Ruomiao Yang

Due to the pressing issue of global warming, there has been a significant focus on zero- and low-carbon fuels globally. Among hydrocarbon fuels, methane is widely used in spark ignition engines due to its abundance and relatively low-carbon footprint. However, to further reduce carbon emissions, interest is growing in the use of ammonia, a zero-carbon fuel, as a partial replacement for methane. Consequently, it is crucial to investigate the impact of ammonia addition on the performance of natural gas spark ignition engines. A key challenge in studying ammonia–methane engines is that the introduction of ammonia alters the formation mechanisms of nitrogen-based pollutants, resulting in the coupling of fuel-borne and airborne nitrogen pollutants. As a result, research on the nitrogen-based emissions of ammonia–methane engines has been limited. This study addresses this issue by differentiating between atmospheric nitrogen and fuel nitrogen elements, effectively decoupling fuel-borne and airborne nitrogen pollutants. This approach provides valuable insights into the effects of ammonia addition on the nitrogen-based pollutant characteristics of natural gas engines. The results indicate that ammonia addition introduces N2O, a species absent in pure methane engines. The N2O primarily originates from cold wall regions and the partial oxidation of ammonia released from engine crevices during the late oxidation process. Although NO remains the dominant nitrogen-based pollutant and the amount of N2O is small, the significant greenhouse gas potential of N2O warrants further attention. Furthermore, while ammonia addition increases the NO concentration in the burning zone, it slightly reduces the NO concentration at chemical equilibrium under stoichiometric conditions. As a result, engines operating with an ammonia energy substitution ratio of 0.4 exhibit lower nitrogen oxide (NOx) emissions compared to those fueled solely by methane. These findings underscore the need for further research into the combustion and emission characteristics of ammonia–methane spark ignition engines.

Amid climate change, reducing reliance on fossil fuels and transitioning to renewable energy sources is crucial. Ammonia, a carbon-free and renewable fuel, shows significant potential as an alternative energy source. By incorporating hydrogen as an additive, its flammability can be enhanced to suit existing spark-ignition engines. However, understanding the characteristics of nitrogen pollutant emissions (i.e., NOx, which includes NO and NO2, and N2O) from ammonia–hydrogen combustion is challenging due to contributions from both fuel-borne and air-borne nitrogen. However, understanding the characteristics of nitrogen pollutant emissions from ammonia–hydrogen combustion is challenging due to contributions from both fuel-borne and air-borne nitrogen. Therefore, a comprehensive understanding of fuel-borne nitrogen pollutants during ammonia–hydrogen combustion is essential. This study focuses on investigating fuel-borne nitrogen pollutants in argon–oxygen atmosphere, thereby eliminating nitrogen from the oxidizer and its role in thermal NOx formation. The research examines the formation and evolution of fuel-borne nitrogen pollutants during ammonia–hydrogen combustion under engine-like conditions. Results indicate that fuel-borne nitrogen pollutants act as intermediates, potentially originating from chemical equilibrium. While fuel NO predominantly forms in the burning zone, it undergoes a reduction in the burned zone. N2O, absent in thermal NOx mechanisms, shows significant concentrations in the burning zone and is mostly converted to N2, leading to limited N2O in the final fuel-borne nitrogen pollutant concentration. Lean-burn conditions, hydrogen addition, and oxyfuel combustion promote fuel NOx formation. Additionally, the equivalence ratio affects the ammonia–hydrogen premixed flame structure due to the de-NOx effect of ammonia. Overall, these findings highlight that fuel-borne nitrogen pollutant mechanisms differ from thermal NOx mechanisms, necessitating specially designed reduction technologies for clean spark-ignition engines.

Ammonia (NH3) has gained considerable attention as a promising carbon-free hydrogen carrier fuel for internal combustion engines, but its direct use in compression-ignition engines presents challenges, often requiring high-reactivity fuels to ignite the premixed NH3/air mixture and initiate combustion. This study focuses on the ignition process of binary NH3 and dimethyl ether (DME) mixtures, as DME is a carbon-neutral, high-reactivity fuel. A key novelty of this paper is the comparison of the ignition processes of DME and NH3/DME mixtures from a temporal, process-oriented perspective, analyzing chemical kinetics across distinct ignition phases rather than focusing solely on instantaneous reactions at discrete time points. The stage-wise analysis reveals that NH3 has minimal impact on the control mechanism governing the two-stage ignition process of DME. Specifically, DME still largely depends on OH radical proliferation during low-temperature oxidation (LTO), which releases heat as the reaction progresses. As the temperature increases, LTO branching pathways gradually shift to chain-propagation pathways, reducing overall reaction activity. The reactivity and temperature rise rate of the system are then governed by the H2O2 loop mechanism before thermal ignition. However, ammonia significantly extends the ignition delay of DME by competing with OH radicals, which are critical for DME oxidation, thus inhibiting ignition. As the ignition reaction proceeds, ammonia kinetics become more involved. For example, nitrogen-containing species from NH3 oxidation, such as NO, NO2, and NH2, react with CH3OCH2 to form CH3OCHO, reducing the flux through the LTO pathway of DME. While ammonia reaction pathways also produce OH radicals, this occurs at the expense of HO2 and H radicals, leading to H2O2 formation. Overall, these findings demonstrate the substantial impact of ammonia addition on DME ignition, highlighting the need for further research to better understand NH3/DME binary fuel ignition and to optimize the design and operation of NH3/DME dual-fuel engines for improved efficiency and reliability.

