Computational analysis of low mass moment of inertia flying wing

被引:0
|
作者
Khushbash, Sara [1 ]
Javed, Ali [1 ]
Shams, Taimur Ali [1 ]
机构
[1] Natl Univ Sci & Technol, Islamabad, Pakistan
关键词
Micro air vehicles; computational fluid dynamic; XFLR; Inflation layer; MICRO-AIR-VEHICLE;
D O I
10.1109/IBCAST51254.2021.9393181
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The primary goal in development of MAVs is to develop a flying body that weigh as less as 90 grams, with a span of 15cm [1]. Since it is challenging to meet these design characteristics of a MAVs with current technology, there has been a lot of research going on in this direction. This paper is also a continuation of the same quest. In this research a delta wing micro air vehicle was analyzed at low sub sonic speed at different angles of attack for determining lift, drag force, stall angle and L/D ratio. CFD and XFLR have been used as computational tools. For CFD analysis, a far field technique was used to calculate the lift and drag coefficients. Rectangular domain was created around the body with a size 10 times greater than body so that flow behavior can be captured accurately. Inflation layers were created around the body to capture boundary layer. Fine mesh was created at the regions of higher gradients. In fluent flow was analyzed using two turbulent models (SA and k-epsilon). A potential flow solver (XFLR) was also used to compare results from RANS methodology with vortex lattice method (VLM). Validation of computational results was carried out by comparing with earlier research concerning wind tunnel testing of MAV [2] at 100,000 Re. This research will help the aero dynamists to understand the complete procedure of computational and XFLR analysis of MAVs. It was found that XFLR predicts higher values of lift coefficient and lower values of drag coefficient at all angles of attack. Consequently, lift to drag ratio as predicted by XFLR was found greater than RANS models. Further no appreciable difference was noticed between lift and drag characteristics predicted by SA and k-epsilon models.
引用
收藏
页码:204 / 211
页数:8
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