Abstract
Hyperbola fitting of test data is an extremely important process in turbulence level measurement test in wind tunnels. The solution of the overdetermined equations (SOE) method is often used to solve hyperbola fitting parameters to obtain turbulence level. However, due to unsteady flow characteristics, the SOE method often results in overfitting phenomena, which makes it impossible to solve turbulence level accurately. This paper proposes using the constrained least-squares (CLS) method to convert the problem of hyperbola fitting of test data into the inequality constrained optimization problem and then using the Lagrange programming neural network (LPNN) method to solve turbulence level iteratively. The stability of the LPNN method is analysed, and three sets of typical turbulence level measurement test data are processed using the LPNN method. The results verify the feasibility of applying the LPNN method to iteratively solve the turbulence level of wind tunnels.
Highlights
Wind tunnel test is the most effective method for aerodynamic research
The hot-wire response function derived by the changing overheat ratio method conforms to a hyperbolic relationship. erefore, the problem of solving turbulence level can be converted into the problem of solving hyperbola fitting parameters of a set of two-dimensional scattered points [15]
In order to solve the problem mentioned above, this paper proposes using the constrained least-squares (CLS) method to convert the problem of hyperbola fitting to the inequality constrained optimization problem and using the Lagrange programming neural network (LPNN) method to solve turbulence level iteratively. e results show that the LPNN method is superior to the traditional solution of the overdetermined equations (SOE) method, which verify the feasibility of the LPNN method for solving turbulence level in wind tunnels
Summary
Wind tunnel test is the most effective method for aerodynamic research. Even if the computer-based numerical simulation technology and the model flight test technology are rapidly improving, wind tunnel test is still an indispensable method for the research of complex aerodynamic characteristics during the research and development of aircraft. e precise design of advanced aircraft requires high accuracy of wind tunnel test results. E LS method has been widely used due to its ease of application and high computational efficiency [21,22,23,24,25] Both Lebiga VA [12, 26, 27] and Radespiel R [28,29,30,31] have done a lot of research in the field of turbulence level measurement and obtaining the fitting solution in compressible flow. E SOE method is based on the LS method, which means it has high computational efficiency, while the SOE method cannot obtain precise turbulence level results due to overfitting if the scattered points deviate from hyperbolic distribution. In order to solve the problem mentioned above, this paper proposes using the constrained least-squares (CLS) method to convert the problem of hyperbola fitting to the inequality constrained optimization problem and using the Lagrange programming neural network (LPNN) method to solve turbulence level iteratively. e results show that the LPNN method is superior to the traditional SOE method, which verify the feasibility of the LPNN method for solving turbulence level in wind tunnels
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