This study aims to advance understanding of in-cylinder combustion processes in medium-speed diesel engines, which are extensively employed in heavy-duty applications where electrification proves inefficient yet remains insufficiently examined in the literature. By modeling a four-stroke engine with dimensions of 210 mm bore and 310 mm stroke, operating at 900 rpm under full load, this research identifies distinct combustion characteristics that differentiate medium-speed engines from their high-speed counterparts. Key findings illustrate that super turbocharging in medium-speed engines enhances the combustion of the fuel–air mixture under elevated temperatures and pressures. Moreover, an increased stroke length promotes gas velocity and turbulence, facilitating fuel atomization and mixing. Notably, rapid fuel ignition occurs near the nozzle due to the high temperature of compressed air, reducing the ignition delay. As a result, the premixed combustion stage nearly disappears, with diffusion combustion dominating, especially pronounced with long-duration injection, a characteristic of medium-speed engines. The study also reveals a more uniform but elevated distribution of nitrogen oxide emissions in medium-speed engines, attributed to prolonged high-temperature conditions that both facilitate their formation. Early stages of diffusion combustion show high concentrations of incomplete combustion products. However, as the combustion process progresses, the conditions favor the complete oxidation of these products at high temperatures, resulting in decreased carbon-based pollutions. In addition, the larger combustion chamber and enhanced turbulence characteristic of medium-speed engines support efficient fuel and air mixing without necessitating the swirl effect required by high-speed engines, diminishing the dependence on wall impingement dynamics for air utilization. Consequently, efficiency optimization strategies for medium-speed engines, emphasizing adjustable injection parameters, encounter fewer constraints than those inherent to the spatial limitations of high-speed engines.

The internal combustion engine will continue to be the primary source of power for transportation. Spark ignition (SI) engines are still widely used for mobility due to their wide range of operating conditions. The key operating variables of an engine are primarily controlled by an engine control unit that has been calibrated. However, a less accurate sensor can lead to large variations in engine performance and emissions. The purpose of this study was to investigate the importance of air–fuel ratio sensor precision during operation of various engines. In this study, a one-dimensional (1D) computational fluid dynamics (CFD) model was used to analyze the engine response due to the variation of the equivalence ratio sensor precision at different engine speeds and loads, to explore the main indicators influenced by the precision of equivalence ratio measurements, and to propose a discriminant criterion for evaluating the suitability of the proposed equivalence ratio precision in relation to the conversion rate of three-way catalyst and vehicle emissions. The results show that for engine performance, it varies slightly with small changes in the fuel-to-air ratio. At higher engine speeds, a slight change in the air–fuel ratio leads to a smaller change in emissions. At the same time, changes in fuel-to-air ratio have a significant effect on carbon monoxide (CO) and nitrogen oxides (NOx) emissions. Carbon monoxide is the most sensitive to the air–fuel ratio, followed by nitrogen oxides, while unburned hydrocarbons are not sensitive to it. And for the three measurement accuracies studied in this paper (0.5%, 1%, and 2%), the accuracies are acceptable, but combining the relative errors of the actual emissions of CO and in order to achieve accurate combustion control, it is recommended that the sensor accuracy should be at least higher than 1% for the port fuel injected engine investigated in this study.

The indicated mean effective pressure (IMEP) is a key parameter for measuring the power output of an internal combustion engine (ICE). This indicator can be used to locate the high efficiency regions of engines. Therefore, it makes sense to predict the IMEP based on the machine learning (ML) approaches. However, different ML models are applicable to different scenarios, so it is important to choose the right model for prediction. The objective of this paper was to compare three ML models’ (ANN, SVR, RF) predictive performance in forecasting IMEP indicator with the input parameters spark timing (ST), speed and load. A validated one-dimensional (1D) computational fluid dynamics (CFD) model was employed to provide 756 sets of data for the training, validation, and testing of the model. The results indicated that the random forest (RF) model had the worst prediction performance, and support vector regression (SVR) had a slightly better prediction performance than the artificial neural network (ANN), at least for the investigations in this study. Overall, the ANN and SVR models showed good predictive performance for IMEP, as the coefficient of determination (R²) was close to unity, and the root mean squared error (RMSE) was close to zero. Whereas the overall prediction results of the RF model are acceptable, the RF model does not learn well for some internal engine laws.

