Elsayed Yasser, Gabbar Hossam A
Faculty of Engineering and Applied Science, Ontario Tech University, 2000 Simcoe St. North, Oshawa, ON L1G0C5, Canada.
Sensors (Basel). 2022 Oct 26;22(21):8203. doi: 10.3390/s22218203.
Fiber Bragg grating (FBG) technology has shown a mutation in developing fiber optic-based sensors because of their tiny size, high dielectric strength, distributed sensing, and immunity to high voltage and magnetic field interference. Therefore, FBG sensors significantly improve performance and accuracy in the world of measurements. The reflectivity and bandwidth are the main parameters that can dramatically affect the sensing performance and accuracy. Each industrial application has its requirements regarding the reflectivity and bandwidth of the reflected wavelength. Optimizing such problems with multi-objective functions that might t with each other based on applications' needs is a big challenge. Therefore, this paper presents an optimization method based on the nondominated sorting genetic algorithm II (NSGA-II), aiming at determining the optimum grating parameters to suit applications' needs. To sum up, the optimization process aims to convert industrial applications' requirements, including bandwidth and reflectivity, into the manufacturing setting of FBG sensors, including grating length and modulation refractive index. The method has been implemented using MATLAB and validated with other research work in the literature. Results proved the capability of the new way to determine the optimum grating parameters for fulfilling application requirements.
光纤布拉格光栅（FBG）技术在开发基于光纤的传感器方面展现出了优势，这是由于其尺寸微小、介电强度高、具有分布式传感能力以及对高压和磁场干扰具有免疫力。因此，FBG传感器在测量领域显著提高了性能和精度。反射率和带宽是能够极大影响传感性能和精度的主要参数。每个工业应用对于反射波长的反射率和带宽都有其特定要求。基于应用需求，使用可能相互冲突的多目标函数来优化此类问题是一项巨大挑战。因此，本文提出了一种基于非支配排序遗传算法II（NSGA-II）的优化方法，旨在确定适合应用需求的最佳光栅参数。总之，优化过程旨在将包括带宽和反射率在内的工业应用需求转化为FBG传感器的制造参数，包括光栅长度和调制折射率。该方法已通过MATLAB实现，并与文献中的其他研究工作进行了验证。结果证明了这种新方法确定满足应用需求的最佳光栅参数的能力。
