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DOE OSTI2023

Hybrid model to evaluate the frequency-dependent leakage inductance of partially-filled transformers

The leakage inductance of a transformer designed for a power electronic converter can drop significantly as the switching frequency is increased due to skin and proximity effects. Although the magnetic image method-based double-2-D model can predict the low-frequency leakage inductance of a partially-filled transformer with sufficient accuracy, it is inherently a frequency-independent model. While Dowell’s 1-D model uses frequency-dependent relations to account for both skin and proximity effects, its accuracy is severely affected by the assumed winding geometry. In this paper, a semianalytical hybrid model is proposed that uses superposition to combine a modified Dowell’s model with the double-2-D model to predict the true leakage inductance of partially-filled transformers at any given frequency. All three conductor types—round, foil, and litz wire—are modeled and analyzed. The quasi-2-D model is further investigated on a variable inductance transformer (VIT) whose winding geometry can be modified mechanically to vary its leakage inductance. With less than 5 % error throughout, the semi-analytically evaluated leakage inductances are in excellent agreement with the finite element method (FEM) simulated and experimentally measured leakage inductances.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Current interrupt method for calculating the electrochemical impedance in a solid oxide electrolysis stack

Here, in this work the time domain response of Solid Oxide Electrolysis Cells (SOEC) to a current interruption was transformed into the frequency domain using a carrier function Laplace transform, which is fit to the experimental data using a MATLAB Complex Nonlinear Least Squares (CNLS) solver. The hardware implementation, consisting principally of a high-speed switch and a fast-logging Analog to Digital Converter (ADC), was assembled and tested using a calibration module to assess the accuracy, repeatability, and speed of acquisition of the prototype device as compared against a calibrated commercial impedance spectrometer. Additionally, the current interrupt device and commercial FRA were used to acquire the impedance spectra of a four cell SOEC stack with a large, 300 cm 2 , active cell area.

SOEC↗

DOE OSTI2020

Ultrasonic analyte concentration and application in flow cytometry

The present invention includes an apparatus and corresponding method for concentrating analytes within a fluid flowing through a tube using acoustic radiation pressure. The apparatus includes a function generator that outputs a radio frequency electrical signal to a transducer that transforms the radio frequency electric signal to an acoustic signal and couples the acoustic signal to the tube. The acoustic signal is converted within the tube to acoustic pressure that concentrates the analytes within the fluid.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting

With hundreds of SARS-CoV-2 lineages circulating in the global population, there is an ongoing need for predicting and forecasting lineage frequencies and thus identifying rapidly expanding lineages. Accurate prediction would allow for more focused experimental efforts to understand pathogenicity of future dominating lineages and characterize the extent of their immune escape. Here, we first show that the inherent noise and biases in lineage frequency data make a commonly-used regression-based approach unreliable. To address this weakness, we constructed a machine learning model for SARS-CoV-2 lineage frequency forecasting, called CovTransformer, based on the transformer architecture. We designed our model to navigate challenges such as a limited amount of data with high levels of noise and bias. We first trained and tested the model using data from the UK and the USA, and then tested the generalization ability of the model to many other countries and US states. Remarkably, the trained model makes accurate predictions two months into the future with high levels of accuracy both globally (in 31 countries with high levels of sequencing effort) and at the US-state level. Our model performed substantially better than a widely used forecasting tool, the multinomial regression model implemented in Nextstrain, demonstrating its utility in SARS-CoV-2 monitoring. Assuming a newly emerged lineage is identified and assigned, our test using retrospective data shows that our model is able to identify the dominating lineages 7 weeks in advance on average before they became dominant. Overall, our work demonstrates that transformer models represent a promising approach for SARS-CoV-2 forecasting and pandemic monitoring.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Embedded Sensing in Additive Manufacturing Metal and Polymer Parts: A Comparative Study of Integration Techniques and Structural Health Monitoring Performance

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications.

