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• A PV/T device with sheet-and-needle finned serpentine tube collector is analyzed. • PV panels are cooled using the water-silver nanofluid. • Random forest machine learning approach is used to develop predictive models. • Thermal, electrical and overall efficiencies are utilized as objective functions. • Utilization of needle fins improve the energy and exergy performance of the system. The Photovoltaic thermal (PVT) collector performance is numerically investigated considering the effect of using needle fins in the serpentine channel with Nanofluid (NF). The influence of increasing the nanoparticle concentration (φ) and Reynolds number (Re) on the energy and exergy features of the PVT device is examined. A comparison is made between the hydrothermal characteristics of the PVT with the finned and plain serpentine channels. The utilization of needle fins improves the thermal efficiency (η th), electrical efficiency (η el), and overall efficiency (η el) by 8.56–10.22%, 0.13–0.24%, 5.12–5.67%, respectively, against the PVT with the plain serpentine channel. Moreover, thermal exergy efficiency (ξ th), electrical exergy efficiency (ξ el), and overall exergy efficiency (ξ ov) by 8.56–1.22%, 0.13–0.24%, and 2.61–2.79%, respectively, versus the PVT with the plain serpentine channel. Moreover, the Random Forest (RF) machine learning approach is used to develop a predictive model for η th , η el , η el , ξ th , ξ el and ξ ov in terms of Re and φ. The outcomes of modeling proved that all the results were in an acceptable level of accuracy and the overall efficiency in both energy and exergy yielded superior precision in comparison with the other targets. [ABSTRACT FROM AUTHOR]

The bottom pivot is a vital support device in the miter gate but often subject to poor lubrication and wear failures. Calculating the hydrodynamic lubrication characteristics of the bottom pivot is a complex three-dimensional (3D) problem, and most of existing models adopt simplified assumptions to reduce the calculation difficulty. To solve this issue, this work develops a 3D model to calculate the hydrodynamic lubrication characteristics of the miter gate bottom pivot. The finite difference method is used to solve the oil film thickness and pressure distribution based on the spherical coordinates Reynolds equation. The component forces in three directions are calculated from the pressure distribution and compared with the theoretical values to generate the calculation difference. Then, the genetic algorithm (GA) is used to minimize the difference to determine the optimal initial parameters for the 3D model. The analysis results show that the calculation accuracy can be significantly improved by using the optimal initial model parameters. When our initial pressure is 5.64MPa, the results meet the engineering accuracy requirements. [ABSTRACT FROM AUTHOR]