While traditional one-dimensional and three-dimensional numerical simulation techniques require a lot of tests and time, emerging Machine Learning (ML) methods can use fewer data to obtain more information to assist in engine development. Combustion phasing is an important parameter of the spark-ignition (SI) engine, which determines the emission and power performance of the engine. In the engine calibration process, it is necessary to determine the maximum brake torque timing (MBT) for different operating conditions to obtain the best engine dynamics performance. Additionally, the determination of the combustion phasing enables the Wiebe function to predict the combustion process. Existing studies have unacceptable errors in the prediction of combustion phasing parameters. This study aimed to find a solution to reduce prediction errors, which will help to improve the calibration accuracy of the engine. In this paper, we used Support Vector Regression (SVR) to reconstruct the mapping relationship between engine inputs and responses, with the hyperparametric optimization method Gray Wolf Optimization (GWO) algorithm. We chose the engine speed, load, and spark timing as engine inputs. Combustion phasing parameters were selected as engine responses. After machine learning training, we found that the prediction accuracy of the SVR model was high, and the R² of CA10−ST, CA50, CA90, and DOC were all close to 1. The RMSE of these indicators were close to 0. Consequently, SVR can be applied to the prediction of combustion phasing in SI gasoline engines and can provide some reference for combustion phasing control.

Engine development needs to reduce costs and time. As the current main development methods, 1D simulation has the limitations of low accuracy, and 3D simulation is a long, time-consuming task. Therefore, this study aims to verify the applicability of the machine learning (ML) method in the prediction of engine efficiency and emission performance. The support vector regression (SVR) algorithm was chosen for this paper. By the selection of kernel functions and hyperparameters sets, the relationship between the operation parameters of a spark-ignition (SI) engine and its economic and emissions characteristics was established. The trained SVR algorithm can predict fuel consumption rate, unburned hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxide (NOx) emissions. The determination coefficient (R²) of experimental measured data and model predictions was close to 1, and the root-mean-squared error (RMSE) is close to zero. Additionally, the SVR model captured the corresponding trend of the engine with the input, though some existed small errors. In conclusion, these results indicated that the SVR model was suitable for the applications studied in this research.

With global warming, and internal combustion engine emissions as the main global non-industrial emissions, how to further optimize the power performance and emissions of internal combustion engines (ICEs) has become a top priority. Since the internal combustion engine is a complex nonlinear system, it is often difficult to optimize engine performance from a certain factor of the internal combustion engine, and the various parameters of the internal combustion engine are coupled with each other and affect each other. Moreover, traditional experimental methods including 3D simulation or bench testing are very time consuming or expensive, which largely affects the development of engines and the speed of product updates. Machine learning algorithms are currently receiving a lot of attention in various fields, including the internal combustion engine field. In this study, an artificial neural network (ANN) model was built to predict three types of indicators (power, emissions, and combustion phasing) together, including 50% combustion crank angle (CA50), carbon monoxide (CO), unburned hydrocarbons (UHC), nitrogen oxides (NOx), indicated mean effective pressure (IMEP), and indicated thermal efficiency (ITE). The goal of this work was to verify that only one machine learning model can combine power, emissions, and phase metrics together for prediction. The predicted results showed that all coefficients of determination (R²) were larger than 0.97 with a relatively small RMSE, indicating that it is possible to build a predictive model with three types of parameters (power, emissions, phase) as outputs based on only one ANN model. Most importantly, when optimizing the powertrain control strategy of a hybrid vehicle, only a surrogate model can help establish the relationship between the input and output parameters of the whole engine, which is the need of the future research. Overall, this study demonstrated that it is feasible to integrate three types of combustion-related parameters in a single machine learning model.

Increasingly stringent regulations to reduce vehicle emissions have made it important to study emission mitigation strategies. Highly accurate control of the air-fuel ratio is an effective way to reduce emissions. However, a less accurate sensor can lead to reduced engine stability and greater variability in engine efficiency and emissions. Additionally, internal combustion engines (ICE) are moving toward higher compression ratios to achieve higher thermal efficiency and alleviate the energy crisis. The objective of this investigation was to analyze the significance of the accuracy of air-fuel ratio measurements at different compression ratios. In this study, a calibrated 1D CFD model was used to analyze the performance and emissions at different compression ratios. The results showed that carbon monoxide (CO) and nitrogen oxides (NOx) were sensitive to the equivalence ratio regardless of the compression ratio. With a slight change in the equivalence ratio, a high compression ratio had little effect on the change in engine performance and emissions. Moreover, with the same air-fuel ratio, an excessively high compression ratio (CR = 12) might result in knocking phenomenon, which increases the fluctuation of the engine output parameters and reduces engine stability. Overall, for precise control of combustion and thermal efficiency improvement, it is recommended that the measurement accuracy of the equivalence ratio is higher than 1% and the recommended value of the compression ratio are roughly 11.