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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 55 records · Page 3

Reimagine fire science for the anthropocene

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

54 ENVIRONMENTAL SCIENCES↗

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit

Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essential for reliable predictions of multiphysics interactions, but remains a grand challenge even for exascale supercomputers and advanced deep learning models. The extreme-resolution data required to represent turbulence, ranging from billions to trillions of grid points, pose prohibitive computational costs for models based on architectures like vision transformers. To address this challenge, we introduce a multiscale hierarchical Turbulence Transformer that reduces sequence length from billions to a few millions and a novel RingX sequence parallelism approach that enables scalable long-context learning. We perform scaling and science runs on the Frontier supercomputer. Our approach demonstrates excellent performance up to 1.1 EFLOPS on 32,768 AMD GPUs, with a scaling efficiency of 94\%. To our knowledge, this is the first AI model for turbulence that can capture small-scale eddies down to the dissipative range in three-dimensional turbulence at high Reynolds numbers.

Yin, Junqi [ORNL] (ORCID:0000000338435520)↗

Advanced Fuels Campaign 2019 Accomplishments

The mission of the Advanced Fuels Campaign (AFC) is to perform or support research, development, and demonstration (RD&D) activities to identify and mature innovative fuels and cladding materials and associated technologies with the potential to improve the performance and safety of current and future reactors; increase the efficient utilization of nuclear energy resources; contribute to enhancing proliferation resistance of the nuclear fuel cycle; and address challenges related to waste management and ultimate disposal. AFC pursues its mission objectives using a goal-oriented, science-based approach that seeks to establish a fundamental understanding of fuel and cladding behaviors under conditions that arise during fabrication, normal steady-state irradiation, off-normal transient scenarios, and storage/disposal. This approach includes advancing the theoretical understanding of fuel behavior, conducting fundamental and integral experiments, and supporting the mechanistic, multi-scale modeling of nuclear fuels to inform and guide fuel development projects, advance the technological readiness of promising fuel candidates, and ultimately support fuel qualification and licensing initiatives. In the area of advanced tools for the modeling and simulation of nuclear fuels, the AFC works in close partnership with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, participating with NEAMS in developing mechanistic fuel behavior models and providing experimental data to inform and validate its most advanced tools. Specifically, AFC objectives in thecoming five year horizon include: 1. support the industry-led development of Accident Tolerant Fuel (ATF) technologies with improved reliability and performance under normal operations and enhanced tolerance during hypothetical accident scenarios, with implementation of lead test rods/assemblies of one or more ATF concepts in commercial reactor(s) by or before 2022 and batch reloads of near-term concept(s) in the 2023-2026 timeframe; 2. lead research and development on innovative fuel and cladding technologies for applications to future advanced reactors, especially fast-spectrum reactors, including reactors that utilize both once-through and recycle scenarios; 3. continue the development and demonstration of a multi-scale,science-based approach to fuel development and testing, andcontribute to the establishment of a state-of-the-art R&D infrastructure necessary to accelerate the development of new fuel concepts; and collaborate with NEAMS on the development and validation of multi-scale, multi-physics, and increasingly predictive fuel performance models and codes. 4. collaborate with NEAMS on the development and validation of multi-scale, multi-physics, and increasingly predictive fuel performance models and codes.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Nanostructure Scaling in Semi-Dilute Triblock Copolymer Gels

Here, there is a considerable body of work that describes the scaling of diblock copolymer micelle dimensions in dilute and semi-dilute solution based upon block degrees of polymerization and copolymer concentration. However, there is a lack of analogous information for semi-dilute ABA triblock copolymer gels, which consist of ABA triblock copolymer dissolved in midblock-selective (B-selective) solvent. The present study uses small angle X-ray scattering to extract micelle dimensions for numerous triblock copolymer gels that vary in copolymer identity (and hence block lengths) and copolymer concentration, as well as gels that contain various ratios of two unique triblock copolymers. Analysis of micelle structural data subsequently translates to universal scaling expressions for the micelle core radius – r A ≈ N A 0.53 N B -0.14 $\phi$ ABA 0.16 where N A and N B are the endblock and midblock degrees of polymerization, respectively, and $\phi$ ABA is the volume fraction of triblock copolymer in the gel – and for the intermicelle spacing – l AA ≈ N A 0.09 N B 0.29 $\phi$ ABA -0.35 . Each scaling expression describes the full collection of experimental data very well. Additionally, these scaling expressions are partially in line with expectations from semi-dilute diblock copolymer solution theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

ARIES/Flatirons Facility - Hydrogen System Capability Buildout

Under the Advanced Research for Integrated Energy Systems (ARIES) initiative, NREL will build out hydrogen system capabilities at the Flatirons campus. The hydrogen infrastructure at the Flatirons campus will include a research-ready megawatt-scale electrolyzer with hydrogen compression and storage, and a fuel cell system. This hardware will support H2@Scale goals by enabling integrated systems R&D (e.g., to demonstrate grid services, energy storage, renewable hydrogen production, and innovative end-use applications). The system will be coupled with the controllable grid interface at the Flatirons campus and will be designed with flexibility to provide a testbed for integrated electrolyzer R&D, hydrogen utilization R&D, and to study the science of scaling for hydrogen energy systems.

