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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Demo Abstract: Toward Data-driven Demand-Response Optimization in a Campus Microgrid

We describe and demonstrate a prototype software architecture to support data-driven demand response optimization (DR) in the USC campus microgrid, as part of the Los Angeles Smart Grid Demonstration Project. The architecture includes a semantic information repository that integrates diverse data sources to support DR, demand forecasting using scalable machine-learned models, and detection of load curtailment opportunities by matching complex event patterns.

Amam, Saima↗
DOE OSTI2017

Thermomechanical Modeling of Sintered Silver - A Fracture Mechanics-based Approach: Extended Abstract: Preprint

Sintered silver has proven to be a promising candidate for use as a die-attach and substrate-attach material in automotive power electronics components. It holds promise of greater reliability than lead-based and lead-free solders, especially at higher temperatures (less than 200 degrees Celcius). Accurate predictive lifetime models of sintered silver need to be developed and its failure mechanisms thoroughly characterized before it can be deployed as a die-attach or substrate-attach material in wide-bandgap device-based packages. We present a finite element method (FEM) modeling methodology that can offer greater accuracy in predicting the failure of sintered silver under accelerated thermal cycling. A fracture mechanics-based approach is adopted in the FEM model, and J-integral/thermal cycle values are computed. In this paper, we outline the procedures for obtaining the J-integral/thermal cycle values in a computational model and report on the possible advantage of using these values as modeling parameters in a predictive lifetime model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A progressive deep transfer learning approach to cycle-skipping mitigation in FWI, In SEG Technical Program Expanded Abstracts 2019, pp. 2348-2352. Society of Exploration Geophysicists, 2019.

To effectively mitigate the cycle-skipping phenomenon in full waveform inversion (FWI), we developed an inception-based deep learning neural network to reconstruct the absent low frequency data by exploiting the subsurface low wavenumber information buried in the acquired high frequency data. Two unique features of our deep learning network for the low frequency data reconstruction are 1) Dual Data Feed; 2) Progressive Transfer Learning. With the Dual Data Feed feature, not only the high frequency data, but also the corresponding Beat Tone data are fed into the neural network, significantly reducing the network complexity and the network training cost. The Progressive Transfer Learning technique enables us to initiate the deep learning network with only one training dataset. Unlike other deep learning approaches in this category, our training dataset is not fixed. Instead, the training dataset and the network accuracy are progressively enhanced in an iterative manner within the Progressive Transfer Learning workflow. The numerical experiments validated that the low frequency data reconstructed by this progressive transfer learning network are sufficiently accurate for a successful FWI inversion without suffering from the cycle-skipping without any priori geological information.

58 GEOSCIENCES↗
DOE OSTI2020

Poster Abstract: Investigating Occupancy Profiles Using Convolutional Neural Networks: Preprint

In this paper, we implement a convolutional neural networks based autoencoder to investigate occupancy profiles. We use the American time use survey data, which contains 191,558 schedules with binary occupancy information. Our results suggest that the trained filters provide an important insight of occupancy profiles (i.e., dominant and distinct patterns), and the latent space compresses the profiles with representative information.

14 SOLAR ENERGY↗
DOE OSTI2023

Abstract for Accelerator Magnets Magnetic Field Measurements

The chapter develops the formalism for the two-dimensional representation of the fields which are commonly found in accelerator magnets. The chapter describes the sense windings and associated components used in the precise measurement of these fields.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Effect of Power Plant Capacity on the CAPEX, OPEX, and LCOC of the CO2 Capture Process in Pre-Combustion Applications - Abstract

Aspen Plus v8.8 was used to simulate the pre-combustion CO2 capture process from a typical fuel gas stream at different power plant capacities ranging from 54.3 to 543 MW. Polyethylene glycol polydimethyl siloxane (PEGPDMS-1) was used as a physical solvent to capture CO2 in a countercurrent packed-bed absorber containing a structured packing (Mellapak 250Y). The process pressure was 51.4 bar and the solvent temperature was varied from 10 to 40 <sup>o</sup>C. The internal diameter of the absorber ranged from 1.9 to 7 m and the packing height from 13.9 to 45.5 m. The physico-chemical properties were obtained and modeled using the Perturbed Chain-Statistical Associating Fluid Theory (PC-SAFT) Equation-of-State (EOS).<p> Four process constraints were imposed in Aspen Plus: (1) no flooding in the absorber, (2) the absorber height to diameter ratio (H/D) is greater than or equal 6, (3) at least 90 mol% of the CO2 capture from the fuel gas stream, and (4) the CO2 stream destined to sequestration sites should contain less than 600 ppm water concentration and less than or equal 0.5 mol% of fuel gases (H2, CO, CH4). The plant lifetime was assumed to be 30 years with an annual discount rate of 10% and an annual maintenance cost of 4% of the total capital expenses.</p><p> Seven power plants with different capacities were used and for each plant, the corresponding CO2 capture process was simulated. The absorber flooding was checked using the generalized pressure drop correlation (GPDC) by Leva [1] and the capital expenditure (CAPEX), operating expenditure (OPEX), and levelized costs per ton of CO2 captured (LCOC) were calculated [2-5]. Normalized by the largest power plant capacity (543 MW) used in the simulation, the results indicated that the capital and operating expenditures increased, however, the LCOC decreased with increasing plant capacity. This behavior was due to the increased annual tonnage of the CO2 captured with plant capacity as shown in Figure 1. The calculated CAPEX, OPEX, LCOC and the tonnage of CO2 captured for the 543 MW power plant were about 52 MM$, 22 MM$/year, 7.46 $/ton and 4 MM ton/year.</p>

