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As one of the most important auto-body moving parts, door hinge is the key point of door design and its accessories arrangement, also the premise of the door kinematic analysis. We proposed an effective layout procedure for door hinge and developed an intelligent system on CATIA CAA platform to execute it. One toolbar and five function modules are constructed - Axis Arrangement, Section, Parting Line, Kinematic, Hinge Database. This system integrated geometrical algorithms, automatically calculate the minimum clearances between doors, fender and hinges on sections to judge if the layout is feasible. As the sizes of the clearances are set to 0s, the feasible layout regions and extreme start/end points are shown in parts window, which help the engineer to check the parting line and design a new one. Our system successfully implemented the functions of five modules for the layout of door hinge axis and parting line based on a door hinge database.

Aiming at the problem of poor robustness after the combination of lateral kinematics control and lateral dynamics control when an autonomous vehicle decelerates and changes lanes to overtake at a certain distance. This paper proposes a trajectory determination and tracking control method based on a PI-MPC dual algorithm controller. To describe the longitudinal deceleration that satisfies the lateral acceleration limit during a certain distance of lane change, firstly, a fifth-order polynomial and a uniform deceleration motion formula are established to express the lateral and longitudinal displacements, and a model prediction controller (MPC) is used to output the front wheel rotation angle. Through the dynamic formula and the speed proportional-integral (PI) controller to control and adjust the brake pressure.

The non-conventional diesel nozzles have attracted more and more attention for their ability to promote jet breakup. In the present study, the internal nozzle flow and jet breakup relying on the convergent-divergent nozzle are investigated by combining the cavitation model and LES model with Multi-Fluid-Quasi-VOF model based on the OpenFOAM code. This is a novel method for which the interphase forces caused by the relative velocity of gas and liquid can be taken into account while sharpening the gas-liquid interface, which is able to accurately present the evolution processes of cavitation and jet breakup. Primarily, the numerical model was verified by the mass flow rate, spray momentum flux, discharge coefficient and effective jet velocity of the prototype Spray D nozzle from the literature.

The increasing demand for lightweight design of the whole vehicle has raised critical weight reduction targets for crash components such as front rails without deteriorating their crash performances. To this end the last few years have witnessed a huge growth in vehicle body structures featuring hybrid materials including steel and aluminum alloys. In this work, a type of tapered tailor-welded tube (TTWT) made of steel and aluminum alloy hybrid materials was proposed to maximize the specific energy absorption (SEA) and to minimize the peak crushing force (PCF) in an oblique crash scenario. The hybrid tube was found to be more robust than the single material tubes under oblique impacts using validated finite element (FE) models. Compared with the aluminum alloy tube and the steel tube, the hybrid tube can increase the SEA by 46.3% and 86.7%, respectively, under an impact angle of 30°.

A solution for cylinder wall scuffing is researched. By means of the finite element method, a mathematical model to investigate the temperature field and thermal deformation of the piston is established, which can be used for predicting the cylinder wall scuffing in both the design process of a new diesel engine and the improvement process of the combustion system of an operating diesel engine. The experimental data support the calculated results obtained by using the model. As an application example, the temperature fields and thermal deformations of the piston in the 6E150C diesel engine, which suffers from the cylinder wall scuffing during the period of operation, are analyzed quantitatively by using the mathematical model. After an improved piston with new structure and good cooling system is used in the 6E150C diesel engine, the cylinder wall scuffing no longer occurs.

The mathematical model for the microwave regeneration of diesel particulate filter is proposed according to the characteristic of microwave regeneration process. The model is used to calculate the temperature field, distribution of particulate and density field of oxygen in the filter during the process of regeneration with typical ceramic foam particulate fitter data. The parametric study demonstrates how some of the main parameters, such as microwave attenuation constant of the filter, filter particulate loading, the power and distribution of microwave energy and so on, affect the efficiency of regeneration, the maximum filter temperature and regeneration duration. The results show that it is possible to regenerate the diesel particulate filters in certain conditions by using microwave energy.

As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels.

The vehicle axle gear whine noise and vibration are key issues for the automotive industry to design a quiet, reliable driveline system. The main source of excitation for this vibration energy comes from hypoid gear transmission error (TE). The vibration transmits through the flexible axle components, then radiates off from the surface of the housing structure. Thus, the design of hypoid gear pair with minimization of TE is one way to control the dynamic behavior of the vehicle axle system. In this paper, an approach to obtain minimum TE and improved dynamic response with optimal tooth profile modification parameters is discussed. A neural network algorithm, named Back Propagation (BP) algorithm, with improved Particle Swarm Optimization (PSO) is used to predict the TE if some tooth profile modification parameters are given to train the model.

