Documents published in Scipedia

• Analytical modeling of shock wave stresses and spall caused by laser plasma in a material interface – application to paint stripping on aluminum substrates

  K. Papadopoulos, K. Tserpes

  eccomas2022.

  
  

  Abstract

  In the present work, analytical and numerical models have been developed to calculate shock wave stresses caused by laser plasma in a material interface. The input pressure causing the shock wave has been derived from a laser-matter interaction model. The material velocity, stresses, and strains versus time in the two materials and the interface have been computed by solving the jump equations for conservation of mass and balance of momentum. The material interface has been considered as an immovable boundary while the back free surface as an unrestrained boundary. Spall fracture strength of the interface was evaluated and was used as stripping criterion. The model has been used for the fast computation of interfacial stresses causing paint stripping on aluminium substrates and the subsequent fast assessment of stripping initiation. An explicit finite element model combined with the cohesive zone modelling method and a spall fracture model have been developed. These models have been compared to each other in terms of time, accuracy and input properties demand and to the experimental results.

  Abstract
  In the present work, analytical and numerical models have been developed to calculate shock wave stresses caused by laser plasma in a material interface. The input pressure [...]

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• Coupled thermomechanical analysis of thermoplastic composite pipe by FEM simulations

  I. Guz, J. Hastie, M. Kashtalyan

  eccomas2022.

  
  

  Abstract

  Spoolable thermoplastic composite pipe (TCP) is an ideal alternative to traditional, heavier metallic counterparts for deepwater riser applications. During operation the pipe is subjected to mechanical loads simultaneously with through-wall thermal gradients arising from the mismatch between temperatures of hot pipe contents and cool surrounding ocean. In this work, structural analysis of TCP under coupled thermomechanical loads is performed using the finite element method (FEM). Temperature-dependent material properties are considered. Material safety factors for different laminate stacking sequences are compared and multi-angle stacking is shown to be effective for both pressure- and tensiondominated scenarios. Safety factors are also generated for TCP bent at reduced and elevated temperatures illustrative of spooling in different environments. It is clear that optimising the laminate for operation will adversely affect spooling capacity and vice-versa, i.e. TCP intended for extreme in-service conditions will require large spools.

  Abstract
  Spoolable thermoplastic composite pipe (TCP) is an ideal alternative to traditional, heavier metallic counterparts for deepwater riser applications. During operation the pipe [...]

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• Parallelization of a finite element solver for chemo-mechanical coupled anode and cathode particles in lithium-ion batteries

  R. Schoof, F. Castelli, W. Dörfler

  eccomas2022.

  
  

  Abstract

  Two chemo-mechanical coupled models for electrode particles of lithium-ion batteries are compared. On the one hand a Cahn­Hilliard-type phase-field approach models lithium intercalation, phase separation and large deformations in phase transforming cathode materials like lithium iron phosphate. On the other hand a chemo-mechanical particle model for lithium intercalation and large deformations for an anode material such as silicon is studied. The comparison of two different ways to define the deformation gradient for the large deformation approach and the two different material properties lead to differences in the resulting quantities and equations for the coupling of the chemo-mechanical model. The usage of an adaptive solution algorithm as well as the parallelization of the finite element solver via the message passing interface concept results in a more reasonable computation time to perform two-dimensional simulations. Both materials are numerically investigated and the results are compared from a physical point of view. When fast charging a battery, higher stress values are reached, which can cause a shorter cycle life. A strong scalability analysis shows good performance for the assembling, however a saturation occurs in the performance of the solver used.

  Abstract
  Two chemo-mechanical coupled models for electrode particles of lithium-ion batteries are compared. On the one hand a Cahn­Hilliard-type phase-field approach models lithium [...]

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• Closed - form expressions for the optimum winding angles of fibres in laminated pressure vessels subjected to internal pressure and axial force

  H. Al-Gahtani

  eccomas2022.

  
  

  Abstract

  The present paper aims at deriving closed-form expressions for the optimum winding angles of fibres in laminated cylindrical pressure vessels subjected to internal pressure, axial load and torque. To achieve this goal, the state of the stress on the surface of the vessel is represented by a composite layered element subjected to general in-plane loading. The symbolic software Mathematica is used to formulate, solve the optimization problem and to generate the data of optimum fibre angles for different loading combinations. The generated data is fitted with simple polynomial expressions capable of accurately predicting the optimum winding angles. The optimization is performed for three candidate composite materials: carbon/epoxy, glass/epoxy, and aramid/epoxy laminates.

