A fast immersed boundary method using a nullspace approach and multi-domain far-field boundary conditions

By Tim Colonius, Kunihiko Taira. 2008

307 citations recorded by the source corpus.

Subfields: Computational Mechanics, Electrical and Electronic Engineering, Numerical Methods & Scientific Computing

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In Computational Mechanics

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Citations in the corpus

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations (2018, methodology): “Speciﬁcally, we simulate the Navier-Stokes equations describing the two-dimensional ﬂuid ﬂow past a circular cylinder at Reynolds number 100 using the Immersed Boundary Projection Method [46, 47].”
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems (2018, methodology): “In particular, following the problem setup presented in [23] and [24], we simulate the Navier-Stokes equations describing the two-dimensional ﬂuid ﬂow past a circular cylinder at Reynolds number 100 using the Immersed Boundary Projection Method [25, 26].”
Discovering governing equations from data by sparse identification of nonlinear dynamical systems (2015, methodology): “The direct numerical simulation involves a fast multi-domain immersed boundary projection method [41, 11].”
Hidden physics models: Machine learning of nonlinear partial differential equations (2017, methodology): “As for the data, following the exact same instructions as the ones provided in [34] and [2], we simulate the Navier-Stokes equations describing the two-dimensional ﬂuid ﬂow past a circular cylinder at Reynolds number 100 using the Immersed Boundary Projection Method [35, 36].”
Super-resolution reconstruction of turbulent flows with machine learning (2018, methodology): “Example 1: Two-dimensional cylinder wake As a preliminary test of machine-learned super-resolution analysis, we consider the two-dimensional cylinder wake flow at ReD = 100 (Taira & Colonius 2007; Colonius & Taira 2008).”
Feedback control of unstable steady states of flow past a flat plate using reduced-order estimators (2009, methodology): “In § 3, we briefly describe the numerical technique of Colonius & Taira (2008) using a fast immersed boundary method, and present the linearized and adjoint formulations of this numerical method.”
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows (2020, methodology): “The snapshots for this wake are generated by two-dimensional direct numerical simulation (DNS) (Taira & Colonius 2007; Colonius & Taira 2008), which numerically solves the incompressible Navier–Stokes equations, (2.9) Here u , p and Re D are the non-dimensionalized velocity vector, pressure and…”
Variants of Dynamic Mode Decomposition: Boundary Condition, Koopman, and Fourier Analyses (2012, methodology): “We generate the data using a multi-domain immersed boundary method; see Taira and Colonius (2007) and Colonius and Taira (2008) for details.”
Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition (2014, methodology): “1 Cylinder wake: simulation data We use an immersed boundary projection method (Taira and Colonius, 2007; Colonius and Taira, 2008), with a domain consisting of a series of nested grids, with the finest grid enclosing the body, and each successive grid twice as large as the previous.”
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (2017, methodology): “[142] T.”
A Sharp-Interface Immersed Boundary Method with Improved Mass Conservation and Reduced Spurious Pressure Oscillations (2011, background): “Also, in the diffused-interface IBM of Taira and Colonius [10,11], they claimed that the global mass is conserved well and there were no”