Neural Network based Aerodynamic Modeling of Cascade Fin and Grid Fin Control Surfaces

Neural Network based Aerodynamic Modeling

Authors

  • Manish Tripathi Defense Institute of Advanced Technology
  • Anshuman Misra Vellore Institute Technology
  • Ajay Misra Defense Institute of Advanced Technology

Keywords:

Grid fins, cascade fins, neural network, lift, drag, stall angle, aerodynamics

Abstract

Unconventional control surfaces called grid fins consisting of intersecting small chord planar surfaces supported by an outer frame have been used on numerous missiles and bombs. Since their inception in the 1950’s, these fins have been a topic of intense research world wide owing to their high angles of attack and wide mach number regime utility, reduced hinge moments and high strength to weight ratio. Cascade fins are similar to grid fins except removal of
cross members. This paper ponders upon a new thrust area in the area of aerodynamic modeling called neural network based aerodynamic modeling using . In this paper, aerodynamic modeling using feed forward neural network is proposed for lattice and cascade fin configurations to estimate the force and moment coefficients. Suitable neural model is identified to establish mapping between motion or control variables and the lift coefficient. It is observed that due to complex aerodynamics associated with lattice and cascade fins, especially at high angles of attack, the modeling based on neural methods appears to be having a distinct advantage. Such models validated using limited wind tunnel tests can be extremely useful in enhancing the aerodynamic data base and time consumption can be greatly reduced. The proposed approach has been validated by applying it on wind tunnel data of grid and cascade fins.

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Published

2018-04-01

How to Cite

[1]
Manish Tripathi, Anshuman Misra, and Ajay Misra, “Neural Network based Aerodynamic Modeling of Cascade Fin and Grid Fin Control Surfaces: Neural Network based Aerodynamic Modeling”, TEMSJ, vol. 1, no. 4, Apr. 2018.

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