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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

This study evaluates the energy performance of ASHRAE Guideline 36–compliant control (ASHRAE 36 control) and reinforcement learning (RL)–based control through experimental field tests and a simulation study. Three field tests were conducted at Oak Ridge National Laboratory’s commercial building test facility in Oak Ridge, Tennessee: a baseline with a baseline conventional control, a test with ASHRAE 36 control, and a test with RL-based control. The selected ASHRAE 36 controls were trim and respond control, as well as variable air volume (VAV) box control. We compared the measured supply air temperature of the rooftop unit, VAV box supply air temperature, and VAV box supply airflow rate across the three test cases. The field data indicated that ASHRAE 36 controls operated as specified by ASHRAE Guideline 36. Based on these data, ASHRAE 36 control achieved a 45 % reduction in hourly averaged HVAC energy consumption compared with the baseline, and RL-based control achieved a 66 % reduction. These potential annual energy savings were confirmed using a calibrated whole-building energy model. Compared with the baseline, ASHRAE 36 control reduced HVAC energy consumption by 42 %, and RL-based control achieved a 54 % reduction. Furthermore, RL-based control reduced total HVAC energy consumption by 21 % more than ASHRAE 36 control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Scheduler Modeling of Distributed Energy Resources for Providing Ancillary Services

Distribution energy resources (DERs) have been integral components of modern power systems, and their capability to provide grid services has been widely studied. To promote the deployment of these resources in providing grid services in real-world utility operations, this paper proposes a day-ahead scheduler model for a distribution system connected DER plant. A certain amount of generation capacity of this DER plant is reserved for frequency services, and some ancillary services for the distribution system-including peak load reduction, voltage regulation, and power factor control-are integrated into the model. The model is tested on a real-world distribution system. From the simulation results, the energy and reserve schedule of the solar photovoltaic (PV) unit and battery energy storage system (BESS) can be determined, and voltage and power factor are well maintained. Additionally, in order to demonstrate the specific characteristics of the co-located and hybrid operation modes for the PV and BESS, these two modes are analyzed both theoretically and through real-time simulation. Simulation results show that most of PV's variability is transferred to the net power in the co-located mode, whereas it is transferred to the BESS in the hybrid mode. This proposed scheduler model and the comparison of co-located and hybrid modes can provide practical guidance for the applications of DER plant in the real-world utility.

14 SOLAR ENERGY↗
DOE OSTI2025

In Situ Data Analysis Through Physics-informed Tensor Decompositions (LDRD Final Report)

We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Assessing the performance of solar radiation management geoengineering simulations

Offsetting the global warming caused by anthropogenic increases in atmospheric greenhouse gases by deliberate injection of aerosols into the stratosphere is the most studied of solar radiation management geoengineering schemes. The long-term success or failure of such schemes in achieving their stated goals is assessed by comparing simulated geoengineered temperature, precipitation and tropical cyclones metrics to equivalent fields in the simulated targeted climate simulations. Results using available data sets from three single model stabilized climate target experiments and three multimodel climate change reduction experiments are presented and compared against a measure of internal variability. While all but one experimental scheme is successful in achieving their targeted global mean annual surface temperature, their success at regional scales varies significantly and is often larger than the internal variability metric used here.

climate model evaluation↗
DOE OSTI2025

Fault-tolerant operation and materials science with neutral atom logical qubits

We report on the fault-tolerant operation of logical qubits on a neutral atom quantum computer, with logical performance surpassing physical performance for multiple circuits including Bell state preparation (12x error reduction), random circuits (15x), and a prototype Anderson Impurity Model ground state solver for materials science applications (up to 6x, non-fault-tolerantly). The logical qubits are implemented via the [[4, 2, 2]] code (C 4 ). Our work constitutes the first complete realization of the benchmarking protocol proposed by Gottesman 2016 demonstrating results consistent with fault tolerance. In light of recent advances on applying concatenated C 4 /C 6 detection codes to achieve error correction with high code rates and thresholds, our work can be regarded as a building block towards a practical scheme for fault tolerant quantum computation. Our demonstration of a materials science application with logical qubits particularly demonstrates the immediate value of these techniques on current experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microscale mechanical property variations of Al-substituted LLZO: insights from compression testing and molecular dynamics simulations

