Improving prediction of preferential concentration in particle-laden turbulence using the neural-network interpolation
Jiajun Hu, Zhen Lu, Yue Yang
Abstract
A neural-network interpolation (NNI) is proposed to improve the prediction of preferential concentration in particle-laden turbulence. The NNI uses the particle position and velocity on neighboring grid points to estimate the fluid velocity at the particle position. To evaluate the NNI, we simulate a two-dimensional homogeneous isotropic turbulence subjected to high-wavenumber forcing. The NNI recovers the effect of small-scale motion on particle distribution from the low-resolution field, adding high-wavenumber energy to the turbulence field. Consequently, the NNI improves the prediction accuracy of the preferential concentration on coarse grids.
Topics & Concepts
TurbulenceWavenumberPosition (finance)Interpolation (computer graphics)IsotropyParticle (ecology)GridPhysicsStatistical physicsHomogeneous isotropic turbulenceArtificial neural networkComputational physicsMechanicsMathematicsClassical mechanicsGeologyComputer scienceGeometryOpticsDirect numerical simulationMotion (physics)Artificial intelligenceOceanographyFinanceEconomicsReynolds numberParticle Dynamics in Fluid FlowsFluid Dynamics and Turbulent FlowsWind and Air Flow Studies