Engine combustion modeling method based on hybrid drive.

Accurate and comprehensive reconstruction of in-cylinder combustion process is essential for timely monitoring of engine combustion state. This article developed a method based on the zero-dimensional (0-D) physical model integrated with big data. The traditional 0-D prediction model based on cumulative fuel mass is improved, the factor of in-cylinder temperature is introduced to adjust the heat release rate, which solves the problem of difficulty in calibrating the heat release rate. Then, convolutional neural network-gated recurrent unit (CNN-GRU), as a deep neural network, including a special convolutional layer and a gated recurrent unit (GRU) neural network is designed for the parameters to be calibrated in the model. The 0-D predictive combustion model is constructed by combining the physical model with CNN-GRU, the combustion process is simplified and reconstructed. The fitting results show that the 0-D physical model based on improved cumulative fuel mass approach is an effective method to reflect the heat release law. Under non-calibration conditions, the root mean square error (RMSE) value of peak firing pressure (PFP) based on CNN-GRU prediction model is 0.5862. The prediction model is a promising method to realize online fitting and optimization of combustion process.

Experimental study on diesel engine exhaust gas recirculation performance and optimum exhaust gas recirculation rate determination method.

In order to study the exhaust gas recirculation (EGR) performance of marine diesel engines, a venturi high-pressure EGR device was established to overcome the exhaust gas reflow problem based on a certain type of turbocharged diesel engine. The EGR performance test is accomplished and an optimal EGR decision-making optimization method based on grey correlation coefficient modified is proposed. The results show that the venturi tube EGR can basically meet the injection requirements of high-pressure exhaust gas and achieve good results. Through the venturi tube EGR, the NO X emissions reduce significantly with the maximum drop of 30.6%. The explosive pressure in cylinder reduces with the EGR rate increases and the cylinder pressure curve shows a single peak at low-speed conditions and double peaks at high-speed condition. However, the fuel consumption rate, NO X and smoke have been negatively affected. Due to small samples, the traditional evaluation method is difficult to determine the optimal EGR rate reasonably, while the proposed method can effectively solve this problem. It can weaken the shortcomings of subjective judgement and greatly improve the rationality of decision-making results.

Compressor map regression modelling based on partial least squares.

In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN). To demonstrate the potential capabilities of PLSO and PLSN for a typical interpolated prediction and an extrapolated prediction, they are compared with two other classical data-driven modelling methods, namely the look-up table and artificial neural network (ANN). PLSO and PLSN are also compared with each other. The results show that PLSO and PLSN have a better prediction performance than the look-up table and the ANN, especially for the extrapolated prediction. The computational time is also decreased sharply. Compared with PLSO, PLSN is characterized by a higher prediction accuracy and shorter computational time than PLSO. It is expected that PLSN could save computational time and also improve the accuracy of a thermodynamic model of a diesel engine.

Optimized modelling and application of exhaust gas recirculation performance evaluation of turbocharged diesel engine.

Aimed at the problem of exhaust gas recirculation (EGR) performance evaluation and optimal EGR rate determination of turbocharged diesel engines, an optimized decision-making method, based on grey theory and entropy weight, was proposed. The internal combustion pressure, fuel consumption rate, NOX, CO and smoke were selected as the decision-making targets and the initial decision-making model was established based on the traditional grey decision-making theory. According to the characteristics and optimization requirements of EGR, the optimal compromise between combustion and emission performance is proposed to transform into decision-making target weighting problem, then an optimized subjective weighting method based on expert scoring and grey relational analysis is proposed. Finally, the entropy weight method was used to solve the objective weight and the optimized multi-objective grey decision-making model was established, which can not only weaken the human error of subjective empowerment, but also fully explore the intrinsic relationship of the evaluation indexes. At last, an optimization simulation platform for EGR performance evaluation based on MATLB/GUIDE was designed and established. The results show that the optimization simulation platform can effectively improve the efficiency of simulation calculation, which is more convenient for practical engineering applications. The optimized method can successfully realize EGR performance evaluation and optimal EGR rate determination under different working conditions. The decision-making result was consistent with the present EGR control strategies, which provide a new research idea for EGR performance optimization.

