  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.
