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RANS-Based Modelling of Turbulent Flow in Submarine Pipe Bends: Effect of Computational Mesh and Turbulence Modelling

• Authors : Qi Yang; Jie Dong; Tongju Xing

Subjects: turbulent flow; 90° curved submarine pipes; Ocean Engineering

• Source: Journal of Marine Science and EngineeringVolume 11Issue 2Pages: 336

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On the Efficacy of Turbulence Modelling for Sloshing

• Authors : Omar Ahmed Mahfoze; Wendi Liu; Stephen M. Longshaw

Subjects: Fluid Flow and Transfer Processes; slosh-induced damping; Process Chemistry and Technology

• Source: Mahfoze, O, Liu, W, Longshaw, S, Skillen, A & Emerson, D 2022, ' On the Efficacy of Turbulence Modelling for Sloshing ', Applied Sciences,

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Numerical Investigation into the Effects of Turbulence Modelling on the Aeroelastic Analysis of Flexible Wings

• Authors : Goonan, TB; Cardoso, RPR; Adetoro, OB

Subjects: Physics::Fluid Dynamics; aeroelasticity; CFD

• Source: IFASD 2022: International Forum on Aeroelasticity and Structural DynamicsInternational Forum on Aeroelasticity and Structural Dynamics, IFASD 2022

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Reynolds stress turbulence modelling of surf zone breaking waves

• Authors : Li, Yuzhu; Larsen, Bjarke Eltard; Fuhrman, David R.

Subjects: Turbulence modelling; Wave breaking; Mechanics of Materials

• Source: Li, Y, Larsen, B E & Fuhrman, D R 2022, ' Reynolds stress turbulence modelling of surf zone breaking waves ', Journal of Fluid Mechanics,

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An Empirical Wall Law for the Mean Velocity in an Adverse Pressure Gradient for RANS Turbulence Modelling

• Authors : Knopp, Tobias

Subjects: Turbulent boundary layer flows; log law; half-power law

• Source: Flow, Turbulence and Combustion. 109:571-601

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A wall-law for the mean velocity in the inner part of turbulent boundary layers at adverse pressure and application for RANS turbulence modelling

• Authors : Knopp, Tobias; Reuther, Nico; Novara, Matteo

Subjects: log law; half-power law; turbulent boundary layer

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Mathematical Methodology and Metallurgical Application of Turbulence Modelling: A Review

• Authors : Yannan Wang; Zhongfu Cheng; Lingling Cao

Subjects: Technology; model validation; DNS

• Source: Metals, Vol 11, Iss 1297, p 1297 (2021)

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A Machine Learning Approach to Improve Turbulence Modelling from DNS Data Using Neural Networks

• Authors : Yuri Frey Marioni; Enrique Alvarez de Toledo Ortiz; Francesco Montomoli

Subjects: Computer science; RANS; Energy Engineering and Power Technology

• Source: International Journal of Turbomachinery, Propulsion and Power; Volume 6; Issue 2; Pages: 17International Journal of Turbomachinery, Propulsion and Power, Vol 6, Iss 17, p 17 (2021)

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Uncertainty Quantification for Data-driven Turbulence Modelling with Mondrian Forests

• Authors : Ashley Scillitoe; Mark Girolami; Pranay Seshadri ...

Subjects: Mondrian forests; Physics and Astronomy (miscellaneous); FOS: Physical sciences

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Probabilistic Machine Learning to Improve Generalisation of Data-Driven Turbulence Modelling

• Authors : Joel Ho; Nick Pepper; Tim Dodwell

Subjects: Computational Engineering, Finance, and Science (cs.CE); FOS: Computer and information sciences; Computer Science - Computational Engineering, Finance, and Science

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