Physical properties optimization of nerve guidance conduits utilizing genetic algorithm: A CFD analysis

Abstract

Nerve guidance conduits, or NGCs, used as structures in tissue engineering, play an important role in reconstruction and the growth of axons after nerve injuries. To produce an appropriate scaffold for nerve growth and transfer the suitable amount of growth factors, these channels should be designed and built with topological, mechanical, and biological properties and functional mass transfer requirements. This study deals with a computational design approach of a nerve guidance conduit which, after validating the geometry, simulates the blood flow utilizing the Carreau-Yasuda model and optimizes physical properties. The physical properties are hydraulic pressure, porosity, diffusivity and water absorption. The optimization process consists of three distinct steps: initially, a single objective function is implemented using a genetic algorithm (GA) to determine the optimal hydraulic pressure for the nerve guidance conduit, resulting in a final value of 0.039 Pa. Subsequently, the GA is employed again to optimize porosity, leading to a final value of 0.6473. Finally, a multi-objective function is utilized to consider porosity, diffusivity, and water absorption, simultaneously.

Keywords

• Nerve guidance conduit
• Nerve injury
• CFD
• Optimization
• Non-Newtonian model
• Genetic algorithm