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.
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
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.
