20,526 research outputs found

    Debates—Stochastic subsurface hydrology from theory to practice: why stochastic modeling has not yet permeated into practitioners?

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    This is the peer reviewed version of the following article: [Sanchez-Vila, X., and D. Fernàndez-Garcia (2016), Debates—Stochastic subsurface hydrology from theory to practice: Why stochastic modeling has not yet permeated into practitioners?, Water Resour. Res., 52, 9246–9258, doi:10.1002/2016WR019302], which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/2016WR019302/abstract. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-ArchivingWe address modern topics of stochastic hydrogeology from their potential relevance to real modeling efforts at the field scale. While the topics of stochastic hydrogeology and numerical modeling have become routine in hydrogeological studies, nondeterministic models have not yet permeated into practitioners. We point out a number of limitations of stochastic modeling when applied to real applications and comment on the reasons why stochastic models fail to become an attractive alternative for practitioners. We specifically separate issues corresponding to flow, conservative transport, and reactive transport. The different topics addressed are emphasis on process modeling, need for upscaling parameters and governing equations, relevance of properly accounting for detailed geological architecture in hydrogeological modeling, and specific challenges of reactive transport. We end up by concluding that the main responsible for nondeterministic models having not yet permeated in industry can be fully attributed to researchers in stochastic hydrogeology.Peer ReviewedPostprint (author's final draft

    Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes

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    In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.Comment: Substantial revisio

    Debates—Stochastic subsurface hydrology from theory to practice: why stochastic modeling has not yet permeated into practitioners?

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    We address modern topics of stochastic hydrogeology from their potential relevance to real modeling efforts at the field scale. While the topics of stochastic hydrogeology and numerical modeling have become routine in hydrogeological studies, nondeterministic models have not yet permeated into practitioners. We point out a number of limitations of stochastic modeling when applied to real applications and comment on the reasons why stochastic models fail to become an attractive alternative for practitioners. We specifically separate issues corresponding to flow, conservative transport, and reactive transport. The different topics addressed are emphasis on process modeling, need for upscaling parameters and governing equations, relevance of properly accounting for detailed geological architecture in hydrogeological modeling, and specific challenges of reactive transport. We end up by concluding that the main responsible for nondeterministic models having not yet permeated in industry can be fully attributed to researchers in stochastic hydrogeology

    Scale Up Reactive Flow in Heterogeneous Porous Media Using Continuous Time Random Walk Approach

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    Reservoir heterogeneities strongly affect the fluid flow in porous media. The behavior of transport and reaction of fluid varies at different scales, leading to the discrepancies between laboratory experiments and field observations. The reactive processes in porous media may alter reservoir properties with different spatial and temporal scale, varying future transport and reaction behaviors. This thesis provides an efficient probabilistic approach to scale up coupled transport and reaction processes in heterogeneous porous media to field scale based on laboratory-scale information. The continuous time random walk (CTRW) is a probabilistic framework which is always incorporating with particle tracking (PT) approach to model solute transport in heterogeneous porous medium. In CTRW-PT approach, the motion of solute particles is described as combination of random independent spatial and temporal increments in each walk step. The spatial and temporal increments, or normally called as transition distance and time, are chosen from a joint space-time probability density function by a stochastic process. The CTRW-PT approach simulates reactive fluid transport as non-reactive fluid. The modelling of reaction and dissolution is followed by in each time step, updating the change of porous medium and its effect on following transport and reaction. The characteristic probability density function (pdf) is used to simulate the transport of fluid. Adjusting the ensemble parameter β and t_2 accounts for the effects of heterogeneity which leads to anomalous flow behavior: the fluid propagates along the “preferential” pathways with short transition times and “trapped” in some zones with long transition times. It mimics the macroscopic behavior that fluid has the tendency to propagate in high-permeability zones and bypass the low-permeability zones. Simulations of non-reactive tracer flow and nanofluid flow under various conditions are performed at core-scale to obtain the key parameters in characteristic pdf by matching the experimental results. The effect of reactive process, heterogeneity and flow rate on flow behavior is analyzed. The CTRW-PT simulation captures the characters of anomalous behavior of delayed breakthrough. The model is run at larger scale as reservoir properties are scaled up properly. The core-scale simulation based on the characteristic pdf agrees with the experimental results. The large-scale simulation is implemented by using the characteristic pdf to describe flow behaviors in a large-scale domain. It is shown that CTRW-PT approach is more effective in large-scale modeling than solving advection-diffusion-reaction equation (ADRE) by finite difference method (FDM). The simulation results at large scale show that the flow response is spatial-dependent. Compared to solving traditional ADRE, the utilization of CTRW-PT approach to model reactive fluid flow captures the characters of anomalous flow behavior, especially in highly heterogeneous porous media. By the probabilistic framework and stochastic process, this approach is more computational-efficient for scaling up lab-scale results to larger scale. It can consolidate the lab-scale understanding with field prediction to optimize the field treatment design

    Modeling and Real-Time Scheduling of DC Platform Supply Vessel for Fuel Efficient Operation

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    DC marine architecture integrated with variable speed diesel generators (DGs) has garnered the attention of the researchers primarily because of its ability to deliver fuel efficient operation. This paper aims in modeling and to autonomously perform real-time load scheduling of dc platform supply vessel (PSV) with an objective to minimize specific fuel oil consumption (SFOC) for better fuel efficiency. Focus has been on the modeling of various components and control routines, which are envisaged to be an integral part of dc PSVs. Integration with photovoltaic-based energy storage system (ESS) has been considered as an option to cater for the short time load transients. In this context, this paper proposes a real-time transient simulation scheme, which comprises of optimized generation scheduling of generators and ESS using dc optimal power flow algorithm. This framework considers real dynamics of dc PSV during various marine operations with possible contingency scenarios, such as outage of generation systems, abrupt load changes, and unavailability of ESS. The proposed modeling and control routines with real-time transient simulation scheme have been validated utilizing the real-time marine simulation platform. The results indicate that the coordinated treatment of renewable based ESS with DGs operating with optimized speed yields better fuel savings. This has been observed in improved SFOC operating trajectory for critical marine missions. Furthermore, SFOC minimization at multiple suboptimal points with its treatment in the real-time marine system is also highlighted

