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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 703 records · Page 39

An On-Demand Electric Transit Case Study of New Rochelle, New York

This work explores the extent to which an on-demand mobility service utilizing lightweight electric vehicles (EVs) provides community and sustainability benefits in New Rochelle, New York. Travel and survey data from September 2019 through 2023 are used to describe the system and estimate impacts on travelers. The system was found to be used more by women (nearly 60%) and younger demographics (>65% under the age of 42), with peak use in the middle of the day and a grocery store as a top origin and destination. The service is utilized primarily for short trips (86% under 2 miles), and the small, right-sized EVs have carbon dioxide emissions associated with charging the fleet that are roughly one-quarter of the fleet emissions of conventional hybrid vans and nearly 50 times less than a fleet of diesel buses. Mapping current socio-spatial dynamics of travel demand can inform equity performance, as well as assist future planning and service area development and possible extensions to similar smaller, lower-density environments that are nearby and connected to major metropolitan areas. The findings in this case study suggest on-demand electric transit may be a significant and growing space for advancing clean and highly valued public mobility services. Sustainable public transport interventions that consider right-sized, electric, on-demand vehicles can help achieve improved accessibility and reduce energy use and greenhouse gas emissions. This work was presented at the Transportation Research Board (TRB) 2025 Annual Meeting on January 7, 2025.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

Constraining high-energy neutrinos from tidal disruption events with IceCube high-energy starting events

Tidal disruption events (TDEs) have been proposed as candidate sources of high-energy neutrinos. Successful and choked jets, as well as the accretion disk, corona, wind, and outflow regions in a TDE have been examined and shown to produce TeV - PeV neutrinos. In this work, we use the IceCube 12.5 year high energy starting events (HESE) dataset and perform a maximum likelihood analysis to investigate the spatial and temporal correlations between HESE dataset and a selected sample of 89 TDEs. Our results indicate that the currently observed data do not show any significant correlation and hence is consistent with the background only hypothesis. Using this result, we place constraints on the fraction of TDEs harboring intrinsic jets ($f_{\rm jet}$) and the corresponding isotropic-equivalent cosmic ray (CR) energy ($\mathcal{E}_{\rm CR}$). We note that even with limited statistics, we can constrain the parameter space as $\mathcal{E}_{\rm CR} \lesssim 3 \times 10^{53}$ erg for $f_{\rm jet} \gtrsim 0.6$ at more than 90% C.L. Finally, we discuss the theoretical implications of our results and the limits on the all-sky diffuse neutrino flux from TDEs. With more observational data in the electromagnetic band for TDEs and neutrino observations from IceCube and KM3NeT, our analysis can be used to place stringent constraints on physical parameters associated with TDEs.

Mukhopadhyay, Mainak [Fermilab; Chicago U., KICP; ↗

2.5D Super-Resolution Approaches for X-Ray Computed Tomography-Based Inspection of Additively Manufactured Parts

X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite its widespread use, obtaining high-resolution CT scans can be extremely time consuming. This issue can be mitigated by performing scans at lower resolutions; however, reducing the resolution compromises spatial detail, limiting the accuracy of defect detection. Super-resolution algorithms offer a promising solution for overcoming resolution limitations in XCT reconstructions of AM parts, enabling more accurate detection of defects. While 2D super-resolution methods have demonstrated state-of-the-art performance on natural images, they tend to under-perform when directly applied to XCT slices. On the other hand, 3D super-resolution methods are computationally expensive, making them infeasible for large-scale applications. To address these challenges, we propose a 2.5D super-resolution approach tailored for XCT of AM parts. Our method enhances the resolution of individual slices by leveraging multi-slice information from neighboring 2D slices without the significant computational overhead of full 3D methods. Specifically, we use neighboring low-resolution slices to super-resolve the center slice, exploiting inter-slice spatial context while maintaining computational efficiency. This approach bridges the gap between 2D and 3D methods, offering a practical solution for high-throughput defect detection in AM parts.

