University of Illinois, Urbana-Champaign

The screening-current-induced stresses are raising growing concerns for the mechanical robustness of REBCO high-field magnets, particularly following the ‘Little Big Coil’ (LBC) experiments, which set a record field of 45.5T. In this paper, we examine the electromagnetic-mechanical behaviors of a set of single-pancake coils, which were originally designed as follow-up studies of the LBCs and tested in ultra-high fields without quench. A comparative study of the sequential model and the coupled model is carried out, demonstrating the perpendicular field modification due to conductor deformation and its effects on current and strain distributions. Local conductor degradation is taken into account by applying a stress/strain-dependent critical current profile while assuming linear elasticity. Under these assumptions, it is found that the suppressed strain response can lead to underestimation of conductor damage when using the strain-dependency, whilst conductor degradation is more sensitive to stress, yielding a conservative estimate on critical currents.

This study investigated the thermal and electrical characteristics of a high-temperature superconducting motor intended for electric vehicle traction. Particular attention was paid to mitigating the temperature increase induced by eddy currents in the aluminum bobbins and to increasing the operating time while maintaining the critical current margin. Adjustments were made to incorporate multiple axial segments composed of anodized aluminum, thereby establishing segmented eddy current loops. Models ranging from two to 40 segments were investigated at the maximum power of 300 kW for the motor. It was observed that the implementation of a 40-segment bobbin reduced the eddy current losses by approximately 78%. A marked decline in the temperature rise was realized, considerably extending the operational time of the motor. The operational time increased from an initial 7 seconds at maximum power to 221 seconds, attributed to the reduced eddy current losses and the resulting decrease in temperature rise.

This paper examines the electrical characteristics of no-insulation (NI) high-temperature superconducting (HTS) coils impregnated with an electrically conductive epoxy. The NI winding technique refers to a method of winding HTS coils without insulating materials between the winding turns and is regarded as the most promising method to protect the HTS coils from the quench phenomenon. Thus far, many analytical and experimental studies focusing on the electrical characteristics of NI HTS coils have been conducted. From these studies, the quench tolerance levels of NI HTS coils were verified, and the possibility of using NI HTS coils in high-field-magnet applications was confirmed. In addition, studies have recently attempted to apply NI HTS coils to electrical rotating machines. The use of NI HTS coils as field coils in electrical rotating machines is expected to enhance their thermal/electrical stability, but there may also be technical issues related to charging/discharging delays and mechanical reliability. In this paper, we propose a NI HTS coil impregnated with an electrically-conductive epoxy to resolve these issues. In the proposed method, a commercially available epoxy resin is mixed with electrically conductive particles, after which HTS coils are wet-wound using the electrically conductive epoxy. In addition, it was confirmed in charging/discharging tests and in a quench test that HTS coils manufactured by the proposed method show reduced charging/discharging delay times in conjunction with quench-tolerant characteristics. The results here demonstrate the potential of the proposed electrically conductive epoxy impregnated NI HTS coil for electrical rotating machine applications.

This paper presents the experiment and analysis on the control of current in Metal Insulation (MI) magnet using a DC power supply and power semiconductor components. A 3 V DC voltage is applied to the 37 mH class MI magnet in a liquid nitrogen environment, 77 K. The operating current is controlled using a half-bridge circuit with SiC MOSFETs and a PI control scheme. The switching frequency varies from 100 Hz to 40 kHz, while the duty cycle remains fixed at 0.5. Experimental results are analyzed using a conventional lumped circuit model which contains the characteristic resistance of No-Insulation (NI) class magnets. Lumped circuit parameters that best describe the current measurement results are simulated using a genetic algorithm. According to the simulation results, as the switching frequency increases, there is a tendency for the inductance to decrease while the characteristic resistance increases. Based on the results of these parameters at different frequencies, a discussion regarding the limitations of the conventional lumped circuit in high-frequency switching scenarios is conducted.

In the case of a synchronous motor, magnetic field variation occurs at the field winding due to field harmonics of non-perfect sinusoidal armature MMF and reluctance change caused by iron-cored stator teeth. So it is necessary to quantitatively analyze the AC loss value of the HTS field winding even for synchronous operation. This paper presents calculation results of AC loss of HTS field winding in synchronous motor considering armature harmonics and slotting effect. We selected two models with different stator topologies: 1) distributed-winding air-cored teeth (DWAT) and 2) concentrated winding iron-cored teeth (CWIT). Then we used 2D A-H formulation for AC loss calculation of HTS field winding according to stator configuration. Through this research, it was confirmed that the CWIT model had three times greater AC loss in synchronous operation due to larger harmonic components.

