Publications

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Coulomb drag experiments have been an essential tool to study strongly interacting low-dimensional systems. Historically, this effect has been explained in terms of momentum transfer between electrons in the active and the passive layer. We report Coulomb drag measurements between laterally coupled GaAs/AlGaAs quantum wires in the multiple one-dimensional (1D) sub-band regime that break Onsager's reciprocity upon both layer and current direction reversal, in contrast to prior 1D Coulomb drag results. The drag signal shows nonlinear current-voltage (I-V) characteristics, which are well characterized by a third-order polynomial fit. These findings are qualitatively consistent with a rectified drag signal induced by charge fluctuations. However, the nonmonotonic temperature dependence of this drag signal suggests that strong electron-electron interactions, expected within the Tomonaga-Luttinger liquid framework, remain important and standard interaction models are insufficient to capture the qualitative nature of rectified 1D Coulomb drag.

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We can improve network pruning by leveraging the loss-topography extraction techniques used by projective integral updates for variational inference. Low-variance Hessians facilitate more aggressive pruning by providing better loss approximations when a parameter is removed.

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As machine learning models for radioisotope quantification become more powerful, likewise the need for high-quality synthetic training data grows as well. For problem spaces that involve estimating the relative isotopic proportions of various sources in gamma spectra it is necessary to generate training data that accurately represents the variance of proportions encountered. In this report, we aim to provide guidance on how to target a desired variance of proportions which are randomly when using the PyRIID Seed Mixer, which samples from a Dirichlet distribution. We provide a method for properly parameterizing the Dirichlet distribution in order to maintain a constant variance across an arbitrary number of dimensions, where each dimension represents a distinct source template being mixed. We demonstrate that our method successfully parameterizes the Dirichlet distribution to target a specific variance of proportions, provided that several conditions are met. This allows us to follow a principled technique for controlling how random mixture proportions are generated which are then used downstream in the synthesis process to produce the final, noisy gamma spectra.

The On-Line Waste Library is a website that contains information regarding United States Department of Energy-managed high-level waste, spent nuclear fuel, and other wastes that are likely candidates for deep geologic disposal, with links to supporting documents for the data. This report provides supporting information for the data for which an already published source was not available.

A mini split cooling system will be used to maintain temperature requirements in a mobile secure transport system. While the split cooling system was designed to be used in static residential or commercial applications, it was selected for this transportation application due to a unique set of security requirements. However, the system’s ability maintain reliability and survive prolonged long-term shock and vibration is a significant concern. The mitigation strategy is to select vibration isolation mounts and perform lifetime shock and vibration testing to demonstrate survivability. The goal of this study is to generate a finite element model of the system and perform modal analysis to inform selection of vibration mounts to minimize the amount of vibrational energy transferred to the split cooling system. The scope of this report is limited to study of the condensing unit only, and geometric variation of the assembly will not be allowed.

The final quality of any AI/ML system is directly related to the quality of the input data used to train the system. In this case, we are trying to build a reliable image classifier that can correctly identify electrical components in x-ray images. The classification confidence is directly related to the quality of the labels in the training data, which are used in developing the AI/ML classifier. Incorrect or incomplete labels can substantially hinder the performance of the system during the training process, as it tries to compensate for variations that should not exist. Image labels are entered by subject matter experts, and in general can be assumed to be correct. However, this is not a guarantee, so developing ways to measure label quality and help identify or reject bad labels is important, especially as the database continues to grow. Given the current size of the database, a full manual review of each component is not feasible. This report will highlight the current state of the “RECON” x-ray image database and summarize several recent developments to try to help ensure high quality labeling both now and in the future. Questions that we hope to answer with this development include: 1) Are there any components with incorrect labels? 2) Can we suggest labels for components that are marked “Unknown”? 3) What kind of overall confidence do we have in the quality of the existing labels? 4) What systems or procedures can we put in place to maximize label quality?

