The fast multi-frame X-ray diffraction detector at the Dynamic Compression Sector
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Sinclair, N.W.↗
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Machine Learning Enabled Position Detection for 6.78 MHz UAV Wireless Power Transfer System
This paper presents a novel supervised machine learning (SML) approach for accurate position detection of the receiver coil in wireless power transfer (WPT) systems using only secondary-side electrical measurements, with applications in autonomous unmanned aerial vehicle (UAV) charging. The proposed method trains a supervised learning model to map measured secondary-side voltage and current features to the receiver’s spatial position with high precision. This enables an autonomous UAV to determine its location relative to the primary coil center, the optimal position for maximizing wireless charging efficiency. The sensing method is fully integrated into a standard WPT system, utilizing the same primary and secondary coils for both power transfer and position detection, thereby eliminating additional sensing hardware. The use of a 6.78 MHz operating frequency enhances positional sensitivity, as high-frequency near-field electromagnetic fields respond strongly to small spatial variations. Experimental validation is performed on a 30 W scaled prototype featuring a 210 mm × 140 mm primary coil, a 50 mm × 80 mm receiver coil, and a 15 mm air gap. Results demonstrate reliable position estimation and a strong correlation between predicted position and optimal coil alignment. This integrated framework unifying position detection and wireless charging offers a promising foundation for future autonomous electric vertical takeoff and landing (eVTOL) systems, enabling compact, hardware-efficient, and high-accuracy charging solutions.
Colak, Kerim [New York University]↗
From Failure to Insight: Analyzing Disk Breakdowns in Large-Scale HPC Environments
Disk failure data provides valuable insights for preventing failures, enhancing storage robustness, guiding system design and deployment, and ensuring reliable operations at data centers. This paper introduces two disk failure datasets collected from large-scale HPC production environments over the past five years, comprising over 5,000 failure records from more than 40,000 disks. We analyzed these datasets across multiple dimensions, including temporal, spatial, and relational trends, and performed a comprehensive reliability assessment. Our analysis yielded numerous observations and insights that influence various operational aspects of HPC storage systems. We believe this study offers a holistic understanding of disk failure trends likely to interest the HPC storage community.
George, Anjus↗
Deep Learning for Intelligent Bubble Size Detection in the Spallation Neutron Source Visual Target
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) will undergo proton power upgrade (PPU), increasing the proton beam power from 1.4 MW to 2.8 MW. From 2.8 MW, 2.0 MW will go to the current First Target Station and the rest will go to the future Second Target Station (STS). The First Target Station uses a liquid mercury target that is contained in a 316L stainless steel vessel. The proton beam is pulsed at 60 Hz, with a pulse of about 0.7μs. When the proton beam hits the target, the intense energy deposition leads to a rapid rise in temperature in the mercury. This temperature rise creates pressure waves that propagate through the mercury and cause cavitation erosion. The power upgrade will cause stronger pressure waves that will further increase damage because of cavitation. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at 1.4 MW. However, at higher power, additional mitigation is necessary. Therefore, the 2 MW target vessel will be equipped with swirl bubblers and an additional gas injection port near the nose to inject more gas in the target. To develop a gas injection strategy and design, flow visualization in water with a transparent prototypical target (“visual target”) was performed. Bubble sizes and their spatial distribution in the flow loop are crucial to understanding the effectiveness of the bubbles in mitigating pressure waves. Bubbles were generated in the visual target under varied conditions of input pressures with helium and air. Images were captured using a high-speed camera at varied frame rates at different positions away from the swirl bubbler and different depths in the flow loop under varying lighting conditions. Initially, methods such as circular Hough transforms were applied after a series of images processing to obtain a general distribution of bubble sizes. Bubbles smaller than 500 μm are preferred to effectively mitigate the effect of pressure waves, which demands an accurate bubble detection and sizing system. Intelligent detection and identification of bubble sizes alleviate misdetection and improves accuracies. Employing neural networks, intelligent detection of bubble sizes and their distribution was developed and provides a robust alternative to traditional techniques. Human intervention was employed to label in-focus and out-of-focus bubbles in the set of training images. An object detection network using a pretrained convolutional neural network was created that extracted the features from the training images. Data augmentation was used to improve network accuracy through a random transformation of the original data.
