The Journal of Engineering Research [TJER]

Contribution To the Evaluation of Soot Deposit Thickness Evolution and Its Impact on Heat Transfer Within Heavy Diesel Engines: An Innovative Simplistic Procedure

Abdelhamid Rahim, larbi Hemmouche, A. Beniaiche , abdelkrim Liazid — Mon, 29 Apr 2024 00:00:00 +0400

This study deals with a numerical procedure designed and built to evaluate the evolution of soot deposits thickness and their impact on heat transfer for a high-pressure common rail 16V280 marine diesel engine piston. Using a combination of 3D numerical computations and an iterative calculation algorithm, this work reveals the complex relationship between soot deposit, temperature distribution, and piston thermal dynamics. Non-uniform soot deposit distribution is observed, concentrated at the piston bowl peripheral regions. This distribution aligns with theoretical expectations, indicating the influence of the swirl effect. The presence of soot deposits alters the temperature distribution, implying displacement of high-temperature zones towards the bowl region of the piston. The reduction in surface temperature of approximately 14%, is attributed to the lower heat transfer coefficient of deposited layers. This greatly influences the distribution of thermo-mechanical stresses of the piston. The proposed procedure offers an approach to assess the impact of soot deposit on heat transfer. In addition, this study contributes to a better understanding of realistic piston conditions and addresses the challenges introduced by soot deposits in heavy-duty diesel engines by combining the proposed procedure with investigations based on CFD software tools.

Artificial Neural Networks for Solar Radiation Prediction: Case Study, Al-Qadisiyah, Iraq

Ahmed Al-rubaye, Marwah M. Al-Khuzaie — Mon, 29 Apr 2024 00:00:00 +0400

For solar energy to develop a clean, renewable alternative to fossil fuels, it is important to be able to correctly predict surface longwave radiation. To improve cost-efficiency and accuracy in surface longwave radiation (SLR) predictions, forecast systems are increasingly utilizing artificial neural networks (ANNs). This study uses two different procedures for predicting solar radiation in great detail. The first model uses weather statistics from a station in Al-Qadisiyah, Iraq. The second model, on the other hand, uses daily statistics from 2022 from NASA's Prediction of Worldwide Energy dataset for the same site. The scaled conjugate gradient technique was used in both models. The goal is to find the best mix of meteorological factors that can be used with an ANN model to achieve accurate predictions. Based on the findings of this study, temperature, relative humidity, and Rainfall all seem to have a big effect on SLR. On the other hand, windy weather doesn't have much of an effect on SLR. ANN models also did very well when trained with data from NASA's Prediction of Worldwide Energy, with an R²-value of 0.823 and an RMSE value of 0.0106. The results show that this mix does better than other models in terms of performance score.

Reinforcement Learning-Driven Decision-Making for Live Virtual Machine Migration in Fog Computing

Shahd Alqam, Nasser Alzeidi, Abderrezak Touzene, Khaled Day — Mon, 29 Apr 2024 00:00:00 +0400

Virtualization is an essential mechanism in fog computing that enables elasticity and isolation, which in turn helps achieve resource efficiency. To bring high flexibility in a fog environment, migration of virtual machines from one node to another is required. This can be achieved by live virtual machine migration to reduce downtime and delays. Multiple existing studies have discussed live virtual machine migration in a fog environment. However, these studies have some limitations, such as pre-migrating the virtual machines based on mobility prediction only or based on the load only, which causes an issue of late and early handover. Due to the dynamic nature of fog environments, VM migration decisions require consideration of multiple factors. Hence, there is a need to develop a system that considers multiple factors to decide to migrate a virtual machine or not to solve the issue of early and late handover. This study proposes a novel approach to live virtual machine migration that applies reinforcement learning for decision-making. Experiments show that the proposed approach significantly reduces the latency of time-critical applications. The proposed system, outperforms the existing systems in terms of total average reward. The system outperformed the mobility-only-based system by 97% when tested with two fog nodes and by 80% when tested with sixteen fog nodes in terms of average reward. Further, the proposed system outperforms the load-based system by 50% and 75% when the environment consists of two fog nodes and sixteen fog nodes, respectively. This proved that considering multiple factors in deciding virtual machine migration in a fog environment can be effectively applied in time-critical applications to reduce latency.

Modified Best Channel Quality Indicator Scheduler for Heterogeneous LTE-A Network

Edidiong-Obong Ekpo, KUFRE UDOFIA, Akaninyene Obot — Mon, 29 Apr 2024 00:00:00 +0400

In this paper, a Long-Term Evolution-Advanced (LTE-A) Modified Best Channel Quality Indicator (M-BCQI) Scheduler is proposed for enhanced cell edge throughput performance. The scheduling algorithm utilizes two time slots, each of 0.5 ms in length, with the First Time Slot (FTS) implemented with the Best Channel Quality Indicator (BCQI) algorithm has a CQI-focused Resource Block (RB) allocation mechanism, while the Robin (RR) algorithm is the mechanism used in the Second Time Slot (STS) for RB allocation. Vienna LTE system level simulator was used for the proposed scheduling algorithm assessment. The results obtained showed significant improvement, indicating 44% superior cell edge performance and 56 % better fairness index in comparison to BCQI. However, M-BCQI slightly underperformed by 4.2% in terms of average throughput in comparison to BCQI and by 7% in comparison to RR in terms of fairness index.

