For the aerodynamic design of multistage compressors and turbines Computational Fluid Dynamics (CFD) plays a fundamental role. In fact it allows the characterization of the complex behaviour of turbomachinery components with high fidelity. Together with the availability of more and more powerful computing resources, current trends pursue the adoption of such high-fidelity tools and state-of-the-art technology even in the preliminary design phases. Within such a framework Graphical Processing Units (GPUS) yield further growth potential, allowing a significant reduction of CFD process turn-around times at relatively low costs. The target of the present work is to illustrate the design and implementation of an explicit density-based RANS coupled solver for the efficient and accurate numerical simulation of multi-dimensional time-dependent compressible fluid flows on polyhedral unstructured meshes. The solver has been developed within the object-oriented OpenFOAM framework, using OpenCL bindings to interface CPU and GPU and using MPI to interface multiple GPUS. The overall structure of the code, the numerical strategies adopted and the algorithms implemented are specifically designed in order to best exploit the huge computational peak power offered by modern GPUS, by minimizing memory transfers between CPUs and GPUS and potential branch divergence occurrences. This has a significant impact in terms of the speedup factor and is especially challenging within a polyhedral unstructured mesh framework. Specific tools for turbomachinery applications, such as Arbitrary Mesh Interface (AMI) and mixingplane (MP), are implemented within the GPU context. The credibility of the proposed CFD solver is assessed by tackling a number of benchmark test problems, including Rotor 67 axial compressor, C3X stator blade with conjugate heat transfer and Aachen multi-stage turbine. An average GPU speedup factor of approximately S ≈ 50 with respect to CPU is achieved (single precision, both GPU and CPU in 100 USD price range). Preliminary parallel scalability test run on multiple GPUS show a parallel efficiency factor of approximately E ≈ 75%

The work aims at showing the importance of flutter predictions in the preliminary design of Propfan-Open Rotors. Both single rotating propellers and contra rotating open rotors are investigated. The related structural sub-systems are modelled through finite element analyses, the aerodynamic sub-systems exploit a finite volume full potential formulation suitable for unsteady transonic flows. An ad hoc technique is developed for simulating the flow field around a contra rotating open rotor configuration. The effectiveness of the proposed aeroelastic analysis is successfully assessed for single propellers through comparisons with reference numerical and experimental data available in the literature, as well as against Euler flow based solutions. Results for contra rotating open rotors cannot be validated because of the lack of corresponding open literature data.

Computational Fluid Dynamics (CFD) is a fundamental tool for the aerodynamic development in industrial applications. In the usual approach structural deformation due to aerodynamic and thermal loads is often neglected. However, in some cases, where power efficiency is the ultimate goal, an accurate prediction of the structure-flow interaction is essential. This is particularly true for trim and flutter analysis of aircrafts, helicopter and turbomachinery blades. Particularly, turbomachinery trim and flutter predictions still represent a challenge due to phenomena like rotor-stator interaction, separations and shock waves. The usual time-linearised, frequency-domain strategies can be inadequate when this kind of strong non-linear phenomena occur in the flow, making necessary full non-linear time-domain simulations or the harmonic balance technique. Beside flutter, another important aspect, not yet adequately investigated, is the trim analysis, which is fundamental for an accurate steady simulation that aims to consider static blade elasticity for the performance evaluation of turbomachines. Moreover, alongside the obvious contribution given by centrifugal loads to the blade deformation, a not less important source of blade displacement is the thermal effect due to the heat exchanged between the solid and the fluid domains. In particular, for some geometries and operating conditions, thermal effects can be more important than centrifugal effects for the blade deformations. Considering multiple sources of blade deformation (elastic, centrifugal and thermal) in a what is often called “multiphysics” approach is nowadays more and more important, if the goal of the analysis is geometry optimization. To achieve this, next to result’s accuracy also computational efficiency is required, when hundreds of aeroelastic simulations have to be performed in a typical optimization loop. Modern GPUs can be exploited to pursue this goal thanks to their high peak computational power available at relatively low costs and low power consumption with respect to the usual CPUs. In this paper a pioneer work describing the impact of static deformation due to blade elasticity, thermal and centrifugal effects on the performances and power efficiency will be provided. Alongside with accurate results, computational efficiency is taken into account. The purpose of this article is to show the architecture of a GPU-accelerated Fluid-Structure Interaction (FSI) solver for compressible viscous flows. The proposed approach is validated with a typical industrial case, i.e. a turbocharger transonic centrifugal-compressor provided by ABB. The effects of trimmed solutions on the most important integral quantities (i.e. mass flow, characteristic curves, mass-averaged outflow profiles) are investigated and a comparison with pure aerodynamic results is provided. Due to the high blade stiffness and thus the very small displacements obtained with the trim solutions, for the particular case presented in the paper the aeroelastic solutions basically provide nearly the same results as the pure aerodynamic solutions.