Methodology for bus structure torsion stiffness and natural vibration frequency prediction based on a dimensional analysis approach

Engineering bus design requires testing of bus structures prototypes in order to guarantee a certain level of strength and an appropriate static and dynamic behavior of the bus superstructure when exposed to road loads. However, experimental testing of real bus structures is very expensive as it requires expensive resources and space. If testing is done on a scale bus model the previous required expenses are considerably reduced. Therefore, a novel methodology based on dimensional analysis applied to bus structure prediction to evaluate the bus structure static and dynamic performance is proposed. The static performance is evaluated attending to torsion stiffness and the dynamic in terms of the natural vibration frequencies and rollover threshold. A scale bus has been manufactured and dimensionless parameters have been defined in order to project the results obtained in the scale bus model to a larger model. Validation of the proposed methodology has been carried out under experimental and finite element analysis.     

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.     

Driver curve speed model and its application to ACC speed control in curved roads

A speed control algorithm for an ACC (Adaptive Cruise Control) system for curved roads is proposed based on driver behavior characteristics. As the foundation of this research, a driver speed model for curved roads is developed using a series of experimental data regarding driver behavior. To adapt the model to each driver’s individual curve speed behavior, the coefficients of the model are identified in real time from the data sequences collected during drivers’ manual operation stage by a self-learning algorithm based on a Recursive Least-Square (RLS) method with a forgetting factor. Using this algorithm, the parameters of the driver model can be identified from the data collected in the manual operation phase, and the identification results are applied during the ACC automatic control phase. Based on the developed model, the ACC speed control algorithm is modified to provide each individual driver with a customized speed profile for the scenario of a curved road with no car ahead. Tests verify the applicability of the modified system.     

Stress sensitivity analysis and optimization of automobile body frame consisting of rectangular tubes

At conceptual design stage, beam element is extensively used to create the frame structure of automobile body, which can not only archive the accurate stiffness but also reduce much computational cost. However, the stress definition of beam element is very complex so that the stress sensitivity and optimization are difficult to analytically derive and numerically program. This paper presents an solution to this problem and an application in the lightweight optimization design of automobile frame. Firstly, maximal Von Mises stress of rectangular tube is calculated by using the superposition of stress, which is together induced by the axial force, bending moments, torsional moment and shear force. Secondly, the sensitivity of Von Mises Stress with respect to size design variables: breadth, height and thickness are derived, respectively. Thirdly, an optimal criterion is constructed by Lagrangian multiplier method to solve the frame optimization with stress constraints. Lastly, numerical example of car frame proves that the proposed method can guarantee the stress of each beam element almost fully reaches at the yielding stress.     

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.