Experiment-numerical value-machine learning fusion-based in-oil liquid drop electric field dynamics modeling and regulation method

By constructing an experimental parameter database and combining phase-field lattice Boltzmann numerical simulation with a sparrow search algorithm to optimize the BP neural network, the stability and accuracy problems in the study of the electric field dynamics of oil droplets in oil were solved, and high-precision prediction and control of oil droplets in oil under strong electric fields were achieved.

CN121706590APending Publication Date: 2026-03-20ZHEJIANG UNIV
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Patent Information

Application Number
CN202511925717.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-19
Publication Date
2026-03-20

AI Technical Summary

Technical Problem

Existing studies on the electric field dynamics of oil droplets in oil have several drawbacks: the effects of temperature and emulsifier concentration on dielectric constant and conductivity are not considered; numerical simulations lack stability and accuracy; and machine learning models suffer from slow convergence and insufficient generalization ability.

Method used

An experimental parameter database was constructed, and a BP neural network optimized by phase-field lattice Boltzmann numerical simulation and sparrow search algorithm was used to realize multi-physics coupling modeling. The neural network was trained and optimized through experimental data to improve the model accuracy and stability.

Benefits of technology

It achieves high-precision prediction and control of oil droplets in oil under strong electric fields, improves the stability of numerical simulation, enhances prediction efficiency, strengthens model adaptability, and can accurately reflect the electro-current-interface coupling behavior of oil-in-oil systems.

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Abstract

The invention discloses an experiment-numerical value-machine learning fusion-based in-oil liquid drop electric field dynamics modeling and regulation method, which comprises the following steps of: firstly, constructing a high-voltage electric field liquid drop experiment system, and testing oil liquid drop experiment data in oil; then establishing a multi-physics field coupling model, and describing deformation and flow field evolution of oil drops in oil under a strong electric field; and finally, constructing a BP neural network and optimizing an initial weight and a threshold value to realize rapid prediction of the deformation behavior of the oil drops in the oil under the action of the electric field. According to the method, an experimental characterization-numerical solution-machine learning optimization three-layer collaborative system is constructed, a whole-process closed loop from physical property determination, equation solution to algorithm prediction is realized, and droplet deformation, charge distribution and interface response are rapidly predicted under the conditions of different electric field intensities, temperatures and emulsifier concentrations; and active control of electric field behaviors can be realized through parameter reverse adjustment, and reliable modeling and optimization basis is provided for an electrowetting chip, a flexible EHD pump and a microfluidic liquid drop regulation and control system.
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