Browsing by Author "Sanchez Moreno, Francisco"

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    Aerodynamic optimisation of civil aero-engine nacelles by dimensionality reduction and multi-fidelity techniques
    (Unknown, 2022-03-30) Tejero, Fernando; MacManus, David G.; Hueso Rebassa, Josep; Sanchez Moreno, Francisco; Goulos, Ioannis; Sheaf, Christopher
    Aerodynamic shape optimisation is complex due to the high dimensionality of the problem, the associated nonlinearity 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 develops a method for transonic aerodynamic design with dimensionality reduction and multi-fidelity techniques. It 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 CFD calculations. In the reduced dimensional space, a co-Kriging model is built to combine Euler lower-fidelity and RANS higher-fidelity CFD evaluations. 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. This work demonstrates that the developed methodology enables the optimisation of complex aerodynamic problems.
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    ItemOpen Access
    Aerodynamic optimisation of civil aero-engine nacelles by dimensionality reduction and multi-fidelity techniques
    (Emerald, 2022-09-30) Tejero, Fernando; MacManus, David G.; Hueso Rebassa, Josep; Sanchez Moreno, Francisco; Goulos, Ioannis; Sheaf, Christopher
    Purpose - Aerodynamic shape optimisation is complex due to 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 develops a method for transonic aerodynamic design with dimensionality reduction and multi-fidelity 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 CFD calculations. In the reduced dimensional space, a co-Kriging model is built to combine Euler lower-fidelity and RANS 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 - This work investigates the viability of CFD optimisation through a combination of dimensionality reduction and multi-fidelity method, and demonstrates that the developed methodology enables the optimisation of complex aerodynamic problems.
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    ItemOpen Access
    Optimization of installed compact and robust nacelles using surrogate models
    (ICAS, 2022) Sanchez Moreno, Francisco; MacManus, David G.; Hueso Rebassa, Josep; Tejero, Fernando; Sheaf, Christopher T.
    The design and optimization of aero-engine nacelles in a configuration installed on the airframe may be an important consideration to realize the cycle benefits of new ultra-high bypass ratio aero-engines. However, this is typically a high-dimensional design problem and there is a need to reduce the associated computational costs. This work presents a method for aerodynamic nacelle optimization for an installed configuration and provides further knowledge about the characteristics of this design space. The methodology includes single fidelity surrogate models built with inviscid flow solutions. Gaussian process regression and artificial neural networks are tested as modelling techniques. Viscous computations are used to assess the optimized designs at cruise and off-design windmilling diversion condition. This approach yielded an optimal design with a reduction in fuel burn of about 0.56% relative to a design optimized in isolated configuration without considering the powerplant integration effects. The optimal design also met the robustness criteria in terms of limited flow separation at the windmilling diversion conditions.