Ceramic solid electrolytes based on LLZO (Li 7 La 3 Zr 2 O 12 ) are promising candidates for all-solid-state batteries due to their high ionic conductivity and good apparent stability vs. lithium metal, however they are prone to mechanical failure. Lithium metal intrusions, alongside cell stack pressure, transition polycrystalline solid electrolyte grains into a compressed state that promotes crack propagation and fracture. Here this work examines the mechanical response of Al-substituted LLZO to compressive forces by measuring ultimate strength under pillar compression with a flat punch tip. Failure modes characterized by in situ scanning electron microscopy show diverse splitting patterns arising from internal porosity, grain boundaries, and slip planes. Large correlated variations in compressive strength (0.93–2.63 GPa) and Young's modulus (72.1–150.97 GPa) are observed across microscale regions of the solid electrolyte. Molecular dynamics simulations of LLZO with different porosities describe the variation of compressive strength and Young's modulus, and enable a microscale porosity model to be fit accounting for Young's modulus reduction across the solid electrolyte. Overall, the results indicate the importance of microscale mechanical testing of ceramic solid electrolytes to identify preferential sites for mechanical degradation and Li intrusion, and ensure the robust design of all-solid-state lithium metal batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Robustness of inflation to kinetic inhomogeneities

We investigate the effects of large inhomogeneities in both the inflaton field and its momentum. We find that in general, large kinetic perturbations reduce the number of e-folds of inflation. In particular, we observe that inflationary models with sub-Planckian characteristic scales are not robust even to kinetic energy densities that are sub-dominant to the potential energy density, unless the initial field configuration is sufficiently far from the minimum. This strengthens the results of our previous work. In inflationary models with super-Planckian characteristic scales, despite a reduction in the number of e-folds, inflation is robust even when the potential energy density is initially sub-dominant. For the cases we study, the robustness of inflation strongly depends on whether the inflaton field is driven into the reheating phase by the inhomogeneous scalar dynamics.

gravity↗
DOE OSTI2025

Uncertainty propagation and sensitivity analysis for constrained optimization of nuclear waste vitrification

Abstract The vitrification of high‐level waste (HLW) by heating a mixture of glass‐forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization algorithm that requires derivative information. Other UP methods often used for optimization under uncertainty (OUU) can be designed to work within an established constrained optimization framework. Methods of UP are evaluated in this study including iterative sampling approaches, first‐order approximations, and surrogate modeling with machine learning (ML). A method of dimensional reduction based on global sensitivity analysis is introduced to support the UP methods for the large dimensionality of the problem. Analytical UP methods able to achieve similar optimums 10 times faster than the baseline MC approach, and produce 93.9% similar output distributions are reported.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models

Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images from diverse sources—such as varying physical groundings or data acquisition systems—and to learn spatio-temporal correlations using transformer architectures. However, tokenizing and aggregating images can be compute-intensive, a challenge not fully addressed by current distributed methods. In this work, we introduce the Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) approach designed for datasets with a large number of channels across image modalities. Our method is compatible with any model-parallel strategy and any type of vision transformer architecture, significantly improving computational efficiency. We evaluated D-CHAG on hyperspectral imaging and weather forecasting tasks. When integrated with tensor parallelism and model sharding, our approach achieved up to a 75% reduction in memory usage and more than doubled sustained throughput on up to 1,024 AMD GPUs on the Frontier Supercomputer.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗
DOE OSTI2025

Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training

With the end of Moore’s law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intelligent subsampling? To explore this, we develop SICKLE, a sparse intelligent curation framework for efficient learning, featuring a novel maximum entropy (MaxEnt) sampling approach, scalable training, and energy benchmarking. We compare MaxEnt with random and phase-space sampling on large direct numerical simulation (DNS) datasets of turbulence. Evaluating SICKLE at scale on Frontier, we show that subsampling as a preprocessing step can, in many cases, improve model accuracy and substantially lower energy consumption, with observed reductions of up to 38×.

