This paper presents an efficient methodology for the robust optimisation of Continuous Flow Polymerase Chain Reaction (CFPCR) devices. It enables the effects of uncertainties in device geometry, due to manufacturing tolerances, on the competing objectives of minimising the temperature deviations within the CFPCR thermal zones, together with minimising the pressure drop across the device, to be explored. We first validate that our training data from conjugate heat transfer simulations of the CFPCR thermal flow problems is noise free and then combine a deterministic surrogate model, based on the mean of a Gaussian Process Regression (GPR) simulator, with Polynomial Chaos Expansions (PCE) to propagate the manufacturing uncertainties in the geometry design variables into the optimisation outputs. The resultant probabilistic model is used to solve a series of robust optimisation problems. The influence of the robust problem formulation and constraints on the design conservatism of the robust optima in comparison with the corresponding deterministic cases is explored briefly.

中文翻译：

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连续流聚合酶链反应热流系统的稳健设计优化

本文提出了一种有效的方法来对连续流动聚合酶链式反应 (CFPCR) 设备进行稳健优化。它能够探索由于制造公差而导致的器件几何形状不确定性对最小化 CFPCR 热区内温度偏差以及最小化整个器件的压降的竞争目标的影响。我们首先验证来自 CFPCR 热流问题共轭传热模拟的训练数据是无噪声的，然后将基于高斯过程回归 (GPR) 模拟器平均值的确定性替代模型与多项式混沌展开 (PCE) 相结合将几何设计变量中的制造不确定性传播到优化输出中。由此产生的概率模型用于解决一系列鲁棒优化问题。与相应的确定性案例相比，鲁棒问题公式和约束对鲁棒最优设计保守性的影响进行了简要探讨。