Rasheed, Fayaz↗
High accuracy single-layer free-space diffractive neuromorphic classifiers for spatially incoherent light
Free-space all-optical diffractive systems have shown promise for neuromorphic classification of objects without converting light to the electronic domain. While the factors that govern these systems have been studied for coherent light, the fundamental properties for incoherent light have not been addressed, despite the importance for many applications. Here we use a co-design approach to show that optimized systems for spatially incoherent light can achieve performance on par with the best linear electronic classifiers even with a single layer containing few diffractive features. This performance is limited by the inherent linear nature of incoherent optical detection. We circumvent this limit by using a differential detection scheme that achieves greater than 94% classification accuracy on the MNIST dataset and greater than 85% classification accuracy for Fashion-MNIST, using a single layer metamaterial.
Léonard, François (ORCID:0000000249736743)↗
Explainable artificial intelligence relates perovskite luminescence images to current-voltage metrics
As the demand for low-cost, high-efficiency solar energy technologies grows, metal halide perovskite (MHP) solar cells have emerged as a promising candidate for next-generation photovoltaics due to their high power conversion efficiencies. However, their poor durability and issues with manufacturing consistency remain significant barriers to commercialization. In this work, we develop deep learning models to support materials characterization and provide insight into features and processes influencing performance. The models are trained using transfer learning of a pretrained model to predict relevant current-voltage (IV) metrics based on different combinations of input electroluminescence (EL) and photoluminescence (PL) images of MHP devices. We examine which image types are most informative in accurately predicting different IV metrics. Additionally, we use explainable artificial intelligence (XAI) techniques to provide insights into specific spatial features in the devices that drive differences in performance. We find that stabilized luminescence images (e.g. those collected after biasing the devices for at least 1 min) are better for predicting metrics of open-circuit voltage (by PL) and short-circuit current (by PL with EL), but that predicting fill factor and overall power output may use the time-evolution of EL images. Based on attribution masks generated by integrated gradients for each device performance metric, we further suggest different loss mechanisms associated with categories of large and small spatial defects. Overall, this case study highlights the potential applicability of XAI methodology for streamlining MHP device analysis and accelerating detailed understanding of the relationships between spatial defects and impacts on performance.
14 SOLAR ENERGY↗
High spatial resolution temperature profile measurements of solid-oxide fuel cells
Temperature gradients resulting from local electrochemical reactions, current distribution and geometry of gas flow channels in solid oxide fuel cells (SOFCs) create thermal stresses, localized thermophysical property gradients and uneven property evolution, contributing to SOFC degradation. This paper presents a new method to perform temperature measurements (up to 800°C) at high spatial resolutions to monitor the operation of SOFCs. Using femtosecond laser irradiation, distributed fiber sensors were hardened for high temperature environment applications. Distributed fiber sensors were embedded in interconnected plates using an additive manufacturing method to perform temperature measurements with 4-mm spatial resolution during the operation of a planar fuel cell. The measurement revealed the impact of various H 2 fuel concentrations and current loads have on temperature profiles of the SOFC tested. Temperature variation on the anode side was found to be less than 5°C, and 3°C on the cathode side. The measurements were compared to results from a multiphysics fuel cell performance model simulating similar conditions. These simulations predicted similar temperature gradients, indicating the experimental data obtained is reasonable. The model also predicts that the effect of the embedded sensor has on the local temperature will be minimal and that the gradient of temperature in the gas channels will be captured despite the separation between the sensor and the gas flow. Finally, the high spatial resolution data harnessed by these distributed fiber sensors provides experimental support for model-based design and optimization to improve the operational efficiency and longevity of solid oxide fuel cells and fuel cell assemblies.
25 ENERGY STORAGE↗
Electron Spin Polarization in Large Electric Fields
This project investigated the effects of large electric fields on electron spin polarization in semiconductors, which is of interest for spin-based electronics (“spintronics”) and quantum information processing. It has been theoretically proposed that large electric fields could potentially preserve or amplify electron spin polarization, which would make it easier to generate, transport, and detect spin polarization in spin-based devices; however, previous measurements had not measured the response of electron spin polarization at large electric fields. During this project, methods to perform measurements of electron spin polarization at large electric fields were developed and performed using ultrafast time- and spatially-resolved magneto-optical spectroscopy, high-voltage pulses, and lock-in detection. The device geometry was engineered to reduce the threshold voltage required to reach the Gunn threshold, and the effect of optical illumination on the sample conductivity and Gunn oscillations was investigated. Electron spin polarization measurements were performed at electric fields up to and beyond the Gunn threshold for the device. The measurements showed that the electron spin polarization magnitude decreased with increasing electric field in the devices measured, and a spin amplification effect was not observed. However, these effects could potentially exist in a different material or in a material with a different doping concentration or in a different device geometry.
