Jack Weatheritt
Jack Weatheritt
Post-Doctoral Researcher, University of Melbourne
Verified email at unimelb.edu.au
TitleCited byYear
A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship
J Weatheritt, R Sandberg
Journal of Computational Physics 325, 22-37, 2016
562016
The development of algebraic stress models using a novel evolutionary algorithm
J Weatheritt, RD Sandberg
International Journal of Heat and Fluid Flow 68, 298-318, 2017
342017
Machine learning for turbulence model development using a high-fidelity hpt cascade simulation
J Weatheritt, R Pichler, RD Sandberg, G Laskowski, V Michelassi
ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, 2017
182017
Applying machine learnt explicit algebraic stress and scalar flux models to a fundamental trailing edge slot
RD Sandberg, R Tan, J Weatheritt, A Ooi, A Haghiri, V Michelassi, ...
Journal of Turbomachinery 140 (10), 101008, 2018
172018
Hybrid Reynolds-averaged/large-eddy simulation methodology from symbolic regression: formulation and application
J Weatheritt, RD Sandberg
AIAA Journal, 3734-3746, 2017
72017
A comparative study of contrasting machine learning frameworks applied to RANS modeling of jets in crossflow
J Weatheritt, RD Sandberg, J Ling, G Saez, J Bodart
72017
The development of data driven approaches to further turbulence closures
J Weatheritt
University of Southampton, 2015
72015
Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in Low-Pressure Turbines
HD Akolekar, J Weatheritt, N Hutchins, RD Sandberg, G Laskowski, ...
Journal of Turbomachinery 141 (4), 041010, 2019
62019
Reynolds stress structures in the hybrid rans/les of a planar channel
J Weatheritt, R Sandberg, A Lozano-Durán
Journal of Physics: Conference Series 708 (1), 012008, 2016
62016
A new Reynolds stress damping function for hybrid RANS/LES with an evolved functional form
J Weatheritt, RD Sandberg
Advances in Computation, Modeling and Control of Transitional and Turbulent …, 2016
62016
Use of Symbolic Regression for construction of Reynolds-stress damping functions for Hybrid RANS/LES
J Weatheritt, RD Sandberg
53rd AIAA Aerospace Sciences Meeting, 0312, 2015
62015
Application of an evolutionary algorithm to LES modelling of turbulent transport in premixed flames
M Schoepplein, J Weatheritt, R Sandberg, M Talei, M Klein
Journal of Computational Physics 374, 1166-1179, 2018
52018
Development and use of machine-learnt algebraic reynolds stress models for enhanced prediction of wake mixing in LPTs
HD Akolekar, J Weatheritt, N Hutchins, RD Sandberg, G Laskowski, ...
ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition …, 2018
52018
Turbulence model development using CFD-driven machine learning
Y Zhao, HD Akolekar, J Weatheritt, V Michelassi, RD Sandberg
arXiv preprint arXiv:1902.09075, 2019
32019
Hybrid simulation of the surface mounted square cylinder
J Weatheritt, RD Sandberg
20th Australiasian Fluid Mechanics Conference, 2016
22016
Data-driven scalar-flux model development with application to jet in cross flow
J Weatheritt, Y Zhao, RD Sandberg, S Mizukami, K Tanimoto
International Journal of Heat and Mass Transfer, 118931, 2019
2019
Improved Junction Body Flow Modeling Through Data-Driven Symbolic Regression
J Weatheritt, RD Sandberg
Journal of Ship Research, 2019
2019
The Construction, Representation and Classification of Axisymmetric Solutions in Higher Dimensions
J Weatheritt
2011
Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot
J Weatheritt, A Ooi, A Haghiri, V Michelassi, G Laskowski
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Articles 1–19