https://www.engineresearch.ir/ The Journal of Engine Research en daily 1 Sat, 22 Nov 2025 00:00:00 +0330 Sat, 22 Nov 2025 00:00:00 +0330 https://www.engineresearch.ir/article_722283.html In this study, the effect of nozzle hole number on fuel injection, atomization, mixing quality, combustion characteristics, and exhaust emissions in an RCCI engine was investigated using three-dimensional simulations in CONVERGE software. The results revealed that increasing the number of nozzle holes leads to a reduction in fuel injection pressure and velocity, consequently cumulative the Sauter mean diameter. This reduction in kinetic energy and turbulence weakens both primary and secondary droplet breakup processes, significantly lowering fuel evaporation rates and mixing quality. With a 3-hole nozzle, the high kinetic energy of the spray resulted in finer droplets, faster evaporation, and improved fuel- air mixing, leading to a shorter ignition delay and enhanced combustion and gross indicated efficiency, which were 17.03 degrees, 92.1%, and 39.4%, respectively. Conversely, increasing the hole number to 6 (baseline) and 9 reduced spray kinetic energy and enlarged droplet sizes, which increased the mixing duration and combustion period, thereby deteriorating the atomization process. From an emissions perspective, a higher number of nozzle holes caused an increase in HC and CO emissions due to incomplete combustion, while NOx emissions decreased as a result of lower peak combustion temperatures. The findings demonstrate that nozzles with fewer holes significantly enhance combustion performance and thermal efficiency in RCCI engines by improving atomization, promoting efficient fuel- air mixing, and reducing ignition delay. https://www.engineresearch.ir/article_731456.html Lithium-sulfur batteries have attracted significant attention as a promising option in the energy storage industry, particularly for electric vehicles, due to their high energy density, low weight and low production cost. one of the main challenges faced by batteries used in electric vehicles is their performance during cold start condition which involve low temperatures and high output current simultaneously. Additionally, in order to evaluate battery performance under real driving condition-consisting of repeated cycles of acceleration, braking and constant speed- the use of the New European Driving Cycle (NEDC) can be useful. In this study, a 3.4 Ah lithium–sulfur battery was numerically simulated using the NTGK model within the ANSYS Fluent software. The purpose of the study is to investigate the thermal and electrochemical behavior of the battery simultaneously and to utilize the results for the design of a battery management system. The NTGK model requires fewer input parameters compared to other models and demonstrates acceptable accuracy in predicting battery behavior. Model validation was performed by comparing voltage versus discharge capacity curves at discharge rates of 0.2C, 0.5C, and 1.5C, with maximum deviations of 11.1%, 6.48%, and 3.07%, respectively. The corresponding temperature rises at these rates were 0.4, 2.2, and 8 K. In the cold start test, the maximum instantaneous voltage drop was 0.76 V, and the highest temperature increase was only 0.6 K. Moreover, after applying ten NEDC cycles, the remaining state of charge (SOC) of the battery was 51%, which is significantly higher compared to two other lithium-ion batteries with final SOCs of 13.3% and 28.5%. The minimum instantaneous voltage drop observed in this test for the lithium–sulfur battery was 0.67 V, whereas it was 0.9 V and 1.2 V for the other batteries. These results confirm the stable performance and favorable thermal behavior of the lithium–sulfur battery under realistic operating conditions. https://www.engineresearch.ir/article_731578.html The use of common rail diesel engines is increasing in commercial, road construction, mining, and agricultural machinery due to their compliance with environmental standards. The fuel system in these engines operates at high rail pressures up to 2000 bar. At such pressures, a temperature rise in the fuel system and physical changes in the fuel are inevitable. In this applied research, three injector nozzles from Bosch, Denso, and Delphi—with different manufacturing technologies but similar operational principles—were tested using the Bosch measurement method on a CRT2000 injector test bench. The evaluation was performed using four types of domestic diesel fuel (Mahshahr 0.5%, Mahshahr 1%, Bandar Abbas 1%, and Lavan 500 ppm) with identical density (820 kg/m³), viscosity (3 cSt), and cetane number (49), alongside one foreign diesel fuel (D6) with a density of 870 kg/m³, viscosity of 5 cSt, and a cetane number of 49. The results indicated that in all three injectors, the mass of fuel injected into the combustion chamber increased with rising pressure. However, the