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Results for “reduction”
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Experimental and numerical study of permeability reduction caused by asphaltene precipitation and deposition during CO2 huff and puff injection in Eagle Ford shale
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Dynamics of ferrihydrite-bound organic carbon during microbial Fe reduction
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Benchmarking the Oxygen Reduction Reaction Activity of Pt-Based Catalysts using Standardized Rotating Disk Electrode Methods
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Development of reactor configurations for an electrofuels platform utilizing genetically modified iron oxidizing bacteria for the reduction of CO2 to biochemicals
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The effect of support morphology on CoOX/CeO2 catalysts for the reduction of NO by CO
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Identifying properties of low-loaded CoO[subscript X]/CeO[subscript 2] via X-ray absorption spectroscopy for NO reduction by CO
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Effects of dioxygen pressure on rates of NO[subscript x] selective catalytic reduction with NH[subscript 3] on Cu-CHA zeolites
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Increasing electrocatalytic nitrate reduction activity by controlling adsorption through PtRu alloying
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High-performance bimetallic alloy catalyst using Ni and N co-doped composite carbon for the oxygen electro-reduction
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Personalized cooling as an energy efficiency technology for city energy footprint reduction
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Mesh refinement for uncertainty quantification through model reduction
We present a novel way of deciding when and where to refine a mesh in probability space in order to facilitate uncertainty quantification in the presence of discontinuities in random space. A discontinuity in random space makes the application of generalized polynomial chaos expansion techniques prohibitively expensive. The reason is that for discontinuous problems, the expansion converges very slowly. An alternative to using higher terms in the expansion is to divide the random space in smaller elements where a lower degree polynomial is adequate to describe the randomness. In general, the partition of the random space is a dynamic process since some areas of the random space, particularly around the discontinuity, need more refinement than others as time evolves. In the current work we propose a way to decide when and where to refine the random space mesh based on the use of a reduced model. The idea is that a good reduced model can monitor accurately, within a random space element, the cascade of activity to higher degree terms in the chaos expansion. In turn, this facilitates the efficient allocation of computational sources to the areas of random space where they are more needed. For the Kraichnan–Orszag system, the prototypical system to study discontinuities in random space, we present theoretical results which show why the proposed method is sound and numerical results which corroborate the theory.
Multiscale model reduction for shale gas transport in poroelastic fractured media
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Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction
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Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty
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