Direct multiphase mesh generation from 3D images using anisotropic mesh adaptation and a redistancing equation

Zhao, Jiaxin; Coupez, Thierry; Decencière, Étienne; Jeulin, Dominique; Cárdenas-Peña, David; Silva, Luísa

doi:10.1016/j.cma.2016.06.009

Cited by 12 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For scientific computation, accuracy of numerical representations may play a key role since the passage from the image to a finite element mesh allows, on one hand, the construction of a numerical representation to be used in further simulations but, on the other hand, may also reduce the size of the stored data. In recent work (Silva et al, 2014; Zhao et al, 2016), we have proposed a novel technique to simultaneously segment and construct a finite element mesh, using directly the 3D image data. In the following, its application on a very large image, requiring massively parallel tools, will be described.…”

Section: Resultsmentioning

confidence: 99%

Massively parallel anisotropic mesh adaptation

Digonnet

Coupez

Laure

et al. 2017

The International Journal of High Performance Computing Applica

Self Cite

View full text Add to dashboard Cite

International audienceMesh adaptation has proven to be very efficient for simulating transient multiphase computational fluid dynamics applications. In this work, we present a new parallel anisotropic mesh adaptation technique relying on an edge based error estimator. It provides a high level of accuracy while substantially reducing the computational effort. This technique enables a good capture of physical phenomena, boundary layers, interfaces, free surfaces and even multiphase turbulent flows, and has a great potential to simulate a large variety of applications. Current investigations explore the performance of the new algorithm on massively parallel resources. In this paper, we show that the developed adaptive meshing works very well in a parallel environment involving topological mesh modifications and dynamic repartitioning of parallel slots. It is also shown that the proposed methodology provides an additional gain in terms of computational cost due the production of a non-uniform mesh size distribution. Runs performed on national and European supercomputers will show the scalability and pertinence of our developments

show abstract

Section: Resultsmentioning

confidence: 99%

Massively parallel anisotropic mesh adaptation

Digonnet

Coupez

Laure

et al. 2017

The International Journal of High Performance Computing Applica

Self Cite

View full text Add to dashboard Cite

show abstract

“…This iterative process starts with a coarse initial mesh, where geometries are immersed and reconstructed using the methods proposed in Section 3.1 . A-posteriori error estimator [ 13 , 14 ] evaluates errors from the level-set results at each computational point, using the smoothed Heaviside function

described in Equation ( 2 ). In order to generate an anisotropic mesh, a tensor is defined at each point, enabling to measure the errors along each dimension.…”

Section: Computational Domain Reconstructionmentioning

confidence: 99%

Octree Optimized Micrometric Fibrous Microstructure Generation for Domain Reconstruction and Flow Simulation

Aissa

Douteau

Abisset-Chavanne

et al. 2021

Entropy

Self Cite

View full text Add to dashboard Cite

Over recent decades, tremendous advances in the field of scalable numerical tools and mesh immersion techniques have been achieved to improve numerical efficiency while preserving a good quality of the obtained results. In this context, an octree-optimized microstructure generation and domain reconstruction with adaptative meshing is presented and illustrated through a flow simulation example applied to permeability computation of micrometric fibrous materials. Thanks to the octree implementation, the numerous distance calculations in these processes are decreased, thus the computational complexity is reduced. Using the parallel environment of the ICI-tech library as a mesher and a solver, a large scale case study is performed. The study is applied to the computation of the full permeability tensor of a three-dimensional microstructure containing 10,000 fibers. The considered flow is a Stokes flow and it is solved with a stabilized finite element formulation and a monolithic approach.

show abstract

“…The specific anatomy of the patient is generally taken into account case-by-case thanks to pre-operative data such magnetic resonance images (MRI) or computed tomography (CT) scans. From these inputs, the surface and the volume of the organs of interest can be identified, reconstructed and meshed [1,18,24,46] . This can be a time-consuming and computationally intensive task and needs to be repeated for any new patient, posing severe limitations to the use of such models within interactive simulation environments.…”

Section: Scope Of the Current Workmentioning

confidence: 99%

A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications

Lauzeral

Borzacchiello

Kugler

et al. 2019

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

et al.. A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications.. Computer Methods and Programs in Biomedicine, Elsevier, 2019, 170, pp.a b s t r a c tBackground and objective: This paper focuses on computer simulation aspects of Digital Twin models in the medical framework. In particular, it addresses the need of fast and accurate simulators for the mechanical response at tissue and organ scale and the capability of integrating patient-specific anatomy from medical images to pinpoint the individual variations from standard anatomical models. Methods:We propose an automated procedure to create mechanical models of the human liver with patient-specific geometry and real time capabilities. The method hinges on the use of Statistical Shape Analysis to extract the relevant anatomical features from a database of medical images and Model Order Reduction to compute an explicit parametric solution for the mechanical response as a function of such features. The Sparse Subspace Learning, coupled with a Finite Element solver, was chosen to create lowrank solutions using a non-intrusive sparse sampling of the feature space.Results: In the application presented in the paper, the statistical shape model was trained on a database of 385 three dimensional liver shapes, extracted from medical images, in order to create a parametrized representation of the liver anatomy. This parametrization and an additional parameter describing the breathing motion in linear elasticity were then used as input in the reduced order model. Results show a consistent agreement with the high fidelity Finite Element models built from liver images that were excluded from the training dataset. However, we evidence in the discussion the difficulty of having compact shape parametrizations arising from the extreme variability of the shapes found in the dataset and we propose potential strategies to tackle this issue. Conclusions:A method to represent patient-specific real-time liver deformations during breathing is proposed in linear elasticity. Since the proposed method does not require any adaptation to the direct Finite Element solver used in the training phase, the procedure can be easily extended to more complex nonlinear constitutive behaviors -such as hyperelasticity -and more general load cases. Therefore it can be integrated with little intrusiveness to generic simulation software including more sophisticated and realistic models.

show abstract

Direct multiphase mesh generation from 3D images using anisotropic mesh adaptation and a redistancing equation

Cited by 12 publications

References 23 publications

Massively parallel anisotropic mesh adaptation

Massively parallel anisotropic mesh adaptation

Octree Optimized Micrometric Fibrous Microstructure Generation for Domain Reconstruction and Flow Simulation

A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications

Contact Info

Product

Resources

About