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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

NuGraph2 with context-aware inputs: physics-inspired improvements in semantic segmentation

Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys.

Other Experiments↗
DOE OSTI2020

CRADA Number NFE-17-06866 with Active Energy Systems, Inc. (CRADA Final Report)

Cooperative Research and Development Agreement (CRADA) NFE-17-06866 between Oak Ridge National Laboratory (ORNL) and Active Energy Systems Inc. (AES) focused on studying and modeling a low cost, high efficiency energy storage technology. The successful development of low-cost, high-efficiency energy storage is a significant bottleneck in the implementation of intermittent renewable energy sources such as wind and solar. AES is a startup company developing a heat exchange technology to utilize a low-temperature phase change material (PCM). The work done under this CRADA explored this technology’s ability to augment a heat pump/heat engine device to convert electricity into thermal energy and vice versa, operating to complement the needs of the electric grid. Modeling, design and development of a prototype to prove the technology was successful, but current production economics and market demand make further exploration undesirable.

25 ENERGY STORAGE↗
DOE OSTI2022

Physics Informed Reinforcement Learning for Power Grid Control using Augmented Random Search

Wide adoption of deep reinforcement learning need to overcome several challenges in energy system domain, including scalability, learning from limited samples, and high-dimensional continuous state and action spaces. In this paper, we integrated physics-based information from the normal generator operation state formula in the reinforcement learning agent's neural network loss function, and applied an augmented random search agent to optimize the generator control under dynamic contingency. Simulation results demonstrated the reliability performance improvements in training speed, reward convergence, sampling efficiency, scalability, and transferability.

physics informed ML, Physics Informed Neural Netwo↗
DOE OSTI2025

Testing and Expertise for Marine Energy (TEAMER) Program Support: MRE Dynamic Seals Performance Investigation (CRADA Final Report)

This TEAMER project allows NREL to use and augment a custom special-purpose testing system to perform accelerated-life testing of rotating seals for use in the Marine Renewable Energy (“MRE”) industry. The requirements of this industry are different from most applications including marine, and relevant performance information is lacking. In order to achieve low costs with extremely effective and reliable operation over long periods deployed underwater, more study of the best seals and arrangements of seals must be performed. By developing a rigorous framework for such testing, NREL hopes to greatly improve understanding of the relevant factors and provide significant data to the industry. Further, this work can inform standards developments for this critical component of MRE systems, leading to maturation and improved acceptance of the industry in commerce.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology

Semiempirical methods like density functional tight-binding (DFTB) allow extensive phase space sampling, making it possible to generate free energy surfaces of complex reactions in condensed-phase environments. Such a high efficiency often comes at the cost of reduced accuracy, which may be improved by developing a specific reaction parametrization (SRP) for the particular molecular system. Thiol–disulfide exchange is a nucleophilic substitution reaction that occurs in a large class of proteins. Its proper description requires a high-level ab initio method, while DFT-GAA and hybrid functionals were shown to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop an SRP for thiol–disulfide exchange based on an artificial neural network (ANN) implementation in the DFTB+ software and compare its performance to that of a standard SRP approach applied to DFTB. Furthermore, as an application, we use both new DFTB-SRP as components of a QM/MM scheme to investigate thiol–disulfide exchange in two molecular complexes: a solvated model system and a blood protein. Demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of DFTB with an ANN only adds a small computational overhead.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

We report the design/discovery of new materials is highly nontrivial owing to the near-infinite possibilities of material candidates and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the forward problem. However, this approach is inefficient and severely constrained by the candidates that the human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the inverse problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature and high electric field, useful for critical structural, electrical, and energy storage applications. This approach to learn from and augment) human ingenuity is general and can be extended to discover polymers with other targeted properties and performance measures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

