The 13th International Conference on Fracture Fatigue and Wear (FFW 2025)

Abstract: Fretting fatigue occurs in many engineering systems where load must be transferred between adjacent components. The consequences for system performance, durability and safety can be very significant. In most cases the cause of the cyclic loading is vibrations in the system. These either result from external loads or are generated in the system itself (e.g. due to out of balance loading or reciprocating mass). Frequently the vibration problem is treated entirely separately from that of fretting at the interface. However this is a simplification which can lead to misleading results. Interface friction is an important source of damping in the system and will have a significant effect on the levels of vibration experienced. Hence, in order to fully characterise the system, the vibration and fretting problems need to be considered in a holistic framework. The input to any model should be the loading experienced by the system and the output is a durability assessment. Interface contact conditions should be treated as internal variables. If the system is treated in this way, there is a possibility to optimise fretting fatigue life by changing interface geometry or friction. The paper will detail the steps required to implement a holistic model and present some sample results for a simple partial slip contact.

Biography: Professor David Nowell is Professor of Machine Dynamics at Imperial College London. He has been involved in research in solid mechanics and tribology for over 35 years and he has developed a particular interest in fretting fatigue. His recent research has focused on the role of frictional interfaces in providing damping in complex engineering systems. Professor Nowell is a Fellow and a Trustee of the Institution of Mechanical Engineers (I.Mech.E.). He is also a Fellow of the Institute of Materials Minerals and Mining (IoM³).

Abstract: Structures and machine components are nowadays manufactured by additive manufacturing processes. This process dominates the resultant microstructural properties of the manufactured part and hence influences its damage behaviour. An effort is required to incorporate this influence into the existing concept of theoretical and applied mechanics models. At Cranfield, the damage mechanics research group is currently working to explore the mentioned influence with a special focus on structures made by fused deposition-based additive printing. So far extensive empirical testing schemes and computations have been used to analyse the trade-off in the values of printing parameters and the damage resistance of printed structures. Both simple and composite structures are tested under pure dynamic, pure thermal and coupled thermo-mechanical loads. The trade-off is evaluated on simple geometries such as plates and beams and also on composite geometries such as battery pack enclosures and metal-polymer riveted panels. This keynote lecture will provide the highlights of the key results, the complexities in data visualisation and modelling and future work.

Biography: Muhammad of experience, hKhan is the Head of the Centre for Life-cycle Engineering and Management and Reader in Damage Mechanics at Cranfield University. With over 23 yearse specializes in damage mechanics, modelling for life extension of engineering assets, and non-invasive techniques for asset health diagnostics. Khan has led and worked on projects sponsored by reputed organizations, including General Dynamics, MoD, QinetiQ, Cummins, UTC Aerospace, ESPRC, Atkins, and PTDF. He has authored a book on machine health diagnostics and published over 150 research articles in international journals and conferences. Dr Khan received his doctorate in machine health diagnostics from the University of Manchester in 2008 and he completed his post-doctoral research in damage diagnosis in aero-transmissions in 2011. He is a Chartered Engineer, a Fellow of the Institute of Mechanical Engineers UK, and a Fellow of Higher Education Academy UK, He is an active member of Condition Monitoring and Structural Health Monitoring Committees of British Institute of Non- Destructive Testing.

Abstract: Numerical methods have been extensively applied to the fracture mechanics, while they cannot simulate the problems without the mechanical models or constitutive equations. Artificial neural networks (ANNs) can be utilized to predict the complex fracture problems, but these approaches require large amounts of data for the training. Therefore, in this paper, to combine the advantages of the numerical methods and the ANNs, an improved back propagation neural network (BPNN) is proposed through introducing the enrichment used in the numerical methods into the activation function utilized in the neural networks. The enrichment is able to represent the crack tip field which can accelerate the convergence. At the field near the crack tip, the improved BP solution can converge to the analytical solutions which validate the high accuracy of the proposed method. Without sufficient data, especially the data are missing in the near field of the crack tip, the improved BP method can also achieve high accuracy and convergence, while the conventional BP method may not converge to the predetermined error bound. Numerical simulations of the quasi-static and fatigue crack problems demonstrate that the improved BP method can accurately predict the crack propagation and its growth rate with relatively little data.
Subsea carbon sequestration technology plays a crucial role in addressing global climate change, but CO2 leakage can harm the subsea ecosystem. Therefore, long-term monitoring and prediction of subsea carbon storage are essential. In this paper, forward and inverse Data-Assisted Physics-Informed Neural Networks (DA-PINNs) are established for subsea CO2 leakage prediction and detection. Firstly, the forward DA-PINN model integrates numerical simulation data and physical constraints including initial conditions, boundary conditions, and governing equations. This model is utilized to predict the CO2 velocity and pressure fields under different leakage widths and initial velocities. The results show that the proposed algorithm outperforms conventional Artificial Neural Networks (ANNs) in accuracy and exceeds the efficiency of conventional numerical simulations. Subsequently, the inverse model incorporates known initial and boundary conditions of leakage as training data, while the governing equations and pressure boundary conditions serve as physical inputs. The inverse DA-PINN model is then used to detect leakage widths and initial velocities under different velocity and pressure fields, achieving a prediction accuracy of over 97%. Compared to conventional ANNs and numerical simulations, the proposed DA-PINNs not only predict CO2 leakage with high accuracy and efficiency but also solve inverse problem with the same high precision and effectiveness.

Biography: Dr. Lihua Wang is a professor at School of Aerospace Engineering and Applied Mechanics in Tongji University, Shanghai, China. She is currently a General Council Member of the International Association for Computational Mechanics (IACM) and the International Chinese Association for Computational Mechanics (ICACM). She is the recipient of several awards, including the APACM Award for Young Investigators in Computational Mechanics, the Qian Linxi Computational Mechanics Award (Young Investigators), the ICACM Young Investigator Award, and the Du Qing-Hua Medal & Young Researcher Award of Computational Methods in Engineering. She has authored more than 120 peer-reviewed journal articles, including CMAME, IJNME, JCP etc., and has been invited to deliver more than 10 plenary and invited lectures at international conferences. She served as associate editor of Chinese Quarterly of Mechanics and as an editorial board member for four international/Chinese journals. Her research interests include development of meshfree methods and machine learning, fluid-structure interactions, high-speed impact, fracture mechanics.