Engine – Diagnose and Rectify Motorsport Vehicle Engine and Component Faults

2012 ◽  
pp. 15-38
Keyword(s):  
Vehicle Engine ◽  
Component Faults

An experimental study on optimal spark timing control for improved performance of a flex fuel vehicle engine

2019 ◽  
Vol 234 (5) ◽  
pp. 1294-1303
Author(s):  
Junsang Yoo ◽  
Taeyong Lee ◽  
Pyungsik Go ◽  
Yongseok Cho ◽  
Kwangsoon Choi ◽  
...  
Keyword(s):  
Empirical Equation ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
Combustion Characteristics ◽  
Combustion Stability ◽  
Timing Control ◽  
Vehicle Engine ◽  
Spark Timing ◽  
Linear Interpolation Method ◽  
Blended Fuels

In the American continent, the most frequently used alternative fuel is ethanol. Especially in Brazil, various blends of gasoline–ethanol fuels are widely spread. The vehicle using blended fuel is called flexible fuel vehicle. Because of several selections for the blending ratios in gas stations, the fuel properties may vary after refueling depending on a driver’s selection. Also, the combustion characteristics of the flexible fuel vehicle engine may change. In order to respond to the flexible fuel vehicle market in Brazil, a study on blended fuels is performed. The main purpose of this study is to enhance performance of the flexible fuel vehicle engine to target Brazilian market. Therefore, we investigated combustion characteristics and optimal spark timings of the blended fuels with various blending ratios to improve the performance of the flexible fuel vehicle engine. As a tool for prediction of the optimal spark timing for the 1.6L flexible fuel vehicle engine, the empirical equation was suggested. The validity of the equation was investigated by comparing the predicted optimal spark timings with the stock spark timings through engine tests. When the stock spark timings of E0 and E100 were optimal, the empirical equation predicted the actual optimal spark timings for blended fuels with a good accuracy. In all conditions, by optimizing spark timing control, performance was improved. Especially, torque improvements of E30 and E50 fuels were 5.4% and 1.8%, respectively, without affecting combustion stability. From these results, it was concluded that the linear interpolation method is not suitable for flexible fuel vehicle engine control. Instead of linear interpolation method, optimal spark timing which reflects specific octane numbers of gasoline–ethanol blended fuels should be applied to maximize performance of the flexible fuel vehicle engine. The results of this study are expected to save the effort required for engine calibration when developing new flexible fuel vehicle engines and to be used as a basic strategy to improve the performance of other flexible fuel vehicle engines.

Analysis of pulsating flow measurement of engine exhaust by a Pitot tube flowmeter

2005 ◽  
Vol 6 (1) ◽  
pp. 85-93 ◽  
Author(s):  
H Nakamura ◽  
I Asano ◽  
M Adachi ◽  
J Senda
Keyword(s):  
Fast Response ◽  
Differential Pressure ◽  
Pulsating Flow ◽  
Pitot Tube ◽  
Engine Exhaust ◽  
Idle Speed ◽  
Pressure Transducers ◽  
Vehicle Engine ◽  
Before And After ◽  
Response Pressure

The Pitot tube flowmetering technique has been used to measure pulsating flow from a vehicle engine exhaust. In general, flowmetering techniques that utilize differential pressure measurements based on Bernoulli's theory are likely to show erroneous readings when measuring an average flowrate of pulsating flow. The primary reason for this is the non-linear relationship between the differential pressure and the flowrate; i.e. the flowrate is proportional to the square root of the differential pressure. Therefore, an average of the differential pressure does not give an average of pulsating flow. In this study, fast response pressure transducers have been used to measure the pulsating pressure. Then the pulsating differential pressure is converted to the flowrate while keeping the pulsation unaveraged. An average flowrate is then calculated in the flowrate domain in order to maintain linearity before and after averaging. The peak amplitude of a pulsation measured here was about 1800 L/min at an average flowrate of 70 L/min when the engine ran at idle speed. This measurement has been confirmed by measuring the pulsation with a gas analyser. The results show a large amount of back and forth gas movement in the exhaust tube. This magnitude of pulsation can cause as much as five times higher erroneous results with the pressure domain averaging when compared to a flowrate domain averaging.

Hybrid Model-Based and Data-Driven Diagnostic Algorithm for Gas Turbine Engines

2020 ◽  
Author(s):  
Amare Fentaye ◽  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Mikael Stenfelt ◽  
Konstantinos Kyprianidis
Keyword(s):  
High Pressure ◽  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Diagnostic Algorithm ◽  
Data Driven ◽  
Intermediate Pressure ◽  
Path Component ◽  
Fault Diagnostic ◽  
Component Faults ◽  
Pressure Compressor

Abstract Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.

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