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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An Overview of the Usefulness of Machine Learning Techniques on Network Packet Data

Understanding the health and behavior of a computer network allows for better network efficiency and security. We present an overview of various machine learning techniques for classifying network packet data via packet metadata. While some classical machine learning approaches achieve reasonable results, the most accurate classification can be achieved with deep learning. On the four data sets studied herein, a basic deep learning model achieved at or near 100\% classification accuracy. We also propose a method for determining variable importance as a means for potential transfer learning applications to classifying yet unseen network packet data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Characterizing Biomass Feedstock Transport Properties Using State of the Art Imaging and Computational Techniques

The microstructure of lignocellulosic biomass determines heat and mass transfer during conversion processes. We present a novel method for characterizing the transport properties of biomass using advanced imaging and computational techniques. The microstructure of two woody feedstocks, red oak and Douglas fir, before and after pyrolysis, is revealed using X-ray computed tomography (XCT). Transport properties are calculated from the XCT images, and principal permeability tensors are calculated using an immersed boundary-based finite volume solver to model gas flow through the geometries. We observe that the permeabilities of native biomass are distinctly anisotropic, however, this anisotropy is greatly reduced after pyrolysis.

adaptive mesh refinement↗
DOE OSTI2022

New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Techniques for collective operations in distributed systems

Various embodiments are generally directed to techniques for collective operations among compute nodes in a distributed processing set, such as by utilizing ring sets and local sets of the distributed processing set. In some embodiments, a ring set may include a subset of the distributed processing set in which each compute node is connected to a network with a separate router. In various embodiments, a local set may include a subset of the distributed processing set in which each compute node is connected to a network with a common router. In one or more embodiments, each compute node in a distributed processing set may belong to one ring set and one local set.

Langer, Akhil↗
DOE OSTI2022

Techniques for charging and/or discharging a battery using frequency modulation

Described herein are techniques that increase the charging and/or discharging rate of a rechargeable battery, at least in part, by using frequency modulated (FM) signals having a frequency in the megahertz (MHz) frequency range. In some embodiments, the MHz frequency range may include any frequency between 0.1 MHz and 1 gigahertz (GHz). In some embodiments, a battery charger described herein may be configured to generate and transmit, to a battery, an FM signal modulated over a frequency range during a period of time, the FM signal having a frequency of at least 0.5 MHz during at least a first portion of the period of time. In some embodiments, a method described herein includes transmitting an FM signal modulated over a frequency range during a period of time and having a frequency of at least 0.5 MHz during at least a first portion of the period of time to a battery.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Multi-modular capacitive wireless power transfer circuit and related techniques

A high performance kilowatt-scale large air-gap multi-modular capacitive wireless power transfer (WPT) system is provided for electric vehicle (EV) charging. In one particular implementation, the multi-modular system achieves high power transfer while maintaining fringing electric fields within prescribed safety limits. The fringing fields are reduced using near-field phased-array field-focusing techniques, wherein the adjacent modules of the multi-modular system are out-phased with respect to one another. The inter-module interactions in this multi-modular system can be modeled, and an approach to eliminate these interactions in a practical EV charging environment is provided. To illustrate one example implementation, a prototype 1.2-kW 6.78-MHz 12-cm air-gap multi-modular capacitive WPT system comprising two 600-W modules is provided. This prototype system achieves 21.2 kW/m2 power transfer density and a peak efficiency of 89.8%. This multi-modular system also achieves a fringing field reduction of 50% compared to its individual modules.

Afridi, Khurram K.↗
DOE OSTI2023

Iron nitride permanent magnet and technique for forming iron nitride permanent magnet

A permanent magnet may include a Fe 16 N 2 phase constitution. In some examples, the permanent magnet may be formed by a technique that includes straining an iron wire or sheet comprising at least one iron crystal in a direction substantially parallel to a <001> crystal axis of the iron crystal; nitridizing the iron wire or sheet to form a nitridized iron wire or sheet; annealing the nitridized iron wire or sheet to form a Fe 16 N 2 phase constitution in at least a portion of the nitridized iron wire or sheet; and pressing the nitridized iron wires and sheets to form bulk permanent magnet.

