Search

• 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]

One of the most important hydraulic structures that has been overlooked is culverts. Regular maintenance and inspections are required to ensure that these structures are used effectively and safely. Culvert inspection, on the other hand, takes a significant amount of time and resources, and agencies cannot afford to do it on a regular basis. To overcome physical inspection challenges, this study proposed employing machine learning algorithms to predict the condition of Utah's culverts based on historical data. This study assessed multiclass classification algorithms that had not been used before. The classification machine learning algorithms employed in this study are Random Forest, Decision Tree, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network. Also, a combined dataset from four states was used to address Utah's limited culvert inventory data. The final dataset contained 2555 culvert records. Out of the developed prediction models, Random Forest had the highest accuracy of 82%. The findings demonstrated that Random Forests could be used to improve culvert network quality while reducing culvert maintenance expenses for UDOT. The proposed model may be applied to similarly existing culvert inventories located around the nation. [ABSTRACT FROM AUTHOR]