Documents published in Scipedia

• Analysis of high-order interpolation schemes for the finite-volume resolution of linear problems on unstructured meshes

  P. Castrillo, E. Schillaci, J. Rigola

  eccomas2022.

  
  

  Abstract

  Finite-volume strategies in fluid-structure interaction problems would be of crucial importance in many engineering applications such as in the analysis of reed valves in reciprocating compressors. The efficient implementation of this strategy passes from the formulation of reliable high-order schemes on 3D unstructured meshes. The development of high-order models is essential in bending-dominant problems, where the phenomenon of shear blocking appears. In order to solve this problem, it is possible to either increase the number of elements or increase the interpolation order of the main variable. Increasing the number of elements does not always yield good results and implies a very high computational cost that, in real problems, is inadmissible. Using unstructured meshes is also vital because they are necessary for real problems where the geometries are complex and depart from canonical rectangular or regular shapes. This work presents a series of tests to demonstrate the feasibility of a high-order model using finite volumes for linear elasticity on unstructured and structured meshes. The high-order interpolation will be performed using two different schemes such as the Moving Least Squares (MLS) and the Local Regression Estimators (LRE). The reliability of the method for solving 2D and 3D problems will be verified by solving some known test cases with an analytical solution such as a thin beam or problems where stress concentrations appear.

  Abstract
  Finite-volume strategies in fluid-structure interaction problems would be of crucial importance in many engineering applications such as in the analysis of reed valves in [...]

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• Aeroelastic Adjoint-Based Optimisation of Highly Flexible Aircraft Wing Configuration

  M. Méheut, M. Carini, C. Blondeau

  eccomas2022.

  
  

  Abstract

  This paper investigates benefits resulting from the use of coupled aeroelastic analysis for aerodynamic shape optimisation of a highly flexible wing. The study is carried out on the eXternal Research Forum model (XRF-1) specified by Airbus Commercial Aircraft, representative of a long-range aircraft configuration. Improvements delivered by considering aeroelastic effects for the evaluation of both the aerodynamic performance and the associated gradients are assessed with respect to the results obtained by freezing the wing flexibility in both primal and adjoint computations. An analysis of the impact on the different drag components is also illustrated based on the far-field drag breakdown. Results show that for induced drag, engaging flexibility only at the primal level still allows to capture first-order gain on the final performance. However, engaging coupled-adjoint sensitivities is key to completely master wave drag reduction on the considered highly flexible wing. Performance improvement obtained by increasing the number of design parameters is also investigated.

  Abstract
  This paper investigates benefits resulting from the use of coupled aeroelastic analysis for aerodynamic shape optimisation of a highly flexible wing. The study is carried [...]

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• Approximating the full-field temperature evolution in 3D electronic systems from randomized “Minecraft” systems

  M. Stipsitz, H. Sanchis-Alepuz

  eccomas2022.

  
  

  Abstract

  Neural Networks as fast physics simulators have a large potential for many engineering design tasks. Prerequisites for a wide-spread application are an easy-to-use workflow for generating training datasets in a reasonable time, and the capability of the network to generalize to unseen systems. In contrast to most previous works where training systems are similar to the evaluation dataset, we propose to adapt the type of training system to the network architecture. Specifically, we apply a fully convolutional network and, thus, design 3D systems of randomly located voxels with randomly assigned physical properties. The idea is tested for the transient heat diffusion in electronic systems. Training only on random 'Minecraft' systems, we obtain good generalization to electronic systems four times as large as the training systems (one-step prediction error of 0.07 % vs 0.8 %).

  Abstract
  Neural Networks as fast physics simulators have a large potential for many engineering design tasks. Prerequisites for a wide-spread application are an easy-to-use workflow [...]

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• In-plane shear capacity response of FRCM-strengthened masonry walls

  F. Nerilli, F. Roscini, B. Ferracuti

  eccomas2022.

  
  

  Abstract

  This paper aims at investigating the in-plane shear response of FRCM-strengthened masonry walls. To this end, available results of experimental tests are collected, accounting for the masonry substrate made with bricks and mortar joints and several FRCM materials applied with different strengthening configurations. The contribution of the composite material to the masonry wall shear capacity is evaluated. The influence of some geometrical and mechanical parameters on the shear strength of the retrofitted walls is assessed. Available analytical design formulations are implemented to the database and commented.

