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DOE OSTI2024

Foreword: Special Section on Multiphysics Aspects of Power Electronics Packaging - Power Die, Power Module, and Converter Level - Part 1

Power electronics are increasingly being used to condition electricity for a wide array of applications, such as transportation (on land, air, and water), data centers, radio frequency, directed energy, wind, solar, and grid-tied applications. To increase power density, performance, efficiency, and reliability and reduce cost, innovations and developments are needed in the multiphysics packaging of power electronics at a die, module, and converter level. This includes fundamental research and development related to emerging high-voltage, high-temperature, and high-switching-frequency power electronics; packaging materials; thermal materials and interfaces; fluid-based thermal management technologies; reliability; condition monitoring; and prognostics. To address these important aspects, this Special Section on Multiphysics Aspects of Power Electronics Packaging includes several articles to be published in two parts. More details are given below on the articles included in the first part.

condition monitoring↗
DOE OSTI2024

Design and Characterization of the Engineering Model of the Spectrometer Onboard LuSEE‐Night

Abstract The Lunar Surface Electromagnetics Explorer—Night, LuSEE‐Night, is a low‐frequency radio astronomy experiment that will explore the cosmic Dark Ages signal on the radio‐quiet farside of the Moon. The LuSEE‐Night carries a radio frequency spectrometer consisting of a set of antennas, analog and digital processing electronics, and will be launched by NASA's Commercial Lunar Payload Services in 2025. The spectrometer is designed to observe the spectrum of the radio sky in the 0.5−50 MHz band. The engineering model (EM) of the four‐channel spectrometer has been developed. The EM has been characterized for linearity, gain, noise, and their temperature dependence, confirming that the EM meets all the requirements for LuSEE‐Night. Three mitigation techniques have been implemented and verified to suppress self‐induced electromagnetic interference. The flight model of the spectrometer is currently being developed and is scheduled to be shipped to the integration site in early 2024.

Tamura, Emi↗
DOE OSTI2025

Passive Wireless Sensors for Realtime Temperature and Corrosion Monitoring of Coal Boiler Components Under Flexible Operation (Final Technical Report)

Researchers at West Virginia University (WVU) propose to demonstrate inexpensive wireless, high-temperature sensors for real-time monitoring of the temperature and corrosion of metal components, which are commonly used in coal-fired boilers. This study presents the development of cost-effective wireless high-temperature sensors for real-time temperature and corrosion monitoring in coal-fired boilers' metal components. The focus is on fabricating and evaluating chipless radio-frequency identification (RFID) sensors capable of operating between 25-1300 ºC. Efforts were directed towards designing passive RFID sensor and interrogator antenna with a broad frequency range, optimizing a microstrip patch antenna sensor integrated into a "peel-and-stick" format for efficient application to various metal specimens without altering their geometry. Additionally, this research aimed to assess the sensor responses under accelerated high-temperature conditions, correlating corrosion and cracking mechanisms with sensor data. An investigation of through-wall data acquisition techniques was also planned, facilitating unobtrusive monitoring of sensor responses housed within metal enclosures. Ultimately, this work sought to establish a robust passive wireless sensor system for the continuous health monitoring of metal components in operational settings, thereby contributing to enhanced safety and efficiency in coal-fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Real-Time High-Accuracy Digital Wireless Time, Frequency, and Phase Calibration for Coherent Distributed Antenna Arrays

his work presents a fully-digital high-accuracy real-time calibration procedure for frequency and time alignment of open-loop wirelessly coordinated coherent distributed antenna array (CDA) modems, enabling radio frequency (RF) phase coherence of spatially separated commercial off-the-shelf (COTS) software-defined radios (SDRs) without cables or external references such as the global navigation satellite system (GNSS). Building on previous work using high-accuracy spectrally-sparse time of arrival (ToA) waveforms and a multistep ToA refinement process, a high-accuracy two-way time transfer (TWTT)-based time–frequency coordination approach is demonstrated. Due to the two-way nature of the high-accuracy TWTT approach, the time and frequency estimates are Doppler and multipath tolerant, so long as the channel is reciprocal over the synchronization epoch. This technique is experimentally verified using COTS SDRs in a lab environment in static and dynamic scenarios and with significant multipath scatterers. Time, frequency, and phase stability were evaluated by beamforming over coaxial cables to an oscilloscope which achieved time and phase precisions of ~60– 70 ps , with median coherent gains above 99% using optimized coordination parameters, and a beamforming frequency root-mean-square error (RMSE) of 3.73 ppb in a dynamic scenario. Finally, experiments were conducted to compare the performance of this technique with previous works using an analog continuous-wave two-tone (CWTT) frequency reference technique in both static and dynamic settings.

