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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

AIMES Final Technical Report

This is the final technical report for the AIMES project. Many important advances in science and engineering are due to large-scale distributed computing. Notwithstanding this reliance, we are still learning how to design and deploy large-scale production Distributed Computing Infrastructures (DCI). This is evidenced by missing design principles for DCI, and an absence of generally acceptable and usable distributed computing abstractions. The AIMES project was conceived against this backdrop, following on the heels of a comprehensive survey of scientific distributed applications. AIMES laid the foundations to address the tripartite challenge of dynamic resource management, integrating information, and portable and interoperable distributed applications. Four abstractions were defined and implemented: skeleton, resource bundle, pilot, and execution strategy. The four abstractions were implemented into software modules and then aggregated into the AIMES middleware. This middleware successfully integrates information across the application layer (skeletons) and resource layer (Bundles), derives a suitable execution strategy for the given skeleton and enacts its execution by means of pilots on one or more resources, depending on the application requirements, and resource availabilities and capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Dakota Uncertainty Quantification Methods Applied to the CFD code Nek5000

Predictive modeling and simulation of the performance of a nuclear reactor and its fuel is a challenging task because of the large number of coupled physical phenomena that must be addressed. In addition to this intrinsic complexity, model uncertainty must be accounted for in any analysis if the model will be used to facilitate design or operational decisions which may impact safety and performance. Rigorous, structured uncertainty analyses are performed by first characterizing the model's input uncertainties and, by propagating the uncertainty through the model in order to estimate the output's uncertainty. This paper presents the state of advancement of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) project to characterize the uncertainty of the CFD code Nek5000 using the Dakota package. Nek5000 is a high order spectral element computational fluid dynamics (CFD) code developed at Argonne National Laboratory for high resolution spectral-filtered large eddy simulations (LESs) and unsteady Reynolds averaged Navier-Stokes (RANS) simulations. The Dakota package developed at Sandia National Laboratory can be integrated with many scientific and engineering codes to facilitate efficient, effective uncertainty quantification (UQ) and sensitivity analyses. Dakota is designed to interface with external packages/codes such as Nek5000 through a script-driven interface called fork. Such interface provides maximum flexibility to the users for pre- and post-processing tasks preceding and following each function evaluation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Materials Characterization using the Center For Synchrotron Radiation Research and Instrumentation (CSRRI), a NSUF Partner Facility

Demanding operating conditions for advanced nuclear energy systems have called for a push in the development of new materials and nuclear fuels with improved performance, safety, and reliability. The development of new materials with improved irradiation, temperature, and corrosion resistance requires significant study of chemical and physical phenomena over multiple time and length scales. To do so, an integrated approach utilizing both computational and experimental capabilities will be required to overcome these scientific and technical challenges. This talk will focus on the application of synchrotron radiation techniques available for characterization of nuclear materials at the Center For Synchrotron Radiation Research and Instrumentation (CSRRI) at the Illinois Institute of Technology. The CSRRI is a partner facility with the U.S. Department of Energy's Nuclear Science User Facility (NSUF), and includes the Materials Research Collaborative Access Team (MRCAT) beam line at Argonne National Laboratory. Using the capabilities of CSRRI, we can study the materials science of irradiated and model materials to improve our understanding of the performance of materials used in nuclear reactors. Current examples of each technique will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

UC LANL Postdoc Entrepreneur Fellowship Cohort Four

Three Laboratory postdoctoral researchers have been selected for this year's cohort of Entrepreneur Fellows. The six-month fellowship program - a joint initiative of the University of California and Los Alamos, now in its fourth year - supports early career scientists explore applications and evaluate market opportunities for their technologies. The Lab selects Entrepreneur Fellows based on the potential for their projects to have both scientific and commercial impact. The fellows receive six months of facilitated training and mentoring, as well as funding to engage in customer discovery, market exploration and technology advancement

36 MATERIALS SCIENCE↗
DOE OSTI2016

Making Advanced Scientific Algorithms and Big Scientific Data Management More Accessible

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory are known as user facilities. They are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Advancing Energy Storage Solutions

ORNL translates scientific discoveries into energy storage solutions that boost energy efficiency, increase energy security, and create economic opportunity for the nation.

