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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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The objective of the Photovoltaic Collaborative to Advance Multi-climate and Performance Research (PVCAMPER) is to create a multi-climate research platform similar to the US DOE Regional Test Center (RTC) program. Overall, the goal is to foster collaborative research and to build an international organization dedicated to sharing data and exchanging best practices related to PV performance.
Due to a lack of additional funds in the account, we have ceased the work on the project in the last quarter of FY21. We plan to resume the work in FY21 when funds are available.
Abstract not provided.
The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g. height, color and grayscale values; all as functions of a location in a plane projection). It is the objective of this project to produce a user-friendly interface that incorporated all operations needed to perform surface examination. For this reason, a Matlab-based Graphical User Interface (GUI) was created to integrate data input with software developed for processing and evaluation. In summary, the GUI permits selective downloading of binary data, interrogation of attributes, data labeling, flagging of significant features, execution of Machine Learning (ML) algorithms, output of parameters for trained ML algorithms, reports of ML model accuracy with respect to labeled data, and generation of graphical representations of various analyses.
The objective of Argonne’s materials research activities for the Transformational Challenge Reactor (TCR) program is to improve the understanding of material properties from additive manufacturing with the focus on understanding the creep and fatigue properties of additively manufactured materials. The material work conducted by Argonne provides support in developing and qualifying advanced materials and manufacturing processes to allow for innovative reactor design and licensing for the TCR. In this report, we present the creep data at temperatures of 550, 600 and 650°C and stresses between 175 and 300 MPa for 316L stainless steel produced by a laser powder bed fusion process. We conducted five different post-build heat treatments, namely 650°C/1h, 750°C/1h, 800°C/1h, 900°C/1h and 1050°C/1h. Creep tests of the heat-treated specimens were performed under the same test condition, 550°C/275 MPa to evaluate and understand the effect of post-build heat treatment on the creep behavior of additively-manufactured 316L SS. Creep tests were conducted to ASME NQA-1 or its lab equivalent for quality assurance.
The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.
Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer, and through reconstruction of data obtained from experimental measurements. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and Inconel 718 plate produced with laser powder bed fusion (LPBF) method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBH’s have the same sizes as the imprinted defects in the LPBF specimens, but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects, and allow for estimation of their sizes and locations. Detection sensitivity of TT is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, we investigate TT of AM stainless steel 316L specimen with imprinted internal porosity defects using relatively low-cost, small form factor infrared (IR) camera based on uncooled micro bolometer detector. Sparse coding related K-means singular value decomposition (SVD) machine learning, image processing algorithms are developed to improve quality of TT images through removal of Additive white Gaussian noise without blurring the images. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact TT can also be used for in-service nondestructive evaluation (NDE). With capability to perform in-service NDE of the AM component lifecycle, TT data can be used for development of a component digital twin.
This project aims to provide the improved Evaluated Nuclear Data File (ENDF) using the newly measured data as well as the latest nuclear reaction model for calculating angular distributions and energy spectra on neutron-induced charged particle reactions through the collaboration of Korea Atomic Energy Research Institute (KAERI) and Los Alamos National Laboratory (LANL). The LANL group will provide the experimental data for angular distributions and spectra of (n,p) and (n,α) on several structural materials such as Fe, Ni and Zn isotopes using the Low Energy Neutron-induced Charged-particle (Z) Chamber (LENZ) instrument at Los Alamos Neutron Science Center (LANSCE). The KAERI group will provide the improved evaluated nuclear library which is based on the LANL experimental data, and further will predict angular distributions and spectra of (n,p) and (n,α) reactions on unmeasured nuclides, such as Cr, Mn, Co, Cu and so on. For the first year of this project, we planned to analyze (n,p) and (n,α) reactions for 54,56 Fe and perform new measurements on those reactions for 58,60 Ni isotopes with the LENZ instrument at LANSCE. For improving our evaluation quality, we have studied reaction models to reproduce LANL’s experimental angular distributions and energy spectra using the full Hauser-Feshbach model code, CoH3 with no approximations used. As the first year’s deliverables, we provided the experimental (n,p) and (n,α) reaction cross sections for 54,56 Fe and incorporate new evaluation on angular distributions and energy spectra of neutron-induced charged particle reactions into the current ENDF/B-VIII.0.
Abstract not provided.
