Partitioned Fluid–Structure–Acoustics Interaction on Distributed Data: Coupling via preCICE

Bungartz, Hans‐Joachim; Lindner, Florian; Mehl, Miriam; Scheufele, Klaudius; Shukaev, Alexander; Uekérmann, Benjamin

doi:10.1007/978-3-319-40528-5_11

Cited by 11 publications

(29 citation statements)

References 13 publications

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“…Thus, already well-resolved features are retained, and linearly dependent or even contradicting information is implicitly overwritten. Eventually, this ought to render the costly tuning step of the reuse parameter ξ unnecessary, which, however, comes at the expense of storing an explicit representation of the inverse Jacobian matrix as it appears explicitly in the update formulas (8) or (11), respectively. We denote the LS method with explicit reuse of ξ time steps in V ξ k and W ξ k by LS(ξ).…”

Section: 2mentioning

confidence: 99%

“…Efficient algorithmic realization and parallelization. We briefly recapitulate the implementational aspects for an efficient algorithmic implementation of the basic building blocks for the two quasi-Newton methods from [8]. In particular, the focus lies on the algorithmic realization on distributed data.…”

Section: Rs-svd Use a Truncation Of The Singular Value Decompositionmentioning

confidence: 99%

“…In particular, the focus lies on the algorithmic realization on distributed data. For an elaborate description, refer to [8]. Above all, we give details on the efficient realization of the newly introduced restart approach for the multivector method.…”

Section: Rs-svd Use a Truncation Of The Singular Value Decompositionmentioning

confidence: 99%

“…using the right-hand side y := r k . This updating scheme is particularly suited for a communication-avoiding and very efficient implementation on distributed data [21,8]. For p processors, Q is decomposed into row blocks corresponding to the interface data belonging to the respective surface partition; cf.…”

Section: Rs-svd Use a Truncation Of The Singular Value Decompositionmentioning

confidence: 99%

“…For the LS method, W k α can be computed locally on each processor without any communication in O( N /p k) as W k is distributed in row blocks analogous to Q. In the original MVJ method [8], the computation of the quasi-Newton update was rather involved, as it comprised three costly and complex matrix operations, namely, the computation of W k = W k − J −1 prev V k , the matrix product W k V † k , and the matrix-vector product J −1 r k . This results in quadratic costs.…”

Section: Rs-svd Use a Truncation Of The Singular Value Decompositionmentioning

confidence: 99%

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Robust Multisecant Quasi-Newton Variants for Parallel Fluid-Structure Simulations---and Other Multiphysics Applications

Scheufele¹,

Mehl²

2017

SIAM J. Sci. Comput.

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Multisecant quasi-Newton methods have been shown to be particularly suited to solve nonlinear fixed-point equations that arise from partitioned multiphysics simulations where the exact Jacobian is inaccessible. In all these methods, the underdetermined multisecant equation for the approximate (inverse) Jacobian is enhanced by a norm minimization condition. The standard choice is the minimization of the Frobenius norm of the approximate inverse Jacobian. In this setting, it is well known that transient fluid-structure simulations typically require the use of secant information also from previous time steps to achieve a small enough number of iterations per implicit time step. The number of these time steps highly depends on the application, the physical parameters, the used solvers, and the mesh resolution. Using too few leads to a relatively high number of iterations, while using too many leads not only to a computational overhead but also to an increase in the number of iterations as well. Determining the optimal number requires a costly trial-and-error process. In this paper, we present results for two different approaches to overcome this issue: The first approach is to use a modified method (presented in [F. Lindner, M. Mehl, K. Scheufele, and B. Uekermann, "A Comparison of Various Quasi-Newton Schemes for Partitioned Fluid-Structure Interaction," in Proceedings of ECCOMAS Coupled Problems, Venice, 2015, pp. 1-12]) that minimizes the Frobenius norm of the difference between the current approximate (inverse) Jacobian and that of the previous time step. Thus, previous time step information is taken into account in an implicit and automatized way without magic parameters. The second approach is to use an unrestricted number of previous time steps in combination with a suitable filtering algorithm automatically removing secant information that is outdated (thus, slowing down convergence) or contradicting newer information (deteriorating the condition of the multisecant equation system). We present a novel algorithm to realize the first idea with linear complexity in the number of coupling surface unknowns (note that already storing the approximate inverse Jacobian would induce quadratic complexity) and the efficient parallel implementation for both approaches. This results in highly efficient, parallelizable, and robust iterative solvers applicable for surface coupling in many types of multiphysics simulations using black-box solver software. In addition, our numerical results for the fluid-structure benchmark (FSI3) from Turek and Hron ["Proposal for Numerical Benchmarking of Fluid-Structure Interaction between an Elastic Object and Laminar Incompressible Flow," in Fluid-Structure Interaction, Springer, Berlin, 2006, pp. 371-385] and for a flow through a flexible tube with a large variety of parameter settings prove the robustness and numerical efficiency of the first approach in particular. The second approach can be shown to be highly sensitive to the choice of the filtering method for the secant information a...

