• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.2
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• pp.228-236
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• 2021

In order to understand the underwater noise source factor of the linear pump type forced ejection system, a reduced-model compressed water experiment device was developed. The reduced-model compressed water experiment device consists of a reverberation tank, a linear pump type forced ejection device, and an underwater vehicle. The underwater noise source was selected from the hydraulic ram moving speed, the hydraulic ram/piston pipe spacing, the ejection pipe inlet/water ram area ratio, and the number of water ram inlets. The underwater vehicle was ejected into the reverberation tank by the device. The source level was derived from the measured sound pressure. The source level tends to increase as the hydraulic ram/piston tube spacing and the hydraulic ram moving speed increase. The source level tended to increase as the area ratio was increased, but the level was weak. The number of water ram inlet did not affect the source level.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.789-792
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• 2005

The dominant noise sources of Diesel Multiple Units are powerpack, which is composed of engine, transmission and cooling system, noise and wheel-rail rolling noise. The interior noise of a running vehicle is determined by structure-borne noise and air-borne noise from these noise sources. The contributions of interior noise from each noise source are calculated by air-borne transfer functions and structure-borne transfer functions of noise sources. In this paper, source separation technique is proposed to determine these transfer functions from the results of stationary and running tests of existing vehicle. With this technique, it is possible to get hold of contributions of interior noise from .noise sources of running vehicle. This source separation technique makes it possible to take efficient measures for reduction of interior noise at the early car-development stage.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.144-151
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• 2006

Sound generated from a moving vehicle often carries information on the condition of vehicle, for example, whether it has faults or not, where the fault exists. The latter is possible especially by MFAH(moving frame acoustic holography) and beamforming method. MFAH is applicable to the sound source of pure tone or narrow band noise. For the beamforming method, we have to know what kind of wave the sound source radiates, for example, plane wave or spherical wave. That is, whether the above methods are applicable depends on the characteristics of sound source. To apply these methods to the fault detection, we have to know the characteristics of wave from faults. In this research, a machine diagnosis technique based on the above holographic approaches is introduced to find the position of faults. The signal due to faults is modeled based on the fact that the faults radiate impulsive noise, and analyzed in time and frequency domain. The way how MFAH and beamforming method can be used is introduced to find the position of source.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.727-728
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• 2007

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.3
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• pp.318-329
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• 2010

The purpose of this study was to develop the $PM_{2.5}$ source profiles for diesel and gasoline-powered vehicles, which contained mass abundances in terms of mass fraction of $PM_{2.5}$ of chemical species. Seven diesel-powered vehicles and nine gasoline-powered vehicles were sampled from a chassis dynamometer exhaust dilution system. The species measured were water-soluble ions, elements, elemental carbon (EC), and organic carbon (OC). From this study, the large abundances of EC (54.5%), OC (26.0%), ${SO_4}^{2-}$ (1.5%), ${NO_3}^-$ (0.8%), and S (0.6%) were observed from the diesel-powered vehicle exhaust showing that carbons were dominant species. The gasoline-powered vehicle exhaust emitted large abundances of OC (38.3%), EC (4.2%), ${SO_4}^{2-}$ (3.6%), ${NH_4}^+$ (3.5%), and ${NO_3}^-$ (3.0%). The abundances of ${SO_4}^{2-}$, ${NH_4}^+$, and ${NO_3}^-$ from gasoline vehicle were greater than those of diesel vehicle. The emissions of P, S, Ca, Fe, and Zn among trace elements for the gasoline vehicle were greater than 1% of the $PM_{2.5}$ mass unlike those for the diesel vehicle. Particularly, the fraction of Zn was five times higher from the gasoline vehicle than that from the diesel vehicle. The source profiles developed in this work were intensively examined by applying chemical mass balance model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.125-129
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• 2003

The identification of location and strength distribution of extended noise sources is important in the practical noise control engineering, especially in the viewpoint of dealing with the inherent nature of noise problem in question. For noise source ranking inside an automotive vehicle, the window method has been mainly used due to its simplicity. However, time and cost drawbacks in the measurement and inaccuracy due to low-frequency tunneling and lack of phase information have been a serious problem in using this method. In this study, the inverse FRF method was employed to carry out the noise source ranking inside an automotive vehicle and it was also used to predict the interior sound pressure with the change of sound insulation materials. As a result, it was found that the source contribution of vehicle panels could be successfully identified in comparison with the window method. The sound pressure at driver's ear position was predicted based on the obtained data and was compared with the measured data. The agreement in spectral trends was acceptable and their difference in level was within 3㏈ above 500㎐.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.5
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• pp.107-114
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• 2006