East Asian Rainbands and Associated Circulation over the Tibetan Plateau Region

Abstract Rainbands that migrate northward from spring to summer are persistent features of the East Asian summer monsoon. This study employs a machine learning algorithm to identify individual East Asian rainbands from May to August in the 6-hourly ERA-Interim reanalysis product and captures rainband events during these months for the period 1979–2018. The median duration of rainband events at any location in East Asia is 12 h, and the centroids of these rainbands move northward continuously from approximately 28°N in late May to approximately 33°N in July, instead of making jumps between quasi-stationary periods. Whereas the length and overall area of the rainbands grow monotonically from May to June, the intensity of the rainfall within the rainband dips slightly in early June before it peaks in late June. We find that extratropical northerly winds on all pressure levels over East China are the most important anomalous flow accompanying the rainband events. The anomalous northerlies augment climatological background northerlies in bringing low moist static energy air and thus generate the front associated with the rainband. Persistent lower-tropospheric southerly winds bring in moisture that feeds the rainband and are enhanced a few days prior to rainband events, but they are not directly tied to the actual rainband formation. The background northerlies could originate as part of the Rossby waves resulting from the jet stream interaction with the Tibetan Plateau. The ageostrophic circulation in the jet entrance region peaks in May and weakens in June and July and does not prove to be critical to the formation of the rainbands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net-Load Forecasting

Accurate net-load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net-load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net-load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.

Bhattacharjee, Kaustav↗
DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗
DOE OSTI2025

Powering the Woods Hole X-Spar Buoy with Ocean Wave Energy—A Control Co-Design Feasibility Study

Despite its success in measuring air–sea exchange, the Woods Hole Oceanographic Institution’s (WHOI) X-Spar Buoy faces operational limitations due to energy constraints, motivating the integration of an energy harvesting apparatus to improve its deployment duration and capabilities. This work explores the feasibility of an augmented, self-powered system in two parts. Part 1 presents the collaborative design between X-Spar developers and wave energy researchers translating user needs into specific functional requirements. Based on requirements like desired power levels, deployability, survivability, and minimal interference with environmental data collection, unsuitable concepts are pre-eliminated from further feasibility study consideration. In part 2, we focus on one of the promising concepts: an internal rigid body wave energy converter. We apply control co-design methods to consider commercial of the shelf hardware components in the dynamic models and investigate the concept’s power conversion capabilities using linear 2-port wave-to-wire models with concurrently optimized control algorithms that are distinct for every considered hardware configuration. During this feasibility study we utilize two different control algorithms, the numerically optimal (but acausal) benchmark and the optimized damping feedback. We assess the sensitivity of average power to variations in drive-train friction, a parameter with high uncertainty, and analyze stroke limitations to ensure operational constraints are met. Our results indicate that a well-designed power take-off (PTO) system could significantly extend the WEC-Spar’s mission by providing additional electrical power without compromising data quality.

autonomous systems↗
DOE OSTI2021

PDG: A Composite Method Based on the Resolution of the Identity

The Gaussian-3 (G3) composite approach for thermochemical properties is revisited in light of the enhanced computational efficiency and reduced memory costs by applying the resolution-of-the-identity (RI) approximation for two-electron repulsion integrals (ERIs) to the computationally demanding component methods in the G3 model: the energy and gradient computations via the second-order Møller–Plesset perturbation theory (MP2) and the energy computations using the coupled-cluster singles–doubles method augmented with noniterative triples corrections [CCSD(T)]. Efficient implementation of the RI-based methods is achieved by employing a hybrid distributed/shared memory model based on MPI and OpenMP. The new variant of the G3 composite approach based on the RI approximation is termed the RI-G3 scheme, or alternatively the PDG method. The accuracy of the new RI-G3/PDG scheme is compared to the “standard” G3 composite approach that employs the memory-expensive four-center ERIs in the MP2 and CCSD(T) calculations. Taking the computation of the heats of formation of the closed-shell molecules in the G3/99 test set as a test case, it is demonstrated that the RI approximation introduces negligible changes to the mean absolute errors relative to the standard G3 model (less than 0.1 kcal/mol), while the standard deviations remain unaltered. Furthermore, the efficiency and memory requirements for the RI-MP2 and RI-CCSD(T) methods are compared to the standard MP2 and CCSD(T) approaches, respectively. The hybrid MPI/OpenMP-based RI-MP2 energy plus gradient computation is found to attain a 7.5× speedup over the standard MP2 calculations. For the most demanding CCSD(T) calculations, the application of the RI approximation is found to nearly halve the memory demand, confer about a 4–5× speedup for the CCSD iterations, and reduce the computational time for the compute-intensive triples correction step by several hours.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A New Concept for Kilotonne Scale Liquid Argon Time Projection Chambers

