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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Flow and transport in three-dimensional discrete fracture matrix models using mimetic finite difference on a conforming multi-dimensional mesh

Here, we present a comprehensive workflow to simulate single-phase flow and transport in fractured porous media using the discrete fracture matrix approach. The workflow has three primary parts: (1) a method for conforming mesh generation of and around a three-dimensional fracture network, (2) the discretization of the governing equations using a second-order mimetic finite difference method, and (3) implementation of numerical methods for high-performance computing environments. A method to create a conforming Delaunay tetrahedralization of the volume surrounding the fracture network, where the triangular cells of the fracture mesh are faces in the volume mesh, that addresses pathological cases which commonly arise and degrade mesh quality is also provided. Our open-source subsurface simulator uses a hierarchy of process kernels (one kernel per physical process) that allows for both strong and weak coupling of the fracture and matrix domains. We provide verification tests based on analytic solutions for flow and transport, as well as numerical convergence. We also provide multiple expositions of the method in complex fracture networks. In the first example, we demonstrate that the method is robust by considering two scenarios where the fracture network acts as a barrier to flow, as the primary pathway, or offers the same resistance as the surrounding matrix. In the second test, flow and transport through a three-dimensional stochastically generated network containing 257 fractures is presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Demonstrating GPU code portability and scalability for radiative heat transfer computations

High performance computing frameworks utilizing CPUs, Nvidia GPUs, and/or Intel Xeon Phis necessitate portable and scalable solutions for application developers. Nvidia GPUs in particular present numerous portability challenges with a different programming model, additional memory hierarchies, and partitioned execution units among streaming multiprocessors. This work presents modifications to the Uintah asynchronous many-task runtime and the Kokkos portability library to enable one single codebase for complex multiphysics applications to run across different architectures. Scalability and performance results are shown on multiple architectures for a globally coupled radiation heat transfer simulation, ranging from a single node to 16,384 Titan compute nodes.

Computer Science↗
DOE OSTI2021

Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization

In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an essential role in capturing the shape of scalar field data. Herein we present a state–of–the–art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time–varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Computational Approaches for Clean Energy Materials

Currently, 80% of the global final energy consumption occurs in form of fuels and only 20% as electricity. On the other hand, renewable energy additions come almost exclusively in the form of electricity (dominantly photovoltaics and wind). Thus, a successful energy transition will require enormous growth in renewables, sufficient to convert excess electricity into fuels, as well as the development of non-electricity based solar fuel technologies. As much as photovoltaic capacities have grown over the past 20 years, it is far from clear that current technologies and materials are up to the task to grow from here by yet another factor 100 until 2050. Therefore, sustained research efforts on emerging inorganic semiconductors for solar electricity and fuels are essential for facing the double challenge of climate change and energy security. Computational materials science can make important contributions, guiding and supporting research activities through both materials search and discovery and through detailed studies that help to develop a mechanistic understanding of materials performance and bottlenecks. This presentation will highlight three recent computational projects with relevance for photovoltaics and solar fuels (1) Defect graph neural networks (dGNN) for materials discovery in solar thermochemical hydrogen (STCH) [1]. The dGNN approach facilitates broad and fast materials screening for defect properties. (2) Modeling highly off-stoichiometric systems by evaluating the free energy of defect interaction [2]. This approach allows quantitative prediction of H2 production in complex STCH oxides. (3) First-principles atomic structure prediction for interfaces [3]. This work showed how an atomically thin CdCl2 interlayer phase enables in principle ideal electron transport across the incommensurate SnO2/CdTe interface. [1] M.D. Witman, A. Goyal, T. Ogitsu, A.H. McDaniel, S. Lany, Nat. Comput. Sci. (2023). https://doi.org/10.1038/s43588-023-00495-2. [2] A. Goyal, M.D. Sanders, R.P. O'Hayre, S. Lany, PRX Energy 3, 013008 (2024). https://doi.org/10.1103/PRXEnergy.3.013008. [3] A. Sharan, M. Nardone, D. Krasikov, N. Singh, S. Lany, Appl. Phys. Rev. 9, 041411 (2022). https://doi.org/10.1063/5.0104008.

density functional theory↗
DOE OSTI2019

X-Ray Computer Methods for Studying the Structural Integrity of Seeds and Their Importance in Modern Seed Science

X-ray computer methods of research (projection microfocus radiography and microtomography), which are used to study the problem of hidden defects of seeds and investigate its impact on sowing quality, have been considered. The description and main characteristics of technical means that were used to obtain digital two-dimensional and three-dimensional (tomographic) X-ray images of seeds have been given and the possible ways of their quantitative computer processing and analysis have been discussed. Conclusions about the abilities of the methods of projection microfocus radiography and microtomography to study the features of the internal structures of a seed that are related to the violation of its integrity have been formulated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Computationally guided discovery of thermoelectric materials

