Part B: Mechanical Engineering

Submit Paper » Modern engineering systems, from vibration-sensitive mechanical devices and safety-critical structures to aerospace, transportation, industrial, and energy applications, are increasingly equipped with advanced sensing, modeling, and control technologies to enhance safety, serviceability, and resilience. However, assessing and predicting their performance are inevitably affected by multiple sources of uncertainty, including variability in material and geometric properties, modeling simplifications, measurement noise, environmental effects, and the stochastic nature of external excitations. ...

We are delighted to announce the recipients of the 2024 Bilal M. Ayyub Research Award and Research Prize for Risk and Uncertainty in Engineering Systems, recognizing the best papers published in our journal in 2024. The awards highlight outstanding scholarly contributions to the advancement of risk and uncertainty analysis in engineering systems. 📰 Best Paper in Part A: Civil Engineering Title: Risk Tolerance, Aversion, and Economics of Energy Utilities in Community Resilience to Wildfires Authors: Bilal M. Ayyub, Ramsay Sawaya, David T. Butry, Jennifer Helgeson, Yumi Oum, Vincent Loh Published: June 2024 DOI: 10.1061/AJRUA6.RUENG-1254 ...

Submissions for this archived call are closed. The rapid evolution of mechanical systems and increasing industrial complexity have driven the need for advanced predictive maintenance strategies. Cognitive Digital Twins (CDTs), integrating AI, real-time data analytics, and cognitive computing, have emerged as a transformative solution. Unlike traditional digital twins, CDTs can learn, reason, and adapt, enabling more accurate and dynamic predictive maintenance. However, their reliability is challenged by modeling uncertainties, sensor noise, environmental variability, and unforeseen operational conditions. ...

Submissions for this archived call are closed. Industrial equipment, such as engine, robot, machine tool, energy harvester, vehicle, etc., plays a pivotal role in enhancing production efficiency, ensuring product quality, and reducing labor expenses. However, the randomness of structural parameters and external excitations can potentially threaten the operation and safety of industrial equipment. Consequently, structural reliability, which can quantify the given performance and safety level of system under various uncertainties, is essential to ensure the quality of industrial equipment. ...

Submissions for this archived call are closed. Large-scale systems are prevalent across critical infrastructure, manufacturing, offshore, automotive, aerospace, energy, and other sectors. These systems are inherently complex, and are characterized by the interactions among various components within the system and between the system and its environment. These systems are plagued with uncertainties stemming from various sources including incomplete or unreliable information, lack of data, and partially known physics. There is a growing demand for advanced techniques that can efficiently manage large-scale system complexity and result in robust and reliable design solutions with limited computational resources, ultimately minimizing failures with catastrophic consequences. ...

Submissions for this archived call are closed. 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 are a 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. ...

Submissions for this archived call are closed. 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.