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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-P-49: Comparison of Mapcheck 2 Commission for Photon and Electron Beams

Purpose: We will investigate the performance variation of the MapCheck2 detector array with different array calibration and dose calibration pairs from different radiation therapy machine. Methods: A MapCheck2 detector array was calibrated on 3 Elekta accelerators with different energy of photon (6 MV, 10 MV, 15 MV and 18 MV) and electron (6 MeV, 9 MeV, 12 MeV, 15 MeV, 18 MeV and 20 MeV) beams. Dose calibration was conducted by referring a water phantom measurement following TG-51 protocol and commission data for each accelerator. A 10 cm × 10 cm beam was measured. This measured map was morphed by applying different calibration pairs. Then the difference was quantified by comparing the doses and similarity using gamma analysis of criteria (0.5 %, 0 mm). Profile variation was evaluated on a same dataset with different calibration pairs. The passing rate of an IMRT QA planar dose was calculated by using 3 mm and 3% criteria and compared with respect to each calibration pairs. Results: In this study, a dose variation up to 0.67% for matched photons and 1.0% for electron beams is observed. Differences of flatness and symmetry can be as high as 1% and 0.7% respectively. Gamma analysis shows a passing rate ranging from 34% to 85% for the standard 10 × 10 cm field. Conclusion: Our work demonstrated that a customized array calibration and dose calibration for each machine is preferred to fulfill a high standard patient QA task.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Leveraging In-Network Computing and Programmable Switches for Streaming Analysis of Scientific Data

With the emergence of programmable network devices that match the performance of fixed function devices, several recent projects have explored in-network computing, where the processing that is traditionally done outside the network is offloaded to the network devices. In-network computing has typically been applied to network functions (e.g., load balancing, NAT, and DNS), caching, data reduction/aggregation, and coordination/consensus functions. In some cases it has been used to accelerate stream-processing tasks that involve small payloads and simple operations. In this work we focus on leveraging in-network computing for stream processing of scientific datasets with large payloads that require complex operations such as floating-point computations and logarithmic functions. We demonstrate in-network computing for a real-world scientific application performing streaming normalization of a 2-D image from a light source experiment. We discuss the challenges we encountered and potential approaches to address them.

Sankaran, Ganesh↗
DOE OSTI2015

Diagnosis of Performance Faults in LargeScale MPI Applications via Probabilistic Progress-Dependence Inference

Debugging large-scale parallel applications is challenging. Most existing techniques provide little information about failure root causes. Further, most debuggers significantly slow down program execution, and run sluggishly with massively parallel applications. This paper presents a novel technique that scalably infers the tasks in a parallel program on which a failure occurred, as well as the code in which it originated. Our technique combines scalable runtime analysis with static analysis to determine the least-progressed task(s) and to identify the code lines at which the failure arose. We present a novel algorithm that infers probabilistically progress dependence among MPI tasks using a globally constructed Markov model that represents tasks' control-flow behavior. In comparison to previous work, our algorithm infers more precisely the least-progressed task. Further, we combine this technique with static backward slicing analysis, further isolating the code responsible for the current state. A blind study demonstrates that our technique isolates the root cause of a concurrency bug in a molecular dynamics simulation, which only manifests itself at 7,996 tasks or more. We extensively evaluate fault coverage of our technique via fault injections in 10 HPC benchmarks and show that our analysis takes less than a few seconds on thousands of parallel tasks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Commerce lab: Mission analysis and payload integration study

Conceived as one or more arrays of carriers which would fly aboard space shuttle, Commerce Lab can provide a point of focus for implementing a series of shuttle flights, co-sponsored by NASA and U.S. domestic concerns, for performing materials processing in research and pre-commercial investigations. As an orbiting facility for testing, developing, and implementing hardware and procedures, Commerce Lab can enhance space station development and hasten space platform production capability. Tasks considered include: (1) synthesis of user requirements and identification of common element and voids; (2) definition of performance and infrastructure requirement and alternative approaches; and (3) carrier, mission model, and infrastructure development.

