Thermal optimization of parabolic trough collector in solar powered ship using hybrid Oldroyd-B nanofluid: Artificial intelligence driven heuristics

In today's quest for renewable energy solutions, solar power is at the front line of reducing carbon excretions. To promote seas' cleanliness, solar powered ships are providing an environment-friendly replacement for the shipping industry and the thermal performance efficacy of parabolic trough solar collectors is of great importance for such vessels. In this study, the nonlinear Oldroyd-B model is executed in the form of hybrid nanofluid composed of silver-magnetite nanoparticles dispersed in ethylene glycol for heat transfer in a parabolic trough solar collector fitted inside the solar powered ship with constant form inclined magnetic field effect using artificial intelligence based neuro-heuristic hybrid approach involving genetic global algorithm composed of sequential quadratic programming local solver. The governing partial differential equations generated through Oldroyd-B hybrid nanofluid are first transformed through similarity transformations in the shape of a nonlinear system of ordinary differential equations and then solved numerically to analyze velocity as well as thermal profiles by engaging sundry scenarios organized through physical parameters. The scrutiny of acquired numerical outcomes is validated through reference solutions generated using the Adams numerical technique and this verification is displayed in terms of graphical and tabulated form of absolute errors. Moreover, it is observed that thermal performance of the parabolic trough solar collector is improved by 12 % with an escalation in magnetic field parameter. A comprehensive statistical and error analyses are also performed to confirm the suitability of the designed scheme. This work provides a robust computational framework for strengthening thermal optimization in solar-powered engaged systems, advancing renewable and effective energy solutions in maritime applications.