Digital twin technology is essential for marine diesel engine optimization, yet purely mechanism-driven methods require excessive computational time and resources, restricting rapid prediction and optimization. This study proposes a hybrid mechanism-data-driven digital twin modeling approach for accurate combustion prediction and simultaneous online optimization of combustion and performance parameters. A complete combustion model from Wiebe parameters to cylinder pressure/heat release rate profiles is first established based on a mechanistic model. For mapping operating parameters to Wiebe parameters, a Snake Optimizer-optimized convolutional bidirectional LSTM (SO-CNN-Bi-LSTM) network is developed using bench test data. This deep learning model is organically integrated with the mechanistic model to construct the digital twin prediction model. For online optimization, the prediction model serves as a virtual engine, employing a closed-loop collaborative combustion strategy and Multi-Objective Snake Optimizer (MOSO) to optimize combustion and emission performance, followed by experimental validation. Results demonstrate that the hybrid-driven model accurately reconstructs and predicts diesel engine combustion processes and output characteristics. The closed-loop collaborative strategy significantly suppresses fluctuations in engine speed and pressure rise rate. Meanwhile, MOSO successfully controls NOx emissions within Tier-III standards. Compared to traditional methods, the proposed framework enables online combustion optimization while maintaining high prediction accuracy.

Fault diagnosis is critical to predictive maintenance and health management of mechanical systems. While sufficient data enables high-precision fault diagnosis models through machine learning, diagnostic accuracy tends to decline with limited samples, rendering single-classifier approaches inadequate. To address this, a hybrid fault diagnosis model incorporating multiple machine learning algorithms is proposed. This model integrates two discriminators and one optimizer: the discriminators select appropriate models and samples, while the optimizer fine-tunes model parameters to achieve high diagnostic accuracy. Comparative analysis shows that the proposed framework effectively enhances the processing capability for fault samples. Moreover, it exhibits extensibility and is not restricted to specific classifier types.

Fault diagnosis is important in monitoring the entire lifecycle of mechanical equipment. And Feature selection can explain the relationships between different fault types and reduce the complexity of the fault diagnosis model. However, due to the correlation among the fault features, a single method is unable to effectively determine the importance of the features. In order to enhance the analytical ability of the feature selection for features and thereby improve the universality of the method, this paper proposes a new feature analysis method - the multi-factor dual analysis method. This method is designed to conduct in-depth analysis of the fault data and utilize statistical methods to determine the optimal ranking of feature importance. By testing on different fault data sets, it has been proved that this method can effectively reduce the feature dimension while maintaining the classification accuracy.

The lifecycle management of marine engine performance is a complex and dynamic process, facing numerous challenges in real-time monitoring and predictive maintenance. This study proposes a novel methodology that integrates transfer learning and engine performance degradation laws to enable the online iteration of the digital twin model for marine engines throughout their entire lifecycle. Initially, a deep learning neural network that integrates Snake Optimization, Convolutional Neural Networks, and Bidirectional Long Short-Term Memory networks (SO-CNN-Bi-LSTM) is employed to develop a performance prediction model for the engine. Then, based on engine wear and bench test data, a nonlinear fitting relationship between running time, wear, and performance degradation is derived, allowing for the generation of a dynamically updated dataset of engine performance parameters. Finally, the digital twin model is iteratively optimized using a feature-model joint migration framework, achieving high-precision lifecycle performance predictions. Experimental results demonstrate that, under maximum wear conditions, the prediction errors for various performance parameters are significantly lower than those before the iteration, validating the effectiveness of the model in performance prediction. This approach establishes a solid theoretical foundation for the comprehensive operation and maintenance management of marine engines throughout their entire lifecycle.

