Sharif Digital Repository

Reliability evaluations play a significant role in engineering applications to ensure the serviceability and safety of advanced structures such as those made of composites. Here, a dynamic reliability evaluation analysis based on the probability density evolution Method (PDEM) has been adapted to assess the reliability of composite structures under uncertainties within the material properties and the external loadings. A Back-Propagation Neural Network approach is employed to identify the system's nonlinear structural response, which is often the case under large deformations. To exemplify, a split Hopkinson pressure bar system was employed to mimic the mechanical behavior of a... 

The seismic risk framework of building structures has been presented to reduce earthquake-induced adverse consequences. In this context, probabilistic analysis of engineering demand parameters (EDPs) is always associated with many uncertainties and high computational demand for use in practical applications. This study has been presented to efficiently estimate the distribution of EDPs in probabilistic seismic risk assessment of irregular steel moment-resisting frames (SMRFs). For this purpose, the incorporation of the consistent fish-bone (CFB) model and the endurance time (ET) analysis method has been used. The proposed method (i.e., CFB-ET) has a substantial impact on reducing the... 

Reliability evaluations play a significant role in engineering applications to ensure the serviceability and safety of advanced structures such as those made of composites. Here, a dynamic reliability evaluation analysis based on the probability density evolution Method (PDEM) has been adapted to assess the reliability of composite structures under uncertainties within the material properties and the external loadings. A Back-Propagation Neural Network approach is employed to identify the system's nonlinear structural response, which is often the case under large deformations. To exemplify, a split Hopkinson pressure bar system was employed to mimic the mechanical behavior of a... 

The seismic risk framework of building structures has been presented to reduce earthquake-induced adverse consequences. In this context, probabilistic analysis of engineering demand parameters (EDPs) is always associated with many uncertainties and high computational demand for use in practical applications. This study has been presented to efficiently estimate the distribution of EDPs in probabilistic seismic risk assessment of irregular steel moment-resisting frames (SMRFs). For this purpose, the incorporation of the consistent fish-bone (CFB) model and the endurance time (ET) analysis method has been used. The proposed method (i.e., CFB-ET) has a substantial impact on reducing the... 

Socio-ecological systems include diverse resources of complexity involving complicated interactions, feedback, dynamic behavior patterns, heterogeneity in agents’ characteristics and behaviors, as well as system uncertainties. Agent-based modeling is introduced as a tool to simulate different levels of stakeholders and interactions, including individual, group, and institutional agents in complex socio-ecological systems. In the present study, an agent-based model was developed to explore sustainable solutions in uncertain conditions for groundwater restoration of a critical aquifer in the Lake Urmia Basin, Iran. Different projects for aquifer restoration such as wells monitoring, license... 

In this study, an auxiliary damping controller based on a robust controller considering the active and reactive power control loops for a doubly-fed induction generator for wind farms is proposed. The presented controller is able to improve the inter-area oscillation damping. In addition, the proposed controller applies only one accessible local signal as the input; however, it can improve the inter-area oscillation damping and, consequently the system stability for the various working conditions and uncertainties. The oscillatory modes of the system are appointed using the linear analysis. Then, the controller’s parameters are determined using the robust control approaches (H∞/ H2) with the... 

Accurate mapping of flood risk areas is the basis for providing basic information on flood hazard reduction strategies and facilitates the relocation process. This study compared statistical approaches and multi-criteria-decision-making (MCDM) in flood hazard susceptibility mapping (FHSM). The performance of two statistical methods, the Evidential Belief Function (EBF) and Weight of Evidence (WOE), was compared with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as an MCDM technique. Mohammad-Abad catchment, known as one of the flood susceptible areas in northern Iran, was selected as a case study. A 100-year flood event with a peak flow of 85 m3/s, known as... 

The present study is set out to systematically investigate the combined impact of operational, geometrical, and model uncertainties on the hemodynamics and performance characteristics in the U.S. Food and Drug Administration (FDA) benchmark centrifugal blood pump. Non-intrusive Polynomial Chaos Expansion (NIPCE) has been utilized to propagate the uncertainty of 12 random input variables in the flow field and the performance characteristics of the blood pump at three working conditions. The global sensitivity of the Quantities of Interest (QoI) to the uncertain input parameters was measured through the Sobol’ indices. The Multiple Reference Frames (MRF) approach and the SST k−ω turbulence... 

In this paper, a reduced multiple-model predictive controller based on gap metric and stability margin is presented to control heating, ventilating, and air conditioning (HVAC) systems. To tackle the strong nonlinearity and large number of degrees of freedom in HVAC system, two approaches, called Reduced Order Model Bank-Multiple Model (ROMB-MM) and Multiple Model-Reduced Order Model (MM-ROM), are introduced. In the first approach, the order reduction is performed prior to multiple models selection and in the second one multiple models selection is implemented before the model order reduction. Furthermore, soft switching is employed to enhance the closed-loop performance as well as to gain... 

