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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Proximal Observations of Epicentral Infrasound Generated by Shallow Low-Magnitude Earthquakes in the Permian Basin, West Texas

Infrasound generated by earthquakes and explosions is generally detected at receivers at epicentral distances of hundreds to thousands of kilometers. However, proximal (<50 km) observations are especially important for low-magnitude earthquakes and low-yield explosions that may not generate signals capable of being detected at great ranges. Here, in this study, we present on the signals detected on an infrasound array 3 km away from two M L 2.9 earthquakes in the Permian Basin of west Texas. Local infrasound (LIS), generated at receivers during the passage of seismic waves, was detected following each earthquake. Epicentral infrasound (EIS), created at or near the epicenter and propagating away as a sound wave, was also detected. Array processing methods show that the EIS signals arrive from the same direction as the earthquake epicenters and at acoustic speeds. To our knowledge, these are the first observations of laterally propagating EIS at proximal ranges following an earthquake of any magnitude.

58 GEOSCIENCES↗
DOE OSTI2024

On Forced RF Generation of CW Magnetrons for SRF Accelerators

CW magnetrons, initially developed for industrial RF heaters, were suggested to power RF cavities of superconducting accelerators due to their higher efficiency and lower cost than traditionally used klystrons, IOTs or solid-state amplifiers. RF amplifiers driven by a master oscillator serve as coherent RF sources. CW magnetrons are regenerative RF generators with a huge regenerative gain. This causes regenerative instability with a large noise when a magnetron operates with the anode voltage above the threshold of self-excitation. Traditionally for stabilization of magnetrons is used injection locking by a quite small signal. Then the magnetron except the injection locked oscillations may generate noise. This may preclude use of standard CW magnetrons in some SRF accelerators. Recently we developed briefly described below a mode for forced RF generation of CW magnetrons when the magnetron startup is provided by the injected forcing signal and the regenerative noise is suppressed. The mode is most suitable for powering high Q-factor SRF cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

pyflowline: a mesh-independent river network generator for hydrologic models

River networks are crucial in hydrologic and Earth system models. Accurately representing river networks in spatially distributed hydrologic models requires considering the model's spatial discretization and computational mesh. However, current methods of generating river networks for hydrologic models do not typically support unstructured meshes. Unstructured meshes offer numerous advantages over traditional, structured meshes. To overcome this limitation, we developed PyFlowline, a Python package that generates mesh-independent river networks. With PyFlowline, hydrologic modelers can generate conceptual river networks and their topological relationships for both structured and unstructured meshes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Uncertainties associated with GAN-generated datasets in high energy physics

Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this paper we point out that data generated by a GAN cannot statistically be better than the data it was trained on, and critically examine the applicability of GANs in various situations, including a) for replacing the entire Monte Carlo pipeline or parts of it, and b) to produce datasets for usage in highly sensitive analyses or sub-optimal ones. We present our arguments using information theoretic demonstrations, a toy example, as well as in the form of a formal statement, and identify some potential valid uses of GANs in collider simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

How to understand limitations of generative networks

Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events. In all cases, the classifier weights make for a powerful test of the generative network, identify potential problems in the density estimation, relate them to the underlying physics, and tie in with a comprehensive precision and uncertainty treatment for generative networks.

Physics↗
DOE OSTI2024

A portable parton-level event generator for the high-luminosity LHC

The rapid deployment of computing hardware different from the traditional CPU+RAM model in data centers around the world mandates a change in the design of event generators for the Large Hadron Collider, in order to provide economically and ecologically sustainable simulations for the high-luminosity era of the LHC. Parton-level event generation is one of the most computationally demanding parts of the simulation and is therefore a prime target for improvements. We present a production-ready leading-order parton-level event generation framework capable of utilizing most modern hardware and discuss its performance in the standard candle processes of vector boson and top-quark pair production with up to five additional jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Forecasting generative amplification

Generative networks are perfect tools to enhance the speed and precision of LHC simulations. Especially when generating events beyond the size of the training dataset, it is important to understand their statistical precision. We present two complementary methods to estimate the amplification factor without large holdout datasets. Averaging amplification uses Bayesian networks or ensembling to estimate amplification from the precision of integrals over given phase-space volumes. Differential amplification uses hypothesis testing to quantify amplification without any resolution loss. Applied to state-of-the-art event generators, both methods indicate that amplification is already possible in specific regions of phase space.

