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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A review of functions, attributes, properties and measurements for the quality control of proton exchange membrane fuel cell components

Quality control (QC) is an essential part of fuel cell technology industrialization, providing means to reduce cost of components, enhancing the reliability of the final product, and offering specification guidance for new entrants in the supply chain. The membrane electrode assembly (MEA), including membrane, catalyst layer (CL), and gas diffusion layer (GDL), as well as bipolar plate (BP), are key components of a proton exchange membrane (PEM) fuel cell, and the attributes of each component strongly correlate with the cell performance and longevity. To ensure the quality of the fuel cell, it is of great importance to characterize the properties with respect to the standardization of component/sub-component specifications. In collaboration with the fuel cell industry, this work aims at establishing compendiums of attributes, or so-called books of attributes, of key fuel cell components for the QC of PEM fuel cells through reviewing, identifying, categorizing, and prioritizing the main attributes/properties that determine their functionalities. The books of attributes for the major PEM fuel cell components include catalyst coated membrane (CCM) as a sub-assembly, GDL, and BP. To address the full spectrum of fuel cell components, gaskets and sub-gaskets are also included.

25 ENERGY STORAGE↗
DOE OSTI2023

Quality Control Methods for Measurement of UCO Kernel Composition and SiC Microstructure

Quality control (QC) is critically important to tristructural-isotropic (TRISO) particle fuels owing to the complexity of and reliance on the fuel form to contain fission products during irradiation. Characterization methods for particle fuel QC have decades of history and have continued to develop as new insights into fuel performance inform revised fuel specifications and as advances in underlying technologies expand the possibilities of what may be characterized. Two relatively new methods for characterization of TRISO fuels have been published in open literature: optical microscopy image analysis for mixed uranium carbide/uranium oxide (UCO) kernel composition analysis and automated grain boundary detection in backscattered electron (BSE) images of the silicon carbide (SiC) layer in TRISO particles for grain size characterization. Suggestions and guidelines for the application of these methods to TRISO fuel qualification are provided in this report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A Refined Method to Translate Solar Data Quality Assessment Flags to Estimated Measurement Uncertainty

Integrating solar resource uncertainties due to radiometer measurement performance and operational data quality assessment can provide improved estimates of economic bankability, system design performance, and compliance of solar energy conversion systems. Estimating radiometer measurement uncertainty is an established procedure consistent with recognized best practices and international guidelines. SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report, the fourth of six for the Data Quality and Uncertainty Integration Project, presents a refined algorithm description for software to translate solar resource data quality assessment results into estimated uncertainty values in a Solar Resource Operational Uncertainty Integrator (SROUI) application. This algorithm requires three-component solar irradiance measurements - global horizontal irradiance, direct normal irradiance, and diffuse horizontal irradiance - collected at 1- to 60-minute intervals, as described in the previous deliverables. The development of this report as Deliverable 6.4 was an iterative process that included reviews and feedback from the project team on initial drafts designed to refine how the new software could best support determining solar resource data uncertainty. The results of this effort will contribute to the final software system development by National Renewable Energy Laboratory staff.

14 SOLAR ENERGY↗
DOE OSTI2020

Using Low-Cost Measurement Systems to Investigate Air Quality: A Case Study in Palapye, Botswana

Exposure to particulate air pollution is a major cause of mortality and morbidity worldwide. In developing countries, the combustion of solid fuels is widely used as a source of energy, and this process can produce exposure to harmful levels of particulate matter with diameters smaller than 2.5 microns (PM 2.5 ). However, as countries develop, solid fuel may be replaced by centralized coal combustion, and vehicles burning diesel and gasoline may become common, changing the concentration and composition of PM 2.5 , which ultimately changes the population health effects. Therefore, there is a continuous need for in-situ monitoring of air pollution in developing nations, both to estimate human exposure and to monitor changes in air quality. In this study, we present measurements from a 5-week field experiment in Palapye, Botswana. We used a low-cost, highly portable instrument package to measure surface-based aerosol optical depth (AOD), real-time surface PM 2.5 concentrations using a third-party optical sensor, and time-integrated PM 2.5 concentration and composition by collecting PM 2.5 onto Teflon filters. Furthermore, we employed other low-cost measurements of real-time black carbon and time-integrated ammonia to help interpret the observed PM 2.5 composition and concentration information during the field experiment. We found that the average PM 2.5 concentration (9.5 μg∙m –3 ) was below the World Health Organization (WHO) annual limit, and this concentration closely agrees with estimates from the Global Burden of Disease (GBD) report estimates for this region. Sulfate aerosol and carbonaceous aerosol, likely from coal combustion and biomass burning, respectively, were the main contributors to PM 2.5 by mass (33% and 27% of total PM 2.5 mass, respectively). While these observed concentrations were on average below WHO guidelines, we found that the measurement site experienced higher concentrations of aerosol during first half our measurement period (14.5 μg∙m –3 ), which is classified as "moderately unhealthy" according to the WHO standard.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?

Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations.

54 ENVIRONMENTAL SCIENCES↗
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↗
DOE OSTI2021

Use Machine Learning to Improve Burnup Measurement in Pebble Bed Reactors

Advanced pebble bed reactor (PBR) designs post new challenges in material control and accountancy (MC&A) because the fuel materials, distributed in many discrete pebbles, are continuously circulated through the reactor core and the refueling path compared to the bulk fuel assembly design in conventional reactors, e.g., light water reactors. In pebble bed reactors, there are hundreds of thousands of fuel pebbles in the reactor core during the normal operation, and the burnup of each pebble is measured when ejected from the core. Accurate burnup measurement is an important step in material control and spent fuel disposition. The measurement is usually based on detection of radiation signatures of fission products accumulated in the pebble fuel over irradiation in the core. 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 pebbles undergoing 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.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
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↗
DOE OSTI2022

A Method for In-Situ Q₀ Measurements of High-Quality SRF Resonators

Accelerator projects such as LCLS-II naturally require low-loss superconducting (SRF) cavities. Due to strong demand for improving intrinsic quality factor (Q₀), importance of accurate cavity characterization increases. We propose a method to measure Q₀ in situ for an SRF resonator installed in its cryogenic module and connected with a RF feed source via a fixed RF coupler. The method exploits measurements of a response for an SRF resonator fed by an amplitude-modulated signal. Such a signal can be synthesized as a beat-wave composed of two frequencies that are close to the resonant frequency. Analyzing the envelope of the reflected signal, one can find the difference in reflection for the chosen frequencies and use them to compute the intrinsic Q. We also develop the methodology to carry out measurements of Q₀ at the nominal cavity operating voltage. We verified our method in experiments with a room temperature copper resonator and with two SRF resonators including Fermilab’s 650 MHz cavity and JLab’s 1500 MHz cavity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Tomographic Reconstruction of the Neutron Time-Energy Spectrum from a Dense Plasma Focus

A dense plasma focus (DPF) nuclear fusion device is an attractive pulsed neutron source in many applications, due to its relatively large neutron yield, produced in a short time duration. In order to design a DPF that generates neutrons within a specified time profile, or generates a neutron energy spectrum with specific properties, it is necessary to be able to characterize and model the results of the fusion process releasing the neutrons. The time-energy spectrum of fusion neutrons is an ideal quantity to use to validate multiphysics codes that simulate the pinch and fusion processes, because it is a quantity that requires the physics of each of the stages leading up to and ending in the fusion reaction to be simulated correctly. In particular, since DPF fusion neutrons are not monoenergetic -- and there can often be several fusion pinches creating neutrons -- a computer simulation matching high quality neutron spectrum measurements provides great confidence in the fidelity of the simulation. In order to make such a comparison, it is first necessary to have quality measurements from which to infer the spectrum. In this work we pose neutron spectroscopy as the classical tomographic inverse problem from neutron time of flight data at multiple distances, but enhanced by using an additional measurement of the time profile of the fusion pinch near its source and by using detector pairs set up in a geometry that allows for scatter background subtraction. The detector pairs enhance the quality of the time of flight measurements, and the additional constraint posed by the measured time profile allows for reconstructions discretized as finely as the time measurements and in energy as finely as 100 keV, without the problem being underdetermined. We present results from a Deuterium-fueled DPF at the U.S. Department of Energy's Nevada National Security Site and show that we can infer the time-energy spectrum from our measurements for both single and multi-pinch fusion reactions with equal fidelity.

