Call for Papers

Please find attached the Call for Papers for the Special Issue on Uncertainty Quantification & Management in Nonlinear Dynamical Systems in Aerospace and Mechanical Engineering. Click to download the CFP Guest Editors Jie Yuan, University of Strathclyde, United Kingdom, jie.yuan@strath.ac.uk Jinglang Feng, University of Strathclyde, United Kingdom, jinglang.feng@strath.ac.uk Enora Denimal, INRIA Institut National de Recherche en Informatique et en Automatique, France, enora.denimal@inria.fr Quan Hu, Beijing Institute of Technology, China, huquan2690@bit.edu.cn Sifeng Bi, University of Strathclyde, United Kingdom, sifeng.bi@strath.ac.uk Alice Cicirello, Delft University of Technology, The Netherlands, A.Cicirello@tudelft.nl Aims & Scope The study of aerospace systems is becoming an increasingly critical challenge due to the presence of a wide range of nonlinearities (such as large structural deformations, joints, fluid-structure interaction, electro-mechanical interaction, etc.), and rigorous requirements of their efficiency and reliability to achieve net zero targets. Moreover, spacecraft dynamics also experience strong nonlinearities due to the perturbation forces (such as non-sphericity of Earth, atmospheric drag, solar radiation pressure, etc.), which make the spacecraft motion very sensitive to the initial state and these unmodeled forces. Therefore, it is important to characterize these uncertainties and quantify their influences on dynamical performance for improved design and analysis, and knowledge contributions in the identification of these nonlinear systems and their high sensitivity to dynamical behaviors. This Special Issue seeks submissions related to the modeling, quantification, and management of uncertainties and nonlinearities for the design, navigation, control, and identification of aerospace systems. ...

Please find attached the Call for Papers for the Special Collection Advances in Efficient Methods in Random Fields Modeling and Analysis. Click to download the CFP Guest Editors Zhenhao Zhang, Changsha University of Science & Technology, zhangzhenhao@csust.edu.cn De-Cheng Feng, Southeast University, dcfeng@seu.edu.cn You Dong, The Hong Kong Polytechnic University, you.dong@polyu.edu.hk Emilio Bastidas-Arteaga, La Rochelle University, ebastida@univ-lr.fr Aims & Scope Spatial and temporal variability widely exists in practical engineering and has a significant influence on structural performance. Generally, it is modeled by the random field/process methods which typically transfer the field into a set of random variables, then it can be implemented in conventional uncertainty analysis framework. Efficient random field modeling and analysis usually involves three aspects, the adopted mathematical representation method, the accurate reflection of the geometric correlations, and the effective sampling of the discretized random variables. With the development of probabilistic mechanics and random process theory, novel methods are developed for efficient random field modeling and convenient uncertainty analysis of structures involving random field properties. Besides, the AI-inspired data-driven approaches bring new insights for resolving the traditional difficulties of random field analysis, e.g., correlation relation identification, surrogate models, dimension reduction methods, etc. This special collection aims to gather contributions presenting the recent advances in efficient random field modeling, analysis, and applications. ...

Please find attached the Call for Papers for the Special Issue on Community Resilience to Disruptive Events: Models and Analyses, Lessons Learned, and Case Studies. Click to download the CFP Guest Editors Cao Wang, University of Wollongong, Australia, wangc@uow.edu.au Matthias G.R. Faes, TU Dortmund University, Germany, matthias.faes@tu-dortmund.de Michael Beer, Leibniz University Hannover, Germany, beer@irz.uni-hannover.de Enrico Zio, Politecnico di Milano, Italy, enrico.zio@polimi.it John W. van de Lindt, Colorado State University, USA, jwv@engr.colostate.edu Aims & Scope Many types of disruptive events, such as earthquakes, tropical cyclones, floods, wildfires, and remarkably the coronavirus (COVID-19) pandemic, have threatened communities around the world with dramatic consequences. With respect to this, society is asking justified questions: how resilient is our community against disruptive events? How can we use resilience approaches to counteract disruptive events? What lessons can we learn from real-world practices to enhance the resilience of our community? This Special Issue is aimed at gathering contributions of methods, lessons, and practices useful to achieve community resilience in the face of disruptive events. Papers discussing the impacts of disruptive events on a community’s resilience in terms of functionality loss and recovery process, uncertainty quantification in community resilience modeling, resilience approaches to counteract impacts of disruptive events, and critical lessons learned from existing practices towards enhancing community resilience are all solicited. ...

Please find attached the Call for Papers for the Special Collection on Extreme Damage Mechanics for Lifecycle Fatigue Resilience of Infrastructure Systems. Click to download the CFP Guest Editors Xuhong Zhou, Chongqing University, zxh@cqu.edu.cn Yongtao Bai, Chongqing University, bai.yongtao@cqu.edu.cn Frédéric Ragueneau, Paris‐Saclay University, frederic.ragueneau@ens‐paris‐saclay.fr Julio Florez‐Lopez, Chongqing University, j.florezlopez@cqu.edu.cn Aims & Scope This Special Collection aims to gather prestigious contributions presenting the state‐of‐the‐art breakthroughs on extreme damage mechanicsfor the lifecycle fatigue resilience of infrastructure systems. Since the 19th century, when the use of steels in civil engineering began to increase, it has been recognized that structural components and systems subjected to repetitive load cycles may fail in service life. This type of failure is well known as “fatigue” due to the formation and propagation of crack damages caused by repeated stress or strain fluctuations. It has been estimated that nearly 90% of the failures can be attributed to fatigue. For instance, bridges and wind turbines subjected to fluctuating live loads may be damaged due to high cycle fatigue. On the other hand, low cycle fatigue is usually characterized by large amplitude and low‐frequency plastic strains such as seismic actions on skyscrapers. Depending on uncertainties of the loading reversal, amplitude/intensity, and occurrence frequency in lifecycle, we should generally couple the probability methodology with computational damage mechanics for risk assessment of large‐scale infrastructure systems. Furthermore, for the goal of “emission peak and carbon neutrality”, there is a demand to develop resilient,sustainable, and long lifecycle infrastructure. To this aim, novel mathematical and computational approaches based on the probability theory, damage and fracture mechanics are needed in the broad topics of lifecycle fatigue assessment of steel and composite structural systems. This challenging aim might today be able to realize with the implementation of valuable data availability, uncertainty quantification, and artificial intelligence technologies. ...

Please find attached the general Call for Papers for Part A: Civil Engineering. Click to download the CFP

Please find attached the general Call for Papers for Part B: Mechanical Engineering. Click to download the CFP