Despite undergoing factory balancing procedures, turbochargers still exhibit significant operational vibration variability under complex working conditions. Long-term service may induce critical failures including impeller wear and turbine carbon deposition. To investigate the vibration mechanisms and control strategies, this study establishes an experimentally validated finite element model of the rotor system. Systematic simulations are conducted by varying the compressor impeller unbalance phase and rotational speed, while maintaining a fixed turbine-end phase. Key findings reveal: Persistent 1X synchronous vibrations with amplitudes below 0.02 mm are observed across the entire speed range, showing negligible correlation with unbalance phase variations. Prominent 0.13X sub-synchronous vibrations displayed phase-dependent characteristics: A pronounced amplification of sub-synchronous vibrations is observed. As the unbalance phase increases from 0° to 180°, the bandwidth of these intensified vibrations within the 30,000–60,000 r/min range expands by approximately 11.7 times. Vibration suppression emerges above 60,000 r/min, reducing bandwidth by 25%. The speed bandwidth of this vibration is increased by 40% when phase varies from 90° to 135°. The variation in the relative phase between the dual ends has minimal impact on the shape of the shaft trajectory; however, it significantly influences the trajectory radius, prompting the analysis to concentrate on the 0° phase condition. At 30,000 r/min, the limit cycle oscillation is dominant. Chaotic motion intensification at elevated speeds (>50,000 r/min). Optimal phase control (0°–90°) narrows the strong sub-synchronous vibration bandwidth across 30,000–60,000 r/min. Rotor orbit analysis confirms safe clearance margins, with maximum vertical trajectory radius measuring 0.064 mm at 60,000 r/min (70% below friction threshold). The findings demonstrate that strategic unbalance phase modulation can effectively suppress sub-synchronous vibrations, providing actionable insights for marine turbocharger rotor dynamics optimization and vibration mitigation.

As an emerging technology, electrically assisted turbocharging (EAT) technology can effectively solve the problem of turbo lag of traditional turbochargers. The turbocharger (TC) rotor was discretized to establish a finite element model and its effectiveness was verified based on experiments, and then the EAT rotor design was carried out. The linear and nonlinear dynamic analysis of EAT rotor semi-floating floating ring bearing (SFRB) system is carried out. The results show that the critical speed of EAT rotor is lower than that of TC rotor. The larger internal clearance will cause strong sub-synchronous vibration of the EAT rotor in the low-speed stage. Properly reducing the internal clearance can effectively suppress the sub-synchronous vibration at low speed, and the high-speed rotor performs balanced motion on the “critical limit cycle.” Taking the EAT with low vibration noise and efficient operation as the goal, it is recommended to keep the inner clearance size at about 0.315 mm.

The inherent high gasification latent heat and sluggish flame propagation speed characteristics of ammonia fuel lead to combustion instability and increased difficulty in emission control during the combustion process of diesel-ignited ammonia engine. This paper aims to adjust the different injection timing of diesel/ammonia to determine the best stratified combustion control strategy under direct injection-directed ammonia injection mode (DI-DAI). In this paper, based on the combustion characteristics of ammonia fuel, a total of 35 diesel/ ammonia injection combinations from 690oCA to 705oCA were set up. The simulation results show that the change of injection timing plays a decisive role in the effect of diesel/ammonia stratified combustion in cylinder. Under rated conditions, all injection strategies meet Tier-II emission standards and can achieve near-zero carbon emissions. Compared to D690/A690, the NOx emissions in the D705/A713combination are reduced by 75.83 %. But, the ammonia escape ratio in the D705/A713 combination reaches 34.40 %. Compared to the D705/A713 combination, the D693/A697 combination exhibited a 90.4 % reduction in N2O emissions and a 76.8 % reduction in NO2. Finally, the D693/A697 combination was selected as the best combustion control combination.

The electrically assisted turbocharger (EAT) enhances engine responsiveness but introduces rotor vibration challenges due to motor integration. However, its electromagnetic effects are often overlooked. This study establishes a floating ring bearing-turbocharger rotor model to investigate these dynamics. Three configurations are compared: Model A (baseline), Model B (added motor mass), and Model C (incorporating electromagnetic effects). Results show Model B's critical speeds decrease significantly (83%-96%) due to mass increase, whereas Model C's electromagnetic effects restore the first-order critical speed by 274% compared to Model B. However, higher-order modes show limited electromagnetic compensation. Nonlinear transient analysis reveals Model B exhibits stable limit cycles at medium-high speeds, while Model C forms elliptical orbits at low speeds and chaotic motion at high speeds due to oil film nonlinearities. Waterfall plots demonstrate Model C suppresses sub-synchronous vibrations (0.12x) by 98.5% at 80,000 r/min and stabilizes synchronous vibration amplitude at 0.006 mm (only 0.6% of that in Model B), despite exhibiting a transient 0.6x vortex. Electromagnetic effects markedly improve stability but necessitate balancing multi-field coupling nonlinearities. This study elucidates the dynamic modulation mechanism of electromagnetic effects, bridging a theoretical gap in traditional research and providing crucial support for stability design in high-speed integrated powertrain systems.

