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A numerical investigation has been performed in the current work on reactivity-controlled compression ignition (RCCI), a low-temperature combustion (LTC) strategy that is beneficial for achieving lower oxides of nitrogen (NOx) and soot emission. A light-duty diesel engine was modified to run in RCCI mode. Experimental data were acquired using diesel as HRF (high-reactivity fuel) and gasoline as LRF (low reactivity fuel) to check the accuracy and fidelity of predicted results. Blends of ethanol and gasoline with DTBP (di-tert-butyl peroxide) addition in a small fraction on an energy basis were used in numerical simulations to promote ignitability and reactivity enhancement of PFI charge. Achieving stable, smooth, and gradual combustion in RCCI is challenging at low loads, especially in light-duty engines, due to misfiring and poor combustion stability. DTBP is known for enhancing cetane number and accelerating combustion, and it is mixed in a PFI blend to avoid combustion deterioration. The factors governing reactivity stratification to achieve optimal combustion phasing were investigated in the present study. DTBP decomposition and its low-temperature oxidation chemistry were found to be responsible for affecting combustion phasing, heat release patterns, and emission trends. DTBP additive and different in-cylinder strategies were applied and studied to reduce unburned emissions. Adopting a multiple injection approach utilizing dual-pulse assisted in reducing HC and CO levels. It enhances combustion quality by providing adequate control over combustion phasing. Altering operating parameters like intake temperatures reduced HC, CO, and soot emissions by 97.6%, 57.6%, and 52.8%, respectively, compared to baseline gasoline/diesel RCCI data. Optimizing the injection timings of the first and second pulse helps achieve optimal combustion phasing and a 72.95% reduction in NOx emissions. The higher injection pressure of DI helped lower the CO and soot emissions by 53.33% and 51.84%, respectively.

Homogeneous charge with direct injection (HCDI) is a single-fuel low-temperature combustion (LTC) strategy that injects diesel into the intake port and inside the engine cylinder. The present study aims to numerically evaluate various oxides of nitrogen (NOₓ) mitigation methods such as split injection, exhaust gas recirculation (EGR), and water vapor induction in a single-cylinder diesel engine operated in HCDI mode. Numerical investigations are carried out using a commercial computational fluid dynamics (CFD) code CONVERGE. Experimental data are generated in a light-duty diesel engine operated in HCDI mode at 2.4 bar indicated mean effective pressure (imep) (low load) and 4.6 bar imep (high load) conditions to validate the CONVERGE predictions. The production engine is modified to run in HCDI mode through suitable modifications in the intake system, cylinder head, and fuel injection systems. The predicted combustion and emission parameters compared well with the measured data. The parametric investigations conducted with CONVERGE show that increasing the main injected fuel quantity in the split injection reduces NOₓ emissions. An increase in EGR fraction reduces NOₓ emissions; however, EGR reduces the indicated thermal efficiency (ITE) significantly even at a moderate proportion. Among the NOₓ mitigation methods chosen for investigation, water vapor induction greatly benefits NOₓ reduction. The efficacy of the three methods is compared at fixed combustion phasing at 2.4 bar and 4.6 bar loads in HCDI. It is observed that water vapor induction reduces NOₓ emissions by up to 71.3%, with a slight penalty of carbon monoxide (CO) and unburned hydrocarbon (UHC) emissions by 8.5% and 28.4%, respectively, at a 4.6 bar load.

Various feedstocks can be employed for biodiesel production, leading to considerable variation in composition and engine fuel characteristics. Using biodiesels originating from diverse feedstocks introduces notable variations in engine characteristics. Therefore, it is imperative to scrutinize the composition and properties of biodiesel before deployment in engines, a task facilitated by predictive models. Additionally, the international commercialization of biodiesel fuel is contingent upon stringent regulations. The traditional experimental measurement of biodiesel properties is laborious and expensive, necessitating skilled personnel. Predictive models offer an alternative approach by estimating biodiesel properties without depending on experimental measurements. This research is centered on building models that correlate mid-infrared spectra of biodiesel and critical fuel properties, encompassing kinematic viscosity, cetane number, and calorific value. The novelty of this investigation lies in exploring the suitability of support vector machine (SVM) regression, a burgeoning machine learning algorithm, for developing these models. Hyperparameter optimization for the SVM models was conducted using the grid search method, Bayesian optimization, and gray wolf optimization algorithms. The resultant SVM models exhibited a noteworthy reduction in mean absolute percentage error (MAPE) for the prediction of biodiesel viscosity (3.1%), cetane number (3%), and calorific value (2.1%). SVM regression, thus, emerges as a proficient machine learning algorithm capable of establishing correlations between the mid-infrared spectra of biodiesel and its properties, facilitating the reliable prediction of biodiesel characteristics.

