Implementation Of Intelligent Commissioning System For Traveling Wave Tube Electrical Parameters

Traveling wave tube is one of the most important devices in microwave and millimeter wave electric vacuum devices.It has the irreplaceable features of other devices:high power,wide bandwidth,and high efficiency.In the microwave high power amplifier parts have an unshakable position.When using the corresponding simulation software for TWT design,optimal performance needs to be achieved under various design parameters.Due to the complexity of the TWT's own theory,and the design parameters that are related to each other and affect each other,the optimization goal may be more than one,making it possible to rely on the experience of the personnel to design the model currently.However,for such complex optimization problems,human experience design is difficult.The optimization objectives of the TWT collector or electron gun are relatively independent,and it is difficult to meet the design requirements using the single-target optimization method.This paper takes TWT optimization as the research object and non-dominated genetic algorithm as the main implementation technology.Design the traveling wave tube optimization method based on non-dominated genetic algorithm(NSGA-?),optimize a variety of complex design parameters,take into account multiple optimization goals,and make the traveling wave tube in an overall optimal working state.Through comparison with the scanning calculation,the effectiveness of the optimization method of this paper is verified,and the optimization effect is better,and the optimization speed is relatively fast.And the method of this paper is simple and easy to use,and has high practical value.The main work of this paper is as follows:1.Learn and master basic genetic algorithms and the fast non-dominated sorting algorithm(NSGA-II)formed on this basis.Understand the influence of the algorithm's population size,cross-operations,selection operations,and mutation operations on the algorithm's convergence speed and optimal solution.And on the algorithm NSGA-? fast non-dominated sorting,crowding degree calculation and elite strategy,in-depth understanding of the basic principles of the algorithm,space-time complexity,convergence speed and optimization results.2.Combining the basic principle of multi-objective genetic algorithm NSGA-? and the optimization idea of NSGA-?,NSGA-? is applied to the complex parameter optimization of TWT.According to the basic principle of multistage depressed collectors and electron gun of traveling wave tube,a multistage depressed collectors and electron gun optimization method based on NSGA-II is designed.3.The design parameters of the collector and electron guns were optimized using design optimization methods.In-depth analysis of the comparison collector's scan calculation results and optimization calculation results to verify the optimization results and the optimization method's efficiency.The optimization algorithm makes the collector efficiency greater than 76.42%,while the electron refluence ratio is less than 1.03%;and make the electron emitter cathode emission current and beam waist radius within the constraint range,the position of electron beam waist up to 12.24mm.4.This paper proposes a method for selecting the best of the best of multistage depressed collectors and electron gun.The final design scheme is determined by analyzing the energy density distribution of the inner surface of the multistage depressed collectors or the electron beam laminar flow of the electron gun of all individuals in the optimized solution set.Based on the results of multi-objective optimization and the relevant theories in the application field,a complete application method for optimization is formed.