For the digitization of turbocharger, the prediction of compressor working state is essential. How to build a model with accurate prediction and less time-consuming is the premise of studying the digitization of turbochargers. As the relationship between compressor parameters is obtained through experiments, it cannot be expressed by simple functional equations, so the surrogate model is often used for fitting the curve. Five surrogate models, the Kriging model, Response Surface Methodology, Artificial Neural Networks, Radial Basis Function, and Support vector machines, were used to fit and regression compressor characteristic curves. And four optimization algorithms, Particle Swarm Optimization, Genetic Algorithm, Gray Wolf algorithm, and Firefly Algorithm, were used to optimize the model. A method to construct a hybrid surrogate model is proposed. The results show that the influencing factors of the modeling pressure ratio and efficiency at all speed groups were confirmed; Different optimization algorithms have different optimization degrees for the five surrogate models; The prediction accuracy of the hybrid surrogate model is better than the optimized model and the single model. The constructed model can be applied in the digital twins system to predict the working state of the compressor in time to achieve the purpose of rapid response.

To improve the problem of high NOx emissions in ammonia/methanol engines, this article explores the impact of different EGR rates on engine performance under three different ammonia/methanol mixing ratios through simulation research. The research results indicate that when the proportion of methanol in ammonia fuel is small, the power and economy of the engine significantly decrease after increasing the EGR rate, and the density of unburned ammonia and N2O in the waste gas grows markedly. When the proportion of methanol in ammonia fuel is high, the decrease in engine power is relatively small after growing the EGR rate, and the density of unburned ammonia and N2O in the waste gas modestly grows. The introduction of EGR rate will reduce the overall performance of the engine, but the effect of reducing NOx emissions is very significant. By increasing the proportion of methanol in ammonia fuel, the adverse effects of EGR technology can be reduced. A60/M40/EGR12 % is the optimal combination of ammonia/methanol mixture ratio and EGR rate in this study. In previous studies, the combustion modes of ammonia/hydrogen fuel, ammonia/natural gas fuel and ammonia/methanol fuel all have the problem of high NOx emission. The research results of this paper can provide solutions and data support for such research.

To improve the low accuracy of the zero-dimensional combustion model established by BP-NN, a particle swarm-neural network (PSO-NN) algorithm was proposed. The PSO optimize weights and thresholds of NN, and the operating and combustion parameters are constructed, and then compared with NN algorithm. The results show that comparing with NN algorithm, the zero-dimensional combustion model constructed by PSO-NN algorithm has higher prediction accuracy, and the mean square error of the main combustion period m is 0.0034, which is 78.21% lower than that before optimization. The particle swarm algorithm has quicker convergence and stronger versatility, which is suitable for the study of diesel engine 0-D model.

In order to achieve real-time mapping and online optimization of the combustion process of a dual-fuel engine that is in prolonged operation, this paper is the first to combine the Wiebe function with a deep learning neural network to propose a zero-dimensional (0-D) combustion prediction model for a biodiesel-diesel dual-fuel engine. First, the parameters of the double Wiebe functions are calculated by the Pelican Optimization Algorithm (POA), and the operating parameters and Wiebe parameters are used as input and output parameters of neural networks, respectively, to construct parameter identification models. Then, the combustion process is simplified and reconstructed by combining the Wiebe function with the deep learning neural network, and a 0-D prediction model based on the hybrid model-driven and data-driven method is established, which can further obtain combustion results such as cylinder pressure curve and indicated mean effective pressure (IMEP). The results show that the coefficient of determination (R2) value of the dual-fuel engine 0-D prediction model based on the double Wiebe function combined with POA–CNN–Bi-LSTM is 0.9827, and the model has good prediction accuracy and generalization. The development of the combustion model provides reliable numerical model support for the online evaluation and optimization of dual-fuel engine performance.

