Effects of injection parameters on the spray characteristics of swirl and slit injectors using the Mie-scattering method

We investigated the effects of injection parameters such as injection pressure, ambient pressure, and ambient temperature on spray characteristics. We calculated the turbulence occurring point (t _c ), defined as the time required to generate a vortex, and the deceleration point (t _b ), defined as the time when spray penetration begins to decelerate, to elucidate the breakup mechanism of the test injectors. The spray velocity coefficient (C_v) was obtained to evaluate the spray characteristics. As the ambient pressure increases in the case of a slit injector, C_v decreases. We investigated the effects of nozzle tip shape according to injection pressure, ambient pressure, and fuel properties on spray characteristics and provide a C_v value of 0.38 for the swirl injector with a spray angle of 60° and the slit injector under atmospheric conditions. The value of C_v in the case of a slit injector was reduced by increasing the ambient pressure. Our results suggest that C_v of a swirl injector is constant regardless of changes in ambient pressure, injection pressure, and fuel properties. On the other hand, C_v of a slit injector is altered by changes in ambient pressure.     

Development of electrostatic diesel particulate matter filtration systems combined with a metallic Flow-Through Filter and electrostatic methods

A 3000 cc diesel engine attached to an engine dynamo was used to test three newly developed electrostatic Diesel Particulate matter filtration Systems (DPS 1, 2, and 3) under four steady-state engine operating conditions: idle, 2000 rpm with no load, and 2000 rpm under 25% and 50% loads. Of the two developed alternatives, DPS 1 and DPS 2, DPS 2 comprises an ionization section, electrostatic field additional section and Flow-Through Filter (FTF), which achieved almost 90% removal of particulate matter (PM) under the engine’s operating conditions, and the efficiency of the FTF was maintained between 20% and 50%. Comparing the long-term performance of DPS 2 and DPS 3 (effectively a serial combination of two DPS 2s) with a commercially-available Diesel Particulate Filter (DPF), the DPS 2 and DPS 3 achieved almost the same efficiency for removing PM as the DPF but had significantly improved (75%∼90% lower) differential pressure drops.     

Design and implementation of a new clamping force estimator in Electro-Mechanical Brake systems

In this paper, an improved clamping force estimator is proposed for Electro-Mechanical Brake (EMB) systems by using the motor rotor position information and the hysteresis characteristics of mechanical parts in the EMB. A cascaded type of a force/position control system with a force sensor or an estimator was designed and implemented to control the clamping force and to keep the clearance gap in EMB systems. The EMB Hardware-In-the-Loop-Simulation (HILS) results show that the proposed force estimator yields better estimation performance than the existing estimator and that the clamping force control system based on the estimator can be also used for the fault tolerant control of the system.     

Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodiesel

This study intends to predict the influence of injection pressure and injection timing on performance, emission and combustion characteristics of a diesel engine fuelled with waste cooking palm oil based biodiesel using the artificial neural network (ANN) model. To acquire data for training and testing in the proposed ANN, experiments were carried out in a single cylinder, four stroke direct injection diesel engine at a constant speed of 1500 rpm and at full load (100%) condition. From the experimental results, it was observed that waste cooking palm oil methyl ester provided better engine performance and improved emission and combustion characteristics at injection pressure of 280 bar and timing of 25.5° bTDC. An ANN model was developed using the data acquired from the experiments. Training of ANN was performed based on back propagation learning algorithm. Multilayer perceptron (MLP) network was used for non-linear mapping of the input and output parameters. Among the various networks tested the network with two hidden layers and 11 neurons gave better correlation coefficient for the prediction of engine performance, emission and combustion characteristics. The ANN model was validated with the test data which was not used for training and was found to be very well correlated.     

Trigger algorithm of vehicle automatic crash notification system

The Automatic Crash Notification (ACN) system is an effective technology to decrease the crash response time, improve the level of post-accident rescue and alleviate the severity of injuries. To realize this system, a vehicle terminal is developed. And based on a moving window integral algorithm, the trigger algorithm of ACN system is designed. By comparing the effect of different window widths on the trigger algorithm, we select the window width of the moving window integral algorithm as 8 ms. After system is triggered, different notify types was determined according to the change of velocity in the moving window. A sled impact simulation test shows that the impact can be identified rapidly and also the notify types can be judged by the trigger algorithm. A vehicle road test proves that the ACN system has no false trigger cases. The outcomes of this study support identifications of accidents and crash severities for both occupants and emergency centers.     

Topology optimization of gas flow channel routes in an automotive fuel cell

An efficient topology optimization method for fluid-structure problems was developed in an effort to determine the optimum flow channel route in a fuel cell bipolar plate from first principles. This study describes the derivation and solution of new mathematical equations for topology optimization combining a density-based algorithm, the interpolation method of moving asymptotes (MMA), and the incompressible Navier-Stokes equation with a term representing the chemical reaction between hydrogen and the catalyst. The present method is based on the finite element method with a newly developed reaction rate equation. In this model, a topology variable of 0 represents viscous flow, whereas a value of 1 indicates porous flow. The flow velocity and pressure were obtained from the Navier-Stokes equation and constraints and element matrices for sensitivity analyses during the optimization. MMA was utilized to calculate the optimum flow routes in the design domain. The influence of the key design parameter q and the pressure drop on the optimum topology were also investigated. The channel topology became smoother with decreasing q, and the number of channels increased with increasing pressure drop.     

Fatigue design for plug/ring type gas welded joints of sts301l including welding residual stresses

This paper presents a fatigue design method for plug and ring type gas welded joints, which incorporates welding residual stress effects. A non-linear finite element analysis (FEA) was first performed to simulate the gas welding process. The numerically predicted residual stresses of the gas welds were then compared to experimental results measured using a hole drilling method. In order to evaluate the fatigue strength of the plug and ring type gas welded joints, a stress amplitude (σ _a ) _R taling the welding residual stress of the gas weld into account was introduced and is based on a modified Goodman equation incorporating the effect of the residual stress. Using the stress amplitude (σ _a ) _R , the ΔP-N _f relations obtained from fatigue tests for plug and ring type gas welded joints having various dimensions and shapes were systematically rearranged into (σ _a ) _R -N _f relations. It was found that the proposed stress amplitude (σ _a ) _R could provide a systematic and reasonable fatigue design criterion for the plug and ring type gas welded joints.     

Lane-following method for high speed autonomous vehicles

This paper presents a steering control method for lane-following in a vehicle using an image sensor. With each image frame acquired from the sensor, the steering control method determines target position and direction, and constructs a travel path from the current position to the target position either as an Arc-path or S-path. The steering angle is calculated from the travel path thus generated, and the vehicle follows the travel path via motor-control. The method was tested using a vehicle dubbed as KAV (Korea Autonomous Vehicle) along an expressway (Seoul Inner Beltway) trajectory with a variety of radii (50 m ∼ 300 m) while traveling at a speed of 60 km/h to 80 km/h. Compared with an experienced human driver, the method showed little much difference in performance in terms of lane-center deviation. The proposed method is currently employed for high speed autonomous driving as well as for stop and go traffic.     

Automotive structure vibration with component mode synthesis on a multi-level

The recursive component mode synthesis method (RCMS) has been implemented for the finite element analysis model of an automobile structure as an efficient free vibration analysis tool. The RCMS method is intended to obtain a better performance relative to the block Lanczos method, which is a traditional method in the industry of obtaining eigenvalues, while obtaining the acceptable accuracy. A numerical example of the automobile finite element model demonstrates the outstanding performance of RCMS compared to the block Lanczos method.