  Abstract
  This paper aims at investigating the in-plane shear response of FRCM-strengthened masonry walls. To this end, available results of experimental tests are collected, accounting [...]

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• Empirical usability fragility curves for unreinforced masonry buildings affected by the 2009 L’Aquila earthquake

  M. Zucconi, M. Di Ludovico, L. Sorrentino

  eccomas2022.

  
  

  Abstract

  Recent earthquakes occurred in Italy highlighted the great vulnerability of the Italian building stoke that registered significant economic losses. In this context, many vulnerability models were developed in the literature to obtain a reliable loss assessment. They often focused on damage fragility curves definitions, intending to estimate the damage suffered by the buildings after the seismic events. Nevertheless, in the last years, the attention of different research groups is moved toward the prediction of the building usability, i.e. the condition of a building being habitable or occupiable after a seismic event. In fact, recent researches highlighted that usability is stronger correlated with direct and indirect costs than structural damage. Consequently, the prediction of usability performance represents a valid indicator for the economic funding distribution after an earthquake. From this perspective, this paper aims to develop typological usability fragility curves for Italian unreinforced-masonry buildings to be used for seismic risk assessment on a large scale. The proposed empirical model was calibrated from the observed data collected after the 2009 L'Aquila earthquake, including more than 56 000 unreinforced-masonry buildings. The database was increased to estimate the effective number of usable buildings in the study area. Then, the structural parameters affecting the usability assessment were investigated, and three parameters (construction timespan, number of stories, and state of repair), available both on the post-earthquake database and Italian census, were selected to define different typological classes. The usability fragility curves were defined as a function of peak ground acceleration for two building usability states strongly correlated to repair and population assistance costs: partially unusable and unusable. The curves represent a sound tool to be used as part of a risk model for assessing earthquake impact in terms of both economic and societal losses.

  Abstract
  Recent earthquakes occurred in Italy highlighted the great vulnerability of the Italian building stoke that registered significant economic losses. In this context, many vulnerability [...]

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• Bayesian system identification and dynamic virtualization using incomplete noisy measurements

  D. Teymouri, O. Sedehi, L. Katafygiotis, C. Papadimitriou

  eccomas2022.

  
  

  Abstract

  This study presents the application of Bayesian Expectation-Maximization (BEM) methodology to coupled state-input-parameter estimation in both linear and nonlinear structures. The BEM is built upon a Bayesian foundation, which utilizes the EM algorithm to deliver accurate estimates for latent states, model parameters, and input forces while updating noise characteristics effectively. This feature allows for quantifying associated uncertainties using response-only measurements. The proposed methodology is equipped with a recursive backward-forward Bayesian estimator that provides smoothed estimates of the state, input, and parameters during the Expectation step. Next, these estimates help calculate the most probable values of the noise parameters based on the observed data. This adaptive approach to the coupled estimation problem allows for real-time quantification of estimation uncertainties, whereby displacement, velocity, acceleration, strain, and stress states can be reconstructed for all degrees-of-freedom through virtual sensing. Through numerical examples, it is demonstrated that the BEM accurately estimates the unknown quantities based on the measured quantities, not only when a fusion of displacement and acceleration measurements is available but also in the presence of acceleration-only response measurements.

  Abstract
  This study presents the application of Bayesian Expectation-Maximization (BEM) methodology to coupled state-input-parameter estimation in both linear and nonlinear structures. [...]

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• Seismic vulnerability of masonry structures through a mechanical-based approach

  F. Liguori, S. Fiore, F. Perelli, D. De Gregorio, G. Zuccaro, A. Madeo

  eccomas2022.

  
  

  Abstract

  Enhancing the territorial resilience to natural events, such as earthquakes, is assuming a primary role in the current political debate. In the context of Disaster Risk Management, developing reliable vulnerability models for the seismic risk assessment at a territorial scale is an aspect of crucial importance. In this perspective, the paper presents a mechanical-based method for the evaluation of local-scale seismic fragility curves for unreinforced masonry buildings, based on the exposure data collected in the Italian CARTIS database. It uses a bidimensional finite element model and static nonlinear analyses to obtain the structural behaviour. Monte Carlo simulations are performed to propagate the uncertainties. Both local and global scale structural behaviour are considered to define the damage grade. A case-study regarding the city centre of Cosenza, in southern Italy, validates the proposal.

