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Results for “accelerated computing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A percolating path to green iron

About 1.9 gigatons of steel is produced every year, emitting 8% (3.6 gigatons) of global CO 2 in the process. More than 50% of the CO 2 emissions come from a single step of steel production, known as ironmaking. Hydrogen- based direct reduction (HyDR) of iron oxide to iron has emerged as an emission-free ironmaking alternative. However, multiple physical and chemical phenomena ranging from nanometers to meters inside HyDR reactors alter the microstructure and pore networks in iron oxide pellets, in ways that resist gaseous transport of H 2 /H 2 O, slow reaction rates, and disrupt continuous reactor operation. Using synchrotron nano X-ray computed tomography and percolation theory, we quantify the evolution of pores in iron oxide pellets and demonstrate how nanoscale pore connectivity influences micro- and macroscale flow properties such as permeability, diffusivity, and tortuosity. Our modeling framework connects disparate scales and offers opportunities to accelerate HyDR.

hydrogen↗
DOE OSTI2023

GPU acceleration of local and semilocal density functional calculations in the SPARC electronic structure code

We present a Graphics Processing Unit (GPU)-accelerated version of the real-space SPARC electronic structure code for performing Kohn–Sham density functional theory calculations within the local density and generalized gradient approximations. In particular, we develop a modular math-kernel based implementation for NVIDIA architectures wherein the computationally expensive operations are carried out on the GPUs, with the remainder of the workload retained on the central processing units (CPUs). Here, using representative bulk and slab examples, we show that relative to CPU-only execution, GPUs enable speedups of up to 6× and 60× in node and core hours, respectively, bringing time to solution down to less than 30 s for a metallic system with over 14 000 electrons and enabling significant reductions in computational resources required for a given wall time.

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

Tensorized Interior Radiative Heat Transfer for a Scalable and Calibrated Building Energy Simulator

Building energy simulation is a critical tool for developing and testing advanced control strategies, such as Reinforcement Learning (RL), to provide demand flexibility and affordable energy costs. The recently introduced Smart Buildings Control Suite (sbsim) provides a lightweight, scalable, and data-calibrated simulation environment based on a 2D finite-difference model. However, the initial model primarily focused on conductive and convective heat transfer, neglecting the significant impact of long-wave radiative heat exchange between interior surfaces. This paper presents a significant extension to the sbsim framework by incorporating a physically-grounded model for interior radiative heat transfer. Our primary contribution is the development and integration of a fully tensorized radiative heat transfer module, which preserves the computational efficiency and scalability of the original simulator. This was achieved by developing a pipeline for view factor calculation, including an algorithm to identify directly seeing surfaces within complex floor plans, and formulating the net radiation equations for efficient execution on modern hardware accelerators. We validate the numerical accuracy of our tensorized implementation by comparing its results against a traditional iterative approach, demonstrating identical outcomes. This enhancement increases the physical fidelity of sbsim, enabling more accurate training of RL agents for building energy optimization.

Ham, Sang woo↗
DOE OSTI2025

PV Degradation Modeling: Applying Geospatial Workflows with "PVDeg"

Accurate degradation modeling is essential for predicting photovoltaic (PV) module performance, estimating longevity and informing design decisions. With degradation rates varying significantly by location, geospatial analysis is critical for PV and broader applications, such as agrivoltaics, weathering and environmental data analysis. This work presents PVDeg, an open-source tool designed for geospatial degradation analysis. PVDeg integrates meteorological data from global sources, including the National Solar Radiation Database (NSRDB) and Photovoltaic Geographical Information System (PVGIS), with degradation models. The toolkit enables users to customize geospatial workflows by integrating weather data, material parameters, and user-defined Python functions. It facilitates accelerated downloads of NSRDB and PVGIS datasets and optimizes geospatial point selection to preserve data density in regions of interest. Additionally, PVDeg provides a local database for storage and spatial queries, supporting large-scale analyses without the need for high-performance computing (HPC) resources. PVDeg provides a foundational workflow that extends its utility beyond PV applications, enabling researchers to analyze geospatial processes across discipline.

