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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics

The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.

Gainaru, Ana↗
DOE OSTI2025

Multi-scale computational screening and mechanistic insights of cyclic amines as solvents for improved lignocellulosic biomass processing

A computational screening workflow for the efficient deconstruction of cellulose, lignin and hemicellulose fractions of lignocellulosic biomass using cyclic amines as solvents. Lignocellulosic biomass is a promising feedstock for production of affordable fuels and chemicals from renewable resources. Effective solubilization and subsequent deconstruction of its cellulose, hemicellulose, and lignin fractions is essential for the viability of future biorefineries. This study used quantum chemistry-based equilibrium thermodynamics methods to evaluate the potential of 650 cyclic amines to solubilize cellulose, hemicellulose, and lignin. The activity coefficients of solvent - biopolymer interactions were predicted using the COSMO-RS (COnductor-like Screening MOdel for Real Solvents) method and used to identify cyclic amines that can efficiently dissolve and extract selective fractions of biopolymers during biomass pretreatment. Among the 650 cyclic amines, 1-piperazineethanmaine was predicted to be an effective solvent for extracting all three polymers and was experimentally shown to achieve the highest lignin removal (97.1%). Non-covalent interaction, reduced density gradient and quantum chemical calculations were performed to elucidate the dissolution mechanism of lignin, cellulose and hemicellulose and gain further molecular level insights into the interactions between the cyclic amines and biomass polymers that promote efficient solubilization and extraction. These analyses indicated that 1-piperazineethanmaine and 1-methylimidazole make noncovalent van der Waals, electrostatic interactions and hydrogen bonding with lignin, leading to enhanced lignin removal, while the strong intramolecular hydrogen bonding interactions in cellulose and hemicellulose result in weaker solvent-biopolymer interactions. Overall, the computational approach provided an efficient method for identifying cyclic amines tailored for optimal biomass pretreatment and resulted in the identification of a potential new class of solvents for effective biomass pretreatment.

Kumar, Nikhil↗
DOE OSTI2021

ExaWorks: Workflows for Exascale

Exascale computers will offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. These software combinations and integrations, however, are difficult to achieve due to challenges of coordination and deployment of heterogeneous software components on diverse and massive platforms. We present the ExaWorks project, which can address many of these challenges: ExaWorks is leading a co-design process to create a workflow Software Development Toolkit (SDK) consisting of a wide range of workflow management tools that can be composed and interoperate through common interfaces. We describe the initial set of tools and interfaces supported by the SDK, efforts to make them easier to apply to complex science challenges, and examples of their application to exemplar cases. Furthermore, we discuss how our project is working with the workflows community, large computing facilities as well as HPC platform vendors to sustainably address the requirements of workflows at the exascale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces

Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. Here we bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Evaluating the active site-substrate interplay between x-ray crystal structure and molecular dynamics in chorismate mutase

Designing realistic quantum mechanical (QM) models of enzymes is dependent on reliably discerning and modeling residues, solvents, and cofactors important in crafting the active site microenvironment. Interatomic van der Waals contacts have previously demonstrated usefulness toward designing QM-models, but their measured values (and subsequent residue importance rankings) are expected to be influenceable by subtle changes in protein structure. Using chorismate mutase as a case study, this work examines the differences in ligand-residue interatomic contacts between an x-ray crystal structure and structures from a molecular dynamics simulation. Select structures are further analyzed using symmetry adapted perturbation theory to compute ab initio ligand-residue interaction energies. The findings of this study show that ligand-residue interatomic contacts measured for an x-ray crystal structure are not predictive of active site contacts from a sampling of molecular dynamics frames. In addition, the variability in interatomic contacts among structures is not correlated with variability in interaction energies. However, the results spotlight using interaction energies to characterize and rank residue importance in future computational enzymology workflows.

Chemistry↗
DOE OSTI2023

Metagenomics harvested genus-specific single-stranded DNA-annealing proteins improve and expand recombineering in Pseudomonas species

The widespread Pseudomonas genus comprises a collection of related species with remarkable abilities to degrade plastics and polluted wastes and to produce a broad set of valuable compounds, ranging from bulk chemicals to pharmaceuticals. Pseudomonas possess characteristics of tolerance and stress resistance making them valuable hosts for industrial and environmental biotechnology. However, efficient and high-throughput genetic engineering tools have limited metabolic engineering efforts and applications. To improve their genome editing capabilities, we first employed a computational biology workflow to generate a genus-specific library of potential single-stranded DNA-annealing proteins (SSAPs). Assessment of the library was performed in different Pseudomonas using a high-throughput pooled recombinase screen followed by Oxford Nanopore NGS analysis. Among different active variants with variable levels of allelic replacement frequency (ARF), efficient SSAPs were found and characterized for mediating recombineering in the four tested species. New variants yielded higher ARFs than existing ones in Pseudomonas putida and Pseudomonas aeruginosa, and expanded the field of recombineering in Pseudomonas taiwanensisand Pseudomonas fluorescens. These findings will enhance the mutagenesis capabilities of these members of the Pseudomonas genus, increasing the possibilities for biotransformation and enhancing their potential for synthetic biology applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Core energies of dislocations in bcc metals

