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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Integrating ytopt and libEnsemble to autotune OpenMC

Ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that consists of sampling a small number of input parameter configurations and progressively fitting a surrogate model over the input-output space until exhausting the user-defined maximum number of evaluations or the wall-clock time. libEnsemble is a Python toolkit for coordinating workflows of asynchronous and dynamic ensembles of calculations across massively parallel resources developed within the ECP PETSc/TAO project. libEnsemble helps users take advantage of massively parallel resources to solve design, decision, and inference problems and expands the class of problems that can benefit from increased parallelism. In this paper we present our methodology and framework to integrate ytopt and libEnsemble to take advantage of massively parallel resources to accelerate the autotuning process. Specifically, we focus on using the proposed framework to autotune the ECP ExaSMR application OpenMC, an open source Monte Carlo particle transport code. OpenMC has seven tunable parameters some of which have large ranges such as the number of particles in-flight, which is in the range of 100,000 to 8 million, with its default setting of 1 million. Setting the proper combination of these parameter values to achieve the best performance is extremely time-consuming. Therefore, we apply the proposed framework to autotune the MPI/OpenMP offload version of OpenMC based on a user-defined metric such as the figure of merit (FoM) (particles/s) or energy efficiency energy-delay product (EDP) on Crusher at Oak Ridge Leadership Computing Facility. In conclusion, the experimental results show that we achieve the improvement up to 29.49% in FoM and up to 30.44% in EDP.

Autotuning↗
DOE OSTI2019

An Energy Consistent Discretization of the Nonhydrostatic Equations in Primitive Variables

We derive a formulation of the nonhydrostatic equations in spherical geometry with a Lorenz staggered vertical discretization. The combination conserves a discrete energy in exact time integration when coupled with a mimetic horizontal discretization. The formulation is a version of Dubos and Tort (2014, https://doi.org/10.1175/MWR-D-14-00069.1) rewritten in terms of primitive variables. It is valid for terrain following mass or height coordinates and for both Eulerian or vertically Lagrangian discretizations. The discretization relies on an extension to Simmons and Burridge (1981, https://doi.org/10.1175/1520-0493(1981)109<0758:AEAAMC>2.0.CO;2) vertical differencing, which we show obeys a discrete derivative product rule. This product rule allows us to simplify the treatment of the vertical transport terms. Energy conservation is obtained via a term-by-term balance in the kinetic, internal, and potential energy budgets, ensuring an energy-consistent discretization up to time truncation error with no spurious sources of energy. We demonstrate convergence with respect to time truncation error in a spectral element code with a horizontal explicit vertically implicit implicit-explicit time stepping algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Predicting Si-Anode Calendar Life Using Machine Learning: Correlating Electrolyte Properties and Electrochemical Signals

This study evaluates novel electrolytes tailored for Si-containing anodes to promote calendar-life. Drawing inspiration from advancements in electrolytes for Li-metal cells, the work investigates correlations between predicted electrolyte properties and measured electrochemical performance using several machine-learning models. By leveraging machine learning and advanced modeling techniques, this study aims to establish predictive frameworks that accelerate calendar-aging experiments and inform rational electrolyte design for Si-containing cells. In the present study, fifteen different electrolytes are evaluated in a Si-containing cell using an accelerated calendar-life protocol. For each electrolyte considered, 87 properties (features) from the Advanced Electrolyte Model were produced to identify key property/performance relationships. In this study, the best performing electrolytes were generally those formulations that included non-coordinating fluoroether solvents, and the most predictive features for long-term calendar-life were features related to salt concentration and electrolyte viscosity as well as early capacity, ionic conductivity, and Coulombic efficiency measurements. The framework developed in this study correlating electrolyte properties to measured electrochemical performance is expected to accelerate electrolyte design for Si-containing anodes and ultimately enable high-energy-density, long-life Li-ion batteries.

