نشریه تحقیقات موتور
پیاپی 73 (زمستان 1402)

In this research, a 3D-CFD model was created to investigate the behavior of spray and combustion in a three-cylinder GDI engine using a six-hole nozzle injector. To validate the model, in-cylinder data pressure was conducted at Iran Khodro Power Train Company (IPCO) under wide-open throttle and 5500 rpm conditions. The obtained data were incorporated into the CFD model to simulate the spray evolution and combustion process inside the cylinder during fuel injection. Droplet temperature and lambda distribution from 30 deg to 279 deg after injection Were displayed. Temperature distribution during 2 to 40 deg ATDC was illustrated in the paper. Finally, evaporated fuel and wall film of the cylinder head, liner, and piston. Intake and exhaust valves and equivalence ratio in the combustion chamber have been studied.

Driving cycle assessment is one of the common methods to evaluate a vehicle’s real-world condition also monitor fuel consumption and emissions. The basic challenge in the extraction of the driving cycle is data analysis to develop and define the suitable behavior of the device. Clustering, classification, and recognition of driving patterns are important steps in the extraction of a suitable driving cycle. Generally, the accuracy of modeling and recognition of AI-based methods is indicated by more than 90% and other outputs comply with big data. Thus, in this research, we endeavored to evaluate the effect of using artificial intelligence on the driving cycle of off-road vehicles. The major part of off-road vehicles are agricultural vehicles such as tractors which are divided into three categories based on agriculture operations; light, heavy, and extra heavy. In addition, the procedure of agricultural operation is effective on fuel consumption, loading, and exhaust emissions. The results of this research showed that the use of conventional machine learning methods for clustering and classification can be used for any volume of features. However, with an increase in features, the complexity of region segmentation and the effect of farm management factors cause overtraining conditions in the learning algorithm and reduce the accuracy of the extracted driving cycle and prediction of driving behavior. Therefore, it is necessary to use advanced algorithms with deep learning capabilities. Therefore, extracting the intelligent driving cycle for agricultural tractors based on the type of agricultural operation with the help of artificial intelligence methods can reduce fuel consumption, pollution, and optimal farm management.

The combustion of fossil-based fuels in ICEs, resulting in a huge amount of greenhouse gases (GHG) and leading to an immense global temperature rise, are the root causes of the more stringent emission legislations to safeguard health and that encourage further investigations on alternative carbon-neutral fuels. In this respect, the hydrogen has been considered as one of the potential clean fuels because of its zero-carbon nature. The current development of hydrogen-based ICEs focuses on the direct injection (DI) strategy as it allows better engine efficiency than the port fuel injection one. The behavior of the fuel jet is a fundamental aspect of the in-cylinder air-fuel mixing ratio, affecting the combustion process, the engine performances, and the pollutants emissions.In the present study, comprehensive investigations on the hydrogen jet behavior, generated by a Compressed Hydrogen Gas (CHG) injector under different operative conditions, were performed. A measuring system, suitable for the gaseous fuels, was used for measuring the flow rate. The fuel jet morphology was studied in a constant volume vessel filled with nitrogen as function of the injection pressure (up to 4.0 MPa) and different backpressure in the vessel, through the measurements of the jet penetrations, total areas, and cone angles showing a strong dependence from the set parameters. The cycle-resolved schlieren imaging technique by a high-speed camera was used to follow the jet spreads while the images were processed by a home-made procedure allowed to identify the contours of the hydrogen jet in the nitrogen gas and hence to measure the main parameters characterizing the jet structure.

Considering the importance and the effect of the rotating flows inside the cylinder on the performance of internal combustion engines and the direct effect of these flows on reducing the amount of greenhouse gas emissions and reducing fuel consumption, it is essential to investigate the characteristics of these flows. Therefore, the issue of ensuring the performance of the swirl meters and the accuracy of the results obtained from the measurement becomes essential. Due to the lack of a specific and standard reference and the loss of performance of the swirl meter over time, it is necessary to have a method and means for its verification and calibration. The present study was carried out to validate the performance of the paddle wheel swirl meter and design a new and easy method for its calibration. The results simulation performed by the dynamic mesh method are very close to the results obtained from the experimental test and have high accuracy, because it directly simulates the performance of the paddle wheel swirl meter, but on the other hand, it requires a very high computational cost. According to the results obtained from the numerical solution and experimental test, the simultaneous use of the calibration equipment along with the correlation relationship is suggested as a fast and high-precision method.

Detecting and rectifying spark plug faults are pivotal in preventing engine-related issues that can have substantial operational and financial consequences. To improve the accuracy and robustness of spark plug fault diagnosis, this research introduces a novel Convolutional Transformer approach that leverages the strengths of Convolutional Neural Networks and Transformers, which effectively capture both local and extended temporal dependencies within spark plug acoustic signals. The results of this groundbreaking approach, as presented in accompanying tables and figures, demonstrate its superior performance, achieving an impressive 97.1% accuracy in a challenging 4-class classification scenario using solely acoustic signals. This achievement signifies a significant advancement in spark plug fault detection, potentially ushering in more reliable and precise diagnostic methods, ultimately contributing to the prevention of costly engine breakdowns and the extension of engine lifespan. Deep learning techniques such as Convolutional Transformers offer a promising way to improve the reliability and performance of internal combustion engines as the automotive industry continues to evolve, highlighting the importance of this research for future automotive developments.

The Atkinson cycle engine plays a crucial role in advancing Hybrid Electric Vehicles (HEVs) due to its superior fuel efficiency compared to the Otto cycle engine. Thermodynamic analysis highlights the Atkinson cycle's significant advantage in cycle efficiency, with factors such as the expansion ratio and effective compression ratio, influencing overall efficiency. This investigation focuses on converting an Otto cycle engine to an Atkinson cycle engine by implementing late intake valve closing and increasing the compression ratio. The optimization of the high geometric compression ratio from 10.7 to 12.4 aims to overcome the Atkinson cycle's drawback, where combustion performance deteriorates due to a reduction in the effective compression ratio. GT-Power software is used to simulate engine performance under various operating points, and comprehensive experiments are conducted to determine the performance, fuel consumption, and combustion characteristics of the developed Atkinson engine. Experimental results show that the Atkinson cycle engine exhibits substantially lower overall fuel consumption compared to the Otto cycle engine, with an improvement in maximum thermal efficiency from 34% to 34.6%. Additionally, the fuel-efficient range of the Atkinson cycle engine surpasses that of the Otto cycle engine, with the minimum fuel consumption area now occurring at low-medium speed and medium-high-load operational conditions.