14 SOLAR ENERGY↗
DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Optimization using pathwise algorithmic derivatives of electromagnetic shower simulations

Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Computing them via algorithmic differentiation typically does not require major manual analysis and rewriting of the code, even for very complex programs like simulations of particle-detector interactions in high-energy physics. However, the pathwise derivative estimator can be biased if there are discontinuities in the program, which may diminish its value for applications. This work integrates algorithmic differentiation into the electromagnetic shower simulation code HepEmShow based on G4HepEm, allowing us to study how well pathwise derivatives approximate derivatives of energy depositions in a sampling calorimeter with respect to parameters of the beam and geometry. We found that when multiple scattering is disabled in the simulation, means of pathwise derivatives converge quickly to their expected values, and these are close to the actual derivatives of the energy deposition. Additionally, we demonstrate the applicability of this novel gradient estimator for stochastic gradient-based optimization in a model example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Interpretable machine learning for knowledge generation in heterogeneous catalysis

Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Here, extracting meaningful physical insights from these black-box models has proved challenging, as the internal logic of these black-box models is not readily interpretable due to their high degree of complexity. Interpretable machine learning methods that merge the predictive capacity of black-box models with the physical interpretability of physics-based models offer an alternative to black-box models. In this Perspective, we discuss the various interpretable machine learning methods available to catalysis researchers, highlight the potential of interpretable machine learning to accelerate hypothesis formation and knowledge generation, and outline critical challenges and opportunities for interpretable machine learning in heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

DLSIA: Deep Learning for Scientific Image Analysis

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Early prediction of antigenic transitions for influenza A/H3N2

Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Machine Learning Software for Cylindrical Battery Design and Performance Prediction

A software that delivers optimal design parameters and performance predictions for cylindrical cells, which can range in size from micro batteries to EV batteries, is developed. The Cylindrical battery design V1.0 is comprised of three types of cylindrical batteries, Micro battery (Primary), Micro battery (Secondary) and 18650/21700/xxxxx cylindrical battery. The software was developed in MATLAB. The software has the capability to output the cell design with the capacity ranges from several mAh to several million Ah. The software utilizes machine learning and includes a graphical user interface to enable rapid prototyping to accelerate energy storage research, development, and manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2020

The challenges of theory-software translation

Background: Software is now ubiquitous within research. In addition to the general challenges common to all software development projects, research software must also represent, manipulate, and provide data for complex theoretical constructs. Ensuring this process of theory-software translation is robust is essential to maintaining the integrity of the science resulting from it, and yet there has been little formal recognition or exploration of the challenges associated with it. Methods: We thematically analyse the outputs of the discussion sessions at the Theory-Software Translation Workshop 2019, where academic researchers and research software engineers from a variety of domains, and with particular expertise in high performance computing, explored the process of translating between scientific theory and software. Results: We identify a wide range of challenges to implementing scientific theory in research software and using the resulting data and models for the advancement of knowledge. We categorise these within the emergent themes of design, infrastructure, and culture, and map them to associated research questions. Conclusions: Systematically investigating how software is constructed and its outputs used within science has the potential to improve the robustness of research software and accelerate progress in its development. We propose that this issue be examined within a new research area of theory-software translation, which would aim to significantly advance both knowledge and scientific practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Parthenon—a performance portable block-structured adaptive mesh refinement framework

On the path to exascale the landscape of computer device architectures and corresponding programming models has become much more diverse. While various low-level performance portable programming models are available, support at the application level lacks behind. To address this issue, we present the performance portable block-structured adaptive mesh refinement (AMR) framework Parthenon, derived from the well-tested and widely used Athena++ astrophysical magnetohydrodynamics code, but generalized to serve as the foundation for a variety of downstream multi-physics codes. Parthenon adopts the Kokkos programming model, and provides various levels of abstractions from multidimensional variables, to packages defining and separating components, to launching of parallel compute kernels. Parthenon allocates all data in device memory to reduce data movement, supports the logical packing of variables and mesh blocks to reduce kernel launch overhead, and employs one-sided, asynchronous MPI calls to reduce communication overhead in multi-node simulations. Using a hydrodynamics miniapp, we demonstrate weak and strong scaling on various architectures including AMD and NVIDIA GPUs, Intel and AMD x86 CPUs, IBM Power9 CPUs, as well as Fujitsu A64FX CPUs. At the largest scale on Frontier (the first TOP500 exascale machine), the miniapp reaches a total of 1.7 × 10 13 zone-cycles/s on 9216 nodes (73,728 logical GPUs) at [Formula: see text] weak scaling parallel efficiency (starting from a single node). In combination with being an open, collaborative project, this makes Parthenon an ideal framework to target exascale simulations in which the downstream developers can focus on their specific application rather than on the complexity of handling massively-parallel, device-accelerated AMR.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

LibERI—A portable and performant multi-GPU accelerated library for electron repulsion integrals via OpenMP offloading and standard language parallelism

A portable and performant graphics processing unit (GPU)-accelerated library for electron repulsion integral (ERI) evaluation, named LibERI, has been developed and implemented via directive-based (e.g., OpenMP and OpenACC) and standard language parallelism (e.g., Fortran DO CONCURRENT). Offloaded ERIs consist of integrals over low and high contraction s, p, and d functions using the rotated-axis and Rys quadrature methods. GPU codes are factorized based on previous developments with two layers of integral screening and quartet presorting. In this work, the density screening is moved to the GPU to enhance the computational efficacy for large molecular systems. Here, the L-shells in the Pople basis set are also separated into pure S and P shells to increase the ERI homogeneity and reduce atomic operations and the memory footprint. LibERI is compatible with any quantum chemistry drivers supporting the MolSSI Driver Interface. Benchmark calculations of LibERI interfaced with the GAMESS software package were carried out on various GPU architectures and molecular systems. The results show that the LibERI performance is comparable to other state-of-the-art GPU-accelerated codes (e.g., TeraChem and GMSHPC) and, in some cases, outperforms conventionally developed ERI CUDA kernels (e.g., QUICK) while fully maintaining portability.

