Publications

A poster presentation on a novel soft ferromagnetic material for transformers and inductors for the REHEDS Research Foundation External Review

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Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency-based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep-learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short-time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. Here, we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN-based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground-truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real-world data shows that both the CNN and MWCNN models outperform standard band-pass filtering (BPF; average improvement in signal-to-noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

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The slides will be presented at a DOE CESER Peer Review for the Risk Management Tools and Technology (RMT) that provide an overview of the ESSec project. The slides discuss the containerization and orchestration tools for energy storage systems and our results along with an overview of the demonstration performed for this project. The peer review is scheduled for August 27-29.

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A series of tension and compression tests were performed on typical transfer tubes made of 304L corrosion-resistant steel. The results were compiled and converted to true stress and true plastic strain for comparison to numerical models. The parameters of a Johnson-Cook constitutive model and fracture criterion were calibrated to mimic the test data. Comparisons were made between the converted data and the empirical formulas in the Johnson-Cook model. Simulations were performed to evaluate the implementation of the formula in finite element analysis, and the parameters were adjusted to improve the correlation.

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Developed for atmospheric science: specifically, a tool for processing sparse photon propagation data

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This report presents the design of defensive cybersecurity architectures (DCSAs) for High Temperature, Gas-Cooled Reactors (HTGRs). A DCSA is a cybersecurity design feature that places systems into security zones in a graded approach according to the importance of the functions performed by the systems. DCSA design efforts for advanced reactors may commence as early as the system-level design phase. This design approach is consistent with the draft regulatory guide for advanced reactor cybersecurity programs (DG-5075) and enables advanced reactor designers to consider the effects of security-by-design (SeBD) features on their DCSAs. Integration of DCSA design and other cybersecurity activities with the traditional design process as part of a SeBD framework may enable advanced reactor designers to improve the security posture of their plants while reducing implementation and operating costs. This report provides a DCSA template for an exemplar HTGR and describes a DCSA design process using event tree analysis so that the template may be optimized for a given HTGR design.

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SULI poster submission

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Paper led by Kapil Khanal (summer intern) to be presented at the UMERC+METS conference. This paper shows the ability to differentiate our BEM code.

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Slides to be used for 10 min introduction to the DOE OE Peer Review 'Zn and Lead Batteries Session' which I will chair

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