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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Benchmarks and Tests of a Multidimensional Cluster Dynamics Model of Helium Implantation in Tungsten

Here, we present a hierarchical multiscale modeling study of implanted helium (He) segregation near grain boundaries (GBs) of tungsten. We extend our spatially dependent cluster dynamics model to two spatial dimensions in order to take into account the biased drift of mobile He clusters toward the GBs observed in atomic-scale simulations. We are also able to reproduce the results from large-scale molecular dynamics simulations near and away from the GBs at low fluence with the extended cluster dynamics model. We suggest and verify that the sink (surface and GB) strengths are attenuated by the increasing concentration of He clusters at high fluence. This cluster dynamics model continues to set the stage for development of fully atomistically informed, coarse-grained models for computationally efficient predictions of He retention and surface morphological evolution, advancing progress toward the goal of efficient and optimal design of plasma-facing components.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Final Technical Report Structural Dynamics in Complex Liquids Studied with Multidimensional Vibrational Spectroscopy

This grant supported work in the Tokmakoff lab at the University of Chicago aimed at understanding the fundamental properties of water at a molecular level, and how water participates in proton transport in aqueous media. The physical properties of water and aqueous solutions are inextricably linked with efforts to develop new sustainable energy sources. Energy conversion, storage, and transduction processes, particularly those that occur in biology and soft matter, make use of water for the purpose of storing and moving charge. Water’s unique physical and chemical properties depend on the ability of water molecules to participate in up to four hydrogen bonds, and the rapid fluctuations and ultrafast energy dissipation of its hydrogenbonded networks. Our work during the grant period led to advances in four areas: (1) the generation of short pulses of broadband infrared light (BBIR) for use in time-resolved twodimensional spectroscopy (2D IR), (2) the investigation of the spectroscopy and transport of excess protons in water, (3) the study of aqueous hydroxide to describe the interaction of the ion and water and the dynamics of proton transfer, and (4) the coupled motion of water and its hydrogen-bonding solutes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Comprehensive Chemical Fingerprinting by Multidimensional GC and Supervised Machine Learning

This project leverages advances in machine learning based data analysis techniques and untargeted omic analytical methods to progress nuclear nonproliferation technologies beyond current capabilities. The developed approaches can be used to identify and detect complex chemical fingerprints of facilities of interest. These techniques have been developed for fields such as metabolomics and genomics but have not been applied to nuclear nonproliferation applications. Adaptation of these techniques for volatile organic compound analysis has far reaching application within the scientific community including environmental chemistry, atmospheric physics, and climate sciences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Modular hybrid modeling to increase efficiency, explore structural uncertainty, and allow multidimensional complexity scaling in land surface models

Land surface models (LSMs) are indispensable tools for predicting hydrologic extremes, as well as a particularly uncertain component of Earth system models that has stubbornly resisted the convergence in projections over several successive generations of model intercomparisons. This uncertainty in LSMs is poorly quantified and poorly attributed to specific processes, which has hampered efforts to focus research in reducing uncertainty. This has resulted from sparse sampling of the possible uncertainty space—which is high-dimensional and has contributions from parametric, structural, initial, and boundary condition uncertainties—as an artifact of CMIP-type ensembles of opportunity and limitations inherent in observational benchmarks. A new approach is needed to understand and reduce this uncertainty, based around individual LSMs that can represent the breadth of assumptions represented in current CMIP-type efforts, while at the same time exploring that uncertainty in a systematic way, confronting multiple types of observations, and where justified, replacing process representations with ML-driven emulators. We propose an approach of modular hybrid modeling to address these challenges.

58 GEOSCIENCES↗
DOE OSTI2021

Comprehensive Chemical Fingerprinting by Multidimensional GC and Supervised Machine Learning

This project leveraged advances in machine learning based data analysis techniques and untargeted analytical methods for organic analysis to progress nuclear nonproliferation technologies beyond current capabilities. The developed approaches can be used to detect and identify complex chemical fingerprints of facilities of interest. These techniques have been developed for fields such as metabolomics and genomics but have not been applied to nuclear nonproliferation applications. Adaptation of these techniques for volatile organic compound analysis has far reaching application within the scientific community including environmental chemistry, atmospheric physics, and climate sciences.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Tracking Photochemical and Photophysical Processes for Solar Energy Conversion Via Multidimensional Visible and Vibrational Spectroscopic Methods

This project was motivated by an overarching goal to elucidate the mechanism of energy and electron transfer that governs the efficient charge separation in photosystem I (PSI) complexes. PSI is a natural light harvesting complex that drives oxygenic photosynthesis in plants, algae, and cyanobacteria. It uses ~300 tightly packed chlorophylls (Chls) to absorb photons, transfer the excitation energy to the reaction center (RC), and generate a charge separated state with near unity quantum efficiency (QE). A better understanding of the mechanism of energy transfer and charge separation in PSI is required for understanding the high QE of natural light harvesting complexes, and it could lead to the further development of artificial photosynthetic systems for solar energy conversion. We applied two-dimensional electronic spectroscopy (2DES) to different cyanobacterial photosystem I complexes to map energy transfer pathways to gain insight into the efficient light harvesting of PSI. We interpreted the 2DES spectra through a global analysis procedure, finding two ultrafast equilibration processes involving red-shifted Chl states. The analysis procedure is general and can be readily applied to other natural and artificial light harvesting systems. In addition to investigating PSI complexes, we also performed measurements on model systems to establish general data analysis procedures for interpreting 2D spectra.