Sullivan, Haley↗

Explicitly Accounting for Multiple Spatial Scales in Data Informing Kd Distribution Development - 18414

This work provides an example of incorporating information about spatial scale into input parameter distribution development for probabilistic performance assessment (PA) models and highlights the importance of having an interdisciplinary team working together in the distribution development process. The novel approach is demonstrated for contaminant partitioning coefficients for water/rock (Kds), providing a specific context for discussing the approach and challenges, but the strategy is general and applies to many other PA model inputs. In this example, a PA model of a radioactive waste disposal site is assumed to have large cells representing spatial scales of up to thousands of square meters in area and several to tens of meters in depth. Spatial variability in Kds is expected to occur at scales much smaller than the units in the model domain over which values are applied, and therefore values are intended to represent possible averages over the modeled volume (i.e., spatial scale represented by a PA model cell). Information about possible Kd values for each material category comes from a variety of literature sources, and the reported results used to inform the distribution are available at a wide range of scales (e.g., individual batch experiment results from small-scale samples, averages based on samples collected over site-scales, averages based on samples from large regions, etc.). Effectively addressing the mismatch in spatial scale between available information and the PA Model is a challenge within PA distribution development and it takes effort from database managers, subject matter experts, modelers, and statisticians. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Resolution-enhanced X-ray fluorescence microscopy via deep residual networks

Abstract Multimodal hard X-ray scanning probe microscopy has been extensively used to study functional materials providing multiple contrast mechanisms. For instance, combining ptychography with X-ray fluorescence (XRF) microscopy reveals structural and chemical properties simultaneously. While ptychography can achieve diffraction-limited spatial resolution, the resolution of XRF is limited by the X-ray probe size. Here, we develop a machine learning (ML) model to overcome this problem by decoupling the impact of the X-ray probe from the XRF signal. The enhanced spatial resolution was observed for both simulated and experimental XRF data, showing superior performance over the state-of-the-art scanning XRF method with different nano-sized X-ray probes. Enhanced spatial resolutions were also observed for the accompanying XRF tomography reconstructions. Using this probe profile deconvolution with the proposed ML solution to enhance the spatial resolution of XRF microscopy will be broadly applicable across both functional materials and biological imaging with XRF and other related application areas.

36 MATERIALS SCIENCE↗

Infrared thermometry and interferential microscopy for analysis of crater formation at the surface of fused silica under CO{sub 2} laser irradiation

In situ spatial and temporal temperature measurements of a fused silica surface heated by a 10.6 {mu}m CO{sub 2} laser were performed using an infrared camera. These measurements were derived from heat flux emission of the fused silica. High temperature measurements--in the range 400-2500 K--were performed at the surface of a semi-transparent media with a high spatial resolution. Particular attention was given to the experimental conception and to the calibration of the infrared device. Moreover, both conventional and interferential microscopes were used to characterize the silica surfaces after CO{sub 2} laser irradiation. By associating these results with thermal camera measurements we identified the major surface temperature levels of silica transformation when heated during 250 ms. Surface deformation of silica is observed for temperatures <2000 K. This is consistent with other recent work using CO{sub 2} laser heating. At higher temperatures, matter ejection, as deduced from microscope observations, occurs at temperatures that are still much lower than the standard boiling point. Such evaporation is described by a thermodynamical approach, and calculations show very good agreement with experiment.

36 MATERIALS SCIENCE↗

Impacts of Substrate Thinning on FPGA Performance and Reliability [Slides]

Substrate thinning is necessary in devices with flip-chip BGA packages to enable both radiation testing and component qualification and high-spatial resolution beam-based failure analysis methods. We investigated three factors affecting device performance: subsurface damage from the thinning process, reduced heat spreading in thin substrates, and changes in device switching speed. We conclude subsurface damage to crystalline Si caused by the thinning process is removable with sufficient SiO 2 slurry polishing. Local temperature differences increase minimally in devices thinned to 3 μm. Compressive stress in the Si increases globally after device thinning and leads to slowing of ring oscillator frequency by about 0.5% compared to full-thickness devices. Future work will include extending the results to submicron Si thickness values, which also has important benefits for failure analysis, debug, and security assessments. We also plan to extend this type of work to other FPGAs and other devices like memory and processors.