36 MATERIALS SCIENCE↗
Unraveling Cu Chemical Signature in CdTe by Spectral Fluorescence Mapping
X-ray absorption spectroscopy (XAS) has been shown to be a powerful tool to unravel the chemical environment of a given atom within a matrix. When used in correlative X-ray microscopy approaches, XAS allows one to probe with nanoscale precision regions of particular interest in an absorber. Herein, we use X-ray absorption near edge structure (XANES) to evaluate the chemical environment of Cu atoms within a CdTe solar cell. The reconstruction of XANES spectra from XRF maps have unfolded 2D maps of Cu chemical structures. In this work, we found that most Cu atoms exist in Cu 2 Te and Cu 1.4 Te phase. Moreover, we found traces of CuTe, Cu 2 O, CuO, Cu 2 S, CuS, and metallic Cu phase. Investigating Cu chemical structures at different performing areas, we found no observable correlation between Cu chemical structures and electrical performance. This approach allows tracking of Cu chemical structures along with electrical performance and elemental distribution simultaneously, with high spatial resolution in a statistically practical way.
CdTe↗
Pooling Data Improves Multimodel IDF Estimates over Median-Based IDF Estimates: Analysis over the Susquehanna and Florida
Traditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the “true” underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-h precipitation intensity–frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations
The offshore wind industry in the U.S. is gaining strong momentum to achieve sustainable energy goals, and the need for observations to provide resource characterization and model validation is greater than ever. Pacific Northwest National Laboratory (PNNL) operates two lidar buoys for the U.S. Department of Energy (DOE) in order to collect hub height wind data and associated meteorological and oceanographic information near the surface in areas of interest for offshore wind development. This work evaluates the performance of commonly used reanalysis products and spatial approximation techniques using lidar buoy observations off the coast of New Jersey and Virginia, USA. Reanalysis products are essential tools in order to set performance expectations and quantify the wind resource variability at a given site. Long-term accurate observations at typical wind turbine hub-heights have been lacking at offshore locations. Using wind speed observations from both lidar buoy deployments, biases and degrees of correspondence for the Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), and the analysis system of the Rapid Refresh (RAP) are examined both at hub height and near surface. Results provide insights on the performance and uncertainty of using reanalysis products for long-term wind resource characterization.
lidar buoy, model validation, offshore wind energy↗
Investigating Multiscale Spatial Distribution of Sulfur in a CNT Scaffold and Its Impact on Li–S Cell Performance
Ever increasing demand on high energy density batteries positions sulfur as a very promising cathode material for next-generation energy storage due to its high theoretical capacity of 1675 mAh/g. Electronically sulfur is highly insulating, and therefore integration of a conducting framework such as a carbon nanotube (CNT) scaffold with sulfur is a key aspect of the cathode design. Despite numerous efforts dedicated to S-CNT cathode development, increasing sulfur loading to above 1 mg/cm 2 while maintaining the cycling stability of the Li–S cell remains challenging. This could be partly due to the lack of understanding of the spatial distribution of sulfur in the CNT matrix and its location with respect to the morphology of the CNT scaffold. We demonstrate herein that the sulfur has a hierarchical distribution in the CNT cathode at high sulfur loading (>5 mg/cm 2 ) spanning multiple length scales (from nanometer to submillimeter). Sulfur infiltration into the CNT rather than the sulfur loading plays a key role in determining the redox reaction kinetics, Li + ion diffusion, and the galvanostatic cycling capacity and stability of Li–S cells. This study provides new insights for the design and fabrication of high loading, binder-free sulfur–carbon-based cathode architectures for next-generation high energy Li–S batteries.
25 ENERGY STORAGE↗
Spatially Resolved Characterisation of Low Mass Fraction Uranium Glass Working Reference Materials
Here, we present the results of a study to generate reference glasses that reflect an environment analogous to historic nuclear fallout samples of interest for post-detonation nuclear forensics. The glasses were generated by melting and then quenching SiO 2 , Al 2 O 3 and CaCO 3 powders. Two suites of glasses with three distinct U isotopic ratios were successfully made with enrichments in the 235 U isotope (~ natural [0.72%], ~ 53% and 94%), but the bulk elemental data showed heterogeneity (~ 10% RSD) with U mass fractions ranging from 331.47 to 373.63 μg g -1 . Spatially resolved U isotopic measurements were performed using three mass spectrometry techniques (secondary ion mass spectrometry-single stage accelerator mass spectrometry [SIMS-SSAMS], large geometry [LG] - SIMS, and laser ablation-inductively coupled plasma-mass spectrometry [LA-ICP-MS]) across five National Laboratories. The results showed good agreement with the bulk U isotopic data for the low, medium, and high U mass fractions. We conclude that despite elemental heterogeneity, these samples can serve as useful working reference materials for spatially resolved nuclear fallout analyses, as well as for other related spatially resolved analyses.