The potential for advances in thermoelectric materials, and thus solid-state refrigeration and power generation, is immense. Progress so far has been limited by both the breadth and diversity of the chemical space and the serial nature of experimental work. In this Review, we discuss how recent computational advances are revolutionizing our ability to predict electron and phonon transport and scattering, as well as materials dopability, and we examine efficient approaches to calculating critical transport properties across large chemical spaces. When coupled with experimental feedback, these high-throughput approaches can stimulate the discovery of new classes of thermoelectric materials. Within smaller materials subsets, computations can guide the optimal chemical and structural tailoring to enhance materials performance and provide insight into the underlying transport physics. Beyond perfect materials, computations can be used for the rational design of structural and chemical modifications (such as defects, interfaces, dopants and alloys) to provide additional control on transport properties to optimize performance. Through computational predictions for both materials searches and design, a new paradigm in thermoelectric materials discovery is emerging.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Long non-coding subgenomic flavivirus RNAs have extended 3D structures and are flexible in solution

Most mosquito-borne flaviviruses, including Zika virus (ZIKV), Dengue virus (DENV), and West Nile virus (WNV), produce long non-coding subgenomic RNAs (sfRNAs) in infected cells that link to pathogenicity and immune evasion. Until now, the structural characterization of these lncRNAs remains limited. Here, we studied the 3D structures of individual and combined subdomains of sfRNAs, and visualized the accessible 3D conformational spaces of complete sfRNAs from DENV2, ZIKV, and WNV by small angle X-ray scattering (SAXS) and computational modeling. The individual xrRNA1s and xrRNA2s adopt similar structures in solution as the crystal structure of ZIKV xrRNA1, and all xrRNA1-2s form compact structures with reduced flexibility. While the DB12 of DENV2 is extended, the DB12s of ZIKV and WNV are compact due to the formation of intertwined double pseudoknots. All 3' stem-loops (3'SLs) share similar rod-like structures. Complete sfRNAs are extended and sample a large conformational space in solution. Our work not only provides structural insight into the function of flavivirus sfRNAs, but also highlights strategies of visualizing other lncRNAs in solution by SAXS and computational methods.

3D structure↗
DOE OSTI2023

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

Alexeev, Yuri↗
DOE OSTI2025

On the Structure–Property Relationship of Semi‐Coherent FeCr 2 O 4 /Cr 2 O 3 Spinel/Corundum Interfaces

Abstract Oxide heterointerfaces are extremely common in both natural and artificial composite structures, including corroded structural materials. Often, key properties such as segregation and atomic transport are dictated by the structure of these interfaces. However, despite this critical link, very few heterointerfaces have been studied in any detail at the atomic scale. Here, one important oxide heterointerface is examined, between spinel and corundum, using the chemical system FeCr 2 O 4 /Cr 2 O 3 as a representative and technologically important case. Using atomistic simulation techniques, it is found that the structure, particularly the local chemistry, of the interface depends on the crystal chemistry at the interface. This atomic and chemical structure further impacts important properties such as defect segregation and mass transport. It is found that defects can nucleate at some regions of these interfaces and migrate back and forth across the corundum layer, suggesting high atomic mobility that may be important for the evolution of spinel/corundum composite structures in extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Rapid prototyping of arbitrary 2D and 3D wireframe DNA origami

Wireframe DNA origami assemblies can now be programmed automatically from the top-down using simple wireframe target geometries, or meshes, in 2D and 3D, using either rigid, six-helix bundle (6HB) or more compliant, two-helix bundle (DX) edges. While these assemblies have numerous applications in nanoscale materials fabrication due to their nanoscale spatial addressability and high degree of customization, no easy-to-use graphical user interface software yet exists to deploy these algorithmic approaches within a single, standalone interface. Further, top-down sequence design of 3D DX-based objects previously enabled by DAEDALUS was limited to discrete edge lengths and uniform vertex angles, limiting the scope of objects that can be designed. Here, we introduce the open-source software package ATHENA with a graphical user interface that automatically renders single-stranded DNA scaffold routing and staple strand sequences for any target wireframe DNA origami using DX or 6HB edges, including irregular, asymmetric DX-based polyhedra with variable edge lengths and vertices demonstrated experimentally, which significantly expands the set of possible 3D DNA-based assemblies that can be designed. ATHENA also enables external editing of sequences using caDNAno, demonstrated using asymmetric nanoscale positioning of gold nanoparticles, as well as providing atomic-level models for molecular dynamics, coarse-grained dynamics with oxDNA, and other computational chemistry simulation approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Neutron and x-ray computed tomography of a natural uranium tristructural isotropic (TRISO) fuel compact

A natural uranium-based, unirradiated tristructural isotropic (TRISO) fuel compact was nondestructively imaged using both X-ray (XCT) and neutron computed tomography (nCT). While XCT of compacts can provide information on fuel kernels, imaging artifacts preclude examination of the graphite matrix. In this work, nCT was used for the first time on a TRISO compact to examine the graphite matrix. A crack was clearly resolved within the graphite matrix, proving that nCT is a viable tool for nondestructive volumetric examination of the matrix material in TRISO fuel compacts. The XCT and nCT data were then fused together to create a more comprehensive dataset containing both matrix and fuel kernels.