36 MATERIALS SCIENCE↗

Emission Inventories from Natural Gas Storage Facilities using Regional Frequency Comb Laser Monitoring and Aircraft Flyovers. Final Report

The project centers around a ground-based regional methane sensor developed by the CU/NIST team members under the DOE ARPA-E MONITOR program, and sophisticated aircraft measurement techniques developed by the UCD/Scientific Aviation team (responsible for a recent Science publication quantifying the Aliso Canyon storage field release). The dual frequency-comb spectrometer is an invisible, eye-safe laser capable of measuring atmospheric methane concentrations along beam paths 1+ miles in length with high precision and stability (<1 ppb methane over 1 mile). The CU/NIST team has developed a unique approach to using a single, central spectrometer to locate and size methane emissions as small as 6 scfh from specific gas field structures within the 1+ mile range of the laser. Deployment involves the stationing of small cubic mirrors at strategic locations across the gas storage site. The mirrors direct light from the spectrometer back to a detector, where methane concentrations are recorded based on light absorption at specific wavelengths. The measurements are coupled with high resolution gas transport models based on meteorological measurements (wind, etc.) to determine the precise location and emission rate of methane sources. The calibration-free and continuous nature of dual frequency-comb spectrometer measurements means that a large area can be monitored continuously for temporal variability of emissions over long periods of time. The aircraft measurements involve spiral flights around storage facilities with a sensitive ethane/methane measurement package. Upwind/downwind concentration comparisons and wind data provide total facility emissions data. During this project, a new micrometeorological package was integrated onto the Scientific Aviation aircraft to provide even greater emissions quantification capability. In this project, the ground-based measurement system was deployed for 11 months (to capture seasonal variability) at a West Coast storage facility, together with a campaign of twice monthly aircraft total facility emissions flights. The data from these two modalities was combined to quantify the methane emissions from the facility with first-of-its-kind temporal and spatial detail. Concurrent with this deployment, the aircraft team performed flights to quantify emissions at a wider array of previously un-surveyed storage sites. The ground-based measurement system was then moved to an additional site of differing total storage and delivery capacity. Aircraft mass balance flights continued, repeatedly surveying (twice monthly) this site, as well as surveying a large number of previously un-assessed sites spanning a variety of size, reservoir type and age. Assessment of ground-based, temporally and spatially detailed methane emissions information alongside aircraft-derived methane total facility emissions information led to an improved understanding of emissions, relevant for inventories of the natural gas storage sector. In particular, this work provided temporal detail (seasonal of emissions and frequency of leaks) to stakeholders and personnel responsible for updates to the EPA’s GHGI. The impact of the proposed work has been a dramatic improvement in the temporal, spatial, and site- and type- specific detail and quantification of emissions inventories for the natural gas storage sector. This work directly benefits the EPA’s GHGI, directly ameliorates the environmental impacts and public health and safety of the natural gas storage sector, and aids policymakers and industry to make sound choices with respect to management and regulation.