This paper investigates the radiating source of electro-magnetic waves in the cabin of automobile with spark ignition engine. Front seats are very close to the engine room where electro-magnetic waves are expected to be radiating. But front seat area is believed to be a blind zone, which is not affected by radiating electro-magnetic waves, because a bulk board and floor board shield the front seat area. The level and frequency spectrum of electro-magnetic waves are measured at the passenger seat and the engine room. The measured frequency range is $145{\sim}365MHz$. As a results, the level of the electro-magnetic waves of automatic transmission vehicle is greater than -82dBm. The shapes of frequency spectrum of both engine room and passenger seat are look alike. But the level of electro-magnetic waves of manual transmission vehicle is less than -82dBm and the shapes of frequency spectrum of engine room and passenger seat are different to each other. From these results, we can say that any mal-function caused by electro-magnetic waves in the automobile cabin are only possible for automatic transmission vehicle. Also, it is believed that the radiating source of electro-magnetic waves is inside the vehicle. Thus, based on the transmission line theory, this paper presumably concludes that the cables which connect all the components inside a automatic transmission vehicle must be a radiating source of electro-magnetic waves in the cabin.

• Journal of the Korean Regional Science Association
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• v.6 no.1
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• pp.57-68
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• 1990

The purpose of this study is to develop the models to predict the noise PCE (Passenger Car Equivalence) of large running vehicles through noise prediction models. The noises were measured at the distance of 7.5M, 11.0M, and 14.5M from the noise source with test vehicles running at the speed of 40 Km/h, 60 Km/h, and 80 Km/h while normal traffic were detoured. Total noise levels were measured while vehicles were running at given speeds, Engine noise level was considered as the noise of its idle running at the three vehicle speeds shown above friction noise level was ascertained by moving the vehicle at given speeds without the engin operating. The noise prediction models for each noise source were developed by factors which affect to the each noise level. As a result of this paper, the reduction of total vehicle noise by increasing the distance to the noise source from 10 M to 15 M is as much as that by dropping its speed from 60 Km/h to 40 Km/h. Also, the reduction of PCE of total noise of large vehicle by making the noise source to that by reducing its speed from 80 Km/h to 60 Km/h. Enging noise PCE, which is in range between 65 and 160, is larger than friction noise PCE which is in range 3.5 and 5.5. Engin noise is the main noise of the large vehicles while friction noise is that of the small vehicles. Machine noise for large vehicles, and engin noise for small vehicles should be tightly controlled to reduce the vehicle noise. A low noise engine and tire, and the shape of vehicle body are needed to be developed to reduce noise further.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.7
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• pp.644-650
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• 2010

Among various elements to affect customer's evaluation of vehicle quality, BSR(Buzz, Squeak, Rattle) are considered to be a mostly contributing factor. In this paper, we provide the test method which can be used to reduce the BSR noise of instrument panel in a vehicle. First, potential source regions of the instrument panel for BSR are localized by using the vibration-excitor and near-acoustic field visualization system. Then, subjective evaluation of BSR noise from the detected potential noise source regions is made with the Zwicker's loudness and time-varying loudness methods. This illustrative analysis reveals that current experimental methods can be used as a test procedure to systematically tackle BSR issues in early stage of the vehicle development cycle, which can result in the reduction of the production cost.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.7
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• pp.774-780
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• 2016

In this paper noise source and transfer path of tactical vehicle are analyzed with partial coherence function and spectrum analysis. Engine, transmission, structure panel and aerodynamic are main source of cabin noise. To reduce cabin noise, identifying transfer path of sources and analyzing their contribution is important. With modeling of transfer path and partial coherence function, transfer path and principal noise source can be identified. Engine/transmission and structural resonance are principal source of low frequency noise and by adding stiffener and sound absorbing material, resonance of vibration and inflow air problem can be solved.