14 SOLAR ENERGY↗
DOE OSTI2023

Overlapping Domain Coupling of Multidimensional and System Codes in NEAMS - Pronghorn and SAM

This report describes the implementation and testing of domain-overlapping coupling of the Pronghorn and SAM codes. Both Pronghorn and SAM are codes developed by the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program for analysis of advanced nuclear reactors. Pronghorn focuses on analyzing multi-dimensional core flow conditions, while SAM focuses on analysis of the entire system including, among other components, piping, pumps, and heat exchangers. It is desirable for many advanced reactor thermal-hydraulics simulations to couple Pronghorn and SAM to obtain self-consistent solutions in the core and the system. The domain-overlapping coupling approach provides a robust and numerically efficient candidate for this coupling. We describe the algorithm for obtaining self-consistent solutions between a SAM network of flow channels and an overlapped multi-dimensional Pronghorn domain. The algorithm is tested for a variety of simple canonical test problems and a model of the Molten Salt Reactor Experiment (MSRE). Results demonstrate that the domain overlapping coupling approach provides consistent pressure, mass flow rate, enthalpy, and passive scalar distributions between Pronghorn and SAM and converges reliably and efficiently (at most 15 iterations, usually much less than 10) for the considered test problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Use of Sobol’ Variance-Based Global Sensitivity Analysis and Multidimensional Legendre Polynomial Fitting for Reduced Order Modeling

Sandia National Laboratories (SNL) has developed a novel reduced order modeling approach. Prioritization of inputs is accomplished using Sobo' indices obtained through a more efficient variance-based global sensitivity analysis. To determine the Sobo' functions, simulated input values are aligned to collocation points to permit the use of Gauss-Lobatto integration, thereby reducing the number of simulation trials needed by more than an order of magnitude compared to standard Monte Carlo approaches. Furthermore, by leveraging the orthogonality of Legendre polynomials in conjunction with those same simulations at the collocation nodes, an efficient fitting method is developed to represent the Sobo' functions from which a reduced order model (ROM) is constructed. The developed method is both more efficient computationally, and the resulting ROM is more accurate. The efficacy of this technique is demonstrated on a nonlinear polynomial test function as well as the nonlinear Ishigami and Sobo' g functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Unified Software Architecture for Advanced Materials and Manufacturing Technologies Data Management and Processing: FY 2023 Multidimensional Data Correlation Platform

This report details the various digital manufacturing activities ongoing at ORNL as part of the Advanced Materials and Manufacturing Technologies (AMMT) program. The AMMT program is exploring a data-driven approach to demonstrate the use of AM for the fabrication of components for nuclear applications, with the goal of providing a greater understanding of manufacturing quality outcomes that would pave the way toward the development of standards for certification and qualification. The objective of this work package is to establish a digital manufacturing discipline common to all participants of the AMMT program to improve the performance, reliability, and lifetime of nuclear components. As part of this effort, we will develop a unified software architecture for AMMT data management and processing, deploy the digital platform across AMMT participants’ facilities, and generate pedigreed datasets in a common format across multiple labs and facilities. To this end, the MDDC work package has focused on three activities during FY23. First, the MDF Digital Tool was overhauled to better serve the needs of the AMMT program. Next, multiple laser powder bed fusion (L-PBF) systems at the MDF were upgraded to a common sensor package for collecting comparable in situ data across machines. Finally, various improvements relevant to the AMMT program were implemented in the ORNL-developed software tool, Peregrine. This report marks the completion of FY23 milestone M3CR-22OR0403051: Report Describing the Architecture of the Digital Platform to Support AMMT Activities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

FY 2024 Multidimensional Data Correlation Platform Data Management Infrastructure Progress: Materials Laboratory

This report provides an inventory of the equipment available at the ORNL Manufacturing Demonstration Facility (MDF) for sample preparation and material characterization, including both destructive and non-destructive techniques that generate critical data to support the development of the Multi-Dimensional Data Correlation (MDDC) framework. The success of the MDDC framework depends heavily on the quality and completeness of the data it can access. Therefore, it is essential to establish a comprehensive inventory of the technologies available to the Advanced Materials and Manufacturing Technologies (AMMT) multi-laboratory team. This starts by gathering information about the types of data they produce, the data collection and transfer protocols used, file formats, and data storage requirements for experiments. This information is then carefully evaluated to create the operations and trackables elements of the Damara Tern platform, which is the foundation of the MDDC framework.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating Photoinduced Processes of Photosystem I Via Multidimensional Electronic and Vibrational Spectroscopies

This project was motivated by an overarching goal to elucidate the mechanism of energy and electron transfer that governs the efficient charge separation in photosystem I (PSI) complexes. PSI is a natural light harvesting complex that drives oxygenic photosynthesis in plants, algae, and cyanobacteria. It uses ~300 tightly packed chlorophylls (Chls) to absorb photons, transfer the excitation energy to the reaction center (RC), and generate a charge separated state with near unity quantum efficiency (QE). A better understanding of the mechanism of energy transfer and charge separation in PSI is required for understanding the high QE of natural light harvesting complexes, and it could lead to the further development of artificial photosynthetic systems for solar energy conversion and modification of light harvesting complexes to improve crop yields. We applied two-dimensional optical spectroscopies to different cyanobacterial photosystem I complexes, including PSI complexes that contain Chl f molecules, to map energy transfer pathways and gain insight into the efficient light harvesting of PSI. We used two-dimensional electronic spectroscopies (2DES) to map energy transfer in Chl a and Chl f containing PSI complexes. To investigate the Chl f PSI complexes, we modified our spectrometer to probe the lower energy states associated with Chl f molecules. We interpreted the 2DES spectra through global analysis procedures to generate maps of energy transfer. We also constructed a two-dimensional electronic vibrational (2DEV) spectrometer that will be used to investigate charge transfer transitions and dynamics within PSI complexes. Measurements were performed on model systems to establish general data analysis procedures for interpreting 2D spectra and gain insight into protein cofactor interactions.

14 SOLAR ENERGY↗