Computational Fluid Dynamics 2020

This book presents a collection of works published in a recent Special Issue (SI) entitled “Computational Fluid Dynamics”. These works address the development and validation of existent numerical solvers for fluid flow problems and their related applications. They present complex nonlinear, non-Newtonian fluid flow problems that are (in some cases) coupled with heat transfer, phase change, nanofluidic, and magnetohydrodynamics (MHD) phenomena. The applications are wide and range from aerodynamic drag and pressure waves to geometrical blade modification on aerodynamics characteristics of high-pressure gas turbines, hydromagnetic flow arising in porous regions, optimal design of isothermal sloshing vessels to evaluation of (hybrid) nanofluid properties, their control using MHD, and their effect on different modes of heat transfer. Recent advances in numerical, theoretical, and experimental methodologies, as well as new physics, new methodological developments, and their limitations are presented within the current book. Among others, in the presented works, special attention is paid to validating and improving the accuracy of the presented methodologies. This book brings together a collection of inter/multidisciplinary works on many engineering applications in a coherent manner

New advances in H∞ control and filtering for nonlinear systems

The main objective of this special issue is to summarise recent advances in H∞ control and filtering for nonlinear systems, including time-delay, hybrid and stochastic systems. The published papers provide new ideas and approaches, clearly indicating the advances made in problem statements, methodologies or applications with respect to the existing results. The special issue also includes papers focusing on advanced and non-traditional methods and presenting considerable novelties in theoretical background or experimental setup. Some papers present applications to newly emerging fields, such as network-based control and estimation

Editorial to special issue “Recent mechanics-based developments in structural dynamics and earthquake engineering”

In structural dynamics, the inertia of accelerated masses and the cornucopia of phenomena that contribute to damping have a significant influence on the internal forces and deformation of building structures. Typical problems of structural dynamics are the prediction of the vibration response of dynamically excited structures, for instance, induced by earthquakes, the identification of structural parameters based on the dynamic response, and the design of vibration-mitigating measures from the fundamental study to the implementation. Many traditionalmethods of structural dynamics and earthquake engineering are based on empirical approaches rather than rigorous application of fundamental mechanical principles. However, dramatic advances in mechanics now make it increasingly possible not only to model but also to solve and predict complex phenomena in dynamics. We are very pleased that, in response to these advances, this special issue of Acta Mechanica provides an insight into the latest mechanics-based developments in various branches of structural dynamics and earthquake engineering. It contains 20 contributions that we selected based on the reactions and feedback we received to our invitation. The first four papers deal with complex dynamic vehicle–bridge interaction (VBI). In the first paper, Homaei et al. [1] investigate the effect of VBI and highlight its similarities and differences to the effect of vibration dampers under earthquake excitation. Based on knowledge of recent experimental studies, König and Adam [2] present a new modeling approach for railway bridges under high-speed traffic, which takes into account both the horizontal and vertical interaction between track and structure. Hirzinger and Nackenhorst [3] apply a model-correction-based strategy for efficient reliability analysis of the uncertain VBI system, where a low-fidelity model is calibrated to the corresponding high-fidelity model close to the most probable point. The paper of Lei et al. [4] proposes a two-step bridge damage detection method based on wavelet transform analysis of the residual contact response of the moving front and rear vehicle wheels to reduce the impact of road surface roughness. Suitably, the next paper by Yang et al. [5] uses a numerical model based on the wave finite element method of wave propagation and attenuation in periodic supported rail, capturing the complex cross-section deformation of the waves. Changing topics away from railways, Amendola et al. [6] also seek a waveform solution but of nonlocal nanobeams dissipating thermal energy by radiation, employing an extension of Type II Green–Naghdi theory. The paper by Abdelnour and Zabel [7] is devoted to the identification of the modal information of complex three-dimensional space truss structures characterized by closely spaced modes as well as global and local vibration mechanisms. Pirrotta and Russotto [8], on the other hand, develop a new operationalmodal analysismethod based on signal filtering and the Hilbert transform of the correlation function matrix for dynamic system identification. The next two papers deal with machine learning in structural dynamics. Milicevic and Altay [9] present a theoretical data generation framework for test-integrated modeling of nonlinear systems in structural dynamics. In particular, a feedforward neural network is used for inverse modeling of nonlinear restoring forces. On the other hand, Maqdah et al. [10] build an unsupervised machine learning model capable of detecting patterns in arch forms under seismic loading and distinguishing between their stress and displacement contours. In one of the three contributions on structures under seismic loads, Zakian and Kaveh [11] provide a comprehensive review on seismic design optimization of engineering structures. Refined probabilistic seismic response evaluation of high-rise reinforced concrete structures is subject of Lyu et al. [12]. In the third contribution, Karaferis et al. [13] present a roadmap for determining comprehensive fragility curves for individual or groups of spherical pressure vessels, tackling the thorny issues of correlation and operational realities. Six other papers can be classified under the topic of vibration control. Rajana and Giaralis [14] introduce a nonlinear rooftop tuned mass damper-inerter system and numerically investigate its efficiency for seismic response mitigation of buildings. The hysteretic tuned mass damper system presented in Xiang et al. [15] is optimized for acceleration control of seismically excited structures. In Masnata et al. [16], both theoretical and experimental studies are conducted on the control performance of a sliding model of a tuned liquid column damper for short-period systems. The study of Li et al. [17] shows that the use of high-static–low-dynamic stiffness floating raft vibration isolation system is beneficial for the shock performance. De Castro Motta et al. [18] present a mechanical model for thermoplastic polyurethane membranes used as components in seismic isolators based on an experimental study. Sezer et al. [19], on the other hand, report the results of experimental investigations on the coefficient of friction at the interface of a PVC-sand-PVC layer, used as part of a low-cost geotechnical seismic isolation system. This special issue is completed with a paper by Minafò et al. [20] on the effect of interface model parameters on the numerical behavior of a finite element model for predicting the bond between fabric-reinforced cementitious matrix and masonry. We would like to thank all the authors who accepted our invitation to contribute to this special issue and the reviewers for their thorough and valuable comments on these studies. Our particular thanks go to Professor Hans Irschik, Editor-in-Chief, for the opportunity to publish this special issue in “Acta Mechanica” and for his guidance during its development. We thank Dr. Michael Stangl, editorial assistant, for his continued support, always responding to our requests in an efficient and timely manner