Homogeneous charge compression ignition (HCCI) is a potential contender to replace conventional diesel combustion due to higher thermal efficiency along with near-zero oxides of nitrogen (NOₓ) and soot emissions. Commercial adaptation of HCCI strategy in automotive engines demands addressing problems associated with narrow operating load range and higher unburned hydrocarbon (HC) and carbon monoxide (CO) emissions. This article intends to address these problems through fuel modifications. A production light-duty diesel engine used for agricultural water pumping applications is modified to run in the HCCI mode through suitable modifications in the intake system. To improve external mixture preparation with low volatile diesel fuel, a high-pressure fuel injection system and a fuel vaporizer are utilized in the intake manifold. The results obtained show that the engine could run only up to 40% of rated load or 2.12 bar BMEP in the HCCI mode with diesel, beyond which it knocks severely owing to early ignition. To address the problems associated with diesel and extend the operating load range, highly volatile and low reactivity gasoline and gasoline-butanol blends with an ignition improver additive are investigated as alternative fuel options. With gasoline and 5% ethylhexyl nitrate (EHN), the operating load range is extended up to 60% of rated load or 3.19 bar BMEP, along with higher brake thermal efficiency and lower HC and CO emissions. Blending 50% butanol with gasoline helps to extend the high load range by up to 65% of rated load and also reduces unburned emissions but poses misfire problems at low loads. Overall, this article demonstrates the feasibility of extending the operating load range and reducing the unburned emissions in HCCI operation through fuel modifications. Utilizing exhaust gas recirculation and intake air preheating along with fuel modifications would help to further extend the load range in HCCI.

Reactivity-Controlled Compression Ignition (RCCI) has emerged as the most promising strategy to achieve high efficiency and clean combustion in diesel engines without any compromise on the achievable load range. Nevertheless, the complexity of the system hardware due to dual fueling and higher unburned fuel emissions are the major challenges to be addressed in RCCI. Although various approaches are proposed in the literature to reduce higher unburned emissions in RCCI, single-fuel strategies without any reactivity stratification that result in higher thermal efficiency and lower unburned emissions are not available. In the present work, a single-fuel novel Homogeneous Charge with Direct Injection (HCDI) strategy is proposed to address the limitations of RCCI in terms of higher unburned emissions. In HCDI, the premixed diesel vapor-air mixture inducted during the engine suction stroke is compressed and subjected to autoignition along with an early direct injection of diesel fuel during the compression stroke. The direct-injected (DI) fuel is used to control the autoignition of the premixed fuel-air mixture in HCDI combustion. To give better insight into HCDI combustion in comparison to other modes, the present research work compares the performance and emissions of HCDI with RCCI and the base conventional combustion in the best-optimized condition. A production light-duty single-cylinder diesel engine used for agricultural and power generation applications is modified to implement HCDI and RCCI strategies through suitable changes in the intake manifold and fuel injection system. The operating parameters in RCCI and HCDI combustion modes are optimized to achieve maximum brake thermal efficiency using a suitable controller. Unlike Homogeneous-Charge Compression Ignition (HCCI), the DI diesel fuel is found to result in improved control over combustion phasing through the absorption of latent heat of vaporization and charge stratification effects. The energy analyses in RCCI and HCDI under optimal operating conditions are also compared among themselves and with that of conventional diesel combustion. The results obtained show that the engine can be operated over the entire operating load range in RCCI and HCDI with higher brake thermal efficiency and near-zero oxides of nitrogen (NOₓ) and smoke emissions. The brake thermal efficiency is higher in HCDI compared to RCCI with a maximum increase of ~14%. The unburned hydrocarbon (HC) and carbon monoxide (CO) emissions are significantly lower in HCDI compared to RCCI with a maximum decrease of ~46% and ~62%, respectively. The available exhaust energy and energy utilization is around ~47% and ~9.3% higher in HCDI compared to RCCI combustion.