Bahl, Henning [Heidelberg Univ. (Germany)] (ORCID:↗
DOE OSTI2018

Aluminum Clad Spent Nuclear Fuel Task 3: Sealed and Vented Systems Episodic Breathing and Gas Generation Modeling Plan

This document presents the test plan for Task 3 of the Aluminum Clad Spent Nuclear Fuel Long Term Dry Storage Technical Issues Action Plan, which covers modeling and predictions of the generations of corrosive gases within both sealed and vented, subject to episodic breathing, storage systems. This includes aluminum clad spent nuclear fuel (ASNF) that is currently or may in the future be held in dry storage for an extended period. The overall objectives of the Task 3 modeling plan are to: 1. Comprehensively model the combined effects of episodic breathing in vented storage systems/canisters using INL INTEC CPP-603 facility as an example. Both bulk gas phase radiolysis reactions and aluminum cladding surface gas generation reactions will be included into a 3-dimensional computational fluid dynamics (CFD) model to model and predict long-term evolutions of the temperature and concentrations of corrosive gases within unsealed canisters subjected to episodic breathing. 2. Comprehensively model sealed and inert canister storage system using DOE standard canister as an example. 3. Closely integrate with Task 1 and Task 2 for identification of main gas-generation reactions and kinetics and provide simulation results back to Task 1 and 2 to further refine experimental designs. This test plan lays out the technical activities that will be performed to achieve the test objectives described above. Simulations will be performed using the Idaho National Laboratory super computer Falcon.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Sample Generation for Nuclear Data

This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on developing a sampling capability that can handle the challenges of generating samples from nuclear cross-section data. The covariance information between energy groups tends to be very ill-conditioned and thus poses a problem using traditional methods for generated correlated samples. This report outlines a method that addresses the sample generation from cross-section matrices. The treatment allows one to assume the cross sections are distributed with a multivariate normal distribution, lognormal distribution, or truncated normal distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Evaluating the Role of Pre-Application Reports in Improving Distributed Generation Interconnection Processes

Several tools have emerged in recent years to increase data exchange and transparency in the distributed generation interconnection process with the goal of reducing interconnection costs. One example is pre-application reports. These reports are generated by a utility at the request of prospective interconnection applicants to provide technical information about a specific point of interconnection. Formal pre-application report processes have been established in 12 states at the distribution system level as well as at the transmission level by the Federal Energy Regulatory Commission. These reports are a relatively low-cost method of informing applicants of grid conditions and potential interconnection limitations early in the interconnection process. Thus, this information may encourage interconnection in locations with little to no detrimental impact to the grid and low interconnection costs. Increasing transparency of these grid conditions may also enable applicants to better plan for interconnection costs and requirements, such as system modifications and complex engineering studies. By providing these benefits, pre-application reports are expected to reduce the number of canceled interconnection applications. This report evaluates this hypothesis by quantifying the impact pre-application report processes have on application approval rate. To evaluate this hypothesis this analysis uses utility-reported distributed generation interconnection data from four utilities in Massachusetts. The analysis considers a four-year time period before and a five-year period after pre-application report process were implemented. The results indicate that once pre-application reports were required for projects 500 kilowatts and larger the approval rate of applications increased by 24%. This suggests that pre-application reports may decrease the percentage of canceled projects. However, due to data and other limitations, these results do not necessarily connote causation. This analysis is an important first step and a prerequisite for future analyses regarding the impact of pre-application reports. Other potential impacts also recognized during this analysis included reducing sunk costs and reducing the number of projects requiring a complex review. Collection of additional data would allow for forming more robust conclusions about these impacts in the future. Such data include circuit-level data, interconnection cost data, policy and regulatory background, national-level data, and utility and developer attributes. These data provide necessary context to account for interrelated factors while evaluating pre-application reports. Filling such knowledge gaps ultimately aids regulators in applying best practices within the industry. As more and more distributed energy resources are interconnected, these best practices will become an integral aspect of efficiently transitioning to an electric grid capable of meeting the needs of the 21st century.

