This paper discusses the noise prediction of Contra Rotating Open Rotors (CROR) in the context of a multi-disciplinary rotor design. Propeller noise is believed to be one of the dominant sources of noise on CROR engines in all flight conditions that require high thrust. It is therefore important to consider acoustics as early as possible in the design process. At Snecma, blade design is tackled as a multi-disciplinary approach that involves among others; mechanics, dynamics, aerodynamics, acoustics and engine integration constraints. In this context, accurate prediction methods based on unsteady Computational Fluid Dynamics (CFD) cannot be used to predict tonal noise radiation because of the prohibitive time required for convergence. This paper focuses on the prediction of far field tonal interaction noise using the fast prediction platform Sandra, which complies with the timescale of a rotor design project. Sandra is composed of an aerodynamic module and an acoustic module. As a first approximation, the aerodynamic module uses either a lifting-line method or a lifting-surface method to compute the pressure fluctuations on the blades. A map of the flow velocity, which contains the velocity gradients of the wake and of the tip vortex behind the front rotor, is extracted from a Reynolds Averaged Navier-Stokes (RANS) CFD simulation downstream of the front rotor. A quasi-stationary approach, which uses the space and time symmetry of the isolated rotors CFD, allows the flow field to be decomposed along the azimuthal angle. The pressure fluctuations on the rear blade are finally computed for each position. The acoustic propagation is then performed in the time domain using Farassat’s formulation 1A of the Ffowcs Williams and Hawkings equation. It is shown that the physics of the interaction between the front and the rear rotor is well captured. Direct comparisons with the far field noise computed from a uRANS solution, and with experimental data, show very good agreement of the position of the angles of maximum noise radiation for harmonics of order 2 or lower. It is also shown that the relative noise radiation between various rotor geometries is reasonably well captured, which is a requirement to provide a fast and relevant method for multi-disciplinary propeller blade design.
The regional aircraft segment plays a crucial role in achieving the EU Flightpath 2050 objectives (increase connectivity through Europe, enforce Europe’s industrial leadership, and significantly reduce the environmental impact of aviation). Despite an outdated perception by the general public, turboprop aircraft are typically less expensive to operate than regional jets. The impact of new technologies is therefore even more evident. Achieving a significant reduction in perceived noise levels remains however a challenge for the success of further turboprop deployment. This twofold paper discusses the design of an innovative low-noise propeller in the framework of the Clean Sky 2 Regional Aircraft IADP, with a focus on the design methodology itself in this second part. The design is inherently multidisciplinary — aerodynamic, acoustic, mechanical — with multiple flight conditions and a wind tunnel condition to be considered. In order to limit the number of expensive high-fidelity computations, an online surrogate-based optimisation (SBO) approach has been deployed. A high-dimensional design space has been considered to enable to identify disruptive low-noise concepts. By exploiting the results of low-fidelity tools (see the first part of the paper), combined with efficient machine learning techniques and data mining capabilities, a gradual increment of the design space from 57 to 111 design parameters has been considered. A significant noise reduction of about 6.5 dB has been achieved without major degradation of the aerodynamic efficiency — fully aligned with the objectives for the Regional Aircraft IADP.
High-fidelity numerical simulations have already contributed in a significant way to the emergence of state-of-the-art turbomachinery components but are still sparingly exploited due to their cost. It is hence relevant to continue the investigations on low-fidelity tools and their combination with higher fidelity methodologies especially when breakthrough technologies are studied. Multi-fidelity approaches are key in supporting turbomachinery engineers to quickly decide on the relevance of an innovative concept, and to accelerate optimisation processes. This paper describes a low-noise propeller design methodology developed during the IRON project (Clean Sky 2). This methodology is driven by Calypso, a platform developed by Cenaero, which integrates machine learning strategies and multifidelity aerodynamic and acoustic solvers. Two key elements of this framework are detailed and validated in this paper. The first one is a low-fidelity tool that offers a fast estimation of the steady aerodynamic performance of a propeller blade using a lifting-surface method. The second one is a far-field tonal noise prediction solver, relying on an acoustic propagation performed with Farassat’s formulation 1A of the Ffowcs-Williams and Hawkings equation. Noise source can be either obtained after a CFD computation or after a low-fidelity aerodynamic evaluation. The low and high-fidelity aero-acoustic methodologies have been applied on 600 samples. It is shown how data mining strategies available within Calypso pave the way to astutely combine both levels of fidelity within an optimisation to quickly and efficiently explore a large design space, and to identify the key design parameters as early as possible in the design process.
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