Explore the vast range of titles available.

The implications of adding cerium oxide (CeO 2 ) nanoparticles as a fuel additive to a castor oil biodiesel–diesel fuel blend on engine performance and emissions in a single-cylinder four-stroke diesel engine under various speed were examined in the current study. The test fuels used were fossil diesel fuels, B5 blend biodiesel (as 5% biodiesel and 95% diesel), B10 blend biodiesel (as 10% biodiesel and 90% diesel), B15 blend biodiesel (as 15% biodiesel and 85% diesel), B20 blend biodiesel (as 20% biodiesel and 80% diesel), and B25 blend biodiesel (as 25% biodiesel and 75% diesel), with cerium oxide (CeO 2 ) nanoparticle additive (75 ppm). The result of the physio-chemical properties of the oil samples was within the limit of the ASTM standard. The addition of CeO 2 nano additive to the biodiesel–diesel blends has demonstrated a significant reduction in emission and increased in engine performance for all biodiesel–diesel blends for the engine operating speed range. From the result B25 have the maximum reduction rate in BSFC and B10 have the minimum reduction rate in BSFC. The average maximum increment of thermal efficiency was 22.2% for B10 with CeO 2 inclusion. CO emission increased as engine speed increased. HC emission was reduced for all blend, with and without CeO 2 nano additions as speed increased. Maximum NO x emission was seen at the rated speed of 2700 rpm without nano additive and at 2900 rpm with nano additive. CeO 2 nano additive reduced the soot opacity by 11.56% for all biodiesel–diesel blends for the engine operating speed range. As the objective of this study the results indicates CeO 2 nano additive reduced emissions and improved the performance. So, using sustainable biodiesel–diesel blends made from castor oil with CeO 2 nano additive advisable in ideal operating conditions for diesel engines.

This study investigates the utilization of biodiesel derived from castor seeds and enhanced with 2% of diethyl ether and CeO 2 NPs to improve engine performance and reduce emissions. Various fuel blends, including biodiesel-diesel mixtures with and without nanoparticles, were tested under fixed load conditions. Results indicate that adding CeO 2 nanoparticles improved combustion and reduced emissions, particularly CO and NO x . Furthermore, biodiesel blends showed in increasing thermal efficiency and reduced BSFC. From the blends B10 showed the highest increase in thermal efficiency. Results indicate that adding CeO 2 NPs improved combustion efficiency, leading to a 22.2% increase in thermal efficiency for the B10 blend. Furthermore, brake power increased by 18% for B10 and 19% for B15 when CeO 2 was added, while brake-specific fuel consumption decreased by up to 26.02% for B25. Emissions were significantly reduced: CO emissions dropped by 12% for B5, and HC emissions decreased by 37.7% for B5 with CeO 2 addition. However, NOx emissions increased by 4.3% for B10 due to improved combustion. Overall, this research suggests that sustainable biodiesel-diesel blends with CeO 2 nanoparticles can effectively enhance engine performance while mitigating emissions, offering a promising solution for cleaner diesel engine operation.Please make justify the abstract to make smart lookup

This study aimed to evaluate the effect of adding 75 ppm cerium oxide nanoparticles (CeO 2 NPs) with 2% diethyl ether (DEE) to castor biodiesel diesel blends on the performance and emissions of a single-cylinder, four-stroke CI engine operated at 80% load across 1700–3000 rpm, revealing that the nano-additive improved brake torque, brake power, thermal efficiency, and reduced carbon monoxide (CO), hydrocarbons (HC), and soot emissions, with only marginal changes in nitrogen oxides (NOx) compared to blends without CeO₂. The results indicate that the addition of CeO 2 NPs led to an increase in brake torque to 15.5 Nm (95% CI: 15.0–16.0 Nm) and brake power to 3.7 kW (95% CI: 3.6–3.8 kW). Furthermore, BSFC decreased to 250 g/kWh at (95% confidence interval (CI): 240–260 g/kWh), enhancing thermal efficiency to 38% (95% CI: 37–39%). Emission analysis revealed a reduction in CO levels to 0.425%V (95% CI: 0.400–0.450%V), HC emissions to 47.5 ppm (95% CI: 45.0–50.0 ppm), and NOx emissions to 80 ppm (95% CI: 75–85 ppm). These findings demonstrate the potential of CeO 2 NPs to enhance engine efficiency while reducing harmful emissions, making them a promising candidate for cleaner combustion technologies.

This study aimed to develop the Addis Ababa Driving Cycle (DC) using real-time data from passenger vehicles in Addis Ababa based on a neural network (NN) and principal component analysis (PCA) approach. Addis Ababa has no local DC for automobile emissions tests and standard DCs do not reflect the current scenario. During the DC’s development, the researchers determined the DC duration based on their experience and the literature. A k-means clustering method was also applied to cluster the dimensionally reduced data without identifying the best clustering method. First, a shape-preserving cubic interpolation technique was applied to remove outliers, followed by the Bayes wavelet signal denoising technique to smooth the data. Rules were then set for the extraction of trips and trip indicators before PCA was applied, and the machine learning classification was applied to identify the best clustering method. Finally, after training the NN using Bayesian regularization with a back propagation, the velocity for each route section was predicted and its performance had an overall R-value of 0.99. Compared with target data, the DCs developed by the NN and micro trip methods have a relative difference of 0.056 and 0.111, respectively, and resolve the issue of the DC duration decision in the micro trip method.

