A data-driven smoothed particle hydrodynamics method for fluids
Year: 2021
Type: dissertation
Abstract: This thesis proposed a novel Data-Driven Smoothed Particle Hydrodynamics (DDSPH) method that, instead of applying the empirical rheological models, utilizes discrete experimental datasets to close the Navier-Stokes equations for hydrodynamic modelling. Besides, the chained hashing algorithm is applied to improve the efficiency of the data retrieval and the robustness of the method with respect to the noisy data is achieved via adding a variable that qualifies the relevance of data points to the clusters. The proposed DDSPH method introduces a new avenue for hydrodynamic modelling and has great potential for modelling complex fluids with highly nonlinear rheological relationships.
Author: Jinshuai Bai
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4236337174