Brewer, Wes [ORNL] (ORCID:0000000236393956)↗
DOE OSTI2024

A Solar Fuels Nexus: Molecules and Materials for Light-Driven Catalysis

The American Chemical Society (ACS) selects two groups of graduate students each year to plan and host a one-day symposium at each national meeting (both fall and spring).This year our Graduate Student Symposium Planning Committee (GSSPC), composed of seven students from four universities, proposal entitled “A Solar Fuels Nexus: Molecules and Materials for Light-Driven Catalysis” was selected for the “Crossroads in Chemistry” ACS Meeting that will take place March 23-26, 2023 in Indianapolis, IN. All members of our GSSPC are affiliated with the DOE Fuels from Sunlight Energy Innovation Hub, with two from the Liquid Sunlight Alliance (LiSA) and five from the Center for Hybrid Approaches in Solar Energy to Liquid Fuels (CHASE). Here we request funds to support this symposium. This symposium will highlight research progress and perspectives in the solar fuels generation field and seeks to advance the four priority research objectives (PROs) established by the Department of Energy’s Office of Basic Energy Sciences (DOE-BES) Roundtable Report that are also central to many research goals within LiSA and CHASE. The symposium will consist of research presentations from 10 invited senior researcher speakers on topics such as molecular catalyst design, computational modeling of electron transfer systems, microenvironmental effects on CO2 reduction and H2O oxidation catalysis, and intelligent design of semiconductor interfaces with ample time for discussions. These research topics fit very well with the Solar Photochemistry supported research areas of “light-driven electron and energy transfer in condensed phase and interfacial molecular systems,” “electrocatalysis and photocatalysis of solar fuels reactions,” and “semiconductor photoelectrochemistry.” More broadly, this symposium seeks to advance the DOE-BES’s mission to: “support fundamental research to understand, predict, and ultimately control matter and energy at the level of electrons, atoms, and molecules” by providing a diverse atmosphere where such research will be disseminated, discussed, and debated. There will be a strong focus on Diversity, Equity, and Inclusivity (DEI) in our symposium. Of our 10 speakers, 7 will be from underrepresented demographics in STEM, including 5 who identify as women. Furthermore, we have representatives from academia accompanied by one national lab scientist and one officer from the Office of Fossil Energy and Carbon Management at the DOE. All speakers will be holding a short DEI moment ahead of their talks. In order to support the career development of attending early career scientists, we will also be hosting a luncheon specifically for graduate students and postdocs to provide them opportunities to network with the distinguished speakers and other attendees. DOE funds for this symposium will be used to support the attendance and participation of 15 graduate students from US institutions by defraying travel and registration costs. These funds will promote engagement and conversation between early career scientists in the solar fuels field, while disseminating solar fuels research funded by and relevant to the DOE.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Consumer Benefits of Clean Energy: Renewable Energy

Meeting national and state decarbonization goals requires a transition to clean energy technologies. Energy efficiency, demand flexibility, renewable energy and storage can reduce consumers’ electricity bills, lower total electricity system costs, and provide health and resilience benefits. Berkeley Lab developed a series of briefs that explore these consumer benefits of a clean energy transition. This brief discusses some of the possible consumer benefits of utility-scale and behind the meter renewable energy, with a focus on how these resources can contribute to a low-cost electricity system. It begins with a literature review of modeled impacts, primarily considering consumer benefits, of the Inflation Reduction Act and Bipartisan Infrastructure Law. Next, it discusses how utility-scale renewable energy can contribute to a low-cost electricity system (e.g., in some cases, low resource costs relative to other alternatives). It concludes with a discussion of behind-the-meter renewable energy consumer benefits (e.g., reduced host electricity bill, increased property value, resilience).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Lie-algebraic Kähler sigma models with U(1) isotropy

We discuss various questions that emerge in connection with the Lie-algebraic deformation of the sigma model in two dimensions. First, we supersymmetrize the original model endowing it with the minimal and extended supersymmetries. Then we derive the general hypercurrent anomaly in both cases. In the latter case this anomaly is one-loop but is somewhat different from the standard expressions one can find in the literature because the target manifold is nonsymmetric. We also show how to introduce the twisted masses and the term, and study the Bogomol’nyi–Prasad–Sommerfield equation for instantons, in particular the value of the topological charge. Then we demonstrate that the second loop in the function of the nonsupersymmetric Lie-algebraic sigma model is due to an infrared effect. To this end we use a supersymmetric regularization. We also conjecture that the above statement is valid for higher loops too, similar to the parallel phenomenon in four-dimensional super-Yang-Mills. In the second part of the paper we develop a special dimensional reduction—namely, starting from the two-dimensional Lie-algebraic model we arrive at a quasi-exactly solvable quantum-mechanical problem of the Lamé type.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Electrocatalytic Reductive Amination of Aldehydes and Ketones with Aqueous Nitrite