25 ENERGY STORAGE↗
DOE OSTI2023

Investigation of local distortion effects on X-ray absorption of ferroelectric perovskites from first principles simulations

Understanding the role of ferroelectric polarization in modulating the electronic and structural properties of crystals is critical for advancing these materials for overcoming various technological and scientific challenges. However, due to difficulties in performing experimental methods with the required resolution, or in interpreting the results of methods therein, the nanoscale morphology and response of these surfaces to external electric fields has not been properly elaborated. Here, in this work, we investigate the effect of ferroelectric polarization and local distortions in a BaTiO 3 perovskite, using two widely used computational approaches which treat the many-body nature of X-ray excitations using different philosophies, namely the many-body, delta-self-consistent-field determinant (mb-ΔSCF) and the Bethe–Salpeter equation (BSE) approaches. We show that in agreement with our experiments, both approaches consistently predict higher excitations of the main peak in the O–K edge for the surface with upward polarization. However, the mb-ΔSCF approach mostly fails to capture the L 2,3 separations at the Ti–L edge, due to the absence of spin–orbit coupling in Kohn–Sham density functional theory (KS-DFT) at the generalized gradient approximation level. On the other hand, and most promising, we show that application of the GW/BSE approach successfully reproduces the experimental XAS, both the relative peak intensities as well as the L 2,3 separations at the Ti–L edges upon ferroelectric switching. Thus simulated XAS is shown to be a powerful method for capturing the nanoscale structure of complex materials, and we underscore the need for many-body perturbation approaches, with explicit consideration of core-hole and multiplet effects, for capturing the essential physics in these systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Volumetric Rendering on Wavelet-Based Adaptive Grid

Numerical modeling of physical phenomena frequently involves processes across a wide range of spatial and temporal scales. In the last two decades, the advancements in wavelet-based numerical methodologies to solve partial differential equations, combined with the unique properties of wavelet analysis to resolve localized structures of the solution on dynamically adaptive computational meshes, make it feasible to perform large-scale numerical simulations of a variety of physical systems on a dynamically adaptive computational mesh that changes both in space and time. Volumetric visualization of the solution is an essential part of scientific computing, yet the existing volumetric visualization techniques do not take full advantage of multi-resolution wavelet analysis and are not fully tailored for visualization of a compressed solution on the wavelet-based adaptive computational mesh. Our objective is to explore the alternatives for the visualization of time-dependent data on space-time varying adaptive mesh using volume rendering while capitalizing on the available sparse data representation. Two alternative formulations are explored. The first one is based on volumetric ray casting of multi-scale datasets in wavelet space. Rather than working with the wavelets at the finest possible resolution, a partial inverse wavelet transform is performed as a preprocessing step to obtain scaling functions on a uniform grid at a user-prescribed resolution. As a result, a solution in physical space is represented by a superposition of scaling functions on a coarse regular grid and wavelets on an adaptive mesh. An efficient and accurate ray casting algorithm is based just on these coarse scaling functions. Additional details are added during the ray tracing by taking an appropriate number of wavelets into account based on support overlap with the interpolation point, wavelet coefficient magnitude, and other characteristics, such as opacity accumulation (front to back ordering) and deviation from frontal viewing direction. The second approach is based on complementing of wavelet-based adaptive mesh to the traditional Adaptive Mesh Refinement (AMR) mesh. Both algorithms are illustrated and compared to the existing volume visualization software for Rayleigh-Benard thermal convection and electron density data sets in terms of rendering time and visual quality for different data compression of both wavelet-based and AMR adaptive meshes.

Vezolainen, Alexei V.↗
DOE OSTI2025

Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions

We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular large language models, have transformed every aspect of software development. For its part, HPC software is recognized as a highly specialized scientific field of its own. We discuss the challenges associated with leveraging state-of-the-art AI technologies to develop such a unique and niche class of software and outline our research directions in the two US Department of Energy–funded projects for advancing HPC Software via AI: Ellora and Durban.

Teranishi, Keita [ORNL] (ORCID:0000000166472690)↗
DOE OSTI2023

TAMM: Tensor algebra for many-body methods

Tensor algebra operations such as contractions in computational chemistry consume a significant fraction of the computing time on large-scale computing platforms. The widespread use of tensor contractions between large multi-dimensional tensors in describing electronic structure theory has motivated the development of multiple tensor algebra frameworks targeting heterogeneous computing platforms. In this paper, we present Tensor Algebra for Many-body Methods (TAMM), a framework for productive and performance-portable development of scalable computational chemistry methods. TAMM decouples the specification of the computation from the execution of these operations on available high-performance computing systems. With this design choice, the scientific application developers (domain scientists) can focus on the algorithmic requirements using the tensor algebra interface provided by TAMM, whereas high-performance computing developers can direct their attention to various optimizations on the underlying constructs, such as efficient data distribution, optimized scheduling algorithms, and efficient use of intra-node resources (e.g., graphics processing units). The modular structure of TAMM allows it to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to the sustainable development of scalable ground- and excited-state electronic structure methods. We present case studies highlighting the ease of use, including the performance and productivity gains compared to other frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Realization and Calibration of Continuously Parameterized Two-Qubit Gates on a Trapped-Ion Quantum Processor

Continuously parameterized two-qubit gates are a key feature of state-of-the-art trapped-ion quantum processors, as they have favorable error scalings and show distinct improvements in circuit performance over more restricted maximally entangling gatesets. In this work, we provide a comprehensive and pedagogical discussion on how to practically implement these continuously parameterized Mølmer–Sørensen gates on the Quantum Scientific Computing Open User Testbed, a low-level trapped-ion processor. To generate the arbitrary entangling angles, θ, we simply scale the amplitude of light used to generate the entanglement. However, doing so requires careful consideration of amplifier saturation as well as the variable light shifts that result. As such, we describe a method to calibrate and cancel the dominant fourth-order effects, followed by a dynamic virtual phase advance during the gate to cancel any residual light shifts, and find a linear scaling between θ and the residual light shift. Once we have considered and calibrated these effects, we demonstrate performance improvement with decreasing θ. Finally, we describe nuances of hardware control to transform the XX-type interaction of the arbitrary-angle Mølmer–Sørensen gate into a phase-agnostic and crosstalk-mitigating ZZ interaction.