25 - ENERGY STORAGE↗
DOE OSTI2022

Quantifying Energetic and Entropic Pathways in Molecular Systems

When examining dynamics occurring at nonzero temperatures, both energy and entropy must be taken into account to describe activated barrier crossing events. Furthermore, good reaction coordinates need to be constructed to describe different metastable states and the transition mechanisms between them. Here we use a physics-based machine learning method called state predictive information bottleneck (SPIB) to find nonlinear reaction coordinates for three systems of varying complexity. SPIB is able to correctly predict an entropic bottleneck for an analytical flat-energy double-well system and identify the entropy- and energy-dominated pathways for an analytical four-well system. Lastly, for a simulation of benzoic acid permeation through a lipid bilayer, SPIB is able to discover the entropic and energetic barriers to the permeation process. Given these results, we thus establish that SPIB is a reasonable and robust method for finding the important entropy, energy, and enthalpy barriers in physical systems, which can then be used to enhance the understanding and sampling of different activated mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Virtual Power Plants: Pilots, Challenges, and Innovations Shaping Future Development

Virtual Power Plants (VPPs) aggregate distributed energy resources (DERs) to provide grid services traditionally delivered by centralized power plants. This article reviews the current state of VPP deployment, highlighting business models, compensation mechanisms, and global pilot projects. While VPPs offer benefits such as grid flexibility, resilience, and cost savings, challenges remain in communication infrastructure, regulatory frameworks, market access, and customer engagement. To address these, we propose a scalable, privacy-preserving hierarchical VPP architecture that coordinates with distribution utilities and preserves customer data. We also present the Integrated T&D Control Room of the Future as a key test bed for validating and accelerating VPP adoption. These innovations can help transition VPPs from pilot programs to integral components of a modern, reliable power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Power Management Solution for Growing Loads at Airport Rental Facilities

Growing electrical loads across airport facilities could expand beyond local grid capacities. Coordinating flexible loads with generation and storage assets can reduce coincident peaks, mitigate costly upgrades, and reduce electricity costs. This report outlines how The Athena Team has developed a suite of tools, resources, and solutions to leverage load flexibility with energy assets to harness this value potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Clebsch canonization of Lie–Poisson systems

We propose a systematic procedure called the Clebsch canonization for obtaining a canonical Hamiltonian system that is related to a given Lie–Poisson equation via a momentum map. We describe both coordinate and geometric versions of the procedure, the latter apparently for the first time. We also find another momentum map so that the pair of momentum maps constitute a dual pair under a certain condition. The dual pair gives a concrete realization of what is commonly referred to as collectivization of Lie–Poisson systems. It also implies that solving the canonized system by symplectic Runge–Kutta methods yields so-called collective Lie–Poisson integrators that preserve the coadjoint orbits and hence the Casimirs exactly. We give a couple of examples, including the Kida vortex and the heavy top on a movable base with controls, which are Lie–Poisson systems on $\mathfrak{so}$ (2,1)* and ($\mathfrak{se}$ (3) ⋉ $\mathbb{R}^3$)* and, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Defying Thermodynamics: Stabilization of Alane Within Covalent Triazine Frameworks for Reversible Hydrogen Storage

Metastable metal hydrides such as AlH 3 have many attractive features as hydrogen storage media, but generally require complex reaction schemes for regeneration following H 2 release. Here in this paper, we demonstrate that the highly unfavorable thermodynamics of direct aluminum hydrogenation can be overcome by stabilizing alane within a nanoporous bipyridine-functionalized Covalent Triazine Framework (AlH 3 @CTF-bipyridine). This material and the counterpart AlH 3 @CTF-biphenyl rapidly desorb H 2 between 95 and 154 °C, with desorption complete at 250 °C. Sieverts measurements, 27 Al MAS NMR and 27 Al{ 1 H} REDOR experiments, and computational spectroscopy reveal that AlH 3 @CTF-bipyridine dehydrogenation is reversible at 60 °C under 700 bar hydrogen, >10 times lower pressure than that required to hydrogenate bulk aluminum. DFT calculations and EPR measurements support an unconventional mechanism whereby strong AlH 3 binding to bipyridine results in single-electron transfer to form AlH 2 (AlH 3 ) n clusters. The resulting size-dependent charge redistribution alters the dehydrogenation/rehydrogenation thermochemistry, suggesting a novel strategy to enable reversibility in high-capacity metal hydrides.