The combustion efficiency of ammonia fuel engines is low, and the concentration of unburned ammonia and N2O emissions in the exhaust gas is high. This article aims to study the impact of diesel-ignited ammonia/methanol engines on combustion and emission performance, explore the improvement of methanol on ammonia fuel combustion, and find the optimal ammonia/methanol mixture ratio. The research indicates that as the methanol mixing ratio increases, the concentration of unburned ammonia in the exhaust gas gradually decreases, and the power performance is greatly improved. The concentration of CO, soot, CO2, and NOx in the exhaust gas has increased, but due to the combustion support of methanol, the engine N2O emissions have significantly decreased. Mixing methanol improves combustion efficiency and solves the problems of ammonia escape and high N2O emissions. In this paper, the optimal mixing ratio of ammonia/methanol is 8:2. The combustion mode of the diesel ignition ammonia/methanol engine studied effectively solves the problems of ammonia escape and high N2O emissions during ammonia combustion, promotes the development of ammonia in the field of compression ignition engines, and promotes the realization of the purpose of carbon reduction.

Ammonia fuel engines face problems such as low combustion efficiency, ammonia escape, and high N2O emissions. However, natural gas reserves are abundant, and the combustion speed is fast. In order to explore whether the mixing of natural gas can promote the combustion of ammonia fuel, this article discusses the combustion and emission performance of ammonia/natural gas hybrid fuel engines through simulation research. The results indicate that mixing ammonia fuel with natural gas can greatly improve the combustion efficiency of ammonia in the cylinder. Appropriate natural gas can greatly improve the power and economic efficiency of engines. Although the N2O emissions of the engine have significantly decreased, the CO2 and NOx emissions of the engine have significantly increased, and the engine's overall emission performance has become worse. Considering the engine's overall performance, this article selects A50/N50 as the optimal mixture ratio for ammonia/natural gas. The article's research results have effectively improved the problems existing in ammonia engines, providing research direction and data support for the development of ammonia fuels.

The target of this paper is to study two combustion control strategies, partial premixed compression ignition (PPCI) and dual fuel combustion (DDF), to solve the problems of low efficiency and high emissions of micro-ignition dual fuel engines. Firstly, the effects of different control parameters on engine performance and emissions in two combustion modes were studied. Then, the encoder-decoder convolutional neural network-gated recurrent unit (ED CNN-GRU) is used for the first time to establish a predictive regression model between the operating parameters and performance of the diesel micro-ignition dual-fuel engine. Finally, NSGA III is used to drive ED CNN-GRU to perform multi-objective optimization of engine performance. The results show that under 75% load, the predicted results of the model and test results show that the corresponding THC emissions in the PPCI combustion mode are 50.65 % and 53.18 % lower than those in the DDF combustion mode, the corresponding CO emissions in the PPCI combustion mode are 69.05% and 70.26% lower than those in the DDF combustion mode respectively. The prediction results of the model and the test results meet the Tier III emission regulations, and the emission is minimized while taking into account the economy. Under the propulsion characteristics, PPCI combustion mode is selected for 0-75% load, and DDF combustion mode is selected for more than 75% load. The control strategy of micro ignition dual fuel engine can effectively promote the development of micro ignition dual fuel engine in the field of marine engine.

The air conditioning (AC) system provides passengers with a comfortable temperature environment and is one of the main energy consumers in modern plug-in hybrid electric vehicles (PHEV). However, the AC system is often overlooked when designing the energy management system (EMS) for PHEVs. To improve the energy efficiency of PHEVs, this paper integrates the Genetic Simulated Annealing Algorithm (GASA) with fuzzy control to propose an energy management system for PHEVs that considers the internal temperature of the vehicle. First, considering the uncertainty of the vehicle's driving environment, an optimized Interval Type-2 Fuzzy Controller (IT2FC) was designed for real-time torque distribution. Second, an iteratively modified genetic simulated annealing algorithm was used to optimize control parameters, correcting the defect of genetic algorithms tending to get trapped in local optima and lingering around optimal positions. Then, considering the energy consumption of the AC system, a vehicle-integrated thermal management model oriented towards control was established. Lastly, the performance of the proposed EMS was discussed. The results show that, compared to the rule-based methods, the proposed EMS reduces fuel consumption by 17.77% and 17.727% in cooling and heating modes, respectively, and compared to the A-ECMS methods, it reduces fuel consumption by 7.38% and 6.31% in cooling and heating modes, respectively. At the same time, the proposed EMS ensures thermal comfort in the cabin. In cooling mode, the traditional rule-based strategy failed to maintain the cabin temperature within the preset range, while the proposed EMS showed significant improvement. In heating mode, the EMS reaches and maintains the cabin's set temperature 35% faster than the rule-based strategy, and 8.75% faster than the A-ECMS strategy.

