Joint Research on Aerodynamic Characteristics and Handling Stability of Racing Car under Different Body Attitudes

With the rapid development of FSAE, the speed of racing cars has increased year by year. As the main research content of racing cars, aerodynamics has received extensive attention from foreign teams. For racing cars, the aerodynamic force on the aerodynamic device ultimately acts on the tires through the transmission of the body and the suspension. When the wheel is subjected to the vertical load generated by the aerodynamic device, the ultimate adhesion capacity of the wheel is improved. Under changing conditions, racing wheels can withstand greater lateral and tangential forces. Therefore, the effects of aerodynamics have a more significant impact on handling stability. The FSAE racing car of Jilin University was taken as the research object, and this paper combines the wind tunnel test, the numerical simulation and the dynamics simulation of the racing system. The closed-loop design process of the aerodynamics of the FSAE racing car was established, and the joint study of aerodynamic characteristics and handling stability of racing car under different body attitudes was realized. Meanwhile, the FSAE car was made the modification of aerodynamic parameter on the basis of handling stability. The results show that, after the modification of the aerodynamic parameters, the critical speed of the car when cornering is increased, the maneuverability of the car is improved, the horoscope test time is reduced by 0.525 s, the downforce of the car is increased by 11.39%, the drag is reduced by 2.85% and the lift-to-drag ratio is increased by 14.70%. Moreover, the pitching moment is reduced by 82.34%, and the aerodynamic characteristics and aerodynamic efficiency of the racing car are obviously improved. On the basis of not changing the shape of the body and the aerodynamic kit, the car is put forward to shorten the running time of the car and improve the comprehensive performance of the car, so as to improve the performance of the car in the race.

Lateral directional aircraft aerodynamic parameter estimation using adaptive stochastic nonlinear filter

This paper aims to accurately estimate the lateral directional aerodynamic parameters in real time irrespective of the variations in the process and measurement covariance matrices. The proposed algorithm for parameter estimation is based on the integration of adaptive techniques into a stochastic nonlinear filter. The proposed adaptive estimation algorithm is applied to flight test data, and the lateral directional derivatives are estimated in real time. The estimates are compared with those obtained from the Filter Error Method (FEM), an offline parameter estimation method accounting for process noise. The estimation results are observed to be very comparable, and the supremacy of the adaptive filter is illustrated by varying the covariance matrices of both process and measurement noises. The parameters estimated by the adaptive filter are found to converge to their actual values, whereas the estimates of the regular filter are observed to diverge from the actual values when changing the noise covariance matrices. The proposed adaptive algorithm can estimate the lateral directional aerodynamic derivatives more accurately without prior knowledge of either process or measurement noise covariance matrices. Hence, it is of great value in online implementations.

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

Research on Matching Characteristics of Wind Turbine and Generator for Small H-shaped Vertical Axis Wind Turbine

Based on the small H-shaped vertical axis wind wheel model (NACA0016), a CFD wind wheel model was constructed. Based on the principle of moving grid, the grid division of the CFD wind wheel model is completed by using GAMBIT software, and the boundary conditions such as the inlet boundary and the outlet boundary are set reasonably. Then, the turbulence model and the couple algorithm are used to carry out transient simulation calculations, and finally the aerodynamic parameter curves of the two-dimensional CFD wind wheel model are obtained. Based on this, the matching characteristics of the wind turbine and generator of the small H-shaped vertical axis wind turbine are studied. The research results show as follows: when the incoming wind speeds change in range of (2 m/s, 12 m/s), and the power characteristic curve and torque characteristic curve of the generator wind wheel are respectively overlap the best power curve and best torque of the generator, the matching characteristics of the small H-shaped vertical axis wind turbine rotor and generator are optimal, which provides reference for carrying out related research.

New approach of inverse design of transonic compressor rotor blade via prescribed isentropic Mach distributions without modification of governing equations

Shock loss is the primary source of total pressure loss of transonic axial compressors. Reducing the shock by redesigning the geometry of rotor is of great interest for turbomachinery designers. However, the complex flow field involving shock waves, shock-boundary interaction, intense secondary flows, etc., in the compressor makes the design of rotor difficult. The conventional method of design and optimization is computationally intensive and time-costly. This study introduces an inverse design method to design rotor blades corresponding to prescribed isentropic Mach number distributions with no modification of flow-governing equations. An artificial neural network is trained to predict the isentropic Mach number distributions of any deformed blades. Then, with the pattern search optimization, the blade corresponding to the prescribed isentropic Mach number distributions can be achieved. When the aerodynamic parameter database is calculated and the neural network is obtained, this method can design large numbers of blades of changed isentropic Mach number distributions immediately, without modifying the computational fluid dynamics (CFD) flow solver. The design process is fully automatic and efficient. In this study, NASA Rotor 37 is redesigned and optimized as test cases. Some analysis on the influence of blade shape on aerodynamic characteristics of the rotor is represented in this study.

Aerodynamic-Parameter Identification and Attitude Control of Quad-Rotor Model with CIFER and Adaptive LADRC

Current research on quadrotor modeling mainly focuses on theoretical analysis methods and experimental methods, which have problems such as weak adaptability to the environment, high test costs, and long durations. Additionally, the PID controller, which is currently widely used in quadrotors, requires improvement in anti-interference. Therefore, the aforementioned research has considerable practical significance for the modeling and controller design of quadrotors with strong coupling and nonlinear characteristics. In the present research, an aerodynamic-parameter estimation method and an adaptive attitude control method based on the linear active disturbance rejection controller (LADRC) are designed separately. First, the motion model, dynamics model, and control allocation model of the quad-rotor are established according to the aerodynamic theory and Newton–Euler equations. Next, a more accurate attitude model of the quad-rotor is obtained by using a tool called CIFER to identify the aerodynamic parameters with large uncertainties in the frequency domain. Then, an adaptive attitude decoupling controller based on the LADRC is designed to solve the problem of the poor anti-interference ability of the quad-rotor and adjust the key control parameter b0 automatically according to the change in the moment of inertia in real time. Finally, the proposed approach is verified on a semi-physical simulation platform, and it increases the tracking speed and accuracy of the controller, as well as the anti-disturbance performance and robustness of the control system. This paper proposes an effective aerodynamic-parameter identification method using CIFER and an adaptive attitude decoupling controller with a sufficient anti-interference ability.