Browsing by Subject "stability"

Now showing 1 - 4 of 4
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    The Effects of Integrated Chemical Catalysis and Reductive Pretreatment on Hydrothermal Liquefaction Derived Bio-oil Yield, Composition, and Stability
    (2018-07) Peterson, Glen
    Bio-oil is a viscous mixture of aldehydes, ketones, etc. It can be used for various applications such as chemicals or fuels. However, due to its acidic nature, bio-oil is unstable. Integrated chemical catalysis (ICC) and reductive pretreatment (RP) hydrothermal liquefaction (HTL) of the biomass feedstock (corn stover and hybrid poplar) were performed in an effort to stabilize the resultant bio-oil. In ICC trials, acidic, basic, and reductive solutions were added to the HTL chamber. RP trials were completed separately. Yield, composition, and stability analysis were conducted using fractionation and GC-MS techniques. Phase distribution was relatively unaffected by varying ICC treatments. Acidic ICC increased furan derivative relative abundance in the bio-oil, while alkaline ICC and RPs decreased furan content. RPs increased double bonded γ-carbon compounds such as eugenol. RPs and alkaline ICC trials increased bio-oil pH and subsequently bio-oil stability, whereas acidic ICC lowered bio-oil pH and destabilized the product.
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    Linear Stability and Sensitivity of a Low-speed Jet in Cross-flow
    (2018-05) Regan, Marc
    The Jet in Cross-flow (JICF) is characterized by a jet of fluid injected transverse to an incoming cross-flow. Complex vortical structures are generated as the cross-flow boundary layer interacts with the jet. The goal of this dissertation is to increase understanding of the stability and sensitivity of the JICF. Achieving this goal will directly benefit the many engineering applications which use the JICF, including gas turbine combustor dilution jets, film cooling, vertical and/or short take-off and landing (V/STOL) aircraft, and thrust vectoring. The JICF is studied . These equations are key components of this research. The JICF is studied using direct numerical simulation of the Navier-Stokes equations, as well as their adjoint, at a Reynolds number of 2000, and two jet-to-cross-flow velocity ratios: R = 2 with an absolutely unstable upstream shear-layer, and R = 4 with a convectively unstable upstream shear-layer. Linear stability analysis of the JICF reveals that the dominant eigenmodes are shear-layer modes whose frequencies match frequencies of the upstream shear-layer observed in simulation (Iyer & Mahesh, 2016) and experiment (Megerian et al., 2007). Asymmetric modes are shown to be more important to the overall dynamics at higher jet-to-cross-flow ratios. Low-frequency modes persist far downstream, and are connected to wake vortices. For R = 4, downstream shear-layer eigenmodes can be more unstable than the upstream shear-layer modes. Adjoint modes show that the upstream shear-layer is most sensitive to perturbations along the upstream side of the jet nozzle exit. Additionally, the lower frequency downstream modes have sensitive regions that extend upstream into the cross-flow boundary layer. Wavemaker results are shown to be consistent with the transition of the upstream shear-layer from absolute to convective instability. Optimal perturbations reveal that for short-time horizons, perturbations that are asymmetric, and grow along the counter-rotating vortex pair, dominate when R = 2. However, as the time horizon increases, growth is focused along the upstream shear-layer. When R = 4, the optimal perturbations for short-time scales are dominated by growth along the downstream shear-layer. For long-time horizons, the optimal perturbations become hybrid modes that grow along the upstream and downstream shear-layers, simultaneously.
