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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Atomic layer deposited aluminum nitride for interface protection of lithium germanium thiophosphate solid electrolytes

Lithium germanium thiophosphate (LGPS) is an attractive solid-state electrolyte material due to its exceptionally high ionic conductivity (~1.2 × 10 –2 S cm –1 ), comparable to many organic liquid electrolytes commonly used in batteries. Despite the high conductivity of LGPS, the susceptibility of LGPS to deleterious degradation reactions has impeded its commercial adoption into solid-state batteries. In particular, the poor voltage stability of LGPS with high-voltage cathode or lithium metal potentials often results in dramatically increasing cell impedance during galvanic cycling. Here, we use aluminum nitride (AlN) as a protection layer for Li metal anode, applied directly to the LGPS at 250 °C using plasma-enhanced atomic layer deposition, to enhance cell performance by preventing LGPS-Li metal reactions. We compare the surface chemistry and electrochemical cycling performance of atomic layer-deposited AlN grown using both plasma N 2 and NH 3 precursors. Galvanostatic cycling and electrochemical impedance spectroscopy show that AlN-coated LGPS cells perform better than bare LGPS cells in contact with Li metal anodes, with the AlN able to improve cycling longevity by over a factor of 3 in certain cases. Finally, we utilize x-ray photoelectron spectroscopy (XPS) line scans to highlight the slow room-temperature reactivity between AlN and evaporated lithium metal, and a computational model is built to aid further XPS analysis.

25 ENERGY STORAGE↗
DOE OSTI2025

Geant4 simulations of sampling and homogeneous hadronic calorimeters with dual readout for future colliders

Calorimeters with dual readout measure both scintillation and Cherenkov light produced in their active media. They offer improvements in energy resolution and, therefore, have become increasingly interesting due to the need for precision jet measurements at Higgs factories. Furthermore, this paper presents GEANT4 simulations of single-particle responses in sampling and homogeneous calorimeters, and demonstrates the effect of inclusion of Cherenkov light in the reconstruction of energies.

Detector modeling and simulations↗
DOE OSTI2022

Molecular dynamics study of silicon atomic layer etching by chlorine gas and argon ions

Classical molecular dynamics (MD) is used to simulate atomic layer etching processes of silicon by alternating exposure to chlorine gas and argon ions. In order to validate our model, a rigorous comparison is done with ion beam experiments found in the literature [Park et al., Jpn. J. Appl. Phys. 44, 389 (2005)]. Here it is shown that the etch per cycle (EPC) as a function of argon ion energy from simulations is in quantitative agreement with experimental results if the correct argon ion fluence is used in the simulations. The EPC as a function of ion irradiation time and amount of chlorine exposure also show good agreement with the experiment. The MD simulations also show the formation of an amorphous silicon region with chlorine atoms mixed uniformly throughout following ion bombardment. Finally, the etch products during the ion irradiation step are analyzed and discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
DOE OSTI2023

Summary: Techno-Economic Analysis of Solar Photovoltaics and Battery Energy Storage at a Vietnam Industrial Park

The Clean Energy Investment Accelerator conducted a case study analysis of battery energy storage system (BESS) feasibility for an industrial park in Vietnam using the National Renewable Energy Laboratory's (NREL's) REopt platform (a distributed energy modeling and optimization tool) to evaluate how BESS may reduce electricity costs, increase utilization of onsite renewable energy generation, and improve resilience to utility grid outages. This presentation summarizes the analysis and key takeaways.

batteries↗
DOE OSTI2025

Innovative Deep-Water Mooring Systems for Floating Wind Farms (DeepFarm): Cooperative Research and Development (Final Report)

The DeepFarm project, led by Principle Power Inc (PPI), focused on design, modeling and development of innovative mooring and anchoring systems for semi-submersible floating wind turbine platforms in a deep water, commercial-size wind farm. It explored the possibility of sharing anchors amongst the floating platforms within the farm, and considers site conditions representative of the West Coast and Hawaii, with a variety of water depths, as case studies to ensure that the sensitivity to water depth and park size are well understood. NREL and PPI worked together to develop and apply NREL's computational tools for modeling floating wind farm dynamics and costs.

17 WIND ENERGY↗
DOE OSTI2020

A Model-Free Voltage Control Approach to Mitigate Motor Stalling and FIDVR for Smart Grids

