Applications and Mechanics of Pressure Adaptive Honeycomb

Author(s):  
Roelof Vos ◽  
Ron Barrett

One of the most compliant structures in aerospace applications that does not suffer from certification constraints is plain honeycomb. It is widely used in primary and secondary structure of FAR 23/25 certified aircraft. In this research, the compliant nature of this material is being exploited by inserting pouches in each of the honeycomb cells. Pressurizing these pouches results in a stiffening of the overall structure. By having an external (spring) force act on the honeycomb structure, this variable stiffness results in an overall deformation of the honeycomb. Strains in excess of 50% can be achieved through this mechanism without encountering the material (yield) limits. It can be shown that based on the maximum pressure that can be extracted from the High-Pressure Compressor in a typical jet engine, the energy density of pressure adaptive honeycomb is on the par with that of shape memory alloy, while exhibiting strains that are an order of magnitude larger at a transfer efficiency that is close to 1. The paper discusses the mechanics of pressure adaptive honeycomb and describes a simple reduced order model that can be used to simplify the geometric model in a finite element environment. The theory that underpins this reduced order model is shown to correlate well to experimental tests. In addition, a proof-of-concept application is presented where pressure-adaptive honeycomb is integrated over the aft 35% of a wing section. It is demonstrated that camber variations in excess of 5% can be generated by a pressure differential of 40kPa. Results of subsequent wind tunnel test show an increase in lift coefficient of 0.3 at a wind speed of 45kts across an angle of attack ranging from −6° to +20°.

Author(s):  
T. Rahman ◽  
S. T. IJsselmuiden ◽  
M. M. Abdalla ◽  
E. L. Jansen

In earlier research the authors optimized variable stiffness panels for maximum buckling load, using lamination parameters. The aim of the present research is to analyze those optimized panels in the postbuckling regime so that further improvement can be achieved in the future with respect to its postbuckling performance. Because the incremental-iterative nonlinear analysis in the postbuckling regime is not feasible within an optimization loop a finite element based perturbation method (Koiter type) is used to compute postbuckling coefficients, which are in turn used to make a quick estimate of the postbuckling stiffness of the panel and to establish a reduced order model. The proposed perturbation method has been implemented in a general purpose finite element code. In the present work the postbuckling analysis of variable stiffness panels carried out using the reduced order model is presented and the potential of the approach for incorporation within the optimization process is demonstrated.


2011 ◽  
Vol 11 (04) ◽  
pp. 735-753 ◽  
Author(s):  
T. RAHMAN ◽  
S. T. IJSSELMUIDEN ◽  
M. M. ABDALLA ◽  
E. L. JANSEN

Modern fiber placement machines allow laminates with spatially varying stiffness properties to be manufactured. In earlier research, the authors optimized variable stiffness plates for maximum buckling load, demonstrating significant improvements in load-carrying capacity. In aerospace applications, panel structures are often permitted to enter the postbuckling regime during service. It is, therefore, not only important to understand their postbuckling behavior, but also to develop fast analysis methods that can subsequently be used in a design optimization framework. The aim of the present research is to study the postbuckling behavior of the optimized plates using a perturbation method that has been developed earlier within a general-purpose finite element environment. The perturbation approach is used to compute postbuckling coefficients, which are used to make a quick estimate of the postbuckling stiffness of the panel and to establish a reduced-order model. In the present work, the postbuckling analysis of variable stiffness plates is carried out using the reduced-order model, and the potential of the approach for incorporation within the optimization process is demonstrated.


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