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    Robustness and Stability of Deep Learning
    (2021-06) Lai, Chieh-Hsin
    This dissertation serves as a collection of my three projects after I received the Ph.D. candidacy in 2018. The first two projects (in Chapter 2 and 3, respectively), joint works with Dongmian Zou and Gilad Lerman, are about novel algorithms for unsupervised and semi-supervised anomaly detection tasks, respectively. Our new methods allow datasets with a high ratio of corruption by outliers. The third project (in Chapter 4), a joint work with Kshitij Tayal, Raunak Manekar, Zhong Zhuang, Vipin Kumar and Ju Sun, brings out a methodology for improving the performance of end-to-end deep learning approaches for inverse problems with many-to-one forward mappings. General features of these three projects are introduced in the following. In Chapter 2, we propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a ``manifold" close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall. In Chapter 3, we propose a new method for novelty detection that can tolerate high corruption of the training points, whereas previous works assumed either no or very low corruption. Our method trains a robust variational autoencoder (VAE), which aims to generate a model for the uncorrupted training points. To gain robustness to high corruption, we incorporate the following four changes to the common VAE: 1. Extracting crucial features of the latent code by a carefully designed dimension reduction component for distributions; 2. Modeling the latent distribution as a mixture of Gaussian low-rank inliers and full-rank outliers, where the testing only uses the inlier model; 3. Applying the Wasserstein-1 metric for regularization, instead of the Kullback-Leibler (KL) divergence; and 4. Using a robust error for reconstruction. We establish both robustness to outliers and suitability to low-rank modeling of the Wasserstein metric as opposed to the KL divergence. We illustrate state-of-the-art results on standard benchmarks. In Chapter 4, we propose a methodology to resolve the irregular approximation of the inverse mapping in some inverse problems with many-to-one forward mappings; especially, we focus on 2D Fourier phase retrieval problem. In many physical systems, inputs related by intrinsic system symmetries generate the same output. So when inverting such systems, an input is mapped to multiple symmetry-related outputs. This causes fundamental difficulties for tackling these inverse problems by the emerging end-to-end deep learning approach. Taking phase retrieval as an illustrative example, we show that careful symmetry breaking on the training data can help get rid of the difficulties and significantly improve learning performance in real data experiments. We also extract and highlight the underlying mathematical principle of the proposed solution, which is directly applicable to other inverse problems.
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    Species richness, but not phylogenetic diversity, influences community biomass production and temporal stability in a re-examination of 16 grassland biodiversity studies
    (Wiley, 2015) Venail, Patrick; Gross, Kevin; Oakley, Todd H; Narwani, Anita; Allan, Eric; Flombaum, Pedro; Isbell, Forest; Joshi, Jasmin; Reich, Peter B; Tilman, David; van Ruijven, Jasper; Cardinale, Bradley J
    Hundreds of experiments have now manipulated species richness (SR) of various groups of organisms and examined how this aspect of biological diversity influences ecosystem functioning. Ecologists have recently expanded this field to look at whether phylogenetic diversity (PD) among species, often quantified as the sum of branch lengths on a molecular phylogeny leading to all species in a community, also predicts ecological function. Some have hypothesized that phylogenetic divergence should be a superior predictor of ecological function than SR because evolutionary relatedness represents the degree of ecological and functional differentiation among species. But studies to date have provided mixed support for this hypothesis. Here, we reanalyse data from 16 experiments that have manipulated plant SR in grassland ecosystems and examined the impact on above-ground biomass production over multiple time points. Using a new molecular phylogeny of the plant species used in these experiments, we quantified how the PD of plants impacts average community biomass production as well as the stability of community biomass production through time. Using four complementary analyses, we show that, after statistically controlling for variation in SR, PD (the sum of branches in a molecular phylogenetic tree connecting all species in a community) is neither related to mean community biomass nor to the temporal stability of biomass. These results run counter to past claims. However, after controlling for SR, PD was positively related to variation in community biomass over time due to an increase in the variances of individual species, but this relationship was not strong enough to influence community stability. In contrast to the non-significant relationships between PD, biomass and stability, our analyses show that SR per se tends to increase the mean biomass production of plant communities, after controlling for PD. The relationship between SR and temporal variation in community biomass was either positive, non-significant or negative depending on which analysis was used. However, the increases in community biomass with SR, independently of PD, always led to increased stability. These results suggest that PD is no better as a predictor of ecosystem functioning than SR. Synthesis. Our study on grasslands offers a cautionary tale when trying to relate PD to ecosystem functioning suggesting that there may be ecologically important trait and functional variation among species that is not explained by phylogenetic relatedness. Our results fail to support the hypothesis that the conservation of evolutionarily distinct species would be more effective than the conservation of SR as a way to maintain productive and stable communities under changing environmental conditions.