Coordination Chemistry↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2022

Deep Reinforcement Learning for Distribution System Restoration Using Distributed Energy Resources and Tie-Switches

Distributed energy resources (DERs), such as solar PVs and energy storage, can be used to restore distribution system critical loads after the extreme weather events to increase grid resilience. However, coordinating multiple DERs together with tie-switches for multi-step restoration process under renewable uncertainty is challenging. This paper proposes a deep reinforcement learning to control discrete actions of switching on/off tie switches and DERs for critical load restoration. The restoration problem is first cast into the Markov decision process suitable for DRL. Then, the original soft actor critic (SAC) method for continuous actions has been extended to handle discrete and continuous actions. Numerical comparison results with other stochastic optimization-based approaches on the modified IEEE 33-bus system show that the proposed method can achieve fast critical load restoration in the presence of substation power outage while maintaining system voltage limit throughout the restoration process.

active distribution systems↗
DOE OSTI2020

MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database

MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Modeling Integrated Electricity and Natural Gas Systems Using Co-Simulation

One approach for modeling natural gas and electricity networks as integrated systems is co-simulation, which involves separating a single model into subproblems that are solved iteratively, with coupling information passed between then. Although a fully integrated problem might be more direct, co-simulation offers advantages in that it can sometimes be more computationally tractable, and can also leverage existing simulation tools to represent the subproblems. Here we present a framework for co-simulation of natural gas and electricity networks using the HELICS tool. We provide validation of the co-simulation approach by comparing results with a fully integrated approach that uses the same simulation tool, finding that the two approaches provide identical results for a range of test cases. We then present results from natural gas and electricity network co-simulation case study of the Colorado Front Range, with an evaluation of the present-day system and also a future system with higher shares of renewable power. The presentation will close with a discussion on future avenues of work for co-simulation of gas and power networks.

co-simulation↗
DOE OSTI2022

Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing

Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the δ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enabling Secure and Resilient XFC: A Software/Hardware-Security Co-Design Approach

Extremely fast charging (XFC) has the potential to reduce the charging time of battery electric vehicles (BEV) to be equivalent to the filling time of internal combustion engine vehicles (ICEV), thus eliminating one of the few advantages ICEV still poses for light- and heavy-duty vehicles. Enabling XFC will, however, require coordination and cooperation between the grid, charging stations, and the vehicles themselves, which leads to an inevitable increase in the attack surface for all systems combined. In securing the overall system, we must not only embrace traditional cybersecurity, which is chiefly concerned with communications and the operation of digital systems, but also cyber-physical systems security as the proper operation of XFC is critically dependent on systems’ abilities to know about (sense) and interact with (actuate) the physical world. The project team consists of academic and industry researchers with backgrounds in cybersecurity, cyber-physical systems security, learning in adversarial environments, transportation security, grid security and resilience, wireless power transfer, converter design, and battery management systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

thornado-hydro: A Discontinuous Galerkin Method for Supernova Hydrodynamics with Nuclear Equations of State

This paper describes algorithms for non-relativistic hydrodynamics in the toolkit for high-order neutrino radiation hydrodynamics (thornado), which is being developed for multiphysics simulations of core-collapse supernovae (CCSNe) and related problems with Runge–Kutta discontinuous Galerkin (RKDG) methods. More specifically, thornado employs a spectral type nodal collocation approximation, and we have extended limiters — a slope limiter to prevent non-physical oscillations and a bound-enforcing limiter to prevent non-physical states — from the standard RKDG framework to be able to accommodate a tabulated nuclear equation of state (EoS). To demonstrate the efficacy of the algorithms with a nuclear EoS, we first present numerical results from basic test problems in idealized settings in one and two spatial dimensions, employing Cartesian, spherical-polar, and cylindrical coordinates. Then, we apply the RKDG method to the problem of adiabatic collapse, shock formation, and shock propagation in spherical symmetry, initiated with a 15 M ⊙ progenitor. Herein, we find that the extended limiters improve the fidelity and robustness of the RKDG method in idealized settings. The bound-enforcing limiter improves robustness of the RKDG method in the adiabatic collapse application, while we find that slope limiting in characteristic fields is vulnerable to structures in the EoS — more specifically, in the phase transition from nuclei and nucleons to bulk nuclear matter. The success of these applications marks an important step toward applying RKDG methods to more realistic CCSN simulations with thornado in the future.

