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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A scalable nonlinear fluid–structure interaction solver based on a Schwarz preconditioner with isogeometric unstructured coarse spaces in 3D

Nonlinear fluid-structure interaction (FSI) problems on unstructured meshes in 3D appear many applications in science and engineering, such as vibration analysis of aircrafts and patient-specific diagnosis of cardiovascular diseases. In this work, we develop a highly scalable, parallel algorithmic and software framework for FSI problems consisting of a nonlinear fluid system and a nonlinear solid system, that are coupled monolithically. The FSI system is discretized by a stabilized finite element method in space and a fully implicit backward difference scheme in time. To solve the large, sparse system of nonlinear algebraic equations at each time step, we propose an inexact Newton-Krylov method together with a multilevel, smoothed Schwarz preconditioner with isogeometric coarse meshes generated by a geometry preserving coarsening algorithm. Here ''geometry'' includes the boundary of the computational domain and the wet interface between the fluid and the solid. We show numerically that the proposed algorithm and implementation are highly scalable in terms of the number of linear and nonlinear iterations and the total compute time on a supercomputer with more than 10,000 processor cores for several problems with hundreds of millions of unknowns.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Elucidating the Determinants of Alkali Ionic Conductivity in Oxide and Sulfide Frameworks

The aim of this project is to elucidate the structural and chemical factors determining alkali conductivity in oxide and sulfide frameworks using large scale first principles calculations and topological analysis. The facile conduction of alkali ions in oxide and sulfide host structures is of critical importance in energy storage. Today, the dominant form of energy storage in portable electronics is the rechargeable alkali-ion battery, a device that functions entirely on the basis of the reversible transport of alkali ions. The Li+/Na+ conductivity of a cathode has a direct influence on the rate capability of a Li/Na-ion battery, i.e., the speed at which it can be charged and discharged. Alkali conductivity is arguably of even greater importance in the solid electrolytes currently being investigated for high safety, high energy all-solid-state batteries. The research performed in this project has provided critical new insights into the diffusion mechanisms in state-of-the-art alkali superionic conductors such as Li 7 P 3 S 11 , Li 3 OCl 1-x Br x anti-perovskite, argyrodite Li 6 PS 5 Cl. Two potential novel lithium superionic conductors (Li 3 Y(PS 4 ) 2 and Li 5 PS 4 Cl 2 ) with improved electrochemical stabilities were predicted. We have also conducted in-depth studies into alkali conduction in several important classes of electrodes, including the layered P2 NaMO 2 oxides and the highly promising novel disordered rock salt Li 3+x V 2 O 5 anode for high-rate applications. This project has resulted in more than 10 peer-reviewed articles in highly regarded journals as well as a new open-source software framework (pymatgen-analysis-diffusion) that is widely used by the materials research community for the study of diffusion in materials.

25 ENERGY STORAGE↗
NASA NTRS2008

Project Integration Architecture

The Project Integration Architecture (PIA) is a distributed, object-oriented, conceptual, software framework for the generation, organization, publication, integration, and consumption of all information involved in any complex technological process in a manner that is intelligible to both computers and humans. In the development of PIA, it was recognized that in order to provide a single computational environment in which all information associated with any given complex technological process could be viewed, reviewed, manipulated, and shared, it is necessary to formulate all the elements of such a process on the most fundamental level. In this formulation, any such element is regarded as being composed of any or all of three parts: input information, some transformation of that input information, and some useful output information. Another fundamental principle of PIA is the assumption that no consumer of information, whether human or computer, can be assumed to have any useful foreknowledge of an element presented to it. Consequently, a PIA-compliant computing system is required to be ready to respond to any questions, posed by the consumer, concerning the nature of the proffered element. In colloquial terms, a PIA-compliant system must be prepared to provide all the information needed to place the element in context. To satisfy this requirement, PIA extends the previously established object-oriented- programming concept of self-revelation and applies it on a grand scale. To enable pervasive use of self-revelation, PIA exploits another previously established object-oriented-programming concept - that of semantic infusion through class derivation. By means of self-revelation and semantic infusion through class derivation, a consumer of information can inquire about the contents of all information entities (e.g., databases and software) and can interact appropriately with those entities. Other key features of PIA are listed.

