Electron Swarm Parameter Calculation With Coupled Ion Dynamics
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Solution Overview
Problem
Existing pulsed Townsend experiments primarily focus on electron dynamics, leading to inaccurate and incomplete electron swarm parameters due to the lack of consideration for ion dynamics, especially in complex gas reactions, which affects the accuracy and universality of plasma simulation results.
Innovation Solution
A parallel electron swarm parameter calculation method that incorporates ion dynamics using a pulsed Townsend experiment, employing a genetic algorithm and finite volume method to optimize electron swarm parameters based on measured current waveforms, with parallel computation on a CUDA platform to enhance accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only electron dynamics model is used in pulsed Townsend experiment, then the experimental setup is simple, but the electron swarm parameters are inaccurate and incomplete
Solution Approach 1:
The patent merges electron dynamics model with ion dynamics model to form a coupled dynamics model. This combination allows simultaneous consideration of both electron and ion behaviors during the electron avalanche process, thereby obtaining complete and accurate electron swarm parameters including drift velocity, diffusion coefficient, and reaction rate coefficients without increasing experimental setup complexity
Solution Approach 2:
The coupled dynamics model serves multiple functions: it describes electron avalanche development, calculates electron transport parameters, and determines reaction rate coefficients for various gas types. This universal model can be applied to different gases and conditions without requiring separate simplified models, improving measurement precision while maintaining experimental simplicity
2Ease of manufacture
If analytical methods are used to solve ion dynamics models, then the computation is straightforward, but the method is only adaptable to single type of gas and computation is difficult
Solution Approach 1:
The patent employs a genetic algorithm that optimizes reaction rate coefficients as parameters. By changing these parameters through iterative optimization rather than relying on fixed analytical solutions, the model can adapt to different gas types and conditions while maintaining computational feasibility. This parameter optimization approach resolves the contradiction between computation ease and gas adaptability
Solution Approach 2:
The patent replaces traditional analytical mechanical solution methods with a computational optimization approach using genetic algorithms. This substitution allows the model to handle complex ion dynamics and various gas types without requiring explicit analytical solutions, thereby improving both adaptability and computational feasibility
3Measurement precision
If pulsed Townsend experiment with ion dynamics is established, then the electron swarm parameters are complete and accurate, but the computation time increases
Solution Approach 1:
The patent segments the computation process into discrete time steps and spatial zones, implementing the coupled electron-ion dynamics model through a finite difference method. This segmentation allows efficient numerical integration of the complex dynamics equations, reducing overall computation time while maintaining complete and accurate electron swarm parameters through systematic step-by-step calculation
Solution Approach 2:
The genetic algorithm incorporates feedback mechanisms that iteratively adjust reaction rate coefficients based on the degree of fit between computed and measured current waveforms. This feedback optimization efficiently converges on accurate parameters without requiring exhaustive computation, reducing computation time while ensuring parameter completeness and accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides comprehensive and accurate electron swarm parameters, suitable for various gases, ensuring reliable plasma simulation data by considering ionization, attachment, detachment, and ion conversion processes, with improved computation speed and universality.
Implementation Method 1
computing the electron swarm parameters of the gas under the reduced field intensity with a minimum deviation between the measured current waveform and the computed current waveform as an optimization target through a genetic algorithm
Implementation Method 2
computing the discharge current waveform of the gas under the reduced field intensity through a finite volume method according to the electron avalanche space-time development model
Implementation Method 3
measuring a discharge current waveform of gas under a reduced field intensity, and obtaining a measured current waveform
Data Source
AI summary
Disclosed are a parallel electron swarm parameter calculation method taking ion dynamics into consideration, and related apparatus. The method includes: measuring a discharge current waveform of gas under a reduced field intensity, and obtaining a measured current waveform; establishing an electron avalanche space-time development model of a coupled electron charge density and different types of ion charge densities; computing the discharge current waveform of the gas under the reduced field intensity through a finite volume method according to the electron avalanche space-time development model, and obtaining a computed current waveform; and computing the electron swarm parameters of the gas under the reduced field intensity with a minimum deviation between the measured current waveform and the computed current waveform as an optimization target through a genetic algorithm.