The electrocatalytic utilization of oxidized nitrogen waste for C–N coupling chemistry is an exciting research area with great potential to be adopted as a sustainable method for generation of organonitrogen molecules. The most widely used C–N coupling reaction is reductive amination. In this work, we develop an alternative electrochemical reductive amination reaction that can proceed in neutral aqueous electrolyte with nitrite as the nitrogenous reactant and via an oxime intermediate. We develop a selection criterion for nitrite reduction electrocatalysts suited for oxime electrosynthesis and, in doing so, find Pd to be a highly efficient catalyst for this reaction, reaching an oxime Faradaic efficiency of 82% at −0.21 V vs the reversible hydrogen electrode. The aliphatic or aromatic structure of the carbonyl reactant impacts the efficacy of the catalyst, with aromatic substrates leading to suppressed oxime formation and detrimental reduction of the carbonyl to the alcohol. We developed a Pb/PbO electrocatalyst that selectively performs oxime reduction in the neutral aqueous electrolyte. With acetone as a model substrate, we demonstrate an efficient one-pot, two-step electrochemical reaction for the conversion of acetone to isopropyl amine with 85% yield and 50% global Faradaic efficiency.

catalysts↗
DOE OSTI2024

A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California

Abstract After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.

Environmental Sciences & Ecology↗
DOE OSTI2024

Solar Thermochemical Carbon Dioxide Splitting Using Ceria and Iron Aluminate Foam Devices and Simulation of a Plant System for Demonstration

An international research project has been undertaken to integrate a unique solar thermal processing reactor system with ceria and iron aluminate as active redox materials for CO2 splitting. Experimental investigations for CO2 splitting were conducted using a solar simulator and tube furnace at Niigata University, followed by demonstrations using a high-flux solar furnace (HFSF) at the National Renewable Energy Laboratory (NREL) in Golden, CO. Each experimental setup consisted of foam devices composed of reticulated porous ceramic (RPC). The RPC has a full ceria or iron aluminate body. It fabricated using the replica method and subjected to a two-step redox reaction, which iteratively separated a stream of CO2 into O2 and CO. Reactivity was evaluated using CO production per mass of the reactive material. The tubular furnace yielded a CO production of 6.41 mL/g at a reduction temperature of 1600degrees C, showcasing a higher CO production rate and total amount than those obtained from experiments conducted with solar simulators and solar furnace setups. For iron aluminate RPC, the productivity was measured as 3.57 mL/g using HFSF at a reduction temperature of 1450degrees C. These results are somewhat higher than those of the previous experiment at lower reduction temperatures of 1400degrees C-1500degrees C. Additionally, the production of CO in the case of ceria RPC was compared with the steady flow model simulation, which assumed chemical equilibrium at various levels of oxygen partial pressure during the reduction process. On the basis of these results, this study proposes a solar fuel system with an open receiver that uses a high-temperature heat transfer fluid.

carbon dioxide thermochemical splitting↗
DOE OSTI2026

Understanding the Role of Extrinsic Impurities in Bulk Nb SRF Cavities

High gradient and high quality factor superconducting radiofrequency (SRF) cavities are essential for efficient particle acceleration in next-generation accelerators. This work demonstrates that the superconducting performance of bulk niobium can be enhanced through a controlled introduction of interstitial oxygen and nitrogen. Thermal and chemical surface treatments modify the concentration and distribution of these impurities, enabling systematic tuning of SRF cavity performance. By combining cavity RF measurements with quantitative materials characterizations, we isolate the impact of impurity concentration on BCS surface resistance and identify the mechanisms by which interstitial impurities alter the superconducting properties of niobium. We find that nitrogen is an order of magnitude more effective than oxygen in reducing BCS resistance at 16 MV/m and that the reduction arises from a combination of mean free path optimization and superconducting gap enhancement. Using these insights, we develop a predictive model that estimates cavity performance directly from room temperature sample material studies, reducing the reliance on full-scale cryogenic tests for surface treatment development. We then apply this model to explore co-doping strategies, demonstrating that combining interstitial oxygen and nitrogen achieves a synergistic reduction in BCS resistance. These results establish co-doping as a viable path for the development of surface-engineered niobium for future high quality factor SRF applications.

Hu, Hannah [Chicago U.]↗
DOE OSTI2025

TorbeamNN: machine learning-based steering of ECH mirrors on KSTAR

We have developed TorbeamNN: a machine learning surrogate model for the TORBEAM ray tracing code to predict electron cyclotron heating (ECH) and current drive locations in tokamak plasmas. TorbeamNN provides more than a 100 times speed-up compared to the highly optimized and simplified real-time implementation of TORBEAM without any reduction in accuracy compared to the offline, full fidelity TORBEAM code. The model was trained using KSTAR ECH mirror geometries and works for both O-mode and X-mode absorption. The TorbeamNN predictions have been validated both offline and real-time in experiment. TorbeamNN has been utilized to track an ECH absorption vertical position target in dynamic KSTAR plasmas as well as under varying toroidal mirror angles and with a minimal average tracking error of 0.5 cm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