  Abstract
  The present paper aims at deriving closed-form expressions for the optimum winding angles of fibres in laminated cylindrical pressure vessels subjected to internal pressure, [...]

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• Deep neural networks for unsupervised damage detection on the Z24 bridge

  V. Giglioni, I. Venanzi, V. Poggioni, A. Baia, A. Milani, F. Ubertini

  eccomas2022.

  
  

  Abstract

  During their life-cycle, civil infrastructures are continuously prone to significant functionality losses, primarily due to material's degradation and exposure to several natural hazards. Following these concerns, many researchers have attempted to develop reliable monitoring strategies, as integration to visual inspections, to efficiently ensure bridge maintenance and early-stage damage detection. In this framework, recent improvements in sensor technologies and data science have stimulated the use of Machine Learning (ML) algorithms for Structural Health Monitoring (SHM). Among unsupervised learning techniques, the potential of autoencoder networks has been attracting notable interest in the context of anomaly detection. In this light, the present paper proposes two different autoencoder-based damage detection techniques, focused on the Multi-Layer Perceptron (MLP) and the Convolutional Autoencoder (CAE) networks, respectively. During the training, the selected ML models learn how reconstructing raw acceleration sequences acquired from sound conditions. Unknown data, including both healthy and damaged bridge responses, are afterwards used to test the implemented networks and to detect damage occurrence. To this aim, a specific index of reconstruction loss is selected as a damage sensitive feature with the aim to quantify the errors between the original and reconstructed sequences. The performance exhibited by the two approaches is compared and evaluated by application to the Z24 benchmark bridge. Results demonstrate the effectiveness of the proposed methodology to perform feature classification and real time damage detection at the level of macro-sequences as new sensor data is collected, resulting suitable for continuous assessment of full-scale monitored bridges.

  Abstract
  During their life-cycle, civil infrastructures are continuously prone to significant functionality losses, primarily due to material's degradation and exposure to several [...]

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• Investigation of higher harmonic Lamb waves for facilitating delamination characterization

  A. Gangwar, D. Joglekar

  eccomas2022.

  
  

  Abstract

  We advance this field by systematically exploring nonlinear interactions of A0 Lamb mode signals with delamination defects of various sizes and interlaminar locations, to facilitate the characterization of delaminations. The interrogation signal, in this regard, is a modulated sinusoid whose frequency varies in steps of 20 kHz between 40 kHz and 100 kHz. Commercial FEM software is used for modelling the contact at delamination interfaces and for simulating the Lamb wave propagation through the waveguide with delamination defect. It is demonstrated that the intermittently acting contact pressure between the two surfaces of delamination acts as a source of nonlinearity, resulting in generation of higher order harmonics of interrogation frequency. A metric for measuring nonlinearity, the nonlinearity index (NI), is used to quantify the strength of wave-damage interactions over a range of interrogation frequencies. The NI index is observed to vary with both the interlaminar location as well as the width of the delamination. The maximum value of NI is further influenced by the frequency of excitation signal. To infer the effect of delamination parameters on the NI, a concept of a concept of contact energy intensity is introduced, which is largely dependent on the size and the interlaminar position of the delamination. The nonlinearity index patterns are explained by combining the intensity of the contact energy with the phase difference between waves traveling through the sub-laminations and the flexural rigidity of the two sub-laminates at the delamination location. The inferences provided can potentially be used for determining the interlaminar location and width of delamination employing higher harmonic Lamb wave signals generated by the breathing delamination.

  Abstract
  We advance this field by systematically exploring nonlinear interactions of A0 Lamb mode signals with delamination defects of various sizes and interlaminar locations, to [...]

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• Forming effects on high-cycle fatigue in an aluminum sheet structure using the Ottosen-Stenström-Ristinmaa model

  S. Tveit, A. Reyes, E. Erduran

  eccomas2022.

  
  

  Abstract

  In this paper a multi-stage numerical analysis is presented with the aim to investigate effects of forming on the high-cycle fatigue performance of a deep-drawn aluminum sheet structure for use in a floating photovoltaic system. Forming simulations of a subsection of the full geometry are performed in a realistic two-step drawing-springback cycle. A simplified global analysis of service load response is performed to obtain displacements at the submodel boundary, that are used to generate boundary conditions for a local service load analysis. The local analysis is then performed on three different models of the subsection: (I) excluding forming effects, (II) including effects of thinning, and (III) including effects of thinning and residual stresses. The critical location with respect to the fatigue limit criterion in the Ottosen-Stenström-Ristinmaa high-cycle fatigue model was identified for case (I), and this location was used to compare the different models to assess effects of forming on high-cycle fatigue performance. Furthermore, the dynamic friction coefficient , and the isotropickinematic mixing coefficient were varied in order to investigate their respective effects.