Improving rolling bearing online fault diagnostic performance based on multi-dimensional characteristics.

As the main cause of failure and damage to rotating machinery, rolling bearing failure can result in huge economic losses. As the rolling bearing vibration signal is nonlinear and has non-stationary characteristics, the health status information distributed in the rolling bearing vibration signal is complex. Using common time-domain or frequency-domain approaches cannot easily enable an accurate assessment of rolling bearing health. In this paper, a novel rolling bearing fault diagnostic method based on multi-dimensional characteristics was developed to meet the requirements for accurate diagnosis of different fault types and severities with real-time computational performance. First, a multi-dimensional feature extraction algorithm based on entropy characteristics, Holder coefficient characteristics and improved generalized fractal box-counting dimension characteristics was performed to extract the health status feature vectors from the bearing vibration signals. Second, a grey relation algorithm was employed to achieve bearing fault pattern recognition intelligently using the extracted multi-dimensional feature vector. This experimental study has illustrated that the proposed method can effectively recognize different fault types and severities after integration of the improved fractal box-counting dimension into the multi-dimensional characteristics, in comparison with existing pattern recognition methods.

A prediction model of compressor with variable-geometry diffuser based on elliptic equation and partial least squares.

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.

An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis.

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.

Suppression of neuropathic pain by peripheral electrical stimulation in rats: mu-opioid receptor and NMDA receptor implicated.

Peripheral electrical stimulation (PES) has been utilized to manage chronic pain associated with nerve injury. However, the data on clinical effectiveness are conflicting and the neurophysiological mechanism is not well known. This study was designed to assess whether PES relieved neuropathic pain and its possible mechanisms. The neuropathic pain model was made with lumbar 5th (L5) and 6th (L6) spinal nerve ligations in rats. Nociceptive responses of the rats were assessed by the cold plate test (the number and duration of paw lifts that occurred in 5 min on a 5 +/- 1 degrees C cold plate). PES with a frequency of 2 Hz and at increasing strengths was given for 30 min via stainless-steel needles inserted into standard acupoints on the leg and back, respectively. Immunochemistry was used to examine the immunoreactivity of the NMDA receptor 1 (NR1) subunit in the spinal cord dorsal horn. The results are as follows: (1) PES relieved neuropathic pain and the effect was blocked by 1.0 mg/kg naloxone. (2) The effect of one session of PES lasted up to 12 h. (3) Repetitive PES showed a cumulative effect and no tolerance was observed. (4) There was a significant increase of NR1 immunoreactivity in the superficial laminae of the spinal cord of neuropathic pain rats as compared with naive rats. This increase could be reversed by repetitive 2 Hz PES. These results suggest that PES can relieve neuropathic pain, and that mu-opioid receptors and NMDA receptors are involved in the effect of PES.

[Effect of electroacupuncture with different frequencies on neuropathic pain in a rat model].

AIM: Our previous studies showed that electroacupuncture (EA) could inhibited radiant heat induced pain and acute or chronic inflammatory pain in rats. In the present study, we observed whether EA with different frequencies could suppress neuropathic pain. METHODS: L5/L6 nerve ligation model was used to assess the effect of EA on neuropathic pain. Mechanical allodynia was represented by 50% withdrawal threshold, while cold-induced ongoing pain was detected by the number of paw lift in 5 min when the rat was put on a 5 degrees cold plate. Han's acupoint nerve stimulator (HANS) was connected to needles inserted into acupoints "jiaji" and "Zusanli" in both sides. The parameters were: (intensity: 0.5-1-2 mA, 10 min each; frequency 2 Hz or 100 Hz; pulse width: 0.6 ms for 2 Hz, 0.2 ms for 100 Hz). RESULTS: EA of both 2 Hz and 100 Hz could relieve the mechanical allodynia, where 2 Hz could induce the effect with shorter latency; they could also relieve the cold-induced ongoing pain, where the effect of 2 Hz outlasted the EA session by up to 48 h after repetitive stimulations over several weeks; a significant relieving effect on cold-induced ongoing pain could also be induced by needle insertion without stimulation. CONCLUSION: EA could relieve neuropathic pain, the analgesic effect of 2 Hz EA is higher than 100 Hz EA.