    Modelo de apoio à decisão para a manutenção condicionada de equipamentos produtivos

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    Doctoral Thesis for PhD degree in Industrial and Systems EngineeringIntroduction: This thesis describes a methodology to combine Bayesian control chart and CBM (Condition-Based Maintenance) for developing a new integrated model. In maintenance management, it is a challenging task for decision-maker to conduct an appropriate and accurate decision. Proper and well-performed CBM models are beneficial for maintenance decision making. The integration of Bayesian control chart and CBM is considered as an intelligent model and a suitable strategy for forecasting items failures as well as allow providing an effectiveness maintenance cost. CBM models provides lower inventory costs for spare parts, reduces unplanned outage, and minimize the risk of catastrophic failure, avoiding high penalties associated with losses of production or delays, increasing availability. However, CBM models need new aspects and the integration of new type of information in maintenance modeling that can improve the results. Objective: The thesis aims to develop a new methodology based on Bayesian control chart for predicting failures of item incorporating simultaneously two types of data: key quality control measurement and equipment condition parameters. In other words, the project research questions are directed to give the lower maintenance costs for real process control. Method: The mathematical approach carried out in this study for developing an optimal Condition Based Maintenance policy included the Weibull analysis for verifying the Markov property, Delay time concept used for deterioration modeling and PSO and Monte Carlo simulation. These models are used for finding the upper control limit and the interval monitoring that minimizes the (maintenance) cost function. Result: The main contribution of this thesis is that the proposed model performs better than previous models in which the hypothesis of using simultaneously data about condition equipment parameters and quality control measurements improve the effectiveness of integrated model Bayesian control chart for Condition Based Maintenance.Introdução: Esta tese descreve uma metodologia para combinar Bayesian control chart e CBM (Condition- Based Maintenance) para desenvolver um novo modelo integrado. Na gestão da manutenção, é importante que o decisor possa tomar decisões apropriadas e corretas. Modelos CBM bem concebidos serão muito benéficos nas tomadas de decisão sobre manutenção. A integração dos gráficos de controlo Bayesian e CBM é considerada um modelo inteligente e uma estratégica adequada para prever as falhas de componentes bem como produzir um controlo de custos de manutenção. Os modelos CBM conseguem definir custos de inventário mais baixos para as partes de substituição, reduzem interrupções não planeadas e minimizam o risco de falhas catastróficas, evitando elevadas penalizações associadas a perdas de produção ou atrasos, aumentando a disponibilidade. Contudo, os modelos CBM precisam de alterações e a integração de novos tipos de informação na modelação de manutenção que permitam melhorar os resultados.Objetivos: Esta tese pretende desenvolver uma nova metodologia baseada Bayesian control chart para prever as falhas de partes, incorporando dois tipos de dados: medições-chave de controlo de qualidade e parâmetros de condição do equipamento. Por outras palavras, as questões de investigação são direcionadas para diminuir custos de manutenção no processo de controlo.Métodos: Os modelos matemáticos implementados neste estudo para desenvolver uma política ótima de CBM incluíram a análise de Weibull para verificação da propriedade de Markov, conceito de atraso de tempo para a modelação da deterioração, PSO e simulação de Monte Carlo. Estes modelos são usados para encontrar o limite superior de controlo e o intervalo de monotorização para minimizar a função de custos de manutenção.Resultados: A principal contribuição desta tese é que o modelo proposto melhora os resultados dos modelos anteriores, baseando-se na hipótese de que, usando simultaneamente dados dos parâmetros dos equipamentos e medições de controlo de qualidade. Assim obtém-se uma melhoria a eficácia do modelo integrado de Bayesian control chart para a manutenção condicionada

    The use of novel mechanical devices for enhancing the performance of railway vehicles

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    Following successful implementation of inerters for passive mechanical control in racing cars, this research studies potential innovative solutions for railway vehicle suspensions by bringing the inerter concept to the design of mechatronic systems. The inerter is a kinetic energy storage device which reacts to relative accelerations; together with springs and dampers, it can implement a range of mechanical networks distinguished by their frequency characteristics. This thesis investigates advantages of inerter-based novel devices to simplify the design of active solutions. Most of the research work is devoted to the enhancement of vertical ride quality; integrated active-plus-novel-passive solutions are proposed for the secondary suspensions. These are defined by different active control strategies and passive configurations including inerters. By optimisation of the suspension parameters, a synergy between passive and active configurations is demonstrated for a range of ride quality conditions. The evidence of cooperative work is found in the reduction of the required active forces and suspension travelling. This reveals a potential for reducing the actuator size. Benefits on power requirements and actuator dynamic compensation were also identified. One of the strategies features a nonlinear control law proposed here to compensate for 'sky-hook' damping effects on suspension deflection; this, together with inerter-based devices attains up to 50% in active force reduction for a setting providing 30% of ride quality enhancement. The study is developed from both, an analytical and an engineering perspective. Validation of the results with a more sophisticated model is performed. The lateral stability problem was briefly considered towards the end of the investigation. A potential use of inerter-based devices to replace the static yaw stiffness by dynamic characteristics was identified. This leads to a synergy with 'absolute stiffness', an active stability solution for controlling the wheelset 'hunting' problem, for reducing the creep forces developed during curve negotiation

    Large-Scale Structure of Multi-Optimised Networks

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