We develop a novel Time Projection Chamber (TPC) concept suitable for deployment in kilotonne-scale detectors, with a charge-readout system free from reconstruction ambiguities, and a robust TPC design that reduces high-voltage risks while increasing the coverage of the light-collection system and maximizing the active volume. This novel concept could be used as a far detector module in the Deep Underground Neutrino Experiment (DUNE). For the charge-readout system, we used the charge-collection pixels and associated application-specific integrated circuits currently being developed for the liquid argon (LAr) component of the DUNE Near Detector design, ArgonCube. In addition, we divided the TPC into a number of shorter drift volumes, reducing the total voltage used to drift the ionization electrons, and minimizing the stored energy per TPC. Segmenting the TPC also contains scintillation light, allowing for precise trigger localization and a more expansive light-readout system. Furthermore, the design opens the possibility of replacing or upgrading components. These augmentations could substantially improve the reliability and the sensitivity, particularly for low-energy signals, in comparison to traditional monolithic LArTPCs with projective-wire charge readouts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Attosecond Probing of Coherent Vibrational Dynamics in CBr 4

A coherent vibrational wavepacket is launched and manipulated in the symmetric stretch (a1) mode of CBr 4 , by impulsive stimulated Raman scattering (ISRS) from nonresonant 400 nm laser pump pulses with various peak intensities on the order of tens of 10 12 W/cm 2 . Extreme ultraviolet (XUV) attosecond transient absorption spectroscopy (ATAS) records the wavepacket dynamics as temporal oscillations in XUV absorption energy at the bromine M 4,5 3d 3/2,5/2 edges around 70 eV. The results are augmented by nuclear timedependent Schrödinger equation simulations. Slopes of the (Br 3d 3/2,5/2 ) –1 10a 1 * core-excited state potential energy surface (PES) along the a 1 mode are calculated to be –9.4 eV/Å from restricted open-shell Kohn–Sham calculations. Using analytical relations derived for the small-displacement limit and the calculated slopes of the core-excited state PES, a deeper insight into the vibrational dynamics is obtained by retrieving the experimental excursion amplitude of the vibrational wavepacket and the amount of population transferred to the vibrational first-excited state as a function of pump-pulse peak intensity. Experimentally, the results show that XUV ATAS is capable of resolving oscillations in the XUV absorption energy on the order of a few to tens of meV with tens of femtosecond time precision. This corresponds to change in C–Br bond length on the order of 10 –4 to 10 –3 Å. The results and the analytic relationships offer a clear physical picture, on multiple levels of understanding, of how the pump-pulse peak intensity controls the vibrational dynamics launched by nonresonant ISRS in the small-displacement limit.

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

Anion photoelectron spectroscopic and relativistic coupled-cluster studies of uranyl dichloride anion, UO 2 Cl 2 –

A joint relativistic coupled-cluster and experimental photoelectron (PE) spectroscopic study of the uranyl dichloride anion, UO 2 Cl 2 – , is reported. Sophisticated electronic-structure calculations predict the photodetachment of UO 2 Cl 2 – to involve a U 5f electron and to be followed by significant geometry relaxation. Therefore, the adiabatic electron affinity (EA a ) of the uranyl dichloride neutral molecule, UO 2 Cl 2 , and the vertical detachment energy (VDE) of its anion, UO 2 Cl 2 – , provide valuable information about its uranium 5f orbital energies. The EA a value was computed to be 3.15 eV. The VDE value was calculated to be 3.55 eV by augmenting the computed EA a with a shift derived from a Franck–Condon simulation using coupled-cluster potential energy surfaces. Here, the VDE, which corresponds to the highest intensity peak in the PE spectrum, was measured to be 3.69 ± 0.20 eV, in good agreement with the computed value. The origin transition in the PE spectrum, whose electron binding energy corresponds to the EA a , was assigned to the feature at 3.2 ± 0.20 eV, consistent with the computed EA a .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Controllable oxygen vacancy defect engineering of BiVO 4 porous structures for room temperature NH 3 detection