Wang, Jian-Ping↗
DOE OSTI2023

USE OF NOVEL REFRACTORY DESIGN AND INSTALLATION TECHNIQUES FOR IMPROVED ENERGY EFFICIENCY IN IRON AND STEEL AND OTHER ENERGY INTENSIVE INDUSTRIES

This work describes a US Department of Energy funded program which unites key players to develop and deploy new technologies aimed at increasing energy efficiencies while reducing their overall energy and environmental footprint. The research brings together a vertically integrated collaborative team consisting of the end user (US Steel), material producers/suppliers (Allied Mineral Products and Reno Refractories), raw material suppliers (American Metallurgical Services, Minerals Manufacturing), and research organizations (Oak Ridge National Laboratory, National Energy Technology Laboratory and University of Alabama-Birmingham) with the objective of designing and producing new refractory materials. Design and production criteria are based on novel aggregates, improved particle packing, and engineered surface textures. In addition, the recycling and use of spent refractory materials has been investigated resulting in development of a novel installation technique that utilizes additive manufacturing technology and existing refractory shotcrete technology. The combination of these new technologies will improve the energy, environmental, and economic efficiencies of the steel industry while reducing the environmental footprint. The developed technology is potentially applicable to additional energy intensive industries including cement, glass, pulp and paper, and non-ferrous metals processing.

Hemrick, James↗
DOE OSTI2020

Evaluation of Techniques for Vegetation Removal from UAV-Based Photogrammetric DSM near WIPP Land Withdrawal Act Boundary, NM

The Waste Isolation Pilot Plant is located within New Mexico's karst landscape and is the United States' only deep geologic radioactive waste repository which isolates transuranic waste from defense activities underground in a bedded salt formation. Characteristic karst topography in this region is made up of material (such as limestone) that can be dissolved by water over a long period of time, and includes features such as springs, surface streams, sinking streams, caves, and sinkholes. Karst features can potentially impact the integrity of the waste repository in the future and compromise WIPP's performance, particularly when coupled with incompatible land-use activities within the Land Withdrawal Act (LWA) boundary as well as increased water withdrawals outside the LWA. In order to better capture the ground surface topography to support hydrology modeling efforts, the U.S. DOE has enlisted FIU to develop a high-resolution digital elevation model (DEM). In a previous field study, a high-resolution digital surface model (DSM) of an adjacent representative site, Basin 6, was developed by photogrammetry using aerial images captured with unmanned aerial vehicles. The DSM generated was based on point clouds representing the entire landscapes, including points of terrain, vegetation and infrastructure. These above-ground features need to be removed to create a DSM/DEM with good representation of the bare ground. In recent years, many techniques including both pre- and post-processing of DSMs, were developed for vegetation removal with varied success. This research focuses on applying and comparing various vegetation removal methods using point clouds generated of the Basin 6 pilot study area. The 3D RGB-based point cloud classification using Python scripts was a modification of the procedure by Themistocleous (2019). The method using Liblas classifies the point cloud three-dimensionally, whereas the method by Themistocleous (2019) is two-dimensional. The preliminary result of the method using Liblas, however, still had a large amount of vegetation remaining compared to the Pix4D Machine Learning method. The vegetation layer remains highly visible in the DEMs processed using the vegetation indices as opposed to the Pix4D Machine Learning method, which produces a DEM where mostly bare ground is seen. Further modifications to the input parameters are required to improve the vegetation vs. bare ground classification using this approach. The next step will be to improve the method using Liblas and to also test other vegetation removal methods found in the literature review to compare them against the two described here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Variance reduction techniques for Monte Carlo neutron noise simulations

The small fluctuations of the neutron flux caused by small perturbations of the macroscopic cross-sections take the name of neutron noise. Advanced Monte Carlo methods have been recently proposed in order to solve the neutron noise equations in the frequency domain, which allows establishing reference solutions to validate faster but approximate deterministic solvers. Due to the presence of particles carrying two statistical weights (for the real and imaginary components of the noise field), both of which may be positive or negative, Monte-Carlo simulations of neutron noise pose distinct challenges in terms of variance reduction. In this work we investigate two variance-reduction techniques, namely branchless collisions and weight cancellation, and probe their effectiveness for a benchmark con- figuration concerning the noise field induced by a pin with oscillating cross sections in a fuel assembly.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Studying the hadron structure with PANDA and CLAS using machine learning techniques