  Abstract
  In this paper a multi-stage numerical analysis is presented with the aim to investigate effects of forming on the high-cycle fatigue performance of a deep-drawn aluminum sheet [...]

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• Using artificial intelligence techniques for the accurate estimation of the ultimate pure bending of steel circular tubes

  M. Ben Seghier, V. Plevris, G. Solorzano

  eccomas2022.

  
  

  Abstract

  In this paper, the potential of building more accurate and robust models for the prediction of the ultimate pure bending capacity of steel circular tubes using artificial intelligence techniques is investigated. Therefore, a database consisting of 104 tests for fabricated and cold-formed steel circular tubes are collected from the open literature and used to train and validate the proposed data-driven approaches which include the Random Forest methodology in two variants: the original version in which the control parameters are manually updated, and an enhanced RF-PSO variant, where Particle Swarm Optimization is used for optimizing these parameters. The data set has four input parameters, namely the tube thickness, tube diameter, yield strength of steel and steel elasticity modulus, while the ultimate pure bending capacity is considered as the target output variable. The obtained results are compared to the real test values through various statistical indicators such as the root mean square error and the coefficient of determination. The results indicate that the proposed enhanced model can provide an accurate solution for modelling the complex behavior of steel circular tubes under pure bending conditions.

  Abstract
  In this paper, the potential of building more accurate and robust models for the prediction of the ultimate pure bending capacity of steel circular tubes using artificial [...]

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• Multi-physical modelling and analysis of lubricated transmissions using a coupled finite element approach

  S. Neeckx, B. Blockmans, W. Rottiers, W. Desmet

  eccomas2022.

  
  

  Abstract

  Lubrication is vital to improve performance, efficiency and durability in transmissions; it serves to minimize wear, noise, vibration and friction. The proper functioning of lubricated transmissions relies on interactions amongst a wide range of physical phenomena (contact dynamics, fluid-structure interaction and heat transfer) operating at different spatial and temporal scales. A lubricated transmission model is proposed in this work to obtain accurate information regarding all physical domains and the coupling thereof to quantify performance, efficiency and durability. The model consists of a thermo-elastically coupled flexible multibody model for the gear pair and a Thermo-Elasto-Hydrodynamic Lubrication (TEHL) model for the lubricant, both based on first principle modelling to ensure high fidelity.

  Abstract
  Lubrication is vital to improve performance, efficiency and durability in transmissions; it serves to minimize wear, noise, vibration and friction. The proper functioning [...]

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• Advanced deep learning comparisons for non-invasive tunnel lining assessment from ground penetrating radar profiles

  M. Rosso, G. Marasco, L. Tanzi, S. Aiello, A. Aloisio, R. Cucuzza, B. Chiaia, G. Cirrincione, G. Marano

  eccomas2022.

  
  

  Abstract

  Innovative, automated, and non-invasive techniques have been developed by scientific community to indirectly assess structural conditions and support the decision-making process for a worthwhile maintenance schedule. Nowadays, machine learning tools are in the spotlight because of their outstanding capabilities to deal with data coming from even heterogeneous sources and their ability to extract information from the structural systems, providing highly effective, reliable, and efficient damage classification tools. In the current study, a supervised multi-level damage classification strategy has been developed regarding Ground Penetrating Radar (GPR) profiles for the assessment of tunnel lining conditions. In previous research, the authors firstly considered a convolutional neural network (CNN), adopting the quite popular ResNet-50, initialized through transfer learning. In the present work, further enhancements have been attempted by adopting two configurations of the newest state-of-art advanced neural architectures: the neural transformers. The foremost is the original Vision Transformer (ViT), whose core is an encoder entirely based on the innovative self-attention mechanism and does not rely on convolution at all. The second is an improvement of ViT which merges convolution and self-attention, the Compact Convolution Transformer (CCT). In conclusion, a critical discussion of the different pros and cons of adopting the above-mentioned different architectures is finally provided, highlighting the actual powerfulness of these technologies in the future civil engineering paradigm nevertheless.

  Abstract
  Innovative, automated, and non-invasive techniques have been developed by scientific community to indirectly assess structural conditions and support the decision-making process [...]

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