Jones, William Henry↗
DOE OSTI2018

Stakeholder Engaged Structured Decision Making to Improve Performance and Reduce Costs While Protecting Human Health and the Environment - 18645

The objective of the Best Practice is to facilitate a paradigm shift when it comes to approaches to the decision making process for nuclear waste management, disposal, and remediation decisions. The stakeholder-engaged structured decision making (SDM) paradigm shift provides a transparent framework for developing optimal solutions to complex problems (Keeney, 1992, Gregory et al, 2012). This is a deliberative-analytical process. The deliberative side centers on understanding stakeholder concerns and values, and developing objectives while identifying options that may achieve those objectives. SDM is implemented with the aid of computer tools that are aimed specifically at capturing these deliberative aspects of a decision analysis. SDM captures this information in a structured format that allows access to the values, objectives, and options that have been identified to help. Through this approach we achieve transparency, traceability, and reproducibility. The goal of SDM at this stage is to provide a formal structure for capturing and processing deliberative information. The deliberative part sets the stage for the analytical part of SDM. A variety of Subject matter experts (SMEs) are engaged in how to evaluate the options through the objectives. Objectives often include minimizing human health risk and minimizing cost, but in the full scope of a sustainability-based approach to decision making, it can include objectives related to economic, environmental and social issues. Subject matter experts might provide other options for achieving the objectives, but their primary role is to evaluate/model the options to the endpoint defined by the objectives or in other words perform a consequence analysis. The structure of SDM makes it clear exactly what is needed from the SMEs, because the options are identified, and the endpoint (objectives) are defined - evaluations and/or models are needed to connect options to objectives. This consequence analysis completes the evaluation, and directly addresses which of the options is the best option. The same SDM approach to finding the best options can also be used for prioritization and resource allocation. Because all of the information is captured in a formal system with the help of computer tools (a software framework for implementing SDM), the decision models that are developed for an application can be fully evaluated numerically and for the insights gained from using a formal process for managing the multiple factors that need to be considered for complex decisions. This includes uncertainty and sensitivity analysis that can be used to guide the need for further data/information collection if the optimal decision is not adequately supported - that is, if there is insufficient confidence in the decision. Also, this feeds directly into adaptive management therefore if more data/information are collected then the efficacy of the current decision can be evaluated, potentially leading to a change in decisions if warranted. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Atomate2: modular workflows for materials science

High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Enabling Real-Time Volume Rendering of Functional Magnetic Resonance Imaging on an iOS Device

Powerful non-invasive imaging technologies like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI) are used daily by medical professionals to diagnose and treat patients. While 2D slice viewers have long been the standard, many tools allowing 3D representations of digital medical data are now available. The newest imaging advancement, functional MRI (fMRI) technology, has changed medical imaging from viewing static to dynamic physiology (4D) over time, particularly to study brain activity. Add this to the rapid adoption of mobile devices for everyday work and the need to visualize fMRI data on tablets or smartphones arises. However, there are few mobile tools available to visualize 3D MRI data, let alone 4D fMRI data. Building volume rendering tools on mobile devices to visualize 3D and 4D medical data is challenging given the limited computational power of the devices. This paper describes research that explored the feasibility of performing real-time 3D and 4D volume raycasting on a tablet device. The prototype application was tested on a 9.7” iPad Pro using two different fMRI datasets of brain activity. The results show that mobile raycasting is able to achieve between 20 and 40 frames per second for traditional 3D datasets, depending on the sampling interval, and up to 9 frames per second for 4D data. While the prototype application did not always achieve true real-time interaction, these results clearly demonstrated that visualizing 3D and 4D digital medical data is feasible with a properly constructed software framework.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Rucio: Scientific Data Management

Rucio is an open-source software framework that provides scientific collaborations with the functionality to organize, manage, and access their data at scale. The data can be distributed across heterogeneous data centers at widely distributed locations. Rucio was originally developed to meet the requirements of the high-energy physics experiment ATLAS, and now is continuously extended to support the LHC experiments and other diverse scientific communities. In this article, we detail the fundamental concepts of Rucio, describe the architecture along with implementation details, and report operational experience from production usage.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing

Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) ms with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600–700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here, in this work, we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Economic analysis of a nuclear hybrid energy system in a stochastic environment including wind turbines in an electricity grid

