Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software

Abstract The optimisation of the cooling system performance in a tokamak reactor requires the accurate analysis of the thermo-hydraulic interactions under complex flow regimes. Although computational fluid dynamics (CFD) provides high-fidelity insight at a lower cost than experimental testing, optimisation remains computationally prohibitive due to the large number of simulations required. To address this, we introduce Hammerhead, the first open-source, transparent and fully integrated machine learning (ML)-CFD framework with automated high-fidelity database generation and modular multi-surrogate modelling for optimisation in pipe flow heat and mass transfer problems. The software provides seamless Python-OpenFOAM integration for automatic high-fidelity model database building and multi-surrogate model comparison from 3 ML algorithms with modular architecture within Python infrastructure. For the high-fidelity database, the software constructs a parametric space using up to four shape parameters to deform pipe wall geometries and perform the conjugate heat transfer simulations. The surrogate models are constructed on the basis of radial basis function interpolation, feed-forward neural networks, and Gaussian processes, and trained on the high-fidelity database to approximate thermo-hydraulic responses efficiently. The framework provides a comparative environment for systematically assessing surrogate model accuracy and reliability within the same optimisation workflow. The approach offers a practical tool that increases the feasibility of design optimisation on small datasets for cooling system applications. On a database of up to 400 cases for a 2D domain, a geometry with thermo-hydraulic performance enhancement of over 500% with respect to that of a smooth surface pipe at Re = 1000 was found with the aid of surrogate modelling, validated through high-fidelity simulation.