Accurate and rapid prediction of aerodynamic loads is an important part of the vehicle digital twinning technology, and is an important link between the real vehicle and its digital twin. At present, building aerodynamic load proxy model based on data modeling method to obtain aerodynamic data efficiently has become an important research direction in vehicle design. However, data modeling methods using a single source are difficult to break the upper limit of accuracy of the existing model predictions. Based on sparse and limited wind tunnel test data, a multi-source data fusion method of wing aerodynamic loads based on pre-training fine-tuning is proposed for the CRM-WB wing body assembly. Considering the difference in prediction accuracy caused by the pressure distribution characteristics on the upper and lower surfaces of the wing, the pre-training grouped fine-tuning strategy is further adopted to construct the aerodynamic load fusion model. The test results show that the average prediction error of the model is 3.17%, and compared with the prediction model based on single data training (an average error of 5.70%), the combined depth neural network fusion modeling method (an average error of 5.11%), and the Gauss process regression uncertainty weighted fusion modeling method (an average error of 6.16%), the multi-source data fusion method proposed in this paper achieves higher accuracy prediction. Generalizability tests show that the pre-training fine-tuning model proposed in this paper has good generalized ability, and the average error of the prediction model is reduced by 11.19% compared to the single data source in the extrapolation case.

Efficient and secure refueling within the vehicle refueling systems exhibits a close correlation with the issues concerning fuel backflow and gasoline evaporation. This paper investigates the transient flow behavior in fuel hose refilling and simplified tank fuel replenishment using the volume of fluid method. The numerical simulation is validated with the simplified hose refilling experiment and the evaporation simulation of Stefan tube. The effects of injection flow rate and injection directions have been discussed in the fuel hose refilling part. For both the experiment and simulation, the pressure at the end of the refueling pipe in the lower located nozzle case is 30% higher than that in the upper located nozzle case at a high flow rate, and the backflow phenomenon occurs at the lower filling mode. The fluid will directly flush into the first pipe elbow, changing the flow pattern from bubble flow to slug flow, which results in low-frequency and high-amplitude flow pressure fluctuations. A hexane refueling system, consisting of a refueling pipe, fuel tank and a vapor return line, is analyzed, in which hexane evaporation is considered. At the early refueling period, a higher refueling rate will lead to more obvious splashing, which leads to a higher average mass of hexane vapor and pressure in the tank. Two optimized fuel tank designs are examined. The lower fuel tank filling port exhibits significantly lower vapor hexane in the fuel tank compared to the other design, resulting in a reduction of 200 Pa in the peak pressure in the tank, which contributes to a substantial reduction of gasoline loss during tank filling.

Flapping Wing Micro Aerial Vehicles (FWMAVs) have caused great concern in various fields because of their high efficiency and maneuverability. Flapping wing motion is a very important factor that affects the performance of the aircraft, and previous works have always focused on the time-averaged performance optimization. However, the time-history performance is equally important in the design of motion mechanism and flight control system. In this paper, a time-history performance optimization framework based on deep learning and multi-island genetic algorithm is presented, which is designed in order to obtain the optimal two-dimensional flapping wing motion. Firstly, the training dataset for deep learning neural network is constructed based on a validated computational fluid dynamics method. The aerodynamic surrogate model for flapping wing is obtained after the convergence of training. The surrogate model is tested and proved to be able to accurately and quickly predict the time-history curves of lift, thrust and moment. Secondly, the optimization framework is used to optimize the flapping wing motion in two specific cases, in which the optimized propulsive efficiencies have been improved by over 40% compared with the baselines. Thirdly, a dimensionless parameter C_variation is proposed to describe the variation of the time-history characteristics, and it is found that C_variation of lift varies significantly even under close time-averaged performances. Considering the importance of time-history performance in practical applications, the optimization that integrates the propulsion efficiency as well as C_variation is carried out. The final optimal flapping wing motion balances good time-averaged and time-history performance.