The hadron spectroscopy and structure are currently very active fields of research to study the non-perturbative regime of quantum chronodynamics. The first one studies the complex structure of excited hadrons by looking at their decay products, while the latter uses lepton scattering on nucleons. Both methods require reconstruction algorithms with great efficiency and good particle identification and background rejection rates. This work aims to provide these by either improving the existing methods or developing new ones. The first part of this document presents a feasibility study of a predicted hybrid charmonium state for the PANDA experiment. Lattice QCD calculations predict the ground state hybrid charmonium to be a spin exotic with quantum numbers of JP C = 1?+ at a mass of around 4.3 GeV with a width to be around 20 MeV. A machine learning based data analysis scheme is proposed to further improve the signal efficiency and the background reduction, alongside with improvements of the analysis software (PandaRoot), that are vital for this study. These improvements include a reworked clustering algorithm for the electromagnetic calorimeter (EMC) and an optimized monte carlo matching for neutral particles. The second part of this document is about studying the proton structure. A multidimensional study of the structure function ratio Fsin(?)LU /FUU has been performed for K±, based on the measurement of beam-spin asymmetries. It uses the high statistics data recorded with the CLAS12 spectrometer at Jefferson Laboratory. Fsin(?)LU is a twist-3 quantity that provides information about the quark gluon correlations in the proton. This document will present for the first time a simultaneous analysis of two kaon channels over a large kinematic range of z, xB , PT and Q2 with virtualities Q2 ranging from 1 GeV2 up to 8 GeV2 using machine learning techniques for improved particle identification.

Kripko, Aron↗
DOE OSTI2024

Dopant Segregation in Single-Crystal Optical Fiber Grown via the Laser-Heated Pedestal Growth Technique

Single crystal (SC) optical fiber has promising potential to be used for optical fiber sensing applications in harsh conditions due to its robustness to high temperature, high radioactivity, and resistance to chemical corrosion as compared to optical sensors using silica fiber. However, SC fiber grown via the laser-heated pedestal growth (LHPG) technique innately does not have a core-cladding structure found in standard optical fiber, resulting in optical losses. In this work we investigate optimization of the growth parameters of a two LHPG process used to grow SC fiber with a graded index via introduction of dopants to the feedstock material. Feedstock material is fabricated with the first LHPG device, then sol-gel dip-coated to create outer films of dopant material. The dip-coated feedstock is used to grow SC fiber in which segregation of the dopant constituents occurs, resulting in a graded index of refraction across the fiber, and an effective core-cladding structure. Hardware and software improvements to both LHPG systems are presented and the growth parameters for short pieces of ~320-330 μm diameter YAG fiber has been established. Characterization techniques/procedures have also been established for future grown SC fiber. These improvements and preparations are anticipated to result in a significant increase in grown fiber quality with a similar growth rate to that previously established.

Lander, Gary↗
DOE OSTI2024

Dopant Segregation in Single-Crystal Optical Fiber Grown via the Laser-Heated Pedestal Growth Technique

Single crystal (SC) optical fiber has promising potential to be used for optical fiber sensing applications in harsh conditions due to its robustness to high temperature, high radioactivity, and resistance to chemical corrosion as compared to optical sensors using silica fiber. However, SC fiber grown via the laser-heated pedestal growth (LHPG) technique innately does not have a core-cladding structure found in standard optical fiber, resulting in optical losses. In this work we investigate optimization of the growth parameters of a two LHPG process used to grow SC fiber with a graded index via introduction of dopants to the feedstock material. Feedstock material is fabricated with the first LHPG device, then sol-gel dip-coated to create outer films of dopant material. The dip-coated feedstock is used to grow SC fiber in which segregation of the dopant constituents occurs, resulting in a graded index of refraction across the fiber, and an effective core-cladding structure. Hardware and software improvements to both LHPG systems are presented and the growth parameters for short pieces of ~320-330 μm diameter YAG fiber has been established. Characterization techniques/procedures have also been established for future grown SC fiber. These improvements and preparations are anticipated to result in a significant increase in grown fiber quality with a similar growth rate to that previously established.

