Sharif Digital Repository

In ray-tracing-based propagation modelling, the electromagnetic field at the receiver is obtained by coherent summation of the fields of multipath components. It is therefore crucial to accurately calculate the phase of the electromagnetic field of each ray. In practice, when preparing the plan of the environment for ray tracing simulation, the lateral positions of the walls may not be included accurately in the database. This alters the phases of the fields as well as the delays of arrival of multipath components which may consequently lead to less accurate results. In this study, the sensitivity of ray tracing results to this type of geometrical inaccuracy is investigated through the... 

In this paper, detailed uncertainty propagation analysis (UPA) and variance-based global sensitivity analysis (GSA) are performed on the widely adopted double-diffuse convection (DDC) benchmark problem of a square porous cavity with horizontal temperature and concentration gradients. The objective is to understand the impact of uncertainties related to model parameters on metrics characterizing flow, heat and mass transfer processes, and to derive spatial maps of uncertainty and sensitivity indices which can provide physical insights and a better understanding of DDC processes in porous media. DDC simulations are computationally expensive and UPA and GSA require large number of simulations,... 

The dynamic state estimation (DSE) applied to power systems with synchrophasor measurements would estimate the system's true state based on measurements and predictions. In this application, as phasor measurement units (PMUs) are not deployed at all power system buses, state predictions would enhance the redundancy of DSE input data. The significance of predicted and measured data in DSE is affected by their confidence levels, which are inversely proportional to the corresponding variances. In practice, power system states may undergo drastic changes during hourly load fluctuations, component outages, or network switchings. In such conditions, the inclusion of predicted values could degrade... 

Probabilistic Seismic Loss Estimation is a methodology used as a quantitative and explicit expression of the performance of buildings using terms that address the interests of both owners and insurance companies. Applying the ATC 58 approach for seismic loss assessment of buildings requires using Incremental Dynamic Analysis (IDA), which needs hundreds of time-consuming analyses, which in turn hinders its wide application. The Endurance Time Method (ETM) is proposed herein as part of a demand propagation prediction procedure and is shown to be an economical alternative to IDA. Various scenarios were considered to achieve this purpose and their appropriateness has been evaluated using... 

A new time-domain hierarchical Bayesian framework is proposed to improve the performance of Bayesian methods in terms of reliability and robustness of estimates particularly for uncertainty quantification and propagation in structural dynamics. The proposed framework provides a reliable approach to account for the variability of the inference results observed when using different data sets. The proposed formulation is compared with a state-of-the-art Bayesian approach using numerical and experimental examples. The results indicate that the hierarchical Bayesian framework provides a more realistic account of the uncertainties, whereas the non-hierarchical Bayesian approach severely... 

A new time-domain hierarchical Bayesian framework is proposed to improve the performance of Bayesian methods in terms of reliability and robustness of estimates particularly for uncertainty quantification and propagation in structural dynamics. The proposed framework provides a reliable approach to account for the variability of the inference results observed when using different data sets. The proposed formulation is compared with a state-of-the-art Bayesian approach using numerical and experimental examples. The results indicate that the hierarchical Bayesian framework provides a more realistic account of the uncertainties, whereas the non-hierarchical Bayesian approach severely... 

In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any misconceptions in the Bayesian framework since it is robust with respect to the modeling assumptions and the observed data. Rather, this issue has deep roots in users’ inability to develop an appropriate class of probabilistic models. This paper bridges this significant gap, introducing a novel Bayesian hierarchical setting, which breaks time-history vibration responses into several segments so as to capture and identify the variability of inferred parameters over the... 

The implementation of Monte Carlo simulations (MCSs) for the propagation of uncertainty in real-world seawater intrusion (SWI) numerical models often becomes computationally prohibitive due to the large number of deterministic solves needed to achieve an acceptable level of accuracy. Previous studies have mostly relied on parallelization and grid computing to decrease the computational time of MCSs. However, another approach which has received less attention in the literature is to decrease the number of deterministic simulations by using more efficient sampling strategies. Sampling efficiency is a measure of the optimality of a sampling strategy. A more efficient sampling strategy requires... 

Real world models of seawater intrusion (SWI) require high computational efforts. This creates computational difficulties for the uncertainty propagation (UP) analysis of these models due the need for repeated numerical simulations in order to adequately capture the underlying statistics that describe the uncertainty in model outputs. Moreover, despite the obvious advantages of moment-independent global sensitivity analysis (SA) methods, these methods have rarely been employed for SWI and other complex groundwater models. The reason is that moment-independent global SA methods involve repeated UP analysis which further becomes computationally demanding. This study proposes the use of... 

In natural gas transmission networks, the efficiency of daily operation is strongly dependent on our knowledge about the customer future demands. Unavailability of accurate demand forecasts makes it more important to be able to characterize the loads and quantify the corresponding uncertainty. A planning strategy for natural gas transmission networks under future demand uncertainty is addressed, which involves coupling of a gas transmission network dynamic simulator with the stochastic optimization framework. Loads from a gas-fired power plant are studied where the loads are characterized by a number of uncertain parameters, and unscented transform is utilized for uncertainty propagation.... 

This paper presents a hierarchical Bayesian modeling framework for the uncertainty quantification in modal identification of linear dynamical systems using multiple vibration data sets. This novel framework integrates the state-of-the-art Bayesian formulations into a hierarchical setting aiming to capture both the identification precision and the variability prompted due to modeling errors. Such developments have been absent from the modal identification literature, sustained as a long-standing problem at the research spotlight. Central to this framework is a Gaussian hyper probability model, whose mean and covariance matrix are unknown, encapsulating the uncertainty of the modal parameters.... 

The interaction between a lake and groundwater has important implications to the quantity and quality of water in both environments. Quantification of lake-groundwater interaction (LGI) has been challenging in regions with limited in-situ data. LGI can be quantified by physically-based models, direct measurement of seepage, measurements of conservative chemical or isotopic tracers, and lake water balance. Despite the accuracy of the methods based on hydrochemical or isotopic measurements and analysis, they require extensive field data that are costly to collect in large lakes. Instead, the data required to quantify LGI by the lake water budget method can be obtained via typical ground... 

The majority of literature regarding optimized Latin hypercube sampling (OLHS) is devoted to increasing the efficiency of these sampling strategies through the development of new algorithms based on the combination of innovative space-filling criteria and specialized optimization schemes. However, little attention has been given to the impact of the initial design that is fed into the optimization algorithm, on the efficiency of OLHS strategies. Previous studies, as well as codes developed for OLHS, have relied on one of the following two approaches for the selection of the initial design in OLHS: (1) the use of random points in the hypercube intervals (random LHS), and (2) the use of...