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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Preservation of Alcator C-Mod Data and Support of ITER Research Through ITPA Participation (Final Technical Report)

This grant supported two primary activities at the MIT Plasma Science and Fusion Center. First, personnel support, hardware upgrades, and software development were used to maintain the C-Mod data archive set. This highly valuable repository contains all of the data generated from operation of the Alcator C-Mod FES User Facility, covering first operation in 1992 through completion of the final experimental campaign on Sept 30, 2016. The archive, stored using the MDSplus architecture, also includes all of the data analysis, which is ongoing, and most of the results of computer modeling of the data. Second, this grant supported activities related to participation of the C-Mod team in the International Tokamak Physics Activites (ITPA), which includes analysis of C-Mod data, in conjunction with data from tokamaks operated around the world, with intense coordination of joint experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

System Background for ITER

Helium-3 rich gases are first sent through ambient z-bed to remove moisture. The helium-3 gas is purified by passing it through three liquid nitrogen cooled Z-beds in series. These Z-beds remove contaminants (i.e. inerts and other small amounts of hydrogen isotopes). Purified gases are stored and sampled for loading inside the Feed Tank.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

IDC Re-Engineering Phase 2 Iteration E2 Use Case Realizations

This architecturally significant use case describes how the System acquires meteorological data to build atmospheric models used in automatic and interactive processing of infrasound data. The System requests the latest available high-resolution global meteorological data from external data centers and puts it into the correct formats for generation of infrasound propagation models. The system moves the meteorological data from Data Acquisition Partition to the Data Processing Partition and stores the meteorological data. The System builds a new atmospheric model based on the meteorological data. This use case is architecturally significant because it describes acquiring meteorological data from various sources and creating dynamic atmospheric transmission model to support the prediction of infrasonic signal detections.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Progress Update on Iterative Reconstruction of Neutron Tomographic Images, 2019

The fiscal year (FY) 2019 update of this report satisfies the technical deliverable “Simultaneous reconstruction of simultaneous observables” for project OR16-ML-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons.” At its inception, this project had two overall goals. The first of these goals was to extend associated-particle fast neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. The second of these goals was to automatically segment the resultant tomographic images into constituent parts and then extract information about the parts, such as the class of shape and potentially shape parameters. This report addresses the component of the project concerned with three-dimensional (3D) image reconstruction and represents the cumulative progress to date. In particular, it addresses the ability to decompose neutrons arriving at times consistent with transmission into transmission and small-angle scattering. Because incremental progress in each year builds on the previous year’s progress and end-ofyear status, progress is most usefully recorded in a cumulative document where incremental progress is reported on a year-by-year basis. Consequently, this report constitutes an update of the last 3 years’ reports [1, 2, 14]. The present year’s progress is detailed in Section 7.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Initial Exploration of the Use of Mixed Precision in Iterative Solvers (B639388 Subcontract Quarter I Report)

The first quarter of the project was primarily spent identifying potential projects at the intersection of finite precision analysis, mixed precision computation, and Krylov subspace methods. We summarize our findings in the remainder of the document. Other activities include attending biweekly xSDK meetings as well as contributing material to the technical report and journal versions of the multiprecision landscape paper. The subsequent quarter will be spent selecting a subset of the described projects to focus on, performing initial numerical experiments to evaluate the potential for the use of mixed precision, and developing initial theoretical analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

IDC Re-Engineering Phase 2 Iteration E2. Draft Component Interface Specification

This report contains a draft Component Interface Specification (CIS) for a signal detector. It is an example of the contents and level of detail of information in a CIS. The classes, interfaces, and data types in this report reflect current understanding of system concepts and potential implementation patterns but should not be interpreted as containing the finalized interface specifications, etc.

42 ENGINEERING↗
DOE OSTI2016

IDC Re-Engineering Phase 2 Iteration E2. Draft Component Interface Specification: Signal Detection Control

This report contains a draft Component Interface Specification (CIS) for an application controlling signal detection calculations. It is an example of the contents and level of detail of information in a CIS. The classes, interfaces, and data types in this report reflect current understanding of system concepts and potential implementation patterns but should not be interpreted as containing the finalized interface specifications, classes, etc. The primary purpose of this report is to discuss the potential OSD APIs represented in class diagrams for the Waveform, QC Mask, and Signal Detection Repository interfaces.

42 ENGINEERING↗
DOE OSTI2021

Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics

Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quarter 4 Report: Report on Final Findings and Opportunities for Future Work in the Use of Mixed Precision in Iterative Solvers

The fourth quarter of the project was spent developing an error analysis of the s-step Lanczos and CG algorithms. Our theoretical bounds and numerical experiments show that the numerical behavior of the algorithm can be significantly improved by using extra precision in a small part of the computation related to the computation and application of the Gram matrix. We have published a technical report which includes all steps of the analysis [8]; a shortened version for journal submission is in preparation. We plan to submit this paper in the following weeks. Activities related to this also include a collaboration with Ichitaro Yamazaki on gathering performance results for these new mixed precision s-step Krylov subspace methods using single/double precision on GPUs. Namely, we would like to obtain performance results that show that the performance overhead of using double the working precision in these select computations is minimal. Other activities include attending biweekly xSDK meetings and presenting a pitch talk on this work to the group on February 25, 2021. In the remainder of the document, we summarize our findings on the potential for mixed precision in classical Krylov subspace methods and s-step Krylov subspace methods, as well as key opportunities for future work.

97 MATHEMATICS AND COMPUTING↗