14 SOLAR ENERGY↗
DOE OSTI2019

Flexible Geothermal Power Generation utilizing Geologic Thermal Energy Storage (Final Seedling Project Report)

This report concludes that there is a cost-effective strategy for seasonal storage of heat that will provide firm, but dispatchable, electrical generating capacity in times when other renewable energy is not available to meet demand. Deployment of the technology appears to require no new technology, but instead combines solar, geothermal, and conventional oil and gas drilling technologies in a novel way. The study basis is the use of sedimentary geologic formations as a medium for thermal energy storage (TES), specifically for heat collected in concentrating solar collectors. The study identifies methodologies that could be used to transport this heat into and out of the subsurface in order to produce dispatchable electrical power, and reports on initial optimization results. The GeoTES system (heat input, storage, heat recovery, and heat to electric conversion) described in this analysis has the potential to provide a unique pathway for increasing the grid penetration of renewable energy in large blocks of power and across many states and regions. Further, the system can be used both to meet the nation’s flexible energy needs while also improving grid stability and reliability. The present study evaluated the use of a large number of dedicated wells to store and recover the heat, essentially creating a synthetic geothermal reservoir. The use of sedimentary geology allows the wells to be drilled at low cost. Dedicated hot and cold wells are used, arranged in a five-spot well pattern with each hot or cold well surrounded at an appropriate distance by the opposite type of well. In large numbers this becomes alternating rows of hot and cold wells. Each hot and cold well is operated using a push-pull strategy. This configuration provides the ability to immediately recover stored hot fluid from a GeoTES reservoir, or to store the heat over many months for recovery at low loss when needed. This is a practical approach for managing the system’s fluid inventory and reducing parasitic load. The production and injection power requirements are reduced because the rows of wells operating in “push” mode provide help to the wells operating in “pull” mode, and vice-versa. Initial charging of a GeoTES system increases the heat recovery temperature. Increasing the duration of the charging period decreases the magnitude of the temperature fluctuations that occur following prolonged system operation. Because of direct contact of the heated water with the reservoir formation, the production of both hot water and steam from the TES, and the temperature ranges of the recovered fluid (190 – 230°C or 375 – 445°F), conventional geothermal power cycles were used to convert the stored heat to electricity. A power cycle configuration for the GeoTES system was selected following a screening study of a number of flash, and flash/binary hybrid options. This analysis concluded that, of the configurations evaluated, a dual-stage flash steam cycle provides the lowest capital costs per unit net power generation with an acceptable hot brine inlet fluid flow rate. The evaluation included the power plant cost estimate, the cost and number of wells and the associated parasitic loads. Annual power generation performance was simulated to evaluate capacity factor and LCOE. The LCOE calculated for the inherently high capacity GeoTES system was $\$$0.13/kWhe. This value was calculated for the case where the solar thermal collector was sized in such a way that the solar collectors permitted an annual power plant capacity factor of up to 97%. The power cycle was able to provide power to the grid every night of the year, and flexible base-load power during the winter, if needed. This LCOE value compares favorably with reported values for solar photovoltaic plus battery energy storage (PV+BES) systems in the open literature, i.e. $\$$0.148/kWhe for a PV+BES system with 4 hours of electrochemical battery energy storage capacity. Addition of battery energy storage with more hours of storage would further increase PV+BES system LCOE and increase the separation between GeoTES and PV+BES. A GeoTES system would therefore provide superior economics for high capacity and long duration solar energy storage.

