1,519 research outputs found

    Letter written to Thomas Bowen from James Blunt.

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    Handwriting is hard to decipher, but was transcribed by Lloyd Holbrook of Goodland, Kansas.https://scholars.fhsu.edu/tj_bowen/1004/thumbnail.jp

    Letter from James G. Blunt to Salmon P. Chase, August 9, 1859

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    In 1856, abolitionist James G. Blunt (1826-1881) moved to Kansas to get involved in the conflict over slavery. He joined a militia with John Brown and was a delegate to the Wyandotte constitutional convention that composed the Kansas state constitution in 1859. In this letter, Blunt writes to Salmon Chase about the Wyandotte convention, ratification of the Kansas constitution, and Chase’s possible presidential nomination.https://nwcommons.nwciowa.edu/abolitionistletters/1001/thumbnail.jp

    Reconstruction of three-dimensional porous media using generative adversarial neural networks

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    To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure

    Continuum-scale characterization of solute transport based on pore-scale velocity distributions

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    We present a methodology to characterize a continuum-scale model of transport in porous media on the basis of pore-scale distributions of velocities computed in three-dimensional pore-space images. The methodology is tested against pore-scale simulations of flow and transport for a bead pack and a sandstone sample. We employ a double continuum approach to describe transport in mobile and immobile regions. Model parameters are characterized through inputs resulting from the micron-scale reconstruction of the pore space geometry and the related velocity field. We employ the outputs of pore-scale analysis to (i) quantify the proportion of mobile and immobile fluid regions, and (ii) assign the velocity distribution in an effective representation of the medium internal structure. Our results (1) show that this simple conceptual model reproduces the spatial profiles of solute concentration rendered by pore-scale simulation without resorting to model calibration, and (2) highlight the critical role of pore-scale velocities in the characterization of the model parameters
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