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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

A parallel data management system for large-scale NASA datasets

The past decade has experienced a phenomenal growth in the amount of data and resultant information generated by NASA's operations and research projects. A key application is the reprocessing problem which has been identified to require data management capabilities beyond those available today (PRAT93). The Intelligent Information Fusion (IIF) system (ROEL91) is an ongoing NASA project which has similar requirements. Deriving our understanding of NASA's future data management needs based on the above, this paper describes an approach to using parallel computer systems (processor and I/O architectures) to develop an efficient parallel database management system to address the needs. Specifically, we propose to investigate issues in low-level record organizations and management, complex query processing, and query compilation and scheduling.

Srivastava, Jaideep↗
DOE OSTI2021

Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression

The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynamics. These quantities exhibit behaviours ranging from sharp plasma gradients and fast transient phenomena (e.g. transitions between low and high confinement regimes) to nominal stationary phases. Analysis of experimental edge measurements therefore require robust fitting techniques to capture potentially stiff spatiotemporal evolution. Additionally, fusion plasma diagnostics inevitably involve measurement errors and data analysis requires a statistical framework to accurately quantify uncertainties. This paper outlines a generalized multidimensional adaptive Gaussian process routine capable of automatically handling noisy data and spatiotemporal correlations. We focus on the edge-pedestal region in order to underline advancements in quantifying time-dependent plasma profiles including transport barrier formation on the Alcator C-Mod tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

The calculation of thermophysical properties of nickel plasma

The thermophysical properties of Nickel plasma have been calculated for the temperatures 10–60 kK and densities less than 1 g/cm{sup 3}. These properties are the pressure, internal energy, heat capacity, and the electronic transport coefficients (electrical conductivity, thermal conductivity, and thermal power). The thermodynamic values have been calculated by means of the chemical model, which also allows one to obtain the ionic composition of considered plasma. The composition has been used to calculate the electronic transport coefficients within the relaxation time approximation. The results of the present investigation have been compared with the calculations of other researchers and available data of measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

EFIT‐AI: Machine Learning and Artificial Intelligence Assisted Equilibrium Reconstruction for Tokamak Experiments and Burning Plasmas (Final Report)

The EFIT-AI project is creating a modern advanced equilibrium reconstruction code suitable for tokamak experiments of burning plasmas. EFIT [1,2] was the first and is the most extensively used equilibrium reconstruction code in the world. This project builds on the production-level experience and adds key elements as follows. 1. A Model Order Reduction (MOR) version of the two-dimensional (2D) Grad-Shafranov equation solver (EFIT-MORNN) using physics-informed neural networks. 2. Improved optimization and data analysis capabilities using a Bayesian framework enhanced with machine learning. 3. A MOR version of the three-dimensional (3D) perturbed equilibrium reconstruction tool.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Impact of fusion reactor neutronics modeling for transmutation and thermal feedback *

Abstract Fusion neutronics calculations provide important metrics pertinent to fusion device operations, such as tritium breeding ratios (TBRs) and data on heat deposition, material activation, and damage. Because of the high computational burden required to generate a high-fidelity Monte Carlo simulation of a 3D fusion device, various assumptions are made to reduce computational time by simplifying the reactor model or the calculation iteration. This paper explores the impact of fusion neutronics metrics such as the TBR and decay heat of structural materials based on assumptions of material composition in the fusion reactor and temperature modeling of materials. Results show that for compact tokamaks with high power and long operational cycles, the transmutation of structural materials is significant enough to cause a substantial change in the flux spectrum and decrease the TBR by 1.68% after 2 years of full power operation. Additionally, assuming a constant temperature and material density can impact the TBR calculations up to 3%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Z Line VISAR Analysis with the LineVISAR SMASH Class

The Z line VISAR system (ZLV) is a spatially-resolved velocimeter that measures surface velocities in high-energy density experiments on the Z Pulsed Power Facility to facilitate the investigation of fusion, power flow, and dynamic material physics. The data measured in these experiments are analyzed with the LineVISAR SMASH class, a MATLAB software suite that provides tools for data importation, streak image correction, spatiotemporal registration, wrapped phase computation, phase unwrapping, shock handling, and velocity calculation. This report overviews the LineVISAR class, discusses its use, provides example implementations, and supplies analysis specifics not typically recorded in journal publications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bringing Different Views Together: A Hybrid Cooperative Perception Framework for Connected Autonomous Vehicles