The electricity market in the U.S. is moving toward an energy mix including more variable renewable energy (VRE) sources like wind and solar. This increasing VRE penetration is altering the profile of the net electricity demand. Normally, flexible generators (e.g. gas turbines) are used to absorb fluctuations in the electricity demand. Therefore, an increase in VRE also leads to an increase in installed capacity for such flexible generators which, in turn, may increase the price of electricity. Nuclear-Renewable Hybrid Energy Systems (N-R HES) are seen as a solution to this problem. The N-R HES contains a nuclear power plant electrically and/or thermally linked to an industrial process (IP). This paper demonstrates a new methodology and presents the corresponding RAVEN/Modelica-based software framework to evaluate the financial performance of N-R HES. The novelty of the method is that it explicitly incorporates the stochastic nature of N-R HES inputs such as electricity demand and wind speeds. The method is applied to a generic (rather than region-specific) N-R HES. The analysis shows that under the right market conditions, economic benefits can be achieved by adding a suitable IP to the base-load generator. In particular, there exists an optimal relationship between the IP capacity and the demand. Furthermore, global trends indicate VRE penetration raises the cost of electricity due to the added volatility, an issue only mitigated when operating with high profit margins for the IP.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Block-oriented modeling of superstructure optimization problems

We present a novel software framework for modeling large-scale engineered systems as mathematical optimization problems. A key motivating feature in such systems is their hierarchical, highly structured topology. Existing mathematical optimization modeling environments do not facilitate the natural expression and manipulation of hierarchically structured systems. Rather, the modeler is forced to "flatten" the system description, hiding structure that may be exploited by solvers, and obfuscating the system that the modeling environment is attempting to represent. To correct this deficiency, we propose a Python-based "block-oriented" modeling approach for representing the discrete components within the system. Our approach is an extension of the Pyomo library for specifying mathematical optimization problems. Through the use of a modeling components library, the block-oriented approach facilitates a clean separation of system superstructure from the details of individual components. This approach also naturally lends itself to expressing design and operational decisions as disjunctive expressions over the component blocks. By expressing a mathematical optimization problem in a block-oriented manner, inherent structure (e.g., multiple scenarios) is preserved for potential exploitation by solvers. In particular, we show that block-structured mathematical optimization problems can be straightforwardly manipulated by decomposition-based multi-scenario algorithmic strategies, specifically in the context of the PySP stochastic programming library. We illustrate our block-oriented modeling approach using a case study drawn from the electricity grid operations domain: unit commitment with transmission switching and N - 1 reliability constraints. Finally, we demonstrate that the overhead associated with block-oriented modeling only minimally increases model instantiation times, and need not adversely impact solver behavior. (C) 2013 Elsevier Ltd. All rights reserved.

Friedman, Z↗
DOE OSTI2017

ELSI: A unified software interface for Kohn–Sham electronic structure solvers

Solving the electronic structure from a generalized or standard eigenproblem is often the bottleneck in large scale calculations based on Kohn-Sham density-functional theory. This problem must be addressed by essentially all current electronic structure codes, based on similar matrix expressions, and by high-performance computation. We here present a unified software interface, ELSI, to access different strategies that address the Kohn-Sham eigenvalue problem. Currently supported algorithms include the dense generalized eigensolver library ELPA, the orbital minimization method implemented in libOMM, and the pole expansion and selected inversion (PEXSI) approach with lower computational complexity for semilocal density functionals. The ELSI interface aims to simplify the implementation and optimal use of the different strategies, by offering (a) a unified software framework designed for the electronic structure solvers in Kohn-Sham density-functional theory; (b) reasonable default parameters for a chosen solver; (c) automatic conversion between input and internal working matrix formats, and in the future (d) recommendation of the optimal solver depending on the specific problem. As a result, comparative benchmarks are shown for system sizes up to 11,520 atoms (172,800 basis functions) on distributed memory supercomputing architectures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Optimization under uncertainty of a hybrid waste tire and natural gas feedstock flexible polygeneration system using a decomposition algorithm

