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Content introduction
In the aviation field, aircraft performance simulation is a key technology that can help pilots and engineers better understand the performance characteristics of the aircraft and its performance under different conditions. Biplane performance simulation is an important branch, which focuses on studying the performance and characteristics of biplanes.
A biplane, as the name suggests, refers to an aircraft with two pairs of wings. This design is very common in aerospace engineering as it offers many advantages. First, the bi-wing design can provide greater lift, allowing the aircraft to take off and land at lower speeds. Secondly, biplanes have a relatively simple structure, lower manufacturing costs, and are more convenient to maintain. In addition, the bi-wing design can also provide better control performance and stability, making the aircraft easier to control and operate.
However, accurately assessing the performance of a biplane requires a series of simulations and tests. Performance simulation is the use of computer software to simulate the performance of an aircraft under different flight conditions. These software are usually based on mathematical models and physical principles, taking into account factors such as the aircraft’s aerodynamic characteristics, power system, weight and balance. Through comprehensive analysis and calculation of these factors, the performance parameters of the aircraft under different speeds, altitudes, loads and other conditions can be obtained, such as lift, drag, thrust, fuel consumption, etc.
An important application of biplane performance simulation is pilot training. By simulating different flight tasks and scenarios, pilots can become familiar with the performance characteristics of the aircraft and learn how to operate under different conditions and respond to emergencies. This kind of virtual training can greatly improve pilots’ skill levels and response capabilities, and reduce risks during actual flights.
In addition to pilot training, biplane performance simulations can be used in aircraft design and improvement. By simulating the performance of different design options and parameters, engineers can evaluate the impact of different designs on aircraft performance and optimize them. This simulation approach can save significant time and costs, improve design efficiency, and ultimately result in better aircraft performance.
Of course, biplane performance simulation has its limitations. First of all, the simulation results are still based on theoretical models and assumptions, and may be somewhat different from actual flight. Secondly, simulations can only consider performance under known conditions and cannot fully predict how the aircraft will perform under unknown or extreme conditions. Therefore, field testing and verification are still needed to ensure the safety and performance of the aircraft in actual flights.
In summary, biplane performance simulation is an important technology that can help us better understand and evaluate the performance characteristics of biplanes. By simulating performance parameters under different flight conditions, we can improve pilot training, optimize aircraft design, and support aircraft safety and performance. Although the simulation method has its limitations, it remains an indispensable tool in the aviation field and will continue to play an important role in aircraft performance research and development.
Part of the code
%% Influence coefficients:
function [b] = SIC(Combined,CP)
x_c = Combined.x_c;
z_c = Combined.z_c;
cos_theta = Combined.cos_theta;
sin_theta = Combined.sin_theta;
x = Combined.x_panel_start;
z = Combined.z_panel_start;
X2 = Combined.panel_length;
Ni = length(x_c);
Nj = Ni;
%Create array:
b = zeros(Ni,Nj);
for i = 1:Ni
%Establish collocation point in the coordinate systems of all the
%panels using equation 11.23a of Katz and Plotkin:
Co_x = cos_theta.*(x_c(i) - x) - sin_theta.*(z_c(i) - z);
Co_z = sin_theta.*(x_c(i) - x) + cos_theta.*(z_c(i) - z);
%Sin_theta and cos_theta are expressing the orientation of the panels
%with respect to the body coordinate system. Note, in this work the
%alpha_i of Katz and Plotkin (figure 11.17 and equation 11.23a) are
%relabelled as theta_i in order to avoid confusion with angle of
%attack. As a result, theta of figure 11.17 is also relabeled as eta.
%Radii squared:
R12 = Co_x.^2 + Co_z.^2;
R22 = (Co_x - X2).^2 + Co_z.^2;
%Angle parts of equation 11.64. Note eta angles are the theta angles of
%figure 11.17.
eta_1 = atan2(Co_z,Co_x);
eta_2 = atan2(Co_z,Co_x - X2);
%Equations 11.63:
b(i,:) = 1/(4*pi)*((Co_x.*log(R12) - (Co_x - X2).*log(R22) + ...
2*Co_z.*(eta_2 - eta_1)));
%Throw in exception to handle cases where collocation point is on the
%panel surface:
if CP == 0
b(i,i) = 1/(2*pi)*Co_x(i)*log(R12(i));
end
end
Running results
References
[1] Guo Weigang, Han Wei, Wang Xiuxia. Visual simulation system of aircraft flight performance based on Matlab/Flightgear [J]. Experimental Technology and Management, 2010, 27(10):3.DOI:10.3969/j.issn.1002-4956.2010 .10.033.
[2] Zheng Zhongpei, Xu Haihang. High-performance UAV flight control system simulation based on Matlab[C]//The 14th China Annual Academic Conference on System Simulation Technology and Its Application.0[2023-10-12].DOI: ConferenceArticle/5af1b370c095d71bc8cb3c1c.