An aerothermodynamic design optimization framework for hypersonic vehicles

In the aviation field great interest is growing in passengers transportation at hypersonic speed. This requires, however, careful study of the enabling technologies necessary for the optimal design of hypersonic vehicles. In this framework, the present work reports on a highly integrated design environment that has been developed in order to provide an optimization loop for vehicle aerothermodynamic design. It includes modules for geometrical parametrization, automated data transfer between tools, automated execution of computational analysis codes, and design optimization methods. This optimization environment is exploited for the aerodynamic design of an unmanned hypersonic cruiser flying at M∞=8 and 30 km altitude. The original contribution of this work is mainly found in the capability of the developed optimization environment of working simultaneously on shape and topology of the aircraft. The results reported and discussed highlight interesting design capabilities, and promise extension to more challenging and realistic integrated aerothermodynamic design problems

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Searching for improvement

Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents

Optimized Placement of Onshore Wind Farms Considering Topography

As the scale of onshore wind farms are increasing, the influence of wake behavior on power production becomes increasingly significant. Wind turbines sittings in onshore wind farms should take terrain into consideration including height change and slope curvature. However, optimized wind turbine (WT) placement for onshore wind farms considering both topographic amplitude and wake interaction is realistic. In this paper, an approach for optimized placement of onshore wind farms considering the topography as well as the wake effect is proposed. Based on minimizing the levelized production cost (LPC), the placement of WTs was optimized considering topography and the effect of this on WTs interactions. The results indicated that the proposed method was effective for finding the optimized layout for uneven onshore wind farms. The optimization method is applicable for optimized placement of onshore wind farms and can be extended to different topographic conditions

OPTIMIZATION OF BLOWING AND SUCTION CONTROL ON NACA0012 AIRFOIL USING GENETIC ALGORITHM WITH DIVERSITY CONTROL

Active control of the flow over an airfoil is an area of heightened interest in the aerospace community. Previous research on flow control design processes heavily depended on trial and error and the designers knowledge and intuition. Such an approach cannot always meet the growing demands of higher design quality in less time. Successful application of computational fluid dynamics (CFD) to this kind of control problem critically depends on an efficient searching algorithm for design optimization. CFD in conjunction with Genetic Algorithms (GA) potentially offers an efficient and robust optimization method and is a promising solution for current flow control designs. But the traditional binary GA and its operators need to be transformed or re-defined to meet the requirements of real world engineering problems. Current research has combined different existing GA techniques and proposed a realcoded Explicit Adaptive Range Normal Distribution (EARND) genetic algorithm with diversity control to solve the convergence problems. First, a traditional binary-coded GA is replaced by a real-coded algorithm in which the corresponding design variables are encoded into a vector of real numbers that is conceptually closest to the real design space. Second, to address the convergence speed problem, an additional normal distribution scheme is added into the basic GA in order to monitor the global optimization process; meanwhile, design parameters boundaries are explicitly updated to eliminate unnecessary evaluations (computation) in un-promising areas to balance the workload between the global and local searching process. Third, during the initial 20% evolution (search process), the diversity of the individuals within each generation are controlled by a formula in order to conquer the problem of preliminary convergence to the local optimum. In order to better understand the two-jet control optimization results and process, at first, a single jet with a width of 2.5% the chord length is placed on a NACA 0012 airfoils upper surface simulating the blowing and suction control under Re=500,000 and angle of attack 18 degree. Nearly 300 numerical simulations are conducted over a range of parameters (jet location, amplitude and angle). The physical mechanisms that govern suction and blowing flow control are determined and analyzed, and the critical values of suction and blowing locations, amplitudes, and angles are discussed. Moreover, based on the results of single suction/blowing jet control on a NACA 0012 airfoil, the design parameters of a two-jet system are proposed. Our proposed algorithm is built on top of the CFD code, guiding the movement of two jets along the airfoils upper surface. The reasonable optimum control values are determined within the control parameter range. The current study of Genetic Algorithms on airfoil flow control has been demonstrated to be a successful optimization application