Consistent inlet and outlet boundary conditions for particle methods

Hu, Fangyuan; Wang, Zidi; Tamai, Terutaka; Koshizuka, Seiichi

doi:10.1002/fld.4768

Cited by 9 publications

(3 citation statements)

References 45 publications

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“…These multi-fidelity methods are classified as either fusion-or adaptation-based depending on whether a single model combines data from sources varying in fidelity or the low-fidelity results are corrected to match the results of the high-fidelity source [35]. Note that the method of Wang et al [32] mentioned in §1 is not considered here due to its poor scalability with many POD modes. Interested readers are encouraged to refer to the respective original articles for more details.…”

Section: (E) Comparison With Existing Multi-fidelity Methodsmentioning

confidence: 99%

“…Lastly, the method proposed by Wang et al [32] uses a nonlinear regression model to map the POD coefficients of the low-fidelity data to the high-fidelity latent coordinates. However, as it will be shown later, the CFD solutions in this work can require upward of 50 POD modes to properly capture the main features of the fields.…”

Section: (B) Multi-fidelity Reduced-order Modellingmentioning

confidence: 99%

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Multi-fidelity non-intrusive reduced-order modelling based on manifold alignment

2021

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This work presents the development of a multi-fidelity, parametric and non-intrusive reduced-order modelling method to tackle the problem of achieving an acceptable predictive accuracy under a limited computational budget, i.e. with expensive simulations and sparse training data. Traditional multi-fidelity surrogate models that predict scalar quantities address this issue by leveraging auxiliary data generated by a computationally cheaper lower fidelity code. However, for the prediction of field quantities, simulations of different fidelities may produce responses with inconsistent representations, rendering the direct application of common multi-fidelity techniques challenging. The proposed approach uses manifold alignment to fuse inconsistent fields from high- and low-fidelity simulations by individually projecting their solution onto a common latent space. Hence, simulations using incompatible grids or geometries can be combined into a single multi-fidelity reduced-order model without additional manipulation of the data. This method is applied to a variety of multi-fidelity scenarios using a transonic airfoil problem. In most cases, the new multi-fidelity reduced-order model achieves comparable predictive accuracy at a lower computational cost. Furthermore, it is demonstrated that the proposed method can combine disparate fields without any adverse effect on predictive performance.

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Section: (E) Comparison With Existing Multi-fidelity Methodsmentioning

confidence: 99%

Section: (B) Multi-fidelity Reduced-order Modellingmentioning

confidence: 99%

Multi-fidelity non-intrusive reduced-order modelling based on manifold alignment

2021

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“…A new particle will be generated in A-type cell when it is vacant, but if the center position of the A-type cell is over the approximated wave surface, the particle will be placed at a distance from the neighboring particles located in the next B-type cell. By using boundary particles and modifying their physical values, Hu et al [96] proposed three kinds of inlet/outlet boundary conditions: a pressure specified inlet/outlet condition, a velocity profile specified inlet/outlet condition, and a fully developed flow outlet condition. Compared with the finite volume method, this approach exhibits accuracy and numerical stability.…”

Section: Import and Export Boundary Conditionsmentioning

confidence: 99%

Review of the State-of-Art of MPS Method in Ocean Engineering

Sun

Dou

Tan

et al. 2022

JMSE

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When dealing with the complex deformation of free surface such as wave breaking, traditional mesh-based Computational Fluid Dynamics (CFD) methods often face problems arising alongside grid distortion and re-meshing. Therefore, the meshless method became robust for treating large displaced free surface and other boundaries caused by moving structures. The particle method, which is an important branch of meshless method, is mainly divided into the Smoothed Particle Hydrodynamics (SPH) and Moving Particle Semi-implicit (MPS) methods. Different from the SPH method, which involves continuity and treat density as a variable when building kernel functions, the kernel function in the MPS method is a weight function which treats density as a constant, and the spatial derivatives are discretized by establishing the gradient operator and Laplace operator separately. In other words, the first- or second-order continuity of the kernel functions in the MPS method is not a necessity as in SPH, though it might be desirable. At present, the MPS method has been successfully applied to various violent-free surface flow problems in ocean engineering and diverse applications have been comprehensively demonstrated in a number of review papers. This work will focus on algorithm developments of the MPS method and to provide all perspectives in terms of numerical algorithms along with their pros and cons.

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