301 research outputs found
Kirchhoff-Love shell representation and analysis using triangle configuration B-splines
This paper presents the application of triangle configuration B-splines
(TCB-splines) for representing and analyzing the Kirchhoff-Love shell in the
context of isogeometric analysis (IGA). The Kirchhoff-Love shell formulation
requires global -continuous basis functions. The nonuniform rational
B-spline (NURBS)-based IGA has been extensively used for developing
Kirchhoff-Love shell elements. However, shells with complex geometries
inevitably need multiple patches and trimming techniques, where stitching
patches with high continuity is a challenge. On the other hand, due to their
unstructured nature, TCB-splines can accommodate general polygonal domains,
have local refinement, and are flexible to model complex geometries with
continuity, which naturally fit into the Kirchhoff-Love shell formulation with
complex geometries. Therefore, we propose to use TCB-splines as basis functions
for geometric representation and solution approximation. We apply our method to
both linear and nonlinear benchmark shell problems, where the accuracy and
robustness are validated. The applicability of the proposed approach to shell
analysis is further exemplified by performing geometrically nonlinear
Kirchhoff-Love shell simulations of a pipe junction and a front bumper
represented by a single patch of TCB-splines
Tissue-scale, personalized modeling and simulation of prostate cancer growth
Recently, mathematical modeling and simulation of diseases and their treatments have enabled the prediction of clinical outcomes and the design of optimal therapies on a personalized (i.e., patient-specific) basis. This new trend in medical research has been termed “predictive medicine.” Prostate cancer (PCa) is a major health problem and an ideal candidate to explore tissue-scale, personalized modeling of cancer growth for two main reasons: First, it is a small organ, and, second, tumor growth can be estimated by measuring serum prostate-specific antigen (PSA, a PCa biomarker in blood), which may enable in vivo validation. In this paper, we present a simple continuous model that reproduces the growth patterns of PCa. We use the phase-field method to account for the transformation of healthy cells to cancer cells and use diffusion-reaction equations to compute nutrient consumption and PSA production. To accurately and efficiently compute tumor growth, our simulations leverage isogeometric analysis (IGA). Our model is shown to reproduce a known shape instability from a spheroidal pattern to fingered growth. Results of our computations indicate that such shift is a tumor response to escape starvation, hypoxia, and, eventually, necrosis. Thus, branching enables the tumor to minimize the distance from inner cells to external nutrients, contributing to cancer survival and further development. We have also used our model to perform tissue-scale, personalized simulation of a PCa patient, based on prostatic anatomy extracted from computed tomography images. This simulation shows tumor progression similar to that seen in clinical practice.Peer ReviewedPostprint (published version
Interpolatory Catmull-Clark volumetric subdivision over unstructured hexahedral meshes for modeling and simulation applications
International audienceVolumetric modeling is an important topic for material modeling and isogeometric simulation. In this paper, two kinds of interpolatory Catmull-Clark volumetric subdivision approaches over unstructured hexahedral meshes are proposed based on the limit point formula of Catmull-Clark subdivision volume. The basic idea of the first method is to construct a new control lattice, whose limit volume by the CatmullClark subdivision scheme interpolates vertices of the original hexahedral mesh. The new control lattice is derived by the local push-back operation from one CatmullClark subdivision step with modified geometric rules. This interpolating method is simple and efficient, and several shape parameters are involved in adjusting the shape of the limit volume. The second method is based on progressive-iterative approximation using limit point formula. At each iteration step, we progressively modify vertices of an original hexahedral mesh to generate a new control lattice whose limit volume interpolates all vertices in the original hexahedral mesh. The convergence proof of the iterative process is also given. The interpolatory subdivision volume has C 2-smoothness at the regular region except around extraordinary vertices and edges. Furthermore, the proposed interpolatory volumetric subdivision methods can be used not only for geometry interpolation, but also for material attribute interpolation in the field of volumetric material modeling. The application of the proposed volumetric subdivision approaches on isogeometric analysis is also given with several examples
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