amount of fuel injected for the foreign diesel (D6) was greater than that for the domestic types. Based on the performance data, it was concluded that this increase is due to the higher density and viscosity of the foreign diesel fuel. Another finding was that the fuel injection rate at a constant pressure increased with a longer pulse width applied to the injector. https://www.engineresearch.ir/article_729317.html Lubricants are essential for the efficient operation of internal-combustion engines. Dispersing nanoparticles—especially hybrid nano-additives—offers a new route to upgrade lubricant performance. Earlier work has relied almost exclusively on experiments, an approach that is expensive and often unable to predict the full behaviour of hybrid systems. Here, we combine molecular-dynamics (MD) simulations with bench tests to examine the viscosity of a 20W-50 engine oil containing a 50:50 mixture of ZnO and CuO nanoparticles. Samples were prepared at 0.25, 0.50 and 0.75 wt % and characterised at 27, 40, 50 and 100 °C. Stability was verified by zeta-potential measurements and visual inspection. The largest discrepancy between simulated and measured viscosities was 9.8 %, confirming that the MD model is reliable. ANOVA and t-test analyses showed that both temperature and concentration exert statistically significant effects on viscosity (p < 0.05). As expected, raising the temperature lowers the viscosity, whereas increasing the nanoparticle loading raises it. Compared with an oil containing only ZnO, the hybrid formulation gives a markedly lower viscosity—by 13–16 % at 100 °C—demonstrating its advantage under realistic engine conditions. The reduction surpasses those reported for conventional additives such as MoS₂ and Al₂O₃ and indicates the potential for lower friction and improved engine efficiency. https://www.engineresearch.ir/article_730027.html In recent years, due to the depletion of fossil energy resources and concerns about greenhouse gas emissions, the use of hydrogen and fuel cells (FCs) has received much attention. Therefore, there is interest in establishing hydrogen production infrastructure and developing fuel cell electric vehicles. Fuel cell electric vehicles with high range, short refueling time, and zero emissions, compared to battery electric vehicles, are considered the roadmap of future vehicles. This paper focuses on modeling and fuel consumption of fuel cell electric vehicles (FCEV) using ADVISOR software. The simulation of the fuel cell electric vehicle system with a thermostat control strategy during different driving cycles is carried out. The aim of applying this strategy is to reduce the fuel consumption of fuel cell electric vehicles. The results obtained from the fuel consumption in the three driving cycles Federal Test Procedure (FTP), New European Driving Cycle (NEDC), and Urban Dynamometer Driving Schedule (UDDS) are 12.1, 7.1, and 7.6 liters, respectively, which were validated with the fuel consumption results of a prototype vehicle from Argonne National Laboratory (ANL). Then, the parameters affecting hydrogen fuel consumption, such as road gradient and aerodynamic drag coefficient, were also investigated in three different driving cycles: FTP, NEDC, and UDDS. Hydrogen fuel consumption in the FTP driving cycle increased by an average of 24.66% for a one percent increase. In the NEDC driving cycle, this increase in hydrogen fuel consumption is equal to 25.92% on average. Also, in the UDDS driving cycle, the increase in hydrogen fuel consumption for a one percent increase in road gradient is equal to 25.87% on average. The results also show that with an increase in the aerodynamic drag coefficient from 0.26 to 0.30, the average hydrogen fuel consumption per 100 km increased by an average of 2.5%. https://www.engineresearch.ir/article_729318.html Engine pistons are subjected to severe thermo-mechanical loading. The resulting high-cycle fatigue (HCF) is the dominant damage mechanism and governs service life; hence, reliable fatigue-life prediction is essential. This study quantifies the influence of switching the fuel from gasoline to compressed natural gas (CNG) on the HCF life of a gasoline-engine piston. A sequentially coupled thermo-mechanical finite-element model was built in ANSYS; temperatures and stresses were exported to nCode Design-Life and the Goodman mean-stress correction was applied to estimate HCF life. Because CNG burns more slowly than gasoline, the combustion event is longer, so the piston crown is exposed to higher temperatures and stresses under CNG operation. The simulation shows that the peak temperature rises by 24 °C and the maximum stress by 5.04 MPa when CNG is used. Consequently, the predicted HCF life drops by 1.44 × 10⁸ cycles (≈ 29 %). The numerically identified critical location coincides with the crack initiation site observed in a failed