36 MATERIALS SCIENCE↗
Rapid subsurface analysis of frequency-domain thermoreflectance images with K-means clustering
K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μm of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K = 3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μm length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel.
36 MATERIALS SCIENCE↗
High-Density, Low-Hysteresis Storage Using Hydrated Salts in Surface-Functionalized Hydrogels (Final Technical Report)
Nearly 70 years ago, Glauber’s salt was identified as a leading phase change material (PCM) in terms of its heat storage density (~2x paraffin), thermal conductivity (~1W/m·K), safety, availability and cost (~$\$$100/ton). However, the complex issues of supercooling and incongruent melting due to phase separation have prevented realization of the promise. The addition of thickeners and nucleating agents such as borax solve these issues but only over few cycles. This work aims to (a) resolve long-standing challenges with Glauber’s salt as a thermal storage material through a unique materials approach, (b) to characterize the new material’s properties that are relevant to performance and (c) to explore its incorporation into commercial water heaters. The materials concept involves encapsulating the salt in custom-designed, large-mesh hydrogels that enable breakthrough advances. Specifically, (1) the choice of mesh size and polymer chemistry control diffusion of salt/water and help to eliminate phase segregation. With the hydrogel itself occupying <10% volume, there is little loss in storage density compared to another encapsulation. (2) Specific nucleation centers that covalently tether to the hydrogel trigger heterogeneous nucleation, eliminating supercooling-associated hysteresis losses. The fact that they are spatially tethered, prevents the loss in performance over multiple freeze/thaw cycles (>100). We report extensive characterization of the hydrogel complex in terms of its storage density, freezing/melting temperature, cycling losses, rheological properties, aging and thermal conductivity. The novel material developed in this work is a significant advancement over the state-of-art. Finally, we investigate its potential as a thermal storage material for commercial/residential water heating and identify scenarios in which its deployment is advantageous.
25 ENERGY STORAGE↗
Mitigation of spatial nonstationarity with vision transformers
Spatial nonstationarity, the location variance of features’ statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechanical compaction trends, in mineral deposits grades vary due to sedimentation and concentration processes, in hydrology rainfall varies due to the atmosphere and topography interactions, and in metallurgy crystalline structures vary due to differential cooling. Conventional geostatistical modeling workflows rely on the assumption of stationarity to be able to model spatial features for geostatistical inference. Nevertheless, this is often not a realistic assumption when dealing with nonstationary spatial data and this has motivated a variety of nonstationary spatial modeling workflows such as trend and residual decomposition, cosimulation with secondary features, and spatial segmentation and independent modeling over stationary subdomains. The advent of deep learning technologies has enabled new workflows for modeling spatial relationships. However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context. We demonstrate the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance and propose the mitigation of such impacts using self-attention (vision transformer) models. We demonstrate the utility of vision transformers for the mitigation of nonstationarity with relative errors as low as 10%, exceeding the performance of alternative deep learning methods such as convolutional neural networks. We establish best practice by demonstrating the ability of self-attention networks for modeling large-scale spatial relationships in the presence of commonly observed geospatial nonstationarity.
58 GEOSCIENCES↗
Downscaling atmospheric chemistry simulations with physically consistent deep learning
Abstract. Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrates the downscaling capabilities of modern CNN-based single image super resolution and video super-resolution schemes and develops modifications to these schemes to ensure they are appropriate for use with physical science data. The CNN-based video super-resolution schemes in particular incur only 39 % to 54 % of the grid-cell-level error of interpolation schemes and generate outputs with extremely realistic small-scale variability based on multiple perceptual quality metrics while performing a large (8×10) increase in resolution in the spatial dimensions. Methods are introduced to strictly enforce physical conservation laws within CNNs, perform large and asymmetric resolution changes between common model grid resolutions, account for non-uniform grid-cell areas, super-resolve lognormally distributed datasets, and leverage additional inputs such as high-resolution climatologies and model state variables. High-resolution chemistry simulations are critical for modeling regional air quality and for understanding future climate, and CNN-based downscaling has the potential to generate these high-resolution simulations and ensembles at a fraction of the computational cost.
54 ENVIRONMENTAL SCIENCES↗
MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting
Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.
Cao, Yang↗