  Abstract
  Enhancing the territorial resilience to natural events, such as earthquakes, is assuming a primary role in the current political debate. In the context of Disaster Risk Management, [...]

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• Bayesian Optimal Sensor Placement for Virtual Sensing and Strain Reconstruction

  T. Ercan, O. Sedehi, C. Papadimitriou, L. Katafygiotis

  eccomas2022.

  
  

  Abstract

  A Bayesian optimal sensor placement (OSP) framework is presented for virtual sensing in structures using output-only vibration measurements. Particularly, this probabilistic OSP scheme aims to enhance the reconstruction of dynamical responses (e.g., accelerations, displacements, strain, stresses) for updating structural reliability and safety, as well as fatigue lifetime prognosis. The OSP framework is formulated using information theory. The information gained from a sensor configuration is defined as the Kullback-Liebler divergence (KL-div) between the prior and posterior distributions of the response quantities of interest (QoI). The Gaussian nature of the response estimate for linear models of structures is employed, and the information gain is characterized in terms of the reconstruction error covariance matrix. A Kalman-based input-state estimation technique is integrated within an existing OSP strategy, aiming to obtain estimates of response QoI and their uncertainties. The design variables include the location, type and number of sensors. Heuristic algorithms are used to solve optimization problem and provide computationally efficient solutions. The effectiveness of the method is demonstrated using an example from structural dynamics.

  Abstract
  A Bayesian optimal sensor placement (OSP) framework is presented for virtual sensing in structures using output-only vibration measurements. Particularly, this probabilistic [...]

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• Hierarchical Bayesian model for simulating the mechanical behavior of bare printed circuit boards with fixing

  H. Schmidt, M. Kaess, M. Huelsebrock, R. Lichtinger

  eccomas2022.

  
  

  Abstract

  A method for probabilistic simulation of a bare printed circuit board fixed with bolted joints based on hierarchical Bayesian updating of a numerical model is presented in this paper. The objective is the determination of parameter uncertainties in a set of nominally identical boards and the propagation of these uncertainties to calculate probability distributions for the behavior of the mechanical system. The numerical model of the system is split into models for the circuit board, the bolts and a contact model that are updated separately.

  Abstract
  A method for probabilistic simulation of a bare printed circuit board fixed with bolted joints based on hierarchical Bayesian updating of a numerical model is presented in [...]

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• LES-aided shape optimization of U-Bend channel

  R. Quadros, J. Mueller

  eccomas2022.

  
  

  Abstract

  Reynolds-Averaged Navier-Stokes (RANS) simulations are inaccurate in predicting complex flow features (ex: separation regions), and therefore deriving an optimised shape using the RANS-adjoint framework does not yield a truly optimal geometry. With the purpose of obtaining accurate sensitivity to objective function of interest, we improve the RANS flowfield using the strategy of Singh et al. [1]. This involves multiplying a corrective factor to the production term in the Spalart-Allmaras (SA) turbulence model equation and solving the inverse problem to determine the appropriate field, which enables the RANS solution to match the high-fidelity data.The geometry of our interest is the U-Bend which is widely studied in literature in the context of gas turbine cooling, and which is known to be a challenging case for RANS simulations to reproduce. We use the mean flowfield from a large-eddy simulation of the U-Bend geometry as the high-fidelity data to which the RANS flowfield is fit using the strategy outlined above. We observe a clear improvement in the RANS flowfield by optimising for the field, the objective function to be minimized being L2-norm of the mean velocity difference between RANS and LES. We further show that adding an additional corrective factor () to the destruction term in the SA turbulence equation and simultaneously optimising for the field alongside the field results in a better match of the RANS flowfield with the corresponding LES flowfield. We also show that surface sensitivity map for the improved LES-aided flowfield varies significantly in comparison to the baseline SA-based flowfield for an objective function of interest, the total pressure loss in the U-Bend.

  Abstract
  Reynolds-Averaged Navier-Stokes (RANS) simulations are inaccurate in predicting complex flow features (ex: separation regions), and therefore deriving an optimised shape using [...]

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