Mechanical Engineering - Research Publications
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ItemNo Preview AvailablePulsed impinging jets: Momentum and heat-transfer(PERGAMON-ELSEVIER SCIENCE LTD, 2022-05-15)
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ItemNo Preview AvailableMomentum boundary-layer characterisation from a pulsed impinging jet(ELSEVIER SCIENCE INC, 2022-04)
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ItemRANS predictions of trailing-edge slot flows using heatflux closures developed with CFD-driven machine learning(GLOBAL POWER PROPULSION SOC, 2021)Accurate prediction of the wall temperature downstream of the trailing-edge slot is crucial to designing turbine blades that can withstand the harsh aerothermal environment in a modern gas turbine. Because of their computational efficiency, industry relies on low-fidelity tools like RANS for momentum and thermal field calculations, despite their known underprediction of wall temperature. In this paper, a novel framework using a branch of machine learning, geneexpression programming (GEP) [Zhao et al. 2020, J. Comp. Physics, 411:109413] is used to develop closures for the turbulent heat-flux to improve upon this underprediction. In the original use of GEP (“frozen” approach), the turbulent heat-flux from a high-fidelity database was used to evaluate the fitness of the candidate closures during the symbolic regression, however, the resulting closure had no information of the temperature field during the optimisation process. In this work, the regression process of the GEP instead incorporates RANS calculations to evaluate the fitness of the candidate closures. This allows the inclusion of the temperature field from RANS to advance the iterative regression, leading to a more integrated heat-flux closure development, and consequently more accurate and robust models. The GEP-based CFD-driven framework is demonstrated on a trailing edge slot configuration with three blowing ratios. Full a posteriori predictions from the new closures are compared to high-fidelity reference data and both conventional RANS closures and closures obtained from the “frozen” approach.
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ItemUNSTEADY SIMULATIONS OF A TRAILING-EDGE SLOT USING MACHINE-LEARNT TURBULENCE STRESS AND HEAT-FLUX CLOSURES(ASME: The American Society of Mechanical Engineers, 2020-06-22)The trailing edge slot is a canonical representation of the pressure-side bleed flow encountered in high pressure turbines. Predicting the flow and temperature downstream of the slot exit remains challenging for RANS and URANS, with both significantly over predicting the adiabatic wall effectiveness. This over prediction is attributable to the incorrect mixing prediction in cases where the vortex shedding is present. In case of RANS the modelling error is rooted in not properly accounting for the shedding scales while in URANS the closures account for the shedding scales twice, once by resolving the shedding and twice with the model for all the scales. Here, we present an approach which models only the stochastic scales that contribute to turbulence while resolving the scales that do not, i.e. scales considered as contributing to deterministic unsteadiness. The model for the stochastic scales is obtained through a data-driven machine learning algorithm, which produces a bespoke turbulence closure model from a high-fidelity dataset. We use the best closure (blowing ratio of 1.26) for the anisotropy obtained in the a priori study of Lav, Philip & Sandberg [A New Data-Driven Turbulence Model Framework for Unsteady Flows Applied to Wall-Jet and Wall-Wake Flows, 2019] and conduct compressible URANS calculations. In the first stage, the energy equation is solved utilising the standard gradient diffusion hypothesis for the heat-flux closure. In the second stage, we develop a bespoke heat-flux closure using the machine-learning approach for the stochastic heat flux components only. Subsequently, calculations are performed using the machine-learnt closures for the heat-flux and the anisotropy together. Finally, the generalisability of the developed closures is evaluated by testing them on additional blowing ratios of 0.86 and 1.07. The machine learnt closures developed specifically for URANS calculations show significantly improved predictions for the adiabatic wall-effectiveness across the different cases.
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ItemA framework to develop data-driven turbulence models for flows with organised unsteadiness(Elsevier, 2019-04-15)Turbulence modelling development has received a boost in recent years through assimilation of machine learning methods and increasing availability of high-fidelity datasets. This paper presents an approach that develops turbulence models for flows exhibiting organised unsteadiness. The novel framework consists of three parts. First, using triple decomposition, the high-fidelity data is split into organised motion and stochastic turbulence. A data-driven approach is then used to develop a closure only for the stochastic part of turbulence. Finally, unsteady calculations are conducted, which resolve the organised structures and model the unresolved turbulence using the developed bespoke turbulence closure. A case study of a wake with vortex shedding behind a normal flat plate, at a Reynolds number of 2,000, based on plate height and freestream velocity, is considered to demonstrate the method. The approach shows significant improvement in mean velocity and Reynold stress profiles compared with standard turbulence models.
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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.