piston, validating the model. https://www.engineresearch.ir/article_735323.html The This research focuses on detecting misfire in a four-cylinder four-stroke gasoline engine inside an acoustic engine test cell using audio signal processing. This research proposes a smart solution by combining signal processing techniques and artificial neural networks. Misfire was created by fuel injection cut off for each cylinder at a constant speed of 760 rpm, and the audio signals were recorded under controlled acoustic conditions. FFT, MFCC and STFT techniques were used for feature extraction. The results showed that the artificial neural network and the one-dimensional convolutional neural network with features extracted from the fast Fourier transform achieved accuracies of 98.40% and 99.36%, respectively. Also, the two-dimensional convolutional neural network using features extracted from the short-time Fourier transform achieved an accuracy of 99.71%. These results show that the proposed methods, especially the use of two-dimensional convolutional neural networks, have a very good performance in identifying the healthy and faulty state of the engine and can serve as an effective tool for real-time monitoring and fault diagnosis of gasoline engines. https://www.engineresearch.ir/article_735324.html Given the importance of improving the efficiency of internal combustion engines, reducing fuel consumption, and emissions, boosting technology, particularly the turbocharger, has been introduced as an effective solution. Turbocharging enables the realization of the engine downsizing concept. In this study, the XU7JP4/L3 engine was first simulated in GT-Power software, and subsequently, a boosting circuit was integrated into the engine model. The performance of the boosting system was evaluated under two wastegate control strategies. In the first case, an internal wastegate with a mechanical control mechanism was employed, while in the second case, an electronic control mechanism was utilized. The results revealed that brake power under boosted conditions with mechanical wastegate control increased by an average of 33%, and with electronic wastegate control by 37%, compared to the naturally aspirated mode. The brake torque in the boosted mode with mechanical wastegate control also increased by 25%, 37%, and 41% at 1500 rpm (simulation start), 2500 rpm (maximum torque), and 6000 rpm (maximum power), respectively, compared to the naturally aspirated mode. The implementation of electronic wastegate control shifted the maximum torque to 3500 rpm and further increased it by 5% compared to the maximum torque achieved under mechanical control. Furthermore, the brake-specific fuel consumption decreased by an average of 2% at low-to-medium speeds and by 3% at medium-to-high speeds in both boosted modes compared to the naturally aspirated mode. In contrast, the instantaneous fuel consumption increased by an average of 32% across the entire engine operating range. Overall, the results of this study confirm the superiority of the electronic wastegate control mechanism in enhancing the impact of the boosting system on the performance of the XU7 engine. https://www.engineresearch.ir/article_735325.html Considering the growing concerns about the depletion of fossil fuel resources and air pollution, the investigation of alternative fuels with cleaner combustion has gained importance. In this study, the effects of gasoline–n-butanol blends on the performance of the Iran National Engine were experimentally examined. The tests were conducted under controlled laboratory conditions at engine speeds of 2000, 2500, and 3000 rpm and four load levels of 25, 50, 75 and 100 percent. The tested fuels included pure gasoline and three blends NB5, NB10 and NB15 containing 5, 10 and 15 percent by volume of butanol, respectively. The results showed that at full load and 3000 rpm, the NB15 blend increased torque by 13.7% and brake power by 12.4% compared to pure gasoline. At medium loads, the NB10 blend exhibited the most stable combustion and a more uniform torque output. Additionally, the addition of butanol reduced exhaust gas temperature by approximately 4 - 5 percent, indicating more controlled combustion and reduced thermal stress on the engine. The observed performance improvement is attributed to the higher octane number, the presence of oxygen in the molecular structure, and the high heat of vaporization of butanol, which lead to a more homogeneous mixture and enhanced flame stability. The results indicate that using fuels containing 10% and 15% n-butanol can effectively improve combustion quality and engine power, representing a step toward the development of sustainable and efficient fuels. https://www.engineresearch.ir/article_735326.html Optimal fuel utilization in internal combustion engines plays a key role in improving fuel economy, reducing emissions, and