Documents published in Scipedia

• Data-driven models for shrinkage porosity prediction in aluminium casting

  M. Nouri, J. Artozoul, A. Caillaud, A. Ammar, F. Chinesta, O. Köser

  eccomas2022.

  
  

  Abstract

  Casting is one of the most used processes to form metals like aluminium. A casting part can contain several defects that threaten its resistance. Shrinkage porosity is one of the major anomalies that designers try to avoid. For this purpose, rounds of numerical simulations should be performed with operating on a selection of parameters in order to minimize the presence of porosity in the casting part. In general, these approaches are time-cost with dependence on the complexity of the study case and the needed accuracy. In this paper, a methodology of data-driven porosity prediction for 3D parts is proposed in order to minimize the time-cost. A supervised learning algorithm is implemented to learn nodal porosity prediction using decision trees based method. A dataset is generated from a casting simulation software with operating on a selection of parameters. The training is realised on critical features vector extracted from nodal thermal history. Model order reduction method is used to interpolate thermal fields allover the parameter space. This interpolation is sufficiently accurate with minor errors. Promising results of shrinkage porosity prediction on a 3D study case are obtained. An evaluation of these results is performed with reference to the simulations results. This solution can contribute to open perspectives for more data-driven solutions that optimize the time-cost in the design stage.

  Abstract
  Casting is one of the most used processes to form metals like aluminium. A casting part can contain several defects that threaten its resistance. Shrinkage porosity is one [...]

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• Model order reduction of solidification problems

  F. Arbes, Ø. Jensen, K. Mardal, J. Dokken

  eccomas2022.

  
  

  Abstract

  Advection driven problems are known to be difficult to model with a reduced basis because of a slow decay of the Kolmogorov N -width. This paper investigates how this challenge transfers to the context of solidification problems and tries to answer when and to what extend reduced order models (ROMs) work for solidification problems. In solidification problems, the challenge is not the advection per se, but rather a moving solidification front. This paper studies reduced spaces for 1D step functions that move in time, which can either be seen as advection of a quantity or as a moving solidification front. Furthermore, the reduced space of a 2D solidification test case is compared with the reduced space of an alloy solidification featuring a mushy zone. The results show that not only the PDE itself, but the smoothness of the solution is crucial for the decay of the singular values and thus the quality of a reduced space representation.

  Abstract
  Advection driven problems are known to be difficult to model with a reduced basis because of a slow decay of the Kolmogorov N -width. This paper investigates how this challenge [...]

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• Data-driven enhanced model reduction for bifurcating models in computational fluid dynamics

  M. Hess, A. Quaini, G. Rozza

  eccomas2022.

  
  

  Abstract

  We investigate various data-driven methods to enhance projection-based model reduction techniques with the aim of capturing bifurcating solutions. To show the effectiveness of the data-driven enhancements, we focus on the incompressible Navier-Stokes equations and different types of bifurcations. To recover solutions past a Hopf bifurcation, we propose an approach that combines proper orthogonal decomposition with Hankel dynamic mode decomposition. To approximate solutions close to a pitchfork bifurcation, we combine localized reduced models with artificial neural networks. Several numerical examples are shown to demonstrate the feasibility of the presented approaches.

  Abstract
  We investigate various data-driven methods to enhance projection-based model reduction techniques with the aim of capturing bifurcating solutions. To show the effectiveness [...]

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• Accelerated nonlinear PDE-constrained optimization by reduced order modelling

  B. Gibson, M. Yano

  eccomas2022.

  
  

  Abstract

  We present a framework to accelerate optimization of problems where the objective function is governed by a nonlinear partial differential equation (PDE) using projection-based reduced-order models (ROMs) and a trust-region (TR) method. To reduce the cost of objective function evaluations by several orders of magnitude, we replace the underlying full-order model (FOM) with a series of hyperreduced ROMs (HROMs) constructed on-the-fly. Each HROM is equipped with an online-efficient a posteriori error estimator, which is used to define a TR. Hyperreduction is performed following a goal-oriented empirical quadrature procedure, which guarantees first-order consistency of the HROM with the FOM at the TR center. This ensures the optimizer converges to a local minimum of the underlying FOM problem. We demonstrate the framework through optimization of a nonlinear thermal fin and pressure-matching inverse design of an airfoil under Euler flow and Reynolds-averaged Navier-Stokes flow.