We demonstrate ultra-compact and highly efficient electro-optic Michelson interferometer modulators on thin film lithium niobate based on spiral-shaped waveguides. The modulator utilizes the in-plane isotropy of the Z-cut lithium niobate refractive index to achieve space-efficient spiral waveguides that are modulated using bottom and top electrodes. Monolithic optical rib waveguides are achieved using dry etching of lithium niobate with bottom and top cladding layers made of silicon dioxide and SU-8 polymer, respectively. The proposed modulator requires a total area of 175×175 μm2 to accommodate a 9-mm long waveguide, owing to the optimized design of the spiral inner radius and the gap between adjacent turns. The vertical distance between electrodes is engineered to achieve a half-wave-voltage-length product Vπl less than 2.02 V.cm with low optical propagation loss of 1.3 dB/cm. The 3-dB electro-optic bandwidth of the fabricated modulators varied between 4.2 GHz and 17.8 GHz for total spiral lengths of 9 mm and 1.2 mm, respectively. The compact modulator architecture fulfills the pressing demand for high-density photonic integrated circuits in modern data centers and telecommunication networks.

Carbon dioxide reduction is a major step toward building a cleaner and safer environment. There is a surge of interest in exploring high-entropy alloys (HEAs) as active catalysts for CO2 reduction; however, so far, it is mainly limited to quinary HEAs. Inspired by the successful synthesis of octonary and denary HEAs, herein, the CO2 reduction reaction (CO2RR) performance of an HEA composed of Ag, Au, Cu, Pd, Pt, Co, Ga, Ni, and Zn is studied by developing a high-fidelity graph neural network (GNN) framework. Within this framework, the adsorption site geometry and physics are employed through the featurization of elements. Particularly, featurization is performed using various intrinsic properties, such as electronegativity and atomic radius, to enable not only the supervised learning of CO2RR performance descriptors, namely, CO and H adsorption energies, but also the learning of adsorption physics and generalization to unseen metals and alloys. The developed model evaluates the adsorption strength of ∼3.5 and ∼0.4 billion possible sites for CO and H, respectively. Despite the enormous space of the AgAuCuPdPtCoGaNiZn alloy and the rather small size of the training data, the GNN framework demonstrated high accuracy and good robustness. This study paves the way for the rapid screening and intelligent synthesis of CO2RR-active and selective HEAs.

Knowledge of condensate shedding droplet dynamics provides important information for the characterization of two-phase heat and mass transfer phenomena. Detecting and segmenting the droplets during shedding requires considerable time and effort if performed manually. Here, we developed a self-supervised deep learning model for segmenting shedding droplets from a variety of dropwise and filmwise condensing surfaces. The model eliminates the need for image annotation by humans in the training step and, therefore, reduces labor significantly. The trained model achieved an average accuracy greater than 0.9 on a new unseen test dataset. After extracting the shedding droplet size and speed, we developed a data-driven model for shedding droplet dynamics based on condensation heat flux and surface properties such as wettability and tube diameter. Our results demonstrate that condensate droplet departure size is both heat flux and tube size dependent and follows different trends based on the condensation mode. The results of this work provide an annotation-free methodology for falling droplet segmentation as well as a statistical understanding of droplet dynamics during condensation.

The semantics of HPC storage systems are defined by the consistency models to which they abide. Storage consistency models have been less studied than their counterparts in memory systems, with the exception of the POSIX standard and its strict consistency model. The use of POSIX consistency imposes a performance penalty that becomes more significant as the scale of parallel file systems increases and the access time to storage devices, such as node-local solid storage devices, decreases. While some efforts have been made to adopt relaxed storage consistency models, these models are often defined informally and ambiguously as by-products of a particular implementation. In this work, we establish a connection between memory consistency models and storage consistency models and revisit the key design choices of storage consistency models from a high-level perspective. Further, we propose a formal and unified framework for defining storage consistency models and a layered implementation that can be used to easily evaluate their relative performance for different I/O workloads. Finally, we conduct a comprehensive performance comparison of two relaxed consistency models on a range of commonly seen parallel I/O workloads, such as checkpoint/restart of scientific applications and random reads of deep learning applications. We demonstrate that for certain I/O scenarios, a weaker consistency model can significantly improve the I/O performance. For instance, in small random reads that are typically found in deep learning applications, session consistency achieved a 5x improvement in I/O bandwidth compared to commit consistency, even at small scales.

Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with obstacles. Traversability estimation–which predicts where in the environment a robot can travel–is one prominent approach that tackles this problem. Existing geometric methods may ignore important semantic considerations, while semantic segmentation approaches involve a tedious labeling process. Recent self-supervised methods reduce labeling tedium, but require additional data or models and tend to struggle to explicitly label untraversable areas. To address these limitations, we introduce a weakly-supervised method for relative traversability estimation. Our method involves manually annotating the relative traversability of a small number of point pairs, which significantly reduces labeling effort compared to traditional segmentation-based methods and avoids the limitations of self-supervised methods. We further improve the performance of our method through a novel cross-image labeling strategy and loss function. We demonstrate the viability and performance of our method through deployment on a mobile robot in outdoor environments. Code is available at: https://github.com/andreschreiber/W-RIZZ .

Microphysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

In recent years, cloud computing has been widely used. This paper proposes an innovative approach to solve complex problems in cloud computing resource scheduling and management using machine learning optimization techniques. Through in-depth study of challenges such as low resource utilization and unbalanced load in the cloud environment, this study proposes a comprehensive solution, including optimization methods such as deep learning and genetic algorithm, to improve system performance and efficiency, and thus bring new breakthroughs and progress in the field of cloud computing resource management.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.

Polyethylene terephthalate (PET) fiber-reinforced polymer (FRR) has been recently developed, which possesses a bilinear tensile stress-strain relationship and a large rupture strain (LRS) capacity. This study presents a novel approach for accurately predicting the stress-strain behavior of PET FRP-confined concrete using machine learning (ML) techniques. A comprehensive dataset comprising 154 axial compression test specimens, including both circular and noncircular cases, was utilized for training and testing ML models. Three advanced ML models, namely extreme gradient boosting (XGBoost), random forest regression (RFR), and k-nearest neighbors (KNN), were applied to predict mechanical properties for both circular and noncircular specimens. XGBoost consistently outperformed RFR and KNN, demonstrating superior accuracy in predicting stress-strain curves for both specimen types. Performance evaluation relied on key metrics such as coefficient of determination (R 2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). Furthermore, the predicted stress-strain curves generated by XGBoost were compared to experimental data and a mechanism model, highlighting the superiority of XGBoost in capturing critical curve points and emphasizing its accuracy and consistency. Moreover, feature importance analysis was carried out and it revealed that parameters like the number of PET FRP layers, fiber thickness, and corner radius significantly influenced the stress-strain behavior.

Overshooting tops (OTs) are manifestations of deep convective updrafts that extend above the tropopause into the stratosphere. They can induce dynamic perturbations and result in irreversible transport of aerosols, water vapor and other mass from the troposphere into the stratosphere, thereby impacting the chemical composition and radiative processes of the stratosphere. These and other effects of OTs depend on their characteristics such as depth and area, which are understood to connect to mid‐tropospheric updraft speed and width, respectively. Less understood is how static stability in the lower stratosphere (LS) potentially modulates these OT–updraft connections, thus motivating the current study. Here, LS static stability and observed OT characteristics are quantified and compared using a combination of reanalysis data, observed rawinsonde data and geostationary satellite data. A weak to moderate relationship between OT depth and LS lapse rate and Brunt‐Väisälä frequency (N²) (R = 0.38, −0.37, respectively) is found, implying that OT depth is reduced with an increasingly stable LS. In contrast, a weak relationship (R = −0.03, 0.03, respectively) is found between OT area and LS static stability, implying that OT area is controlled primarily by mid to upper tropospheric updraft area. OT duration has a weak relationship to LS lapse rate and N² (R = 0.02, −0.02, respectively). These relationships may be useful in interpreting mid‐ and low‐level storm dynamics from satellite‐observed characteristics of OTs in near real‐time.

The fusion peptide of SARS-CoV-2 spike protein is functionally important for membrane fusion during virus entry and is part of a broadly neutralizing epitope. However, sequence determinants at the fusion peptide and its adjacent regions for pathogenicity and antigenicity remain elusive. In this study, we perform a series of deep mutational scanning (DMS) experiments on an S2 region spanning the fusion peptide of authentic SARS-CoV-2 in different cell lines and in the presence of broadly neutralizing antibodies. We identify mutations at residue 813 of the spike protein that reduced TMPRSS2-mediated entry with decreased virulence. In addition, we show that an F823Y mutation, present in bat betacoronavirus HKU9 spike protein, confers resistance to broadly neutralizing antibodies. Our findings provide mechanistic insights into SARS-CoV-2 pathogenicity and also highlight a potential challenge in developing broadly protective S2-based coronavirus vaccines.