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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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Multiple stress cushion (SX-358) uniaxial compression tests are performed. There are 640 (= 4 cycles x 160 tests) compression test curves coming to Granta. Find CHIP-Foam material model parameters. Efficient method is needed to process test data to calibrate custom material models.
Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.
The uranium UF 6 coincidence counter (UFCC) was designed to measure 5A/B cylinders containing UF 6 in the passive neutron self-interrogation mode. The purpose of the present report is to extend the UFCC capability to other sample types such as oxides and liquids that might be found during verification activities. A 252 Cf source was introduced into the detector to make the active mode measurements possible, because only the UF 6 samples have adequate neutron emission for self-interrogation. The sample enrichments varied from 0.72% to 91% to establish the shape of the calibration curve and the measurement uncertainties. The measurements included the singles, doubles, and triples rates. The time correlations between the 252 Cf source and the induced fission reactions reduced the statistical error in the doubles and triples rates to be small enough for useful measurements in 5-10 minutes.
Sandia National Laboratories Radiation Protection department did not successfully address the causes leading to the issue NTS-SS-SNL-NMSITE-2017-0004, “Contamination Monitor Left in Service After Inadequate Calibration”, nor identify through the verification and validation process that corrective actions were not fully implemented and effective at addressing the issue(s). This report is the third causal in four years associated with the iPCM12 instrument and the Radiation Protection Instrumentation (RPI) organization within Radiation Protection (00628).
Focal Area(s): Automated custom calibration for the Energy Exascale Earth System Model (E3SM) applies primarily to focal area 2: Predictive modeling through the use of AI techniques, and includes elements of focal area 3: Insight gleaned from complex data (both observed and simulated).
The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High- quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation (Frick, Eckman, and Goldman 2017; Frick 2019). To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project - End-Use Load Profiles for the U.S. Building Stock - that culminated in the release of a publicly available dataset1 of simulated EULPs representing residential and commercial buildings across the contiguous United States. The motivation for this work is further detailed in a November 2019 report: Market Needs, Use Cases, and Data Gaps (Mims Frick et al. 2019). This Methodology and Results report provides detailed descriptions of how the dataset was developed, intended for an audience of dataset and model users interested in the technical details. These details include descriptions of all of the model improvements made for calibration and the final comparisons to empirical data sources. A companion report, End-Use Load Profiles for the U.S. Building Stock: Applications and Opportunities, will be published subsequently and will describe example applications and considerations for using the dataset, intended for an audience of general dataset users.
This report describes time-of-flight (ToF) beam energy measurements with multiple phase monitors in a beam line with no acceleration. Measurement uncertainty is quantified via rigorous treatment of beam-based phase monitor calibration and jitter analysis of the accelerator system. Experimental results in the Superconducting Linac (SCL) of the Spallation Neutron Source (SNS) are presented.
A beam position monitor (BPM) provides information on electron beam current and position, essential to meeting the performance requirements for the Scorpius accelerator as laid down in FR1.1 –FR1.8 and FR2.2. The critical elements in employing BPM’s to meet those requirements are design of the sensor, design of calibration equipment for the sensor, and design of methods to record and process data.
Beam position monitors (BPMs) provide time-resolved measurements of the current and centroid position of high-current electron beams in linear induction accelerators (LIAs). One of the types of detectors used in BPMs is the B-dot loop, which generates a signal from the EMF due to the time varying magnetic flux through the loop. If some of the boundaries of the loop are composed of thick metal walls with finite conductivity, the resulting signal must be corrected for the magnetic field diffusion into the metal. From first principles, we have derived the perturbation to BPM measurements due to this effect. The theoretical framework was used to design an algorithm for signal correction that does not require knowledge of the time history of the magnetic flux being measured. Corrected signals based on that process compared favorably with the know reference signals in a laboratory calibration test sequence.
Understanding the nature of dark energy and dark matter remains one of the fundamental questions in physics today; impacting our understanding of particle physics, cosmology, and possibly theories of gravity. Given the scale and complexity of the next generation of cosmology experiments (e.g., the Rubin Observatory, the Euclid satellite mission, and the Roman space telescope) we are entering an era where statistical noise no longer determines the accuracy to which we can measure cosmological parameters. Our ability to control and correct for systematics will ultimately determine the scientific impact of these experiments. This award addressed the challenge of how we determine what limits the accuracy of our cosmological measures, what techniques are appropriate for measuring and calibrating the properties of galaxies to best constrain cosmological models, how to develop statistical techniques that are insensitive to systematic errors, and how to optimize survey strategies in order to minimize systematics while maximizing the speed at which an experiment can achieve its science objectives. In this final technical report for award DE-SC0011635 we describe a set of open-source frameworks that simulate the characteristics and properties of current and planned cosmology surveys and the application of these frameworks to the development of new methodologies for estimating the properties and distances to galaxies that are robust to noisy and incomplete data.
Here we present a new 1-step Arrhenius thermal decomposition model of PBX 9501. As opposed to previous studies, we use high-energy asymptotic theory, and previously published equations of state to parameterize the model to experimental critical-time data. The model parameters were fit to experimental data using a bootstrapping procedure to capture the effects of experimental uncertainty. Subsequent implementation of the model in a chemicalkinetics ODE solver shows good agreement of times to ignition vs initial temperature in isochoric thermal explosion calculations with the results of the high-energy asymptotic theory. We also compare the resultant ZND structures to those of one of our previously calibrated burn models.
