Mechanical Engineering - Research Publications
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ItemA New Data-Driven Turbulence Model Framework for Unsteady Flows Applied to Wall-Jet and Wall-Wake Flows(The American Society of Mechanical Engineers, 2019-11-05)The unsteady flow prediction for turbomachinery applications relies heavily on unsteady RANS (URANS). For flows that exhibit vortex shedding, such as the wall-jet/wake flows considered in this study, URANS is unable to predict the correct momentum mixing with sufficient accuracy. We suggest a novel framework to improve that prediction, whereby the deterministic scales associated with vortex shedding are resolved while the stochastic scales of pure turbulence are modelled. The framework first separates the stochastic from the deterministic length scales and then develops a bespoke turbulence closure for the stochastic scales using a data-driven machine-learning algorithm. The novelty of the method lies in the use of machine-learning to develop closures tailored to URANS calculations. For the walljet/wake flow, three different mass flow ratios (0.86, 1.07 and 1.26) have been considered and a high-fidelity dataset of the idealised geometry is utilised for the sake of model development. This study serves as an a priori analysis, where the closures obtained from the machine-learning algorithm are evaluated before their implementation in URANS. The analysis looks at the impact of using all length scales versus the stochastic scales for closure development, and the impact of the extent of the spatial domain for developing the closure. It is found that a two-layer approach, using bespoke trained models for the near wall and the jet/wake regions, produce the best results. Finally, the generalisability of the developed closures is also evaluated by applying a given closure developed using a particular mass flow ratio to the other cases.
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ItemImprovement In Unsteady Wake Prediction Through Machine Learning Based Rans Model Training(ISUAAAT15, 2018)The inability of RANS to correctly capture profile wake dynamics and decay prevents the accurate prediction of unsteady losses and poses additional challenges to the aeromechanic verification of both turbines and compressors. This paper addresses this problem by introducing a novel technique applied to improve wake prediction through a RANS based calculation. In particular, for the first time, a data-driven approach is used to develop bespoke turbulence closures to be used in the context of unsteady RANS (URANS) calculations. The new closure is obtained from an evolutionary machine learning algorithm. The algorithm requires a high-fidelity dataset which, in this study, is provided through a DNS for the wake downstream of a normal flat plate with a Reynolds number = 2,000, based on the freestream velocity and plate height. URANS are conducted with the developed closure and the mean flow statistics are compared with the high-fidelity data. The results from the developed closure show excellent agreement with the reference data. Furthermore, the generalisability of the developed closure is evaluated by considering other flat plate wake data, which differ in both the aspect ratio and the Reynolds number from the case used in this study to develop the closures. The results once again show remarkable improvement compared with the standard URANS.
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ItemInfluence of Pressure Gradient on Plane Wake Evolution in a Constant Area Section(Australasian Fluid Mechanics Society, 2018)This study focusses on understanding the influence of streamwise pressure gradients acting in a constant area section on the spatial wake evolution. First, the impact of a constant area section in contrast to the usual variable area section is studied analytically for a simplified 2D inviscid flow. Second, high-fidelity data (DNS) is used to verify some of the conclusions of the above study. A flat plate normal to the flow at a Reynolds number of 2,000, based on the plate height and freestream velocity is considered as the test case for the high-fidelity calculations. Multiple pressure gradients are simulated to identify any global trends and compare with the existing experimental findings of variable area sections. The results indicate that the wake evolution in presence of a pressure gradient is dissimilar for constant and variable area sections.