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A study of unsteady compressible flow for two types of exhaust manifold and CCC (Close-Coupled Catalyst) systems attached to a 4-cylinder DOHC gasoline engine was carried out to investigate the flow distribution of exhaust gases and finally to make the conversion efficiency of catalyst better. An experimental study was conducted, using LDV technique, to measure the velocity distributions inside exhaust manifolds and CCC under practical engine conditions. In this study, through experiment and calculation, the effects of geometric configuration of exhaust manifold on flow maldistribution in monolith were mainly investigated to understand the exhaust flow structure in terms of flow uniformity and to improve the conversion efficiency. As a result of this fundamental study, the modified exhaust manifold (Type B) was designed and manufactured. Full load performance tests and vehicle emission tests were performed to see the effects of flow characteristics on engine performance and emission.

In diesel engine development, the new technology is coming out to meet the stringent exhaust emission regulation. The regulation demands more eco-friendly vehicles. Euro6c demands to meet not only WLTP mode, but also RDE(Real Driving Emission). In order to satisfy RDE mode, the new technology to reduce emissions should cover all operating areas including High Load & High Speed. It is a big challenge to reduce NOx on the RDE mode and a lot of DeNOx technologies are being developed. So the new DeNOx technology is needed to cover widened operating area and strict acceleration / deacceleration. The existing LNT(Lean NOx Trap) and Urea SCR(Selective Catalytic Reduction) is necessary to meet the typical NEDC or WLTP, but the RDE mode demands the powerful DeNOx technology. Therefore, the LNT & Urea SCR on DPF was developed through this study.

Road friction estimation algorithms had been studied for many years because it is very important factor for safety control and fuel efficiency of vehicle. But traditional solutions are hard to adapt in automotive industry because their performance is not sufficient enough and expensive to implement. Therefore, this paper proposes a road friction estimation algorithm based on a trained artificial neural-network which is low cost and robust. The suggested method doesn’t need expensive additional sensors such as optical or lidar sensor, also it shows better performance in real car environment compared to other algorithms based on vehicle dynamics. In this paper, we would describe this algorithm in detail and analyze the test results evaluated in real road conditions.

Tuning the size and position of the cooling water holes in the head gasket during engine cooling system development is generally positioned at the final stage of the cooling system hardware design. Until now, the gasket hole tuning operation was dependent on the case study through repetitive CFD analysis. In this process, there was a difference in the optimization level by know-how and expertise of the person in charge. In this study, a gasket hole tuning technique was developed using optimization algorithms to improve the level of optimization. First, select factors and perform screening using the DOE(Design Of Experiments) method, and then find the optimal gasket hole size and arrangement through the optimal design process based on the results of the CFD analysis planned by DOE.

Hydroforming process is an emerging manufacturing technology which allows engineers to use continuous closed section without flange for spot weld in a given package envelope. In this research, Hydroforming is applied to a front pillar and a roof side rail for improvement of obstruction angle, body stiffness and roof crush resistance. In addition, the joints of front / center pillar that were integrated into the hydroforming part and structure of package tray were improved. As a result, front pillar width is reduced by 23%, body torsional stiffness is increased by 45% and roof crush resistance is improved by 35%.

Environmental and fuel economy regulations (Eu 6d and WLTP RDE) on automobiles have been tightened recently. To counter this regulation, the global automobile industry is focusing on weight reduction, fuel efficient turbo charger, cooled EGR, thermal management, low friction and so on. However, the high-speed turbocharger makes turbulence, and resulting in airflow noise. This noise is transmitted indoor through the air intake system, which adversely affects the vehicle's competitiveness. Therefore, for turbo engine, it is essential to reduce the noise of the air intake system. The air intake system consists of air cleaner, air filter, air intake hose and air duct. The air flow noise of turbo-engine is mainly the emission noise emitted from the walls of air intake system. And the transfer path of turbo noise is in order of air intake hose, air cleaner and air duct. Therefore, it is effective to reduce the noise of the air intake hose located at the beginning of noise transfer path.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