Yale, Christopher Gordon [Sandia National Laborato↗
DOE OSTI2018

Forming IDEAS: Interactive Data Exploration & Analysis System

Modern cyber security operations collect an enormous amount of logging and alerting data. While analysts have the ability to query and compute simple statistics and plots from their data, current analytical tools are too simple to admit deep understanding. To detect advanced and novel attacks, analysts turn to manual investigations. While commonplace, current investigations are time-consuming, intuition-based, and proving insufficient. Our hypothesis is that arming the analyst with easy-to-use data science tools will increase their work efficiency, provide them with the ability to resolve hypotheses with scientific inquiry of their data, and support their decisions with evidence over intuition. To this end, we present our work to build IDEAS (Interactive Data Exploration and Analysis System). We present three real-world use-cases that drive the system design from the algorithmic capabilities to the user interface. Finally, a modular and scalable software architecture is discussed along with plans for our pilot deployment with a security operation command.

Bridges, Robert↗
DOE OSTI2022

FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

Abstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale ® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗
DOE OSTI2020

The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20,000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

ZEM: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making - 16438

Typically environmental management (EM) problems require analysis of large and complex data sets originating from concurrent data streams with different data collection frequencies and pedigree. These big data sets require on-the-fly analysis, assimilation and integration into a series of models with different complexity for various types of model-based analyses. This is needed to provide fast iterative model analyses based on the latest available data to guide decision-making. Furthermore, the data and model are associated with uncertainties. The uncertainties are probabilistic (e.g. measurement errors) and non-probabilistic (unknowns, e.g. alternative conceptual models characterizing site conditions). To address all of these issues, we have developed an integrated framework for real-time data and model analyses for environmental decision-making called ZEM. The framework allows for seamless and on-the-fly integration of data and modeling results for robust and scientifically-defensible decision-making applying advanced decision analyses tools such as Bayesian-Information-Gap Decision Theory (BIG-DT). The framework also includes advanced methods for optimization that are capable of dealing with a large number of unknown model parameters, and surrogate (reduced order) modeling capabilities based on support vector regression techniques. The framework is predominantly coded in Julia, a state-of-the-art high-performance programing language (http://julialang.org) and incorporates the MADS computational tools (Model Analysis and Decision Support; http://mads.lanl.gov). We demonstrate the application of ZEM for data and model analyses at the LANL Chromium site. The ZEM framework will be released as open-source code and can be applied to any environmental management site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Review of multi-fidelity models

Multi-fidelity models provide a framework for integrating computational models of varying complexity, allowing for accurate predictions while optimizing computational resources. These models are especially beneficial when acquiring high-accuracy data is costly or computationally intensive. This review offers a comprehensive analysis of multi-fidelity models, focusing on their applications in scientific and engineering fields, particularly in optimization and uncertainty quantification. It classifies publications on multi-fidelity modeling according to several criteria, including application area, surrogate model selection, types of fidelity, combination methods and year of publication. The study investigates techniques for combining different fidelity levels, with an emphasis on multi-fidelity surrogate models. Here this work discusses reproducibility, open-sourcing methodologies and benchmarking procedures to promote transparency. The manuscript also includes educational toy problems to enhance understanding. Additionally, this paper outlines best practices for presenting multi-fidelity-related savings in a standardized, succinct and yet thorough manner. The review concludes by examining current trends in multi-fidelity modeling, including emerging techniques, recent advancements, and promising research directions.

42 ENGINEERING↗
DOE OSTI2021

Making sense of uncertain nuclear data

At its core, and in an imperfect world, the scientific method relies heavily on the concept of uncertain knowledge. Understanding the nature and magnitude of our lack of knowledge on nuclear data has important ramifications on a wide range of nuclear technologies that encompass defense, energy, medicine, astrophysics, among others. Over the span of a long and fruitful carrier, Dr. Massimo Salvatores has made significant contributions to the fields of nuclear reactor physics, nuclear data, and uncertainty quantification and propagation. Advances in machine learning techniques and computational capabilities are enabling a new level of confidence in our ability to predict key nuclear metrics as well as their associated uncertainties across many applications. Here this short article reviews some of those concepts with an eye towards developing a new paradigm to provide nuclear data libraries of benefit to various applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