Lander, Gary↗
DOE OSTI2024

Ultra-Long Distance BOTDA Sensor System Employing Hybrid Amplification and Advanced Noise Reduction Techniques

Brillouin Optical Time Domain Analysis (BOTDA) sensor systems play a pivotal role in distributed sensing, which enables precise measurements of strain and temperature across extensive fiber lengths. This research offers a comprehensive strategy to extend the sensing range of BOTDA systems beyond tens of kilometers while maintaining high spatial resolutions. Such enhanced sensing is realized through the integration of distributed Raman amplification, inline amplification using erbium-doped fiber amplifiers (EDFA), and advanced noise reduction techniques. Optimizing Raman pump and probe wave profiles ensures the robustness of Brillouin scattering signals, effectively countering attenuation-induced losses. Additionally, enhancing weak Brillouin signals by strategically placing EDFAs at optimal fiber locations keeps sensor sensitivity high. Leveraging inherent redundancy in measured data as a function of frequency and fiber distance, the non-local means (NLM) filter removes noise while preserving essential physical information. In summary, this research has shown a holistic exploration of extending BOTDA’s distance sensing capabilities up to 150 km with spatial resolutions of 8 meters.

Bhatta, Hari↗
DOE OSTI2023

Applying AI/ML Techniques to U.S. Nuclear Operating Experience Program

Idaho National Laboratory (INL) has provided technical assistance to the U.S. Nuclear Regulatory Commission (NRC) in reliability and risk analysis including the operating experience (OpE) program since the 1980s. The U.S. nuclear OpE program provides input parameters to the NRC Standardized Plant Analysis Risk models and the industry probabilistic risk assessment (PRA) models. While earlier PRA focuses were on at-power, internal event analysis, the risks from external hazards and during low power shutdown (LPSD) operation could be significant and the needs to develop LPSD PRA and external hazards PRA are on the rise. One issue in developing LPSD PRA is the reasonable estimation of shutdown initiative event (SDIE) frequencies. INL has developed and is maintaining an SDIE database for the NRC. However, this database is based on the reviewing of Licensee Event Reports (LERs), which is believed to be only a subset of “actual” shutdown initiating events occurred in the industry. This paper investigates a new approach to identify and characterize shutdown initiating events from the Institute of Nuclear Power Operations (INPO) industry database using machine learning techniques. The main process in this approach is to find out the relationship between key words in event descriptions and the SDIE categories as in the NRC SDIE database. The relationship can then be applied to the INPO database and search for SDIEs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

High-Throughput Electric-Field-Assisted Sintering and Characterization Techniques for Materials Discovery

Despite improvements in computing and modeling capabilities, the performance of new materials, particularly those which deviate greatly in composition from well-studied materials (e.g., high-entropy alloys), can be difficult to simulate given the lack of available experimental property data. While some modeling techniques may attempt to predict the properties of these exotic materials, most are forced to make extrapolations from more traditional materials. To fulfill the need for accelerated material synthesis and property measurement, a high-throughput methodology has been developed. Utilizing electric-field-assisted sintering (EFAS), also known as spark plasma sintering (SPS), equipped with custom tooling, samples of differing alloy compositions can be produced simultaneously as a single alloy array. Several arrays have been produced with compositions spanning the Co-Cr-Fe-Mn-Ni alloy family, including many high-entropy alloys, while the novel array geometry has enabled the samples to be polished and characterized in parallel, using X-ray diffraction, scanning-electron microscopy, and laser-based thermal diffusivity measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Commercial EV Charging Infrastructure: Modeling Concepts & Techniques

This presentation offers an overview of key concepts and techniques for modeling global commercial EV charging infrastructure. We begin by comparing the unique needs of commercial vehicles with those of personal vehicles. Next, we delve into network design topics, including phased deployment of public infrastructure, port sharing across and within fleets, power requirements for en-route charging, and strategies for estimating depot charging availability. Finally, we outline the primary steps NREL follows in conducting an EV charging infrastructure needs assessment.

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