Mechanical Engineering - Research Publications
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ItemNo Preview AvailableResolvent analysis-based pressure modeling for trailing edge noise prediction(American Institute of Aeronautics and Astronautics, 2019-05-20)This paper presents the first development steps of a hybrid computational aeroacoustics (CAA) method for the prediction of trailing edge (TE) noise, based on a physics-driven prediction of the surface pressure on an airfoil. Starting frommean flowdata for a given configuration, the dominant pressure modes over a foil are modeled with an incompressible formulation of the resolvent method. As a conceptual test of its suitability to predict surface pressures, the framework is used to model the unsteady surface pressure fluctuations generated by instability waves on an infinite flat plate. While a canonical test case, the flat plate is a good starting point for the investigation of airfoil TE noise. Subsequently, the framework is applied to a NACA0012 airfoil at 0° angle of attack. In the flat plate case, hydrodynamic instabilities are excited by a single frequency volume forcing and result in streamwise propagating Tollmien-Schlichting waves. The resolvent captures these instabilities and the resulting surface pressure field with good accuracy. A Mach number dependence is observed for the agreement between resolvent and DNS pressure modes, which may explain the difference in wavelengths between DNS and resolvent results in the NACA0012 airfoil case. Bearing in mind this dependence of the pressure prediction accuracy on the Mach number, the results show promise for resolvent-based TE noise predictions.
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ItemDNS of a turbulent premixed jet flame with fully developed turbulent inlet(Australasian Fluid Mechanics Society, 2018-01-01)A Direct Numerical Simulation (DNS) study is conducted of a premixed round jet flame with a fully turbulent flow exiting a long pipe as inlet condition. The inclusion of the pipe in the simulation with a fully turbulent flow inside ensures that the turbulence-flame interaction, in particular near the inlet region, is well represented. The orientation of turbulence structures in different regions of the domain is identified. The interaction between the turbulence structures and the flame is examined using vector alignments. Streamwise oriented vortical structures formed in the pipe boundary layer are convected into the base of the flame. Preferential alignment of the flame normal with the compressive eigenvector of the strain rate tensor is observed close to the pipe exit suggesting that the turbulence characteristics of the jet inlet have an effect on the flame front close to the flame base.
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ItemDEVELOPMENT AND USE OF MACHINE-LEARNT ALGEBRAIC REYNOLDS STRESS MODELS FOR ENHANCED PREDICTION OF WAKE MIXING IN LPTS(AMER SOC MECHANICAL ENGINEERS, 2018-01-01)Non-linear turbulence closures were developed that improve the prediction accuracy of wake mixing in low-pressure turbine (LPT) flows. First, Reynolds-averaged Navier-Stokes (RANS) calculations using five linear turbulence closures were performed for the T106A LPT profile at exit Mach number 0.4 and isentropic exit Reynolds numbers 60,000 and 100,000. None of these RANS models were able to accurately reproduce wake loss profiles, a crucial parameter in LPT design, from direct numerical simulation (DNS) reference data. However, the recently proposed kv2w transition model was found to produce the best agreement with DNS data in terms of blade loading and boundary layer behavior and thus was selected as baseline model for turbulence closure development. Analysis of the DNS data revealed that the linear stress-strain coupling constitutes one of the main model form errors. Hence, a geneexpression programming (GEP) based machine-learning technique was applied to the high-fidelity DNS data to train non-linear explicit algebraic Reynolds stress models (EARSM). In particular, the GEP algorithm was tasked to minimize the weighted difference between the DNS and RANS anisotropy tensors, using different training regions. The trained models were first assessed in an a priori sense (without running any CFD) and showed much improved alignment of the trained models in the region of training. Additional RANS calculations were then performed using the trained models. 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, in particular those trained on just the wake region.
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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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ItemUsing a new Entropy Loss Analysis to Assess the Accuracy of RANS Predictions of an HPT Vane(The American Society of Mechanical Engineers, 2019)Entropy loss is widely used to quantify the efficiency of components in turbomachines, and empirical relations have been developed to estimate the contribution of different mechanisms. However, further analysis is still needed to not only get a deeper insight of the physics, but also to more accurately quantify the loss generation caused by different terms. In the present study, the entropy transport equations based on averaged flow quantities are first derived, and the entropy generation process is fully decomposed into several terms representing different physical mechanisms, such as mean viscous dissipation, turbulence production, mean and turbulent heat flux, etc. This decomposition framework is then applied to available high-resolution LES and RANS results of a VKI LS-89 HPT vane, and a detailed quantification of different entropy generation terms is obtained. The results show that the entropy generation caused by mean flow features like mean viscous dissipation and mean heat flux are in close agreement between LES and RANS, indicating that RANS provides an overall good prediction for the mean flow. Furthermore, we find that turbulence production plays an important role in entropy generation as it represents the energy extracted from the mean flow to turbulent fluctuations. However, the difference between RANS and LES results for the turbulence production term is not negligible, particularly in the wake region. This implies that the failure of RANS to predict the correct total loss might be largely caused by errors in capturing the correct turbulence production in the near wake region.