39 EE - Hydrogen and Fuel Cell Technologies (EE-3F↗

Challenges for monitoring and data analytics in a leadership public data repository

The availability and disposition of data has assumed increasing importance in large-scale computational science. Data repositories are evolving to meet new classes of requirements: compliance with government access guidelines, support for reproducibility of experimental results, and long-term availability of data products. The Constellation public data repository at the Oak Ridge Leadership Computing Facility faces these issues while being situated in one of the most productive data centers in the world. While monitoring and operational data analysis are ingrained in the operation of the OLCF’s large-scale high performance computing platforms, data repositories do not have this history of support. Problems faced by Constellation range from data size (over 7 petabytes in current holdings) to analytic complexity (detailed curation is both absolutely necessary for many data sets and absolutely impossible for humans to accomplish in any practical manner) to deployment environment (OLCF storage resources are oriented toward the needs of the compute platforms). In this paper we describe some of the challenges for collecting monitoring and analytic data from a leadership public data repository. We also discuss various strategies we are pursuing in order to address these challenges, from manual data collection to plans for introducing machine learning-based curatorial techniques.

Widener, Patrick [ORNL] (ORCID:0000000258820816)↗

Brochure on the 2024 ASCR Workshop on Energy-Efficient Computing for Science

Large-scale computing has enabled numerous scientific discoveries, including ground-breaking achievements facilitated by the US Department of Energy (DOE) supercomputers and advances in applied mathematics and computer science. While important advances were made in energy efficiency to enable exascale computing, continued efforts are needed to dramatically improve the energy efficiency of the next generation of high-performance computing (HPC) systems and, more broadly, AI data centers. Without substantial improvements in energy efficiency, the energy consumption associated with computing could become a limiting factor for future scientific discovery, national security, and technological advancement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Milligrams to kilograms: making microbes work at scale

Energy is one of the most complex fields of study and an issue that influences nearly every aspect of modern life. Over the past century, combustion of fossil fuels, particularly in the transportation sector, has been the dominant form of energy release. Refining of petroleum and natural gas into liquid transportation fuels is also the centerpiece of the modern chemical industry used to produce materials, solvents, and other consumer goods. In the face of global climate change, the world is searching for alternative, sustainable means of producing energy carriers and chemical building blocks. The use of biofuels in engines predates modern refinery optimization and today represents a small but significant fraction of liquid transportation fuels burnt each year. Similarly, white biotechnology has been used to produce many natural products through fermentation. The evolution of recombinant DNA technology into modern synthetic biology has expanded the scope of biofuels and bioproducts that can be made by biocatalysts. This opinion examines the current trends in this research space, highlighting the substantial growth in computational tools and the growing influence of renewable electricity in the design of metabolic engineering strategies. In short, advanced biofuel and bioproduct synthesis remains a vibrant and critically important field of study whose focus is shifting away from the conversion of lignocellulosic biomass towards a broader consideration of how to reduce carbon dioxide to fuels and chemical products.

59 BASIC BIOLOGICAL SCIENCES↗

A multiscale cohesive law for carbon fiber networks

Better predictive models of mechanical failure in low-weight heat shield composites would aid material certification for missions with aggressive atmospheric entry conditions. In this study, we develop such a model for the rapid engineering analysis of the failure limits of phenolic impregnated carbon ablator (PICA) - a leading heat shield material whose structural component is a carbon fiber network. We hypothesize inelastic deformation failure mechanisms and model their behavior using molecular dynamics simulations to calculate the binding energy. We then upscale this binding energy to the macroscale using a renormalization argument. The approach delivers insightful and reasonably accurate macroscale predictions that compare favorably to experiments. In application, the model is validated for a particular variety of PICA by comparison to experiment and would then be used to study design scenarios in different entry conditions.

