16,342 research outputs found
Coupling techniques for partitioned fluid-structure interaction simulations with black-box solvers
In partitioned simulations of fluid‐structure interaction, the flow and the displacement of the structure are calculated separately and coupling iterations between the flow solver and the structural solver are required to calculate the solution of the coupled problem if the interaction is strong. This work is a comparison of three coupling algorithms which use the flow solver and structural solver as a “black box”. Consequently, these algorithms are suitable for implementation in future versions of MpCCI. It is demonstrated that the algorithm of the interface quasi‐Newton technique with an approximation for the inverse of the Jacobian from a least‐squares model is straightforward and that this technique needs a relatively low number of coupling iterations in the simulation of an oscillating flexible beam and the propagation of a pressure wave in a flexible tube
An Overview of Multi-Processor Approximate Message Passing
Approximate message passing (AMP) is an algorithmic framework for solving
linear inverse problems from noisy measurements, with exciting applications
such as reconstructing images, audio, hyper spectral images, and various other
signals, including those acquired in compressive signal acquisiton systems. The
growing prevalence of big data systems has increased interest in large-scale
problems, which may involve huge measurement matrices that are unsuitable for
conventional computing systems. To address the challenge of large-scale
processing, multiprocessor (MP) versions of AMP have been developed. We provide
an overview of two such MP-AMP variants. In row-MP-AMP, each computing node
stores a subset of the rows of the matrix and processes corresponding
measurements. In column- MP-AMP, each node stores a subset of columns, and is
solely responsible for reconstructing a portion of the signal. We will discuss
pros and cons of both approaches, summarize recent research results for each,
and explain when each one may be a viable approach. Aspects that are
highlighted include some recent results on state evolution for both MP-AMP
algorithms, and the use of data compression to reduce communication in the MP
network
A multi-solver quasi-Newton method for the partitioned simulation of fluid-structure interaction
In partitioned fluid-structure interaction simulations, the flow equations and the structural equations are solved separately. Consequently, the stresses and displacements on both sides of the fluid-structure interface are not automatically in equilibrium. Coupling techniques like Aitken relaxation and the Interface Block Quasi-Newton method with approximate Jacobians from Least-Squares models (IBQN-LS) enforce this equilibrium, even with black-box solvers. However, all existing coupling techniques use only one flow solver and one structural solver. To benefit from the large number of multi-core processors in modern clusters, a new Multi-Solver Interface Block Quasi-Newton (MS-IBQN-LS) algorithm has been developed. This algorithm uses more than one flow solver and structural solver, each running in parallel on a number of cores. One-dimensional and three-dimensional numerical experiments demonstrate that the run time of a simulation decreases as the number of solvers increases, albeit at a slower pace. Hence, the presented multi-solver algorithm accelerates fluid-structure interaction calculations by increasing the number of solvers, especially when the run time does not decrease further if more cores are used per solver
Generalized Filtering Decomposition
This paper introduces a new preconditioning technique that is suitable for
matrices arising from the discretization of a system of PDEs on unstructured
grids. The preconditioner satisfies a so-called filtering property, which
ensures that the input matrix is identical with the preconditioner on a given
filtering vector. This vector is chosen to alleviate the effect of low
frequency modes on convergence and so decrease or eliminate the plateau which
is often observed in the convergence of iterative methods. In particular, the
paper presents a general approach that allows to ensure that the filtering
condition is satisfied in a matrix decomposition. The input matrix can have an
arbitrary sparse structure. Hence, it can be reordered using nested dissection,
to allow a parallel computation of the preconditioner and of the iterative
process
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