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Accurate measurement of proton conductivity in Proton Exchange Membranes (PEMs) is vital for fuel cell performance and durability. This study explores eddy current testing as a non-destructive, non-contact and high-rate quality control (QC) method for measuring Nafion membrane proton conductivity, with potential for high-volume manufacturing applications.
Purpose: Pretreatment patient plan verification with gamma index (GI) metric analysis is standard procedure for intensity modulated radiation therapy (IMRT) treatment. The aim of this paper is to evaluate the variability of the local and global gamma index obtained during standard pretreatment quality assurance (QA) measurements for plans performed with Tomotherapy unit. The QA measurements were performed with a 3D diode array, using variable passing criteria: 3%/3 mm, 2%/2 mm, 1%/1 mm, each with both local and global normalization.Methods: The authors analyzed the pretreatment QA results for 73 verifications; 37 were prostate cancer plans, 16 were head and neck plans, and 20 were other clinical sites. All plans were treated using the Tomotherapy Hi-Art System. Pretreatment QA plans were performed with the commercially available 3D diode array ArcCHECK™. This device has 1386 diodes arranged in a helical geometry spaced 1 cm apart. The dose measurements were acquired on the ArcCHECK™ and then compared with the calculated dose using the standard gamma analysis method. The gamma passing rate (%GP), defined as the percentage of points satisfying the condition GI < 1, was calculated for different criteria (3%/3 mm, 2%/2 mm, 1%/1 mm) and for both global and local normalization. In the case of local normalization method, the authors set three dose difference threshold (DDT) values of 2, 3, and 5 cGy. Dose difference threshold is defined as the minimum absolute dose error considered in the analysis when using local normalization. Low-dose thresholds (TH) of 5% and 10% were also applied and analyzed.Results: Performing a paired-t-test, the authors determined that the gamma passing rate is independent of the threshold values for all of the adopted criteria (5%TH vs 10%TH, p > 0.1). Our findings showed that mean %GPs for local (or global) normalization for the entire study group were 93% (98%), 84% (92%), and 66% (61%) for 3%/3 mm, 2%/2 mm, and 1%/1 mm criteria, respectively. DDT was equal to 2 cGy for the local normalization analysis cases. The authors observed great variability in the resulting %GP. With 3%/3 mm gamma criteria, the overall passing rate with local normalization was 4.6% less on the average than with global one, as expected. The wide difference between %GP calculated with global or local approach is also confirmed by an unpaired t-test statistical analysis.Conclusions: The variability of %GP obtained confirmed the necessity to establish defined agreement criteria that could be universal and comparable between institutions. In particular, while the gamma passing rate does not depend on the choice of threshold, the choice of DDT strongly influences the gamma passing rate for local calculations. The difference between global and local %GP was statistically significant for prostate and other treatment sites when DDT was changed from 2 to 3 cGy.
Natural gas infrastructure releases methane (CH 4 ), a potent greenhouse gas, into the atmosphere. The estimated emission rate associated with the production and transportation of natural gas is uncertain, hindering our understanding of its greenhouse footprint. This study presents a new application of inverse methodology for estimating regional emission rates from natural gas production and gathering facilities in north-eastern Pennsylvania. An inventory of CH 4 emissions was compiled for major sources in Pennsylvania. This inventory served as input emission data for the Weather Research and Forecasting model with chemistry enabled (WRF-Chem), and atmospheric CH 4 mole fraction fields were generated at 3 km resolution. Simulated atmospheric CH 4 enhancements from WRF-Chem were compared to observations obtained from a 3-week flight campaign in May 2015. Modelled enhancements from sources not associated with upstream natural gas processes were assumed constant and known and therefore removed from the optimization procedure, creating a set of observed enhancements from natural gas only. Simulated emission rates from unconventional production were then adjusted to minimize the mismatch between aircraft observations and model-simulated mole fractions for 10 flights. To evaluate the method, an aircraft mass balance calculation was performed for four flights where conditions permitted its use. Using the model optimization approach, the weighted mean emission rate from unconventional natural gas production and gathering facilities in north-eastern Pennsylvania approach is found to be 0.36 % of total gas production, with a 2 σ confidence interval between 0.27 and 0.45 % of production. Similarly, the mean emission estimates using the aircraft mass balance approach are calculated to be 0.40 % of regional natural gas production, with a 2 σ confidence interval between 0.08 and 0.72 % of production. Furthermore these emission rates as a percent of production are lower than rates found in any other basin using a top-down methodology, and may be indicative of some characteristics of the basin that make sources from the north-eastern Marcellus region unique.
