Defence University College

Biodiesel production from waste cooking oil is a potential feedstock to alternative fuel sources. Produced alternative fuel sources were to have a strong emphasis on energy efficiency to substitute the declining world supply of fossil fuels while also improving emission characteristics in diesel engine applications. The development of industrialization and mechanization causes increased fossil fuel consumption and its usage. This study aimed at an experimental investigation of biodiesel blended fuel combustion efficiency for single-cylinder diesel engines under varying engine loads at constant engine speed 1500 rpm. The transesterification process was used to produce biodiesel from waste cooking oil using NaOH as catalysts. Biodiesel reaction takes place with oil to methanol ratio of 6:1 at 60 °C reaction temperature for 90 min with 600 rpm stirring speed while 90% yield gained. Eight sample biodiesel blends were prepared with percentage value B5%, B10%, B15%, B20%, B25%, B30%, B35%, B40% v/v and B100% to investigate the effect of biodiesels on engine performances. The experimental result reveals that the mixed biodiesel blends have lower average value in terms of brake torque, brake power, and brake thermal efficiency than diesel fuels as a percentage of 16.79%, 4.08%, and 27.9% respectively. In addition, the average BSFC of biodiesel blends was increased by 4.8% than baseline diesel fuel. Related to exhaust gases emission test results were shows that the average CO and HC emissions decreased by 52.2% and 60% with increasing loads and blends and the increment of CO2 and NOx by 28.1% and 45.4% respectively than baseline fuel with increasing blends and loads. Biodiesel derived from waste cooking oil was less costly and environmentally friendly and viable to substitute petro-diesel.

The use of autonomous robots has grown in popularity during the last few years. Robots, including such wheeled and legged autonomous robots, have become such a simple answer to dull, dirty, and hazardous chores. Outdoor applications, like as on a mudd

This paper mainly addresses Ethiopia's defence diplomacy through its participation in peace support operations. This qualitative research study aims to analyze and synthesize the findings from relevant literature reviews to show to what extent Ethiopia has used peacekeeping participation as a tool of defence diplomacy in contributing to peace and security, especially in the Horn of Africa region. The paper finds that Ethiopian defence diplomacy is essential for Ethiopia to achieve its global foreign policy objectives, promote regional stability and cooperation, and seek regional influence.

Abstract This paper focuses on the thermal analysis of the synchronous reluctance generator with a rating of 2.1 kW. It mainly uses explicit, and implicit finite difference methods for thermal analysis to reduce the complexity of thermal calculation for the machine's components. It compares the results with the results obtained using a finite element analysis (FEA) and includes the experimental verification of the obtained results. The explicit, and implicit finite difference thermal analysis is relatively simple and computationally fast. Once the design parameters are known, the electric losses and iron losses of the synchronous reluctance generator are evaluated. These machine parameters are utilized in developing the explicit finite difference (EFD), an implicit finite difference (IFD), and a 3D FEA model for thermal analysis. It is observed that the obtained results from the EFD, IFD, FEA, and experiments are very close to each other, and the temperature rise for the designed machine is within the desired and acceptable range.

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

The present work focuses on the experimental study of multi-cylinder DI diesel engine using diesel-biodiesel-ethanol based fuel blends. The blends contain diesel, biodiesel and alcohol are 60:30:10% on a volume basis. The measured emission was converted into a specific basis and the overall cycle emission was also calculated. The combustion parameters such as cylinder pressure history, maximum pressure and its angle, rate of pressure rise, heat release rate, and angle of 5, 10, 50, and 90% mass burning were compared. A little lower peak pressure was observed for hybrid fuel blends (< 7%), the premixed heat release was lower than diesel fuel whereas the diffusion stage heat release rate was higher. The lower rate of pressure rise for hybrid fuel blends shows its suitability in diesel engines in terms of combustion noise, higher thermal efficiency, lower specific and cyclic emissions, than diesel expect unburned hydrocarbon in the exhaust. [Received: September 4, 2021; Accepted: November 14, 2022]

The increasing demand for eco-friendly materials and technology has made the industry focus on bio-compatible composites. This made the researchers explore the potential of eco-friendly, bio-degradable, and inexpensive banana fibre for automotive applications. This work reports the preparation and testing of banana fibre natural hybrid composite fibres randomly oriented with and without adding silica filler (5–15 wt.%) through a hand lay-up process. The mechanical properties such as tensile modulus, flexural modulus, hardness, impact strength, and water absorption capacity were measured. Composite specimens having a fibre length of 30 mm (15 wt.% of silica) exhibited better mechanical properties. The hardness, tensile, flexural, and impact strength measured were 46.74 HV, 54.71 MPa, 127.94 MPa, and 15.19 kJ/m2. The results showed significant improvement in mechanical properties in silica-reinforced hybrid composite compared to composites without silica filler. The wt.% of banana fibre increases, and the number of free hydroxyls (-OH) groups increases in cellulose, increasing moisture absorption. The pattern in which the composite absorbs the moisture at room temperature is called “Fickian behaviour.” Furthermore, scanning electron microscope (SEM) characterisation studied the interaction between fibre matrix and the distribution of silica reinforcement. This research concludes that bio-composites that exhibit improved mechanical properties are eco-friendly and are found to be suitable for automotive applications that meet present-day requirements.

The Chapter discusses the turbine characteristics to design low-power rating generators. The low-power machine results in small wind turbines, hence distribution of power generators have attracted a growing interest from the demand, for remote and rural electrification. In renewable energy generation the design of the generator from the wind turbine is the most challenging part of the design. The generator specifications have been obtained from wind turbine models such as torque, speed and power. Based on these specifications the design of the generator with rating of 1 kW has been achieved. The turbine characteristics have been studied and various parameters of the designed machine are analyzed through analytical model and finite element analysis.