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Aircraft Aerodynamic Design: Geometry And Optim... ##TOP##

Optimal aircraft design is impossible without a parametric representation of the geometry of the airframe. We need a mathematical model equipped with a set of controls, or design variables, which generates different candidate airframe shapes in response to changes in the values of these variables. This model's objectives are to be flexible and concise, and capable of yielding a wide range of shapes with a minimum number of design variables. Moreover, the process of converting these variables into aircraft geometries must be robust. Alas, flexibility, conciseness and robustness can seldom be achieved simultaneously.

Aircraft Aerodynamic Design: Geometry and Optim...

Lately, commercial aviation has been moving towards the reduction of environmental impact and direct operating costs, due to the rapid increase in aircraft demand and air traffic congestion predicted for the next years. Several researchers have been abandoning the fully exploited conventional configuration and exploring novel arrangements, such as flying wings and blended wing bodies. The Flying V concept, proposed at TU Berlin in collaboration with Airbus GmbH, represents the focus of this research project, since the preliminary analyses have estimated remarkable aerodynamic benefits and weight savings. It is a V-shape flying wing with two cylindrical pressurised cabins placed in the wing leading edge and engines over the trailing edge; elevons provide longitudinal control and vertical tails double as winglets. The primary research goal is the aerodynamic design of the Flying V aircraft to assess whether this concept has better aerodynamic performances than the reference conventional configuration during cruise. The design philosophy selected for this project consists of a multi-fidelity design space exploration followed by two different design paths: dual step optimization, where planform and airfoil variables are subsequently varied, and single step optimization. Athena Vortex Lattice is used to rapidly investigate the feasible design space, whereas the Stanford University Unstructured code in the Euler mode is adopted for an accurate wave and vortex-induced drag estimation. The profile drag is computed by a separate empirical module. The three-dimensional geometry is automatically generated within the ParaPy framework (a Knowledge Based Engineering environment) according to a multi-level parametrization: the wing planform shape is parametrized with 10 variables, the profiles with 43 parameters, and the winglets are defined by 3 additional variables. Subsequently, the unstructured volume grid is produced by the Salome platform wrapped in ParaPy and then fed into the aerodynamic solver. The aerodynamic design is performed at one single cruise condition: the Mach number is equal to 0.85, the lift coefficient to 0.26, and the altitude to 13,000 m. The baseline configuration is progressively improved: the wave and vortex induced drag components are reduced, the wetted area is slightly decreased and the pitching moment coefficient about the reference centre of gravity location is almost null. The maximum lift to drag ratio of the single step optimized configuration is 23.7 at the cruise point: this value confirms the estimation of 25 of the conceptual phase. A 12% reduction in subsonic drag is achieved, with the desired pitching moment. The Flying V is then compared to the NASA Common Research Model, a conventional configuration benchmark, by using the same solver and a similar mesh refinement. The maximum lift to drag ratio of the NASA Common Research Model is 18.9, hence the Flying V is 25% aerodynamically more efficient at the design cruise condition.

Koreanschi et al. used Xfoil for the evaluation of the base airfoil performance in their study that presented numerical optimization and experimental wind tunnel testing of a morphing wing tip equipped with an adaptable upper surface, and a rigid aileron [4]. Gabor et al. used Xfoil in the high angle of attack optimization of a morphing airfoil as a function evaluator [5]. Della Vecchia et al. investigated the effect of a morphing trailing edge to the performance of a high-altitude long-endurance (HALE) aircraft. The variation of the lift coefficient and the profile drag coefficient of the airfoils with trailing edge deflection was obtained with Xfoil [6]. Magrini and Benini investigated the aerodynamic optimization of a morphing leading-edge airfoil. The optimization problem was solved by using the transition SST model, Spalart-Allmaras model, and Xfoil [7]. Liu et al. studied the optimization of the airfoil at an ultra-low Reynolds number for nanorotor performance [8]. In the study, Xfoil results were compared with the results of a two-dimensional incompressible Navier-Stokes solver in which the artificial compressibility method was utilized to deal with incompressible flow. In this validation study, the National Advisory Committee for Aeronautics (NACA) 0006 airfoil was used and was 6000. Xfoil captured and values up to stall correctly. Silvestre et al. coupled the blade element momentum theory with Xfoil and developed a new propeller design code named as JBLADE [9].