The present study examines the effect of the multiple injection strategies in a common rail diesel engine using machine learning, image processing, and object detection techniques. The study demonstrates a novel approach of utilizing image-processing tools to gain information from heat release rates and in-cylinder visualizations from experimental or computational studies. The 3D CFD combustion and emission predictions of a commercial code ANSYS FORTE© are validated with small-bore common rail diesel engine data with known injection strategies. The validated CFD tool is used as a virtual plant model to optimize the injection schedule for reducing oxides of nitrogen (NOₓ) and soot emissions using an apparent heat release rate image-based machine learning tool. A methodology of the machine learning tool is quite helpful in predicting the NO–soot trade-off. This methodology shows a significant reduction in soot and NO emissions using a pilot–main–post-injection schedule of 25% pilot, 25% post-, and 50% main injection, compared to a baseline pilot–main injection schedule. In addition, this work attempts a robust and high-fidelity optimization of the fuel injection schedule using the random forest algorithm for predicting the NO and soot emissions using 73 simulations done with different pilot–main and pilot–main–post-injection strategies on a small-bore diesel engine. Further, the object detection algorithm is trained on simulation data from the small-bore engine for detecting the interaction between the developed combustion from the pilot or main with sprays of subsequent injections using in-cylinder 3D CFD simulation and experimental data. A small-bore engine dataset shows that the trained object detection algorithm successfully corroborates the simulation and experimental data interaction. This investigation, therefore, presents a novel application of object detection methodology by automating the process and providing a general-purpose object detection algorithm. This approach can be used on any new simulation or experimental data for automated detection of the spray–thermal zone interaction without human intervention.

Biodiesel is a suitable alternative to diesel because of its carbon neutrality, renewability, lubricity, and lower pollutant emissions. However, extensive research indicates higher oxides of nitrogen (NOₓ) emissions with biodiesel. A practical method to combat this problem is utilizing water and biodiesel as emulsions. The effect of biodiesel-water emulsion in high-pressure fuel injection systems is not fully explored in the existing literature. The present study addresses this research gap by utilizing biodiesel-water emulsions in a modified light-duty diesel engine. The governor-controlled injection system was adapted to a fully flexible electronic system capable of high-pressure injection. Unlike other literature studies, the fuel injection timings were optimized with biodiesel-water emulsions to maximize brake thermal efficiency (bte) at every load condition. In a novel attempt, the biodiesel source, i.e., raw Karanja oil (RKO), a triglyceride, was utilized as the surfactant to stabilize the biodiesel-water emulsions containing 6%, 12%, and 18% water. The emulsions reduced the ignition delay and cylinder pressures, with less-intense premixed combustion and a more significant diffusion phase combustion than biodiesel. The emulsions also present a delayed combustion phasing following the injection timing trends. Among the tested emulsions, at 5.08 bar brake mean effective pressure (BMEP), 18% biodiesel-water emulsion resulted in an 18% reduced brake specific fuel consumption (bsfc), 5% increase in bte, 30% and 7% mitigation in NOₓ and smoke levels, with an increase of 10% and 28% for unburned hydrocarbon (HC) and carbon monoxide (CO) emissions.

Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NOx–soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO–soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot–main and a pilot–main–post-fuel injection strategy. Change of fuel injection quantity was not attempted in this study as the main objective was to demonstrate the use of RL algorithms while maintaining a constant indicated mean effective pressure. A change in fuel quantity has a larger influence on the indicated mean effective pressure than a change in fuel injection timing. The focus of this work was to present a novel methodology of using the 3D combustion data from analysis software in the form of a functional mock-up unit (FMU) and showcasing the implementation of a RL algorithm in Python language to interact with the FMU to reduce the NO and soot emissions by suggesting changes to the main injection timing in a pilot–main and pilot–main–post-injection strategy. RL algorithms identified the operating injection strategy, i.e., main injection timing for a pilot–main and pilot–main–post-injection strategy, reducing NO emissions from 38% to 56% and soot emissions from 10% to 90% for a range of fuel injection strategies.