Clock synchronization↗
DOE OSTI2025

Microstructure and 77K mechanical properties of electron beam welded Cu101- Inconel 625 joints

In developing large-scale next-generation superconducting radio frequency (SRF) linear accelerators using superconducting films on Cu, the design and development of dissimilar welding and joining metals, such as Cu to Inconel, stainless steel, and Nb, are essential. In this talk, we present the development of procedures for electron-beam welding of Cu¿Inconel 625 and evaluation of the microstructure and 77K mechanical properties of the weld and base material in the welded condition and heat treatments in the 750°C ¿950°C heat treatment range. The results will be presented in the context of developing joining techniques for low-temperature applications where high conductivity and strength, vacuum hygiene, and magnetic properties of the material need consideration. The methods presented here are being deployed to thin film SRF Cu cavities at Jefferson Lab. Acknowledgment: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear

Khanal, Bashu↗
DOE OSTI2026

CHARACTERIZING AND CONTROLLING RECOVERY AND RECRYSTALLIZATION IN NIOBIUM FOR IMPROVED SRF CAVITY PERFORMANCE

Crystal defects, such as dislocations and low-angle boundaries, provide sources of magnetic flux trapping in the Nb materials used for superconducting radio frequency (SRF) resonating cavities. Improving the performance of SRF cavities, as measured through the quality factor, requires reducing these defects. SRF cavity production involves deformation processing, such as rolling and forming, and strategic annealing heat treatments. The resulting microstructures can be recovered, recrystallized, or both. Because recovery leaves many defects that can trap flux, recrystallization should improve cavity performance. Thus, processing schedules that produce complete recrystallization without excessive grain growth need to be designed. Solutions to this problem require understanding physical metallurgy and differentiating between recovered and recrystallized regions of microstructure. Backscattered electron microscopy techniques are applied to this end. We demonstrate that the conditions required to produce fully recrystallized microstructures depend on Nb impurity content, suggesting that processing schedules may need to be adjusted by material heat or lot. We also demonstrate that processing can be used to control growth of recrystallized grains to maintain mechanical strength in fully recrystallized materials. Forming cavities from cold-rolled Nb sheet material may provide strategic new routes to obtain microstructures that improve SRF cavity performance.

Taleff, E. [The University of Texas at Austin]↗
DOE OSTI2025

Results and lessons learned from accelerating radio frequency modeling using machine learning [slides]

The “advanced tokamak” reactor concept is a leading candidate for a steady state fusion pilot plant. An advanced tokamak (AT) sustains a majority of the required plasma current with effects resulting from maintenance of the peaked pressure at the device center. This current is augmented by auxiliary current drive sources. These auxiliary actuators may consist of neutral particle beams and/or radio frequency (RF) systems such as lower hybrid current drive (LHCD) and high harmonic fast wave (HHFW) current drive using radio and microwaves from antennas. The primary focus of this work is to develop models of RF current profile control suitable for use in integrated modeling frameworks and for real-time control in experiments. Direct physics models of RF current drive can be computationally intensive. In order to achieve predictive times appropriate for the thousands of calls needed in real-time control of experiments and for use in integrated models, we will apply modern machine learning (ML) techniques to accelerate these models and interpolate their results. To generate the fast and accurate models for use in control level algorithms and integrated modeling we need to replace present models with high dimensional interpolation of their results. We will perform additional simulations across a broader parameter range for EAST and other tokamaks in different physics regimes (Alcator C-Mod, DIII-D, WEST, CFETR, ARC, ITER) and combine them into a larger database for training and testing of the ML models. Further testing of the control level models with experimental current profile data from EAST and C-Mod tokamaks will provide additional confirmation of the control level model before integration in a tokamak control system or integrated modeling suite. ML will be used to optimize the selection of training data consisting of RF current driven at different values of density profile, temperature profile, plasma current, and wavenumber. ML will also be used to facilitate classification of current drive from these input data. The output of this effort will be a validated classifier capable of determining the current drive profiles for HHFW CD and LHCD on a mille-second timescale. This will provide a breakthrough capability enabling real-time control of RF driven current profiles in experiments including ITER ICRF and use integrated modeling frameworks requiring thousands of current profile calculations in discharge simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