Mathematical problems for complex networks

Copyright @ 2012 Zidong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is made available through the Brunel Open Access Publishing Fund.Complex networks do exist in our lives. The brain is a neural network. The global economy is a network of national economies. Computer viruses routinely spread through the Internet. Food-webs, ecosystems, and metabolic pathways can be represented by networks. Energy is distributed through transportation networks in living organisms, man-made infrastructures, and other physical systems. Dynamic behaviors of complex networks, such as stability, periodic oscillation, bifurcation, or even chaos, are ubiquitous in the real world and often reconfigurable. Networks have been studied in the context of dynamical systems in a range of disciplines. However, until recently there has been relatively little work that treats dynamics as a function of network structure, where the states of both the nodes and the edges can change, and the topology of the network itself often evolves in time. Some major problems have not been fully investigated, such as the behavior of stability, synchronization and chaos control for complex networks, as well as their applications in, for example, communication and bioinformatics

Nonlinear analysis of dynamical complex networks

Copyright © 2013 Zidong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Complex networks are composed of a large number of highly interconnected dynamical units and therefore exhibit very complicated dynamics. Examples of such complex networks include the Internet, that is, a network of routers or domains, the World Wide Web (WWW), that is, a network of websites, the brain, that is, a network of neurons, and an organization, that is, a network of people. Since the introduction of the small-world network principle, a great deal of research has been focused on the dependence of the asymptotic behavior of interconnected oscillatory agents on the structural properties of complex networks. It has been found out that the general structure of the interaction network may play a crucial role in the emergence of synchronization phenomena in various fields such as physics, technology, and the life sciences

Mathematical control of complex systems

Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Introduction: Localized Structures in Dissipative Media: From Optics to Plant Ecology

Localised structures in dissipative appears in various fields of natural science such as biology, chemistry, plant ecology, optics and laser physics. The proposed theme issue is to gather specialists from various fields of non-linear science toward a cross-fertilisation among active areas of research. This is a cross-disciplinary area of research dominated by the nonlinear optics due to potential applications for all-optical control of light, optical storage, and information processing. This theme issue contains contributions from 18 active groups involved in localized structures field and have all made significant contributions in recent years.Comment: 14 pages, 0 figure, submitted to Phi. Trasaction Royal Societ

Filtering and control for unreliable communication: The discrete-time case

Copyright © 2014 Guoliang Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the past decades, communication networks have been extensively employed in many practical control systems, such as manufacturing plants, aircraft, and spacecraft to transmit information and control signals between the system components. When a control loop is closed via a serial communication channel, a networked control system (NCS) is formed. NCSs have become very popular for their great advantages over traditional systems (e.g., low cost, reduced weight, and power requirements, etc.). Generally, it has been implicitly assumed that the communication between the system components is perfect; that is, the signals transmitted from the plant always arrive at the filter or controller without any information loss. Unfortunately, such an assumption is not always true. For example, a common feature of the NCSs is the presence of significant network-induced delays and data losses across the networks. Therefore, an emerging research topic that has recently drawn much attention is how to cope with the effect of network-induced phenomena due to the unreliability of the network communication. This special issue aims at bringing together the latest approaches to understand, filter, and control for discrete-time systems under unreliable communication. Potential topics include but are not limited to (a) multiobjective filtering or control, (b) network-induced phenomena, (c) stability analysis, (d) robustness and fragility, and (e) applications in real-world discrete-time systems

New perspectives in human movement variability

Movement variability is defined as the normal variations that occur in motor performance across multiple repetitions of a task.2 Bernstein1 described movement variability quite eloquently as ‘‘repetition without repetition.’’ Traditionally, movement variability has been linked to noise and error, being considered to be random and independent. This theoretical approach blends well with traditional statistical and assessment methods of movement variability that assume randomness and independence of observations. However, numerous studies have indicated that when movement is observed over time variations are closely related with each other neither being random nor independent. Practically, traditional methods can mask the temporal structure of movement variability and contain little information about how movement changes over time