Thermal limit of helical-nut surfaced tubes for divertor cooling

Abstract The exigencies imposed by the elevated thermal flux directed at a divertor under a unidirectional heating paradigm necessitate a rigorous thermo-hydraulic scrutiny of cooling channels with enhanced cooling efficacy. In this exposition, we systematically scrutinize the thermal limit characteristics of a helical-nut surfaced (HNS) tube, a prospective cooling conduit for fusion reactors. To accurately simulate the unidirectional high thermal load and subcooled flow conditions endemic to a tokamak, we have meticulously engineered a Joule heating apparatus capable of imparting heat loads up to 14.8 MW m −2 alongside a hermetically sealed experimental loop. Our empirical investigations delineate the influence of systemic parameters on the thermal limit. It is discerned that the thermal limit appreciably escalates in concordance with rising subcooling and mass flow rates. This phenomenon stems from the fact that elevated subcooling expedites vapor condensation within the channel, while an augmented flow rate enhances forced convective heat transfer dynamics. Concomitantly, the onset of nucleate boiling heat flux is observed to diminish, which can be attributed to the decrement in latent heat and liquid surface tension as pressure rises. Nonetheless, it is observed that system pressure does not exert a substantive impact on the thermal limit. Historical investigations have grappled with the challenge posed by predicting the thermal limit for HNS tubes under unidirectional high thermal loads, as extant thermal limit correlations—analyzed under subcooled flow boiling conditions—fail to account for the intricate helical fin surface structures intrinsic to HNS tubes. Consequently, these correlations consistently underestimate the thermal limit. In response, this study pioneers the formulation of a novel correlation, capable of prognosticating the thermal limit of HNS tubes subjected to unilateral high thermal loads. This correlation is meticulously derived deploying Python-based artificial intelligence regression techniques. The veracity of this innovative thermal limit correlation is rigorously validated, showcasing its proficiency in precisely predicting both the experimental results delineated in this study and the thermal limit data previously documented in extant literature with commendable accuracy.