This paper introduces an approach for parameterizing airfoil geometries using a Variational Auto encoder (VAE)with a focus on achieving a low-dimensional and interpretable model. The primary focus is to facilitate efficient use in design optimization environments by capturing essential airfoil features in a minimal number of latent dimensions. To address the black-box nature of VAEs and enhance interpretability, a correlation analysis is performed to uncover the relationships between the airfoil properties and these inferred latent dimensions. Key to this research is the incorporation of both geometric and aerodynamic properties in this analysis, enabling the generation of air foils with desired aerodynamic characteristics through manual tuning of the latent vector by a designer. The method is evaluated using the extensive UIUC airfoil database, which includes a diverse range of airfoil categories. The VAE is trained on airfoil surface coordinate points, and the generated output geometries are refined using a composite Bezier curve to smooth out local imperfections. Results demonstrate that theVAE can successfully extract and parameterize key airfoil features using a limited number of interpretable latent parameters. These parameters show clear correlations with geometric and aerodynamic airfoil properties, providing a practical and understandable parameterization model that facilitates the intuitive generation of new airfoil designs through smooth interpolation of the training data.

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This paper introduces an approach for parameterizing airfoil geometries using a Variational Autoencoder (VAE) with a focus on achieving a low-dimensional and interpretable model. The primary focus is to facilitate efficient use in design optimization environments by capturing essential airfoil features in a minimal number of latent dimensions. To address the black-box nature of VAEs and enhance interpretability, a correlation analysis is performed to uncover the relationships between the airfoil properties and these inferred latent dimensions. Key to this research is the incorporation of both geometric and aerodynamic properties in this analysis, enabling the generation of airfoils with desired aerodynamic characteristics through manual tuning of the latent vector by a designer. The method is evaluated using the extensive UIUC airfoil database, which includes a diverse range of airfoil categories. The VAE is trained on airfoil surface coordinate points, and the generated output geometries are refined using a composite Bezier curve to smooth out local imperfections. Results demonstrate that the VAE can successfully extract and parameterize key airfoil features using a limited number of interpretable latent parameters. These parameters show clear correlations with geometric and aerodynamic airfoil properties, providing a practical and understandable parameterization model that facilitates the intuitive generation of new airfoil designs through smooth interpolation of the training data. @en

A design can only be as good as its mathematical representation. In engineering design optimization, the chosen method of parameterization can have significant impact on the outcomes. This paper introduces a novel methodology for airfoil design parameterization utilizing variational autoencoders (VAEs), a class of neural networks known for their proficiency in reducing dimensionality. However, a significant challenge with VAEs is the interpretability of the encoded latent space. This work aims to address this issue by creating a network with an interpretable latent space, yielding parameters that are understandable to humans. The effectiveness of this approach is evaluated using the comprehensive UIUC airfoil database, which offers a diverse range of airfoil shapes for analysis. We show that a VAE can successfully extract key features of airfoil geometries and parameterize them using six parameters, which show a clear correlation with airfoil properties in a way that remains understandable by the designer. Additionally, it smoothly interpolates the data points, allowing the generation of new airfoils and thus offering a practical and interpretable airfoil parameterization.@en

The present paper shows the main characteristic of a numerical simulation tool, developed in the framework of the European H2020 project MAHEPA, to estimate optimal flight performance of a generic aircraft featuring a Hybrid powertrain. The purpose of the study is to determine optimal flight trajectories together with optimal power controls when a powertrain with multi-energy or multi-power sources (as the case of a generic hybrid one) is considered. For this purpose a complete new software has been developed, which is composed by three main parts: a mission performance “analyser” where the system dynamics of the problem is determined and it solves the aircraft Equation of Motion; a powertrain simulator that determines the operating conditions of the powertrain components and it ultimately computes the consumption of each energy source; a numerical algorithm that optimizes the aircraft control variables to determine both the optimum flight trajectory and the power management according to a certain objective functions and a variety of constraints. Different study cases are discussed when two existing flying hybrid aircraft are considered: a Hybrid-Electric (HE) Pipistrel Panthera aircraft and a Fuel-Cell hybrid (FCH) Pipistrel HY4. Results are presented also depending on the capability to simulate the entire mission as a whole (Single-Phase approach) as well as through the distinction of different flight segments as in the case of the Multi-Phase approach. In addition, two different resolution algorithms are tested in order to evaluate what are the aspects that might dictate the selection of the most suitable one.@en

The paper focusses on a methodology developed to determine both mission parameters and power management for a generic Hybrid aircraft in such a way its energy consumption and/or flight time can be minimised. In fact the system dynamics of a Hybrid aircraft has generally one or more degree of freedom, when compared to a conventional one, related to the number of energy (or power) sources present on board with the consequences that classic solutions of mission analysis (e.g. climb rate that minimise the burn fuel) can be no longer valid. The approach adopted in this study is to couple mission perfomance analysis to optimal control methods so that the core mathematical problem is reduced to an optimiziation problem that aims at finding optimal values for certain control variables in such a way an arbitrary objective function can be minimized in presence of a certain set of constraints deriving from limitations of both flight envelope and propulsion operating conditions. To fully characterize the problem, the optimal contol algorithms are coupled to a simulator developed in house to model the operating characterisitic of a generic Hybrid powertrain. The simulator, based on Objected Oriented Paradigm, consists of modules that are used to describe the operation conditions (in terms of efficiency map and delivered Power/Energy) of each powertrain component. Subsequently, those modules can be assembled together to model either a Serial or a Parallel architecture, with several possible power sources: batteries, Combustion Engine, Fuel Cell. Two optimal control approaches (Single Phase and Multi-Phase) have been tested on a Hybrid Electric study case considering different objective functions, namely minimum energy, minimum fuel (when a Combustion Engine is considered) and minimum time. Prelimiary results show an agreement of both the Single-Phase and Multi-Phase approaches. In addition, both the power management and the mission profile differ greatly at varying the considered objective function.@en