Standard driving cycles (DCs) and real driving emissions (RDE) legislation developed by the European Commission contains significant gaps with regard to quantifying local area vehicle emission levels and fuel consumption (FC). The aim of this paper was to review local DCs for estimating emission levels and FC under laboratory and real-world conditions. This review article has three sections. First, the detailed steps and methodologies applied during the development of these DCs are examined to highlight weaknesses. Next, a comparison is presented of various recent local DCs using the Worldwide Harmonized Light-Duty Test Cycle (WLTC) and FTP75 (Federal Test Procedure) in terms of the main characteristic parameters. Finally, the gap between RDE with laboratory-based and real-world emissions is discussed. The use of a large sample of real data to develop a typical DC for the local area could better reflect vehicle driving patterns on actual roads and offer a better estimation of emissions and consumed energy. The main issue found with most of the local DCs reviewed was a small data sample collected from a small number of vehicles during a short period of time, the lack of separate phases for driving conditions, and the shifting strategy adopted with the chassis dynamometer. On-road emissions measured by the portable emissions measurement system (PEMS) were higher than the laboratory-based measurements. Driving situation outside the boundary conditions of RDE shows higher emissions due to cold temperatures, road grade, similar shares of route, drivers’ dynamic driving conditions, and uncertainty within the PEMS and RDE analysis tools.

Battery electric vehicles (BEV) are suitable alternatives for achieving energy independence and meeting the criteria for reducing greenhouse emissions in the transportation sector. Evaluating their performance and energy consumption in the real-data driving cycle (DC) is important. The purpose of this work is to develop a BEV DC for the interlinked urban and suburban route of Addis Ababa (AA) in Ethiopia. In this study, a new approach of micro-trip random selection-to-rebuild with behaviour split (RSBS) was implemented, and its effectiveness was compared via the k-means clustering method. When comparing the statistical distribution of velocity and acceleration with measured real data, the RSBS cycle shows a minimum error of 2% and 2.3%, respectively. By splitting driving behaviour, aggressive drivers were found to consume more energy because of frequent panic stops and subsequent acceleration. In braking mode, coast drivers were found to improve the regenerative braking possibility and efficiency, which can extend the range by 10.8%, whereas aggressive drivers could only achieve 3.9%. Also, resynthesised RSBS with the percentage of behaviour split and its energy and power consumption were compared with standard cycles. A significant reduction of 14.57% from UDDS and 8.9% from WLTC-2 in energy consumption was achieved for the AA and its suburbs DC, indicating that this DC could be useful for both the city and suburbs.

In this work, wire cut electrical discharge machining (WEDM) is used for the material removing processes; it is utilized for machining conductive parts where it is required to produce complicated shapes, new profiles, new geometry, new product development, and high-accuracy components. This machining process is best suitable for high-end applications such as aerospace, automations, automobile, and medical devices. At present, most of the industrial sectors choose the WEDM process because it is used to develop products in a very short development cycle and at a better economic rate. In this paper, the selected complex geometry of the metal sample was eroded away from the wire during the WEDM process, which eliminates mechanical tensions during machining. The effect of different WEDM operation variables set as wire speed, wire tension, discharge current, dielectric flow rate, and pulse on and off time on the parameter, stainless steel 304 material removing rate (MRR) using RSM, has been studied. The MRR will be maximized if the optimum sets of operational variations are used and also achieve a superior surface finish.

The aim of this study is to carry out a focused literature review on the mechanical and tribological behaviour of AlCoCrFeNi High Entropy Alloys (HEA). HEAs are a proficient class of alloys designed by the use of several constituent alloying elements in equiatomic or close to equiatomic ratios. In view of their distinctive property range, there has been huge attention on this class of alloys. Among the various group of HEAs, AlCoCrFeNi-based HEAs have attracted interest due to their enhanced properties. Various AlCoCrFeNi-based HEAs are developed by adding additional elements such as Mo, Ti, and Zr. The effect of these alloying constituents on the mechanical, metallurgical, and tribological performance of the AlCoCrFeNi HEA is discussed in detail. In addition to that, the various techniques used to produce these HEAs are also discussed.

The main objective of this work is to analyse the impact of different EGR configurations (no EGR, cold EGR, and hot EGR) on exhaust emissions of PCCI-DI engine. Methanol port injection, dieseline direct injection, advanced injection timing, and different EGR rates were adapted and optimized on the baseline engine. A hybrid algorithm of grey relational analysis with the Taguchi method was implemented for optimization. Results were compared among the PCCI-DI combustion strategy with the baseline using cold EGR, hot EGR, and no EGR configurations. Both cold and hot EGR configurations resulted in lower emission of NOx plus smoke at different loads. At low loads, hot EGR showed promising results of lower HC and CO than the cold EGR with a difference of 18.33% and 33.3%, respectively. NOx and smoke reductions simultaneously and better trade-offs were obtained using cold EGR configuration.

PVC (polyvinyl chloride) is a tough polymer used in applications, including plumbing and construction materials. As natural fibre-reinforced composites have more advantages over conventional synthetic composites, this paper focuses on the fatigue analysis of PVC composite which is reinforced with coir fibre. The influences of three input parameters, namely, the size of the coir fibre, coir fibre content, and the chemicals that are used in the treatment of coir fibre on the fatigue life of the composite are examined. In the response surface model (RSM), Box-Behnken designs (BBD) are employed for the preparation/analysis/optimization of the samples. ANSYS software is used to perform the fatigue analysis of different samples containing various combinations of the parameters. To determine the effects of various input parameters on the fatigue behaviour of composites, ANOVA is employed to determine their optimal levels. Regression equations are established to determine the fatigue limit. When treated with triethoxy(ethyl)silane, coir with a concentration of 6 wt.% and a particle size of 75 μm exhibits a maximum fatigue limit of 2.819 MPa.