(in lingua inglese)

Ottimizzazione di una pala rotorica di un compressore transonico con lo scopo di aumentare rendimento isentropico e rapporto di compressione. Lo studio comprende due fasi: ottimizzazione del tip della pala e ottimizzazione dell'intera pala rotorica. Per la discretizzazione della geometria sono state utilizzate le curve di Bezier e per il processo di ottimizzazione si è deciso di utilizzare un algoritmo genetico. Il risultato dell'ottimizzazione ha portato un aumento dell'efficienza dello 0.7%.

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= 56.8'. [1] blading has a subsonic axial velocity it follows that the tangential component of velocity will be high, hence supersonic compressor blades are highly staggered, often by more than 60'to the axial direction' [1]. The deceleration of the relative velocity in the blade passage would normally be accomplished with one or more shockwaves. Therefore, supersonic blades have two features: a very small camber and a very low thickness (about 2 per cent of chord for the tip section of a transonic fan). The shock pattern is not only affected by the geometry of the blade but also very strongly by the inlet Mach number, the inlet flow direction and the back pressure behind the blade row. The Fig. 2.1 shows blades in linear cascade with an inlet Mach number just above unity. Two of the pictures are for the choked condition but with different back pressure. The flow pattern is simpler at the lower flow rate (higher incidence), with the shock at the leading edge that dominates and creates the majority of the pressure rise. The complexity of the flow field makes the aerodynamic design of transonic compressor rotors very hard. Some complex flow features are not still completely understood and consequently the optimization process remains hard.

P1 (3.21) The isentropic efficiency is: η = Tt2,is '' Tt1 Tt2 '' Tt1 (3.22) where Tt2,is is the isentropic total temperature at the outlet of the rotor, Tt1 is the total temperature at the inlet and Tt2 is the total temperature at the outlet. If the isentropic relationships between total temperature and total pressure have been considered: P2

P1 = Tt2

Tt1 γ γ''1 (3.23) the equation Eq. 3.22 becomes: η = P R γ''1 γ '' 1 Tt2 Tt1''1 (3.24) The values of the initial geometry are: P R = 1.5558 η = 0.8916 As the MATLAB GA is implemented to minimize the parameters chosen, hence, the ratios 1/P R and 1/η are considered as fitness functions of the optimization. If some error is generated during the optimization process (mesh error or simulation divergence), a penalty function is used. The ratios 1/P R and 1/η are assumed 1000.

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