Developing novel big-data based models for designing greener turbines

Grant number: LP160100228 | Funding period: 2016 - 2020

Completed

Abstract

Developing novel big-data based models for designing greener turbines. 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 cos..

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Related publications (9)

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HIGH-FIDELITY SIMULATIONS OF A HIGH-PRESSURE TURBINE VANE SUBJECT TO LARGE DISTURBANCES: EFFECT OF EXIT MACH NUMBER ON LOSSES

Yaomin Zhao, Richard Sandberg

2021-01-01

We report on a series of highly resolved large-eddy simulations of the LS89 high-pressure turbine (HPT) vane, varying the exit Mac..

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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

2021-01-01

This paper presents development of accurate turbulence closures for wake mixing prediction by integrating a machine-learning appro..

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RANS predictions of trailing-edge slot flows using heatflux closures developed with CFD-driven machine learning

Chitrarth Lav, Ali Haghiri, Richard D Sandberg

2021-01-01

Accurate prediction of the wall temperature downstream of the trailing-edge slot is crucial to designing turbine blades that can w..

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MACHINE LEARNING FOR THE DEVELOPMENT OF DATA DRIVEN TURBULENCE CLOSURES IN COOLANT SYSTEMS

James Hammond, Francesco Montomoli, Marco Pietropaoli, Richard Sandberg, Vittorio Michelassi

2020-06-22

This work shows the application of Gene &pression Pro­gramming to augment RANS turbulence closure modelling for flows through comp..

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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

2019-12-01

In low-pressure turbines (LPTs), around 60–70% of losses are generated away from end-walls, while the remaining 30–40% is controll..

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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

2019-05-01

In low-pressure turbines (LPT) at design point, around 60–70% of losses are generated in the blade boundary layers far from end wa..

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On the Identification and Decomposition of the Unsteady Losses in a Turbine Cascade

D Lengani, D Simoni, R Pichler, RD Sandberg, V Michelassi, F Bertini

2019-03-01

The present paper describes the application of proper orthogonal decomposition (POD) to large eddy simulation (LES) of the T106A l..

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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

2018-10-01

Machine learning was applied to large-eddy simulation (LES) data to develop nonlinear turbulence stress and heat flux closures wit..

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DEVELOPMENT AND USE OF MACHINE-LEARNT ALGEBRAIC REYNOLDS STRESS MODELS FOR ENHANCED PREDICTION OF WAKE MIXING IN LPTS

Harshal D Akolekar, Jack Weatheritt, Nicholas Hutchins, Richard D Sandberg, Gregory Laskowski, Vittorio Michelassi

2018-01-01

Non-linear turbulence closures were developed that improve the prediction accuracy of wake mixing in low-pressure turbine (LPT) fl..