Accurate methods and an efficient workflow for computing and documenting dislocation core energies are developed and applied to 1 2 ( 111 ) and ( 100 ) dislocations in five body-centered cubic (bcc) metals W, Ta, V, Mo, and α -Fe represented by 13 model interatomic potentials. For each dislocation type, dislocation core energies are extracted for a large number of dislocation characters thoroughly sampling the entire 2-space of crystallographic line orientations of the bcc lattice. Of particular interest, core energies of the 1 2 ( 111 ) { 110 } dislocations are found to be distinctly asymmetric with respect to the sign of the character angle, whereas core energies of ( 100 ) { 110 } junction dislocations exhibit marked cusps for line orientations vicinal to the closed-packed ( 111 ) directions. Overall, our findings furnish substantial insights for developing accurate models of dislocation core energies employed in mesoscale dislocation dynamics simulations of crystal plasticity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

NOODLES [SWR-22-78]

NOODLES is a protocol specification for collaborative visualization. It allows software tools of any type to participate in an analysis or visualization session. Use cases include, but are not limited to, distributed analysis, workflow interoperability, computational steering, etc.

Brunhart-Lupo, Nicholas↗
DOE OSTI2023

Federated IRI Science Testbed (FIRST): A Concept Note

The Department of Energy’s (DOE’s) vision for an Integrated Research Infrastructure (IRI) is to empower researchers to smoothly and securely meld the DOE’s world-class user facilities and research infrastructure in novel ways in order to radically accelerate discovery and innovation. Performant IRI arises through the continuous interoperability of research workflows with compute, storage, and networking infrastructure, fulfilling researchers’ quests to gain insight from observational and experimental data. Decades of successful research, pilot projects, and demonstrations point to the extraordinary promise of IRI but also indicate the intertwined technological, policy, and sociological hurdles it presents. Creating, developing, and stewarding the conditions for seamless interoperability of DOE research infrastructure, with clear value propositions to stakeholders to opt into an IRI ecosystem, will be the next big step. Governance, funding, and resource allocation are beyond the scope of this document: it seeks to provide a high-level view of potential benefits, focus areas, and the working groups whose formation would further define the testbed’s design, activities, and goals.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Solar Spectrum Conversion for an Algae Bioreactor (CRADA Final Report)

This project focused on developing advanced optical coatings to improve solar energy utilization. The research aimed to create lanthanide-doped upconversion nanoparticles (UCNPs) capable of capturing unused near-infrared (NIR) light from the sun and converting it into visible light (blue and red photons) that can be used for photosynthesis. The primary goal was to identify, synthesize, and integrate highly efficient UCNPs into a transparent thin-film device. Through a comprehensive workflow involving computer simulations, high-throughput robotic synthesis, and detailed optical characterization, the project successfully developed a high-performance material. The key technical achievement was the creation of a core-shell UCNP (NaYF₄:20%Yb³⁺, 2%Er³⁺ coated with a 10 nm NaYF₄ shell) that demonstrated a quantum yield of 3.2% for converting 980 nm NIR light into visible light. Transparent thin films fabricated from these nanoparticles showed excellent optical properties, confirming their potential for practical applications. This research adds to the scientific understanding of energy transfer in lanthanide materials and demonstrates a technically effective method for creating efficient light-converting coatings. The primary benefit to the public lies in the potential for these coatings to enhance the efficiency of solar-driven processes, such as boosting the growth of algae in photobioreactors for biofuel production.

14 SOLAR ENERGY↗
DOE OSTI2026

Combining Generative Modeling and Advanced Control for Building Scenario Generation

Buildings make up a large portion of energy consumption in the U.S. today. Understanding their energy consumption patterns can improve their efficiency, but requires detailed models that rely on incomplete or unknown information. Previous work has shown that artificial intelligence (AI) can be used to predict missing information and even suggest upgrades to improve building efficiency. However, building upgrades may require undesirable upfront costs. Oppositely, advanced control could improve building efficiency with negligible upfront cost. To explore the tradeoffs between these two approaches, in this work we propose a workflow to compute optimal temperature setpoint schedules to minimize energy consumption and operational cost. Results show that modifying the temperature setpoints in a building using model predictive control (MPC) can effectively reduce its energy consumption and operational cost. This optimal operation cannot fully meet a desired goal. However, we show that by considering MPC in addition to component upgrades, a desired goal can be met with significantly less upfront costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2010

Polyphony: A Workflow Orchestration Framework for Cloud Computing

Cloud Computing has delivered unprecedented compute capacity to NASA missions at affordable rates. Missions like the Mars Exploration Rovers (MER) and Mars Science Lab (MSL) are enjoying the elasticity that enables them to leverage hundreds, if not thousands, or machines for short durations without making any hardware procurements. In this paper, we describe Polyphony, a resilient, scalable, and modular framework that efficiently leverages a large set of computing resources to perform parallel computations. Polyphony can employ resources on the cloud, excess capacity on local machines, as well as spare resources on the supercomputing center, and it enables these resources to work in concert to accomplish a common goal. Polyphony is resilient to node failures, even if they occur in the middle of a transaction. We will conclude with an evaluation of a production-ready application built on top of Polyphony to perform image-processing operations of images from around the solar system, including Mars, Saturn, and Titan.