The present work proposes a viable approach to develop single-cylinder diesel engines for the future by implementing regulated intake air boosting (RIAB) and engine downspeeding (ED) along with the well-established low compression ratio (LCR) approach. The investigations were conducted in a mass-production light-duty single-cylinder diesel engine initially equipped with a naturally aspirated (NA) intake system. By lowering the compression ratio (CR) and implementing the intake air boosting (IAB) using a belt-driven supercharger, the maximum brake mean effective pressure (BMEP) of the engine could be increased by 50%. More importantly, the improved performance could be achieved without violating the peak firing pressure (PFP) limits. However, a significant penalty was observed in the brake-specific fuel consumption (BSFC) at low-load operating points due to the additional power consumption of the IAB system. Hence, RIAB was implemented to optimize the boost pressure with respect to engine load to simultaneously reduce the BSFC and oxides of nitrogen (NOₓ) and soot emissions. Further, the increased full-load performance of the engine was leveraged to implement the ED approach that could reduce the operating speeds of the engine by 37.8%. It was observed that the benefits of downspeeding a supercharged engine are significantly high due to the simultaneous reduction of the frictional losses of the base engine and the power consumption of the supercharger. Overall, by combining the above concepts and the proven LCR approach, significant benefits could be achieved in fuel economy and exhaust emissions that are quantified for the regulatory Modified Indian Drive Cycle (MIDC) using a one-dimensional tool. The obtained results show a net reduction of 77.8% and 39.5% in the soot and NOₓ emissions, respectively. Moreover, a significant benefit of 14.8% could be achieved in the fuel economy. Thus the proposed approach can be used to develop single-cylinder diesel engines for the future to improve vehicle performance and comply with stringent emission regulations.

Reactivity-Controlled Compression Ignition (RCCI) has emerged as the most promising strategy to achieve high efficiency and clean combustion in diesel engines without any compromise on the achievable load range. Nevertheless, the complexity of the system hardware due to dual fueling and higher unburned fuel emissions are the major challenges to be addressed in RCCI. Although various approaches are proposed in the literature to reduce higher unburned emissions in RCCI, single-fuel strategies without any reactivity stratification that result in higher thermal efficiency and lower unburned emissions are not available. In the present work, a single-fuel novel Homogeneous Charge with Direct Injection (HCDI) strategy is proposed to address the limitations of RCCI in terms of higher unburned emissions. In HCDI, the premixed diesel vapor-air mixture inducted during the engine suction stroke is compressed and subjected to autoignition along with an early direct injection of diesel fuel during the compression stroke. The direct-injected (DI) fuel is used to control the autoignition of the premixed fuel-air mixture in HCDI combustion. To give better insight into HCDI combustion in comparison to other modes, the present research work compares the performance and emissions of HCDI with RCCI and the base conventional combustion in the best-optimized condition. A production light-duty single-cylinder diesel engine used for agricultural and power generation applications is modified to implement HCDI and RCCI strategies through suitable changes in the intake manifold and fuel injection system. The operating parameters in RCCI and HCDI combustion modes are optimized to achieve maximum brake thermal efficiency using a suitable controller. Unlike Homogeneous-Charge Compression Ignition (HCCI), the DI diesel fuel is found to result in improved control over combustion phasing through the absorption of latent heat of vaporization and charge stratification effects. The energy analyses in RCCI and HCDI under optimal operating conditions are also compared among themselves and with that of conventional diesel combustion. The results obtained show that the engine can be operated over the entire operating load range in RCCI and HCDI with higher brake thermal efficiency and near-zero oxides of nitrogen (NOx) and smoke emissions. The brake thermal efficiency is higher in HCDI compared to RCCI with a maximum increase of ~14%. The unburned hydrocarbon (HC) and carbon monoxide (CO) emissions are significantly lower in HCDI compared to RCCI with a maximum decrease of ~46% and ~62%, respectively. The available exhaust energy and energy utilization is around ~47% and ~9.3% higher in HCDI compared to RCCI combustion.