Development of Selenium-Silica Nanocomposites By Sol-Gel Process Utilizing Three Different Reducing Agents and Its Characterization

Soumya Mukherjee — Mon, 29 Apr 2024 00:00:00 +0400

Nanocrystalline Selenium was synthesized within a silica matrix using a sol-gel method with high-purity SeO₂, ethyl alcohol, distilled water, and TEOS (Tetraethyl orthosilicate). This process encapsulated the selenium dioxide in the silica matrix, which was then reduced at about 100^°C using an oil bath. Three reducing agents were employed: acetone vapor, and sodium borohydride (NaBH₄) and hydrazine (N₂H₄) in liquid form. DSC-TGA analyses of the precursor mixture determined the crystallization temperature for selenium nanoparticle formation within the matrix to be around 100^°C. Post-heating phase analysis via XRD revealed hexagonal structures, with crystallite sizes between 33 and 43 nm determined using Debye-Scherrer's formula. Morphological studies showed irregular polygonal shapes with rough surfaces, with particulate sizes under 0.2µ for acetone vapor, around 0.1µ for hydrazine, and slightly over 0.1µ for sodium borohydride. Time variations were explored to observe phase and crystallite size changes. FTIR analysis was conducted for bonding assessment, revealing M-O coordination. The composite's absorbance was examined through UV-VIS spectroscopy, and its morphological attributes were investigated using FESEM analysis, complemented by EDX to determine elemental composition

Minimizing the Effect of Integrating Wind Farms on Transmission Power Systems

Mon, 29 Apr 2024 00:00:00 +0400

Expanding the renewable energy sources usage in power systems will help the government meet its energy policy goals of lowering emissions, ensuring high power supply reliability, and promoting spot markets with high competitivity. However, due to the intermittent nature of these sources, this will necessitate the mitigation of network vulnerabilities. High wind energy penetration in the grid could have an impact on the system frequency, voltage, real and reactive power response, and power quality of the system. The main objective of this paper is to investigate how the integration of wind farms in the Jalan Bani Bu Ali (JBBA) area will affect Oman's main interconnected system (MIS). Results show that the effect is minor on voltage and frequency stability by all disturbances taken into account.

Comparison of Seismic and Wind Actions on Medium to High-Rise Buildings in Muscat, Oman

Muhammad Bilal Waris, Khalifa Al-Jabri, Uhoud Al-Rawahi, Ali Al-Nuaimi — Mon, 29 Apr 2024 00:00:00 +0400

This study is a comparison of wind and seismic loads on medium and high-rise buildings in Muscat, Oman. It uses the proposed Omani Seismic Code and Eurocode EN1991 for seismic and wind calculations, respectively. Muscat falls under Zone-1 in the Omani seismic code and experience basic wind speed of 30 m/sec. The research investigates buildings with varying aspect ratios (1:1 and 1:2), heights (11, 15, and 19 stories), and structural layouts (frame only, core shear wall, and corner shear wall), using ETABS for structural analysis. The findings reveal that seismic actions are generally more significant than wind actions for buildings in Muscat. In frame-only structures, wind-induced base shear ranges from 16%-33% for 1:1 aspect ratio and 21%-43% in the x-direction and 10%-20% in the y-direction for 1:2 aspect ratio, when compared to seismic actions. This difference decreases with increasing building height. Incorporating shear walls notably reduces the maximum lateral displacement across all scenarios, with core-located walls being most effective, leading to a 49% reduction in lateral displacement. Shear walls also substantially mitigate first-story column shear forces and bending moments. The study concludes that seismic actions are more critical than wind actions in Muscat for simple moment-resisting frame systems. Additionally, using shear walls in these buildings is highly beneficial for controlling lateral displacements and reducing member forces.

Improve The Conversion Efficiency and Electrical Output of The Solar PV System by Developing and Implementing a Cooling System

Alaa N. Abed, Naseer K. Kasim — Mon, 29 Apr 2024 00:00:00 +0400

The performance of the grid-connected solar photovoltaic (PV) system in the present study is improved using cooling technology. This technology reduces the temperature of solar PV modules and thus increases their efficiency and lifespan. The current solar PV system has been installed in northern Baghdad in the city of Taji. This study included improving electrical power, conversion efficiency, and performance ratio. All of these improvements are achieved by cooling using water. The performance ratio (PR) and efficiency maximum values of the reference (unimproved) and improved solar PV modules are at 10:10 am at 88.30% & 96.30% and 13.4% & 14.6%, respectively. Improved and reference PV modules maximum solar radiation values occur at 12:00 pm at 1.91 W and 1.566 W, respectively. The maximum solar radiation values of the reference and improved PV modules are at 12:00 pm at 922.9 W/m² and 1012.22 W/m², respectively. The maximum daily gain of the power and solar irradiance are 22% and 10%, respectively. The significant innovation in the study is the successful performance optimization of the grid-connected solar PV system. Using different equations to study performance by following different behaviors. Along with improving and studying CIGS PV module performance.