As depicted in Fig. , a crossed barrel has n hollow columns with outer radius r and thickness t, twisted at an angle θ . Here, n, r,t and θ are the design variables we want to optimize, whose data types (discrete/continuous) and domains are outlined in Table 2. Due to the involvement of continuous variables (n, r,t), exhaustively enumerating all possible combinations is impractical. Hence, an emulator, as recommended by Hickman et al. , is utilized to simulate the process and therefore make it possible to sample over the whole feasible domain. The emulator was modeled as Bayesian neural nets (BNN) and trained on over 2,500 HTE data points collected by Gongora et al. , where the weights and biases are modelled with a normal distribution. We note that the trained emulator serves the purpose of method comparisons in this case study. Nevertheless, the accuracy of the trained model can be improved if more data could be provided.