The reduction of intake noise is a very important factor in controlling the interior noise levels of vehicles, particularly at low and major engine operating speeds. A vehicle intake system generally consists of air cleaner box, hose, duct, and filter element. Also, resonators and porous duct are included, being used to reduce intake noise. For more accurate estimation of the transmission loss (TL), it seems important to develop a CAE model that accurately describes this system. In this paper, simple methods, which can consider the effects of filter element and vibro-acoustic coupling, are suggested which could remarkably improve estimation accuracy of the TL. The filter element is assumed as equivalent semi-rigid porous materials characterized by the flow resistivity defined by the pressure drop, velocity, and thickness.

In the early stages of vehicle development, it is critical to establish performance goals for the major systems. The fundamental modes of body and chassis frames are typically assessed using FE models that are discretized using shell elements. However, the use of the shell-based FE method is problematic in terms of fast analysis and quick decision-making, especially during the concept phase of a vehicle design because it takes much time and effort for detailed modeling. To overcome this weakness, a one-dimensional (1D) method based on beam elements has been extensively studied over several decades, but it was not successful because of low accuracy for thin-walled beam structures. This investigation proposes a 1D method based on thin-walled beam theory with comparable accuracy to shell models. Most body pillars and chassis frame members are composed of thin-walled beam structures because of the high stiffness-to-mass ratio of thin-walled cross sections.

In recent years, with the advent of the Fourth Industrial Revolution and the COVID-19 pandemic, people's lives worldwide have undergone significant changes. Additionally, the emergence of a new generation of consumers known as the millennial generation has led to a high demand for multipurpose family cars. The perspective is shifting towards choosing premium products that enhance the quality of life and pursue their own happiness and comfort through technology, rather than simply selecting a midsize SUV based on the increase in family size. We aim to meet the needs of these global customers by conducting research and developing various new features that were not previously available in midsize SUVs. In this study, we defined the actual target users for midsize SUVs and established UX concepts by analyzing their characteristics. Based on this, we employed an optimal design approach by analyzing the evaluation results by country for the various features implemented within the vehicle.

Impact resistance of plastic underbody parts was studied using simulated injection-molded specimen which can be tested according to different types of material used, injection molding variants like position and number of injection molding gates, and features of ribs. Material applied was glass fiber reinforced polyamide which can be used in underbody parts. Test was performed using several combinations of injection molding gates and rib types. From the test result, optimal design guide for plastic underbody parts was determined. Also, new high impact resistant plastic material made of glass fiber reinforced polyamide 66 (PA66) and polyamide 6 (PA6) alloy was developed and the material properties useful for CAE were determined. As a case study, oil pan and muffler housing were designed following the optimal design guide and CAE. And the reliability of the sample muffler housing designed was verified.

Bad weather conditions such as torrential rain, heavy snow, and thick fog frequently occur worldwide. Vehicle accidents in such bad weather conditions account for a significant portion of all vehicle accidents, and the level of damage is relatively severe compared to other accidents that occur in clear weather. This paper analyzes the driver's driving stability in bad weather conditions, which has such a significant meaning, in various ways through experiments on the inconvenience experienced by the driver. In this study, three levels of bad weather conditions were implemented in a driving simulator environment to evaluate driver inconvenience for six activities. Through driving experiment, quantitative bio-signals and vehicle signals were analyzed in each weather condition. The SD survey was used to assess the driver's inconvenience level for activities performed while driving and analyze the ranking of inconvenience.

In this paper, an application process is studied at which the insertion loss (IL) test data of sound insulating parts or noise control treatments are utilized for the sound transmission loss (STL) simulation of the trimmed dash structure. The considered sound barrier assemblies were composed of a felt layer, a mass layer, and a decoupler layer. Flat samples of sound barrier assemblies with several different thicknesses were prepared, and ILs of them were measured by using a sound transmission loss facility. Flat samples were assumed to have mass-spring-mass resonance frequencies. The mass was set as the area mass of the sound barrier layer of the felt layer and the mass layer. The spring constant of the decoupler layer was assumed as the multiplication of that of an air spring and a spring correction factor.

