Neural network-based regression for heat transfer and fluid flow over in-line cylinder arrays with random pitch distances at low Reynolds number

Abstract

Finding an arrangement, leading to a higher heat transfer and lower pressure drop, is crucial in the design of heat exchangers. Previous studies have primarily focused on regular arrangements with uniform pitch distances, which lack applicability to general configurations. In this study, we proposed a new procedure of a flow-learned building block (FLBB) to predict heat transfer in an in-line cylinder array with random pitch distances using a neural network-based regression analysis with a systematic data generation process. As a first step, we demonstrated the FLBB’s capability to predict the heat transfer and pressure drop in in-line cylinder arrays with random pitch distances at low Reynolds numbers from 1 to 100 for air ((Formula presented.)). Subsequently, a high-order FLBB approach was proposed to address the spatial interdependency between neighbouring cylinders, particularly in scenarios where vortex shedding occurs in the wake of cylinders at increased Reynolds numbers. The high-order FLBB approach was then shown to successfully describe various flow and temperature patterns using cylinder arrays with random pitch distances. The proposed procedure exhibited remarkable efficiency, requiring only about 1 s. Furthermore, the FLBB was successfully extended to various flow regimes, even encompassing unseen Reynolds numbers from 1 to 100.