03 NATURAL GAS↗

The nanoscale distribution of copper and its influence on charge collection in CdTe solar cells

For decades, Cu has been the primary dopant in CdTe photovoltaic absorbers. Typically, Cu acceptor concentrations in these devices are on the order of 1 1014 cm-3, which has made it notoriously difficult to directly correlate nanoscale Cu distributions to the local charge transport properties of these devices. To measure and correlate these properties, measurement techniques require high sensitivity to elemental concentration, large penetration depth, and operando compatibility. Techniques such as secondary-ion mass spectroscopy and X-ray energy dispersive spectroscopy are widely adopted to measure Cu concentrations, but they are limited by penetration depth, sensitivity, or spatial resolution. Additionally, they lack the operando capabilities required to correlate one-to-one Cu concentrations to electrical performance. In this work, correlative X-ray microscopy is used to investigate the spatial distribution of Cu and its impact on charge collection through the depth and breadth of CdTe photovoltaic devices. Plan-view, nanoscale X-ray fluorescence maps clearly demonstrate the spatial segregation of copper around regions thought to be CdTe grain boundaries. Complementary cross-section imaging unveils the transition of the maximum charge-collection efficiency from the ZnTe-CdTe interface to the CdS-CdTe interface as a function of Cu incorporation. The copper concentration through the depth of the CdTe layer is characterized by slow and fast diffusion components, and cross-section charge-transport modeling shows that the experimentally obtained charge collection can be explained by the modeled acceptor distribution through the depth of the CdTe layer.

14 SOLAR ENERGY↗

Probing plasmons in three dimensions by combining complementary spectroscopies in a scanning transmission electron microscope

The nanoscale optical response of surface plasmons in three-dimensional metallic nanostructures plays an important role in many nanotechnology applications, where precise spatial and spectral characteristics of plasmonic elements control device performance. Electron energy loss spectroscopy (EELS) and cathodoluminescence (CL) within a scanning transmission electron microscope have proven to be valuable tools for studying plasmonics at the nanoscale. Each technique has been used separately, producing three-dimensional reconstructions through tomography, often aided by simulations for complete characterization. Here we demonstrate that the complementary nature of the two techniques, namely that EELS probes beam-induced electronic excitations while CL probes radiative decay, allows us to directly obtain a spatially- and spectrally-resolved picture of the plasmonic characteristics of nanostructures in three dimensions. Furthermore, the approach enables nanoparticle-by-nanoparticle plasmonic analysis in three dimensions to aid in the design of diverse nanoplasmonic applications.

36 MATERIALS SCIENCE↗

Isotope specific resolution recovery image reconstruction in high resolution PET imaging

Purpose: Measuring and incorporating a scanner-specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However, due to the short half-life of clinically used isotopes, other long-lived isotopes not used in clinical practice are used to perform the PSF measurements. As such, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction, usually underestimating the true PSF owing to the difference in positron range. In high resolution brain and preclinical imaging, this effect is of particular importance since the PSFs become more positron range limited and isotope-specific PSFs can help maximize the performance benefit from using resolution recovery image reconstruction algorithms. Methods: In this work, the authors used a printing technique to simultaneously measure multiple point sources on the High Resolution Research Tomograph (HRRT), and the authors demonstrated the feasibility of deriving isotope-dependent system matrices from fluorine-18 and carbon-11 point sources. Furthermore, the authors evaluated the impact of incorporating them within RM image reconstruction, using carbon-11 phantom and clinical datasets on the HRRT. Results: The results obtained using these two isotopes illustrate that even small differences in positron range can result in different PSF maps, leading to further improvements in contrast recovery when used in image reconstruction. The difference is more pronounced in the centre of the field-of-view where the full width at half maximum (FWHM) from the positron range has a larger contribution to the overall FWHM compared to the edge where the parallax error dominates the overall FWHM. Conclusions: Based on the proposed methodology, measured isotope-specific and spatially variant PSFs can be reliably derived and used for improved spatial resolution and variance performance in resolution recovery image reconstruction. The benefits are expected to be more substantial for more energetic positron emitting isotopes such as Oxygen-15 and Rubidium-82.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Adjoint-based optimization of mechanical performance in polycrystalline materials and structures through texture control

