Part B

Submit Paper » Engineering systems are increasingly complex. They need to meet advanced requirements for mission-critical fields with a low failure tolerance. As unexpected failures during the designed lifespan of a system may lead to catastrophic consequences, their reliability modeling and assessment are of utmost importance. The reliability modeling should achieve the assessment at a reasonable confidence level to help decision-makers arrive at sound decisions in practice. ...

Submit Paper » Fuel cells area vital component of renewable energy warranting significant consideration and ever-increasing applications. With the increase in the application of these systems, the need for safety analysis also increases. These systems can fail due to several reasons that can result in economic losses and catastrophes. Increasing the life expectancy of fuel cells is an important aspect that needs substantial attention. Hence, to avoid sudden failures and achieve better life expectancy the discovery, identification, and implementation of enhanced health indicators for effective diagnosis and prognosis is critical. ...

Submit Paper » In recent decades, fault diagnostics and failure prognostics have demonstrated their great potential for health monitoring and risk management of complex engineering systems, including smart factories, power plants, space systems, and heavy equipment. The credibility and applicability of fault diagnostics and failure prognostics, however, are significantly affected by various uncertainties, such as model uncertainty, data uncertainty, process uncertainty, environmental uncertainty, and the inherent uncertainty of engineered systems. Therefore, accurately quantifying the effects of these uncertainties is essential and one of the most widely-held concerns to ensure trustworthy decision-making based on diagnostic and prognostic results. The purpose of this special issue is to present the latest advancements in the field of uncertainty-aware diagnostics and prognostics for the health management of engineered systems. ...

Please find attached the Call for Papers for the Special Issue on Probabilistic Digital Twins in Additive Manufacturing. Click to download the CFP Guest Editors Zequn Wang, Assistant Professor, Michigan Technological University, USA, zequnw@mtu.edu Zhen Hu, Assistant Professor, University of Michigan-Dearborn, USA, zhennhu@umich.edu Moon Seung Ki, Associate Professor, Nanyang Technological University, Singapore, skmoon@ntu.edu.sg Hong-Zhong Huang, Professor, University of Electronic Science and Technology of China, China, hzhuang@uestc.edu.cn Qi Zhou, Associate Professor, Huazhong University of Science and Technology, China, qizhou@hust.edu.cn Aims & Scope Additive manufacturing (AM) has made enormous progress over the past decade, as it is capable of producing complex parts with significantly less fabrication constraints compared to existing manufacturing technologies over a broad dimensional scale. Complicated AM process variability is one of the greatest obstacles in performance evaluation and quality control of additively manufactured materials and products, and thus hinders widespread implementation of AM techniques. Digital twin, as a digital replica of a production system or process, has great potential in overcoming quality variability and reliability issues in AM processes. With the development of probabilistic digital twins in AM and uncertainty management techniques, it becomes possible to reduce the computational burden for multi-scale modeling and realize reliable AM processes by taking advantage of large volumes of in situ sensor data to optimize process parameters, detect, and prevent process faults. ...

Please find attached the Call for Papers for the Special Issue on Modeling and Analysis of Inspection Uncertainties in Structural Health Monitoring. Click to download the CFP Guest Editors Yi Zhang, Associate Professor, School of Civil Engineering, Tsinghua University, China, zhang-yi@tsinghua.edu.cn Chul-Woo Kim, Professor, Department of Civil and Earth Resources Engineering, Kyoto University, Japan, kim.chulwoo.5u@kyoto-u.ac.jp Yan-Gang Zhao, Professor, Department of Architecture, Kanagawa University, Japan, zhao@kanagawa-u.ac.jp Pei-Pei Li, Postdoctoral Research Fellow, School of Civil Engineering, Tsinghua University, China, lipeipei626@gmail.com Aims & Scope In recent years, structural health monitoring (SHM) technology has developed rapidly and is now gradually applied to civil, mechanical, automobile, and aerospace engineering practices. One of the most widely-held concerns is to utilize useful information provided by SHM for quantitative assessment of structural health and updating structural models. To accomplish this, inspection or measurement information is gathered through monitoring, followed by a thorough analysis that uses finite element analysis models, mathematical statistics tools, artificial intelligence technologies, or other advanced methods to obtain an objective quantitative assessment of structural health or updating structural models. However, due to uncertainties in mathematical modeling and analysis in SHM, objectives for satisfactory results of the quantitative structural health assessment and structural model updating have not been fully realized in real-world conditions, and many problems still require further research. ...

Please find attached the Call for Papers for the Special Issue on Digital Twins: A New Frontier in Critical Infrastructure Protection and Resilience. Click to download the CFP Update: Submissions are open until January 31, 2023. Guest Editors Nii Attoh-Okine, PhD, University of Delaware, USA, okine@udel.edu Yaw Adu-Gyamfi, PhD, University of Missouri, USA, adugyamfiy@missouri.edu Aims & Scope A digital twin is a computational model (or set of coupled) that evolves over time to persistently represent the critical structure, its components, system or process. Digital twin underpins intelligent automation by supporting data-driven decision making and enabling asset specific analysis and system behavior. Within the contexts of critical Infrastructure systems, the digital twins represent the flow of information among connected platforms. In the future, as many agencies turn to digital twin capabilities, they have to migrate towards continuous real-time performance models and calibrate by pairing data from real-time sensors, meters, weather, and other data. The digital twin can be used to run “what-if” scenarios, predict and prevent failures, provide early alerts of anomalies and conduct predictive analysis. The strength of a digital twin is the interconnectivity of data and models. The main characteristics of a digital twin are ...

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