This report presents an enhanced performance evaluation of the emergency power system (EPS) and high-pressure core spray (HPCS) emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS) data from 1998 through 2022 and (2) maintenance unavailability performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2022. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in Nuclear Regulatory Commission (NRC) probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. The EDG failure modes considered are fail to start (FTS), fail to load and run (FTLR), and fail to run after one hour of operation (FTR>1H). Engineering analyses were performed with respect to time-period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed include subcomponent, failure cause, detection method, recovery, manufacturer, and EDG rating. The following increasing trends were identified for EDGs for the most recent 10-year period: (1) HPCS EDG FTS failure probability, (2) EPS and HPCS EDG frequency of start demands (demands per reactor year), and (3) EPS and HPCS EDG frequency of FTLR demands. The following decreasing trends were identified for EDGs for the most recent 10-year period: (1) EPS EDG FTS failure probability, (2) EPS EDG FTLR failure probability, (3) EPS EDG unavailability, and (4) EPS and HPCS EDG frequency of FTLR events (failures per reactor year).
Further deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs—now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters increased from 66 to 87%, and the true negative rate of predicting non-adopters increased from 75 to 88%. We attribute the enhanced performance to complex variable interactions and nonlinear effects incorporated by machine learning. With more accurate predictions, machine learning is able to reduce customer acquisition costs by 15% ($0.07/Watt) and identify new market opportunities for solar companies to expand and diversify their customer bases. Our research methods and findings provide broader implications for the adoption of similar clean energy technologies and related policy challenges such as market growth and energy inequality.
Microbial electrolysis cells (MECs) have demonstrated high-rate H 2 production while concurrently treating wastewater, but the transition in scale from laboratory research to systems that can be practically applied has encountered challenges. It has been more than a decade since the first pilot-scale MEC was reported, and in recent years, many attempts have been made to overcome the barriers and move the technology to the market. This study provided a detailed analysis of MEC scale-up efforts and summarized the key factors that should be considered to further develop the technology. We compared the major scale-up configurations and systematically evaluated their performance from both technical and economic perspectives. We characterized how system scale-up impacts the key performance metrics such as volumetric current density and H 2 production rate, and we proposed methods to evaluate and optimize system design and fabrication. In addition, preliminary techno-economic analysis indicates that MECs can be profitable in many different market scenarios with or without subsidies. Here, we also provide perspectives on future development needed to transition MEC technology to the marketplace.
Colloidal silicon quantum dots (Si QDs) with a very broad photoluminescence (PL) band are proposed as a probe to monitor the Purcell enhancement in a plasmonic nanostructure. Si QDs placed on an arbitrary plasmonic nanostructure enable us to determine the Purcell enhancement factors in a broad spectral range (600–900 nm). As a proof-of-concept experiment, a layer of Si QDs is spin-coated on gold film-over nanosphere structures, and the Purcell enhancement is quantitatively determined from the analyses of the PL spectra and the decay rates. The method proposed in this work provides a facile approach to quantitatively measure the performance of plasmonic substrates for PL and Raman enhancements.
Specific rate constants for the S{sup +}+H{sub 2} reaction are calculated using the ground quartet state potential energy surface and quasi-classical trajectories method. The calculations are performed for H{sub 2} in different vibrational states v = 0-4 and thermal conditions for rotational and translational energies. The calculations lead to slow rate constants for the H{sub 2} vibrational levels v = 0, 1, but a significant enhancement of reactivity is observed when v > 1. The inverse reaction is also studied and rate constants for v = 0 are presented. For comparison, we also recompile previous results of state-to-state rate constants of the C{sup +}+H{sub 2} for H{sub 2} in rovibrational state v, j = (0,0), (1,0), (1,1), and (2,0). The calculated rate coefficients are fitted using an improved form of the standard three-parameter Arrhenius-like equation, which is found to be very accurate in fitting rate constants over a wide range of temperatures (10-4000 K). We investigate the impact of the calculated rate coefficients on the formation of SH{sup +} in the photon-dominated region Orion Bar and find an abundance enhancement of nearly three orders of magnitude when the reaction of S{sup +} with vibrationally excited H{sub 2} is taken into account. The title reaction is thus one of the principal mechanisms in forming SH{sup +} in interstellar clouds.
