A one-dimensional model of the diesel engine working process was established, and thermal boundary conditions of gases contacting with a cylinder head were determined by comparing them with the results of a routine test. A fluid-structure interaction model between the cooling water and cylinder head passages was established in which boundary conditions of cooling water were obtained by computational fluid dynamics analysis. Simultaneously, considering the pressure mechanical load in the cylinder, temperature and the stress distribution of the cylinder head were analysed by the model with a thermo-mechanical coupling load. The model was validated using the temperature hardness plug method. Four parameters of intake valve opening, exhaust valve opening, fuel supply beginning, and compression ratio were selected as influencing factors, and the thermo-mechanical coupling load of the cylinder head was optimised by the Taguchi and analysis of variance method subsequently. The study indicates that the error of the calculation model for the cylinder head’s thermal-mechanical coupling load is within ±1.5%, and the proportion of the thermal stress in the cylinder head thermal-mechanical coupling stress is above 90%. The fuel supply beginning has the greatest influence on the thermal load of the cylinder head. Based on the optimisation methods within the required power range, the maximum temperature and maximum thermo-structural coupling stress of the cylinder head are decreased by about 10.05 K and 7.13 MPa in the nose bridge area, respectively.

To address the issues of the high cost of marine turbocharger fault simulation testing and the difficulties in obtaining fault sample data, a multi-body dynamics model of a marine turbocharger was developed. The simulation approach was used to acquire the turbocharger vibration signals. The result shows that the amplitude of the 1× vibration signal power spectrum drops as the bearing surface roughness increases. However, the amplitude of the 2× and 9× vibration signal power spectra increases as the roughness increases. The TrAdaBoost transfer learning method is used to develop a marine turbocharger diagnosis model. The validation results of 2040 simulated fault samples reveal that when the desired sample number is 20, the diagnostic model has an accuracy of 87%. When the desired number of samples is 40, the diagnostic model’s accuracy is 96%. The diagnosis model may perform diagnosis information transfer between the actual turbocharger and the simulation model.

Compared to diesel, liquefied natural gas (LNG), often used as an alternative fuel for marine engines, comes with significant advantages in reducing emissions of particulate matter (PM), SO_x, CO₂, and other pollutants. Promoting the use of LNG is of great significance for achieving carbon peaking and neutrality worldwide, as well as improving the energy structure. However, compared to diesel engines, medium- and high-speed marine LNG engines may produce higher methane (CH₄) emissions and also have nitrogen oxide (NO_x) emission issues. For the removal of CH₄ and NO_x from the exhaust of marine LNG engines, the traditional technical route of combining a methane oxidation catalyst (MOC) and an HN₃ selective catalytic reduction system (NH₃-SCR) will face problems, such as low conversion efficiency and high operation cost. In view of this, the technology of non-thermal plasma (NTP) combined with CH₄-SCR is proposed. However, the synergistic mechanism between NTP and catalysts is still unclear, which limits the optimization of an NTP-CH₄-SCR system. This article summarizes the synergistic mechanism of NTP and catalysts in the integrated treatment process of CH₄ and NO_x, including experimental analysis and numerical simulation. And the relevant impact parameters (such as electrode diameter, electrode shape, electrode material, and barrier material, etc.) of NTP reactor energy optimization are discussed. The work of this paper is of great significance for guiding the high-efficiency removal of CH₄ and NO_x for an NTP-CH₄-SCR system.

Turbocharger is one of the vital parts of a diesel engine causing a high failure rate. Its surface vibration signal contains important time-varying features. To better process the nonstationary signals with time-varying features and perform the time-frequency transformation on the turbocharger surface vibration signal, a novel multi-objective matched synchrosqueezing chirplet transform method is proposed in this paper. The method is based on the Linear Chirplet Transform to optimize the selection of the demodulation rate. Parameters such as Rayleigh entropy and signal-to-noise ratio are used as targets to select the value of the optimal demodulation rate. Then the local maximum synchrosqueezing transform is used as a post-processing method for time-frequency rearrangement of the signal. This method improves the energy concentration of the transformation result while maintaining the ability of signal reconstruction. On the test stand, the turbocharger fault samples were obtained. The time-domain signals of the turbocharger at the 1×, 2×, and 9× frequencies were reconstructed based on the time-frequency transformation results, and fault characteristic parameters were extracted from them. Then the effectiveness of the fault feature parameter identification ability was validated by Principal Component Analysis. The study showed that our proposed MOMSSCT method is capable of processing and analysing the strongly time-varying surface vibration signals of turbochargers. Its transformation results have high time-frequency energy aggregation, clear trajectories, and a 37.5% reduction in frequency spread width. The visualization results show that data with different fault types are more clearly differentiated in the two-dimensional graph than common features. The extracted fault characteristic parameters have good fault classifiability under various turbocharger operating conditions. Using the fault features extracted by MOMSSCT, the diagnostic accuracy rate can reach more than 85%.

To improve the non-uniformity of a multi-cylinder marine diesel engine caused by manufacturing assembly errors and performance degradation of the fuel injection system, with the instantaneous speed applied as the control target, the feedback variable of each cylinder’s exhaust temperature was used to obtain the non-uniformity information and the injection quantity of each cylinder was applied as the control variable; the inhomogeneity control was accomplished by modifying the injection pulse spectrum. The model of AVL Cruise M was established and validated by bench test data. The non-uniformity control strategy based on the instantaneous speed and the exhaust temperature of each cylinder was developed in SIMULINK, and the control effect was compared with the closed-loop control of cylinder pressure by software in-loop simulation. The results showed that the non-uniformity control strategy based on exhaust temperature could significantly improve the uniformity of each cylinder; although the improvement effect was not as great as the non-uniformity control strategy based on cylinder pressure, the cost was significantly reduced, and the practicality and reliability were better. With the closed-loop control of exhaust temperature and instantaneous speed, the CV (Coefficient of Variation) of IMEP (indicated effective pressure) was close to the closed-loop control of cylinder pressure; the maximum occurred at 25% load when it was 0.199%. This co-simulation provided a theoretical basis for the subsequent hardware-in-the-loop simulation and actual engine tests.

The strong coupling between the ship’s sway and yaw motion increases the complexity of identifying hydrodynamic derivatives in mathematical models and reduces accuracy. To solve this problem, this paper proposes an identification method Alpha Evolution Multi-output Support Vector Regression (AE-MSVR) based on MSVR combined with AE. This method approaches the yaw and sway motion equations as a multi-input and multi-output (MIMO) problem, utilizing MSVR for modeling and optimizing hyperparameters with AE. It reduces parameter drift by restructuring the regression model’s input–output. Identification data is obtained via zigzag test simulation. The AE-MSVR method successfully identifies linear and nonlinear hydrodynamic derivatives in the 3 degree of freedom (DOF) Abkowitz model. Using clean simulation data, the results show promising agreement with experimental values from planar motion mechanism (PMM) tests and achieve improved accuracy compared with the standard SVR identification method. To assess robustness, simulated noise is introduced at different levels; maneuvering characteristics are evaluated using turning circle tests. Results demonstrate that AE-MSVR achieves promising accuracy in identifying ship hydrodynamic derivatives and shows encouraging robustness against noise. The method provides potential support for ship motion prediction and maneuverability forecasting.