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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science

The Energy Efficient Mobility Systems (EEMS) technology landscape is complex and rapidly evolving, which provides both tremendous opportunities and formidable challenges. Significant alterations to the mobility landscape are underway due to the advent of vehicle and infrastructure connectivity, autonomous driving, and rapid passenger- and freight-vehicle electrification. Advanced computing will play an increasingly important role in enabling the EEMS program to understand and identify the most important levers to improve the energy productivity of future integrated mobility systems. It is also driving new approaches to mobility and the research to unlock an affordable, efficient, safe, and accessible transportation future. Driving much of this change is the collection, analysis, and strategic use of massive amounts of diverse, complex data from infrastructure and vehicles with on-board sensors and data storage and transmission capabilities. Diverse and representative data are key to implementing approaches to maximize mobility energy productivity. While high-fidelity modeling of integrated transportation networks has strengthened our understanding of dynamic movement and behavior patterns, existing tools must be expanded beyond their current focus. This work necessitates data infrastructure investments (e.g., secure-streaming data platforms driven by ubiquitous sensors and video analytics) as well as investments in critical capabilities for large-scale automated analysis and organization using modern machine learning, statistics, and artificial intelligence. Other chief needs include agile, large-scale storage that can be quickly searched and queried for relevant data to support validation and model development, data-sharing agreements, and formatting standards for key data types. The future of public transit must be explored in greater detail, research must inform design, and opportunities must be identified for improving the mobility productivity of public transit in both urban and rural America.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Predictive Dynamic Security Assessment through Advanced Computing

Abstract— Traditional dynamic security assessment is limited by several factors and thus falls short in providing real-time information to be predictive for power system operation. These factors include the steady-state assumption of current operating points, static transfer limits, and low computational speed. This addresses these factors and frames predictive dynamic security assessment. The primary objective of predictive dynamic security assessment is to enhance the functionality and computational process of dynamic security assessment through the use of high-speed phasor measurements and the application of advanced computing technologies for faster-than-real-time simulation. This paper presents algorithms, computing platforms, and simulation frameworks that constitute the predictive dynamic security assessment capability. Examples of phasor application and fast computation for dynamic security assessment are included to demonstrate the feasibility and speed enhancement for real-time applications.

advanced, computing, security, assessment, predict↗
DOE OSTI2024

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

09 BIOMASS FUELS↗
DOE OSTI2013

Modeling Multiscale-Multiphase-Multicomponent Subsurface Reactive Flows Using Advanced Computing

The DOE SciDAC program funded a team that developed PFLOTRAN, the next-generation (peta-scale) massively parallel, multiphase, multicomponent reactive flow and transport code. These codes are required to improve understanding and risk management of subsurface contaminant migration and geological sequestration of carbon dioxide. The important fate and transport processes occurring in the subsurface span a wide range of spatial and temporal scales, and involve nonlinear interactions among many different chemical constituents. Due to the complexity of this problem, modeling subsurface processes normally requires simplifying assumptions. However, tools of advanced scientific computing that have been used in other areas such as energy and materials research can also help address challenging problems in the environmental and geoscience fields. The overall project was led by Los Alamos National Laboratory and included Argonne, Oak Ridge and Pacific Northwest National Laboratories, in addition to the University of Illinois. This report summarizes the results of the research done at the University of Illinois, which focused on improvements to the underlying physical and computational modeling of certain transport and mixing processes.

58 GEOSCIENCES↗
DOE OSTI2014

Advanced computational tools for optimization and uncertainty quantification of carbon capture processes

Advanced multi-scale modeling and simulation has the potential to dramatically reduce development time, resulting in considerable cost savings. The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and universities that is developing, demonstrating, and deploying a suite of multi-scale modeling and simulation tools. One significant computational tool is FOQUS, a Framework for Optimization and Quantification of Uncertainty and Sensitivity, which enables basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to rapidly synthesize and optimize a process and determine the level of uncertainty associated with the resulting process. The overall approach of CCSI is described with a more detailed discussion of FOQUS and its application to carbon capture systems.

Carbon capture↗
DOE OSTI2014

Scientific Discovery through Advanced Computing (SciDAC-3) Partnership Project Annual Report

The Applying Computationally Efficient Schemes for BioGeochemical Cycles ACES4BGC Project is advancing the predictive capabilities of Earth System Models (ESMs) by reducing two of the largest sources of uncertainty, aerosols and biospheric feedbacks, with a highly efficient computational approach. In particular, this project is implementing and optimizing new computationally efficient tracer advection algorithms for large numbers of tracer species; adding important biogeochemical interactions between the atmosphere, land, and ocean models; and applying uncertainty quanti cation (UQ) techniques to constrain process parameters and evaluate uncertainties in feedbacks between biogeochemical cycles and the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Application of Robust Design and Advanced Computer Aided Engineering Technologies: Cooperative Research and Development (Final Report)

