Mechanical Engineering - Theses
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ItemUnderstanding loss mechanisms in turbomachinery to increase efficiency( 2016)The improvement in the design of any mechanical device is always carried out to increase its efficiency. Turbomachinery, more specifically, the low pressure turbine (LPT) in aircraft engines, is no exception. In order to enhance its efficiency, the specific fuel consumption has to be reduced, which implies different loss mechanisms (mechanisms of entropy generation) in the LPT have to be studied. Conventional loss calculations consider a control volume around the blade and find the total loss in that region. However, there are a number of sources of losses around LPT blades, prominently the mixing out of incident wakes from the stator to the rotor. In order to design more efficient blades, knowing the total loss in the blade control volume is not enough. It is important to quantify the various sources of loss due to wake mixing. Denton has derived an analytical expression for losses due to mixing, which consists of 3 terms - losses from the trailing edge region, losses due to boundary layer effects, and losses due to blade blockage effects. This equation, however, is built upon a number of assumptions such as steady, incompressible flow conditions which are not realised for real flows. Denton does not define a distinctive trailing edge region, methods to calculate boundary layer thickness and base pressures. In order to enhance the applicability of Denton’s mixed out loss equation, it is important to identify the dependence of Denton’s equation on these parameters; which is an important objective of this work. This work aims to improve the robustness of Denton’s equation by analysing the effect of not defining the trailing edge region properly on the total loss; and then proposes 4 criteria to define the trailing edge region. This analysis was conducted on a LPT blade with steady flow conditions and it was found that the losses calculated with the aid of the trailing edge criteria lie within 5% of the loss in the blade control volume. Sensitivity analyses on the total loss from Denton’s equation has been conducted using boundary layer thickness criteria, base pressure and non-uniform flow averaging techniques as input parameters. It was found that there are large variances in the total loss, if the boundary layer thickness is not defined correctly, and therefore researchers need to be very careful and consistent in their selection of boundary layer thickness. Visual observation, along with the boundary layer thickness criteria identified in this work, will serve as a good method to determine the boundary layer thickness. Denton’s equation does not deal with unsteady dissipation effects. In order to analyse those effects, an analysis has been conducted on an unsteady flow field consisting of incoming wakes; which has been broken up into 20 quasi-steady phases. Denton’s losses were calculated for each of these phases in an attempt to understand the effects of unsteady flow on Denton’s equation. Based on the analysis conducted, it was concluded that better averaging techniques are required to average the losses from the 20 phases to quantify the losses due to unsteady effects. Overall, Denton’s analysis, which is dependent on a number of criteria, has the potential to give a good rough estimate of the loss source. Due to the absence of more accurate methods, it is helpful for designers; and if conducted by the same user, allows for good qualitative comparison of different flow configurations. It is however difficult to quantify the exact amounts of losses based on this method. For a quantitative comparison with total losses in the control volume, it is probably necessary to come up with new ways of quantifying the different sources. The report provides ideas for future analyses to further improve the understanding of loss mechanisms via Denton’s equation and other methods.
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ItemTurbulence Model Development and Implementation for Low Pressure Turbines using a Machine Learning Approach( 2019)The design of the gas turbine, which is the work horse of the aviation industry, has reached a high degree of maturity; given that the first gas turbine flew in the late 1930s. Despite this, the industrial sector is looking towards harnessing even incremental points of efficiency with novel methods, which can translate to millions of dollars of savings and large reductions in carbon emissions. Current gas turbine design is primarily carried out using low-fidelity simulations due to their low cost and user-friendliness. However, these simulations lack the accuracy of high-fidelity simulations, largely due to the use of a linear stress-strain relation – the Boussinesq approximation. With the increase in the power of computing, high-fidelity simulations are becoming increasingly commonplace but are still not feasible as an iterative industrial design tool. In order to bridge the gap between high and low-fidelity simulations, certain high-fidelity data sets can be harvested to extract meaningful physics-based insights with machine learning processes to improve the accuracy of iterative low-fidelity calculations. This thesis focuses on improving low-fidelity modelling strategies (Reynolds–Averaged Navier–Stokes (RANS)) for low pressure turbine (LPT) flows, by harnessing meaningful physics-based information from high-fidelity data using a machine learning approach – gene expression programming (GEP). Improvement in the accuracy of the existing linear stress-strain closure relations is sought by developing machine-learnt explicit algebraic Reynolds stress models (EARSM). Of the many physical phenomena that occur in an LPT, designers are very interested in being able to accurately model the wake mixing using RANS, as this phenomenon governs the stagnation pressure loss in a turbine and also because existing RANS-based turbulence models fail to accurately predict this phenomenon. Therefore, the goal of this thesis is to develop and implement non-linear EARSMs to enhance the wake mixing in LPTs using GEP and high-fidelity data sets at realistic engine operating conditions. Firstly, an extensive analysis of the existing RANS-based turbulence models for LPTs with steady inflow conditions was conducted. None of these RANS models were able to accurately reproduce wake loss profiles based on high-fidelity data. However, the recently proposed k-v2-omega transition model was found to produce the best agreement with high-fidelity data in terms of blade loading and boundary layer behaviour and was thus selected as the baseline model for turbulence closure development. Using different training regions for model development, the resulting closures were extensively analysed in an a priori sense (without running any CFD) and also while running CFD calculations. Importantly, to assess their robustness, the trained models were tested both on the cases they were trained for and on testing, i.e. previously not seen, cases with different flow features. The developed models improved prediction of the Reynolds stress, TKE production, wake loss profiles and wake maturity across all cases. The existing GEP framework was extended to include RANS feedback during the model development process. It was found that the models generated via this method allow greater flexibility to the user in terms of selecting metrics of direct interest. The models returned offer a higher degree of numerical stability and robustness across different flow conditions and even geometries. Models developed on the LPT were tested on a high pressure turbine case and vice-versa and some of the models were able to reduce the peak wake loss error by up to 90% over the Boussinesq approximation in this cross-validation study. A zonal based model development approach was proposed with an aim to enhance the wake mixing prediction of unsteady RANS calculations for LPTs with unsteady inflow conditions. High-fidelity time-averaged and phase-lock averaged data at a realistic isentropic Reynolds number and two reduced frequencies, i.e. with discrete incoming wakes and with wake ‘fogging’, were used as reference data. This is the first known study to develop machine learning based turbulence models for unsteady flows, and also the first study to use phase-lock averaged data for the same. Models developed via phase-lock averaged data were able to capture the effect of certain prominent physical phenomena in LPTs such as wake-wake interactions, whereas models based on the time-averaged data could not. Correlations with the flow physics led to a set of models that can effectively enhance the wake mixing prediction across the entire LPT domain for both cases. Based on a newly developed error metric, the developed models have reduced the a priori error over the Boussinesq approximation on average by 45%. Based on the analysis conducted in this work, a few best practice guidelines have been proposed which can offer future designers an insight into the GEP-based model development process. Overall, this study showcases that GEP is a promising avenue for future RANS-based turbulence model development.