Source record↗
DOE OSTI2015

Delayed Gamma-Ray Spectroscopy for Non-Destructive Assay of Nuclear Materials

This project has been a collaborative effort of researchers from four National Laboratories, Lawrence Berkley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), Los Alamos National Laboratory (LANL), Pacific Northwest National Laboratory (PNNL), and Idaho State University’s (ISU) Idaho Accelerator Center (IAC). Experimental measurements at the Oregon State University (OSU) were also supported. The research included two key components, a strong experimental campaign to characterize the delayed gamma-ray signatures of the isotopes of interests and of combined targets, and a closely linked modeling effort to assess system designs and applications. Experimental measurements were performed to evaluate fission fragment yields, to test methods for determining isotopic fractions, and to benchmark the modeling code package. Detailed signature knowledge is essential for analyzing the capabilities of the delayed gamma technique, optimizing measurement parameters, and specifying neutron source and gamma-ray detection system requirements. The research was divided into three tasks: experimental measurements, characterization of fission yields, and development of analysis methods (task 1), modeling in support of experiment design and analysis and for the assessment of applications (task 2), and high-rate gamma-ray detector studies (task 3).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Integrated Testing of a Membrane CO 2 Capture Process with a Coal-Fired Boiler

This final technical report describes work conducted by Membrane Technology and Research, Inc. (MTR) for the Department of Energy, National Energy Technology Laboratory (DOE NETL) to demonstrate integrated operation of the MTR small pilot membrane CO 2 capture system with Babcock & Wilcox Enterprises, Inc.’s (B&W) Small Boiler Simulator-II (SBS-II) pilot coal boiler (award number DE-FE0026414). The work was performed from July 1, 2015 through March 31, 2018. The overall goal of this project was to operate the existing MTR small pilot membrane CO 2 capture system at an appropriately-sized coal-fired boiler to test integrated operation with CO 2 recycle to the boiler. MTR was assisted in this project by B&W, which hosted the integrated membrane-boiler field test at the B&W Research Facility in Barberton, OH. This report details the work performed to prepare the B&W site, modify the existing MTR membrane system, install the MTR system at B&W, and operate an integrated membrane-boiler field test with CO 2 recycle to the boiler. Detailed analysis of the integrated membrane-boiler test data was conducted to validate prior B&W computational fluid dynamics (CFD) modeling and SBS-II boiler tests with CO 2 -laden air that showed modest effects of recycled CO 2 on boiler performance. The integrated test results also demonstrated how various membrane parameters impact the performance of a dynamic boiler system. Key results for all tasks and recommendations for future research are discussed in this report.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Verification of Triso Fuel Burnup Using Machine Learning Algorithms

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134 Cs, 137 Cs, 154 Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2010

Lateral Load Testing of the Advanced Stirling Convertor (ASC-E2) Heater Head

Free-piston Stirling convertors are fundamental to the development of NASA s next generation of radioisotope power system, the Advanced Stirling Radioisotope Generator (ASRG). The ASRG will use General Purpose Heat Source (GPHS) modules as the energy source and Advanced Stirling Convertors (ASCs) to convert heat into electrical energy, and is being developed by Lockheed Martin under contract to the Department of Energy. Achieving flight status mandates that the ASCs satisfy design as well as flight requirements to ensure reliable operation during launch. To meet these launch requirements, GRC performed a series of quasi-static mechanical tests simulating the pressure, thermal, and external loading conditions that will be experienced by an ASC E2 heater head assembly. These mechanical tests were collectively referred to as lateral load tests since a primary external load lateral to the heater head longitudinal axis was applied in combination with the other loading conditions. The heater head was subjected to the operational pressure, axial mounting force, thermal conditions, and axial and lateral launch vehicle acceleration loadings. To permit reliable prediction of the heater head s structural performance, GRC completed Finite Element Analysis (FEA) computer modeling for the stress, strain, and deformation that will result during launch. The heater head lateral load test directly supported evaluation of the analysis and validation of the design to meet launch requirements. This paper provides an overview of each element within the test and presents assessment of the modeling as well as experimental results of this task.