Catenacci, Jared↗
DOE OSTI2026

Improvements to In-Canal Assay of High Specific Activity 60Co at the ATR

Improvements to the quality of measurements in the assaying of High Specific Activity (HSA) cobalt 60 produced at the Advanced Test Reactor at the Idaho National Laboratory are presented. Such assays are conducted regularly between cycles at the ATR, and due to the radiological safety concerns, they are performed within the ATR canal. A standard ion chamber detector was placed in a collimated assay tube within the canal. Another tube, parallel to the detector, was used to position various cobalt 60 standards and targets. The collimated port between these tubes allows for the targets to be pulled across via a winch system. Previous measurements were performed at discrete 1.5-inch steps using a hand-cranked winch system. Charge was collected and integrated for 10 seconds at each discrete step during a scan. Two substantial improvements to the assay include an electronically controlled, motor-driven winch system and a software controlled continuous charge collection. Combined, these changes improved the quality of measurements as well as streamline the assay process. Further examination of these charge profiles indicated a reasonable amount of photon scattering, or shine, before a line-of-sight is established. This created inaccuracies in the reported activity of the cobalt targets. A series of MCNP-6.3 calculations were performed in an effort to quantify and correct for this shine effect. The results of the calculations and how they were employed to a create shine correction factor for the in-canal cobalt assay at the ATR are presented. The overall bias showed an overestimation of the HSA cobalt 60 activity by around 7-8%.

07 - ISOTOPES AND RADIATION SOURCES↗
DOE OSTI2025

Deliverable 6.7-Final Technical Report: Development Summary and Evaluation of the Solar Uncertainty Integrator (SUNI) Software

The Data Quality and Uncertainty Integration Project was a three-year effort to address stakeholder needs for assessing solar radiation resource data quality based on existing tools for estimating radiometer measurement uncertainties and assessing post-measurement data quality. The annual research objectives for the project addressed a logical progression of effort needed to achieve the ultimate project goal of developing the Solar Uncertainty Integrator (SUNI) software. This final technical report summarizes the development process for achieving these key research objectives and addresses the outreach and code development efforts in the final year of the project to develop a new solar irradiance data uncertainty integration software package.

14 SOLAR ENERGY↗
DOE OSTI2026

High Contrast Pattern Projection To Enable Background Oriented Schlieren Based Air Leak Detection Through Any Interior Or Exterior Building Surface

Air leakage in buildings wastes an estimated 4 quads of energy per year in the United States. Finding and sealing leakage sites is critical in existing buildings. Previous work has shown that background oriented Schlieren (BOS) imaging can be used to visualize air leakage but requires the leak to exit through a high contrast surface like a brick wall. To remedy this, different techniques of projecting various high contrast patterns on low contrast building surfaces like interior gypsum walls or vinyl siding were investigated. For each technique, the background quality was measured and compared to an ideal printed random dot background. The background quality metrics were correlated with the measured visualization metrics to understand which metrics are most important for maximizing air leak visualization performance. In this work, leakage visualization performance is presented for these various projected backgrounds with an aim to expand the building surfaces suitable for the BOS leak detector.

Jatana, Gurneesh [ORNL] (ORCID:0000000288903225)↗
DOE OSTI2024

ML Classifier Fusion for Three Data Streams with Quality Inversely Proportional to Time Resolution

We consider a monitoring scenario of phenomenon using three different streams of measurements whose quality is proportional to their constant inter-arrival times. Each measurement of a stream needs to be binary-classified to reflect the state of interest of the phenomenon. A set of classifiers is separately trained and fused for each stream at its time resolution using measurements collected under known states. We present a machine learning method to fuse the outputs of these fusers to provide a final classification at the finest time resolution. We show that this fused-fusers method provides decisions with likely superior classification probability compared to the best individual classifiers and fused-classifiers. We derive generalization equations that guarantee a superior classification probability of fused-fusers with a confidence probability specified by the classifiers’ generalization equations. We apply these results to study a practical problem of classifying Pu/Np target dissolution events at a radiochemical processing facility using gamma spectral measurements of effluent flows.