14 SOLAR ENERGY↗
DOE OSTI2019

Generation of Multigroup Multiband Nuclear Data

Historically, deterministic pre-processing methods have been used for generating multigroup (MG) and multigroup multiband (MGMB) cross sections. These deterministic methods require an assumption of the shape for the scalar ux. There has been increasing interest in utilizing Monte Carlo transport as a method to generate these cross sections since the energy self-shielding and resonance overlap are fully accounted for. The purpose of this project is to implement and test the capability of generating these cross sections in Mercury.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Bench Scale Testing of Next Generation Hollow Fiber Membrane Modules

This project seeks to develop a next generation membrane material (PI-2) for application in Air Liquide’s (AL) hybrid process. Combining the cold membrane operation with an integrated CO 2 Compression and Purification Unit (CPU) significantly reduces the overall cost of CO 2 capture. The proposed work will accelerate the membrane bundle fabrication, scale-up and performance validation of this new material. The proposed novel membrane development, combined with the results of the previous Cold Membrane Project, will provide the best opportunity for AL to meet the long term development targets of the DOE with respect to timing, cost, and performance for CO 2 capture. Our previous field test program (DE-FE0013163) showed promising results for the hybrid process utilizing the existing commercial AL membrane bundles (PI-1 material). The project work has also shown the potential for significant improvements through initial laboratory tests with the novel material (PI-2). The initial PI-2 results show a step-change in membrane permeance. This will enable further reduction in the cost of CO 2 capture by reducing the number of membrane modules and associated equipment in the system. In order to capture this value, however, the new material needs to be validated by field testing of large bundles, representative of commercial production. Lastly, a comprehensive evaluation of novel hybrid processes and costs needs to be completed to ensure optimal use of this improved material performance. The current project seeks to develop this next generation membrane to TRL 5 (approximately 600 - 1,000 Nm3/h flue gas, 350 - 600 scfm, 0.2 - 0.3 MWe equivalent). Air Liquide manufacturing methodology will be applied to fabricate prototype (4”) membrane modules from PI-2 material. Once the manufacturing methods prove reliable with the new material, and performance is demonstrated in line with expectation, 6” commercial scale bundles will be fabricated. Recognizing that a low cost membrane solution by itself may not meet DOE’s LCOE target for CO 2 capture, we will also perform simulation studies of novel hybrid processes in combination with the next generation membrane technology. This approach has the best potential for exceeding the DOE targets. The best evaluation of the new commercial units will be performance testing under real flue gas at the NCCC. Finally, a rigorous TEA, which incorporates the novel process studies and field performance, will be completed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Conditional Generative Adversarial Networks for Solving Heat Transfer Problems

Generative Adversarial Networks (GANs) have been used as a deep learning approach to solving physics and engineering problems. Using deep learning for these problems is attractive in that reasonably accurate models can be inferred from only raw data, eliminating the need to define the exact physical equations governing a problem. We expand on previous work using GANs to generate steady-state solutions to the two-dimensional heat equation. Using a basic conditional GAN (cGAN), we generate accurate solutions for rectangular domains conditioned on four edge boundary conditions (MAE < 0.5%). For finding steady-state solutions over arbitrary two-dimensional domains (not constrained to rectangles), we use a cGAN designed for image-to-image translation. We train this GAN on various types of geometric domains (circles, squares, triangles, shapes with one circular or rectangular hole), achieving accurate results on test data made up of geometries similar to those in training (MAE < 1%). For both of these GANs, we experiment with different loss function terms, showing that a term using the gradients of solution images significantly improves the basic cGAN but not the image-to-image GAN. Lastly, we show that the image-to-image GAN performs poorly when applied to two-dimensional geometries that vary in structure from training data (MAE < 8% for shapes with multiple holes or different shaped holes). This demonstrates the cGAN's lack of generalizability. While the cGAN is an accurate and computationally efficient method when trained and tested on similarly structured data, it is a much less reliable method when applied to data that is slightly different in structure from the training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Mesh Generation for Microstructures

A parallel, adaptive overlay grid procedure is proposed for use in generating all-hex meshes for stochastic (SVE) and representative (RVE) volume elements in computational materials modeling. The mesh generation process is outlined including several new advancements such as data filtering to improve mesh quality from voxelated and 3D image sources, improvements to the primal contouring method for constructing material interfaces and pillowing to improve mesh quality at boundaries. We show specific examples in crystal plasticity and syntactic foam modeling that have benefitted from the proposed mesh generation procedure and illustrate results of the procedure with several practical mesh examples.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Test Plan for Investigation of Hydrogen Generation in SNF Canisters