Cooperative perception will be essential for connected autonomous vehicles to enhance object recognition and optimize path planning by sending data information about the surrounding environment. However, an inherent challenge in existing systems is the high bandwidth cost of transmitting information in real-time, which restricts cooperative perception’s practicality. Here, this work presents a hybrid cooperative perception fusion framework aimed at mitigating this issue by optimizing data transmission according to available bandwidth or through data reduction techniques. Our methods ensure that vehicles can rapidly transmit high-confidence data without overwhelming the network. Experimental results indicate that our methodology substantially diminishes data transmission sizes while maintaining object detection accuracy. For cooperative perception in autonomous vehicle systems, our approach provides a scalable and effective way to get past the bandwidth barrier.

Carrillo, Dominic [Univ. of North Texas, Denton, T↗
DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1989

Robotic Vision Would Combine Optics And Microwaves

Conceptual robot-vision system processes data from both microwave and visible-wavelength sensors. Concept, called "fusion of sensors," yields better information on shapes of some objects than obtained from one type of sensor alone. Proposed system fuses data from television images with polarized low-resolution radar-scattering cross sections. Using iterative procedure, generates successive approximations to shape of target by minimizing differences between observed radar returns and radar returns observed if object characterized by computed scattering transfer matrix.

Krishen, Kuma↗
DOE OSTI2016

A novel technique for real-time estimation of edge pedestal density gradients via reflectometer time delay data

A new model-based technique for fast estimation of the pedestal electron density gradient has been developed. The technique uses ordinary mode polarization profile reflectometer time delay data and does not require direct profile inversion. Because of its simple data processing, the technique can be readily implemented via a Field-Programmable Gate Array, so as to provide a real-time density gradient estimate, suitable for use in plasma control systems such as envisioned for ITER, and possibly for DIII-D and Experimental Advanced Superconducting Tokamak. The method is based on a simple edge plasma model with a linear pedestal density gradient and low scrape-off-layer density. By measuring reflectometer time delays for three adjacent frequencies, the pedestal density gradient can be estimated analytically via the new approach. Using existing DIII-D profile reflectometer data, the estimated density gradients obtained from the new technique are found to be in good agreement with the actual density gradients for a number of dynamic DIII-D plasma conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The Microwave-Heated Bumpy Torus: A Concept for Fusion Energy

In the last 3 decades, giant steps in technology and simulations capability have been made for the benefit of all magnetic fusion concepts. This is especially true for virtually a one-of-a-kind device, a microwave-driven bumpy-torus magnetic-fusion-energy system. In the 1980s, the prototype experiment was technology limited, and it was judged based on unrealistic expectations from an incomplete understanding of the machine performance. For those experiments, the machine did not achieve the theoretical stability threshold to demonstrate good plasma confinement. This paper examines the premise that the experimental platform was underpowered, which prevented acceptance of its value for fusion. In this paper, the experimental plasma scaling data with power are reviewed. If, with the advancements of technology and simulations capability of the last 30 years, stable operation can be proven, a much improved device competitive with tokamak and stellarator performance may be possible. The advantages of this concept include steady-state operation, gentle shut down, modular construction for fabrication and maintenance, and the natural diversion of impurities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Distributed Target Tracking With Optimal Data Migration

The paper presents an Extended Kalman Filter based framework for airborne target tracking using dynamic information fusion from multi-modal sensors with geodiversity. First, the algorithm execution location is determined using an optimal data migration strategy, next the sensors information is dynamically fused at each estimation instance using validity flag for each sensor reading, finally the target estimation is updated based on the fused innovation vector. The approach is applied to synthetic data generated from the radar and camera models located on the ground for the simulated target flight in Reflection simulation environment.

