?url_ver=Z39.88-2004&rft_id=10.5204%2Fthesis.eprints.211354&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=A+data-driven+smoothed+particle+hydrodynamics+method+for+fluids&rft.creator=Bai%2C+Jinshuai&rft.subject=Hydrodynamics+Modelling&rft.subject=Data-Driven+Computational+Mechanics&rft.subject=Rheology&rft.subject=Smoothed+Particle+Hydrodynamics&rft.subject=Data+Retrieval&rft.subject=Chained+Hashing+Algorithm&rft.subject=Information+Theory&rft.subject=Data+Clustering&rft.description=This+thesis+proposed+a+novel+Data-Driven+Smoothed+Particle+Hydrodynamics+(DDSPH)+method+that%2C+instead+of+applying+the+empirical+rheological+models%2C+utilizes+discrete+experimental+datasets+to+close+the+Navier-Stokes+equations+for+hydrodynamic+modelling.+Besides%2C+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.&rft.publisher=Queensland+University+of+Technology&rft.date=2021&rft.type=Thesis&rft.format=application%2Fpdf&rft.relation=https%3A%2F%2Feprints.qut.edu.au%2F211354%2F1%2FJinshuai_Bai_Thesis.pdf&rft.rights=free_to_read&rft.rights=http%3A%2F%2Fcreativecommons.org%2Flicenses%2Fby-nc-nd%2F4.0%2F&rft.relation=doi%3A10.5204%2Fthesis.eprints.211354&rft.relation=Bai%2C+Jinshuai+(2021)+A+data-driven+smoothed+particle+hydrodynamics+method+for+fluids.+Master+of+Philosophy+thesis%2C+Queensland+University+of+Technology.&rft.id_number=https%3A%2F%2Feprints.qut.edu.au%2F211354%2F&rft.identifier=Faculty+of+Engineering%3B+School+of+Mechanical%2C+Medical+%26+Process+Engineering