Publications

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This report summarizes the international collaboration work conducted by Sandia and funded by the US Department of Energy Office (DOE) of Nuclear Energy Spent Fuel and Waste Science & Technology (SFWST) as part of the Sandia National Laboratories Salt R&D and Salt International work packages. This report satisfies the level-three milestone M3SF-20SN010303062. Several stand-alone sections make up this summary report, each completed by the participants. The sections discuss international collaborations on geomechanical benchmarking exercises (WEIMOS), granular salt reconsolidation (KOMPASS), engineered barriers (RANGERS), and model comparison (DECOVALEX). Lastly, the report summarizes a newly developed working group on the development of scenarios as part of the performance assessment development process, and the activities related to the Nuclear Energy Agency (NEA) Salt club and the US/German Workshop on Repository Research, Design and Operations.

Despite their wide use in terahertz (THz) research and technology, the application spectra of photoconductive antenna (PCA) THz detectors are severely limited due to the relatively high optical gating power requirement. This originates from poor conversion efficiency of optical gate beam photons to photocurrent in materials with subpicosecond carrier lifetimes. Here we show that using an ultra-thin (160 nm), perfectly absorbing low-temperature grown GaAs metasurface as the photoconductive channel drastically improves the efficiency of THz PCA detectors. This is achieved through perfect absorption of the gate beam in a significantly reduced photoconductive volume, enabled by the metasurface. This Letter demonstrates that sensitive THz PCA detection is possible using optical gate powers as low as 5 μW-three orders of magnitude lower than gating powers used for conventionalPCAdetectors.We show that significantly higher optical gate powers are not necessary for optimal operation, as they do not improve the sensitivity to the THz field. This class of efficient PCA THz detectors opens doors for THz applications with low gate power requirements.

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This report represents the milestone deliverable M4SF-21SN010309021 “Modeling Activities Related to Waste Form Degradation: Progress Report” that describes the progress of R&D activities of ongoing modeling investigations specifically on nuclear waste glass degradation, Density Functional Theory (DFT) studies on clarkeite structure and stability, and electrochemical modeling of spent nuclear fuel (SNF). These activities are part of the newly-created Waste form Testing, Modeling, and Performance work package at Sandia National Laboratories (SNL). This work package is part of the “Inventory and Waste Form Characteristics and Performance” control account that includes various experimental and modeling activities on nuclear waste degradation conducted at Oak Ridge National Laboratory (ORNL), SNL, Argonne National Laboratory (ANL), and Pacific Northwest National Laboratory (PNNL).

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Performing terrain classification with data from heterogeneous imaging modalities is a very challenging problem. The challenge is further compounded by very high spatial resolution. (In this paper we consider very high spatial resolution to be much less than a meter.) At very high resolution many additional complications arise, such as geometric differences in imaging modalities and heightened pixel-by-pixel variability due to inhomogeneity within terrain classes. In this paper we consider the fusion of very high resolution hyperspectral imaging (HSI) and polarimetric synthetic aperture radar (PolSAR) data. We introduce a framework that utilizes the probabilistic feature fusion (PFF) one-class classifier for data fusion and demonstrate the effect of making pixelwise, superpixel, and pixelwise voting (within a superpixel) terrain classification decisions. We show that fusing imaging modality data sets, combined with pixelwise voting within the spatial extent of superpixels, gives a robust terrain classification framework that gives a good balance between quantitative and qualitative results.

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This paper serves as the Interface Control Document (ICD) for the Seascape automated test harness developed at Sandia National Laboratories. The primary purposes of the Seascape system are: (1) provide a place for accruing large, curated, labeled data sets useful for developing and evaluating detection and classification algorithms (including, but not limited to, supervised machine learning applications) (2) provide an automated structure for specifying, running and generating reports on algorithm performance. Seascape uses GitLab, Nexus, Solr, and Banana, open source codes, together with code written in the Python language, to automatically provision and configure computational nodes, queue up jobs to accomplish algorithms test runs against the stored data sets, gather the results and generate reports which are then stored in the Nexus artifact server.

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