Nuclear astrophysics (1129)↗
DOE OSTI2020

Novel Homogeneous Electrocatalysts for the Nitrogen Reduction Reaction

This project funded a broad range of fundamental studies in the research labs of Prof. John Berry at the University of Wisconsin – Madison. The overall goals of this research are to identify and explore promising new fundamental chemistry of metal-metal bonded coordination compounds in catalysis, with a particular focus on exploring the technologies needed for the transition to a nitrogen economy. We focus on two key technologies for this overall goal: Electrochemical synthesis of ammonia from nitrogen and water, and Electrochemical ammonia oxidation to produce nitrogen. Ammonia is the most hydrogen-rich material known aside from hydrogen itself, and is therefore an ideal fuel unit. Currently, ammonia synthesis uses significant fossil fuel inputs, has a large carbon footprint, and is performed in large, centralized facilities, mandating the exploration of carbon-neutral approaches that can be done in a distributable manner so that ammonia transportation does not pose a bottleneck. Our work towards these goals is organized as follows: Goal 1: Synthesis and characterization of new catalysts and N2RR intermediates; Goal 2: Thermodynamic investigations of all catalysts and intermediates; Goal 3: Exploration of the electrocatalytic N¬2RR using new catalysts. We additionally made progress toward a related Goal 4: Exploration of new types of catalysts with other, cheaper transition metals. We had a major setback in year 1 of the grant due to the student driving this project (Tristan Brown) contracting an incurable disease that made it impossible for him to perform lab work. Tristan transitioned to a computational chemistry project and was subsequently able to complete his PhD.

02 PETROLEUM↗
DOE OSTI2021

Per-Phase and 3-Phase Optimal Coordination of Directional Overcurrent Relays Using Genetic Algorithm

Penetration of the power grid by renewable energy sources, distributed storage, and distributed generators is becoming increasingly common. Increased utilization of these distributed energy resources (DERs) has given rise to additional protection coordination concerns, particularly where they are utilized in an unbalanced manner or where loading among phases is unbalanced. Digital relays such as the SEL-751 (produced by Schweitzer Engineering Laboratories, Pullman, WA, USA) series have the capability of being set on a per-phase basis. This capability is underutilized in common practice. Additionally, in optimization algorithms for determining relay settings, the time-overcurrent characteristics (TOCs) of relays are generally not treated as variables and are assigned before running the optimization algorithm. In this paper, TOC options themselves are treated as discrete variables to be considered in the optimization algorithm. A mixed integer nonlinear programming problem (MINLP) is set up where the goal is to minimize relay operating times. A genetic algorithm (GA) approach is implemented in MATLAB where two cases are considered. In the first case, the TOC and Time dial setting (TDS) of each relay is set on a three-phase basis. In the second case, per-phase settings are considered. Relay TDSs and TOCs are both considered as simultaneous discrete control variables. Despite the stochastic nature of using per-phase settings for unbalanced systems is found to generally allow for shorter operating times. However, for relatively balanced systems, it is best to use three-phase settings if computation time is of importance.

Matthews, Ronald C.↗
DOE OSTI2022

Simulating single-particle dynamics in magnetized plasmas: The RMF code

The RMF (Rotating Magnetic Field) code is designed to calculate the motion of a charged particle in a given electromagnetic field. It integrates Hamilton’s equations in cylindrical coordinates using an adaptive predictor-corrector double-precision variable-coefficient ordinary differential equation solver for speed and accuracy. RMF has multiple capabilities for the field. Particle motion is initialized by specifying the position and velocity vectors. Here, the six-dimensional state vector and derived quantities are saved as functions of time. A post-processing graphics code, XDRAW, is used on the stored output to plot up to 12 windows of any two quantities using different colors to denote successive time intervals. Multiple cases of RMF may be run in parallel and perform data mining on the results. Recent features are a synthetic diagnostic for simulating the observations of charge-exchange-neutral energy distributions and RF grids to explore a Fermi acceleration parallel to static magnetic fields.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