Dynamics of structures

http://sstl.cee.illinois.edu/papers/Engineering_Structures.pdf

Large-scale MR fluid dampers: modeling and dynamic performance considerations

Public awareness of the need to reduce global warming and the significant increase in the prices of conventional energy sources have encouraged many countries to provide new energy policies that promote the renewable energy applications. Such renewable energy sources like wind, solar, hydro based energies, etc. are environment friendly and have potential to be more widely used. Combining these renewable energy sources with back-up units to form a hybrid system can provide a more economic, environment friendly and reliable supply of electricity in all load demand conditions compared to single-use of such systems. One of the most important issues in this type of hybrid system is to optimally size the hybrid system components as sufficient enough to meet all load requirements with possible minimum investment and operating costs. There are many studies about the optimization and sizing of hybrid renewable energy systems since the recent popular utilization of renewable energy sources. In this concept, this paper provides a detailed analysis of such optimum sizing approaches in the literature that can make significant contributions to wider renewable energy penetration by enhancing the system applicability in terms of economy.

Optimum design of hybrid renewable energy systems: Overview of different approaches

Due to the inherent nonlinear nature of magnetorheological (MR) dampers, one of the challenging aspects for developing and utilizing these devices to achieve high performance is the development of models that can accurately describe their unique characteristics. In this review, the characteristics of MR dampers are summarized according to the measured responses under different conditions. On these bases, the considerations and methods of the parametric dynamic modelling for MR dampers are given and the state-of-the-art parametric dynamic modelling, identification and validation techniques for MR dampers are reviewed. In the past two decades, the models for MR dampers have been focused on how to improve the modelling accuracy. Although the force–displacement behaviour is well represented by most of the proposed dynamic models for MR dampers, no simple parametric models with high accuracy for MR dampers can be found. In addition, the parametric dynamic models for MR dampers with on-line updating ability and the inverse parametric models for MR dampers are scarcely explored. Moreover, whether one dynamic model for MR dampers can portray the force–displacement and force–velocity behaviour is not only determined by the dynamic model itself but also determined by the identification method.

https://iopscience.iop.org/article/10.1088/0964-1726/20/2/023001/pdf

Magnetorheological fluid dampers: a review of parametric modelling

Shape memory alloys (SMAs) are a class of alloys that possess numerous unique characteristics. They offer complete shape recovery after experiencing large strains, energy dissipation through hysteresis of response, excellent resistance to corrosion, high fatigue resistance, and high strength. These features of SMAs, which can be exploited for the use in control of civil structures subjected to seismic events, have attracted the interest of many researchers in structural engineering over the past decades. This article presents an extensive review of seismic applications of SMAs. First, a basic description of two unique effects of SMAs, namely shape memory and superelastic effect, is provided. Then, the mechanical characteristics of the most commonly used SMAs are discussed. Next, the material models proposed to capture the response of SMAs in seismic applications are briefly introduced. Finally, applications of SMAs to buildings and bridges to improve seismic response are thoroughly reviewed.

Seismic Response Control Using Shape Memory Alloys: A Review

The magnetorheological (MR) damper is a newly developed semiactive control device that possesses unique advantages such as low power requirement and adequately fast response rate. The device has been previously tested in a laboratory to determine its dynamic properties and characterized by a system of nonlinear differential equations. This paper presents an alternative representation of the damper in terms of a multilayer perceptron neural network. A neural network model with 6 input neurons, one output neuron and twelve neurons in the hidden layer is used to simulate the dynamic behavior of the MR damper. Training of the model is done by a Gauss-Newton based Levenberg-Marquardt method using data generated from the numerical simulation of the nonlinear differential equations. An optimal brain surgeon strategy is adopted to prune the weights and optimize the neural networks. An optimal neural network is presented that satisfactorily represents dynamic behavior of the MR damper.

Neural Network Modeling of a Magnetorheological Damper

Design of fuzzy logic controller for smart base isolation system using genetic algorithm

GA-optimized fuzzy logic control of a large-scale building for seismic loads

Smart base-isolation strategies are being widely investigated as a way to reduce structural damage caused by severe loads. This study uses a friction pendulum system (FPS) as the isolator and a magnetorheological (MR) damper as the supplemental damping device of a smart base-isolation system. Neuro-fuzzy models are used to represent dynamic behavior of the MR damper and FPS. A fuzzy logic controller (FLC) is used to modulate the MR damper so as to minimize structural acceleration while maintaining acceptable base displacement levels. To this end, a multi-objective optimization scheme that uses a nondominated multi-objective genetic algorithm (NSGA-II) is used to optimize parameters of membership functions and find appropriate fuzzy rules. To demonstrate the effectiveness of the proposed multi-objective genetic algorithm for FLC, a numerical study of a smart base-isolation system is conducted using several historical earthquakes. The findings show that the proposed method can find optimal fuzzy rules and that the NSGA-II-optimized FLC outperformed a passive control strategy, a conventional semiactive control algorithm and a human-designed FLC.

Paul N. Roschke

Papers

Neural Network Modeling of a Magnetorheological Damper

Design of fuzzy logic controller for smart base isolation system using genetic algorithm

GA-optimized fuzzy logic control of a large-scale building for seismic loads

Fuzzy Control of Base‐Isolation System Using Multi‐Objective Genetic Algorithm

Neuro-fuzzy model of hybrid semi-active base isolation system with FPS bearings and an MR damper