62,942 research outputs found

    Numerical approximation of poroelasticity with random coefficients using Polynomial Chaos and Hybrid High-Order methods

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    In this work, we consider the Biot problem with uncertain poroelastic coefficients. The uncertainty is modelled using a finite set of parameters with prescribed probability distribution. We present the variational formulation of the stochastic partial differential system and establish its well-posedness. We then discuss the approximation of the parameter-dependent problem by non-intrusive techniques based on Polynomial Chaos decompositions. We specifically focus on sparse spectral projection methods, which essentially amount to performing an ensemble of deterministic model simulations to estimate the expansion coefficients. The deterministic solver is based on a Hybrid High-Order discretization supporting general polyhedral meshes and arbitrary approximation orders. We numerically investigate the convergence of the probability error of the Polynomial Chaos approximation with respect to the level of the sparse grid. Finally, we assess the propagation of the input uncertainty onto the solution considering an injection-extraction problem.Comment: 30 pages, 15 Figure

    Spatial Wireless Channel Prediction under Location Uncertainty

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    Spatial wireless channel prediction is important for future wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the channel parameters and in predicting the channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel

    Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

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    This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

    A Compressed Sampling and Dictionary Learning Framework for WDM-Based Distributed Fiber Sensing

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    We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a pre-processing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber. The performance is evaluated by simulations and experimental data for a fiber sensor system with common core architecture.Comment: Accepted for publication in Journal of the Optical Society of America A [ \copyright\ 2017 Optical Society of America.]. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibite

    The Complex Interstellar Na I Absorption toward h and Chi Persei

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    Recent high spatial and spectral resolution investigations of the diffuse interstellar medium (ISM) have found significant evidence for small-scale variations in the interstellar gas on scales less than or equal to 1 pc. To better understand the nature of small-scale variations in the ISM, we have used the KPNO WIYN Hydra multi-object spectrograph, which has a mapping advantage over the single-axis, single-scale limitations of studies using high proper motion stars and binary stars, to obtain moderate resolution (~12 km/s) interstellar Na I D absorption spectra of 172 stars toward the double open cluster h and Chi Persei. All of the sightlines toward the 150 stars with spectra that reveal absorption from the Perseus spiral arm show different interstellar Na I D absorption profiles in the Perseus arm gas. Additionally, we have utilized the KPNO Coude Feed spectrograph to obtain high-resolution (~3 km/s) interstellar Na I D absorption spectra of 24 of the brighter stars toward h and Chi Per. These spectra reveal an even greater complexity in the interstellar Na I D absorption in the Perseus arm gas and show individual components changing in number, velocity, and strength from sightline to sightline. If each of these individual velocity components represents an isolated cloud, then it would appear that the ISM of the Perseus arm gas consists of many small clouds. Although the absorption profiles vary even on the smallest scales probed by these high-resolution data (~30";~0.35pc), our analysis reveals that some interstellar Na I D absorption components from sightline to sightline are related, implying that the ISM toward h and Chi Per is probably comprised of sheets of gas in which we detect variations due to differences in the local physical conditions of the gas.Comment: 27 pages text; 8 figure

    Dynamic Robust Transmission Expansion Planning

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    Recent breakthroughs in Transmission Network Expansion Planning (TNEP) have demonstrated that the use of robust optimization, as opposed to stochastic programming methods, renders the expansion planning problem considering uncertainties computationally tractable for real systems. However, there is still a yet unresolved and challenging problem as regards the resolution of the dynamic TNEP problem (DTNEP), which considers the year-by-year representation of uncertainties and investment decisions in an integrated way. This problem has been considered to be a highly complex and computationally intractable problem, and most research related to this topic focuses on very small case studies or used heuristic methods and has lead most studies about TNEP in the technical literature to take a wide spectrum of simplifying assumptions. In this paper an adaptive robust transmission network expansion planning formulation is proposed for keeping the full dynamic complexity of the problem. The method overcomes the problem size limitations and computational intractability associated with dynamic TNEP for realistic cases. Numerical results from an illustrative example and the IEEE 118-bus system are presented and discussed, demonstrating the benefits of this dynamic TNEP approach with respect to classical methods.Comment: 10 pages, 2 figures. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TPWRS.2016.2629266, IEEE Transactions on Power Systems 201
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