  Abstract
  We present a framework to accelerate optimization of problems where the objective function is governed by a nonlinear partial differential equation (PDE) using projection-based [...]

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• On the matching of eigensolutions to parametric partial differential equations

  M. Alghamdi, F. Bertrand, D. Boffi, F. Bonizzoni, A. Halim, G. Priyadarshi

  eccomas2022.

  
  

  Abstract

  In this paper a novel numerical approximation of parametric eigenvalue problems is presented. We motivate our study with the analysis of a POD reduced order model for a simple one dimensional example. In particular, we introduce a new algorithm capable to track the matching of eigenvalues when the parameters vary.

  Abstract
  In this paper a novel numerical approximation of parametric eigenvalue problems is presented. We motivate our study with the analysis of a POD reduced order model for a simple [...]

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• The impact of tube corrugation within the multi-disciplinary design optimization of a charge air cooler.

  A. Singh, D. Toal, E. Richardson, C. Ibsen, K. Jose, A. Bhaskar

  eccomas2022.

  
  

  Abstract

  The following paper explores the impact of corrugated tubes within a charge air cooler (CAC) on overall cooler performance, cost and size, for the first time. Corrugated tubes have been demonstrated to perform better in terms of heat transfer, when compared to a smooth tube [2], however they have not been optimized in the context of a CAC. In this study, a CAC with corrugated tubes is compared against a similar system comprising of smooth tubes as a baseline design. Both CACs have common design parameters, such as number of tubes per rows, number of rows, number of passes, fins per meter, fin material, and tube material, while two additional design parameters exist i.e., groove depth, and pitch for the CAC with corrugated tubes, that characterizes the helical corrugation. These two systems are optimized to minimize manufacturing cost where cost is a function of cooler dimensions and material selection. Feasible designs are then obtained by satisfying dimension, pressure, weight, performance and vibrations based constraints. A vibration constraint introduced here is an addition to the current state of the art [3], making this approach, a multi-disciplinary one and the first of its kind. Finally, the optimum is compared which signifies the importance of a multi-disciplinary analysis for both cooler configurations.

  Abstract
  The following paper explores the impact of corrugated tubes within a charge air cooler (CAC) on overall cooler performance, cost and size, for the first time. Corrugated tubes [...]

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• Application of multiresolution analysis and deep learning to obtain failure pressure of corroded pipelines

  A. Marques Ferreira, S. Bastos Afonso, R. Willmersdorf

  eccomas2022.

  
  

  Abstract

  The assessment of corroded pipelines is considered a very important task in the oil and gas industry. The present work aims to develop an efficient system to accurately predict the burst pressure of corroded pipelines with complex corrosion profiles through hybrid models using multiresolution analysis, numerical analysis, and metamodels. The corrosion profile is obtained from ultrasonic inspections and the data is provided as a river bottom profile. The real corrosion shapes are parametrized considering a discrete wavelet transform to reduce the amount of data that describes the defect. The coefficients obtained from the wavelet transform are used as inputs to feed a deep neural network system for quickly and accurately predict the burst pipeline pressure. Eight different steel materials are considered in the NN build process. Synthetic models that have similar statistics to real corrosion profiles are created and submitted to non-linear FEM analysis, for the different materials. The failure pressures obtained from the synthetic defects are used to train a neural network to predict the burst pressure of the pipelines. The results obtained with the deep neural networks are very accurate for all cases presented in this work.

  Abstract
  The assessment of corroded pipelines is considered a very important task in the oil and gas industry. The present work aims to develop an efficient system to accurately predict [...]