Rao, Nageswara↗
DOE OSTI2023

High Dose Rate Irradiations of Enduray Vision System for Nuclear Inspections

Higher radiation-hardened video cameras are needed in the operation and remote handling of equipment in nuclear reactor inspection and refueling applications. Vega Wave Systems, Inc. has developed a radiation-hardened vision system for nuclear energy applications. This vision system has been developed under several small business innovative research programs (SBIRs) from the U.S. Department of Energy, and the previous GAIN voucher program at Argonne National Laboratory (ANL) was successful in demonstrating that a prototype of the camera is radiation-hard up to at least 525 kGy (5.25 × 106 rad, Si equivalent) at dose rates of 10 kGy/hr with no measurable degradation in image quality and no measurable radiation-induced noise (RIN). Vega Wave Systems has developed a new version of the camera with more than 3X the resolution using a new design and new components, and this new design requires radiation-hardness qualification for marketplace acceptance. The program described in this report provided high radiation-hardness testing of this redesigned high-resolution vision system using the Argonne Low-Energy Accelerator Facility (LEAF). This report presents the results of irradiation tests performed on Vega Wave System’s redesigned high resolution vision system at a dose rate of 9.3 kGy/hr and up to total doses of 1823 kGy. This was an accumulation of 197 hours of irradiation. The results were excellent, providing proof of the redesigned high-resolution vision system’s immunity to high levels of radiation.

42 ENGINEERING↗
DOE OSTI2024

EPCAPE-PT-LANL Measurements: Ground based counterflow virtual impactor

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Ground based counter flow virtual impactor (Brechtel Inc) Data Notes: A factor of 6.7 needs to be applied to all cloud droplet residual concentration to correct the enhancement of the concentration because all the residual samples collected at 100 lpm were delivered into the 15 lpm of CVI sample flow. [https://amt.copernicus.org/articles/5/1259/2012/amt-5-1259-2012.html] Header: - Visibility[m]: The atmospheric visibility at the time of measurement, expressed in meters. - QualityControl_Flag[bool]: A boolean flag indicating whether the measurement passed quality control checks. - Temperature[C]: The ambient temperature at the time of the measurement, expressed in degrees Celsius. - CVI_Flag[bool]: A boolean flag indicating whether the Counterflow Virtual Impactor (CVI) was active (true) or inactive (false) during the measurement. - RelativeHumidity[%]: The relative humidity at the time of the measurement, expressed as a percentage

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Beyond optimization—supervised learning applications in relativistic laser-plasma experiments

We explore the applications of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. We predict the beam charge of electrons produced in a laser wakefield accelerator given the laser wavefront change caused by a deformable mirror. Machine learning enables feature analysis beyond merely searching for an optimal beam charge, showing that specific aberrations in the laser wavefront are favored in generating higher beam charges. Supervised learning models allow characterizing the measured data quality as well as recognizing irreproducible data and potential outliers. Furthermore, we also include virtual measurement errors in the experimental data to examine the model robustness under these conditions. This work demonstrates how machine learning methods can benefit data analysis and physics interpretation in a highly nonlinear problem of relativistic laser-plasma interaction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A Catalog of Temporally Localized Systematic Deviations in Global Body Wave Travel-Time Measurements

Accurate measurements of the arrival times of seismic waves are crucial for seismological analyses such as robust locations of earthquakes, characterization of seismic sources, and high-fidelity imaging of the Earth’s interior. However, these travel-time measurements can sometimes be contaminated by timing errors at the stations which record this data. In this study, we apply a classical approach, based on identifying time-dependence in measured body wave arrival times, to identify these timing errors in a dataset on the order of 107 individual measurements. We find timing deviations at a subset of the stations in our dataset and document the temporal location, extent, and severity of these errors, finding errors at 83 stations, and impacting ~100,000 measurements. This catalog of deviations may enable future investigators to obtain a more accurate dataset through the implementation of quality control measures to eliminate the contaminated data we have identified.

58 GEOSCIENCES↗