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Drag force and drag coefficient
Published in Mohammad H. Sadraey, Aircraft Performance, 2017
This section deals with two types of struts: (1) Landing gear strut and (2) wing strut. Landing gear is often attached to the aircraft structure via a strut. In some general aviation (GA)/homebuilt aircraft, wings are attached through a few struts to support wing structure, that is, strut-braced wings (see Figure 3.14). Modern aircraft use advanced material for structure that is stronger, and there is no need for any strut to support their wings, that is, cantilever. In some aircraft (such as hang gliders), the cross section of the wing strut is a symmetrical airfoil in order to reduce the strut drag. In both cases, the strut produces an extra drag for aircraft.
Aerodynamic optimisation of a parametrised engine pylon on a mission path using the adjoint method
Published in International Journal of Computational Fluid Dynamics, 2019
Damien Guénot, François Gallard, Joël Brézillon, Yann Mérillac
FFD was proposed by Sederberg and Parry (1986): the idea is to surround the shape within a geometrical object such as a cube, deform the geometrical object in which the shape is embedded, and finally transform the shape. It has been successfully applied by Secco and Martins (2019) on a strut-braced wing: intersections among components are recomputed after every geometry update using triangulated surfaces. The main drawback of this approach is that the output is a deformed mesh, and not a deformed CAD, which is an issue for digital continuity in industry, where CAD is the standard for exchanging geometrical data. As it directly deforms the mesh, the technique is straightforward to use in a CFD optimisation context (Li et al. 2013; Skinner and Zare-Behtash 2018; Rusch, Siggel, and Becker 2018). In studies of Koc and Nakahashi (2005) and Mouton, Laurenceau, and Carrier (2007), dealing with wing/pylon/engine optimisation, Hicks–Henne functions are used. With this deformation process, linear combinations of localised deformations are added to the initial configurations. The main drawback of this parametrisation is that it has no physical meaning for the designers. Moreover, as mentioned by Banović et al. (2018), it only produces the optimal shape as a deformed mesh rather than a CAD surface.
Alternative jet fuels and climate geopolitics: What, why does it and who matters in the environmental policy-making process
Published in International Journal of Sustainable Transportation, 2022
Mónica Soria Baledón, Marcel Trudel, Nicolás Kosoy
Overall, there is general confidence in the dynamism and strong adaptive capacity of the aviation sector to successfully undertake the industry’s environmental commitments10 to address its climate impact. In the view of participants, this entails greater investments in alternative propulsion (e.g. electrical, hybrid, hydrogen fuel cells), aircraft design technologies (e.g. blended wing body, strut-braced wing), alternative jet fuels, infrastructure and operational improvements to meet the mid-century goal of 325 MtCO2 by 2050 (ATAG, 2012).
An efficient geometric constraint handling method for surrogate-based aerodynamic shape optimization
Published in Engineering Applications of Computational Fluid Mechanics, 2023
Kai Wang, Zhong-Hua Han, Ke-Shi Zhang, Wen-Ping Song
However, handling a large number of geometric constraints presents a big challenge to the surrogate-based aerodynamic shape optimization. First, it is not feasible to calculate the geometric constraint functions directly during the sub-optimization performed by a global optimization algorithm such as genetic algorithm within the framework of a SBO, in which these functions defined by massive surface meshes and parameterization methods such as free form deformation (FFD) are evaluated thousands of times for each updating cycle, the total cost of a number of cycles can be prohibitive. Second, the method of building surrogate models for all geometric constraints during the optimization will suffer from difficulties associated with the prohibitive computational cost caused by training the large number of hyper-parameters in a one by one manner. Finally, it is improper to replace all constraints with the most violated one such as the maximum thickness constraint, since the maximum thickness tmax may changes during the optimization, which leads the constraint function to be discontinuous, and may result in an increased number of iterations of the optimization and even premature convergence. At present, a large number of geometric constraints have been widely applied in gradient-based aerodynamic shape optimization of different aerodynamic configurations, such as the one-stage turbine (Xu et al., 2015), regional Jet (Bons et al., 2018), large civil aircraft wing (Lei et al., 2019; Kenway & Martins, 2016; Chen et al., 2016), strut-braced wing (Secco & Martins, 2019), 10 MW wind turbine (Madsen et al., 2019), blended-wing-body aircraft (Reist & Zingg, 2017; Lyu et al., 2017), as well as fairing (Brelje et al., 2020). Therefore, it is necessary to investigate an efficient geometric constraint handling method for SBO to solve ASO problems involving a large number of geometric constraint.