High-resolution high-throughput spatiotemporal strain imaging reveals loss mechanisms in a surface acoustic wave device

Surface acoustic wave devices are key components for processing radio frequency signals in wireless communication because these devices offer simultaneously high performance, compact size and low cost. The optimization of the device structure requires a quantitative understanding of energy conversion and loss mechanisms. Here we use stroboscopic full-field diffraction x-ray microscopy to reveal an unanticipated acoustic loss in a prototypical one-port resonator device. A non-uniform acoustic excitation in the active area was responsible for the substantial end and side leakages observed at the design frequency. Quantitative analysis of the strain amplitude using a wave decomposition method allowed the determination of several key device parameters. This high-resolution high-throughput spatiotemporal strain imaging technique is more generally applicable to the study of dynamic strain modulation in nanoscale acoustic, electronic, optical and quantum devices. The high sensitivity allows precise measurement of the strain modulation with picometer-scale amplitude.

Zhou, Tao [ESRF - The European Synchrotron, Grenob↗
DOE OSTI2024

Phase-controlled improvement of photon lifetime in coupled superconducting cavities

High-quality cavities are crucial for various fundamental physical studies and applications. Here we find that by coupling two cavities directly or via a phase-tunable coupling channel, the photon lifetime of the local field can exceed that of the bare cavities. The cavity photon lifetime is modified by the phases of the initial states and the phase accumulation on the coupling channel, which affect the interference between cavities. In experiments, by coupling superconducting radio-frequency cavities via phase-tunable cables, we realize a factor of 2 improvement in the cavity photon lifetime. The results can bring rich revenue to quantum information science, sensing, and high-energy physics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

High Power Test Results For A Cylindrical-Shell SiC Higher-Order-Mode DAMPER

The next high-current Electron Ion Collider (EIC) is a new accelerator to be built at Brookhaven National Laboratory (BNL) in collaboration with Thomas Jefferson National Accelerator Facility (TJNAF). In the EIC Electron Storage Ring (ESR), there will be beam currents up to 2.5 A, which will excite massive Higher-Order-Modes (HOM) power in the 17 single-cell 591 MHz Superconducting Radio Frequency (SRF) cavities. To damp the HOM power in the ESR SRF cavities is a challenge. A room temperature cylindrical shell shape Silicon Carbide (SiC) Beamline HOM Absorber (BLA) was chosen as the baseline design, due to its broadband and high-power capability, and previous demonstrations at other accelerator facilities, albeit at much lower power. Because the EIC BLA HOM power dissipation is significantly greater than the previous applications, it is imperative to carry out high power testing to determine the maximum device performance levels achievable for thermal transport, RF breakdown, and mechanical stress, prior to finalizing the design. A SiC HOM absorber with state-of-the-art geometry size was prototyped to verify shrink-fit technique, test outgassing rate, and high power handling capability. This paper presents the HOM damper’s prototyping and test results.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

High-power test results for a cylindrical-shell silicon carbide higher-order-mode damper

The next high-current Electron-Ion Collider (EIC) is a new accelerator to be built at Brookhaven National Laboratory in collaboration with Thomas Jefferson National Accelerator Facility. In the EIC Electron Storage Ring (ESR), there will be beam currents of up to 2.5 A, which will excite massive higher-order-mode (HOM) power in the 17 single-cell 591 MHz superconducting radio-frequency (SRF) cavities. Damping the HOM power in the ESR SRF cavities is a challenge. A room temperature cylindrical shell shape silicon carbide (SiC) beamline HOM absorber (BLA) was chosen as the baseline design, due to its broadband and high-power capability, and previous demonstrations at other accelerator facilities, albeit at much lower power. Because the EIC BLA HOM power dissipation is significantly greater than the previous applications, it is imperative to carry out high-power testing to determine the maximum device performance levels achievable for thermal transport, rf breakdown, and mechanical stress, prior to finalizing the design. A SiC HOM absorber with a state-of-the-art geometry size was prototyped to verify the shrink-fit technique, test outgassing rate, and high-power handling capability. This paper presents the HOM damper’s prototyping and test results. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2026

