Prof. Dr. Alexander Tyrsin,
Institute for Risk and Reliability
Title: Entropy-probabilistic Methods and Models of Risk Analysis to Increase the Reliability of Complex Adaptive Systems
Nowadays, systems are getting more and more effective but are increasing in their complexity in parallel. Consequently, it becomes harder to manage such systems in a way that they consistently remain within a range of operations that can be flagged as reliable. This is mainly driven by growing information uncertainty, increasing risk complexity, and the emergence of system risks that are not well defined.
Therefore, the development of effective methods and models for timely prediction and prevention of crisis situations in complex adaptive systems is highly important in today’s and future world.
In order to tackle these issues, a combination of multivariate risk models and vector entropy models of stochastic systems can be used. This allows both system properties and risk stochasticity to be taken into account. The risk analysis of complex adaptive systems can be implemented based on the principle of system composition. Moreover, the complex adaptive system can be modelled on three levels: first, via random vectors with correlated components, second a set of correlated random vectors and third network correlation structures.
This method can be applied to many systems such as the socio-economic development of regions, stock index systems, oil field flooding systems, territories, transport systems, and communication systems. For instance, periods of economic downturns can now be identified via entropy analysis, allowing the assessment of the current behavior of participants within the economy of cities.
Dr. Abhishek Kundu,
School of Engineering, Cardiff University, UK
Title: Uncertainty quantification in industrial structural design and maintenance
Uncertainty quantification (UQ) is a pervasive aspect of industrial structural applications, encompassing design, optimisation, monitoring, and prognostics. This presentation will provide an overview of a selection of research projects that employ a Bayesian approach to modelling, robust design under uncertainty, and degradation assessment for safety-critical industrial structures.
Robust early-stage industrial design of complex structures, such as aircraft wings, presents significant challenges due to design immaturity, a wide range of loading and operating conditions, and potential variability during manufacturing and assembly. The study presented herein aims to develop an approach for exploring the design space with a data-driven surrogate model that maps design parameters to a high-dimensional vector-valued output describing the response of quantities at locations under a range of operating scenarios. The optimal design of structural parameters under uncertainty is undertaken using a Bayesian inversion to identify probabilistic margins on design parameters in the presence of stochastic variability. The proposed approach is guided by a novel confidence-based criterion of meeting designer-specified performance thresholds. The results demonstrate the accuracy and computational savings of complex industrial design, and facilitates the expansion of the design space for complex industrial design workflows.
Continuous monitoring of safety-critical structures ensures reliable performance, maintenance, and prognostics. Acousto-ultrasonic signals monitor and interrogate these structures for concealed damage. Probabilistic characterisation of elastic properties reconstructs dispersive wave modes using Bayesian inversion, quantifying localised structural degradation. We explore minimal signal acquisition hardware footprint by integrating edge computing and physics-informed machine learning techniques for real-time structural monitoring. This approach is conceptualised as a Cyberphysical Structural Health Monitoring (CyberSHM) system, an automated monitoring framework integrated with the internet and collaborating with human end-users. The study utilises carbon-fibre composite panels with stiffeners as a test bench, subjecting them to impact and fatigue loading and monitoring with a CyberSHM system, demonstrating its effectiveness, challenges, and a step towards realising the futuristic vision of automated continuous monitoring systems.
Additional information
The workshop will take place in the institutes library, room 116, on Friday, March 21, 2025. Start is at 10:00 a.m. The seminar will also be available via Webex online meeting. If you want to participate online please contact Torsten Ilsemann.