Cornell, Peggy A.↗
NASA NTRS2010

Lateral Load Testing of the Advanced Stirling Convertor (ASC-E2) Heater Head

Free-piston Stirling convertors are fundamental to the development of NASA s next generation of radioisotope power system, the Advanced Stirling Radioisotope Generator (ASRG). The ASRG will use General Purpose Heat Source (GPHS) modules as the energy source and Advanced Stirling Convertors (ASCs) to convert heat into electrical energy, and is being developed by Lockheed Martin under contract to the Department of Energy. Achieving flight status mandates that the ASCs satisfy design as well as flight requirements to ensure reliable operation during launch. To meet these launch requirements, GRC performed a series of quasi-static mechanical tests simulating the pressure, thermal, and external loading conditions that will be experienced by an ASC-E2 heater head assembly. These mechanical tests were collectively referred to as "lateral load tests" since a primary external load lateral to the heater head longitudinal axis was applied in combination with the other loading conditions. The heater head was subjected to the operational pressure, axial mounting force, thermal conditions, and axial and lateral launch vehicle acceleration loadings. To permit reliable prediction of the heater head s structural performance, GRC completed Finite Element Analysis (FEA) computer modeling for the stress, strain, and deformation that will result during launch. The heater head lateral load test directly supported evaluation of the analysis and validation of the design to meet launch requirements. This paper provides an overview of each element within the test and presents assessment of the modeling as well as experimental results of this task.

Cornell, Peggy A.↗
NASA NTRS2011

Lateral Load Testing of the Advanced Stirling Convertor (ASC-E2) Heater Head

Free-piston Stirling convertors are fundamental to the development of NASA s next generation of radioisotope power system, the Advanced Stirling Radioisotope Generator (ASRG). The ASRG will use General Purpose Heat Source (GPHS) modules as the energy source and Advanced Stirling Convertors (ASCs) to convert heat into electrical energy, and is being developed by Lockheed Martin under contract to the Department of Energy. Achieving flight status mandates that the ASCs satisfy design as well as flight requirements to ensure reliable operation during launch. To meet these launch requirements, GRC performed a series of quasi-static mechanical tests simulating the pressure, thermal, and external loading conditions that will be experienced by an ASC-E2 heater head assembly. These mechanical tests were collectively referred to as "lateral load tests" since a primary external load lateral to the heater head longitudinal axis was applied in combination with the other loading conditions. The heater head was subjected to the operational pressure, axial mounting force, thermal conditions, and axial and lateral launch vehicle acceleration loadings. To permit reliable prediction of the heater head s structural performance, GRC completed Finite Element Analysis (FEA) computer modeling for the stress, strain, and deformation that will result during launch. The heater head lateral load test directly supported evaluation of the analysis and validation of the design to meet launch requirements. This paper provides an overview of each element within the test and presents assessment of the modeling as well as experimental results of this task.

Cornell, Peggy A.↗
DOE OSTI2021

VERIFICATION OF TRISO FUEL BURNUP USING MACHINE LEARNING ALGORITHMS

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134Cs, 137Cs, 154Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1977

Silicon materials task of the low cost solar array project, phase 2

The object of phase 2 of this program is to investigate and define the effects of various processes, contaminants and process-contaminant interactions in the performance of terrestrial solar cells. The major effort this quarter was in the areas of crystal growth and thermal processing, comparison of impurity effects in low and high resistivity silicon, modeling the behavior of p-type ingots containing Mo, and C and, quantitative analysis of bulk lifetime and junction degradation effects in contaminated solar cells. The performance of solar cells fabricated on silicon web crystals grown from melts containing about 10 to the 18th power/cu cm of Cr, Mn, Fe, Ni, Ti, and V, respectively were measured. Deep level spectroscopy of metal-contaminated ingots was employed to determine the level and density of recombination centers due to Ti, V, Ni, and Cr.

Hopkins, R. H.↗
NASA NTRS1978

Development and Evaluation of Die Materials for Use in the Growth of Silicon Ribbons by the Inverted Ribbon Growth Process. Task 2: LSSA Project

Several ribbon growth experiments were performed from V-shaped dies coated with CVD Si3N4. The most significant result was the ability to perform five consecutive growth runs from the same die without mechanical degradation of the die through temperature cycling. The die was made from vitreous carbon coated with CVD Si3N4. Silicon oxynitride, Si2N2O, was examined with respect to thermal stability in contact with molten silicon. The results of X-ray analysis indicate that this material is converted to both alpha - and beta-Si3N4 in the presence of molten silicon. Experiments on the stability of CVD SiOxNy shoe that this material can be maintained in contact with molten silicon (sessile drop test) for greater than 30 h at 1450 C without total decompositon. These layers are converted mainly to beta-Si3N4.

