Aerodynamic optimisation of civil aero-engine nacelles by dimensionality reduction and multi-fidelity techniques

Abstract

Purpose Aerodynamic shape optimisation is complex because of the high dimensionality of the problem, the associated non-linearity and its large computational cost. These three aspects have an impact on the overall time of the design process. To overcome these challenges, this paper aims to develop a method for transonic aerodynamic design with dimensionality reduction and multifidelity techniques. Design/methodology/approach The developed methodology is used for the optimisation of an installed civil ultra-high bypass ratio aero-engine nacelle. As such, the effects of airframe-engine integration are considered during the optimisation routine. The active subspace method is applied to reduce the dimensionality of the problem from 32 to 2 design variables with a database compiled with Euler computational fluid dynamics (CFD) calculations. In the reduced dimensional space, a co-Kriging model is built to combine Euler lower-fidelity and Reynolds-averaged Navier stokes higher-fidelity CFD evaluations. Findings Relative to a baseline aero-engine nacelle derived from an isolated optimisation process, the proposed method yielded a non-axisymmetric nacelle configuration with an increment in net vehicle force of 0.65% of the nominal standard net thrust. Originality/value This work investigates the viability of CFD optimisation through a combination of dimensionality reduction and multifidelity method and demonstrates that the developed methodology enables the optimisation of complex aerodynamic problems.

Deep-Learning for Flow-Field Prediction of 3D Non-Axisymmetric Aero-Engine Nacelles

“…The CFD data used to build low order models for flow-field prediction of 3D non-axisymmetric aero-engine nacelles is generated with the approach developed by Tejero et al [14,24,25]. A thorough description of this computational method was provided in the past [14,24,25], and as such, a brief overview is presented below. The nacelle shape is defined with a parametric definition using intuitive Class-Shape Transformation (iCST) [26,27].…”

Coupled propulsive and aerodynamic analysis of an installed ultra-high bypass ratio powerplant at high-speed and high-lift conditions

“…However, this increases the computational cost in comparison with other approaches to optimise the housing components in isolation [21,22]. Thus, advanced design techniques such as multi-fidelity methods and dimensionality reduction [9] should be explored to enable the concurrent optimisation of the powerplant integration and the engine cycle.…”

“…Nevertheless, the increase on the fan diameter will require a closer integration of the propulsion system within the airframe. This is expected to increase the aerodynamic coupling between the engine and the airframe [8,9]. Thus, to evaluate the aerodynamic performance of the integrated powerplant design a combined assessment of the airframe and powerplant aerodynamics is required.…”

“…Thus, there is a limited amount of information about the integration effects on the aerodynamic performance of the powerplant for UHBR engines. Tejero et al [9] and Sánchez-Moreno et al [14] analysed the nacelle aerodynamics for integrated powerplants under cruise conditions. Goulos et al [8] also carried out an evaluation of the impact of the engine installation position in the overall aerodynamics of the aircraft.…”

An efficient gradient-enhanced kriging modeling method assisted by fast kriging for high-dimension problems

“…Incorporating cheap auxiliary information has been demonstrated to be a promising strategy to alleviate the curse of dimensionality of kriging. Such cheap information usually refers to lower-fidelity data or inexpensive gradients (Tejero et al , 2022). In this paper, kriging assisted with cheap gradient toward breaking curse of dimensionality is concerned, for its extensive application in many fields such as numerical design optimization or modeling of complex simulation problem (Laurent et al , 2019; Li et al , 2020).…”