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

Beam impedance and heating analysis of the diagnostic stripline

In this work the heat transfer analysis has been performed for the stripline kicker, design of which is used at many accelerator facilities as a part of the transverse bunch-by-bunch feed-back system, including NSLS-II. The experimental data of the electrode temperature have been collected from a special diagnostic stripline designed with six infrared view ports. The temperature analysis is based on the power loss calculations and the loss factor computed by the 3-D electromagnetic simulation code GdfidL. To estimate the power loss, we analyze the real part of the longitudinal impedance and the loss factor for two different regimes. We use the ANSYS finite element code to correlate the temperature with the power loss. Lambertson and Shafer formalisms of the longitudinal and transverse beam impedances are discussed for the simplified stripline geometries. The required characteristic impedances and the geometric factors are determined analytically for the simplified geometries and compared with the results for the designed stripline geometry using the 2-D POISSON code by solving the Laplace’s equation in two dimensions. Lambertson’s equations are compared with numerical results. We introduce the frequency range, within which we can apply the analytical approach.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Diffusion of NiH on Ni(111) and of Ni with Adsorbed H on Ni(111) in the Context of Ni Coarsening in Solid Oxide Cells

In the Ni-based hydrogen electrode of a solid oxide cell (SOC), Ni coarsening is an important degradation mechanism and could be enhanced by NiH diffusing on the Ni particle surfaces. Here, in this work, the average lifetime and diffusion distance of NiH on Ni(111) are computed using density-functional theory and kinetic Monte Carlo methods. It is found that NiH is extremely short-lived and, thus, cannot promote coarsening in the SOC. Also, the diffusion of Ni on Ni(111) is shown to be at most slightly accelerated by H along NiH dissociation paths involving the movement of Ni with a nearby H. Based on this result, coarsening is not likely to be dramatically accelerated by the diffusion of Ni with H on Ni(111). However, support is provided for the experimental procedure of measuring the product of surface coverage and single-species diffusivity of Ni on Ni(111) in a hydrogen-rich atmosphere.

Ni coarsening↗
DOE OSTI2021

Thermal Experiments for Fractured Rock Characterization: Theoretical Analysis and Inverse Modeling

Abstract Field‐scale properties of fractured rocks play a crucial role in many subsurface applications, yet methodologies for identification of the statistical parameters of a discrete fracture network (DFN) are scarce. We present an inversion technique to infer two such parameters, fracture density and fractal dimension, from cross‐borehole thermal experiments data. It is based on a particle‐based heat‐transfer model, whose evaluation is accelerated with a deep neural network (DNN) surrogate that is integrated into a grid search. The DNN is trained on a small number of the heat‐transfer model runs and predicts the cumulative density function of the thermal field. The latter is used to compute fine posterior distributions of the (to be estimated) parameters. Our synthetic experiments reveal that fracture density is well constrained by data, while fractal dimension is harder to determine. Adding nonuniform prior information related to the DFN connectivity improves the inference of this parameter.

Zhou, Zitong↗
DOE OSTI2024

Sub-100-fs energy transfer in coenzyme NADH is a coherent process assisted by a charge-transfer state

Excitation energy transfer (EET) is a key photoinduced process in biological chromophoric assemblies. Here we investigate the factors which can drive EET into efficient ultrafast sub-ps regimes. We demonstrate how a coherent transport of electronic population could facilitate this in water solvated NADH coenzyme and uncover the role of an intermediate dark charge-transfer state. High temporal resolution ultrafast optical spectroscopy gives a 54 ± 11 fs time constant for the EET process. Nonadiabatic quantum dynamical simulations computed through the time-evolution of multidimensional wavepackets suggest that the population transfer is mediated by photoexcited molecular vibrations due to strong coupling between the electronic states. The polar aqueous solvent environment leads to the active participation of a dark charge transfer state, accelerating the vibronically coherent EET process in favorably stacked conformers and solvent cavities. Our work demonstrates how the interplay of structural and environmental factors leads to diverse pathways for the EET process in flexible heterodimers and provides general insights relevant for coherent EET processes in stacked multichromophoric aggregates like DNA strands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