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• A new numerical limit analysis-based strategy to retrofit masonry curved structures with FRCM systems

  R. Fugger, R. Maia Avelino, A. Iannuzzo, P. Block, G. de Felice

  eccomas2022.

  
  

  Abstract

  In most historic masonry structures, curved geometries, such as arches or vaults, are key structural components to the overall building stability. Therefore, it is crucial to assess their safety level with respect to changes in the boundary conditions (increased loads or settlements). If the safety level of the structure needs to be enhanced, a strategy to intervene and retrofit structural members is represented by the use of Fabric Reinforced Cementitious Matrix (FRCM) systems. These types of externally bonded composite materials, made of high-strength textiles embedded in inorganic matrices, are proven to be a particularly advantageous strengthening solution for curved masonry structures. Even though limit analysis approaches such as Thrust Network Analysis (TNA) have been widely used to assess structural stability, their use in a retrofitting framework is seldom explored. This paper proposes an automated procedure to design the FRCM reinforcement required in masonry structures based on an initial TNA assessment analysis. To perform these analyses, a nonlinear programming problem is implemented and solved to compute the minimum reinforcement required for stability. These quantities are then used to design the FRCM reinforcement according to existing regulations. Finally, the load-bearing capacity of the reinforced structure can be re-evaluated for different load cases ensuring that the structure is safe. The effectiveness of the proposed approach is benchmarked against laboratory tests and demonstrated on arched structures.

  Abstract
  In most historic masonry structures, curved geometries, such as arches or vaults, are key structural components to the overall building stability. Therefore, it is crucial [...]

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• Design relationships for the strengthening of masonry walls with mortar-based composites

  S. De Santis, P. Meriggi, S. Fares, G. de Felice

  eccomas2022.

  
  

  Abstract

  The paper presents a design method for the strengthening of masonry walls with fabric reinforced cementitious matrix (FRCM), steel reinforced grout (SRG) and composite reinforced mortar (CRM) systems. They have proved effective for the enhancement of structural capacity and are suitable for seismic retrofitting and for applications to architectural heritage. More recently, significant research efforts have been devoted to the development of testing/certification methods and of design guidelines. For this latter purpose, analytical relationships were developed, which are consistent with Eurocodes, are suitable for engineering practice, and have been incorporated in design guides. Both the bending strengthening under out-of-plane loads and the shear strengthening under in-plane loads are dealt with in the paper. The validation of the resisting models and the calibration of partial coefficients according to the design-by-testing approach are described. Assumptions, limitations and advantages are discussed, to promote the knowledge transfer from the academia to engineering practice and the proper use of FRCM, SRG and CRM for enhancing the safety level of the built environment.

  Abstract
  The paper presents a design method for the strengthening of masonry walls with fabric reinforced cementitious matrix (FRCM), steel reinforced grout (SRG) and composite reinforced [...]

  • 4
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• Challenges of integrating adjoint simulations in industrial turbomachinery MDO

  J. Backhaus, C. Voß, C. Frey

  eccomas2022.

  
  

  Abstract

  The design of turbomachinery creates a strong demand for the simultaneous optimization of multiple blade rows with regard to different disciplines including aerodynamics, aeroelasticity, and solid mechanics. Established gradient-free methods, typically surrogatebased methods, have been successfully applied to the optimization of single blade rows and pairs of adjacent rows, typically featuring in the order of 50 design variables per blade row. Gradient-free methods become inhibitively expensive through the increased number of design variables from simultaneous optimizations of many rows. Gradients obtained from adjoint simulations can help in transitioning to larger design spaces as they provide derivatives with respect to each design variable at a computational cost that only depends on the number of objectives. For the transition from gradient-free to gradientbased optimizations, a variety of challenges had to be solved, which will be outlined in this paper.

  Abstract
  The design of turbomachinery creates a strong demand for the simultaneous optimization of multiple blade rows with regard to different disciplines including aerodynamics, [...]

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