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Developing an accurate combustion model for a Sequential Turbocharging diesel engine is an efficient way to reduce the time consumption and the expensive experiment costs in the process of determining the control strategy of a Sequential Turbocharging system. In this paper, 142 operations of a marine diesel engine with two Sequential Turbocharging systems are used for this research. A novel method is proposed to calculate the initial values and limit bounds of the Wiebe parameters, and the use of this method could effectively avoid the occurrence of impossible solutions or multiple solutions caused by only using nonlinear least square fitting. The calculated Wiebe parameters could achieve a high level of accuracy that the mean value of the determination coefficient between the simulated and experimental heat release rate profiles is 0.98 for all the operations, and they are accurate enough for developing the combustion model. The partial least square regression is first imposed to relate the Wiebe parameters to the operating conditions. For the ability to solve the multiple correlations among the operating conditions, the prediction performance of the combustion model developed by partial least square regression is much higher than that by least square regression. The mean absolute percentage errors of the brake specific fuel consumption and maximum in-cylinder pressure simulated by the model are 0.284% and 1.39%, respectively. The model could accurately predict the engine performance and is sufficient to make the reasonable control strategies for the different Sequential Turbocharging systems.

Artificial Neural Network (ANN) and Support Vector Machine (SVM), due to their accuracy and ability to analyze nonlinear problems, have been applied in many research areas. However, their performances vary with the application area. This paper investigates the performances of these two approaches for the responses prediction of a common rail direct injection system (CRDI)-assisted marine diesel engine. Moreover, considering that the experiments of marine diesel engines are always time, money and energy consuming, the main purpose of this study is to determine the better predictive approach based on a small amount of training data. The Taguchi orthogonal array is employed for the operating points determination of training data; then, based on the same training data, which contain only 25 samples, the predictive performances of ANN and SVM are evaluated and compared. The comparison of ANN and SVM indicates that with limited experimental data, SVM can find the optimal global solution and has excellent predictive accuracy and generalization capability, while ANN may converge to local minima and face the overfitting problem. Eventually, this study suggests that SVM is well-suited for application to diesel engine response predictions and will reduce the experimental cost significantly.

A comparison of four different control-oriented models has been carried out in this paper for the simulation of the main combustion metrics in diesel engines, i.e., combustion phasing, peak firing pressure, and brake mean effective pressure. The aim of the investigation has been to understand the potential of each approach in view of their implementation in the engine control unit (ECU) for onboard combustion control applications. The four developed control-oriented models, namely the baseline physics-based model, the artificial neural network (ANN) physics-based model, the semi-empirical model, and direct ANN model, have been assessed and compared under steady-state conditions and over the Worldwide Harmonized Heavy-duty Transient Cycle (WHTC) for a Euro VI FPT F1C 3.0 L diesel engine. Moreover, a new procedure has been introduced for the selection of the input parameters. The direct ANN model has shown the best accuracy in the estimation of the combustion metrics under both steady-state/transient operating conditions, since the root mean square errors are of the order of 0.25/1.1 deg, 0.85/9.6 bar, and 0.071/0.7 bar for combustion phasing, peak firing pressure, and brake mean effective pressure, respectively. Moreover, it requires the least computational time, that is, less than 50 s when the model is run on a rapid prototyping device. Therefore, it can be considered the best candidate for model-based combustion control applications.

The main tasks of computer vision are image classification/location, target detection, target tracking, semantic segmentation and instance segmentation. The task of target detection is to output the borders and labels of a single target from the image. Object detection is an important issue in the field of computer vision. It has important research significance and application value in video monitoring, autonomous driving and human-computer interaction. In recent years, deep learning has made a breakthrough in the research of image classification and led to the rapid development of object vision detection. This paper briefly introduces the object detection algorithm based on deep learning. First, the basic process of object detection is introduced, then several current algorithms of object detection are introduced, and finally the future development trend is prospected.

The purpose of this study is to propose a multi-objective decision making optimization method based on subjective and objective empowerment optimization. This paper intend to achieve the evaluation of turbocharged diesel engine EGR performance. First, the main diesel engine EGR parameters were selected as decision target and corresponding experimental data are used as effect sample matrix to establish the initial multi-objective-making model. The characteristics and optimization requirements of turbocharged diesel engine EGR are considered. Secondly, the expert scoring, grey correlation analysis are used to solve the optimized weight vector. Finally, the optimized decision-making model was established to explore the intrinsic objective relationship of EGR evaluation index parameters and give the best evaluation and optimal decision. The results show that the optimized method can successfully solve the turbocharged diesel engine EGR performance evaluation and optimal decision problem, which can provides theoretical support and reference for the further optimization of EGR.

Exhaust gas recirculation (EGR) is one of the main methods of reducing NOX emissions and has been widely used in marine diesel engines. This paper proposes an optimized comprehensive assessment method based on multi-objective grey situation decision theory, grey relation theory and grey entropy analysis to evaluate the performance and optimize rate determination of EGR, which currently lack clear theoretical guidance. First, multi-objective grey situation decision theory is used to establish the initial decision-making model according to the main EGR parameters. The optimal compromise between diesel engine combustion and emission performance is transformed into a decision-making target weight problem. After establishing the initial model and considering the characteristics of EGR under different conditions, an optimized target weight algorithm based on grey relation theory and grey entropy analysis is applied to generate the comprehensive evaluation and decision-making model. Finally, the proposed method is successfully applied to a TBD234V12 turbocharged diesel engine, and the results clearly illustrate the feasibility of the proposed method for providing theoretical support and a reference for further EGR optimization.

To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.