Abstract

Experimental tests in wind tunnels have been traditionally employed as a fundamental tool to evaluate aerodynamic and aeroelastic effects due to wind action on civil engineering structures, such as bridges and slender buildings. In the last decades, due to the versatility presented by numerical methods to change physical as well as geometrical parameters, numerical simulation has become a very attractive tool. Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD) together with Fluid‐Structure Interaction (FSI) techniques are employed in aerodynamic and aeroelastic analysis in several engineering fields. Aerodynamic and aeroelastic behavior due to wind action on the Guama River Bridge, located at Pará State, Brazil, is first studied, taking into account experimental tests performed in the Wind Tunnel Joaquim Blessman of the Building Aerodynamic Laboratory, UFRGS. Numerical procedures are used to simulate experimental tests in order to determine aerodynamic and aeroelastic characteristics of the bridge, which is idealized by sectional models. Finally, an aeroelastic analysis of a flexible slender building is presented. Good results are obtained using numerical simulation, when compared with experimental tests.

Abstract

Shape optimization is a largely studied problem in aeronautics. It can be applied to many disciplines in this field, namely efficiency improvement of engine blades, noise reduction of engine nozzles, or reduction of the fuel consumption of aircraft. Optimization for general purposes is also of increasing interest in many other fields.

Traditionally, optimization procedures were based on deterministic methodologies as in Hamalainen et al (2000), where the optimum working point was fixed. However, not considering what happens in the vicinity of the defined working conditions can produce problems like loose of efficiency and performance. That is, in many cases, if the real working point differs from the original, even a little distance, efficiency is reduced considerably as pointed out in Huyse and Lewis (2001)

Non deterministic methodologies have been applied to many fields (Papadrakakis, Lagaros and Tsompanakis, 1998; Plevris, Lagaros and Papadrakakis, 2005). One of the most extended nondeterministic methodologies is the stochastic analysis. The time consuming calculations required on Computational Fluid Dynamics (CFD) has prevented an extensive application of the stochastic analysis to shape optimization. Stochastic analysis was firstly developed in structural mechanics, several years ago. Uncertainty quantification and variability studies can help to deal with intrinsic errors of the processes or methods. The result to consider for design optimization is no longer a point, but a range of values that defines the area where, in average, optimal output values are obtained. The optimal value could be worse than other optima, but considering its
vicinity, it is clearly the most robust regarding input variability.

Uncertainty quantification is a topic of increasing interest from the last few years. It provides several techniques to evaluate uncertainty input parameters and their effects on the outcomes. This research presents a methodology to integrate evolutionary algorithms and stochastic
analysis, in order to deal with uncertainty and to obtain robust solutions.

Abstract

Presents a numerical strategy for the aerodynamic analysis of large buildings, with an application to the simulation of the air flow within a telescope building. The finite element formulation is presented first, and then the methodology followed to obtain significant data from the calculations is described. The quality of the ventilation of the building is defined by the average residence times, and the feasibility of this ventilation by the actions created on the instruments and the general flow pattern.

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Boundary Layer Ingestion (BLI) is a promising concept that helps improving aircraft aeropropulsive performance. However, it remains difficult to bring together OAD (Overall Aircraft Design) process and high-fidelity tools due to the time of response of complex disciplinary tools. ONERA has thus developed a mixed fidelity approach inside its in-house OAD platform. The purpose is to mix conventional and robust OAD methods with high levels of fidelity from disciplinary tools such as CFD (Computational Fluid Dynamics), FEM (Finite Element Model) or CAA (Computational Aero Acoustics). This paper focuses on the integration of rapid CFD tools inside the OAD process, in order to assess BLI benefits. The resulting process is validated against reference and experimental cases when available, or high-fidelity RANS data elsewhere. The paper intends to present the tools and their validation process. These modules are applied to the common inlet concept, which aims at ingesting00% of the fuselage boundary layer. The results demonstrate the potential gain on the Power Saving Coefficient (up to 3% obtained with the L2 BLI module without resizing loop) but with non-negligible fan losses (up to 1.5%).

Abstract

During each aircraft program a vast amount of aerodynamics data has to be generated to judge performance, structural loads as well as handling qualities. Within the past years the usage of computational fluid dynamics has significantly increased providing accurate insights into aircraft behaviour at early design stages and therefore at least partially enabled the mitigation of costly design changes. However, fully relying on high fidelity aerodynamic data is still computational prohibitive. Hence, data-driven models have gained an increasing attention in recent years. These methods not only provide continuous models but also enable the inclusion of highly accurate aerodynamic results in time-critical environments. This paper aims at applying deep learning techniques to derive such models and compare them to state of the art reduced order modeling techniques. In particular, three deep learning methods, a Multilayer perceptron for distribution predictions, a Multi-layer perceptron for pointwise predictions and an Autoencoder coupled with an interpolation technique are compared to Proper Orthogonal Decomposition and Isomap with latent space interpolation. For all methods an efficient methodology to determine hyperparameters is outlined and applied. Results are presented for an Airbus provided XRF1 dataset which includes surface pressure distributions at various Mach numbers and angles of attack.

Abstract

The feasibility of laminar flow control technology for future wing is bound to the development of a leading edge high-lift system that complies with the requirements on smooth surfaces to enable maintaining the laminar boundary layer flow, such as a Krueger flap. Although in principle the aerodynamic performance of a Krueger flap is known, the unsteady behaviour of the flow during deployment and retraction is completely unknown. This is as even more important as during deployment the Krueger flap is exposed to highly unfavourable positions perpendicular to the flow. To mitigate the risk of unfavourable aircraft behaviour, it is therefore expected that a Krueger flap has to be deflected significantly fast and may trigger unsteady aerodynamic effects. The European H2020 project UHURA, running from September 2018 to August 2022, has been focusing on the unsteady flow behaviour around such high-lift system and will first time deliver a deeper understanding of critical flow features at this type of high-lift device during their deployment and retraction together with a validated numerical procedure for its simulation. UHURA performed detailed experimental measurements in several wind tunnels to obtain a unique data set for validation purposes of Computational Fluid Dynamics (CFD) software, including detailed flow measurements by Particle Image Velocimetry (PIV) and other optical measurement technologies.