The application of Computational Fluid Dynamics (CFD) for three-dimensional (3D) combustion analysis coupled with detailed chemistry in engine development is hindered by its expensive computational cost. Chemistry computation may occupy as much as 90% of the total computational cost. In the present paper, a new two-step iso-octane combustion model was developed for spark-ignited (SI) engine to maximize computational efficiency while maintaining acceptable accuracy. Starting from the model constants of an existing global combustion model, the new model was developed using an approach based on sensitivity analysis to approximate the results of a reference skeletal mechanism. The present model involves only five species and two reactions and utilizes only one uniform set of model constants. The validation of the new model was performed using shock tube and real SI engine cases.

After the COVID-19 pandemic, leisure activities and cultures have undergone significant transformations. Particularly, there has been an increased demand for outdoor camping. Consequently, the need for capabilities that allow vehicles to navigate not only paved roads but also unpaved and rugged terrains has arisen. In this study, we aim to address this demand by utilizing AI to introduce a 'Stuck Probability Estimation Algorithm' for vehicles on off-road. To estimate the 'Stuck Probability' of a vehicle, a mathematical model representing vehicle behavior is essential. The behavior of off-road driving vehicles can be characterized in two main aspects: firstly, the harshness of the terrain (how uneven and rugged it is), and secondly, the extent of wheel slip affecting the vehicle's traction.

The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

It is an important factor in electric vehicles to show customers how much they can drive with the energy of the remaining battery. If the remaining mileage is not accurate, electric vehicle drivers will have no choice but have to feel anxious about the mileage. Additionally, the potential customers have range anxiety when they consider Electric Vehicles. If the remaining mileage to drive is wrong, drivers may not be able to get to the charging station and may not be able to drive because the battery runs out. It is important to show the remaining available driving range exactly for drivers. The previous study proposed an advanced model by predicting the remaining mileage based on actual driving data and based on reflecting the pattern of customers who drive regularly. The Bayesian linear regression model was right model in previous study.

Aerodynamic simulation results are most of the time compared to wind tunnel results. It is too often simplistically believed that it suffice to take the CAD geometry of a car, prepare and run a CFD simulation to obtain results that should be comparable. With the industry requesting accuracies of a few drag counts when comparing CFD to wind tunnel results, a careful analysis of the element susceptible of creating a difference in the results is in order. In this project a detailed 1:4 scale model of the Hyundai Genesis was tested in the model wind tunnel of the FKFS. Five different underbody panel configurations of the car were tested going from a fully paneled car to a car without panels. The impact of the moving versus static ground was also tested, providing over all ten different experimental results for this car model.

Binder jetting of sand molds and cores for metal casting provides a scalable and efficient means of producing metal components with complex geometric features made possible only by Additive Manufacturing. Topology optimization software that can mathematically determine the optimum placement of material for a given set of design requirements has been available for quite some time. However, the optimized designs are often not manufacturable using standard metal casting processes due to undercuts, backdraft and other issues. With the advent of binder-based 3D printing technology, sand molds and cores can be produced to make these optimized designs as metal castings.

A combined experimental and computational study of 3-D unsteady compressible flow in exhaust manifold and CCC system was performed to understand the flow characteristics and to improve the flow distribution of pulsating exhaust gases within monolith. An experimental study was carried out to measure the velocity distribution in production exhaust manifold and CCC under engine operating conditions using LDV (Laser Doppler Velocimetry) system. Velocity characteristics were measured at planes 25 mm away from the front surface of first monolith and between two monolithic bricks. To provide boundary conditions for the computational study, velocity fields according to crank angle were also measured at the entrance of exhaust manifold. The comparisons of exhaust gas flow patterns in the junction and mixing pipe between experimental and computational results were made.