Distributed sensing↗
DOE OSTI2015

Polar-direct-drive experiments on the National Ignition Facility

To support direct-drive inertial confinement fusion experiments at the National Ignition Facility (NIF) [G. H. Miller, E. I. Moses, and C. R. Wuest, Opt. Eng. 43, 2841 (2004)] in its indirect-drive beam configuration, the polar-direct-drive (PDD) concept [S. Skupsky et al., Phys. Plasmas 11, 2763 (2004)] has been proposed. Ignition in PDD geometry requires direct-drive–specific beam smoothing, phase plates, and repointing the NIF beams toward the equator to ensure symmetric target irradiation. First experiments to study the energetics and preheat in PDD implosions at the NIF have been performed. These experiments utilize the NIF in its current configuration, including beam geometry, phase plates, and beam smoothing. Room-temperature, 2.2-mm-diam plastic shells filled with D{sub 2} gas were imploded with total drive energies ranging from ∼500 to 750 kJ with peak powers of 120 to 180 TW and peak on-target irradiances at the initial target radius from 8 × 10{sup 14} to 1.2 × 10{sup 15 }W/cm{sup 2}. Results from these initial experiments are presented, including measurements of shell trajectory, implosion symmetry, and the level of hot-electron preheat in plastic and Si ablators. Experiments are simulated with the 2-D hydrodynamics code DRACO including a full 3-D ray-trace to model oblique beams, and models for nonlocal electron transport and cross-beam energy transport (CBET). These simulations indicate that CBET affects the shell symmetry and leads to a loss of energy imparted onto the shell, consistent with the experimental data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

First neutron spectroscopy measurements with a pixelated diamond detector at JET

A prototype Single crystal Diamond Detector (SDD) was installed at the Joint European Torus (JET) in 2013 along an oblique line of sight and demonstrated the possibility to carry out neutron spectroscopy measurements with good energy resolution and detector stability in discharges heated by neutral beam injection and radio-frequency waves. Starting from these positive results, within the Vertical Neutron Spectrometer project of the Joint European Torus, we have developed a pixelated instrument consisting of a matrix of 12 independent SDDs, called the Diamond Vertical Neutron Spectrometer (DVNS), which boosts the detection efficiency of a single SDD by an order of magnitude. In this paper we describe the main features of the DVNS, including the detector design, energy resolution, and data acquisition system for on-line processing. Preliminary spectroscopy measurements of 2.5 MeV neutrons from the present deuterium plasma at JET are finally presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1990

Evolution of an Intelligent Information Fusion System

Consideration is given to the hardware and software needed to manage the enormous amount and complexity of data that the next generation of space-borne sensors will provide. An anthology is presented illustrating the evolution of artificial intelligence, science data processing, and management from the 1960s to the near future. Problems and limitations of technologies, data structures, data standards, and conceptual thinking are addressed. The development of an end-to-end Intelligent Information Fusion System that embodies knowledge of the user's domain-specific goals is proposed.

Campbell, William J.↗
DOE OSTI2021

Development of a deep learning based automated data analysis for step-filter x-ray spectrometers in support of high-repetition rate short-pulse laser-driven acceleration experiments

We present a deep learning based framework for real-time analysis of a differential filter based x-ray spectrometer that is common on short-pulse laser experiments. The analysis framework was trained with a large repository of synthetic data to retrieve key experimental metrics, such as slope temperature. With traditional analysis methods, these quantities would have to be extracted from data using a time-intensive and manual analysis. Furthermore, this framework was developed for a specific diagnostic, but may be applicable to a wide variety of diagnostics common to laser experiments and thus will be especially crucial to the development of high-repetition rate (HRR) diagnostics for HRR laser systems that are coming online.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Alpha-heating analysis of burning plasma and ignition experiments on the National Ignition Facility

A recent experiment conducted on the National Ignition Facility (NIF) described in the study by Abu-Shawareb et al. achieved a fusion yield output of 1.3 MJ from ~ 220 kJ of x-ray energy absorbed by the capsule, demonstrating remarkable progress in the field of laser driven inertial confinement fusion. In the study by A. R. Christopherson [“Effects of charged particle heating on the hydrodynamics of inertially confined plasmas,” Ph.D. thesis (2020)], the plasma conditions needed to claim the onset of ignition and burn propagation were outlined and multiple criterion were provided to assess progress in inertial fusion experiments. In this work, we modify the metrics from A. R. Christopherson to accurately calculate performance metrics for indirect-drive experiments on the NIF. We also show that performance metric trends observed in NIF data are consistent with theory and simulations. This analysis indicates that all the identified criterion for ignition and burn propagation have been exceeded by experiment 210 808.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