The texture of a polycrystalline material refers to the preferred orientation of the grains within the material. In metallic materials, texture can significantly affect the mechanical properties such as elastic moduli, yield stress, strain hardening, and fracture toughness. Recent advances in additive manufacturing of metallic materials offer the possibility in the not too distant future of controlling the spatial variation of texture. In this work, we investigate the advantages, in terms of mechanical performance, of allowing the texture to vary spatially. We use an adjoint-based gradient optimization algorithm within a finite element solver (COMSOL) to optimize several engineering quantities of interest in a simple structure (hole in a plate) and loading (uniaxial tension) condition. As a first step to general texture optimization, we consider the idealized case of a pure fiber texture in which the homogenized properties are transversely isotropic. In this special case, the only spatially varying design variables are the three Euler angles that prescribe the orientation of the homogenized material at each point within the structure. This work paves a new way to design metallic materials for tunable mechanical properties at the microstructure level.

36 MATERIALS SCIENCE↗

Wavelet multiresolution analysis of particle-laden turbulence

Direct numerical simulations of incompressible homogeneous-isotropic turbulence laden with a dilute suspension of inertial point particles are performed in conjunction with a wavelet multiresolution analysis of the results. The use of spatially localized wavelet basis functions enables the simultaneous consideration of physical and scale spaces in the spectral characterization of the flow field of the carrier phase and the concentration field of the dispersed phase. The multiresolution analysis of the dispersed phase provides statistical information about the spatial variabilities of a scale-dependent coarse-grained number density field and the local energy spectra of its fluctuations, characterizing the sensitivities of those quantities to variations in scale and Stokes number. In particular, the spatial variabilities of the wavelet energy spectrum of the particle concentration fluctuations are observed to be maximum in regimes where the particles preferentially concentrate. The results highlight the scale-dependent inhomogeneities of the structures in the concentration field generated by preferential concentration, and the existence of characteristic scales of interaction between the dispersed and carrier phases. Additionally, an interphase multiresolution analysis is performed that indicates the occurrence of a spatial anticorrelation between the enstrophy and kinetic-energy spectra of the carrier phase and the particle concentration at small scales in regimes where preferential concentration is important. In conclusion, this anticorrelation vanishes as the scale is increased and is largely suppressed when the preferential-concentration effect is negligible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data

Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem. A significant challenge for this problem is the lack of high-resolution data that indicates hesitancy. Here, we develop a hybrid VaxHesSTL framework that combines a Graph Neural Network (GNN) and a Recurrent Neural Network (RNN) to address the VaxHesitancy problem. The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. We train and evaluate VaxHesSTL using a large dataset, namely the All-Payer Claims Databases (APCD), for Virginia, consisting of insurance claims from over five million individuals for six years. We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. Experiments demonstrate that VaxHesSTL outperforms a range of state-of-the-art baselines, which rely solely on historical time series data without accounting for spatial relationships. Since hesitancy data at higher spatial resolution is often unavailable or hard to get, we incorporate an active learning approach with our VaxHesSTL framework to optimize the training set without compromising the prediction performance. We find that hesitancy data for only 18% of ZIP Codes selected by active learning allows us to forecast hesitancy for all the ZIP Codes in the Virginia.

60 APPLIED LIFE SCIENCES↗

On the origin of spatially dependent electronic excited-state dynamics in mixed hybrid perovskite thin films

The fundamental photophysics underlying the remarkable performance of organic-inorganic hybrid perovskites in optoelectronic device applications has been increasingly studied using complementary spectroscopic techniques. However, the spatially heterogeneous polycrystalline morphology of the solution-processed thin films is often overlooked in conventional ensemble measurements and therefore the reported results are averaged over hundreds or even thousands of nano- and micro-crystalline grains. Here, we apply femtosecond transient absorption microscopy to spatially and temporally probe ultrafast electronic excited-state dynamics in chloride containing mixed lead halide perovskite (CH 3 NH 3 PbI 3-x Cl x ) thin films. We found that the electronic excited-state relaxation kinetics are extremely sensitive to the spatial location probed, which was manifested by position-dependent transient absorption signal amplitude and decay behaviour, along with an obvious rise component at some positions. The analysis of transient absorption kinetics acquired at several distinct spatial positions enabled us to identify Auger recombination as the dominant mechanism underlying the initial portions of the spatially dependent dynamics with variable rate constants. The different rates observed suggest occurrence of distinct local electronic structures and variable contributions from impurities/defects and phonons in this nonlinear dynamical process.

