Developing novel big-data based models for designing greener turbines

Using a New Entropy Loss Analysis to Assess the Accuracy of RANS Predictions of an High-Pressure Turbine Vane

Yaomin Zhao, Richard D Sandberg

2020-08-01

Entropy loss is widely used to quantify the efficiency of components in turbomachines, and empirical relations have been developed..

UNSTEADY SIMULATIONS OF A TRAILING-EDGE SLOT USING MACHINE-LEARNT TURBULENCE STRESS AND HEAT-FLUX CLOSURES

Chitrarth Lav, Richard Sandberg

2020-06-22

The trailing edge slot is a canonical representation of the pressure-side bleed flow encountered in high pressure turbines. Predic..

RANS turbulence model development using CFD-driven machine learning

Y Zhao, HD Akolekar, J Weatheritt, V Michelassi, RD Sandberg

2020-06-15

This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. The CF..

Bypass transition in boundary layers subject to strong pressure gradient and curvature effects

Y Zhao, RD Sandberg

2020-04-10

This paper aims at characterizing the bypass transition in boundary layers subject to strong pressure gradient and curvature effec..

A New Data-Driven Turbulence Model Framework for Unsteady Flows Applied to Wall-Jet and Wall-Wake Flows

Chitrarth Lav, Jimmy Philip, Richard Sandberg

2019-11-05

The unsteady flow prediction for turbomachinery applications relies heavily on unsteady RANS (URANS). For flows that exhibit vorte..

Machine-Learnt Turbulence Closures for Low Pressure Turbines with Unsteady Inflow Conditions

Harshal D Akolekar, Richard Sandberg, Nick Hutchins, Vittorio Michelassi, Gregory M Laskowski

2019-10-01

The design of low-pressure turbines (LPT) must account for the losses generated by the unsteady interaction with the upstream blad..

A framework to develop data-driven turbulence models for flows with organised unsteadiness

Chitrarth Lav, Richard D Sandberg, Jimmy Philip

2019-04-15

Turbulence modelling development has received a boost in recent years through assimilation of machine learning methods and increas..

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, V Michelassi

2019-04-01

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

The Current State of High-Fidelity Simulations for Main Gas Path Turbomachinery Components and Their Industrial Impact

Richard D Sandberg, Vittorio Michelassi

2019-04-01

Over the past two decades high-fidelity simulations have become feasible for most main gas path turbomachinery components. This pa..

Using a new Entropy Loss Analysis to Assess the Accuracy of RANS Predictions of an HPT Vane

Richard Sandberg, Yaomin Zhao

2019-01-01

Entropy loss is widely used to quantify the efficiency of components in turbomachines, and empirical relations have been developed..

Les and RANS analysis of the end-wall flow in a linear LPT cascade, part I: Flow and secondary vorticity fields under varying Inlet condition

R Pichler, Yaomin Zhao, RD Sandberg, V Michelassi, R Pacciani, M Marconcini, A Arnone

2018-11-06

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

Identification and quantification of losses in a LPT cascade by POD applied to LES data

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

2018-04-01

A POD based procedure has been developed to identify and account for the different contributions to the entropy production rate ca..

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

2018-01-01

A form of supervised machine learning was applied to highly resolved large-eddy simulation (LES) data to develop non linear turbul..

LES and RANS Analysis of the End-Wall Flow in a Linear LPT Cascade With Variable Inlet Conditions: Part II — Loss Generation

Michele Marconcini, Roberto Pacciani, Andrea Arnone, Vittorio Michelassi, Richard Pichler, Yaomin Zhao, Richard Sandberg

2018-01-01

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

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

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

2018-01-01

The present paper describes the application of Proper Orthogonal Decomposition (POD) to Large Eddy Simulation (LES) of the T106A l..

A Comparative Study of Contrasting Machine Learning Frameworks Applied to RANS Modeling of Jets in Crossflow

Jack Weatheritt, Richard D Sandberg, Julia Ling, Gonzalo Saez, Julien Bodart

2017-01-01

Classical RANS turbulence models have known deficiencies when applied to jets in crossflow. Identifying the linear Boussinesq stre..

HIGH-FIDELITY SIMULATIONS OF A LINEAR HPT VANE CASCADE SUBJECT TO VARYING INLET TURBULENCE

Richard Pichler, Richard D Sandberg, Gregory Laskowski, Vittorio Michelassi

2017-01-01

The effect of inflow turbulence intensity and turbulence length scales have been studied for a linear high-pressure turbine vane c..

Machine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation

Jack Weatheritt, Richard Pichler, Richard D Sandberg, Gregory Laskowski, Vittorio Michelassi

2017-01-01

The validity of the Boussinesq approximation in the wake behind a high-pressure turbine blade is explored. We probe the mathematic..