additive manufacturing↗

DOE OSTI2021

Spatially resolved x-ray detection with photonic crystal scintillators

We study the self-collimation phenomenon in photonic crystals (PhC) of wide bandgap materials for ultra-fast and high spatial resolution x-ray detection. We work on various heavy inorganic scintillators: BaF 2 , GaN, ZnO, CsI:Tl, NaI:Tl, LYSO, WO 4 compounds, and plastic scintillators. Conventional scintillator detectors do not rely on a direct detection mechanism; hence, they require intricate design and fabrication processes. We offer a PhC design to observe self-collimation phenomena and overcome the ongoing spatial resolution challenges with these types of materials. We investigate the photonic band diagrams and iso-frequency contours. Fourier transforms based on finite-difference time-domain and frequency domain simulations are done for verifying and analyzing the self-collimation with the selected material. Light extraction efficiency at the PhC–air interface, depending on the truncation distance from the excitation point, is measured. Beam divergence values are calculated at 1 mm propagation distance. The vertical field profiles are obtained to observe the confinement. For the spatial resolution analysis, cross-sectional beam profiles have been examined. Furthermore, Gaussian envelopes are fitted to beam profiles for a consistent data analysis, and full-width-at-half-maximum values are considered. As a result, we theoretically prove and demonstrate the spatially resolved x-ray detection at the sub-micrometer level for a wide range of scintillator materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Radio-Frequency Rapid Thermal Processing Enabling Spatial Phase Transformation and Nanocrystallization of Soft Magnetic Amorphous Alloys

Thermal processing of soft magnetic amorphous and nanocrystalline alloys is explored under the influence of radio-frequency induction-heating techniques. Direct induction-heating concepts based on longitudinal and transverse flux heating are examined and the details of electromagnetic fields interaction with metallic strips are discussed by analytical calculations as well as finite element analysis. Initial experimental results confirming spatial control of phase transformations and nanocrystallization within a single strip of Finemet Fe-based amorphous ribbons are reported. Here, the degree to which primary and secondary crystallization temperature are achieved depends on the spacing between the ribbon relative to the induction coil as well as the coil design and configuration. For transverse coil configurations, the local temperature and therefore microstructural evolution is different across the lateral dimension of processed ribbons, with reduced gap sizes producing enhanced peak temperatures and larger temperature distributions with greater spatial variation in microstructure. In addition, indirect susceptor-based induction heating under tension is performed and the impact of microstructure is demonstrated. Herein, potential for exploiting spatially optimized phase transformations is illustrated through electromagnetic field–assisted processing in a scalable manufacturing process with amorphous and nanocrystalline soft magnetic alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Real-Time Wave Energy Converter Control Using Instantaneous Frequency

Wave Energy Converters (WECs) rely on effective Power Take-Off (PTO) control strategies to maximize energy absorption under dynamic sea conditions. Traditional hydrodynamic modeling techniques may require computationally intensive convolution calculations, making real-time control implementation challenging. This paper presents an alternative approach by leveraging instantaneous frequency estimation to dynamically adjust PTO damping in response to varying wave frequencies. Two real-time frequency estimation methods are explored: the Hilbert Transform (HT) and Phase-Locked Loop (PLL). The Hilbert Transform method provides accurate frequency tracking but introduces a delayed response due to its dependence on causal data. Conversely, the PLL approach demonstrates strong potential in frequency tracking but requires careful gain tuning, particularly in complex sea states. Comparative evaluations across multiple test cases—including sinusoidal variations, amplitude steps, frequency step changes, and real-world JONSWAP spectrum waves—highlight the strengths and limitations of each method. The two different PTO control techniques across the various frequency estimation methods were tested under real-sea states using a state-space model of a point-absorbing Wave Energy Converter. The Capture Width Ratio (CWR) is used as a performance metric, with results showing that the HT achieves a 10.6% improvement, while the PLL estimation yields a 0.9% improvement relative to the fixed parameter control baseline. These results highlight the effectiveness of real-time frequency estimation in improving energy absorption compared to static control parameters.

WEC control↗

DOE OSTI2023

Quantum frequency processor for provable cybersecurity

Methods of quantum key distribution include receiving a frequency bin photon at a location, selecting a frequency bin photon quantum key distribution measurement basis, with a quantum frequency processor, performing a measurement basis transformation on the received frequency bin photon so that the frequency bin photon is measurable in the selected frequency bin photon quantum key distribution measurement basis, and detecting the frequency bin photon in the selected quantum key distribution measurement basis and assigning a quantum key distribution key value based on the detection to a portion of a quantum key distribution key. Apparatus and methods for encoding, decoding, transmitting, and receiving frequency bin photons are disclosed.