36 MATERIALS SCIENCE↗

In situ compression artifact removal in scientific data using deep transfer learning and experience replay

The massive amount of data produced during simulation on high-performance computers has grown exponentially over the past decade, exacerbating the need for streaming compression and decompression methods for efficient storage and transfer of this data---key to realizing the full potential of large-scale computational science. Lossy compression approaches such as JPEG when applied to scientific simulation data realized as a stream of images can achieve good compression rates but at the cost of introducing compression artifacts and loss of information. This paper develops a unified framework for in situ compression artifact removal in which the fully convolutional neural network architectures are combined with scalable training, transfer learning, and experience replay to achieve superior accuracy and efficiency while significantly decreasing the storage footprint as compared with the traditional optimization-based approaches. We demonstrate the proposed approach and compare it with compressed sensing postprocessing and other baseline deep learning models using climate simulations and nuclear reactor simulations, both of which are driven by hyperbolic partial differential equations. Our approach when applied to remove the compression artifacts on the JPEG-compressed nuclear reactor simulation data (using a transfer-trained model that was pretrained on the climate simulation data and updated incrementally as the nuclear reactor simulation progressed), achieved a significant improvement---mean peak signal-to-noise ratio of 42.438 as compared with 27.725 obtained with the compressed sensing approach.

97 MATHEMATICS AND COMPUTING↗

Laser-Interference Surface Preparation for Enhanced Coating Adhesion and Adhesive Joining of Multi-Materials (Final Project Report)

This report investigates a laser-interference surface treatment as a non-contact, i.e., without major solid/liquid medium application or abrasion, and non-chemical surface preparation method for aerospace coating systems. It was proposed to use the laser-interference technique to structure surfaces of Al and/or Ti, creating "rough" surfaces with pre-engineered series of ridges and valleys at submicron scale. The science goal of this study was to develop an understanding of the surface microstructure, topology, and physical mechanisms that would improve adhesion and corrosion protection of Al2024-T3. Microstructure analysis indicates that the laser-interference structuring (LIS) was found to reduce the formation of CuMn-rich precipitates in Al 2024-T3 over a 500-800 nm depth from top surface. The X-cut and cross-hatch coating adhesion ratings indicate that the LIS specimens meet the performance requirements in the coating adhesion specifications by having a higher or identical ranking to those specimens prepared with current state-of-the-art chemical conversion or sulfuric acid anodizing. After the ASTM B117 corrosion exposure, it was found that the laser processed specimens exhibited only few blisters. It was found that the corrosion damage was minimized at a laser rastering speed of 4 mm/s, for which only 33% of specimens developed very minor corrosion damage. The ASTM D1654 creepage ratings, used to evaluate corrosion damage along the scribe lines, were found to be at least nine for all coated panels. These results indicate that the laser-interference technique with the additional acetone wiping has the potential to be further developed as a minor chemical surface preparation technique for chromate-containing epoxy primers coatings.

36 MATERIALS SCIENCE↗

Can the United States Maintain Its Leadership in High-Performance Computing? - A report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR Office

The United States (U.S.) is no longer the unambiguous leader in the vitally important field of high performance computing (HPC). Japan, the European Union (EU), and China have fielded systems that are on par with our fastest supercomputers. The supply chain for everything from semiconductors to scientific software is globally distributed. Yet our economic future and security depend critically on our ability to innovate faster than our competitors, and the speed of innovation depends increasingly on large-scale computational science and engineering and thus HPC. How should the United States respond to this challenge? This report seeks to initiate a new and potentially transformative national discussion on this vital question. The Department of Energy’s (DOE) Advanced Scientific Computing Research (ASCR) program is well-positioned to make informed, targeted decisions about where the United States should cooperate and where it should compete in the global market for scientific exploration and discovery. By setting its sights on problems critical to our nation and the world, by establishing productive new collaborations, and by making strategic investments, ASCR can restore and maintain U.S. scientific leadership in the critical areas described in this report while strengthening our research infrastructure and training a large, diverse cohort of scientists. In doing so, ASCR and its scientists will pave the way for a secure and prosperous future for America. For more than 30 years, the ASCR program has provided the HPC and networking capabilities and expertise needed to support DOE’s mission to advance the national, economic, and energy security of the United States. The program now faces the challenge of developing and deploying the next generation of HPC systems and technologies, as well as supporting the application of HPC and artificial intelligence (AI) technologies to a wide range of scientific and engineering research problems. Through its research and development efforts, the ASCR program must also advance the state of the art in HPC and accelerate the pace of scientific discovery and technological innovation. Fulfilling this promise will require significantly increased investments, as well as innovative policies and programs. This subcommittee is aware that we are making recommendations and calls for action at a time when federal resources are limited. We understand that a wide range of competing priorities must be balanced by the nation’s leaders and that there is a need to leverage resources in new ways and seek efficiencies in facilities and operations. However, we must not let these realities limit our imagination or silence our advocacy. The ASCR program is a key part of the U.S. research infrastructure and an important component of economic growth and U.S. competitiveness. ASCR has a responsibility to pursue its mission, including advanced scientific computing, applications of AI technologies, and the required advanced research facilities, with determination and enthusiasm. To fulfill the scientific enterprise’s responsibility to the nation, the ASCR program must not only develop and publish a clear vision with an associated list of goals, priorities, and recommendations but also demonstrate scientific leadership by consistently securing long-term funding. This will allow the program to build on its achievements to date, to realize its ambitious vision, and to make lasting contributions to the field.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The Importance of Sharing Data in Systems Biology