consequently mitigating environmental impacts. In this study, the combustion chamber geometry of a common-rail diesel engine is optimized with the aim of lowering fuel consumption and exhaust emissions while maintaining the engine’s current power output. For this purpose, a closed-cycle engine model was first developed in AVL FIRE software and validated at an operating speed of 2000 rpm. During the optimization process, ISFC, NOx, and Soot were defined as the main objective functions. The geometric parameters of the piston bowl, with specified upper and lower bounds, were used as inputs to the multi-objective genetic algorithm (MOGA). CFD outputs, including NOx and Soot levels for each design configuration, were evaluated, and the variations of emissions throughout the optimization process were analyzed. A total of 240 design cases were generated by the algorithm. Among the five initially selected designs, two were discarded due to power reduction. From the remaining three designs, one was finally chosen. In this selected case, Soot and ISFC changes were negligible; ISFC increased by only 0.052%, which is almost identical to the baseline, whereas NOx emissions were reduced by approximately 2%. Overall, the optimization process resulted in a piston bowl design that produces the lowest NOx and Soot emissions while maintaining fuel consumption close to that of the baseline combustion chamber. https://www.engineresearch.ir/article_735327.html The use of new and innovative methods in the design of internal combustion engines is essential for the significant development of the automotive industry. In spark ignition engines with a conventional fuel supply circuit, the amount of fuel injected by the injector is strongly dependent on the pressure difference between the back and the injection location of the injector, as well as, the injection period. The pressure at the injection location cannot be controlled due to the phenomena of air flow disconnection and fuel evaporation fraction, and causes complications in estimating the amount of injected fuel. In the present work, a gas pressure fueling system (GPFS) was designed to estimate the mass of injected fuel per cycle. In this study, a single-cylinder research spark ignition engine under certain operating conditions was used, First, for a given injection period with the conventional engine fueling system (CEFS), experimental data of cylinder and intake manifold pressures, TDC, and crank angle were extracted for more than 550 consecutive cycles. Then, using the GPFS method at different gas pressures, in addition to the aforementioned data, the fuel volume consumed for more than 2500 consecutive cycles was measured and stored. With this method, the fuel injected per cycle was estimated. A lot of effort was made to find a case that would provide similar performance to the CEFS method, and an experiment in which the gas pressure was 0.25 bar higher than the CEFS line pressure was found to show relatively similar performance of the CEFS case. The data of these two cases have been examined and compared in this study. The results showed that the GPFS method, with an increase in pressure of 0.25 bar, had similar performance to the CEFS method, meanwhile it was also possible to measure fuel mass per cycle. https://www.engineresearch.ir/article_735328.html The persistent use of internal combustion engines in the transportation sector underscores a growing need for efficient methods to optimize their performance and emissions calibration. Conventional calibration methods, which rely heavily on experimentation, are often time-consuming, costly, and incapable of capturing the complex, non-linear interactions among critical parameters. This study presents a hybrid methodology integrating Design of Experiments (DOE) and Machine Learning (ML) to model Brake Specific Fuel Consumption (BSFC) and HC and NOx emissions in a turbocharged gasoline engine. Two advanced ML algorithms were trained using an optimized DOE based on the Fedorov algorithm. Their predictive performance was systematically evaluated and compared across varying data volumes, ranging from 12.5% to 50% of the design space. Under limited data conditions (25% of data), both models demonstrated stable performance for predicting HC and NOx emissions, attributable to their inherent robustness against overfitting. Nevertheless, the prediction errors for these pollutants remained significant compared to those for BSFC, highlighting the necessity for further model refinement and experimental validation. When the data volume was increased to 50%, both models achieved high predictive accuracy, with mean R² values of 99% for BSFC, 96% for NOx, and 90% for HC. This approach demonstrates that a desired calibration accuracy can be achieved using only 50% of the experimental data, thereby potentially reducing testing time and associated costs by half.