Most of algorithms of lane detection mainly aim at the scenes of daytime. However, those algorithms are unstable for the lane detection at night because the camera is very sensitive to the light change. This paper proposed a lane detection algorithm that largely improves the detection system’s performance when it is used at night. The algorithm has two main stage: Image processing and Kalman filter (KF). The key process steps of Stage 1 are: extracting the Region of Interesting (ROI)→Edge Detection →Binarization→Hough→ Lane Selection→Lane fitting. First step, a ROI could be extracted according to the relatively fixed location of lanes. In step of edge detection, we use a creative filter named Correlation filter to remove image noise and remain the feature of lane. The filter matrix looks like “[0 1 1, −1 0 1; −1 −1 0]”. Next, the candidate lines are detected by the Hough transform, then, the equations of lane are acquired by fitting spots obtained from Hough.

The purpose of this study is to investigate the effect of intake air hydrogenation coupled with intake air humidification (IAH) on the combustion and emission of marine diesel engines. A 3D numerical model of four-stroke turbocharged intercooled marine diesel engine was established by using commercial software AVL-Fire. The effects of hydrogen and water injected into the intake port on engine in-cylinder combustion and emission characteristics at 1350 r/min and partial load were studied. The novelty of this study is to combine different hydrogen-fuel ratios and water-fuel ratios, so as to find the optimization method that can reduce NOx and soot emissions and ensure the thermal efficiency of the engine doesn’t decrease.

Unlike RANS method, LES method needs more time and much more grids to accurately simulate the spray process. In KIVA, spray process was modeled by Lagrangain-drop and Eulerian-fluid method. The coarse grid can cause errors in predicting the droplet-gas relative velocity, so for reducing grid dependency due to the relative velocity effects, an improved spray model based on a gas-jet theory is used in this work and in order to validate the model seven different size grids were used. In this work, the local dense grid was used to reduce the computation cost and obtain accurate results that also were compared with entire dense grid. Another method to improve computation efficiency is the MUSCL (Monotone Upstream-centered Schemes for Conservation Laws) differencing scheme that was implemented into KIVA3V-LES code to calculate the momentum convective term and reduce numerical errors.

This paper discusses the development of engine and vehicle model for performing dynamic emulation experiments on vehicle transmissions. In order to reduce costs and shorten new vehicle development cycle time, vehicle simulation on the driveline test bench is an attractive alternative at the development phase to reduce the quantity of proto vehicles. This test method moves the test site from the road to the bench without the need for real chassis parts. Dynamic emulation of mechanical loads is a Hardware-in-the-loop (HIL) procedure, which can be used as a supplement of the conventional simulations in testing of the operation of algorithms without the need for the prototypes. The combustion engine is replaced by an electric drive dynamometer, which replicates the torque and speed signature of an actual engine. The road load resistance of the vehicle on a real test road is accurately simulated on Load dynamometer.

Multi-sensor fusion strategies have gradually become a consensus in autonomous driving research. Among them, radar-camera fusion has attracted wide attention for its improvement on the dimension and accuracy of perception at a lower cost, however, the processing and association of radar and camera data has become an obstacle to related research. Our approach is to build a concise framework for camera and radar detection and data association: for visual object detection, the state-of-the-art YOLOv5 algorithm is further improved and works as the image detector, and before the fusion process, the raw radar reflection data is projected onto image plane and hierarchically clustered, then the projected radar echoes and image detection results are matched based on the Hungarian algorithm. Thus, the category of objects and their corresponding distance and speed information can be obtained, providing reliable input for subsequent object tracking task.

Deep reinforcement learning (DRL) has not been widely used in the engineering field yet because RL needs to be learned through ‘trial and error’, which makes the application of this kind of algorithm in real physical environment more difficult, and it is impossible to carry out ‘trial and error’ learning on real vehicles. By analyzing the motion state of the vehicle in the car following mode, the algorithm that combined traditional longitudinal motion control with DRL improves the safety of RL in the real physical environment and the poor adaptability of the traditional longitudinal motion control algorithm. In this paper, the longitudinal motion of the unmanned vehicle is taken as the research object, and the PID algorithm is combined with the Deep Deterministic Policy Gradient (DDPG) algorithm to control the longitudinal motion of the unmanned vehicle.