Lukens, Joseph M.↗

DOE OSTI2024

Quantum frequency processor for provable cybersecurity

Lukens, Joseph M.↗

DOE OSTI2022

Traveling Wave Energy Analysis of Faults on Power Distribution Systems

This paper explores the most important factors that define the Traveling Wave (TW) propagation on distribution systems. The factors considered in this work are: the distance to the fault location, the fault type, and the crossing of system elements (such as regulators, capacitor banks, laterals, and extra loads within the protection zones). This work uses a realistic, yet simplified, distribution system composed of two protection zones, in which, several combinations of the previously mentioned factors are considered. The simulated fault measurements undergo a signal processing stage in which, first, they are decomposed into independent modes using the Karrenbauer transform. Second, a time–frequency representation is obtained using the Stationary Wavelet Transform (SWT), dividing the signal into several frequency bands. Finally, the Parseval’s Energy (PE) theorem is applied to calculate the signal energy in each frequency band. A qualitative analysis is performed based on the previously calculated energies to outline which are the factors that most affect the TW energy during propagation. The results show that distance, the presence of regulators, either in the propagation path or upstream, and the type of fault are the main factors that affect TW propagation across the system, and therefore they should be considered for TW-based protection schemes for distribution systems.

Jiménez-Aparicio, Miguel (ORCID:000000016864461X)↗

DOE OSTI2020

Parallel high-frequency magnetic sensing with an array of flux transformers and multi-channel optically pumped magnetometer for hand MRI application

Here, we investigate an approach for parallel high-frequency magnetic sensing based on a multi-channel radio frequency (RF) optically pumped magnetometer (OPM) coupled to multiple flux transformers (FTs) with a focus on hand magnetic resonance imaging (MRI) application at ultra-low field (ULF). Multiple RF OPM sensing channels are realized by using a single large-area alkali-metal vapor cell and two laser beams for pumping and probing, shared for all the channels. This design leads to significant cost reduction when multi-channel sensing is desirable, as in the case of ULF MRI. The FT, composed of two connected coils, serves as a transmitter of a target magnetic field to the OPM, while decoupling the OPM from untargeted magnetic fields in the sensing area that can limit the OPM performance. For hand MRI application, theoretical and numerical analysis is performed to determine an optimal geometry for the FT array that could improve signal-to-noise ratio (SNR) and sufficiently reduce crosstalk between FTs. We estimate that the optimized multi-channel FT-OPM sensor can achieve a magnetic field sensitivity of the order of 1 fT / Hz 1 / 2 above 100 kHz, which would be sufficient for 1 mm resolution MRI. In general, the multi-channel capability enables simultaneous magnetic measurements, thus reducing the sensing time and improving the SNR, and we anticipate many applications of the multi-channel FT-OPM sensor beyond the targeted here hand MRI: anatomical parallel ULF MRI of the human brain and other parts of the body, airport security screening, magnetic material imaging, and many others.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Laminated Permanent Magnets Enable Compact Magnetic Components in Current Source Converters

Magnetic components play a key role in determining the size, cost, efficiency of a power converter. They are more significant in current source converters, where the dc-link energy is stored in an inductor/transformer. In such cases, the magnetics have a predominantly DC flux, superimposed by an AC flux at switching frequency. Previously, permanent magnets have been proposed to offset the DC flux and thereby reduce the size, cost, and losses of the inductors/transformers. This paper starts by analyzing the issue of eddy currents when using such standard permanent magnets, especially in medium frequency applications, making them practically unusable. Alternatively, the paper proposes the use of laminated permanent magnets to reduce these eddy currents and thereby realize the benefits of offsetting the DC flux to reduce the size, cost, and loss of inductors/transformers. Using a soft-switching solid-state transformer as an example, experimental results are presented to show the impact of eddy currents in standard permanent magnets when used with a medium frequency transformer. Here, experimental results showing the benefits of using laminated magnets in reducing the eddy currents are presented.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Instantaneous ultra-wideband sensing using frequency-domain channelization