Duffy, M. T.↗
DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗
DOE OSTI2022

Quantitative Performance Assessment of Proxy Apps and Parents (Report for ECP Proxy App Project Milestone ADCD-504-28)

The ECP Proxy Application Project has an annual milestone to assess the state of ECP proxy applications and their role in the overall ECP ecosystem. Our FY22 March/April milestone (ADCD- 504-28) proposed to: Assess the fidelity of proxy applications compared to their respective parents in terms of kernel and I/O behavior, and predictability. Similarity techniques will be applied for quantitative comparison of proxy/parent kernel behavior. MACSio evaluation will continue and support for OpenPMD backends will be explored. The execution time predictability of proxy apps with respect to their parents will be explored through a carefully designed scaling study and code comparisons. Note that in this FY, we also have quantitative assessment milestones that are due in September and are, therefore, not included in the description above or in this report. Another report on these deliverables will be generated and submitted upon completion of these milestones. To satisfy this milestone, the following specific tasks were completed: Study the ability of MACSio to represent I/O workloads of adaptive mesh codes. Re-define the performance counter groups for contemporary Intel and IBM platforms to better match specific hardware components and to better align across platforms (make cross-platform comparison more accurate). Perform cosine similarity study based on the new performance counter groups on the Intel and IBM P9 platforms. Perform detailed analysis of performance counter data to accurately average and align the data to maintain phases across all executions and develop methods to reduce the set of collected performance counters used in cosine similarity analysis. Apply a quantitative similarity comparison between proxy and parent CPU kernels. Perform scaling studies to understand the accuracy of predictability of the parent performance using its respective proxy application. This report presents highlights of these efforts.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

The Automated Array Assembly Task of the Low-cost Silicon Solar Array Project, Phase 2

An advanced process sequence for manufacturing high efficiency solar cells and modules in a cost-effective manner is discussed. Emphasis is on process simplicity and minimizing consumed materials. The process sequence incorporates texture etching, plasma processes for damage removal and patterning, ion implantation, low pressure silicon nitride deposition, and plated metal. A reliable module design is presented. Specific process step developments are given. A detailed cost analysis was performed to indicate future areas of fruitful cost reduction effort. Recommendations for advanced investigations are included.

Coleman, M. G.↗
NASA NTRS2000

SAVANT: Solar Array Verification and Analysis Tool Demonstrated

The photovoltaics (PV) industry is now being held to strict specifications, such as end-oflife power requirements, that force them to overengineer their products to avoid contractual penalties. Such overengineering has been the only reliable way to meet such specifications. Unfortunately, it also results in a more costly process than is probably necessary. In our conversations with the PV industry, the issue of cost has been raised again and again. Consequently, the Photovoltaics and Space Environment Effects branch at the NASA Glenn Research Center at Lewis Field has been developing a software tool to address this problem. SAVANT, Glenn's tool for solar array verification and analysis is in the technology demonstration phase. Ongoing work has proven that more efficient and less costly PV designs should be possible by using SAVANT to predict the on-orbit life-cycle performance. The ultimate goal of the SAVANT project is to provide a user-friendly computer tool to predict PV on-orbit life-cycle performance. This should greatly simplify the tasks of scaling and designing the PV power component of any given flight or mission. By being able to predict how a particular PV article will perform, designers will be able to balance mission power requirements (both beginning-of-life and end-of-life) with survivability concerns such as power degradation due to radiation and/or contamination. Recent comparisons with actual flight data from the Photovoltaic Array Space Power Plus Diagnostics (PASP Plus) mission validate this approach.

Chock, Ricaurte↗
DOE OSTI2017

Beam Based RF Voltage Measurements and Longitudinal Beam Tomography at the Fermilab Booster

Increasing proton beam power on neutrino production targets is one of the major goals of the Fermilab long term accelerator programs. In this effort, the Fermilab 8 GeV Booster synchrotron plays a critical role for at least the next two decades. Therefore, understanding the Booster in great detail is important as we continue to improve its performance. For example, it is important to know accurately the available RF power in the Booster by carrying out beam-based measurements in order to specify the needed upgrades to the Booster RF system. Since the Booster magnetic field is changing continuously measuring/calibrating the RF voltage is not a trivial task. Here, we present a beam based method for the RF voltage measurements. Data analysis is carried out using computer programs developed in Python and MATLAB. The method presented here is applicable to any RCS which do not have flat-bottom and flat-top in the acceleration magnetic ramps. We have also carried out longitudinal beam tomography at injection and extraction energies with the data used for RF voltage measurements. Beam based RF voltage measurements and beam tomography were never done before for the Fermilab Booster. The results from these investigations will be very useful in future intensity upgrades.

43 PARTICLE ACCELERATORS↗