Particle-based simulations of electrophoretic deposition with adaptive physics models☆
Particle-based simulations of electrophoretic deposition with adaptive physics models
Particle-Based Simulations of Electrophoretic Deposition with Adaptive Physics Models
Electrophoretic deposition (EPD) is a powerful and versatile technique for fabricating functional coatings and advanced materials, particularly in the fields of nanotechnology, energy storage, and biomedical devices. To optimize this complex process and predict deposition behavior under varying electric fields and particle dynamics, high-fidelity simulation tools are essential.
Particle-based simulation approaches, such as the Discrete Element Method (DEM), Lattice Boltzmann Method (LBM), and Molecular Dynamics (MD), are increasingly employed to model EPD at the micro- and meso-scale. These simulations allow for tracking the motion of charged particles suspended in a liquid medium, subjected to electric fields and hydrodynamic interactions. However, the highly nonlinear, multiscale nature of the process demands more than static physics models.
Adaptive physics models have emerged as a key advancement in improving the realism and efficiency of simulations. These models dynamically adjust physical parameters—such as local field strength, particle-particle interactions, and fluid viscosity—based on evolving system conditions. This adaptability enables accurate modeling of critical phenomena such as particle agglomeration, field-induced alignment, and non-uniform deposition patterns.
Incorporating multiphysics capabilities is also crucial. EPD involves coupled electrostatic, hydrodynamic, and sometimes thermal effects, making single-physics approximations inadequate. Adaptive models, often integrated with finite element or finite volume methods, support real-time refinement of spatial and temporal resolution, enabling better convergence and predictive accuracy.
These simulations are not only academic exercises; they have practical implications in optimizing deposition parameters for tailored coating thickness, density, and morphology. This is especially relevant for next-generation materials like carbon nanotubes, graphene oxide films, and bioactive ceramic coatings.
Moreover, the integration of machine learning with particle-based adaptive simulations opens the door for data-driven surrogate models, enabling faster optimization and control in industrial applications.
As research continues, combining adaptive physics with high-performance computing and data analytics is expected to revolutionize how we understand and engineer the EPD process across disciplines.
Global Particle Physics Excellence Awards
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