Low Pressure Turbine Lapse Rate Study: CFD Model vs. Rig Data

2002 ◽  
Author(s):  
J.-S. Liu ◽  
M. L. Celestina ◽  
G. B. Heitland ◽  
D. B. Bush ◽  
M. L. Mansour ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Flow Field ◽  
Fluid Dynamic ◽  
Low Reynolds Number ◽  
Low Pressure ◽  
Lapse Rate ◽  
Bypass Transition ◽  
Blade Row ◽  
Low Reynolds ◽  
Lp Turbine

As an aircraft engine operates from sea level take-off (SLTO) to altitude cruise, the low pressure (LP) turbine Reynolds number decreases. As Reynolds number is reduced the condition of the airfoil boundary layer shifts from bypass transition to separated flow transition. This can result in a significant loss. The LP turbine performance fall-off from SLTO to altitude cruise, due to the loss increase with reduction in Reynolds number, is referred to as a lapse rate. A considerable amount of research in recent years has been focused on understanding and reducing the loss associated with the low Reynolds number operation. A recent 3-1/2 stage LP turbine design completed a component rig test program at Honeywell. The turbine rig test included Reynolds number variation from SLTO to altitude cruise conditions. While the rig test provides detailed inlet and exit condition measurements, the individual blade row effects are not available. Multi-blade row computational fluid dynamics (CFD) analysis is used to complement the rig data by providing detailed flow field information through each blade row. A multi-blade row APNASA model was developed and solutions were obtained at the SLTO and altitude cruise rig conditions. The APNASA model predicts the SLTO to altitude lapse rate within 0.2 point compared to the rig data. The global agreement verifies the modeling approach and provides a high confidence level in the blade row flow field predictions. Additional Reynolds number investigation with APNASA will provide guidance in the LP turbine Reynolds number research areas to reduce lapse rate. To accurately predict the low Reynolds number flow in the LP turbine is a challenging task for any computational fluid dynamic (CFD) code. The purpose of this study is to evaluate the capability of a CFD code, APNASA, to predict the sensitivity of the Reynolds number in LP turbines.

Calculation of High-Lift Cascades in Low Pressure Turbine Conditions Using a Three-Equation Model

2010 ◽  
Vol 133 (3) ◽  
Author(s):  
Roberto Pacciani ◽  
Michele Marconcini ◽  
Atabak Fadai-Ghotbi ◽  
Sylvain Lardeau ◽  
Michael A. Leschziner
Keyword(s):  
Reynolds Number ◽  
Kinetic Energy ◽  
Eddy Viscosity ◽  
Low Reynolds Number ◽  
Low Pressure ◽  
Equation Model ◽  
Bypass Transition ◽  
High Lift ◽  
Wide Range ◽  
Low Reynolds

A three-equation model has been applied to the prediction of separation-induced transition in high-lift low-Reynolds-number cascade flows. Classical turbulence models fail to predict accurately laminar separation and turbulent reattachment, and usually overpredict the separation length, the main reason for this being the slow rise of the turbulent kinetic energy in the early stage of the separation process. The proposed approach is based on solving an additional transport equation for the so-called laminar kinetic energy, which allows the increase in the nonturbulent fluctuations in the pretransitional and transitional region to be taken into account. The model is derived from that of Lardeau et al. (2004, “Modelling Bypass Transition With Low-Reynolds-Number Non-Linear Eddy-Viscosity Closure,” Flow, Turbul. Combust., 73, pp. 49–76), which was originally formulated to predict bypass transition for attached flows, subject to a wide range of freestream turbulence intensity. A new production term is proposed, based on the mean shear and a laminar eddy-viscosity concept. After a validation of the model for a flat-plate boundary layer, subjected to an adverse pressure gradient, the T106 and T2 cascades, recently tested at the von Kármán Institute, are selected as test cases to assess the ability of the model to predict the flow around high-lift cascades in conditions representative of those in low-pressure turbines. Good agreement with experimental data, in terms of blade-load distributions, separation onset, reattachment locations, and losses, is found over a wide range of Reynolds-number values.