Optimizing the combustion process by predicting combustion parameters during prolonged engine operation is crucial for engine maintenance. This study presents a zero-dimensional (0-D) prediction model that integrates the advantages of model-driven and data-driven approaches. Initially, the snake optimization algorithm (SO) is employed to address the challenges related to low parameter fitting accuracy and multiple solutions in calculating Wiebe parameters. Subsequently, a convolutional neural network-bidirectional long short-term memory neural network (CNN–Bi-LSTM) is devised to establish a nonlinear correlation between operating parameters and Wiebe parameters. The structural parameters of CNN–Bi-LSTM are then optimized using the SO algorithm (SO–CNN–Bi-LSTM). Ultimately, a 0-D prediction combustion model is formulated by amalgamating the Wiebe function with the neural network, enabling real-time prediction of combustion results and generalization analysis of prediction performance under non-calibrated conditions. The findings demonstrate that the combustion model exhibits heightened accuracy, thereby establishing a robust technical foundation for the development of a digital twin in the engine combustion process.

In this paper, for the application requirements of an electrically-assisted turbocharger brushless DC motor (BLDC), the drive control is simulated and studied in the Simulink platform. By establishing the motor control model, the speed and current response characteristics of the inductors square wave closed-loop control under different operating conditions are analyzed in detail, and the dynamic response performance of the system is significantly improved by optimizing the PID control parameters. The variable operating condition characteristics and stability of the electrically assisted turbocharger are analyzed, which provides the theoretical basis and data support for optimizing the control strategy and improving the system’s reliability.

Electrically assisted turbocharging (EAT) technology is an important technical means to effectively solve the problems of insufficient intake air and poor transient response of traditional turbocharged engines at low speed or acceleration, and to realize the electrification of marine internal combustion engine power system. In the EAT design stage, the original rotor model of a marine turbocharger was established and verified. Subsequently, the EAT rotor dynamics design and analysis were carried out, and the influence of the unbalanced phase and external clearance on rotor vibration was discussed. The results show that when the external clearance is small, extensive sub-synchronous vibration occurs and bifurcation occurs at high speed. When the external clearance is large, the sub-synchronous vibration component almost disappears but high synchronous vibration occurs at a high speed. At the same time, the shaft is significantly affected by the gravity load at low speeds, and the rotor makes a balanced motion on the critical limit cycle at high speed. In order to minimize the vibration and noise of the same type of EAT, it is recommended to control the outer clearance of the EAT bearing in the range of 0.30-0.36mm. The research content provides a reference for the dynamic design and analysis of the EAT, which effectively helps the development of the original engine of the ship electric auxiliary turbocharger and improves the operation stability of the EAT.

To tackle the challenges of low efficiency and excessive NOx emissions in biodiesel-diesel dual-fuel engines, this paper presents an optimization method using the Pareto-multiple objective snake optimizer (Pareto-MOSO) to drive a convolutional neural network-gated recurrent unit (CNN-GRU). In the Pareto-MOSO, brake specific fuel consumption (BSFC), NOx, Soot, and CO are optimized by changing the engine control parameters, including injection timing (Tinject), engine speed (n), biodiesel blending ratio, torque (Ttorque), exhaust gas recirculation (EGR rate), intake pressure (Pin), and rail pressure (Prail). The experiment generates training data for CNN-GRU and verifies the accuracy of the Pareto-MOSO optimization results. The findings suggest that the optimized engine exhibits a balanced correlation between economy and emissions under propulsion characteristics. Furthermore, the NOx emissions are all in accordance with the IMO Tier III emission regulations. Under the rated condition, scheme 2 demonstrates a significant reduction of 76.57% in NOx emissions. However, this optimization has resulted in a 1.63% increase in BSFC compared to its pre-optimized state. Therefore, the adoption of suitable control strategies proves advantageous in addressing the trade-off between economy and emissions of engines.