Controlling structural features of sensing material while judiciously introducing vacancy defect states for revamping the electronic properties of the sample to obtain its superior gas sensing performance, is quite rare. Herein, we report for the first time, the room temperature (RT) ammonia (NH 3 ) detection of peanut-like porous bismuth vanadate (BiVO 4 ) with a stable monoclinic phase. The developed BiVO 4 possess abundant oxygen vacancies and porosity by virtue of calcinations (400–800 ℃). BiVO 4 calcined at 400 ℃ exhibits high selectivity towards NH 3 with a maximum response of 1421 @ 270 ppm at RT, which is 2.5 fold enhanced compared to without calcined BiVO 4 (response of 547 @ 270 ppm NH 3 ). The oxygen defective BiVO 4 appeared highly durable and stable even under high humid conditions (∼60 %). Besides, the porosity of BiVO 4 not only enhances the specific surface area (19.8 m 2 /g) but also results in fast diffusion of NH 3 molecules, leading to a reduction in the decay time (34 s for 90 ppm NH 3 ). The density functional theory (DFT) uncovers that the oxygen vacancy formation in BiVO 4 augments the NH 3 sensing capabilities by enhancing the adsorption energy of NH 3 . This work provides insight into the sensing mechanism of increased response caused by defect engineering and porosity, which will be favourable for fabricating high-performance NH 3 sensors at RT.

DFT analysis↗
DOE OSTI2021

Decomposing Loosely Coupled Mixed-Integer Programs for Optimal Microgrid Design

Microgrids are frequently employed in remote regions, in part because access to a larger electric grid is impossible, difficult, or compromises reliability and independence. Although small microgrids often employ spot generation, in which a diesel generator is attached directly to a load, microgrids that combine these individual loads and augment generators with photovoltaic cells and batteries as a distributed energy system are emerging as a safer, less costly alternative. In this work, we present a model that seeks the minimum-cost microgrid design and ideal dispatched power to support a small remote site for one year with hourly fidelity under a detailed battery model; this mixed-integer nonlinear program (MINLP) is intractable with commercial solvers but loosely coupled with respect to time. A mixed-integer linear program (MIP) approximates the model, and a partitioning scheme linearizes the bilinear terms. We introduce a novel policy for loosely coupled MIPs in which the system reverts to equivalent conditions at regular time intervals; this separates the problem into subproblems that we solve in parallel. We obtain solutions within 5% of optimality in at most six minutes across 14 MIP instances from the literature and solutions within 5% of optimality to the MINLP instances within 20 minutes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

AI-assisted rapid crystal structure generation towards a target local environment

In material design, traditional crystal structure prediction approaches are expensive as they require extensive structural sampling through expensive energy minimization methods. Emerging artificial intelligence (AI) generative models have shown great promise in rapidly generating realistic crystals, but they typically handle only a few tens of atoms per unit cell. To overcome this limitation, we introduce a symmetry-informed approach, the Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal). Our method generates initial structures using AI models trained on an augmented dataset, and then optimizes them using structure descriptors rather than energy-based optimization. We demonstrate its effectiveness by expanding from 25 known low-energy sp2 carbon allotropes to over 1700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and battery materials.

Ridwan, Osman Goni [University of North Carolina a↗
DOE OSTI2024

Cryptate binding energies towards high throughput chelator design: metadynamics ensembles with cluster–continuum solvation

A tiered forcefield/semiempirical/meta-GGA pipeline together with a thermodynamic scheme designed with error cancellation in mind was developed to calculate binding energies of [2.2.2] cryptate complexes of mono- and divalent cations. Stable complexes of Na, K, Rb, Ca, Zn and Pb were generated, revealing consistent cation–N lengths but highly variable cation–O lengths and an amine stacking mechanism potentially augmenting the cation size selectivity. Metadynamics, used for searching the high-dimensional potential energy surface, together with a cluster–continuum model for affordable – yet accurate – solvation modeling, enabled the discovery of more stable geometries than those previously reported. Similar solvation energy curve shapes for lone vs. coordinated ions enabled rapid solvation convergence via the cancellation of errors stemming from finite cluster sizes. In conclusion, an R 2 of 0.850 vs. experimental aqueous binding energies was obtained, validating this scheme as the backbone of a high-throughput workflow for chelator design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