Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

超算与机器学习辅助高渗透性海水反渗透膜系统优化设计

当前,高渗透性反渗透膜材料的研究引起了广泛的关注,然而高渗透导致的浓差极化与膜污染加剧等瓶颈问题限制了高性能膜材料的应用发展.本工作采用机器学习结合超级计算提出了针对先进反渗透膜材料的组件进水隔网(亚毫米级)与系统(米级)的多尺度优化设计新方法.在进料含盐度35,000 ppm,回收率50%典型工况下,对标目前国际先进海水反渗透淡化工艺,本文提出的优化方案能使淡水制备比能耗(1.66 k Wh/m^(3))降低27.5%,所需膜面积减少约37.2%,系统最大浓差极化因子控制在工程允许范围以内(<1.20),可有效缓解高渗透膜系统中膜污染问题,为高性能膜材料精准设计提供理论依据、计算工具和大数据支撑,有重要的应用潜力.本文提出的机器学习结合超算的多尺度设计新研究范式,突破了基于“试错法”的传统单一尺度组件设计限制,高通量并行计算规模可扩展至93,120核以上,较串行算法计算效率提升3000倍以上,可大幅度缩短高性能膜组件的设计周期.