Study on Aerodynamic Optimization Design Method Analysis and Control Algorithm of the Pod

Author(s):  
Wei Huang ◽  
Meilin Xie ◽  
Xiaoxu Yang ◽  
Xuezheng Lian ◽  
Yihang Zhang
Author(s):  
Xiaodong Liu ◽  
Peiliang Zhang ◽  
Guanghong He ◽  
Yongen Wang ◽  
Xudong Yang

In order to solve the multi-objective multi-constraint design in aerodynamic design of flying wing, the aerodynamic optimization design based on the adjoint method is studied. In terms of the principle of the adjoint equation, the boundary conditions and the gradient equations are derived. The Navier-Stokes equations and adjoint aerodynamic optimization design method are adopted, the optimization design of the transonic drag reduction for the two different aspect ratio of the flying wing configurations is carried out. The results of the optimization design are as follows: Under the condition of satisfying the aerodynamic and geometric constraints, the transonic shock resistance of the flying wing is weakened to a great extent, which proves that the developed method has high optimization efficiency and good optimization effect in the multi-objective multi-constraint aerodynamic design of the flying wing.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mi Baigang ◽  
Wang Xiangyu

Dynamic stability is significantly important for flying quality evaluation and control system design of the advanced aircraft, and it should be considered in the initial aerodynamic design process. However, most of the conventional aerodynamic optimizations only focus on static performances and the dynamic motion has never been included. In this study, a new optimization method considering both dynamic stability and general lift-to-drag ratio performance has been developed. First, the longitudinal combined dynamic derivative based on the small amplitude oscillation method is calculated. Then, combined with the PSO (particle swarm optimization) algorithm, a dynamic stability derivative that must not be decreased is added to the constraints of optimization and the lift-drag ratio is chosen as the optimization objective. Finally, a new aerodynamic optimization method can be built. We take NACA0012 as an example to validate this method. The results show that the dynamic derivative calculation method is effective and conventional optimization design can significantly improve the lift-drag ratio. However, the dynamic stability is enormously changed at the same time. By contrast, the new optimization method can improve the lift-drag performance while maintaining the dynamic stability.


Author(s):  
Xiang Liu ◽  
Xiaogeng Liang

In this study, an improved cooperative integrated guidance and control (IGC) design method is proposed based on distributed networks to address the guidance and control problem of multiple interceptor missiles. An IGC model for a leading interceptor is constructed based on the relative kinematic relations between missiles and a target and the kinematic equations of the missiles in a pitch channel. The unknown disturbances of the model are estimated using a finite-time disturbance observer (FTDO). Then, the control algorithm for the leading interceptor is designed according to the disturbance estimation and nonsingular fast dynamic surface sliding mode control (SMC). To enhance the rate of convergence of the cooperative control commands for the interceptors, an improved cooperative control strategy is proposed based on the leader-follower distributed network. Consequently, the two velocity components of the interceptor in the pitch channel can be obtained, which are subsequently converted to the total velocity and flight path angle commands of the interceptor using kinematic relations. The control algorithm for the following interceptor is similarly designed using an FTDO and dynamic surface SMC. The effectiveness of the improved distributed cooperative control strategy for multiple interceptors is validated through simulations.


2011 ◽  
Vol 230-232 ◽  
pp. 453-456
Author(s):  
Wei Dong Wang

The importance of cam contour curve and the necessity of cam reverse engineering are discussed. The error sources during the course of cam reverse design are analyzed. The growth form innovative design method is researched, especially product structure growth form design method. The information of structure, dimension and tolerance should be perfected gradually during the course of growth form innovative design, and finally perfected when the product structure is finished, furthermore, using the current structure, dimension and tolerance information to conduct evaluation and control in the grow direction during the design process, to achieve the ultimate purpose of optimization design. Taken the innovative design of an precision cam as an example, the reconstruction of product structure and the product prototype reverse design are finished, and then the product is innovative designed. A practical designs technical ways based on growth form innovative design is systemly provided.


2010 ◽  
Vol 34-35 ◽  
pp. 1656-1660 ◽  
Author(s):  
Ying Cai Yuan ◽  
Yi Lun Liu ◽  
Yan Li

Clearance is inevitable,so, with the increasing of machine’s speed, nonlinear vibration phenomenon caused by clearance is more apparent, which influences the precision and stability of mechanical system. For increasing the stability of mechanical system, a robust design method based on sensitivity analysis is studied, by using four-bar linkage as the research object, deducing the nonlinear dynamic model with clearance and the sensitivity of dynamic response, based on the rational planning to the tracks and control the sensitivities. In the design example, it shows that although the track deviation of robust design is slightly bigger than that of optimization design, the comprehensive dynamic performance of the mechanical system is much better than the latter, which means the stability of mechanical system is improved greatly. Thus, the robust design based on sensitivity analysis is an effective way to improve the stability of the mechanical system.


2005 ◽  
Vol 42 (5) ◽  
pp. 1375-1375 ◽  
Author(s):  
Shinkyu Jeong ◽  
Mitsuhiro Murayama ◽  
Kazuomi Yamamoto

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