Designing high-efficiency parabolic trough receiver tubes via AI-assisted simulation

This study investigates the application of twisted lobed tubes in parabolic troughs to enhance thermal performance by overcoming limitations in heat transfer efficiency. Twisted lobed tubes can promote turbulent flow and increase surface area, but finding the optimum design and operation parameters (e.g., twisted pitch ratio, lobe number, and Reynolds number) is difficult since these parameters are linked together for influencing the heat transfer rate, pressure drop, and thermal performance. To address this technical gap, artificial neural networks were integrated with numerical simulations (using k-ω shear-stress transport turbulence model and the finite volume method) to evaluate how design parameters affect performance. The results indicate that thermal performance and the relative Nusselt number exhibit similar behaviour, while the relative friction factor has minimal impact on performance variations. The optimal configuration identified is a tube with 7 lobes, a relative pitch of 4, and a Reynolds number of 5,000, achieving a thermal performance of 1.97, a relative Nusselt number of 2.28, and a relative friction factor of 1.56. The presented approach demonstrates the potential of combining numerical simulations, artificial neural networks, and optimization algorithms to design more efficient solar thermal receivers, paving the way for broader solar thermal system adoption.