Superconducting low-beta Nb 3 Sn cavity for ATLAS and future ion accelerators

We report on a Nb 3 Sn-coated low-beta superconducting radio frequency (RF) cavity intended for accelerating ions. We aim to apply the cavity in ATLAS, our Argonne National Laboratory user facility for nuclear physics studies with ion beams in the energy range of 5–20 MeV u −1 . The Nb 3 Sn-coated cavity, a 145 MHz quarter-wave optimized for ions moving with velocity β = $v/c$ = 0.08 exhibits an order-of-magnitude reduction in RF losses into helium at 4.4 K compared to a superconducting niobium (Nb) cavity at the same frequency and temperature. Experimentally measured fields are among the highest to date for any Nb 3 Sn-coated cavity, reaching a peak surface magnetic field of 105 mT. We also present a practical solution to the problem of cavity frequency tuning. Tuning by mechanical deformation has been a challenge with Nb 3 Sn due to its brittle nature, however, using a set of techniques tailored to the properties of thin-film Nb 3 Sn on Nb, we can repeatably tune the cavity to the ATLAS master clock frequency after it is cooled, while maintaining the excellent performance characteristics. The same Nb 3 Sn cavity technology offers broad benefits for future ion accelerators.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Cryogenic RF characterization of the MAGO cavity for high-frequency gravitational-wave detection

Superconducting radio-frequency (SRF) cavities are promising resonant sensors for gravitational-wave detection in the kHz-MHz frequency range. We report the cryogenic RF characterization of a prototype superconducting niobium cavity with an unconventional geometry designed for narrow electromagnetic mode separation. Following an adapted surface preparation procedure, cryogenic tests were performed at Fermilab and DESY at temperatures down to 2 K. Mechanical tuning at room temperature achieved a mode splitting of approximately 11 kHz at cryogenic temperature. High electromagnetic quality factors consistent with previous prototype cavities were measured. The measurements further revealed phase transfer characteristics relevant for stable low-level RF control as well as indications of mode coupling potentially caused by one-point multipacting. In addition, first cryogenic measurements of the mechanical eigenmodes yielded mechanical quality factors significantly below commonly assumed theoretical values. These results demonstrate the successful application of established SRF preparation and characterization techniques to a non-standard resonator geometry and provide important experimental input for the development of future SRF-based gravitational-wave detectors.

Dokuyucu, Can [DESY]↗
DOE OSTI2025

Microwave microscope studies of trapped vortex dynamics in superconductors

Trapped vortices in superconductors introduce residual resistance in superconducting radio-frequency (SRF) cavities and disrupt the operation of superconducting quantum and digital electronic circuits. Understanding the detailed dynamics of trapped vortices under oscillating magnetic fields is essential for advancing these technologies. We have developed a near-field magnetic microwave microscope to study the dynamics of a limited number of trapped vortices under the probe when stimulated by a localized rf magnetic field. By measuring the local second-harmonic response (𝑃 2⁢f ) at subfemto-Watt levels, we isolate signals exclusively arising from trapped vortices, excluding contributions from surface defects and Meissner screening currents. Toy models of niobium superconductor hosting vortex pinning sites are introduced and studied with time-dependent Ginzburg-Landau (TDGL) simulations of probe/sample interaction to better understand the measured second-harmonic response. The simulation results demonstrate that the second-harmonic response of trapped vortex motion under a localized rf magnetic field shares key features with the experimental data. Here, this measurement technique provides access to vortex dynamics at the micrometer scale, such as depinning events and spatially resolved pinning properties, as demonstrated in measurements on a niobium film with an antidot flux pinning array.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Towards Construction of a Novel Nanometer-Resolution MeV-STEM for Imaging Thick Frozen Biological Samples

