A data-driven smoothed particle hydrodynamics method for fluids
|
Jinshuai Bai Thesis
(PDF 5MB)
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
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.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads:
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
| ID Code: | 211354 |
|---|---|
| Item Type: | QUT Thesis (Master of Philosophy) |
| Supervisor: | Gu, YuanTong & Sauret, Emilie |
| Keywords: | Hydrodynamics Modelling, Data-Driven Computational Mechanics, Rheology, Smoothed Particle Hydrodynamics, Data Retrieval, Chained Hashing Algorithm, Information Theory, Data Clustering |
| DOI: | 10.5204/thesis.eprints.211354 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Mechanical, Medical & Process Engineering |
| Institution: | Queensland University of Technology |
| Deposited On: | 06 Jul 2021 05:26 |
| Last Modified: | 06 Jul 2021 05:26 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page