Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows

This paper presents an assessment of machine-learned turbulence closures, trained for improving wake-mixing prediction, in the context of LPT flows. To this end, a three-dimensional cascade of industrial relevance, representative of modern LPT bladings, was analyzed, using a state-of-the-art RANS approach, over a wide range of Reynolds numbers. To ensure that the wake originates from correctly reproduced blade boundary-layers, preliminary analyses were carried out to check for the impact of transition closures, and the best-performing numerical setup was identified. Two different machine-learned closures were considered. They were applied in a prescribed region downstream of the blade trailing edge, excluding the endwall boundary layers. A sensitivity analysis to the distance from the trailing edge at which they are activated is presented in order to assess their applicability to the whole wake affected portion of the computational domain and outside the training region. It is shown how the best-performing closure can provide results in very good agreement with the experimental data in terms of wake loss profiles, with substantial improvements relative to traditional turbulence models. The discussed analysis also provides guidelines for defining an automated zonal application of turbulence closures trained for wake-mixing predictions.

Aerodynamic interaction of turbines in a regulated two-stage turbocharging system

Two-stage turbocharging becomes prevailing in internal combustion engines due to its advantage of flexibility of boosting for the variation of operational conditions. Two turbochargers are closely coupled by engine manifolds in the system especially under the requirement of compactness. This paper studies the influence of the interaction of two turbines in a two-stage turbocharging system on the performance. Results show that the performance of low-pressure turbine is highly sensitive to the stage interaction. Specifically, compared with the cases without interaction, the efficiency of low-pressure turbine increases maximumly by 2.8% when the bypass valve is closed, but reduces drastically by 7.5% when the valve is open. Detailed flow analysis shows that the combined results of swirling flow from the high-pressure turbine and the Dean vortex caused by the manifold elbow result in the alleviation of entropy generation in the turbine rotor. However, when the bypass valve is open, interaction of the swirling flow with the injected bypass flow results in strong secondary flow in the volute and distorted inlet flow condition for the rotor, leading to the enhancement of entropy generation in low-pressure turbine. The study provides valuable insights into turbine performance in a two-stage turbocharging system, which can be used for the modeling and optimization of multi-stage turbocharging systems.

Turbine Broadband Noise Predictions Using Linearised Frequency Domain Navier-Stokes Solvers

A linear frequency domain Navier-Stokes solver is used to retain the influence of turning, thickness, and main geometric parameters on turbine broadband noise. The methodology has been applied to predict the broadband interaction noise produced by a representative low-speed low-pressure turbine section. The differences in the spectra with respect to those yielded by state-of-the-art flat plate based methodologies are up to 6 dB. The differences are caused by multiple effects that semi-analytical methodologies do not account for. The most important are blade thickness and turning, which have been studied separately to quantify their impact on the broadband noise footprint. The influence of changing the turbine operating conditions has been discussed as well. The outlet sound pressure level scales with the third and second power of the inlet and outlet Mach number, respectively, for constant turbulence intensity, within most of the frequency range considered.

Direct Numerical Simulation on the Unsteady Flow Field of Low-Pressure Turbine Cascades With and Without Upstream Wake at Low Reynolds Number

The spectral/hp element method is a high fidelity method that has good numerical dispersion-diffusion characteristics and is flexible and applicable to quasi-three-dimensional aerodynamic problems with complex geometric configurations in the streamline direction and the pitch direction. This paper uses this method to directly solve the incompressible Navier-Stokes equations, and analyzes the aerodynamic performance of the T106A low-pressure turbine cascade at low Reynolds number. Two different conditions, i.e. uniform inlet flow and cylinder’s wake flow, are adopted and their basic characteristics of flow separation and transition are quantitatively analyzed and compared, by observing the distribution of cascade wall surface pressure and friction coefficient, the distribution of wake profile pressure loss and the evolution characteristics of boundary layer flow structure. The numerical results show that the spectral/hp element method can accurately predict the flow separation and transition performance of low-pressure turbine cascades, which implies that it can be used as a high-fidelity simulation and calculation tool for the optimal design of this type of cascade. It is also found that cylinder’s wake can effectively inhibit boundary layer separation of the T106A LPT blade and improve aerodynamic performance and efficiency of it.

Low-Pressure Turbine Cooling Systems

Modern low-pressure turbine engines are equipped with casings impingement cooling systems. Those systems (called Active Clearance Control) are composed of an array of air nozzles, which are directed to strike turbine casing to absorb generated heat. As a result, the casing starts to shrink, reducing the radial gap between the sealing and rotating tip of the blade. Cooling air is delivered to the nozzles through distribution channels and collector boxes, which are connected to the main air supply duct. The application of low-pressure turbine cooling systems increases its efficiency and reduces engine fuel consumption.