Multi-objective hydro-thermal optimization of spiral conformal cooling channels using a kriging-CFD framework

Abstract This study presents a surrogate-based computational framework for the thermo-hydraulic optimization of spiral conformal cooling channels (CCCs) used in injection moulding applications. Sixteen design configurations were generated using Latin Hypercube Sampling and evaluated through CFD simulations to determine cooling time and pressure drop. Based on the simulation data, a Universal Kriging surrogate model was developed to describe the nonlinear relationships between channel diameter, helix pitch, channel-to-surface distance, and the resulting thermo-fluid performance. The predictive capability of the surrogate model was evaluated using leave-one-out cross-validation. The model showed excellent agreement for pressure drop prediction (R² ≈ 0.99) and good predictive accuracy for cooling time (R² ≈ 0.83) with relatively low prediction error. Owing to the computational efficiency of the surrogate model, more than 3 × 10⁵ design points were explored to generate detailed response surfaces and investigate the influence of geometric parameters on system performance. Multi-objective optimization was subsequently performed using Pareto front analysis, revealing a clear trade-off between cooling efficiency and hydraulic resistance. Several representative optimal solutions were identified, including designs corresponding to minimum cooling time, minimum pressure drop, and balanced performance conditions. The results indicate that channel diameter is the dominant parameter affecting pressure drop, whereas cooling performance is governed by coupled interactions among the geometric variables. Overall, the proposed Kriging-based framework provides an efficient and reliable approach for the design and optimization of conformal cooling channels. The methodology can support advanced mould design while significantly reducing the computational cost associated with extensive numerical simulations.