This study presents a systematic causal analysis of the fuel consumption rate reduction phenomenon observed in mortar-carrier tracked vehicles during driving tests. The investigation focused on identifying the root causes and developing effective improvement measures. Through comprehensive inspections and tests of the chassis and power pack components, along with data analysis, the study identified the damage of the engine flywheel housing gasket and the clogging of the transmission exhaust pump strainer as the main causes of the reduced fuel consumption rate. The causal relationship between the two phenomena was empirically proven using material composition analysis and statistical techniques, enhancing the reliability and validity of the diagnosis. Based on the root cause analysis results, improvements were implemented, including the replacement of the engine gasket and the cleaning of the transmission exhaust pump strainer. The effectiveness of the improvements was quantitatively verified, confirming a significant enhancement in fuel consumption rate and cruising range. By employing a systematic and scientific analysis methodology, this study provides a foundation for improving the reliability and maintenance efficiency of similar weapon systems and power transmission systems in general.

The effect of EGR on fuel economy was investigated in a gasoline direct injection engine. The 1-D cycle simulation program of GT-Power was utilized to evaluate fuel consumption rate. At high load, fuel consumption increased by about 2~6% according to EGR rate. Knock mitigation was the main effects, gaining about 80% of the total fuel consumption improvement. At low load, fuel consumption reduction was 0.6~2%, which was much lower than that for high load. The lower improvement of fuel consumption at low load is attributed to solely dilution and chemical effects of exhaust gas.

PURPOSES :This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors.METHODS :Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling.RESULTS :The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables.CONCLUSIONS :Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

The test was done on cars travelling at the speeds of 20km/h, 60km/h and 100km/h using the performance testing mode for chassis dynamometer. In this test, the secondary ignition waveform, exhaust emissions and fuel consumption were measured in case of faulty MAP sensor, faulty oxygen sensor and spark plugs. The following results from the related analysis of secondary waveform, emission and fuel consumption measurements were obtained : 1) The fuel consumption was higher in the order of oxygen sensor trouble, MAP trouble, spark plug trouble, before maintenance and after maintenance. Maximum fuel economy is 9.3km/L, the minimum fuel economy is 3.2km/L, the difference between max. and min. is 65.5%. 2) If you compare the oxygen sensor trouble with after maintenance, the CO has improved an average of 98%, fuel economy average of 60%. And the HC has improved an average of 87%, fuel economy average of 60%. The fuel consumption and exhaust gas was bad in the order of oxygen sensor trouble, MAP trouble and S/P trouble.

높은 차량운행비용(VOC : Vehicle Operating Cost)은 포장도로 복구작업의 주요한 원인이고, 차량운행비용(VOC)은 연료소모량, 오일소모량, 부품교체비용 등으로 구성된다. 이중 연료소모량이 VOC에서 차지하는 비중이 높고, 다른 도로조건에 비해 도로 표면 거칠기가 도로의 노화 정도를 대표적으로 지시하는 값이기 때문에, 본 연구에서는 포장도로의 표면 거칠기(IRI : International Roughness Index) 변화에 따른 차량의 연료소모량 변화를 측정하였다. 차량의 연료분사 인젝터의 전압변화를 측정하여 연료소모량을 계산하였고, 속도는 GPS센서를 사용하여 측정하였다. 본 실험 결과를 이용하여 IRI 변화에 대한 연료소모량의 변화율을 계산할 수 있었다. 계산 결과, 40~100km/h 속도영역에서 중형 및 대형 승용차의 연료소모량(L/100km)은 3.5m/km 정도의 IRI 수준에서 IRI(m/lm) 증가율의 7배 정도로 증가하였고, 60km/h의 속도에서 가장 연비가 우수하였다.