Mathematical Modelling and Hierarchical Encourage Particle Swarm Optimization Genetic Algorithm for Jet Pipe Servo...

Area of Science:

• Aerospace Engineering
• Mechanical Engineering
• Fluid Dynamics

Background:

• The jet pipe servo (JPS) valve is critical for aircraft maneuverability.
• Existing models and optimization methods may not fully capture JPS valve dynamics.

Purpose of the Study:

• To develop a more accurate mathematical model for JPS valves.
• To propose a novel multiobjective hierarchical encourage particle swarm optimization genetic algorithm (HEPGA) for JPS valve optimization.
• To improve the dynamic performance of JPS valves, specifically reducing adjustment and overshoot.

Main Methods:

• Computational Fluid Dynamics (CFD) integrated with an analytical model to account for turbulent submerged free jets.
• Dynamic mesh technique for flow field analysis under actual working conditions, considering force interactions.
• A novel HEPGA algorithm inspired by a staff welfare system for population division, optimizing for conflicting objectives (adjustment and overshoot).

Main Results:

• The optimized JPS valve demonstrated a 24.28% reduction in adjustment and a 51.39% reduction in overshoot compared to the prototype.
• Experimental validation confirmed the accuracy of the developed mathematical model.
• The HEPGA algorithm showed improved convergence compared to other hybrid methods.

Conclusions:

• The proposed accurate mathematical model and HEPGA significantly enhance JPS valve dynamic performance.
• The optimized JPS valve design leads to improved aircraft maneuverability.
• The study validates the effectiveness of integrating CFD and advanced optimization algorithms in aerospace component design.