A multigrid mesh-embedding technique for three-dimensional transonicpotential flow analysis

Brown, Jeffrey J.

doi:10.2514/6.1982-107

Cited by 7 publications

(7 citation statements)

References 3 publications

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“…To solve the problem of high computational cost while calculating high-precision solutions in traditional discretization methods, researchers have proposed acceleration techniques to speed up the numerical discretization solution. Multigrid technology stood out among various approaches Dwyer et al (1982); Brown (1982); Berger and Oliger (1984); Phillips and Schmidt (1984); Phillips and Schmidt (1985); Zhang (1997); Mazhukin et al (1993); Jin et al (2014). Among them, Brown et al Brown (1982) used the multigrid mesh-embedding technique to solve three-dimensional transonic potential flow.…”

Section: Traditional Discretization Acceleration Techniques For Fluid...mentioning

confidence: 99%

“…Multigrid technology stood out among various approaches Dwyer et al (1982); Brown (1982); Berger and Oliger (1984); Phillips and Schmidt (1984); Phillips and Schmidt (1985); Zhang (1997); Mazhukin et al (1993); Jin et al (2014). Among them, Brown et al Brown (1982) used the multigrid mesh-embedding technique to solve three-dimensional transonic potential flow. They used small grids to model regions of large local gradients and largescale grids to model regions with relatively small gradients.…”

Section: Traditional Discretization Acceleration Techniques For Fluid...mentioning

confidence: 99%

“…A scale of previous works has been carried out to reduce the computation load from the perspective of decreasing the resolution of the grids. As early as 1982, Brown et al Brown (1982) applied a multigrid method to accelerate the numerical solution process of the three-dimensional transonic potential flow. The multigrid method was considered a classic method to reduce computational costs in the traditional numerical solution process because it uses different mesh divisions for different regions instead of highresolution mesh modeling.…”

Section: Introductionmentioning

confidence: 99%

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TSI-SD: A time-sequence-involved space discretization neural network for passive scalar advection in a two-dimensional unsteady flow

Song

Tian²,

Nie³

et al. 2023

Front. Mar. Sci.

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Numerical simulation of fluid is a great challenge as it contains extremely complicated variations with a high Reynolds number. Usually, very high-resolution grids are required to capture the very fine changes during the physical process of the fluid to achieve accurate simulation, which will result in a vast number of computations. This issue will continue to be a bottleneck problem until a deep-learning solution is proposed to utilize large-scale grids with adaptively adjusted coefficients during the spatial discretization procedure—instead of traditional methods that adopt small grids with fixed coefficients—so that the computation cost is dramatically reduced and accuracy is preserved. This breakthrough will represent a significant improvement in the numerical simulation of fluid. However, previously proposed deep-learning-based methods always predict the coefficients considering only the spatial correlation among grids, which provides relatively limited context and thus cannot sufficiently describe patterns along the temporal dimension, implying that the spatiotemporal correlation of coefficients is not well learned. We propose the time-sequence-involved space discretization neural network (TSI-SD) to extract grid correlations from spatial and temporal views together to address this problem. This novel deep neural network is transformed from a classic CONV-LSTM backbone with careful modification by adding temporal information into two-dimensional spatial grids along the x-axis and y-axis separately at the first step and then fusing them through a post-fusion neural network. After that, we combine the TSI-SD with the finite volume format as an advection solver for passive scalar advection in a two-dimensional unsteady flow. Compared with previous methods that only consider spatial context, our method can achieve higher simulation accuracy, while computation is also decreased as we find that after adding temporal data, one of the input features, the concentration field, is redundant and should no longer be adopted during the spatial discretization procedure, which results in a sharp decrease of parameter scale and achieves high efficiency. Comprehensive experiments, including a comparison with SOTA methods and sufficient ablation studies, were carried out to verify the accurate and efficient performance and highlight the advantages of the proposed method.

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Section: Traditional Discretization Acceleration Techniques For Fluid...mentioning

confidence: 99%

Section: Traditional Discretization Acceleration Techniques For Fluid...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TSI-SD: A time-sequence-involved space discretization neural network for passive scalar advection in a two-dimensional unsteady flow

Song

Tian²,

Nie³

et al. 2023

Front. Mar. Sci.

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“…SLOR was used for solution of the difference equations in the Reyhner algorithm. Brown 3 improved the accuracy and efficiency of this analysis by incorporating a local mesh refinement scheme used in conjunction with multigrid convergence acceleration.…”

Section: Introductionmentioning

confidence: 99%

Approximate factorization algorithm for three-dimensional transonic nacelle/inlet flowfield computations

Vadyak¹,

Atta²

1985

Journal of Propulsion and Power

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A highly efficient computer analysis has been developed for predicting transonic nacelle/inlet flowfields. This algorithm can compute the three-dimensional transonic flowfield about axisymmetric or asymmetric nacelle/inlet configurations at zero or nonzero incidence. The flowfield is determined by solving the fullpotential equation in conservative form on a body-fitted curvilinear computational mesh. The difference equations are solved using the AF2 approximate factorization scheme. The effects of boundary-layer viscous entrainment are approximated in the inviscid algorithm by applying a surface transpiration velocity determined from the calculated boundary-layer growth. Computed results and correlations with existing methods and experiment are presented to illustrate application of the analysis.

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“…Unlike the standard FAS where one level directly communicates with only the next coarser level, NAM levels may directly restrict or prolongate information to any or all levels depending on the particular system of grids. NAM is actually a generalized local refinement method [9,11,211 Figure 4 for the three level treatment of grids just described.…”

Section: Chapter 5 Nonaligned Multigrid (Nam)mentioning

confidence: 99%

A multigrid approach to embedded-grid solvers

Johnson

Belk

1993

31st Aerospace Sciences Meeting

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A multigrid mesh-embedding technique for three-dimensional transonicpotential flow analysis

Cited by 7 publications

References 3 publications

TSI-SD: A time-sequence-involved space discretization neural network for passive scalar advection in a two-dimensional unsteady flow

TSI-SD: A time-sequence-involved space discretization neural network for passive scalar advection in a two-dimensional unsteady flow

Approximate factorization algorithm for three-dimensional transonic nacelle/inlet flowfield computations

A multigrid approach to embedded-grid solvers

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