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Section: 2mentioning

confidence: 99%

Section: Rs-svd Use a Truncation Of The Singular Value Decompositionmentioning

confidence: 99%

Section: Rs-svd Use a Truncation Of The Singular Value Decompositionmentioning

confidence: 99%

Section: Rs-svd Use a Truncation Of The Singular Value Decompositionmentioning

confidence: 99%

Section: Rs-svd Use a Truncation Of The Singular Value Decompositionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Multisecant Quasi-Newton Variants for Parallel Fluid-Structure Simulations---and Other Multiphysics Applications

Scheufele¹,

Mehl²

2017

SIAM J. Sci. Comput.

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ExaFSA: Parallel Fluid-Structure-Acoustic Simulation

Lindner

Totounferoush

Mehl

et al. 2020

Software for Exascale Computing - SPPEXA 2016-2019

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In this paper, we present results of the second phase of the project ExaFSA within the priority program SPP1648-Software for Exascale Computing. Our task was to establish a simulation environment consisting of specialized highly efficient and scalable solvers for the involved physical aspects with a particular focus on the computationally challenging simulation of turbulent flow and propagation of the induced acoustic perturbations. These solvers are then coupled in a modular, robust, numerically efficient and fully parallel way, via the open source coupling library preCICE. Whereas we made a first proof of concept for a three-field simulation (elastic structure, surrounding turbulent acoustic flow in the near-field, and pure acoustic wave propagation in the far-field) in the first phase, we removed several scalability limits in the second phase. In particular, we present new contributions to (a) the initialization of communication between processes of

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A scalable framework for the partitioned solution of fluid–structure interaction problems

et al. 2020

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In this work, we present a scalable and efficient parallel solver for the partitioned solution of fluid-structure interaction problems through multi-code coupling. Two instances of an in-house parallel software, Ter-moFluids, are used to solve the fluid and the structural sub-problems, coupled together on the interface via the preCICE coupling library. For fluid flow, the Arbitrary Lagrangian-Eulerian form of the Navier-Stokes equations is solved on an unstructured conforming grid using a second-order finitevolume discretization. A parallel dynamic mesh method for unstructured meshes is used to track the moving boundary. For the structural problem, the nonlinear elastodynamics equations are solved on an unstructured grid using a second-order finite-volume method. A semi-implicit FSI coupling method is used which segregates the fluid pressure term and couples it strongly to the structure, while the remaining fluid terms and the geometrical nonlinearities are only loosely coupled. A robust and advanced multivector quasi-Newton method is used for the coupling iterations between the solvers. Both the fluid and the structural solver use distributed-memory parallelism. The intra-solver communication required for data update in the solution process is carried out using non-blocking point-to-point communicators. The inter-code communication is fully parallel and point-to-point, avoiding any central communication unit. Inside each single-physics solver, the load is balanced by dividing the computational domain into fairly equal blocks for each process. Additionally, a load balancing model is used at the inter-code level to minimize the overall idle time of the processes. Two strating the accuracy and computational efficiency of the coupled solver. Strong scalability test results show a parallel efficiency of 83% on 10,080 CPU cores.

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Partitioned Fluid–Structure–Acoustics Interaction on Distributed Data: Coupling via preCICE

Cited by 11 publications

References 13 publications

Robust Multisecant Quasi-Newton Variants for Parallel Fluid-Structure Simulations---and Other Multiphysics Applications

Robust Multisecant Quasi-Newton Variants for Parallel Fluid-Structure Simulations---and Other Multiphysics Applications

ExaFSA: Parallel Fluid-Structure-Acoustic Simulation

A scalable framework for the partitioned solution of fluid–structure interaction problems

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