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This report documents the results of a long-term (5.79 year) exposure of 4-point bend corrosion test samples in the inlet and outlet vents of four spent nuclear fuel dry storage systems at the Maine Yankee Independent Spent Fuel Storage Installation. The goal of the test was to evaluate the corrosiveness of salt aerosols in a realistic near-marine environment, providing a data set for improved understanding of stress corrosion cracking of spent nuclear fuel dry storage canisters. Examination of the samples after extraction showed minor corrosion was present, mostly on rough-ground surfaces. However, dye penetrant testing showed that no SCC cracks were present. Dust collected on coupons co-located with the corrosion specimens was analyzed by scanning electron microscopy and leached to determine the soluble salts present. The dust was mostly organic material (pollen and stellate trichomes), with lesser detrital mineral grains. Salts present were a mix of sea-salts and continental salts, with chloride dominating the anions, but significant amounts of nitrate were also present. Both corrosion samples and dust samples showed evidence of wetting, indicating entry of water into the vents. The results of this field test suggest that the environment at Maine Yankee is not highly aggressive, although extrapolation from the periodically wetted vent samples to the hot, dry, canister surface may be difficult. No stress corrosion cracks were observed, but minor corrosion was present despite high nitrate concentrations in the salts. These observations may help address the ongoing question of the importance of nitrate in suppressing corrosion and SCC.

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Commercial nuclear power plants typically use nuclear fuel that is enriched to less than five weight percent in the isotope ²³⁵U. However, recently several vendors have proposed new nuclear power plant designs that would use fuel with ²³⁵U enrichments between five weight percent and 19.75 weight percent. Nuclear fuel with this level of ²³⁵U enrichment is known as “high assay low-enriched uranium.” Once it has been irradiated in a nuclear reactor and becomes used (or spent) nuclear fuel, it will be stored, transported, and disposed of. However, irradiated high assay low-enriched uranium differs from typical irradiated nuclear fuel in several ways, and these differences may have economic effects on its storage, transport, and disposal, compared to typical irradiated nuclear fuel. This report describes those differences and qualitatively discusses their potential economic effects on storage, transport, and disposal.

Derivative computation is a key component of optimization, sensitivity analysis, uncertainty quantification, and the solving of nonlinear problems. Automatic differentiation (AD) is a powerful technique for evaluating such derivatives, and in recent years, has been integrated into programming environments such as Jax, PyTorch, and TensorFlow to support derivative computations needed for training of machine learning models, facilitating wide-spread use of these technologies. The C++ language has become the de facto standard for scientific computing due to numerous factors, yet language complexity has made the wide-spread adoption of AD technologies for C++ difficult, hampering the incorporation of powerful differentiable programming approaches into C++ scientific simulations. This is exacerbated by the increasing emergence of architectures, such as GPUs, with limited memory capabilities and requiring massive thread-level concurrency. C++ AD tools must effectively use these environments to bring novel scientific simulations to next-generation DOE experimental and observational facilities. In this project, we investigated source transformation-based automatic differentiation using LLVM compiler infrastructure to automatically generate portable and efficient gradient computations of Kokkos-based code. We have demonstrated that our proposed strategy is feasible by investigating the usage of a prototype LLVM-based source transformation tool to generate gradients of simple functions made of sequences of simple Kokkos parallel regions. Speedups of up to 500x compared to Sacado were observed on NVIDIA V100 GPU.

Binary classification using machine learning is needed to address engineering problems such as identifying passing/failing parts based on measured features from aging hardware. In these classifications, providing the uncertainty of each prediction is essential to support engineering decision making. One popular classifier is the support vector machine (SVM). There are many variations, with the simplest being a linear division between two classes with a hyperplane. Kernel methods can be implement

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A dry etching process to transfer the pattern of a photonic integrated circuit design for high-speed laser communications is described. The laser stack under consideration is a 3.2-µm-thick InGaAs/InAlAs/InAlGaAs epitaxial structure grown by molecular beam epitaxy. The etching was performed using Cl2-based inductively-coupled-plasma and reactive-ion-etching (ICP-RIE) reactors. Four different recipes are presented in two similar ICP-RIE reactors, with special attention paid to the etched features formed with various hard mask compositions, in-situ passivations, and process temperatures. The results indicate that it is possible to produce high-aspect-ratio features with sub-micron separation on this multilayer structure. Additionally, the results of the etching highlight the tradeoffs involved with the corresponding recipes.

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