Activities for most tasks continued smoothly from FY19 into FY20. A Science Plan was developed for the F2019 Nuclear Forensics Venture in Q1, and was presented at HQ on Jan17, 2020. The Science plan included input from all tasks in the F2019 Venture plus this project, and was assembled and presented jointly by all three labs.
After receiving funds at the beginning of FY21 we were able to resume work on the project. We continued data analysis of 156 Eu decay. The project has also gone through an Independent Review in November. Following the previously reported steps in the analysis of 156 Eu, specifically removing random coincidences from the gamma ray spectrum, we look not only at the peak of interest (811 keV) but all other gamma rays that are visible. Determining the area of all gamma ray peaks in this random coincidence free spectrum is not only necessary to calculate the gamma ray branching ratios but combined with the peak areas of the gamma ray singles spectrum we are able to determine experimentally the efficiency of the beta detector.
Regulatory drivers and market demands for lower pollutant emissions, lower carbon dioxide emissions, and lower fuel consumption motivate the development of clean and fuel-efficient engine operating strategies. Most current production engines use a combination of both in-cylinder and exhaust emission control strategies to achieve these goals. The emissions and efficiency performance of in-cylinder strategies depend strongly on flow and mixing processes associated with fuel injection. Both heavy- and light-duty engine/vehicle manufacturers use multiple-injection strategies to reduce noise, emissions, and fuel consumption. For both conventional and low-temperature diesel combustion, the state of knowledge and modeling tools for multiple injections are far less advanced than for single-injection strategies. Engine efficiency is limited to some degree by tradeoffs that must be accepted to meet particulate matter (including soot) emissions limits. Recent work on this project has filled some knowledge gaps on soot oxidation with multiple injections, and the current work for Fiscal Year (FY) 2018 addresses knowledge gaps on soot formation for multiple injections. While total in-cylinder soot is readily measured, discerning formation from oxidation is difficult. The FY 2018 experiments are designed to create in-cylinder conditions at the threshold of soot formation, where processes that affect soot formation can be more readily discerned. Soot formation pathways under such conditions are fraught with uncertainties, and soot models significantly overpredict polyaromatic hydrocarbon (PAH) and soot, so experimental data at these conditions will provide much needed data for improvements to PAH and soot models.
Traditional batteries are composed of two-dimensional films that are stacked and/or rolled. Thin film batteries display high power density while their thick film counterparts show good energy density, but it has proven difficult to concurrently achieve both within these planar form factors. In addition, conventional Li-ion batteries based on liquid organic electrolytes or gel polymer electrolytes have raised severe safety concerns due to the intrinsic flammable properties of the organic electrolytes.
Abstract not provided.
The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.
The current one-year allocation (w20 qburst) is the first year in a two year allocation of the same name. The project has supported two publications in peer-review journals, another manuscript in preparation for publication, a white paper for the Astro2020 Decadal Survey, and a number of talks (see below). Code development continues on implementing a neutrino quantum kinetic equation solver into BURST, now called QBURST. While implementing coherent and forward-scattering processes into the dynamical neutrino evolution equations, multiple numerical issues have appeared. We have remedied a number of these issues through extensive debugging and testing, which include streamlining interpolation and implementing new ODE integration techniques. Further testing is required to solve the problem at a precision of better than one part in 10 6 . We expect that we will understand the numerical difficulties and implement solutions by end of year 2021.
This research is focused on applying the NLem method to the full thermal radiative transfer (TRT) equations in the context of a Monte Carlo solution strategy. A framework has been developed for obtaining a solution to the TRT equations using the nonlinear elimination method (NLem) method. Current efforts are focused of the development and verification of the required Monte Carlo algorithm, diffusion algorithm, and material energy solver. Once all three pieces have been verified, the algorithms will then be coupled into a fully functioning method for applying NLem to the TRT equations.
Several 100 μg aliquots of NBL CRM 137 Pu sulfate tetrahydrate were prepared in 20 mL glass vials at some time in the past and are expected to have similar chemical composition and similar concentrations of contaminants as the bulk 250 mg CRM 137 Pu sulfate tetrahydrate units. Thus, these units were characterized and used to test Pu purification chemistry to be employed on the larger unit. For preliminary characterization, the residue of bottle 6 was dissolved in 4 M HNO3/0.01 M HF and transferred quantitatively to a pre-weighed 30 mL Savillex Teflon vial with a total solution mass of approximately 18 g. A gravimetric aliquot was removed from this solution for characterization and the remaining solution was dried down and reconstituted in 8 M HNO 3 to test the anion exchange purification column.