Oshkosh Corporation (OSK) is taking an aggressive approach to implementing advanced technologies, including hybrid electric vehicle (HEV) technology, throughout their commercial and military product lines. These technologies have important implications for OSK's commercial and military customers, including fleet fuel efficiency, quiet operational modes, additional on-board electric capabilities, and lower thermal signature operation. However, technical challenges exist with selecting the optimal HEV components and design to work within the performance and packaging constraints of specific vehicle applications. SK desires to use unique expertise developed at the Department of Energy?s (DOE) National Renewable Energy Laboratory (NREL), including HEV modeling and simulation. These tools will be used to overcome technical hurdles to implementing advanced heavy vehicle technology that meet performance requirements while improving fuel efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Advanced Computational Techniques for Improving Resilience of Critical Energy Infrastructure under Cyber-Physical Attacks

In this chapter, we present recent advances in improving the resilience of cyber-physical systems, especially with regards to energy systems. We provide discussions around various types of cyber-physical events that can cause disruptions and new advances in optimization, control, and reinforcement learning (RL) to deal with the challenges posed by such cyber-physical events. The presented methods range from distributed robust optimization, autonomous and coordinated control, reinforcement learning based resilient control and topology reconfiguration in Inter-System resilient control.

Nazir, Mohammad Nawaf [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2018

Advanced Computational Methods for Monte Carlo Calculations

This course is intended for graduate students who already have a basic understanding of Monte Carlo methods. It focuses on advanced topics that may be needed for thesis research, for developing new state-of-the-art methods, or for working with modern production Monte Carlo codes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Simulation of Wave Interactions with Magnetohydrodynamics (SWIM) SciDAC (Scientific Discovery through Advanced Computing) Project

The Center for Simulation of Wave Interactions with Magnetohydrodynamics (SWIM) was begun in 2005 with as a partnership between the DOE Office of Fusion Energy (FES) and the Office of Advanced Supercomputing Research (ASCR) to serve as a prototype for a comprehensive Fusion Simulation Project (FSP). The purpose was to begin the process of multi-physics, multi-scale coupling, at a reduced scope from a full FSP, while addressing problems of programmatic importance, and to develop approaches to large-scale code integration as required for the full FSP. The scientific objectives of SWIM were to improve our understanding of interactions that both RF wave and particle sources have on extended-MHD phenomena, to improve our capability for predicting and optimizing the performance of burning plasmas, to develop an integrated computational system for treating multi-physics phenomena with the required flexibility and extensibility to serve as a prototype for the Fusion Simulation Project, to address mathematics issues related to the multi-scale, coupled physics of RF waves and extended MHD, and to optimize the integrated system on high performance computers. The SWIM project ended in 2012 and the FSP, for which SWIM was to serve as a prototype, was not begun.

Batchelor, Donald↗
DOE OSTI2024

FY24 Advanced Computing HPC Annual Report for allocation "vtocei"

This project aims to elucidate the mechanisms underlying the formation of the cathodeelectrolyte interphase (CEI) and its impact on the performance of Li-ion batteries during electrochemical cycling. Our approach integrates advanced multiscale modeling with multimodal characterization to offer a comprehensive understanding of CEI dynamics. We employ a validated multiscale modeling framework to analyze microstructure-dependent transport properties, alongside joint theory-experiment protocols for detailed resolution of interfacial chemistry via spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Community Petascale Project for Accelerator Science and Simulation: Advancing Computational Science for Future Accelerators and Accelerator Technologies

The design and performance optimization of particle accelerators are essential for the success of the DOE scientific program in the next decade. Particle accelerators are very complex systems whose accurate description involves a large number of degrees of freedom and requires the inclusion of many physics processes. Building on the success of the SciDAC-1 Accelerator Science and Technology project, the SciDAC-2 Community Petascale Project for Accelerator Science and Simulation (ComPASS) is developing a comprehensive set of interoperable components for beam dynamics, electromagnetics, electron cooling, and laser/plasma acceleration modelling. ComPASS is providing accelerator scientists the tools required to enable the necessary accelerator simulation paradigm shift from high-fidelity single physics process modeling (covered under SciDAC1) to high-fidelity multiphysics modeling. Our computational frameworks have been used to model the behavior of a large number of accelerators and accelerator R&D experiments, assisting both their design and performance optimization. As parallel computational applications, the ComPASS codes have been shown to make effective use of thousands of processors. ComPASS is in the first year of executing its plan to develop the next-generation HPC accelerator modeling tools. ComPASS aims to develop an integrated simulation environment that will utilize existing and new accelerator physics modules with petascale capabilities, by employing modern computing and solver technologies. The ComPASS vision is to deliver to accelerator scientists a virtual accelerator and virtual prototyping modeling environment, with the necessary multiphysics, multiscale capabilities. The plan for this development includes delivering accelerator modeling applications appropriate for each stage of the ComPASS software evolution. Such applications are already being used to address challenging problems in accelerator design and optimization. The ComPASS organization for software development and applications accounts for the natural domain areas (beam dynamics, electromagnetics, and advanced acceleration), and all areas depend on the enabling technologies activities, such as solvers and component technology, to deliver the desired performance and integrated simulation environment. The ComPASS applications focus on computationally challenging problems important for design or performance optimization to all major HEP, NP, and BES accelerator facilities. With the cost and complexity of particle accelerators rising, the use of computation to optimize their designs and find improved operating regimes becomes essential, potentially leading to significant cost savings with modest investment.

43 PARTICLE ACCELERATORS↗