Driven by life-science applications, a mega-electron-volt Scanning Transmission Electron Microscope (MeV-STEM) has been proposed here to image thick frozen biological samples as a conventional Transmission Electron Microscope (TEM) may not be suitable to image samples thicker than 300–500 nm and various volume electron microscopy (EM) techniques either suffering from low resolution, or low speed. The high penetration of inelastic scattering signals of MeV electrons could make the MeV-STEM an appropriate microscope for biological samples as thick as 10 μm or more with a nanoscale resolution, considering the effect of electron energy, beam broadening, and low-dose limit on resolution. The best resolution is inversely related to the sample thickness and changes from 6 nm to 24 nm when the sample thickness increases from 1 μm to 10 μm. To achieve such a resolution in STEM, the imaging electrons must be focused on the specimen with a nm size and an mrad semi-convergence angle. This requires an electron beam emittance of a few picometers, which is ~1000 times smaller than the presently achieved nm emittance, in conjunction with less than 10 -4 energy spread and 1 nA current. We numerically simulated two different approaches that are potentially applicable to build a compact MeV-STEM instrument: (1) DC (Direct Current) gun, aperture, superconducting radio-frequency (SRF) cavities, and STEM column; (2) SRF gun, aperture, SRF cavities, and STEM column. Beam dynamic simulations show promising results, which meet the needs of an MeV-STEM, a few-picometer emittance, less than 10 -4 energy spread, and 0.1–1 nA current from both options. Also, we designed a compact STEM column based on permanent quadrupole quintuplet, not only to demagnify the beam size from 1 μm at the source point to 2 nm at the specimen but also to provide the freedom of changing the magnifications at the specimen and a scanning system to raster the electron beam across the sample with a step size of 2 nm and the repetition rate of 1 MHz. This makes it possible to build a compact MeV-STEM and use it to study thick, large-volume samples in cell biology.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deep-learning-based domain adaptation for cavity fault prediction at Jefferson Laboratory

Superconducting radio-frequency (SRF) cavities are the core components of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab, providing high-power electron beams for nuclear physics experiments. The facility comprises 418 SRF cavities, and any fault in these cavities can lead to interruptions in the electron beam supply. Cavity faults are the leading cause of beam trips in CEBAF. Predicting and mitigating those faults before onset can help maintain normal operation. Existing models face challenges in distinguishing between normal and fault signals when changes occur in the underlying time-series data, from changes in control software, operational parameters, or the environment. This work proposes a deep learning domain adaptation model that leverages transfer learning to address fault prediction challenges by improving accuracy. The model is trained and fine-tuned using a dataset collected for faulty and normal operation using a data acquisition system in CEBAF. Our deep learning-based domain adaptation model achieves a prediction accuracy of 89.61% of the fault and normal signals. The developed model effectively predicts normal running signals compared to the baseline approach without domain adaptation. This capacity is essential for the fault prediction task in the CEBAF because of heavily imbalanced data containing vast amounts of normal signals. The model performs well for predicting faults several hundred milliseconds before the fault onset compared to other models where no adaptation is applied. Incorporating deep learning-based domain adaptation techniques will significantly improve the fault prediction performance.

Rahman, Md Monibor [Old Dominion Univ., Norfolk, V↗
DOE OSTI2024

Foreword: Special Section on Multiphysics Aspects of Power Electronics Packaging—Power Die, Power Module, and Converter Level: Part 2

Power electronics are increasingly being used to condition electricity for a wide array of applications, such as transportation (on land, air, and water), data centers, radio frequency, directed energy, wind, solar, and grid-tied applications. Here, to increase power density, performance, efficiency, and reliability-as well as to reduce cost-innovations and developments are needed in the multiphysics packaging of power electronics at a die, module, and converter level. This includes fundamental R&D related to emerging high-voltage, high-temperature, and high-switching-frequency power electronics, packaging materials, thermal materials and interfaces, fluid-based thermal management technologies, reliability, condition monitoring, and prognostics. Latest developments in this area are published as a Special Section on Multiphysics Aspects of Power Electronics Packaging. The first part was published in the May 2024 issue of the IEEE Transactions on Components, Packaging and Manufacturing Technology (Volume 14, Issue 5). The second part of that Special Section is being published in this issue. A brief summary of the papers included in the second part are given below.

24 POWER TRANSMISSION AND DISTRIBUTION↗