[1] or Vesting and Bensow [2]. When it comes to assessing cavitation effects in propeller design, necessary to explore option (b), potential flow codes represent a ...
Computational Tool Development for Propeller Design Considering Cavitation Rickard E. Bensow* and Göran Bark* *
Dept. of Shipping and Marine Technology Chalmers University of Technology, 412 96 Gothenburg, Sweden E-mail: {rickard.bensow, bark}@chalmers.se, web page: http://www.chalmers .se/smt
ABSTRACT In recent years, there’s been an increased interest in high efficiency ship propulsion. Two paths to achieve this are to (a) to utilize interaction effects between propeller, hull and rudder in a system design, and (b) allow for a higher loaded blade with smaller blade area, which then leads to more cavitation on the propeller. The fast development in CFD the last decade has made the (a) option more and more feasible. Ship hull computations in self propelled conditions, using either a hybrid RANS/potential flow solver or a full RANS approach, is a standard feature in many commercial and in-house software today. The next step in this development is to use these tools in an automated optimization as is done in e.g. Han et al. [1] or Vesting and Bensow [2]. When it comes to assessing cavitation effects in propeller design, necessary to explore option (b), potential flow codes represent a quick and fairly reliable tool to determine the main influence of cavitation on thrust and efficiency around the design point. This includes to some extent also pressure pulses of the lowest harmonics. However, more detailed effects of cavitation, such as high frequency or broadband noise or erosion, are not possible to assess with any computational tools today. Recent results using LES [3,4] or Euler methods [5] indicate that these methods are capable of capturing much of the cavitation dynamics, but to expect high fidelity predictions of all aspects of cavitation nuisance in reasonable computational time is. Also experiments can only provide an incomplete picture to assist the designer; experience and knowledge is still a necessary tool. This paper will discuss the possibilities to combine the tools described above. The combination will then open up to assess both option (a) and (b) simultaneously through: • Improved design knowledge and guidelines, based on improved understanding of cavitation physics gained through detailed studies of experiments and LES results, • The application of these guidelines to cavitation predictions of a potential flow (or RANS) solution in order to assess the risk of e.g. erosion, and • Including this assessment in an automated optimization procedure using simulations of a self propelled ship. Efforts done at Chalmers relating to the first bullet, e.g. [3,4,6,7] will be summarized and the possibilities to use these in the second and third bullet, to some extent already exemplified in [2], will be highlighted.
REFERENCES [1] Han, K.-J., Larsson, L., and Regnström, B., Numerical optimization of the propeller behind a ship hull at full scale. 26th Symposium on Naval Hydrodynamics, Rome, Italy, (2006). [2] Vesting F. and Bensow R.E., Propeller Optimisation considering Sheet Cavitation in Hull Interaction, 2nd Int. Symposium on Marine Propulsors, Hamburg, Germany, (2011). [3] Bensow R.E. and Bark G., ”Implicit LES predictions of the cavitating flow on a propeller” J. Fluids Engng., 132(4), (2010). [4] Lu, N.-X., Bensow, R.E., and Bark, G., “LES of Unsteady Cavitation on the Delft Twisted Foil“, J. Hydrodynamics, Ser. B, Vol 22(5) [5] Schmidt, S.J., Thalhamer, M., Schnerr, G.H., Inertia Controlled Instability and Small Scale Structures of Sheet and Cloud Cavitation, CAV2009, Ann Arbor, MI, USA, (2009). [6] Bark, G., Grekula, M., Berchiche, N., and Bensow, R.E., On Some Physics to Consider in Numerical Simulation of Erosive Cavitation, CAV2009, Ann Arbor, MI, USA, (2009). [7] Bark and Grekula, HTA Analysis of high-speed video data for assessment of the risk of cavitation erosion, AMT09, Nantes, France, (2009).
