Three-dimensional modeling of performance degradation of planar SOFC with phosphine exposure
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Charcoal canisters are a common method of 222 Rn screening. The calibrations of different batches of activated charcoal used in different canister designs are typically performed in large volume controlled environmental chambers with known and controlled radon concentration, temperature, and humidity. Radon screening could be facilitated in locations without ready access to environmental chambers if canisters could be accurately calibrated and undergo quality control in less controlled environments. This study compares charcoal canister calibrations from a highly controlled radon chamber with calibrations from a basement storage area experiencing temporally varying radon. In addition, the impacts of exposure time and spectral region of interest selection on calibration accuracy are investigated by comparing calibrations calculated using three different choices. Approximately 30 mo after calibrations were completed, groups of canisters were exposed for different durations of time to investigate measurement accuracy and calibration validity over an extended time. A digital simulation of charcoal canister kinetics was also performed to establish limits on how stable radon must be in a space for equilibrium-based calibration to be performed there. Overall, the accuracy of measurements using calibrations from each space differed by less than 10% after 2 d exposure time, showing that carefully controlled conditions are not necessary for the accurate calibration of charcoal canisters. Furthermore, measurement accuracy differed by less than 2% for different spectral region selections. Accuracy improved slightly with canister exposure duration. Simulations suggest that radon instability is most tolerable near the beginning of canister calibration exposures, but this merits further experimental study.
Manual labeling for machine learning tasks such as image classification is tedious and labor-intensive; as a result, scientific datasets suitable for deep learning applications are scarce and limited. While data augmentation techniques have shown promise for extending image datasets, very little work has been done to understand the impact of combining multiple augmentation methods sequentially or the limits of their effectiveness when combined. Our work addresses this gap by examining how standard and combinatorial data augmentation affects the performance of machine learning models when trained on small datasets for label classification tasks. For our analysis, we generate single, double and quadruple-augmented datasets for a microscopy image classification task using six standard augmentation methods, and compare the resultant improvements observed in binary classification accuracy with three standard image classification models (DenseNet169, MobileNetV2, ResNet101V2). Our experiments show a non-monotonic relationship between the number of simultaneous augmentation methods and classification accuracy, indicating that there is a trade-off between the degree of augmentation and the model performance. These findings suggest that the optimal number of augmentation methods will vary by domain and use case. We also find that the order in which augmentation methods are applied to a limited dataset matters when combining augmentation schemes, with our use case showing performance differences up to 2.6% when the augmentation order is reversed for double-augmented datasets. Our work offers insights to the limits of data augmentation when working on image classification tasks with limited datasets.
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Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 to undertake cooperative research in the key topic of blade erosion. Participants in the task are given in Table 1.
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Presentation at INFORM Annual Meeting
Atmospheric processes over the Southern Ocean have a profound influence on regional and global climate. This is a part of the world where global climate models perform particularly poorly, with models persistently overpredicting the amount of sunlight reaching the Earth's surface (Regayre et al. 2020). A major challenge when trying to improve the representation of atmospheric processes in this part of the globe is the scarcity of observations capable of constraining our understanding. During 2024/25 a U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility field campaign (CAPE-k) deployed a suite of instrumentation at the kennaook/Cape Grim atmospheric monitoring station in Tasmania, Australia to provide detailed cloud-aerosol observations in this region in order to address the greatest source of uncertainty in climate models – atmospheric aerosols and how they influence cloud formation (Carslaw et al. 2013). In support of the ARM observations, an Australian Research Council (ARC)-funded project “Southern Ocean aerosols: sources, sinks and impact on cloud properties,” led by the Queensland University of Technology, complemented the ARM measurements with a suite of chemical measurements at the kennaook/Cape Grim site in northwestern Tasmania to provide more information on the processes controlling aerosol formation and growth. The deployment of the University of York LIF-SO2 instrument (Temple et al. 2025) in the ARM mobile facility container was part of this chemistry-focused deployment. Increased observational efforts are critical for understanding sources and sinks of Southern Ocean aerosols, in particular the role of marine micro-organisms (phytoplankton, algae) on aerosols formation, their properties and growth, cloud droplet formation, and the conversion of cloud droplets to ice crystals and precipitation. The observational focus of the ARC-funded project is aerosol chemical composition and their gaseous precursors. The remote nature of the Southern Ocean poses a significant analytical challenge when studying gaseous precursors, as low concentrations are often below the limits of detection of commercial instrumentation. Historically this has meant that observations have had to be time-averaged over days to weeks to achieve the limits of detection required. Although useful when considering overall average levels of aerosol precursors, this time-averaging obscures temporal variability that can provide insight into the controlling processes. Over recent years advances in analytical techniques have made high-time-resolution measurements of key gas-phase aerosol precursors achievable with sufficiently low limits of detection.
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In monolithic UMo fuels, the interaction between the Al cladding and large gas bubble volumetric swelling causes both elastic-plastic and creep deformation. In this work, a phase-field model of gas bubble evolution in polycrystalline UMo under elastic-plastic deformation was developed for studying the dynamic interaction between evolving gas bubble/voids and deformation. A crystal plasticity model, which assumes that the plastic strain rate is proportional to resolved shear stresses of dislocation slip systems on their slip planes, was used to describe plastic deformation in polycrystalline UMo. Xe diffusion and gas bubble evolution are driven by the minimization of chemical and deformation energies in the phase-field model, while evolving gas bubble structure was used to update the mechanical properties in the crystal plasticity model. With the developed model, we simulated the effect of gas bubble structures (different volume fractions and internal gas pressures) on stress-strain curves and the effect of local stresses on gas bubble evolution. The results show that 1) the effective Young’s modulus and yield stress decrease with the increase of gas bubble volume fraction; 2) the hardening coefficient increases with the increase of gas bubble volume fraction, especially for gas bubbles with higher internal pressure; and 3) the pressure dependence of Xe thermodynamic and kinetic properties in addition to the local stress state determine gas bubble growth or shrinkage. The simulated results can serve as a guide to improve material property models for macroscale fuel performance modeling.
Modelling the water transport along the soil–plant–atmosphere continuum is fundamental to estimating and predicting transpiration fluxes. A Finite-difference Ecosystem-scale Tree Crown Hydrodynamics model (FETCH3) for the water fluxes across the soil–plant–atmosphere continuum is presented here. The model combines the water transport pathways into one vertical dimension, and assumes that the water flow through the soil, roots, and above-ground xylem can be approximated as flow in porous media. This results in a system of three partial differential equations, resembling the Richardson–Richards equation, describing the transport of water through the plant system and with additional terms representing sinks and sources for the transfer of water from the soil to the roots and from the leaves to the atmosphere. The numerical scheme, developed in Python 3, was tested against exact analytical solutions for steady state and transient conditions using simplified but realistic model parameterizations. The model was also used to simulate a previously published case study, where observed transpiration rates were available, to evaluate model performance. With the same model setup as the published case study, FETCH3 results were in agreement with observations. Through a rigorous coupling of soil, root xylem, and stem xylem, FETCH3 can account for variable water capacitance, while conserving mass and the continuity of the water potential between these three layers. FETCH3 provides a ready-to-use open access numerical model for the simulation of water fluxes across the soil–plant–atmosphere continuum.
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