In this paper, we investigate the feasibility of using DNS data and machine learning algorithms to assist RANS turbulence model development. High-fidelity DNS data are generated with the incompressible Navier–Stokes solver implemented in the spectral/hp element software framework Nektar++. Two test cases are considered: a turbulent channel flow and a stationary serpentine passage, representative of internal turbo-machinery cooling flow. The Python framework TensorFlow is chosen to train neural networks in order to address the known limitations of the Boussinesq approximation and a clustering based on flow features is run upfront to enable training on selected areas. The resulting models are implemented in the Rolls-Royce solver HYDRA and a posteriori predictions of velocity field and wall shear stress are compared to baseline RANS. The paper presents the fundamental elements of procedure applied, including a brief description of the tools and methods and improvements achieved.

This paper shows the current research to move towards the full digital design of a gas turbine. In the last years new manufacturing technologies, such as additive manufacturing, become more common for gas turbine applications, allowing greater flexibility in the design space. There is a need to...

Ultra-high bypass ratio turbofans offer significant reductions in fuel and pollution due to their higher propulsive efficiency. Short intakes might lead to a stronger fan-intake interaction, which creates uncertainty in stability at off-design conditions. Due to the prohibitive cost of full-scale experimental testing, subscale testing in wind tunnels is used to understand this behaviour. The low Reynolds number of subscale models results in unrepresentative laminar shock-boundary layer interactions. The boundary layer state thus needs to be conditioned to better represent full-scale transonic fans. This paper proposes the use of an inexpensive and robust flow control method for the suction side of a fan blade. Design guidelines are given for the location and height of the discrete roughness elements used to control the boundary layer state. This paper also presents a rapid experimental validation methodology to ensure and de-risk the application of the boundary layer trip to 3D rig blades. The experimental methodology is applied to a generic aerofoil representative of a fan tip section. The experimental method proves that it is possible to reproduce boundary layers and pressure distributions of a full-scale fan blade on a 1/10 subscale model. The results obtained confirm that the boundary layer trip method successfully promotes transition at the location representative of full-scale blades, avoiding unrepresentative laminar shock wave boundary layer interactions. This highlights the importance of conditioning boundary layers in low Reynolds number fan rig testing.

Direct numerical simulations (DNSs) of the T106A low-pressure turbine were conducted for various turbulence intensities and length scales to investigate their effects on flow behaviour and transition. A source-term formulation of the synthetic eddy method (SEM) was implemented in the Nektar++ spectral/hp element framework to introduce anisotropic turbulence into the flow field. A single sponge layer was imposed, which covers the inflow and outflow regions just downstream and upstream of the inflow and outflow boundaries, respectively, to avoid acoustic wave reflections on the boundary conditions. Additionally, in the T106A model, mixed polynomial orders were utilized, as Nektar++ allows different polynomial orders for adjacent elements. A lower polynomial order was employed in the outflow region to further assist the sponge layer by coarsening the mesh and diffusing the turbulence near the outflow boundary. Thus, this study contributes to the development of a more robust and efficient model for high-fidelity simulations of turbine blades by enhancing stability and producing a more accurate flow field. The main findings are compared with experimental and DNS data, showing good agreement and providing new insights into the influence of turbulence length scales on flow separation, transition, wake behaviour, and loss profiles.

Within the turbomachinery industry, components are currently assessed deploying standard second-order steady solvers. These are unable to capture complicated unsteady phenomena that have a critical impact on component performance. In this work, the high-order spectral h/p solver Nektar++ will be applied to a compressor blade to study the turbulent transition mechanisms and assess the effect of incoming disturbances with quasi-DNS resolution. The case will be modelled at an angle of incidence of $53.5°$ to match the original experimental loading at $52.8°$. At clean inflow conditions, Kelvin–Helmholtz instabilities appear on both sides of the blade due to a double separation, with the pressure side one not being reported in the experiments. The separation is gradually removed by the incoming turbulent structures but at different rates on the two sides of the blade. It will be shown that there is an optimal amount of turbulence intensity that minimises momentum thickness, which is strongly related to losses. Moreover, a discussion on the spanwise extrusion will be included, this being a major player in the modelling costs. Finally, the wall-clock time and the exact expenditure to run this case will be outlined, providing quantitative evidence of the feasibility of considering a quasi-DNS resolution in an industrial setting.