Basis enrichment and solid–fluid coupling for model‐reduced fluid simulation

Gerszewski, Dan; Kavan, Ladislav; Sloan, Peter-Pike; Bargteil, Adam W.

doi:10.1002/cav.1612

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…Since then, improvements have been made to make them modular [WST09], consistent with widely‐used integrators [KD13], more energy‐preserving [LMH∗15] and memory‐efficient [JSK16]. A related “Laplacian Eigenfunctions” approach [DLF12] has also been introduced and refined [GKSB15], removing the need for snapshot training data when computing the linear subspace. The basis functions used by these methods are all linear however, and various methods are then used to coerce the state of the system onto some nonlinear manifold.…”

Section: Related Workmentioning

confidence: 99%

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Kim

Azevedo

Thuerey

et al. 2019

Computer Graphics Forum

307

267

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This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in‐betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence‐free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re‐sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700× faster than re‐simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300×.

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Section: Related Workmentioning

confidence: 99%

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Kim

Azevedo

Thuerey

et al. 2019

Computer Graphics Forum

307

267

View full text Add to dashboard Cite

show abstract

“…Wicke et al [WST09] improve basis reusability by precomputing modular, reconfigurable flows. Gerszewski et al [GKSB15] instead enrich a set of existing basis functions for task adaptation. In both cases, however, the initial basis and any interactions with additional basis elements must still be precomputed from a costly full-space simulation.…”

Section: Previous Workmentioning

confidence: 99%

Local Bases for Model‐reduced Smoke Simulations

Mercier

Nowrouzezahrai

2020

Computer Graphics Forum

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We present a flexible model reduction method for simulating incompressible fluids. We derive a novel vector field basis composed of localized basis flows which have simple analytic forms and can be tiled on regular lattices, avoiding the use of complicated data structures or neighborhood queries. Local basis flow interactions can be precomputed and reused to simulate fluid dynamics on any simulation domain without additional overhead. We introduce heuristic simulation dynamics tailored to our basis and derived from a projection of the Navier‐Stokes equations to produce physically plausible motion, exposing intuitive parameters to control energy distribution across scales. Our basis can adapt to curved simulation boundaries, can be coupled with dynamic obstacles, and offers simple adjustable trade‐offs between speed and visual resolution.

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Interlinked SPH Pressure Solvers for Strong Fluid-Rigid Coupling

et al. 2019

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We present a strong fluid-rigid coupling for Smoothed Particle Hydrodynamics (SPH) fluids and rigid bodies with particle-sampled surfaces. The approach interlinks the iterative pressure update at fluid particles with a second SPH solver that computes artificial pressure at rigid-body particles. The introduced SPH rigid-body solver models rigid-rigid contacts as artificial density deviations at rigid-body particles. The corresponding pressure is iteratively computed by solving a global formulation that is particularly useful for large numbers of rigid-rigid contacts. Compared to previous SPH coupling methods, the proposed concept stabilizes the fluid-rigid interface handling. It significantly reduces the computation times of SPH fluid simulations by enabling larger time steps. Performance gain factors of up to 58 compared to previous methods are presented. We illustrate the flexibility of the presented fluid-rigid coupling by integrating it into DFSPH, IISPH, and a recent SPH solver for highly viscous fluids. We further show its applicability to a recent SPH solver for elastic objects. Large scenarios with up to 90 M particles of various interacting materials and complex contact geometries with up to 90 k rigid-rigid contacts are shown. We demonstrate the competitiveness of our proposed rigid-body solver by comparing it to Bullet.

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Basis enrichment and solid–fluid coupling for model‐reduced fluid simulation

Cited by 4 publications

References 20 publications

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Local Bases for Model‐reduced Smoke Simulations

Interlinked SPH Pressure Solvers for Strong Fluid-Rigid Coupling

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