Browsing by Author "Heidebrecht, Alexander"

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    Artificial neural network for preliminary design and optimisation of civil aero-engine nacelles
    (Cambridge University Press, 2024-04-29) Tejero, Fernando; MacManus, David; Heidebrecht, Alexander; Sheaf, Christopher T.
    Within the context of preliminary aerodynamic design with low order models, the methods have to meet requirements for rapid evaluations, accuracy and sometimes large design space bounds. This can be further compounded by the need to use geometric and aerodynamic degrees of freedom to build generalised models with enough flexibility across the design space. For transonic applications, this can be challenging due to the non-linearity of these flow regimes. This paper presents a nacelle design method with an artificial neural network (ANN) for preliminary aerodynamic design. The ANN uses six intuitive nacelle geometric design variables and the two key aerodynamic properties of Mach number and massflow capture ratio. The method was initially validated with an independent dataset in which the prediction error for the nacelle drag was 2.9% across the bounds of the metamodel. The ANN was also used for multi-point, multi-objective optimisation studies. Relative to computationally expensive CFD-based optimisations, it is demonstrated that the surrogate-based approach with ANN identifies similar nacelle shapes and drag changes across a design space that covers conventional and future civil aero-engine nacelles. The proposed method is an enabling and fast approach for preliminary nacelle design studies.
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    An automated approach for the aerodynamic design of close-coupled propulsion/airframe configurations
    (Council of European Aerospace Societies (CEAS), 2020-02-28) Matesanz García, Jesús; Christie, Robert; Tejero, Fernando; MacManus, David G.; Heidebrecht, Alexander
    Reducing aircraft emissions is a key element in mitigating the environmental impact of aviation. Within this context, different novel aircraft propulsion configurations have been proposed. A common feature of many of these novel configurations is the closer integration of the propulsive system and the aircraft airframe with an expected increase of the aerodynamic coupling. Therefore, is necessary to assess the performance of the aerodynamic installation of the propulsive system of these configurations with a systematic approach. A systematic and automated methodology for the design and performance evaluation of embedded propulsion systems is defined. This methodology is demonstrated with a Boundary Layer Ingestion propulsive fuselage concept. This approach covers the geometry design of the selected configuration, an automatic aerodynamic numerical computation and a novel performance evaluation for the design. A Design Space Exploration was performed to characterize the relative importance of the individual parameters of the geometry and their correlation with the key performance metrics. Finally, a multi-objective optimization was carried to demonstrate the capabilities of this approach.
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    Parametric geometry and CFD process for turbofan nacelles
    (American Society of Mechanical Engineers, 2016-09-20) Heidebrecht, Alexander; Stańkowski, Tomasz; MacManus, David G.
    A parametric geometry definition for a generic turbofan nacelle was developed for use in preliminary design, based on Class-Shape Transformation curves. This takes as input a set of six intuitive variables which describe the main dimensions of a nacelle. This set is the same set of inputs as required by a preliminary nacelle design method to which the aerodynamic properties of resulting shapes were compared. An automated computational fluid simulation process was developed and implemented which generates meshes and quickly conducts an analysis of the resulting nacelle shapes using a commercial code. Several geometries were generated and analysed using this process to show whether the aerodynamic properties of the generated shapes are in line with the expected performance of a fan cowl of equal dimensions. It was found that the aerodynamic performance of the parametric fan cowls significantly exceeds predictions from an established preliminary fan cowl design method and is very close in performance to existing designs. The drag of an equivalent parametric fan cowl can therefore be used as a predictor of nacelle performance with greater accuracy than established preliminary design methods. It is therefore suited as a tool to develop improved preliminary design methods, and for studies of the design space for preliminary nacelle design.
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    Surrogate model of complex non-linear data for preliminary nacelle design
    (Elsevier, 2018-10-03) Heidebrecht, Alexander; MacManus, David G.
    Most response surface methods typically work on isotropically sampled data to predict a single variable and fitted with the aim of minimizing overall error. This study develops a metamodel for application in preliminary design of aircraft engine nacelles which is fitted to full-factorial data on two of the eight independent variables, and a Latin hypercube sampling on the other six. The specific set of accuracy requirements for the key nacelle aerodynamic performance metrics demand faithful reproduction of parts of the data to allow accurate prediction of gradients of the dependent variable, but permit less accuracy on other parts. The model is used to predict not just the independent variable but also its derivatives, and the Mach number, an independent variable, at which a certain condition is met. A simple Gaussian process model is shown to be unsuitable for this task. The new response surface method meets the requirements by normalizing the input data to exploit self-similarities in the data. It then decomposes the input data to interpolate orthogonal aerodynamic properties of nacelles independently of each other, and uses a set of filters and transformations to focus accuracy on predictions at relevant operating conditions. The new method meets all the requirements and presents a marked improvement over published preliminary nacelle design methods.