74 ATOMIC AND MOLECULAR PHYSICS↗

A TCN-Based Hybrid Forecasting Framework for Hours-Ahead Utility-Scale PV Forecasting

This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven fluctuation forecasting (FF) model. Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the FF model to achieve more accurate forecast of intra- hour PV power drops, and iii) reconciling TF and FF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved. To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the FF model on capturing large power fluctuations caused by cloud movements. Here, the framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina. Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by 20% - 30% compared with state-of-the-art methods.

42 ENGINEERING↗

Spatial Transferability of Machine Learning Based Volume Estimation Models

High-quality traffic volume data is essential for efficient transportation planning and operations. However, such high-quality data is expensive to collect, owing primarily to the high capital cost of installing and maintaining continuous counting stations (CCSs). Recent availability of probe-based vehicle data offers a cost-effective solution for increasing the observability of traffic volumes. However, having ample ground truth traffic data is a prerequisite for developing robust volume estimation models. Though this might not be a big issue in many states, states with scarce CCS data might be able to benefit from robust volume estimation models developed in (adjacent) data-rich states. While there is a reasonable amount of spatial transferability research in the transportation domain, there is a dearth of knowledge on the spatial transferability of probe-based volume estimation models. To address this gap, this paper explores spatial transferability of volume estimation models developed from data in three states (Colorado, North Carolina, and Pennsylvania). Results indicate that it is extremely important to maintain temporal consistency when attempting spatial transferability of volume estimation models. It was also found that models trained on regions with lower peak traffic volumes will limit the performance of models transferred to states with higher peak hourly traffic volumes. Corroborating findings from existing spatial transferability research on other topics, it was found that a meta-model (developed using data from multiple states) performs better than volume estimation models developed within any one of the states.

ADVANCED PROPULSION SYSTEMS↗

Critical heat flux on zircaloy and accident tolerant fuel cladding under prototypical conditions of pressurized and boiling water reactors

First of a kind high spatial and temporal resolution temperature measurements of Pressurized Water Reactor and Boiling Water Reactor simulated fuel pins (high heat flux cosine profile heaters) in annular rod type geometry during nucleate boiling, departure from nucleate boiling and re-wetting has been performed. Distributed fiber optic measurements of the cladding axial temperature with spatial resolutions down to 2.5 mm axially at up to three equally spaced azimuthal locations per rod have demonstrated the similarities and differences of the different accident tolerant fuel cladding materials. These experiments show the performance of accident tolerant cladding with a precise measurement of the location and evolution of dryout followed by the re-wetting phenomena (see supplementary time history of a critical heat flux event). Experimental parameters varied coolant mass fluxes from 1695 to 2712 kg/m 2 s, pressures from 10 to 20 MPa, and inlet subcooling from 10 to 55 °C. It was found that the coatings and different material have little effect on the value of the critical heat flux. Any discrepancies between the local critical heat flux verified among the cladding material are attributed to different departure from nucleate boiling phenomena captured during the experiment with the high-resolution optical fiber temperature data. Substantial differences in the performance of the different cladding material to survive a critical heat flux event were however observed by post-test X-ray and optical inspection. External evident damage and internal cracks to the simulated fuel pin having the bare zircaloy cladding were observed in regions associated with the occurrence of the critical heat flux for longer periods. Such damages were associated with zirconium oxidation, and not noticed on the Cr coated zircaloy or FeCrAl claddings. These results indicate that the use of FeCrAl or a Cr coated zircaloy as cladding in Pressurized Water Reactor and Boiling Water Reactor may increase the survivability of the cladding material for short transients.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