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ItemLES and RANS Analysis of the End-Wall Flow in a Linear LPT Cascade With Variable Inlet Conditions: Part II — Loss Generation(AMER SOC MECHANICAL ENGINEERS, 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-walls, while the remaining 30%–40% is controlled by the interaction of the blade profile with the end-wall boundary layer. Increasing attention is devoted to these flow regions in industrial design processes. Experimental techniques have shed light on the mechanism that controls the growth of the secondary vortices, and scale-resolving CFD have provided a detailed insight into the vorticity generation. Along these lines, this paper discusses the end-wall flow characteristics of the T106 profile with parallel end-walls at realistic LPT conditions, as described in the experimental setup of Duden and Fottner (1997) “Influence of Taper, Reynolds Number and Mach Number on the Secondary Flow Field of a Highly Loaded Turbine Cascade”, P. I. Mech. Eng. A-J. Pow., 211 (4), pp.309–320. The simulations target first the same inlet conditions as documented in the experiments, and determines the impact of the incoming boundary layer thickness by running additional cases with modified incoming boundary layers. Calculations are carried out by both RANS, due to its continuing role as the design verification workhorse, and highly-resolved LES. Part II of the paper focuses on the loss generation associated with the secondary end-wall vortices. Entropy generation and the consequent stagnation pressure losses are analyzed following the aerodynamic investigation carried out in the companion paper. The ability of classical turbulence models generally used in RANS to discern the loss contributions of the different vortical structures is discussed in detail and the attainable degree of accuracy is scrutinized with the help of LES and the available test data. The purpose is to identify the flow features that require further modelling efforts in order to improve RANS/URANS approaches and make them able to support the design of the next generation of LPTs.
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ItemLes and RANS analysis of the end-wall flow in a linear LPT cascade, part I: Flow and secondary vorticity fields under varying Inlet condition(AMER SOC MECHANICAL ENGINEERS, 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 by the interaction of the blade profile with the end-wall boundary layer. Experimental and numerical studies have shown how the strength and penetration of the secondary flow depends on the characteristics of the incoming end-wall boundary layer. Experimental techniques did shed light on the mechanism that controls the growth of the secondary vortices, and scale-resolving CFD allowed to dive deep into the details of the vorticity generation. Along these lines, this paper discusses the end-wall flow characteristics of the T106 LPT profile at Re = 120K and M = 0.59 by benchmarking with experiments and investigating the impact of the incoming boundary layer state. The simulations are carried out with proven Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) solvers to determine if Reynolds Averaged models can capture the relevant flow details with enough accuracy to drive the design of this flow region. Part I of the paper focuses on the critical grid needs to ensure accurate LES, and on the analysis of the overall time averaged flow field and comparison between RANS, LES and measurements when available. In particular, the growth of secondary flow features, the trace and strength of the secondary vortex system, its impact on the blade load variation along the span and end-wall flow visualizations are analysed. The ability of LES and RANS to accurately predict the secondary flows is discussed together with the implications this has on design.
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ItemMachine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation(American Society of Mechanical Engineers, 2017-01-01)The validity of the Boussinesq approximation in the wake behind a high-pressure turbine blade is explored. We probe the mathematical assumptions of such a relationship by employing a least-squares technique. Next, we use an evolutionary algorithm to modify the anisotropy tensor a priori using highly resolved LES data. In the latter case we build a non-linear stress-strain relationship. Results show that the standard eddy-viscosity assumption underpredicts turbulent diffusion and is theoretically invalid. By increasing the coefficient of the linear term, the farwake prediction shows minor improvement. By using additional non-linear terms in the stress-strain coupling relationship, created by the evolutionary algorithm, the near-wake can also be improved upon. Terms created by the algorithm are scrutinized and the discussion is closed by suggesting a tentative non-linear expression for the Reynolds stress, suitable for the wake behind a high-pressure turbine blade.
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ItemHIGH-FIDELITY SIMULATIONS OF A LINEAR HPT VANE CASCADE SUBJECT TO VARYING INLET TURBULENCE(AMER SOC MECHANICAL ENGINEERS, 2017-01-01)The effect of inflow turbulence intensity and turbulence length scales have been studied for a linear high-pressure turbine vane cascade at Reis = 590,000 and Mis = 0.93, using highly resolved compressible large-eddy simulations employing the WALE turbulence model. The turbulence intensity was varied between 6% and 20% while values of the turbulence length scales were prescribed between 5% and 20% of axial chord. The analysis focused on characterizing the inlet turbulence and quantifying the effect of the inlet turbulence variations on the vane boundary layers, in particular on the heat flux to the blade. The transition location on the suction side of the vane was found to be highly sensitive to both turbulence intensity and length scale, with the case with turbulence intensity 20% and 20% length scale showing by far the earliest onset of transition and much higher levels of heat flux over the entire vane. It was also found that the transition process was highly intermittent and local, with spanwise parts of the suction side surface of the vane remaining laminar all the way to the trailing edge even for high turbulence intensity cases.
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ItemOn the Identification and Decomposition of the Unsteady Losses in a Turbine Cascade(American Society of Mechanical Engineers, 2018-01-01)The present paper describes the application of Proper Orthogonal Decomposition (POD) to Large Eddy Simulation (LES) of the T106A low-pressure-turbine profile with unsteady incoming wakes at two different flow conditions. Conventional data analysis applied to time averaged or phase-locked averaged flow fields is not always able to identify and quantify the different sources of losses in the unsteady flow field as they are able to isolate only the deterministic contribution. A newly developed procedure allows such identification of the unsteady loss contribution due to the migration of the incoming wakes, as well as to construct reduced order models able to highlight unsteady losses due to larger and/or smaller flow structures carried by the wakes in the different parts of the blade boundary layers. This enables a designer to identify the dominant modes (i.e. phenomena) responsible for loss, the associated generation mechanism, their dynamics and spatial location. The procedure applied to the two cases shows that losses in the fore part of the blade suction side are basically unaffected by the flow unsteadiness, irrespective of the reduced frequency and the flow coefficient. On the other hand, in the rear part of the suction side the unsteadiness contributes to losses prevalently due to the finer scale (higher order POD modes) embedded into the bulk of the incoming wake. The main difference between the two cases has been identified by the losses produced in the core flow region, where both the largest scale structures and the finer ones produces turbulence during migration. The decomposition into POD modes allows the quantification of this latter extra losses generated in the core flow region, providing further inputs to the designers for future optimization strategies.