The maximization of is defined in equation (24). The upper limit of the at the maximum condition is 25 whereas the lower limit is -8. The speed at the maximum condition is also defined as a design variable so that the aerodynamic design tool calculates the aerodynamic forces for the appropriate . Equation (27) is defined to satisfy the level flight condition at the maximum condition. The equations between equations (28) and (33) define the upper and the lower limit of the airfoil shape and the wing planform geometry design variables. The second optimization problem is obtained by adding three functional inequality constraints to the first optimization problem as follows:

These Rhino/Python codes are included in a package named AirCONICS (Aircraft CONfiguration through Integrated Cross-disciplinary Scripting), an aircraft geometry toolbox built upon the principles described in the book. In order to run the Rhino/Python codes referred to in the book (and much more), follow these steps to install AirCONICS and verify your installation:

Modern aerospace engineering design synthesis relies upon two key ingredients: a flexible, parametric description of the object being designed e.g., the external surface geometry of an aircraft, and a measure of merit capable of evaluating the performance of each candidate geometry. A performance optimization process then searches the broad range of geometry parameter combinations and, upon computing the measure-of-merit corresponding to each, it converges towards the best design. Over the last decade, András Sóbester and Alexander Forrester, within the Faculty of Engineering and the Environment at the University of Southampton, have developed and honed a range of software tools to assist with this design process.

To optimize the aircraft geometry, use the problem-based approach. Start by defining problem constants and the optimization variables using the helper function initializeAircraft, which is included with this example. Organize these variables into six structures: aircraft, wing, hTail, vTail, fuselage, and payload. See a list of the optimization variables and their physical representation below.

To compute these derivatives, the addStability function calculates all aerodynamic coefficients and the mass moment of inertia matrix for the aircraft. With this information, use the staticStability method of the Aero.FixedWing object to obtain the ten stability derivatives.

Abstract:For a wind turbine to extract as much energy as possible from the wind, blade geometry optimization to maximize the aerodynamic performance is important. Blade design optimization includes linearizing the blade chord and twist distribution for practical manufacturing. As blade linearization changes the blade geometry, it also affects the aerodynamic performance and load characteristics of the wind turbine rotor. Therefore, it is necessary to understand the effects of the design parameters used in linearization. In this study, the effects of these parameters on the aerodynamic performance of a wind turbine blade were examined. In addition, an optimization algorithm for linearization and an objective function that applies multiple tip speed ratios to optimize the aerodynamic efficiency were developed. The analysis revealed that increasing the chord length and chord profile slope improves the aerodynamic efficiency at low wind speeds but lowers it at high wind speeds, and that the twist profile mainly affects the behaviour at low wind speeds, while its effect on the aerodynamic performance at high wind speeds is not significant. When the blade geometry was optimized by applying the linearization parameter ranges obtained from the analysis, blade geometry with improved aerodynamic efficiency at all wind speeds below the rated wind speed was derived.Keywords: design optimization; wind turbine blade; blade geometry linearization; tip speed ratio; simulated annealing algorithm

Pat Piperni recently joined the engineering faculty at Clarkson University after working in the aerospace industry for 30 years. Prior to joining Clarkson, he headed the Multidisciplinary Design Optimization (MDO) initiative at Bombardier Aerospace, where his responsibilities included leading the development of a company-wide MDO capability and overseeing its application to aircraft design projects. Prior to that, he led the High-Speed Aerodynamics Group in Bombardier's Advanced Aerodynamics Department. In that role he was responsible for the high-speed aerodynamic design of several Bombardier aircraft currently in service, including the C Series commercial jet, while also being involved in research and development in high-fidelity multidisciplinary optimization and computational fluid dynamics. He is an Associate Fellow of the Canadian Aeronautics and Space Institute (CASI), a member of the CASI Aircraft Design & Development Committee, and a member of the AIAA MDO Technical Committee. 041b061a72

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