Combining Shaping and Flow Control for Aerodynamic Optimization

Abstract

Shape optimization and flow control have been extensively studied in the past to improve the performance of wings and blades. However, they are commonly applied as two separate disciplines, with their interactions being rarely studied. The present work aims to examine and identify the potential to explore the interactions between the two disciplines and is chiefly concerned about two related issues: 1) how much more performance improvement can be obtained by combining the shape optimization with the flow control optimization, and 2) the effects of the sequencing of the disciplines when the optimizations are combined. To address these issues, five optimization approaches have been studied, representing the shaping only, the flow control optimization only, and three different sequencings of the disciplines in the combined optimizations. All the optimization approaches are applied consistently by using the same optimization system developed in this research. The system uses the Kriging surrogate method as the optimization algorithm, and it incorporates a standard computational fluid dynamics solver for which the validity and numerical sensitivities have been assessed. The developed methodology is applied to two optimization cases of practical interest: a compressor blade and a circulation controlled airfoil. The results of both cases show that, when shaping and flow control optimizations are combined, the performance enhancement is considerably higher than when the two disciplines are applied separately. Furthermore, in the combined optimizations for the airfoil, it is observed that the concurrent optimization leads to the highest performance, whereas the sequential optimizations tend to be "stuck" at less optimal solutions. Nomenclaturechord length in the x direction c 0 = chord length of the baseline airfoil L = lift Ma = Mach number _ m = mass flow rate N s = number of samples N v = number of design variables P j = power required by blade flow control p = static pressure p 0 = total pressure Re = Reynolds number t = blade cascade pitch length V = velocity magnitude x co;cc = chordwise position of the start of a Coanda surface sector x s;cc = chordwise position of the slot for circulation control x s1 = chordwise position of the blowing slot on the blade x s2 = chordwise position of the suction slot on the blade β = blade inlet flow angle β max = maximum operating inlet flow angle γ = blade stagger angle δ = direction normal to the blade chord η = efficiency ξ = direction along the blade chord Subscripts d = condition at the design inlet flow angle eq= equivalent quantity j = jet parameter s = slot parameter 0 = stagnation/baseline parameter 1 = condition of inlet-mass-averaged (blade), freestream (airfoil), or blowing (slot) 2 = condition of outlet-mass-averaged (blade) or suction (slot)

Turbine blade tip aero-thermal management: Some recent advances and research outlook

“…The strong coolant-base flow interaction in the tip region would suggest that cooling injection can be to some extent considered as a means of flow control. It has been shown that combining flow control and geometry shaping would provide extra performance gain than either shaping or flow control respectively (Zhang and He, 2015).…”

Interpolation of Turbulent Boundary Layer Profiles Measured in Flight Using Response Surface Methodology

“…The RSM has been applied to aerodynamic optimization studies and data fitting to reduce errors in the data acquired in flight conditions [24][25][26][27][28][29]. Rosenbaum et al [24] and Sevant et al [25] applied the RSM to design the wing geometry of aircraft.…”

“…Rosenbaum et al [24] and Sevant et al [25] applied the RSM to design the wing geometry of aircraft. Zhang et al [26] applied the RSM to a coupling problem between the shape optimization and flow control in order to improve the performance of the wings and blades. Papila et al [27] applied the RSM to reduce both noise and modeling errors for designing a high-speed civil transport wing structure, and successfully demonstrated reduced error inherent in the modeling and optimizing the wing geometry and structure [27].…”