GPU-accelerated estimation of heat transfer coefficients in continuous casting under large interference by a novel multiagent-based dimensional learning Jaya algorithm

This paper addresses the challenge of estimating heat transfer coefficients (HTCs) through the measured surface temperatures under large interference in the secondary cooling zone (SCZ) of continuous casting processes. Conventional methods for calculating HTCs, typically reliant on surface temperature data, face significant challenges in achieving accuracy. These challenges primarily stem from the limitations inherent in traditional methodologies and the distortions caused by temperature outliers, which are a consequence of large interference. To address these issues more effectively, we introduced a novel algorithm–multi-agent and dimensional learning based Jaya algorithm (MADL-Jaya)–specifically designed to estimate HTCs. Furthermore, a hybrid method that integrates weighted least squares (WLS) with the MADL-Jaya was proposed to mitigate the impact of outliers. Additionally, estimating HTCs entails substantial computational demands. Consequently, a double-deck parallel algorithm employing graphics processing unit (GPU) acceleration was presented. This architecture facilitates both parallel heat transfer models and parallel MADL-Jaya calculations, thus significantly reducing the computational time required for HTC estimation. The experimental results unequivocally affirm the efficacy of the proposed algorithm in the accurate estimation of HTCs within the SCZ of continuous casting, effectively neutralizing the detrimental influence of outlier values while markedly improving computational speed.