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At present, regression modeling methods fail to achieve higher simulation accuracy, which limits the application of simulation technology in more fields such as virtual calibration and hardware-in-the-loop real-time simulation in automotive industry. After fully considering the abruptness and complexity of engine predictions, a Gaussian process regression modeling method based on a combined kernel function is proposed and verified in this study for engine torque, emission, and temperature predictions. The comparison results with linear regression, decision tree, support vector machine (abbreviated as SVM), neural network, and other Gaussian regression methods show that the Gaussian regression method based on the combined kernel function proposed in this study can achieve higher prediction accuracy. Fitting results show that the R 2 value of engine torque and exhaust gas temperature after the engine turbo (abbreviated as T4) prediction model reaches 1.00, and the R 2 value of the nitrogen oxide (abbreviated as NOx) prediction model reaches 0.9999. The model generalization ability verification test results show that for a totally new world harmonized transient cycle data, the R 2 value of engine torque prediction is 0.9993, the R 2 value of exhaust gas temperature is 0.995, and the R 2 value of NOx emission prediction result is 0.9962. The results of model generalization ability verification show that the model can achieve high prediction accuracy for performance prediction, temperature prediction, and emission prediction under steady-state and transient operating conditions.

Low accuracy is the main challenge that plagues the application of engine modeling technology at present. In this paper, correlation analysis technology is used to analyze the main influencing factors of engine torque and NOx (nitrogen oxides) raw emission performance from a statistical point of view, and on this basis, the regression algorithm is used to construct the engine torque and NOx emission prediction model. The prediction RMSE between engine torque prediction value and true value reaches 4.6186, and the torque prediction R2 reaches 1.00. Prediction RMSE between NOx emission prediction value and true value reaches 67.599, and NOx emission prediction R2 reaches 0.99. When using the new WHTC data for model prediction verification, the RMSE between the engine torque predicted value and true value reaches 4.9208, and the prediction accuracy reaches 99.60%, the RMSE between NOx emission prediction value and true value reaches 72.38, and the prediction accuracy reaches 99.2%, indicating that the model is relatively accurate. The evaluation result of the ambient temperature impact on torque shows that ambient temperature is positively correlated with engine torque.

In the application of DPFs (diesel particulate filters), temperature prediction and control technology during the regeneration phase has always been a great challenge, which directly affects the safety and performance of diesel vehicles. In this study, based on theoretical analysis and sample gas bench test results, a one-dimensional simulation model is built with GT-POWER software. The effects of soot loading quantity and oxygen concentration on regeneration temperature performance are studied. Simulation results show that, when the soot loading quantity exceeds 46 g (12.7 g/L), the maximum temperature inside DPF during the regeneration phase would be higher than 800 °C, and the risk of burning crack would be high. When the oxygen concentration in the exhaust gas is low (lower than 7%), the fuel injected into exhaust gas fails to give off enough heat, and the exhaust gas temperature fails to reach the target regeneration temperature, hydrocarbon emission could be found from the DPF outlet position; when the oxygen concentration in the exhaust gas reaches 7% or above, the DPF inlet temperature could reach the target temperature, accompanied by less hydrocarbon emission. Combined with the simulation results, engine test bench validation was carried out. The results show that the simulation results and test results agree well.

To investigate the potential bioavailability and mobility of Phosphorus (P) in sediment, Measurements and Testing (SMT) programme was employed to characterize the distribution of P in the middle-lower reaches of East Tiaoxi River. The results showed that the inorganic phosphorus (IP) was the predominant fraction of total phosphorus (TP). There was a watershed for the percentage of non-apatite inorganic phosphorus (NAIP) and apatite inorganic phosphorus (AP) in IP in Deqing County. The IP was mainly distributed in the NAIP fraction from Pingyao town to Deqing County, while the IP mainly distributed in the AP fraction from Deqing county to Huzhou city. Keywords-sediment; phosphorus fractions; bioavailable phosphorus; aquatic plantI.