This paper focuses on oxidation reactivity and nanostructural characteristics of particulate matter (PM) emitted from diesel engine fuelled with different volume proportions of diesel/polyoxymethylene dimethyl ethers (PODEn) blends (P0, P10 and P20). PM was collected using a metal filter from the exhaust manifold. The collected PM samples were characterized using thermogravimetric analysis (TGA), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Raman spectroscopy. The TGA results indicated that the PM produced by P20 had the highest moisture and volatility contents and the fastest oxidation rate of solid carbon followed by P10 and P0 derived PM. SEM analysis showed that PM generated from P20 was looser with a lower mean value than PM emitted from P10 and P0. Quantitative analysis of high-resolution TEM images presented that fringe length was reduced along with increased separation distance and tortuosity with an increase in PODEn concentration. These trends improved the oxidation reactivity. According to Raman spectroscopy data, the intensity, full width at half-maximum and intensity ratio of the bands also changed demonstrating that PM nanostructure disorder was correlated with a faster oxidation rate. The results show the use of PODEn affects the oxidation reactivity and nanostructure of PM that is easier to oxidize.

When searching for the optimal solution, Equivalent Consumption Minimum Strategy (ECMS) has to calculate and compare the total equivalent fuel rate of huge candidates covered all over the control domain for each time instant. Therefore, this strategy still has a heavy computation burden problem; it is a challenge for ECMS to be implemented online for real-time control. To reduce ECMS's calculation load, this paper proposes an adaptive Simplified-ECMS-based strategy for a parallel plug-in hybrid electric vehicle (PHEV). A convex piecewise function is applied to fit the total equivalent fuel rate with respect to the motor torque, which is the control variable. Then, the ECMS problem is simplified to calculate and compare only five candidates' total equivalent fuel rate to determine the optimal torque distribution. Particle swarm optimization (PSO) algorithm is applied to optimize the equivalent factor, and the MAPs of this factor under different driving cycles, driving distances and initial SOC are obtained. Based on this, the adaptive Simplified-ECMS-based strategy is proposed. Simulations were performed, and the results show that the Simplified-ECMS-based strategy can obviously shorten the calculation time compared to ECMS-based strategy, and the adaptive Simplified-ECMS-based strategy can decrease fuel consumption of plug-in hybrid electric vehicle by 16.43% under the testing driving cycle, compared to CD-CS-based strategy. A road test on the prototype vehicle is conducted and the effectiveness of the Simplified-ECMS-based strategy is validated by the test data.

The airflow dynamics observed during a cough process in a CT-scanned respiratory airway model were numerically analyzed using the computational fluid dynamics (CFD) method. The model and methodology were validated by a comparison with published experimental results. The influence of the cough peak flow rate on airflow dynamics and flow distribution was studied. The maximum velocity, wall pressure, and wall shear stress increased linearly as the cough peak flow increased. However, the cough peak flow rate had little influence on the flow distribution of the left and right main bronchi during the cough process. This article focuses on the mathematical and numerical modelling for human cough process in bioengineering.

With plug-in hybrid electric vehicles (PHEVs), the catalyst temperature is below the light-off temperature due to reduced engine load, extended engine off period, and frequent engine on/off shifting. The conversion efficiency of a three-way catalyst (TWC) and tailpipe emissions were proven to depend heavily on the temperature of the catalyst. The existing energy management strategy (EMS) of the PHEVs focuses on the improvement of fuel efficiency and emissions based on hot engine characteristics, but neglects the effect of catalyst temperature on tailpipe emissions. This paper presents a new EMS that incorporates a catalyst thermal management method. First, an additional cost is established to implement additional constraints on catalyst temperature, and then the global cost function is created using this additional cost and the fuel consumption. Second, we find the global optimal solution using Pontryagin's minimum principle method, which provides an optimal control policy and state trajectories. Then, based on the analysis of the optimal control policy, an engine on/off filter (eng on/off filter) is introduced to command the engine on/off shifting. This filter plays an important role in adjusting both the energy and catalyst thermal management strategy for PHEVs. Finally, a practical approach based on the eng on/off filter is developed, and a genetic algorithm is applied to optimize the time constants of this filter. Simulation results demonstrate that the proposed approach's fuel consumption increased slightly, but the tailpipe emissions of HC (hydrocarbons), CO (carbon monoxide) and NOx (nitrogen oxide) significantly decreased compared with the standard approach.