The meson spectroscopy plays nowadays a central role in the investigation of hadron structure thanks to the possible existence of exotic hybrid mesons, quark-antiquark-gluon bound states. Their explicit gluonic degrees of freedom which should clearly emerge from a Partial Wave Analysis (PWA) of the corresponding Dalitz plot of the exotic particle decay, may result in final JPC configurations not allowed in the constituent quark model. Besides this clear signature, hybrid mesons are also expected to have a large particle multiplicity decays, requiring for their search an experimental apparatus with high performances in terms of rate capability, resolution and almost a full acceptance to apply PWA methods. New-generation experiments are planned at Thomas Jefferson National Laboratory (VA, USA) for which an unprecedented statistics of large multiplicity decay events with fully reconstructed kinematics will be available. In particular for the MesonEx (CLAS12) experiment in Hall B, a wide scientific program that will start in 2016 has been deployed to study the meson spectrum at energies up to 11 GeV. A key role in such program is played by the Forward Tagger apparatus of the experiment, which will allow to extend the study of meson electro-production to very low Q2 values, in a quasi-real photo production kinematical region, where the production of hybrid mesons is expected to be favorite. Currently a new analysis framework for the search of the hybrid mesons is being set up by the HASPECT network, an international structure which gather people involved into theoretical and experimental hadronic physics all over the world. The goals of the network is to develop new analysis models and statistical techniques to unfold the signal and background distributions in high-statistics datasets. In this work are briefly presented the first preliminary results from the application of a statistical technique, namely the sPlot, to the data already acquired by the CLAS experiment for the decay f 1 (1285) --> (eta)pi+pi-.
Silicon is regarded as the next generation anode material for LIBs with its ultra-high theoretical capacity and abundance. Nevertheless, the severe capacity degradation resulting from the huge volume change and accumulative solid-electrolyte interphase (SEI) formation hinders the silicon based anode material for further practical applications. Hence, a variety of methods have been applied to enhance electrochemical performances in terms of the electrochemical stability and rate performance of the silicon anodes such as designing nanostructured Si, combining with carbonaceous material, exploring multifunctional polymer binders, and developing artificial SEI layers. Silicon anodes with low-dimensional structures (0D, 1D, and 2D), compared with bulky silicon anodes, are strongly believed to have several advanced characteristics including larger surface area, fast electron transfer, and shortened lithium diffusion pathway as well as better accommodation with volume changes, which leads to improved electrochemical behaviors. Finally, in this review, recent progress of silicon anode synthesis methodologies generating low-dimensional structures for lithium ion batteries (LIBs) applications is listed and discussed.
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). Analytic gradients are also (optionally) provided for the distributed loads, thrust, torque, and power with respect to design variables of interest.
The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety and reliability for hydrogen facilities by implementing component reliability data taxonomies that support hydrogen infrastructure failure rate analysis. The project aims to quantify failure rates of hydrogen components through high-quality data collection and analysis on root causes and maintenance needed. HyCReD provides a common database for cataloging hydrogen component failures which exists for reliability research in many other mature industries [2]. The database fills a gap for the hydrogen community by providing a scientifically rigorous approach to quantitative risk assessment (QRA), prognostic health management (PHM), and reliability-centered maintenance (RCM) analysis. High level results will be aggregated and anonymized to protect company sensitive information; detailed results will be used to help address issues of hydrogen components. These advanced analytics will support accelerated deployment of hydrogen infrastructure by enabling better: design and safety of projects (safety codes and standards development), infrastructure reliability and cost (component failure rates, maintenance protocols), and component R&D needs (robust supply chain). A key to a successful HyCReD implementation is facilitating the ease of reporting and data quality in the database that can be used for analysis. Maintenance data was a previously identified gap in initial efforts to populate and validate the database taxonomies [3]. Collection of maintenance data will be instrumental in identifying failure modes and rates, identifying incipient component failures or reduced performance, cataloging best practices for maintenance routines and methods for prognostic health management, and quantifying the risk and effect of different failure modes. Several key priorities are identified for streamlined data collection to achieve quality and detailed failure data: Applicability, Ease of Use, Accessibility, and Information Security. The HyCReD team has now begun deployment of the database to several companies and groups that have signed non-disclosure agreements to facilitate the data collection of failures in industry hydrogen refueling station infrastructure. This paper will provide an update into the process of HyCReD deployment including the development of a coding guide for facility personnel to reference and ensure data quality and consistency from one station to another as well as implementation of contextually dependent data fields of system taxonomy and formatted entries to provide ease of use. The goal is to communicate the lessons learned from the roll-out to technicians and engineers in the field, and the addition of need for high level of security to protect all stakeholders.
Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during train and inference of large networks. Given its easy implementation and flexibility, one promising approach is layer factorization, which reshapes weight tensors into a matrix format and parameterizes it as the product of two rank-r matrices. However, this family of approaches often requires an initial full-model warm-up phase, prior knowledge of a feasible rank, and it is sensitive to parameter initialization.In this work, we introduce a novel approach to train the factors of a Tucker decomposition of the weight tensors. Our training proposal proves to be optimal in locally approximating the original unfactorized dynamics and stable for the initialization. Furthermore, the rank of each mode is dynamically updated during training.We provide a theoretical analysis of the algorithm, showing convergence, approximation and local descent guarantees. The method's performance is further illustrated through a variety of experiments, showing remarkable training compression rates and comparable or even better performance than the full baseline and alternative layer factorization strategies.
Deterministic transport codes play a fundamental role in the modelling and simulation of neutron transport. One of the most common deterministic methods is the method of discrete ordinates, also known as the S method. While offering significant advantages over other deterministic methods or stochastic methods like Monte Carlo, the method of discrete ordinates suffers from non-physical artifacts in its local solution due to its discretization of angle. These artifacts, referred to as ray effects because of their ray-like appearance, tend to be worse in problems with small sources in areas with little scattering. Significant effort has gone into developing methods to mitigate ray effects, such as the first-collision source treatment, which separates the angular flux into the uncollided and collided fluxes and solving them using non-traditional techniques such as ray tracing. One such code capable of doing this is Lawrence Livermore National Laboratory's deterministic transport code ARDRA. Current ray tracing methods typically trace to a set of points inside a zone to compute an overall flux. However, this approach has significant drawbacks, such as a low order of convergence and not being conservative. Therefore, a new method has been developed that traces instead to a set of points on each of a zone's surfaces and computing the currents, before using these to obtain the flux. A comparison between these two ray tracing methods showed significant advantages to the new surface method, including inherent conservation, a higher convergence rate, and an increase in calculable information like leakage. This work performed under the auspices of the U.S. Department of Energy by Lawrence Liver- more National Laboratory under Contract DE-AC52-07NA27344. (authors)
This report presents an enhanced performance evaluation of the emergency power system (EPS) and high-pressure core spray (HPCS) emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS) data from 1998 through 2024 and (2) maintenance unavailability performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2024. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in Nuclear Regulatory Commission (NRC) probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. The EDG failure modes considered are fail to start (FTS), fail to load and run (FTLR), and fail to run after one hour of operation (FTR>1H). Engineering analyses were performed with respect to time-period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed include subcomponent, failure cause, detection method, recovery, manufacturer, and EDG rating. The following increasing trends were identified for EDGs for the most recent 10-year period: • EPS and HPCS EDG frequency of start demands (demands per reactor year) • EPS and HPCS EDG frequency of FTLR demands • EPS and HPCS EDG frequency of run>1H hours. The following decreasing trends were identified for EDGs for the most recent 10-year period: • EPS EDG FTR>1H failure rate • EPS EDG unreliability • EPS and HPCS EDG frequency of FTR>1H events (failures per reactor year).
A key aspect of achieving acceptable indoor air quality is source control. Cooking has been recognized as a significant source of pollutants for health impacts (e.g., PM2.5 and NO2) as well as moisture and odour. A common method of controlling this pollutant source is by using a range (or cooker) hood that vents to outside. However, field and laboratory experiments have shown highly variable performance for these devices. We use the capture efficiency metric (the fraction of the pollutants that are exhausted to outside at steady state) to characterize the range hood performance. To address this issue and provide useful information for builders, contractors, designers and home occupants, a laboratory rating method for range hood capture efficiency has recently been developed by LBNL and ASTM. The test method uses standardized emitters to create a heated plume and seed it with tracer gas. The tracer gas measurements in the room, the range hood exhaust and in the ambient air are used to estimate capture efficiency. However, this test method only applies to wall-mounted range hoods. Some range hoods are not wall-mounted: island range hoods are designed to operate over a cooktop in the middle of a room rather than against a wall and downdraft hoods draw air from near the cooktop rather than overhead. This paper discusses the development of a new test apparatus for island and downdraft hoods and presents measured capture efficiency data from example hoods. The results of this work will be used in future revisions to the ASTM standard.
Quality control of a periodic structure is performed using the damping rate of acoustic waves generated in the periodic structure. In this technique, an excitation light beam illuminates the first layer in the periodic structure to excite an acoustic wave. Possible irregularities in the periodic structure can scatter the acoustic wave, thereby increasing the damping rate of the acoustic wave. A sequence of probe light beams illuminates the periodic structure to measure the acoustic wave as a function of time to generated a temporal signal representing the damping rate of the acoustic signal. The acquired damping rate is employed to evaluate the quality of the periodic structure.