Electric power networks are large and highly nonlinear dynamical systems that present unique challenges to control design. Though there is a large number of dynamic models for power system stability and control, many models are only useful with right assumptions and wrong for other tasks. Moreover, the dynamic behavior of the grid is increasingly complex under the banner of smart grids. These lead to the difficulty of developing appropriate dynamic modeling, and thus an efficient control strategy. To avoid such modeling challenges, here we present a novel dynamic voltage control strategy based on a model-free control (MFC) approach, requiring no modeling procedure. In particular, it focuses on fault-induced delayed voltage recovery (FIDVR) events, which require complex and accurate dynamic load models to replicate such events. This work utilizes MFC as an online controller to achieve the desired voltage stability under the FIDVR event. The proposed MFC strategy allows simple implementation and low computational cost for efficient mitigation of FIDVR. For benchmarking, a reasonably accurate dynamic performance model is explored. Simulation results with the IEEE 57 bus test network demonstrate the enhanced dynamic voltage profile for load buses having induction motors with the support of reactive power resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable, when interacting with its surroundings. The combined law of thermodynamics derived by Gibbs about 150 years ago laid the foundation of thermodynamics. In Gibbs combined law, the entropy production due to internal processes was not included, and the 2nd law was thus practically removed from the Gibbs combined law, so it is only applicable to systems under equilibrium, thus commonly termed as equilibrium or Gibbs thermodynamics. Gibbs further derived the classical statistical thermodynamics in terms of the probability of configurations in a system in the later 1800's and early 1900's. With the quantum mechanics (QM) developed in 1920's, the QM-based statistical thermodynamics was established and connected to classical statistical thermodynamics at the classical limit as shown by Landau in the 1940's. In 1960's the development of density functional theory (DFT) by Kohn and co-workers enabled the QM prediction of properties of the ground state of a system. On the other hand, the entropy production due to internal processes in non-equilibrium systems was studied separately by Onsager in 1930's and Prigogine and co-workers in the 1950's. In 1960's to 1970's the digitization of thermodynamics was developed by Kaufman in the framework of the CALculation of PHAse Diagrams (CALPHAD) modeling of individual phases with internal degrees of freedom. CALPHAD modeling of thermodynamics and atomic transport properties has enabled computational design of complex materials in the last 50 years. Our recently termed zentropy theory integrates DFT and statistical mechanics through the replacement of the internal energy of each individual configuration by its DFT-predicted free energy. The zentropy theory is capable of accurately predicting the free energy of individual phases, transition temperatures and properties of magnetic and ferroelectric materials with free energies of individual configurations solely from DFT-based calculations and without fitting parameters, and is being tested for other phenomena including superconductivity, quantum criticality, and black holes. Those predictions include the singularity at critical points with divergence of physical properties, negative thermal expansion, and the strongly correlated physics. Furthermore, those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of the materials genome®. This has the potential to shift the paradigm of CALPHAD modeling from being heavily dependent on experimental inputs to becoming fully predictive with inputs solely from DFT-based calculations and machine learning models built on those calculations and existing experimental data through newly developed and future open-source tools. Furthermore, through the combined law of thermodynamics including the internal entropy production, it is shown that the kinetic coefficient matrix of independent internal processes is diagonal with respect to the conjugate potentials in the combined law, and the cross phenomena that the phenomenological Onsager flux and reciprocal relationships are due to the dependence of the conjugate potential of a molar quantity on nonconjugate molar quantities and other potentials, which can be predicted by the zentropy theory and CALPHAD modeling.

42 ENGINEERING↗
DOE OSTI2020

Performance and Feature Improvements in Parareal-based Power System Dynamic Simulation

In recent years, a novel Parareal-based approach has been developed for fast transient simulations of large power system interconnections. Parareal belongs to the class of Parallel-in-time algorithms for solution of systems of differential-algebraic equations in parallel over an interval of time. The selection of a reasonably fast and accurate coarse solution is crucial to improve the performance of Parareal algorithm. Semi-analytical solution methods are one promising approach to achieve this goal. They have been investigated, and some preliminary results are presented here. In addition, Parareal-based simulator has been expanded to enable co-simulation with OpenDSS, a widely used open-source distribution system simulator. Preserving the parallel nature of the Parareal approach and taking advantage of the parallel capabilities of the latest versions of OpenDSS, each distribution system can be solved in their entirety on different processors in parallel within the main Parareal simulator. This paper also presents the structure of the transmission and distribution co-simulation and some results with different dynamic models of inverter-based resources in the distribution systems.

Park, Byungkwon↗
DOE OSTI2020

Updated Available Drawdowns for Big Hill SPR Caverns - Model Including the Caprock Fault

This report updates the estimated values of the baseline available drawdowns for the caverns at the Big Hill storage facility, and an updated table listing the available drawdowns. An updated finite element numerical analysis model, which included a fault in the caprock layers, was constructed and the daily data of actual wellhead pressures and oil-brine interfaces was used. The number of available drawdowns for each of the Big Hill SPR caverns is estimated using the new model. All caverns are predicted to have five available drawdowns remaining from a geomechanical perspective. BH-101 and 105 have a region of concern at the floor edge and/or sloping floor, where tensile and dilatant stresses are predicted to occur during each workover. The tensile state is predicted to occur because of the geometries of the edge and floor. Therefore, geomechanical examination for two caverns would be recommended after a drawdown leach. The well integrity of each cavern is not investigated in this report. The estimates for the number of baseline available drawdowns are subject to change in the future as the knowledge of physical phenomena at the sites, and the further development of the models of geomechanical behavior at the sites, evolve over time.

02 PETROLEUM↗
DOE OSTI2026

Performance-Aligned LLMs for Generating Fast HPC Code

Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others. Causes of poor performance can originate from disparate sources and be difficult to diagnose. Recent years have seen a multitude of work that use large language models (LLMs) to assist in software development tasks. However, these tools are trained to model the distribution of code as text, and are not specifically designed to understand performance aspects of code. In this work, we introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance. This allows us to build upon the current code modeling capabilities of LLMs and extend them to generate better performing code. Here, we demonstrate that our fine-tuned model improves the expected speedup of generated code over base models for a set of benchmark tasks from 0.9 to 1.6 for serial code and 1.9 to 4.5 for OpenMP parallel code.

Computer science↗
DOE OSTI2026

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data

As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2022

Scale-invariant machine-learning model accelerates the discovery of quaternary chalcogenides with ultralow lattice thermal conductivity

We design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM'Q 3 (A/M/M' = alkali, alkaline earth, post-transition metals, lanthanides, and Q = chalcogens). These compounds are shown to possess ultralow lattice thermal conductivity (κ l ), a desired requirement for thermal-barrier coatings and thermoelectrics. Upon screening the thermodynamic stability of ~1 million compounds using the ML model iteratively and performing density-functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their κ l using Peierls–Boltzmann transport equation, which reveals ultralow κ l (<2 Wm -1 K -1 at room temperature) due to their soft elasticity and strong phonon anharmonicity. Our work demonstrates the high efficiency of scale-invariant ML model in predicting novel compounds and presents experimental-research opportunities with these new compounds.

36 MATERIALS SCIENCE↗