Numerical Simulation of Turbine Blade Boundary Layer and Heat Transfer and Assessment of Turbulence Models

1997 ◽  
Vol 119 (4) ◽  
pp. 794-801 ◽  
Author(s):  
J. Luo ◽  
B. Lakshminarayana
Keyword(s):  
Heat Transfer ◽  
Boundary Layer ◽  
Reynolds Number ◽  
Low Reynolds Number ◽  
Turbulence Models ◽  
Navier Stokes ◽  
Bypass Transition ◽  
Boundary Layer Development ◽  
Low Reynolds ◽  
Layer Development

The boundary layer development and convective heat transfer on transonic turbine nozzle vanes are investigated using a compressible Navier–Stokes code with three low-Reynolds-number k–ε models. The mean-flow and turbulence transport equations are integrated by a four-stage Runge–Kutta scheme. Numerical predictions are compared with the experimental data acquired at Allison Engine Company. An assessment of the performance of various turbulence models is carried out. The two modes of transition, bypass transition and separation-induced transition, are studied comparatively. Effects of blade surface pressure gradients, free-stream turbulence level, and Reynolds number on the blade boundary layer development, particularly transition onset, are examined. Predictions from a parabolic boundary layer code are included for comparison with those from the elliptic Navier–Stokes code. The present study indicates that the turbine external heat transfer, under real engine conditions, can be predicted well by the Navier–Stokes procedure with the low-Reynolds-number k–ε models employed.

Time Resolved Flow Field Investigations of Low Reynolds number Transient Maneuvers

2016 ◽  
Author(s):  
Hauke Ehlers ◽  
Robert Konrath ◽  
Marcel Börner ◽  
Ralf Wokoeck ◽  
Rolf Radespiel
Keyword(s):  
Reynolds Number ◽  
Flow Field ◽  
Low Reynolds Number ◽  
Time Resolved ◽  
Field Investigations ◽  
Low Reynolds

A Fluid Dynamics Study of a Modified Low-Reynolds-Number Flapping Motion

2010 ◽  
Author(s):  
M. R. Amiralaei ◽  
H. Alighanbari ◽  
S. M. Hashemi
Keyword(s):  
Fluid Dynamics ◽  
Reynolds Number ◽  
Phase Angle ◽  
Fluid Dynamic ◽  
Low Reynolds Number ◽  
Dynamic Performance ◽  
Navier Stokes ◽  
Fluid Forces ◽  
Low Reynolds ◽  
Volume Method

The objective of the present study is to investigate the low Reynolds number (LRN) fluid dynamics of an elliptic airfoil performing a novel figure-eight-like motion. To this mean, the influence of phase angle between the pitching and translational (heaving and lagging) motions and the amplitude of translational motions on the fluid flow is simulated. Navier-Stokes (NS) equations with Finite Volume Method (FVM) are used and the instantaneous force coefficients and the fluid dynamics performance, as well as the corresponding vortical structures are analyzed. Both the phase angle and the amplitudes of horizontal and vertical motions are of great importance to the fluid dynamic characteristics of the model as they are shown to change the peaks of the fluid forces, fluid dynamic performance, and the vortical patterns around the model.

A Comparative Study of Performance of Low Reynolds Number Turbulence Models for Various Heat Transfer Enhancement Simulations

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Ankit Tiwari ◽  
Savas Yavuzkurt
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Nusselt Number ◽  
Heat Transfer Enhancement ◽  
Friction Factor ◽  
Fluid Dynamic ◽  
Low Reynolds Number ◽  
Turbulence Models ◽  
Transfer Enhancement ◽  
Low Reynolds

The goal of this study is to evaluate the computational fluid dynamic (CFD) predictions of friction factor and Nusselt number from six different low Reynolds number k–ε (LRKE) models namely Chang–Hsieh–Chen (CHC), Launder–Sharma (LS), Abid, Lam–Bremhorst (LB), Yang–Shih (YS), and Abe–Kondoh–Nagano (AKN) for various heat transfer enhancement applications. Standard and realizable k–ε (RKE) models with enhanced wall treatment (EWT) were also studied. CFD predictions of Nusselt number, Stanton number, and friction factor were compared with experimental data from literature. Various parameters such as effect of type of mesh element and grid resolution were also studied. It is recommended that a model, which predicts reasonably accurate values for both friction factor and Nusselt number, should be chosen over disparate models, which may predict either of these quantities more accurately. This is based on the performance evaluation criterion developed by Webb and Kim (2006, Principles of Enhanced Heat Transfer, 2nd ed., Taylor and Francis Group, pp. 1–72) for heat transfer enhancement. It was found that all LRKE models failed to predict friction factor and Nusselt number accurately (within 30%) for transverse rectangular ribs, whereas standard and RKE with EWT predicted friction factor and Nusselt number within 25%. Conversely, for transverse grooves, AKN, AKN/CHC, and LS (with modified constants) models accurately predicted (within 30%) both friction factor and Nusselt number for rectangular, circular, and trapezoidal grooves, respectively. In these cases, standard and RKE predictions were inaccurate and inconsistent. For longitudinal fins, Standard/RKE model, AKN, LS and Abid LRKE models gave the friction factor and Nusselt number predictions within 25%, with the AKN model being the most accurate.

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