Market uncertainties motivate the development of flexible polygeneration systems that are able to adjust operating conditions to favor production of the most profitable product portfolio. However, this operational flexibility comes at the cost of higher capital expenditure. A scenario-based two-stage stochastic nonconvex Mixed-Integer Nonlinear Programming (MINLP) approach lends itself naturally to optimizing these trade-offs. This work studies the optimal design and operation under uncertainty of a hybrid feedstock flexible polygeneration system producing electricity, methanol, dimethyl ether, olefins or liquefied (synthetic) natural gas. A recently developed C++ based software framework (named GOSSIP) is used for modeling the optimization problem as well as its efficient solution using the Nonconvex Generalized Benders Decomposition (NGBD) algorithm. Two different cases are studied: The first uses estimates of the means and variances of the uncertain parameters from historical data, whereas the second assesses the impact of increased uncertain parameter volatility. The value of implementing flexible designs characterized by the value of the stochastic solution (VSS) is in the range of 260–405 M$ for a scale of approximately 893 MW of thermal input. Increased price volatility around the same mean results in higher expected net present value and VSS as operational flexibility allows for asymmetric exploitation of price peaks.

42 ENGINEERING↗
DOE OSTI2025

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cross-Code verification and sensitivity analysis to effectively model the electrothermal instability

This manuscript presents verification cases that are developed to study the electrothermal instability (ETI). Specific verification cases are included to ensure that the unit physics components necessary to model the ETI are accurate, providing a path for fluid-based codes to effectively simulate ETI in the linear and nonlinear growth regimes. Two software frameworks with different algorithmic approaches are compared for accuracy in their ability to simulate diffusion of a magnetic field, linear growth of the ETI, and a fully nonlinear ETI evolution. The nonlinear ETI simulations show early time agreement, with some differences emerging, as noted in the wavenumber spectrum, late into the nonlinear development of ETI. Here, a sensitivity study explores the role of equation-of-state (EOS), vacuum density, and vacuum resistivity. EOS and vacuum resistivity are found to be the most critical factors in the modeling of nonlinear ETI development.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Simulation studies related to the particle identification by the forward and backward RICH detectors at Electron Ion Collider

The Electron-Ion collider (EIC) will be the ultimate facility to study the dynamics played by colored quarks and gluons in the phenomenology of nucleons and nuclei, described by Quantum Chromodynamics. The physics programs will greatly rely on efficient particle identification (PID) in both the forward and the backward regions. The forward and the backward RICHes of the EIC have to be able to cover wide acceptance and momentum ranges; in the forward region a dual radiator RICH (dRICH) is foreseen and in the backward region a proximity-focusing RICH can be foreseen to be employed. The geometry and the performance studies of the dRICH have been performed as prescribed in the EIC Yellow Report using the ATHENA software framework. Furthermore, this part of our work reports the effort following the call for EIC detector proposal and the studies related to the forward and the backward RICH performance. In the forward region, the dRICH performance showed a pion- kaon separation from around 1 GeV/c to 50 GeV/c at a three sigma level; the proximity focusing RICH (pfRICH) foreseen for the backward region can reach three sigma separation up to 3 GeV/c for e/$π$ and up to 10 GeV/c for $π$/K mass hypotheses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Constellation: The autonomous control and data acquisition system for dynamic experimental setups

The operation of instruments and detectors in laboratory or beamline environments presents a complex challenge, requiring stable operation of multiple concurrent devices, often controlled by separate hardware and software solutions. These environments frequently undergo modifications, such as the inclusion of different auxiliary devices depending on the experiment or facility, adding further complexity. The successful management of such dynamic configurations demands a flexible and robust system capable of controlling data acquisition, monitoring experimental setups, enabling seamless reconfiguration, and integrating new devices with limited effort. This paper presents Constellation, a flexible and network-distributed control and data acquisition software framework tailored to laboratory and beamline environments, that addresses the limitations of existing solutions. The framework is designed with a focus on extensibility, providing a streamlined interface for instrument integration. It supports efficient system setup via network discovery mechanisms, promotes stability through autonomous operational features, and provides comprehensive documentation and supporting tools for operators and application developers such as controllers and logging interfaces. At the core of the architectural design is the autonomy of the individual components, called satellites, which can make independent decisions about their operation and communicate these decisions to other components. This paper introduces the design principles and framework architecture of Constellation, presents the available graphical user interfaces, shares insights from initial successful deployments, and provides an outlook on future developments and applications.

Autonomy↗
DOE OSTI2022

The Terrestrial Biosphere Model Farm

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

54 ENVIRONMENTAL SCIENCES↗