system dynamics: why does subdiffusion better approximate the variation ing 1 than standard diffusion? Why is there an upper bound ong 1 , but not a lower bound that would prevent it from reachingg 5 ?

Astronomy & Astrophysics↗
DOE OSTI2020

Monte-Carlo Neutrino Transport in Neutron Star Merger Simulations

Gravitational waves and electromagnetic signals from merging neutron star binaries provide valuable information about the the properties of dense matter, the formation of heavy elements, and high-energy astrophysics. To fully leverage observations of these systems, we need numerical simulations that provide reliable predictions for the properties of the matter unbound in these mergers. An important limitation of current simulations is the use of approximate methods for neutrino transport that do not converge to a solution of the transport equations as numerical resolution increases, and thus have errors that are impossible to quantify. Here, we report on a first simulation of a binary neutron star merger that directly solves the transport equations using Monte-Carlo techniques. We demonstrate that a computationally cheap Monte-Carlo transport can be implemented without ill effects from sampling noise. We apply Monte-Carlo transport to an unequal mass neutron star binary merger up to 5ms past merger, and report on the properties of the matter and neutrino outflows. Finally, we compare our results to the output of our best approximate `M1' transport scheme, demonstrating that an M1 scheme that carefully approximates the neutrino energy spectrum only leads to ~10% uncertainty in the composition and velocity of the ejecta, and ~20% uncertainty in the electron (anti)neutrino luminosities and energies. Here, the most significant disagreement found between M1 and Monte-Carlo results is a factor of ~2 difference in the luminosity of heavy-lepton neutrinos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Cholesky-based experimental design for Gaussian process and kernel-based emulation and calibration.

Gaussian processes and other kernel-based methods are used extensively to construct approximations of multivariate data sets. The accuracy of these approximations is dependent on the data used. This paper presents a computationally efficient algorithm to greedily select training samples that minimize the weighted L p error of kernel-based approximations for a given number of data. The method successively generates nested samples, with the goal of minimizing the error in high probability regions of densities specified by users. The algorithm presented is extremely simple and can be implemented using existing pivoted Cholesky factorization methods. Training samples are generated in batches which allows training data to be evaluated (labeled) in parallel. For smooth kernels, the algorithm performs comparably with the greedy integrated variance design but has significantly lower complexity. Numerical experiments demonstrate the efficacy of the approach for bounded, unbounded, multi-modal and non-tensor product densities. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data using Bayesian inference.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multiqubit entanglement generation with squeezed modes

We present a hybrid continuous variable–discrete variable entanglement generation protocol using linear optics and homodyne measurements, capable of producing multiple high-fidelity Bell pairs per protocol iteration, with an approximate 0.5 success probability. The effectiveness of the protocol is determined by the squeezing strength. To increase the number of Bell pairs, approximately 3 dB of extra squeezing is needed for each additional Bell pair. The protocol also generates single Bell pairs with an approximate 0.75 probability for squeezing strengths ⪅ 15 dB , achievable with current technology.

Macridin, Alexandru [Fermilab] (ORCID:000000022228↗