A Shape Memory Alloy-Based Morphing Axial Fan Blade—Part II: Blade Shape and Computational Fluid Dynamics Analyses

The ability of a morphing blade to change its geometry according to the different operating conditions represents a challenging approach for the optimization of turbomachinery performance. In this paper, experimental and computational fluid dynamics (CFD) numerical analyses on a morphing blade for a heavy-duty automotive cooling axial fan are proposed. Starting from the experimental results proposed in the first part of this work, a morphing blade, made of shape memory alloy (SMA) strips embedded in a polymeric structure, was thoroughly tested. In order to assess the ability of the strips to reach a progressive and smooth shape changing evolution, several experiments were performed in a purpose-built wind tunnel. The morphing blade changed its shape as the strips were thermally activated by means of air stream flow. The bending deformation evolution with the increasing number of thermal cycles was evaluated by digital image analysis techniques. After the analyses in the wind tunnel, CFD numerical simulations of a partially shrouded fan composed of five morphing blades were performed in order to highlight the evolution of the fan performance according to air temperature conditions. In particular, the capability of the blade activation was evaluated by the comparison between the fan performance with nonactivated blades and with activated blades. The results show a progressive stabilization of the shape memory behavior after the first cycle. The blade deformation led to a significant improvement in the fan performance at a constant rotational velocity. The CFD numerical simulation points out the differences in the overall performance and of three-dimensional fluid dynamic behavior of the fan. This innovative concept is aimed at realizing a sensorless smart fan control, permitting (i) an energy saving that leads to fuel saving in the automotive application fields and (ii) an increase in engine life, thanks to a strong relationship between the engine thermal request and the cooling fan performance.

[1]  Tilo Pfeifer,et al.  Optical Methods for Dimensional Metrology in Production Engineering , 2002 .

[2]  A. Suman,et al.  Morphing blades with embedded SMA strips: An experimental investigation , 2015 .

[3]  M. Mertmann,et al.  Design and application of shape memory actuators , 2008 .

[4]  Paul M. Weaver,et al.  Review of morphing concepts and materials for wind turbine blade applications , 2013 .

[5]  Weihai Chen,et al.  Fast 3D modeling in complex environments using a single Kinect sensor , 2014 .

[6]  G. Sansoni,et al.  Combination of a Vision System and a Coordinate Measuring Machine for the Reverse Engineering of Freeform Surfaces , 2001 .

[7]  Zhao Changlu,et al.  A Simulation Study of an Advanced Thermal Management System for Heavy Duty Diesel Engines , 2012 .

[8]  Shaker A. Meguid,et al.  Shape morphing of aircraft wing: Status and challenges , 2010 .

[9]  Bengt Sundén,et al.  Vehicle Cooling Systems for Reducing Fuel Consumption and Carbon Dioxide: Literature Survey , 2010 .

[10]  Haji Hassan Masjuki,et al.  A review on air flow and coolant flow circuit in vehicles’ cooling system , 2012 .

[11]  C. Oliet,et al.  Parametric studies on automotive radiators , 2007 .

[12]  Shaolin Mao,et al.  Off-highway heavy-duty truck under-hood thermal analysis , 2010 .

[13]  Liang-Chia Chen,et al.  An integrated reverse engineering approach to reconstructing free-form surfaces , 1997 .

[14]  Inderjit Chopra,et al.  In-flight tracking of helicopter rotor blades using shape memory alloy actuators , 1999 .

[15]  Adolfo Senatore,et al.  High Performance Engine Warm-Up Thermo-Fluid-Dynamic Analysis , 2008 .

[16]  Michele Pinelli,et al.  A Shape Memory Alloy-Based Morphing Axial Fan Blade—Part I: Blade Structure Design and Functional Characterization , 2015 .

[17]  Rahizar Ramli,et al.  UNDERHOOD GEOMETRY MODIFICATION AND TRANSIENT COOLANT TEMPERATURE MODELLING FOR ROBUST COOLING NETWORKS , 2012 .

[18]  Cees Bil,et al.  Application of Smart Materials for Adaptive Airfoil Shape Control , 2009 .

[19]  Paul Galpin,et al.  Experimental and Computational Analysis of a Multistage Axial Compressor Including Stall Prediction by Steady and Transient CFD Methods , 2014 .

[20]  Thomas Hallqvist The Cooling Airflow of Heavy Trucks – a Parametric Study , 2008 .

[21]  Pedro Arias,et al.  Metrological evaluation of Microsoft Kinect and Asus Xtion sensors , 2013 .

[22]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[23]  Giovanna Sansoni,et al.  In-field performance of an optical digitizer for the reverse engineering of free-form surfaces , 2005 .

[24]  Gordon G. Parker,et al.  Design and Development of a Model Based Feedback Controlled Cooling System for Heavy Duty Diesel Truck Applications Using a Vehicle Engine Cooling System Simulation , 2001 .

[25]  Ibrahim Dincer,et al.  Computational Aerodynamic Study of Automotive Cooling Fan in Blocked Conditions , 2013 .

[26]  Kwonhue Choi,et al.  Active coolant control strategies in automotive engines , 2010 .

[27]  Robert D. Chalgren Thermal Comfort and Engine Warm-Up Optimization of a Low-Flow Advanced Thermal Management System , 2004 .