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An adaptive discrete Newton method for regularization-free Bingham model

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An adaptive discrete Newton method for regularization-free Bingham model

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dc.contributor.author Fatima, Arooj es_ES
dc.contributor.author Turek, Stefan es_ES
dc.contributor.author Ouazzi, Abderrahim es_ES
dc.contributor.author Afaq, Muhammad Aaqib es_ES
dc.date.accessioned 2022-09-29T10:16:38Z
dc.date.available 2022-09-29T10:16:38Z
dc.date.issued 2022-05-11
dc.identifier.isbn 9788490489697
dc.identifier.uri http://hdl.handle.net/10251/186720
dc.description.abstract [EN] Developing a numerical and algorithmic tool which correctly identifies unyielded regions in yield stress fluid flow is a challenging task. Two approaches are commonly used to handle the singular behaviour at the yield surface, i.e. the Augmented Lagrangian approach and the regularization approach, respectively. Generally in the regularization approach, solvers do not perform efficiently when the regularization parameter gets very small. In this work, we use a formulation introducing a new auxiliary stress. The three field formulation of the yield stress fluid corresponds to a regularization-free Bingham formulation. The resulting set of equations arising from the three field formulation is solved efficiently and accurately by a monolithic finite element method. The velocity and pressure are discretized by the higher order stable FEM pair Q2/Pdisc 1 and the auxiliary stress is discretized by the Q2 element. Furthermore, this problem is highly nonlinear and presents a big challenge to any nonlinear solver. Therefore, we developed a new adaptive discrete Newton method, which evaluates the Jacobian with the divided difference approach. We relate the step length to the rate of the actual nonlinear reduction for achieving a robust adaptive Newton method. We analyse the solvability of the problem along with the adaptive Newton method for Bingham fluids by doing numerical studies for a prototypical configuration ”viscoplastic fluid flow in a channel”. es_ES
dc.description.sponsorship We would like to thank the Deutsche Forschungsgemeinschaft (DFG) for their financial support under the DFG Priority Program SPP 1962. The authors also acknowledge the support by LS3 and LiDO3 team at ITMC, TU Dortmund University es_ES
dc.format.extent 10 es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Yield stress fluids es_ES
dc.subject Bingham fluid es_ES
dc.subject Directional divided difference es_ES
dc.subject FEM es_ES
dc.subject Adaptive Newton's method es_ES
dc.subject Multigrid es_ES
dc.subject Regularization-Free es_ES
dc.subject Viscoplastic Fluids es_ES
dc.title An adaptive discrete Newton method for regularization-free Bingham model es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/YIC2021.2021.12389
dc.relation.projectID info:eu-repo/grantAgreement/DFG//SPP 1962 es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Fatima, A.; Turek, S.; Ouazzi, A.; Afaq, MA. (2022). An adaptive discrete Newton method for regularization-free Bingham model. En Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. 180-189. https://doi.org/10.4995/YIC2021.2021.12389 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename VI ECCOMAS Young Investigators Conference es_ES
dc.relation.conferencedate Julio 07-09, 2021 es_ES
dc.relation.conferenceplace Valencia, España es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/YIC/YIC2021/paper/view/12389 es_ES
dc.description.upvformatpinicio 180 es_ES
dc.description.upvformatpfin 189 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\12389 es_ES
dc.contributor.funder Deutsche Forschungsgemeinschaft es_ES
dc.contributor.funder TU Dortmund University es_ES


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