Documents published in Scipedia

• 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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• Using conservation laws to infer deep learning model accuracy of Richtmyer-Meshkov instabilities

  C. Jekel, D. Sterbentz, S. Aubry, Y. Choi, D. White, J. Belof

  eccomas2022.

  
  

  Abstract

  Richtmyer-Meshkov Instability (RMI) is a complicated phenomenon that occurs when a shockwave passes through a perturbed interface. Over a thousand hydrodynamic simulations were performed to study the formation of RMI for a parameterized high velocity impact. Deep learning was used to learn the temporal mapping of initial geometric perturbations to the full-field hydrodynamic solutions of density and velocity. The continuity equation was used to include physical information into the loss function, however only resulted in very minor improvements at the cost of additional training complexity. Predictions from the deep learning model appear to accurately capture temporal RMI formations for a variety of geometric conditions within the domain. First principle physical laws were investigated to infer the accuracy of the model's predictive capability. While the continuity equation appeared to show no correlation with the accuracy of the model, conservation of mass and momentum were weakly correlated with accuracy. Since conservation laws can be quickly calculated from the deep learning model, they may be useful in applications where a relative accuracy measure is needed.

  Abstract
  Richtmyer-Meshkov Instability (RMI) is a complicated phenomenon that occurs when a shockwave passes through a perturbed interface. Over a thousand hydrodynamic simulations [...]

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• Vectorial limitation for multislope MUSCL schemes

  A. Tételin, C. Le Touze, P. Villedieu

  eccomas2022.

  
  

  Abstract

  In finite volume schemes with MUSCL interpolation of scalar variables at the faces of control volumes, a slope limiting function is used in order to prevent non-physical oscillations of the solution. More particularly, these functions are designed to ensure a certain monotonicity criterion at each face of the control volume, criterion which then ensures a stability property of the scheme. For vectorial variables, these slope limiting functions are generally applied componentwise, but this may result in a frame-dependance, as well as a loss of accuracy due to false detection of extrema. In this paper, a new vectorial interpolation method is introduced, which is frame-invariant, second-order accurate and stable in a sense that will be defined.

  Abstract
  In finite volume schemes with MUSCL interpolation of scalar variables at the faces of control volumes, a slope limiting function is used in order to prevent non-physical oscillations [...]

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• Remaining Useful Life prediction with a Deep Self-Supervised Learning Approach

  A. Akrim, C. Gogu, R. Vingerhoeds, M. Salaün

  eccomas2022.

  
  

  Abstract

  With the increasing availability of data for Prognostics and Health Management (PHM), Deep Learning (DL) techniques are now the subject of considerable attention in Prognostics for Predictive Maintenance, achieving more accurate Remaining Useful Life (RUL) predictions. However, one of the major challenges for DL techniques resides in the difficulty of obtaining large amounts of labeled data on industrial systems. To overcome this lack of labeled data, an emerging learning technique is considered in this work : Self-Supervised Learning, a sub-category of unsupervised learning approaches. This paper aims to investigate whether pre-training DL models in a self-supervised way on unlabeled sensors data can be useful for downstream tasks in PHM (i.e. RUL estimation) with only limited amount of labelled data. A synthetic dataset composed of strain data is used. Results show that the self-supervised pretrained models significantly outperform the non pre-trained models in downstream Remaining Useful Life (RUL) prediction task, showing promising results in prognostic tasks when only limited labeled data is available.

  Abstract
  With the increasing availability of data for Prognostics and Health Management (PHM), Deep Learning (DL) techniques are now the subject of considerable attention in Prognostics [...]

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• Pros and cons of various equivalent frame models for nonlinear analysis of URM buildings

  A. Shabani, M. Kioumarsi

  eccomas2022.

  
  

  Abstract

  Brick masonry is considered as one of the old construction materials, and several cultural heritage assets are made of unreinforced masonry (URM), which is susceptible to earthquakes due to its brittle behavior. The equivalent frame method (EFM) is a nonlinear modeling method widely utilized for the seismic analysis of URM buildings with lower computational efforts than finite and discrete element methods. In this study, three macroelements, including the unified method (UM), composite spring method (CSM), and double modified multiple vertical line element model (DM-MVLEM), were utilized to model three case studies. The first case study is a full-scale two-story URM wall that was tested by applying the cyclic prescribed displacements, and two other case studies were developed by changing the configuration of openings. The second case study is with short piers, and weak spandrels exist in the third model. The efficiency of the methods in terms of the accuracy of the pushover results, prediction of damage patterns, and duration of the incremental dynamic analysis (IDA) are discussed. Finally, seismic fragility curves are provided to compare the IDA results.

  Abstract
  Brick masonry is considered as one of the old construction materials, and several cultural heritage assets are made of unreinforced masonry (URM), which is susceptible to [...]

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• Simulation of reacting flows using artificial neural networks: application to multi-regime combustion

  C. Mehl, D. Aubagnac-Karkar

  eccomas2022.

  
  

  Abstract

  The simulation of reactive flows is a major challenge in several industrial sectors, such as aeronautics or energy production. The coupling between fluid dynamics and chemistry comes however at a cost, as chemical processes involve a wide range of spatial and temporal scales. The resulting equations are stiff and require specific, and expensive, numerical methods. The use of machine learning to estimate the reaction rates has been recently proposed. In particular, Artificial Neural Networks (ANN) have the ability to perform interpolation on high-dimensional data and are thus particularly adapted to chemistry problems. A major issue is then to select an appropriate database on which to train the ANN. It must: (i) be representative of the targeted application; (ii) be sufficiently quick to generate. A promising strategy is to use 0-D stochastics reactors, which mimic reactive and mixing processes in systems while being cheap to compute. This methodology has been successfully applied to non-premixed combustion in the literature. In the present work, the aim is to investigate the ability of the 0D stochastic reactors to be used as a database for a wider range of combustion systems. More specifically, the focus will be on the ability to predict auto-ignition followed by premixed flame propagation. To that purpose, a 2-D turbulent case involving the auto-ignition of a hotspot in a hydrogen/air mixture and the subsequent propagation of a premixed flame is proposed. An ANN model based on stochastic reactors is then built and tested on (i) a 0-D auto-ignition case; (ii) a 1-D laminar premixed flame propagation; (ii) the full 2-D turbulent configuration. Using adequate data transformation at the input and output of the neural network, accurate results are obtained, highlighting the ability of the proposed strategy to deal with a large range of combustion applications.

  Abstract
  The simulation of reactive flows is a major challenge in several industrial sectors, such as aeronautics or energy production. The coupling between fluid dynamics and chemistry [...]

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