36 - MATERIALS SCIENCE↗
DOE OSTI2016

SPOKES: An end-to-end simulation facility for spectroscopic cosmological surveys

The nature of dark matter, dark energy and large-scale gravity pose some of the most pressing questions in cosmology today. These fundamental questions require highly precise measurements, and a number of wide-field spectroscopic survey instruments are being designed to meet this requirement. A key component in these experiments is the development of a simulation tool to forecast science performance, define requirement flow-downs, optimize implementation, demonstrate feasibility, and prepare for exploitation. We present SPOKES (SPectrOscopic KEn Simulation), an end-to-end simulation facility for spectroscopic cosmological surveys designed to address this challenge. SPOKES is based on an integrated infrastructure, modular function organization, coherent data handling and fast data access. These key features allow reproducibility of pipeline runs, enable ease of use and provide flexibility to update functions within the pipeline. The cyclic nature of the pipeline offers the possibility to make the science output an efficient measure for design optimization and feasibility testing. We present the architecture, first science, and computational performance results of the simulation pipeline. The framework is general, but for the benchmark tests, we use the Dark Energy Spectrometer (DESpec), one of the early concepts for the upcoming project, the Dark Energy Spectroscopic Instrument (DESI). As a result, we discuss how the SPOKES framework enables a rigorous process to optimize and exploit spectroscopic survey experiments in order to derive high-precision cosmological measurements optimally.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

ParCAT: Parallel Climate Analysis Toolkit

Climate science is employing increasingly complex models and simulations to analyze the past and predict the future of Earth's climate. This growth in complexity is creating a widening gap between the data being produced and the ability to analyze the datasets. Parallel computing tools are necessary to analyze, compare, and interpret the simulation data. The Parallel Climate Analysis Toolkit (ParCAT) provides basic tools to efficiently use parallel computing techniques to make analysis of these datasets manageable. The toolkit provides the ability to compute spatio-temporal means, differences between runs or differences between averages of runs, and histograms of the values in a data set. ParCAT is implemented as a command-line utility written in C. This allows for easy integration in other tools and allows for use in scripts. This also makes it possible to run ParCAT on many platforms – from laptops to supercomputers. ParCAT outputs NetCDF files so it is compatible with existing utilities such as Panoply and UV-CDAT. This paper describes ParCAT and presents results from some example runs on the Titan system at ORNL.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗
DOE OSTI2025

ADEPT: A Pedagogical Framework for Integrating Agentic AI with Deterministic Scientific Workflows

The integration of Large Language Models (LLMs) into scientific research promises to accelerate discovery, yet a significant gap remains between the dynamic reasoning of Artificial Intelligence (AI) agents and the static, deterministic nature of canonical scientific workflows. This paper introduces ADEPT (Agentic Discovery and Exploration Platform for Tools), a reference architecture and pedagogical framework explicitly designed to bridge this gap. ADEPT's primary mission is to provide a transparent, "glass-box" environment where researchers and engineers can learn to effectively wrap established scientific software (e.g., BLAST, Nextflow pipelines) and compose it into reliable, agent-driven workflows. We describe its modular, multi-server architecture, which leverages the Model Context Protocol (MCP) for tool serving, LangGraph for robust agentic orchestration, and a secure nsjail-based sandbox for safe code execution. By prioritizing architectural clarity, safety, and modularity, ADEPT serves as an extensible blueprint for building trustworthy AI-augmented systems and fosters the collaborative development necessary to responsibly employ agentic AI for science. We provide practical examples of how to adapt and extend this framework, highlighting its utility in workforce development and AI-readiness capabilities across research and development projects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

2D numerical simulation of unsteady flows for large scale floods prediction in real time

The challenge of finding a compromise between computational time and level of accuracy and robustness has traditionally expanded the use simplified models rather than full two-dimensional (2D) models for flood simulation. This work presents a GPU accelerated 2D shallow water model for the simulation of flood events in real time. In particular, an explicit first-order finite volume scheme is detailed to control the numerical instabilities that are likely to appear when used in complex topography. The model is first validated with the benchmark test case of the Toce River (Italy) and numerical fixes are demonstrated to be necessary. The model is next applied to reproduce real events in a reach of the Ebro River (Spain) in order to compare simulation results with field data. The second case deals with a large domain (744 km 2 ) and long flood duration (up to 20 days) allowing an analysis of the performance and speed-up achieved by different GPU devices. We report the high values of fit between observed and simulated results as well as the computational times achieved are encouraging to propose the use of the model as forecasting system.

54 ENVIRONMENTAL SCIENCES↗