SS 02
随着现代工业向高端化、智能化迈进，高可靠元器件及系统已成为航空航天、能源电力、轨道交通和国防安全等国家战略与关键基础设施领域的核心基石。其性能与寿命受到复杂多物理场耦合、制造公差、材料退化、时变负载与环境扰动等多重不确定性的严峻挑战。传统的基于单一物理场、确定性假设及固定周期维护的策略，在精准表征失效机理、量化全生命周期风险以及实现前瞻性健康管理方面面临显著瓶颈。
近年来，融合多物理场建模仿真、不确定性量化理论与数据驱动人工智能的跨学科方法，为破解上述难题提供了革命性途径。通过构建集成物理机理与不确定性的元器件及系统数字孪生，能够深入揭示从微观失效到系统级性能退化的跨尺度关联规律。运用先进的不确定性量化技术，可实现对可靠性边界、剩余寿命分布的概率化精准评估。进一步结合机器学习、深度学习等智能算法，能够赋能状态的实时感知、故障的早期诊断与预测性维护决策的自主优化，从而在设计、制造、运维全链条实现可靠性、安全性与经济性的综合提升。
本专题旨在汇聚学术界与产业界在相关方向的前沿探索与创新实践，聚焦于利用多物理场建模、不确定性量化与智能健康管理等关键技术，系统解决高可靠元器件及系统在可靠性设计、评估、预测与运维管理中的核心挑战，推动该领域方法创新与工程应用的深度融合。征稿主题包括但不限于：高可靠元器件及系统的多物理场耦合建模与仿真验证；不确定性来源表征、传播分析与灵敏度研究；可靠性稳健性设计与多目标优化；故障机理研究、诊断算法与剩余寿命预测技术；数字孪生驱动或数据融合的智能健康管理框架与系统；相关先进实验方法与重大工程应用案例。
With the advancement of modern industry towards high-end and intelligent development, highly reliable components and systems have become the cornerstone of national strategic and critical infrastructure fields such as aerospace, energy and power, rail transportation, and national defense. Their performance and lifespan face severe challenges from multiple uncertainties, including complex multiphysics coupling, manufacturing tolerances, material degradation, time-varying loads, and environmental disturbances. Traditional strategies based on single-physics fields, deterministic assumptions, and fixed-period maintenance encounter significant bottlenecks in accurately characterizing failure mechanisms, quantifying lifecycle risks, and achieving proactive health management.
In recent years, interdisciplinary approaches integrating multiphysics modeling and simulation, uncertainty quantification theory, and data-driven artificial intelligence have provided revolutionary pathways to address these challenges. By constructing digital twins for components and systems that integrate physical mechanisms and uncertainties, the cross-scale correlation from microscopic failure to system-level performance degradation can be deeply revealed. Employing advanced uncertainty quantification techniques enables probabilistic and accurate assessment of reliability boundaries and remaining useful life distribution. Furthermore, combining intelligent algorithms such as machine learning and deep learning empowers real-time condition perception, early fault diagnosis, and autonomous optimization of predictive maintenance decisions, thereby achieving comprehensive improvement in reliability, safety, and cost-effectiveness throughout the entire chain of design, manufacturing, operation, and maintenance.
This Special Session aims to gather cutting-edge research and innovative practices from academia and industry in related areas. It focuses on leveraging key technologies such as multiphysics modeling, uncertainty quantification, and intelligent health management to systematically address the core challenges in the reliability design, assessment, prediction, and operational management of highly reliable components and systems. The session seeks to promote the deep integration of methodological innovation and engineering application in this field. Topics of interest include, but are not limited to: Multiphyics Coupling Modeling and Simulation Verification for Highly Reliable Components and Systems; Characterization of Uncertainty Sources, Propagation Analysis, and Sensitivity Studies; Reliability Robust Design and Multi-objective Optimization; Research on Failure Mechanisms, Diagnostic Algorithms, and Remaining Useful Life Prediction Techniques; Digital Twin-Driven or Data-Fusion Intelligent Health Management Frameworks and Systems; Relevant Advanced Experimental Methods and Significant Engineering Application Cases.
