Poly Journal of Engineering and Technology (PJET)

Utilization of Catchment Morphometric Features to Estimate Design Peak Flow Ungauged Catchments: Insights Lake Tana Sub-Basin, Ethiopia

Walelign Kassie Endalew — 2025-08-26

A reliable estimate of design peak flow is one of the main and most frequently required engineering hydrology tasks. The necessity for estimating design peak flow is associated with civil engineering structures required for water storage, diversion, transport, and waterways crossings. The objective of this paper is to develop an alternative peak flow estimation model for the ungauged catchments using catchment morphometric features as input variables. Daily stream flow data from gauged catchments are collected and annual maximum daily flow series are extracted. At-site flood frequency analysis is used to estimate peak flow quantiles for 10, 25, 50, 100, and 200-year return periods. Based on previous similar experiences catchment area, longest stream length, channel slope, and catchment circularity ratio are selected as the most important catchment morphometric features for peak flow generation. Using GIS, the selected catchment morphometric features are quantified and regression analysis is applied to relate them with the quantiles. The result shows that Pearson type III, general extrem value, Log Normal, and general Pareto distributions are the best-fit probability distributions in the sub-basin. The regression analysis shows that design Peak flows have a very good degree of association with the catchment area, longest stream length, channel slope, and catchment circularity ratio with the coefficient of determination (R²) ranging from 0.962 to 0.993. The developed models can be used as alternative simplified means in estimating design peak flow for ungauged catchments in the Lake Tana sub-basin and other hydrologically homogeneous catchments.

Performance analysis of solar photovoltaic panels integrated with PCMs and Fins

Muluken Zegeye Getie — 2025-09-05

The design of cooling systems for solar PV panel systems has become increasingly important. The phase change materials, which are lower cost, have a long life, have reasonable efficiency, and can be integrated with different techniques, are a potential cooling mechanism for solar PV panels. In this study, numerical investigation and experimental testing of solar PV panels have been conducted using phase change materials (PCM) at different temperatures and fin arrangements. Transient numerical simulations were carried out with ANSYS Fluent software using a 2-D simplified geometry. Numerical simulations were used to evaluate the PV cell temperature and PCM thermal behavior. The numerical study was validated experimentally with PV-PT58/fins configured externally. The panel temperature was maintained below the reference panel temperature for the entire day by using PT58 and PT58/fins configured internally and externally. The average temperatures of the panel obtained from the numerical analysis were 32°C and 34.5°C for the experimental analysis, which are comparable. The experimental results show that the cooled PV module had an average efficiency of 12.03% compared to the uncooled panel's 10.84%, which is an improvement of 10.98% in the panel's electric efficiency.

Advancements and Applications of Artificial Neural Networks in Structural Engineering A Comprehensive Review

Habtamu Alemayehu Tadesse — 2025-08-04

Artificial Neural Networks (ANNs) have revolutionized the field of civil engineering by offering efficient and accurate solutions for complex material behavior predictions. This paper reviews the applications of ANNs in civil engineering, emphasizing their role in predicting the load capacities of structural components under various conditions. The study highlights the development and application of a deep feed forward neural network (FFNN) for predicting the load capacities of post-installed adhesive anchors in cracked concrete. Additionally, it explores a hybrid methodology combining nonlinear finite element (NLFE) techniques with FFNN to enhance prediction accuracy and reduce computational effort. The research demonstrates the significant potential of ANNs in diverse civil engineering applications, including crack detection, structural analysis, design optimization, and strength estimation. Despite challenges such as data quality, computational resources, model interpretability, and generalization, the opportunities for enhanced prediction accuracy, reduced computational effort, and adaptability to various applications are substantial. The study particularly emphasizes the potential for ANN adoption and development in Ethiopia, presenting opportunities for capacity building and infrastructure improvement. The findings underscore the robustness and efficiency of ANNs, particularly deep FFNNs, as a vital tool in advancing structural engineering practices.