This article proposes a novel index along with a user-friendly platform for resilience evaluation of the urban water distribution networks (WDN). WDN is of great importance in preserving the safety and sustainability of communities and cities. Resilience of a given system results in its sustainability. In recent years, many studies have focused on resilient urban WDN. However, these studies did not provide a comprehensive evaluation index and platform that could simultaneously consider hydraulic analysis, resilience parameters, and significant uncertainties. In this study, a novel resilience index was defined, and a user-friendly and practical platform (REWAT) was developed to calculate the... 

One of the most critical issues in manufacturing systems is the operator management. In this paper, the operator assignment problem is studied within a cellular manufacturing system. The most important novelty of this research is the consideration of operator learning and forgetting effects simultaneously. The skill level of an operator can be increased/decreased based on the time spent on a machine. Moreover, the issues related to operators like hiring, firing, and salaries are considered in the proposed model. The parameters are considered to be uncertain in this model, and a robust optimization approach is developed to handle it. Using this approach, the model solution remains feasible... 

Between 2010 and 2017, we have collected new optical and radar observations of the potentially hazardous asteroid (2102) Tantalus from the ESO NTT and Danish telescopes at the La Silla Observatory, and from the Arecibo planetary radar. The object appears to be nearly spherical, showing a low-amplitude light-curve variation and limited large-scale features in the radar images. The spin-state is difficult to constrain with the available data; including a certain light-curve subset significantly changes the spin-state estimates, and the uncertainties on period determination are significant. Constraining any change in rotation rate was not possible, despite decades of observations. The convex... 

Reinforcement learning methods such as Q-learning have shown promising results in the model-free design of linear quadratic regulator (LQR) controllers for linear time-invariant (LTI) systems. However, challenges such as sample-efficiency, sensitivity to hyper-parameters, and compatibility with classical control paradigms limit the integration of such algorithms in critical control applications. This paper aims to take some steps towards bridging the well-known classical control requirements and learning algorithms by using optimization frameworks and properties of conic constraints. Accordingly, a new off-policy model-free approach is proposed for learning the Q-function and designing the... 

This article focuses on the robust D-stabilization analysis of fractional-order control systems where each of the system and the controller may be of fractional order. The coefficients of the system are considered as complex linear functions of interval uncertain parameters, so this article deals with fractional-order polytopic systems. First, a necessary and sufficient condition is introduced for the robust D-stabilization of the closed-loop control system based on the zero exclusion condition and the value set concept. Then, the geometric pattern of the value set of the characteristic polynomial is obtained analytically using the exposed vertices. Second, a function is presented to check... 

Smart home is a small but an important energy segment that has a significant potential to implement authentic energy policies, where human is a major decision-maker in the home energy management dilemma. Therefore, humans’ emotions and tendencies plays a vital role as the End-User's daily decisions. In this paper, we develop a behavioral home energy management model based on time-driven prospect theory incorporating energy storage devices, distributed energy resources, and smart flexible home appliances. The conventional prospect theory is then modified by considering the psychological time discount effect in the value function. The non-flexible load and photovoltaic generation uncertainties... 

Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge (SoC) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC... 

Nowadays, Autonomous Underwater Vehicles (AUV) are used in environmental studies, ocean floor mapping, and measuring water properties. Navigation of these vehicles is one of the most challenging issues due to the unavailability of global positioning system (GPS) signal underwater. Inertial navigation is a method commonly used for underwater navigation. If a low-cost Inertial Measurement Unit (IMU) is used, navigation quality will decline rapidly due to sensor inherent error. Although using a Doppler Velocity Log (DVL) speedometer sensor helps limit this error to some extent, it does not yield acceptable accuracy in low-cost IMUs. Filtering the gyro based on the AUV rotational dynamics model... 

This work develops a systematic uncertainty quantification framework to assess the reliability of prediction delivered by physics-based material models in the presence of incomplete measurement data and modeling error. The framework consists of global sensitivity analysis, Bayesian inference, and forward propagation of uncertainty through the computational model. The implementation of this framework on a new multiphase model of novel porous silica aerogel materials is demonstrated to predict the thermomechanical performances of a building envelope insulation component. The uncertainty analyses rely on sampling methods, including Markov-chain Monte Carlo and a mixed finite element solution of... 

This article deals with solving distributed optimization problems with equality constraints by a class of uncertain nonlinear heterogeneous dynamic multiagent systems. It is assumed that each agent with an uncertain dynamic model has limited information about the main problem and limited access to the information of the state variables of the other agents. A distributed algorithm that guarantees cooperatively solving of the constrained optimization problem by the agents is proposed. Via applying this algorithm, the agents do not need to continuously broadcast their data. It is shown that the proposed algorithm can be useful in solving resource allocation problems. IEEE  

The heat transfer simulations of turbine blades with internal cooling are faced with so many uncertainties, of which some originate from the secondary air system, including the inlet hot gas temperature and pressure and the cooling side boundary conditions, and the blade material. The main objective of this work is to carry out a suitable sensitivity analysis on a specific novel turbine vane to improve the thermal performance of its internal cooling system and to quantify how the uncertainties on the designed/calculated values can desirably/undesirably affect the maximum blade surface temperature, which can consequently affect the gas turbine engine efficiency. Furthermore, the sensitivity...