Developing novel big-data based models for designing greener turbines
Grant number: LP160100228 | Funding period: 2016 - 2020
This project aims to improve the fuel efficiency of gas turbines, the backbone of power generation and aircraft propulsion, for efficient and affordable power generation and air travel. Australia is large, remote and has some of the world’s highest carbon dioxide emissions per capita. Improving fuel efficiency will reduce cost and emissions, but current design tools lack the accuracy to advance technology. This project will investigate fluid flow in gas turbines and use big-data analytics to develop more accurate design tools. Gas turbines with reduced fuel usage and carbon dioxide emissions are expected to reduce the cost and environmental impact of power generation and air travel in Austra..View full description
Related publications (7)
INTEGRATION OF MACHINE LEARNING AND COMPUTATIONAL FLUID DYNAMICS TO DEVELOP TURBULENCE MODELS FOR IMPROVED TURBINE WAKE MIXING PREDICTION
Harshal D Akoleka, Yaomin Zhao, Richard Sandberg, Roberto Pacciani
This paper presents development of accurate turbulence closures for wake mixing prediction by integrating a machine-learning appro..
MACHINE LEARNING FOR THE DEVELOPMENT OF DATA DRIVEN TURBULENCE CLOSURES IN COOLANT SYSTEMS
James Hammond, Francesco Montomoli, Marco Pietropaoli, Richard Sandberg, Vittorio Michelassi
This work shows the application of Gene &pression Programming to augment RANS turbulence closure modelling for flows through comp..
Large-Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure Turbine Cascade, Part I: Flow and Secondary Vorticity Fields Under Varying Inlet Condition
Richard Pichler, Yaomin Zhao, Richard Sandberg, Vittorio Michelassi, Roberto Pacciani, Michele Marconcini, Andrea Arnone
In low-pressure turbines (LPTs), around 60–70% of losses are generated away from end-walls, while the remaining 30–40% is controll..
Large Eddy Simulation and RANS Analysis of the End-Wall Flow in a Linear Low-Pressure-Turbine Cascade-Part II: Loss Generation
Michele Marconcini, Roberto Pacciani, Andrea Arnone, Vittorio Michelassi, Richard Pichler, Yaomin Zhao, Richard Sandberg
In low-pressure turbines (LPT) at design point, around 60–70% of losses are generated in the blade boundary layers far from end wa..
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, G Laskowski
Machine learning was applied to large-eddy simulation (LES) data to develop nonlinear turbulence stress and heat flux closures wit..