This report presents a test plan to investigate radiolytic hydrogen generation in spent nuclear fuel (SNF) canisters containing Zr-based cladding materials. The initial primary contributor to the generation of radiolytic hydrogen is estimated to be the residual physisorbed water on the ZrO 2 film, post-dry out. Over the longer term the primary hydrogen source is associated with the aluminum hydroxides and water vapor. The proposed testing to be conducted in FY22 would consist of lab-scale testing on non-radioactive surrogates of oxidized ZIRLO tubing and unalloyed zirconium using a miniature steel canister (“mini-canister") designed to allow intermittent, in-situ sampling of the canister gas during irradiation. The mini-canister approach for irradiation with in-situ monitoring system was used in a recent and on-going study [McNamara and Verst, 2020a & 2020b; Verst et al., 2021] to measure radiolytic hydrogen generation from aluminum SNF cladding surrogate materials. This proposed testing will form part of the overall materials performance evaluation of the commercial SNF-in-canister system and is part of the technical bases for their continued safe dry storage

08 HYDROGEN↗
DOE OSTI2021

Methane Mitigation Thermoelectric Generator (MMTEG) (Final Scientific/ Technical Report)

Gas Technology Institute (GTI) has completed a $1.815M (plus $500K cost share), 54-month Co-operative agreement with the Department of Energy’s National Energy Technology Laboratory to develop a Methane Mitigation Thermoelectric Generator (MMTEG) system for gas field applications. This novel system uses fugitive gas to produce electrical power and consists of Thermoelectric Generators (TEG) driven by a linear burner, an air compressor, an accumulator, valves, batteries, and power electronics. The electrical power generated by the system is used to compress air and, in turn, the air is used to operate the pneumatic valves at the well site instead of using natural gas (NG) which would then be vented. The team completed the project objectives which included (1) Design, fabricate and test an integrated 6We nominal MMTEG prototype system, (2) Design a “retrofit kit” MMTEG system capable of being field tested at a gas well production site, and (3) build and test the field MMTEG system in a laboratory environment. Multiple system options were developed prior to selecting a “Passive” system which meets cost, NG savings, and greenhouse gas (GHG) reduction targets although not as efficient as initially planned. The MMTEG system is built primarily from commercial off-the-shelf parts (in some cases re-purposed) including the heat exchanger, heat rejection, electronic components, and also the TEGs. The team developed and implemented a novel system including the control system developed by Morrison Applied Sciences (MAS). The team completed incremental demonstrations of the hardware prior to the MMTEG system demonstration. A one-year simulation of the air delivery and battery charging subsystems was completed prior to the integration into the MMTEG system. In addition, the team simulated two-years of thermal cycles for integrated Burner/TEG/heat rejection subsystem. Finally, the entire MMTEG system was assembled, and troubleshooting was completed over a two-week period. Next, the MMTEG system was tested to simulate over 15 weeks of entire system operation over approximately four weeks in an accelerated test fashion with minimal intervention (such as changing fuel tanks). The MMTEG system met the key goals of a unit cost of under $1500 while saving 97% of NG expended today (including leakage) on average by pneumatic systems venting to the atmosphere. The MMTEG system reduced GHG emissions by 1000X (using the methane intensification factor of 28 relative to CO2). GTI is currently pursuing a field test of the MMTEG system. Interfacing with producers has provided additional insight into system improvements. Planned improvements include additional weather protection and control system improvements including the implementation of a long- range radio capability to notify operators if there is a fault.

03 NATURAL GAS↗
DOE OSTI2021

Quantum Random Number Generator (QRNG)

The Los Alamos Quantum Random Number Generator (QRNG) is a hardware-based, high-performance Random Number Generator capable of generating 200 Mbit/s or more of true random numbers. Like flipping a coin, it is very much random and essential for information security like encrypting data on the internet, checking email, or purchasing something from an online vendor. The device harvests entropy from fluctuations in an optical source that arise from quantum mechanical properties of light. Qrypt, Inc., a company launched in 2017, began making strategic investments and developing partnerships to advance cutting-edge quantum hardware solutions. One of those key investments was licensing QRNG from Los Alamos and subsequently collaborating with advanced quantum materials and technology researcher Dr. Raymond Newell to create high-quality random keys at scale.

97 MATHEMATICS AND COMPUTING↗