Space Exploration,↗
DOE OSTI2022

Towards an HPC Complementary Computing Facility

This Letter considers the design for computing facilities that are complementary to the leadership class High Performance Computing (HPC) facilities. This design envisions a future where funding agencies are allocating greater resources for leadership class facilities and these facilities will provide a significant part of the total compute cycles for HEP Experiments. While a leadership class facility (LCF) may provide cycles and advanced architectures, the facility does not necessarily provide all of the services needed to help HEP users make the best use of the HPC facility, as well as the services needed to provide computing for workflows that are not a good fit for the HPC facilities. This Letter outlines some of the necessary components of a facility designed to provide those services and capabilities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

An open-source hybrid unstructured mesh - CAD fusion multiphysics analysis workflow in SALAMANDER

Plasma facing components in fusion devices will endure extreme neutron and heat fluxes. To facilitate their design using simulation tools, the open-source Fusion Module, Fusion ENergy Integrated multiphys-X (FENIX) framework is being developed to model these components with a high-fidelity multi-physics multi-dimensional approach. It can iteratively resolve couplings between all the physics at play, from neutron radiation, to thermomechanics, to near-wall plasma dynamics. This framework is based on the Multiphysics Object Oriented Simulation Environment (MOOSE), which is developed by a collaboration of US National Laboratories since 2008, for advanced nuclear, geomechanics simulations and other applications. FENIX couples numerous simulation tools, including OpenMC, the Tritium Migration Analysis Program v8, the NekRS CFD software, and most MOOSE modules. For the coupling of radiation transport and other physics, FENIX supports a hybrid workflow between Computer Assisted Design (CAD) and unstructured mesh geometries. The CAD can be generated from skinning the unstructured mesh, to enable a coarse geometry for efficient particle transport, but still resolving the local material compositions and temperature gradients. Neutron transport is performed using DAGMC on the CAD, and Cardinal, integrated in FENIX, maps tallied quantities, such as the heat deposition or the tritium generation rates, from a tally volumetric mesh to the other physics’ unstructured mesh. This coupling was exercised on a simplified tokamak geometry, coupling neutron transport with the heat conduction equation, and on a monoblock divertor problem, coupling additionally with tritium migration. Mesh convergence studies highlight the importance of the mapping conservativeness. Coupling with thermo-mechanics is further enabled by the generalization of the approach to moving meshes. The presentation will include these coupled analysis as well as an update on status of the FENIX framework.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Core Characterization: Computer Tomography, Geochemical, and Core Petrophysical Workflows for Emerging Critical Mineral Plays

The National Energy Technology Laboratory’s geoimaging lab in Morgantown, WV has been performing core characterization using multi-resolution Computed Tomography (CT) imaging and multisensor corelogger which allows for cm-resolution elemental and petrophysical measurements along whole core. This workflow has historically been used in unconventional gas plays and potential carbon storage locations. This rapid core characterization leads itself well as a first phase process in CM/REE evaluation efforts allowing for high-resolution, non-destructive investigation of elemental concentration using X-Ray Fluorescence and examination of core features through CT imaging.

Paronish, Thomas J.↗
DOE OSTI2024

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Alnajjar, Anees↗
DOE OSTI2026

Image processing workflow yielding high contrast synchrotron nanoscale computed tomography data from Ni-YSZ electrodes

The operating lifetime of Ni-YSZ fuel electrodes used in solid oxide electrolysis cells and fuel cells (SOECs and SOFCs) is limited by Ni redistribution, one of the primary degradation mechanisms that must be overcome to extend the longevity and maximize the performance of SOECs and SOFCs. To achieve this, 3D microstructural data is needed to relate both initial performance and performance loss over time to microstructural properties and their evolution throughout operation under various conditions. However, 3D microstructure data remains relatively scarce within the literature due to multiple challenges in acquiring and analyzing such data reliably. This work presents a workflow for acquiring and processing synchrotron X-ray nanoscale computed tomography (nano-CT) data from Ni-YSZ electrodes. Parameters for each step in the nano-CT workflow are described up to the final result (a 3D reconstruction), with particular emphasis on image alignment using freely available software. Following the results of a parametric sweep of the image alignment step, high contrast, low signal-to-noise 3D nano-CT data is obtained with relatively short compute times. While the exact methods best suited to samples with different microstructural qualities, or similar Ni-YSZ nano-CT data obtained from other sources may deviate from the solution found herein, this work also generalizes the decision points and evaluation of each step to provide a starting point to adapt this workflow to other datasets.

08 HYDROGEN↗