The stable working window of a dual-fuel engine is narrow, and it is prone to knock during operation. The occurrence of knock limits the load and torque output of a dual-fuel engine, and even causes engine damage in severe cases. The existing volumetric model of marine dual-fuel engine has little research on the related problems of knock simulation. In order to analyze the causes of knock phenomenon and the influence of operating parameter changes on knock, under the Matlab/Simulink simulation environment, a quasi-dimensional model was established with MAN 8L51/60DF dual-fuel engine as the prototype, and the model was calibrated using the bench data. The knock intensity index coefficient (KI) was used as the evaluation index of knock intensity. Three parameters, the intake air temperature, compression ratio, and natural gas intake, were selected as variables to simulate the engine. According to the analysis of the simulation results, the influence of the parameter changes on the occurrence of engine knock phenomenon and knock intensity could be further studied. The results showed that the combination of the KI model and the quasi-dimensional model could effectively and accurately predict the engine performance and knock trend. The change of gas inlet quality, compression ratio, and inlet temperature could promote the occurrence of detonation, the engine knock could be avoided by controlling the intake air temperature below 336 K, compression ratio not exceeding 15 or the intake volume of natural gas per cycle not exceeding 11.25 g/cycle.

False Data Injection Attacks (FDIA) on ship Direct Current (DC) microgrids may result in the priority trip of a large load, a black-out, and serious accidents of ship collisions when maneuvering in the port. The key of the prevention of FDIA is the detection of and countermeasures to false data. In this paper, a defense strategy is developed to detect and mitigate against FDIA on ship DC microgrids. First, a DC bus voltage estimator is trained with an Artificial Neural Network (ANN) model. The error between the estimate value and the measure value is compared with a threshold generated from history data to detect the occurrence of FDIA. Combined with the correlation of artificial neural network inputs, bad data are identified and recovered. The method is tested under six cases with different network and physical disturbances in Matlab/Simulink. The results show that the method can identify and mitigate the FDIA effectively; the error of identifying FDIA by ANN is less than 0.5 V. Therefore, the deviation between the actual bus voltage and the reference voltage is less than 0.5 V.

Although the exhaust gas recirculation (EGR) technology has been proven effective to decrease the marine engine’s nitrogen oxides (NOx) emissions, it is associated with a considerable fuel consumption increase and challenges to the engine–turbocharger matching. This study aims to parametrically optimise the EGR and turbocharging system settings of a large marine two-stroke engine with the objective of obtaining the highest engine efficiency whilst ensuring compliance with the prevailing NOx emissions limits. Two typical configurations of the investigated engine (baseline and alternative) are modelled in the GT-SUITE software. Parametric simulations are performed with EGR rates up to 40% along with cylinder bypass rates up to 50%, and the simulation results are analysed to quantify the impact of the engine operation with EGR on the performance and NOx emissions parameters. For the baseline engine configuration, the EGR rate increase considerably deteriorates the brake specific fuel consumption (BSFC), which is attenuated by opening the cylinder bypass valve. The optimal combinations of the EGR and cylinder bypass rates for each operating point are identified for both configurations. Following the comparative assessment between the two engine configurations, recommendations for the engine operating modes are proposed, leading to BSFC improvement in the region of 0.7 to 2.9 g/kWh. This study provides insights for the operational settings optimisation of two-stroke engines equipped with EGR systems, contributing towards the reduction of the associated environmental carbon footprint.

As the core component of a ship’s engine room, the operation of a marine diesel engine (MDE) directly affects the economy and safety of the entire vessel. Predicting the future changes in the status parameters of a MDE helps to understand the operational status, enabling timely warnings to the engine crew, and to ensure the safe navigation of the vessel. Therefore, this paper combines the temporal pattern attention mechanism with the bidirectional long short-term memory (BiLSTM) network to propose a novel trend prediction method for short-term exhaust gas temperature (EGT) forecasting. First, the Pearson correlation analysis (PCA) is conducted to identify input feature variables that are strongly correlated with the EGT. Next, the BiLSTM network models input feature variables such as load, fuel oil pressure, and scavenging air pressure and capture the interrelationships between different vectors from the hidden layer matrix within the BiLSTM network. This allows the selection of valuable information across different time steps. Meanwhile, the temporal pattern attention (TPA) mechanism has the ability to explore complex nonlinear dependencies between different time steps and series. This assigns appropriate weights to the feature variables within different time steps of the BiLSTM hidden layer, thereby influencing the input effect. Finally, the improved slime mold algorithm (ISMA) is utilized to optimize the hyperparameters of the prediction model to achieve the best level of short-term EGT trend prediction performance based on the ISMA-BiLSTM-TPA model. The prediction results show that the mean square error, the mean absolute percentage error, the root mean square error and the coefficient of determination of the model are 0.4284, 0.1076, 0.6545 and 98.2%, respectively. These values are significantly better than those of other prediction methods, thus fully validating the stability and accuracy of the model proposed in this paper.

Compared to alternating current (AC) grids, direct current (DC) grids are becoming more and more popular. A power distribution approach is suggested in order to solve the issue of uneven power distribution of distributed generation (DG) in a ship DC microgrid. Power control is carried out using a tracking differentiator (TD), while the output power change rate is not greater than the maximum power ramp rate permitted by the battery, and state-of-charge balance is attained quickly. The proposed strategy also reduces the communication pressure on the power grid. A distributed hierarchical control model of a DC microgrid based on a consensus algorithm is created in order to validate the suggested methodology. The simulation results demonstrate that the established model is capable of simulating the DC microgrid accurately, that the states of charge values of the five batteries gradually converge under the adjustment of the secondary strategy, and that the suggested strategy is reasonable and efficient.