Interpretable A-posteriori error indication for graph neural network surrogate models
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Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, in this work, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent X-ray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non-disperse and disperse size distributions.
Understanding the relationship between the evolution of microstructures of irradiated LiAlO2pellets and tritium diffusion, retention and release could improve predictions of tritium performance. Given expert-labeled segmented images of irradiated and unirradiated pellets, we trained Deep Convolutional Neural Networks to segment images into defect, grain, and boundary classes. Qualitative microstructural information was calculated from these segmented images to facilitate the comparison of unirradiated and irradiated pellets. We tested modifications to improve the sensitivity of the model, including incorporating meta-data into the model and utilizing uncertainty quantification. The predicted segmentation was similar to the expert-labeled segmentation for most methods of microstructural qualification, including pixel proportion, defect area, and defect density. Overall, the high performance metrics for the best models for both irradiated and unirradiated images shows that utilizing neural network models is a viable alternative to expert-labeled images.
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The Southwest Carbon Partnership (SWP), one of the U.S. Department of Energy (U.S. DOE) seven Regional Carbon Sequestration Partnerships, is currently working to demonstrate the utilization and storage of CO 2 in the Farnsworth Unit (FWU) Enhanced Oil Recovery (EOR) site under the final development phase of this U.S. DOE initiative. A component of the research is to use fluid tracers to understand the multifluid flow patterns that develop between injection and production wells via collected field data and supporting numerical reservoir models. The FWU, located in the Anadarko Basin, Ochiltree County, Texas, and being operated by Chaparral Energy, is a mature EOR water-flood field, which is currently being converted to a CO 2 flow, with inverted 5-spot patterns transitioning from pure water to alternating CO 2 and water floods (i.e., water alternating gas (WAG)) at an approximate rate of one every 6 to 10 months. The SWP tracer program is conducting a suite of tracer injections into the active 5-spot patterns at the FWU. Tracers have been selected to be nonreactive and either principally soluble in CO 2 (gas soluble) or water (aqueous soluble). In addition to characterizing the multifluid flow behaviour within reservoir, the gas and aqueous tracers have roles in detecting any leakage from the reservoir. A total of seven unique perfluorocarbon tracer (PFT) compounds make up the suite of gas soluble tracers and eight unique naphthalene sulfonate tracer (NPT) compounds comprise the aqueous soluble tracers. Lastly, all selected tracers are significantly detectable below the parts per billion concentrations, allowing for high resolution for the inter-well tests at relatively low injection volumes.
Inadequate sanitation is amongst the causes of escalating pollution problems in developing countries, as municipal wastewater treatment systems remove only a fraction of pollutants that could be removed with best available technologies (BAT). Although BAT is a proven instrument of environmental policies, its potential for municipalities remains largely unused in developing countries. In order to ease its implementation, the paper developed a simplified assessment approach towards identifying an approximating of BAT in terms of a “flexible BAT” (FlexiBAT), which is based on the identification of national reference plants assessed with respect to pollutant removal (environmental impact, health impact), costs (economic viability, affordability) and social acceptability. The concept was tested for 58 case studies in India, where none of the technologies passed all tests for FlexiBAT. Therefore, there is a need to improve or develop better and more innovative technologies. Amongst the most promising ones, membrane bioreactors provided good physical water quality, but costs were high, while for moving bed biofilm reactors costs were low, but water quality was insufficient. Conventional onsite systems require separate consideration. In order to ease the identification of FlexiBAT, a national environmental information system with data from the regular monitoring of existing plants would be needed.
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