Systems biology research spans a range of biological scales and science domains, and often requires a collaborative effort to collect and share data so that integration is possible. However, sharing data effectively is a challenging task that requires effort and alignment between collaborative partners, as well as coordination between organizations, repositories, and journals. As a community of systems biology researchers, we must get better at efficiently sharing data, and ensuring that shared data comes with the recognition and citations it deserves.

Wood-Charlson, Elisha M. (ORCID:0000000195577715)↗

The Early Data Release of the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) completed its 5 month Survey Validation in 2021 May. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra.

79 ASTRONOMY AND ASTROPHYSICS↗

BOXKIT

SF-23-067 BoxKit is a library that provides building blocks to parallelize and scale data science, high performance computing, and machine learning applications for block-structured datasets. Spatial data from simulations and experiments can be accessed and managed using tools available in this library when working with more data analysis oriented packages like SciKit (https://github.com/scikit-learn/scikit-learn) and FlowNet (https://github.com/NVIDIA/flownet2-pytorch)

DHRUV, AKASH↗

Multimetallic Metal-Organic Frameworks as Heterogeneous Catalysts for Gas Phase Hydroformylation and Hydrogenation Reactions

This project focused on the development bimetallic metal-organic frameworks (MOFs) as gas phase heterogeneous catalysts for hydrogenation and hydroformylation reactions. MOFs are a new class of hybrid inorganic/organic materials that are highly crystalline, with structures consisting of metal nodes of specific geometries connected by organic linkers. Although there have been a number of studies of catalysis at MOF nodes in solution, there is little experimental data in the literature for gas phase reactions despite the fact that industrial heterogeneous catalysis on MOFs is more economically viable than homogeneous catalysis. The use of MOFs as heterogeneous catalysts presents the unique opportunity to carefully control the composition, geometry and ensemble sizes of the active sites, which are all critical factors for the rational design of new catalysts. Specific objectives of the project are as follows: (1) to tailor the geometry, composition and ensemble size of the active sites; (2) to understand how the adsorption of molecules at metal sites can be modified by interactions with a neighboring metal site; (3) to determine how oxidation states of the metal change during reaction and how these states can be modified by metal-metal electronic interactions; and (4) to elucidate reaction mechanisms and intermediates. For these studies, we have chosen to investigate selective hydrogenation of hydrocarbons and hydroformylation, which are industrially relevant reactions. Our interdisciplinary team of Chen, Shustova and Vogiatzis/Henkelman provides critical expertise in novel MOF synthesis (Shustova), atomic-scale surface science and catalysis (Chen) and computational studies of reaction mechanisms (Vogiatzis/Henkelman) that are necessary for the development of these catalysts. We believe that this work will lead directly to the rational design of new catalysts that are versatile, highly active/selective and suitable for industrial processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗

Towards Predictive Plasma Science and Engineering through Revolutionary Multi-Scale Algorithms and Models (Final Report)

This report describes the high-level accomplishments from the Plasma Science and Engineering Grand Challenge LDRD at Sandia National Laboratories. The Laboratory has a need to demonstrate predictive capabilities to model plasma phenomena in order to rapidly accelerate engineering development in several mission areas. The purpose of this Grand Challenge LDRD was to advance the fundamental models, methods, and algorithms along with supporting electrode science foundation to enable a revolutionary shift towards predictive plasma engineering design principles. This project integrated the SNL knowledge base in computer science, plasma physics, materials science, applied mathematics, and relevant application engineering to establish new cross-laboratory collaborations on these topics. As an initial exemplar, this project focused efforts on improving multi-scale modeling capabilities that are utilized to predict the electrical power delivery on large-scale pulsed power accelerators. Specifically, this LDRD was structured into three primary research thrusts that, when integrated, enable complex simulations of these devices: (1) the exploration of multi-scale models describing the desorption of contaminants from pulsed power electrodes, (2) the development of improved algorithms and code technologies to treat the multi-physics phenomena required to predict device performance, and (3) the creation of a rigorous verification and validation infrastructure to evaluate the codes and models across a range of challenge problems. These components were integrated into initial demonstrations of the largest simulations of multi-level vacuum power flow completed to-date, executed on the leading HPC computing machines available in the NNSA complex today. These preliminary studies indicate relevant pulsed power engineering design simulations can now be completed in (of order) several days, a significant improvement over pre-LDRD levels of performance.

42 ENGINEERING↗