Ashkanani, Husain↗
DOE OSTI2021

A Predictive Model for Offgas Composition in Waste Glass Melters - Spring 2021 SULI General Audience Abstract

Approximately 56 million gallons of nuclear waste are currently stored in 177 underground storage tanks at the Hanford site in Washington state. A portion of the tank waste is targeted for immobilization by converting the nuclear waste to borosilicate glass through a process called vitrification. During vitrification, emissions are produced in addition to the intended glass product. These emissions, contained in a volume above the molten glass called the plenum, are composed of water vapor, air components, aerosols, particulates, and other gaseous species that result from evaporation, reactions, and air influx. The formation of these gasses causes the surface of the glass to foam. To reduce foaming, sucrose is added to the waste stream before the conversion to glass. Sucrose also reduces the formation of hazardous gasses in the plenum including nitrous oxides, NOx, and carboxides, COx. However, if excess sucrose is present, the reactions that produce the gasses in the plenum will be incomplete, and increased levels of NOx and COx can be observed. The Waste Treatment and Immobilization Plant at the Hanford site is equipped with the technology to treat the plenum emissions. Though many tests have been run to determine the presence of particulates in the plenum emissions, there is does not exist an established method to confidently predict the composition of the gaseous species in the plenum prior to treatment without physical testing. A model was created in the process modeling software Aspen Plus to predict the composition of NOx and COx in the plenum. The model employs a constant stirred tank reactor and user-defined reaction chemistry to determine the plenum composition. To verify the model, the offgas predicted by the model were compared to available offgas data for ten different feed compositions. It is demonstrated that the developed Aspen Plus model is capable of predicting the composition of NOx and COx in the plenum.

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

Cooperative Research and Development Agreement between National Energy Technology Laboratory and West Virginia University Research Corporation (Abstract)

The National Energy Technology Laboratory (NETL) and West Virginia University Research Corporation (Participant) will collaborate on the testing of a 3D printed sparger in a temperature and pressure chamber, for the purpose of supporting the “American-Made Geothermal Prize” competition, designed to catalyze manufacturing innovation in the American geothermal industry by harnessing the rapid advances additive manufacturing can provide in improving design, fabrication, and functionality, which is accomplished through a series of prize competitions and the development of a diverse and powerful support network that leverages national laboratories, energy incubators, and other resources from across the United States. The intent is to connect competitor teams with national laboratories that can help accelerate the development of innovative solutions and products. Teams who have won the Set!, Go!, Make! Contests are eligible to utilize vouchers at national laboratories to advance their ideas.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Reactivity Coefficient Measurements and Sensitivity Studies [Abstract]

Nuclear data validation is often performed today using criticality measurements. The gold standard for criticality measurements is the International Criticality Safety Benchmark Experiment Project (ICSBEP). The validation specifically focuses on the effective multiplication factor (k eff ). K eff is a relatively easy parameter to infer and has reduced uncertainty due to being at or above critical. However, while k eff is a well-documented parameter with detailed sensitivity and uncertainty analysis, it cannot be used as a standalone metric to determine inaccuracies in nuclear data (e.g., cross section data, PFNS, nu), which is based on theory, physics, and differential measurements. The Experiments Underpinned by Computational Learning for Improvements in nuclear Data (EUCLID) project aims to identify compensating errors in specific nuclear data by optimally designing experiments that are sensitive to a suite of measurement parameters beyond k eff . By identifying how each parameter's nuclear data sensitivity differs from others, experiments can be designed to constrain questionable nuclear data. One sensitivity that is of particular interest to this project includes reactivity coefficient sensitivities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Sensitivity/Uncertainty Comparison Study Involving IRSN, LANL, and ORNL Tools to Support Validation [Abstract]

Under a DOE Nuclear Criticality Safety Program (NCSP) task involving Analytical Methods, three Laboratories collaborated in a comparison of results obtained from Sensitivity/Uncertainty (S/U) packages relevant to validation of transport codes. The task involves Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Los Alamos National Laboratory (LANL), and Oak Ridge National Laboratory (ORNL) comparing results of MORET 5/MACSENS V3.0, MCNP6.2/Whisper-1.1, and SCALE 6.2.3/TSUNAMI/USLSTATS respectively. All Monte Carlo transport code results utilize nuclear data from ENDF/B-VII.1 evaluation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