The Laser-Based X-ray Radiographic Imaging team at Los Alamos National Lab is looking for a monoenergetic MeV X-ray source to both verify our Monte Carlo N-particle simulations of detector performance and calibrate the instruments for future measurements. Funded by the Laboratory Directed Research and Development (LDRD) program, our overall goal is to improve the radiographic quality and reliability of laser-based X-ray sources for deployment at both dynamic and static radiography facilities. Laser-based X-ray sources have demonstrated smaller spot sizes to current electron accelerator based sources. The smaller laser spot size leads to significantly improved resolution.
The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Fuel behavior in a nuclear reactor is governed by a complex network of mechanisms interacting with various other physics aspects in the reactor system. Any model developed to represent the fuel behavior will likely be idealized resulting in uncertainties in their predictions compared to the observed data. As such, this report was motivated by the need to identify the sources of uncertainties and quantify and propagate them through the fuel model outputs. Such quantification of uncertainties will establish a level of model trustworthiness, identify approaches to improve the model trustworthiness, and even guide optimal experiment design for maximal information gain. To accomplish the uncertainty quantification for computational models, this report has relied on the Bayesian framework which provides probabilistic treatment of models their inputs and outputs. The current state-of-the-art on performing Bayesian Uncertainty Quantification (UQ) for nuclear engineering models using High Performance Computing (HPC) resources have been reviewed. Implementation of capabilities for massively parallel Bayesian UQ in Multiphysics Object-Oriented Simulation Environment (MOOSE) is discussed. Several verification cases are discussed to verify the accuracy of the quantified uncertainties using the developed computational capabilities in MOOSE. Then, the problem of quantifying the uncertainties in TRI-Structural isOtropic (TRISO) fuel silver release is addressed. For the first time, the uncertainties arising from the TRISO Fission Gas Release (FGR) model due to model inadequacy and experimental noise are quantified. Also, the Bayesian capabilities are applied to the calibration of the MATPRO creep model, a widely used model in several fuel assessment cases. The impact of the prediction uncertainties in the MATPRO model on the fuel cladding behavior as part of the TRIBULATION assessment case (which is an integral effects case) is investigated. This report concludes with a discussion on the future work for the UQ for computational models.
A set of thin Fe foils was fabricated at GA for non-destructive areal density measurements by X-ray AutoEdge. This experiment was aimed at separate (without including Mg) calibration of Fe areal density parameters by comparing AutoEdge and RBS results.
This document demonstrates a calibration of the Preston-Tonks-Wallace (PTW) model of plastic deformation that fairly accurately reproduces the flow stress of half-hard copper across strain rates varying by a factor of more than ten billion. The experiments included are quasistatic compression at low rates, Split Hopkinson Pressure Bar (SHPB) compression at intermediate rates, and arrested Richtmyer-Meshkov instability (RMI) growth at high strain rates. The temperatures vary from liquid nitrogen (77 K) to around 500 K. The largest true strains obtained are approximately 0.5. It would be interesting to compare the results to copper deformation data at higher pressures and higher strains.
The MicroBooNE detector is a Liquid Argon Time Projection Chamber (LArTPC) located along the Booster Neutrino Beam (BNB) at Fermilab. Its primary physics goal is to contribute to addressing the elusive short-baseline MiniBooNE low energy excess. MicroBooNE records and utilises both the ionisation charge and scintillation light produced inside the TPC to select and reconstruct neutrino interactions. The scintillation light collected using a plane of PhotoMultiplier Tubes (PMTs) is also used for accurate event timing and cosmic muon rejection with the latter being an important background for detectors, such as MicroBooNE, located on the surface. A good understanding of light modeling and its related systematic uncertainties is crucial to evaluate their impact on physics analyses. The experience acquired from MicroBooNE regarding how the stability of the scintillation light behaves and evolves during the 5 years of the primary physics run will be discussed as well as the calibration method used. This experience will help with understanding the physics of scintillation light in LArTPCs for the future long-running Short-Baseline Neutrino (SBN) and Deep Underground Neutrino Experiment (DUNE) programs.
The Broadband Reflector Experiment for Axion Detection (BREAD) is an ongoing experiment searching for the conversion of yet undiscovered axion-like dark matter particles to photons in the presence of a magnetic field. InfraBREAD, a sub-experiment of BREAD, uses a superconducting nanowire single photon detector (SNSPD), a high-efficiency and low-noise device, to specifically detect infrared-range photons produced by $\mathcal{O}$(eV) axion-like particles. The unique BREAD reflector setup allows for the focusing of all converted signal photons to a 1mm $\times$ 1mm SNSPD. However, when the detector is cooled to cryogenic temperatures during operation, uneven thermal contraction of reflector components may lead to a small shift in the location of the focal spot. A novel calibration method using blackbody radiation is proposed to locate the true focal spot of the detector \textit{in situ}. Through ray tracing simulations done in FRED Optical Engineering Software, this method is demonstrated to locate the focus to within 50 $\mu$m in three dimensions.
Distribution system model calibration is a key enabling task for incipient failure identification within the distribution system. This report summarizes the work and publications by Sandia National Laboratories on the GMLC project titled “Incipient Failure Identification for Common Grid Asset Classes”. This project was a joint effort between Sandia National Laboratories, Lawrence Livermore National Laboratory, National Energy Technology Laboratory, and Oak Ridge National Laboratory. The included work covers distribution system topology identification, transformer groupings, phase identification, regulator and tap position estimation, and the open-source release and implementation of the developed algorithms.