This paper presents a framework for automated optimization of double-heater convective PCR (DH-cPCR) devices by developing a computational fluid dynamics (CFD) simulation database and artificial neural network (ANN) model. The optimization parameter space that includes the capillary tube geometries and the heater sizes of DH-cPCR is established, and a database consisting of nearly 10,000 CFD simulations is constructed. The database is then used to train a two-stage ANN models that select practically relevant data for modeling and predict PCR device performance. The trained ANN model is then combined with the gradient-based and the heuristics optimization approaches to search for optimal device configuration that possesses the shortest DNA doubling time. The entire design process including model meshing and configuration, parallel CFD computation, database organization, and ANN training and utilization is fully automated. Case studies confirm that the proposed framework can successfully find the optimal device configuration with an error of less than 0.3 s, and hence, representing a cost-effective and rapid solution of DH-cPCR device design.

本文通过开发计算流体动力学 (CFD) 模拟数据库和人工神经网络 (ANN) 模型，提出了双加热器对流 PCR (DH-cPCR) 设备的自动优化框架。建立了包括毛细管几何形状和 DH-cPCR 加热器尺寸的优化参数空间，并构建了一个由近 10,000 个 CFD 模拟组成的数据库。然后使用该数据库来训练两阶段 ANN 模型，该模型选择实际相关的数据进行建模和预测 PCR 设备性能。然后将经过训练的 ANN 模型与基于梯度和启发式优化方法相结合，以搜索具有最短 DNA 倍增时间的最佳设备配置。整个设计过程包括模型网格划分和配置、并行 CFD 计算、数据库组织，ANN 培训和使用是完全自动化的。案例研究证实，所提出的框架可以成功找到最佳设备配置，误差小于 0.3 s，因此，代表了 DH-cPCR 设备设计的具有成本效益和快速的解决方案。