A radar system wherein a radar return is channelized by frequency into a plurality of channels, and each of the channels is sampled by a respective analog-to-digital converter that has a sampling rate less than the Nyquist rate of the radar return. The digitally sampled channel signals are Fourier transformed into the frequency domain, where each of the frequency-domain channel signals is matched-filtered according to a respective partial matched filter. The channel signals are then transformed back to the time domain, whereupon they are added together to generate the impulse response of the radar system responsive to the radar return.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Nonreciprocal Frequency Domain Beam Splitter

The canonical beam splitter—a fundamental building block of quantum optical systems—is a reciprocal element. It operates on forward- and backward-propagating modes in the same way, regardless of direction. The concept of nonreciprocal quantum photonic operations, by contrast, could be used to transform quantum states in a momentum- and direction-selective fashion. In this study, we demonstrate the basis for such a nonreciprocal transformation in the frequency domain through intermodal Bragg scattering four-wave mixing (BSFWM). Since the total number of idler and signal photons is conserved, the process can preserve coherence of quantum optical states, functioning as a nonreciprocal frequency beam splitter. We explore the origin of this nonreciprocity and find that the phase-matching requirements of intermodal BSFWM produce an enormous asymmetry ( 76 × ) in the conversion bandwidths for forward and backward configurations, yielding ~ 25 dB of nonreciprocal contrast over several hundred GHz. We also outline how the demonstrated efficiencies ( ~ 10 – 4 ) may be scaled to near-unity values with readily accessible powers and pumping configurations for applications in integrated quantum photonics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Low modulation index 3-phase solid state transformer

A solid-state transformer (SST) comprises a transformer core, a primary winding, a secondary winding, a primary-side switch bank, and a secondary-side switch bank. Each of the switch banks includes six 4-quadrant switches. The twelve 4-quadrant switches are toggled on and off over six clock cycles in a repetitive sequence with a period that is a function of a desired operating frequency of the transformer. The sequence is configured such that at any given time, 2 of 3 input and output phases are connected to the primary and secondary windings. The SST further includes L-C filter circuits that are configured to filter high-frequency components of current and voltage waveforms such that these components are not back-fed to the electrical mains or delivered to a load. The SST includes a primary-side filter circuit and a secondary-side filter circuit that can each include respective L-C filters for three input or output phases.

Koplow, Jeffrey P.↗

DOE OSTI2023

Development of Multiresolution Capabilities for the Holistic Energy Resource Optimization Network (HERON) tool A progress update

INL researchers work on technoeconomic analyses for integrated energy systems (IES) using the Framework for Optimization of ResourCes and Economics (FORCE). Within FORCE, researchers use the Holistic Energy Resource Optimization Network (HERON) tool to conduct optimization of grid portfolios under uncertain market conditions. These optimizations determine optimal capacities for all IES components and strategies for resource dispatch which maximize some economic metric (e.g., net present value). Resource dispatch occurs on finer timescales (typically hours) and thus are asked to respond to a given time series (e.g. hourly load demand profiles for a grid, or pre-determined electricity prices). Volatile and complex bidding dynamics as well as poorly forecasted weather events within deregulated markets add uncertainty to the time series; FORCE can address this uncertainty by training a reduced order model on historical time series and generate unique synthetic time series which represent individual scenarios or realizations of the market. The IES configuration can be simulated under these different sampled realizations and a stochastic optimization is conducted which optimizes the expected value of the desired economic metric. The training of a synthetic time series generator is limited by the chosen time resolution; dynamics can occur on different time scales. Seasonal demand trends can dominate faster dynamical events (such as power outages from certain sectors or severe weather events) which might not get captured correctly by the trained model. In this report, we investigate different ways of addressing the training and generation of time series on multiple time scales using three main algorithms: wavelet decomposition, dynamic mode decomposition, and generative adversarial networks for time series. We demonstrate a time series analysis that yields information on not just the frequency space but also temporal space: where a fast Fourier transform can provide what frequencies dominate, the new algorithms can provide when the frequencies dominate as well. These analyses can help improve IES optimization by allowing researchers to couple simulations at different timescales when it is most needed - seasonal, day-ahead, and real time optimization - with greater computational efficiency. Future work will include implementation of a subset of the proposed algorithms into the FORCE toolset and application of these analyses into multiple timescale optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