Cylinder by Cylinder Engine Pressure and Pressure Torque Waveform Determination Utilizing Speed Fluctuations

1989 ◽  
Author(s):  
Stephen J. Citron ◽  
John E. O'Higgins ◽  
Lillian Y. Chen
Keyword(s):  
Speed Fluctuations

Modeling and validation of crankshaft speed fluctuations of a single-cylinder four-stroke diesel engine

2021 ◽  
pp. 095440702110262
Author(s):  
Mustafa Babagiray ◽  
Hamit Solmaz ◽  
Duygu İpci ◽  
Fatih Aksoy
Keyword(s):  
Diesel Engine ◽  
Dynamic Model ◽  
Moment Of Inertia ◽  
Mass Motion ◽  
Cylinder Pressure ◽  
Motion Equations ◽  
Single Cylinder ◽  
Mass Moment ◽  
Mass Moment Of Inertia ◽  
Speed Fluctuations

In this study, a dynamic model of a single-cylinder four-stroke diesel engine has been created, and the crankshaft speed fluctuations have been simulated and validated. The dynamic model of the engine consists of the motion equations of the piston, conrod, and crankshaft. Conrod motion was modeled by two translational and one angular motion equations, by considering the kinetic energy resulted from the mass moment of inertia and conrod mass. Motion equations involve in-cylinder gas pressure forces, hydrodynamic and dry friction, mass inertia moments of moving parts, starter moment, and external load moment. The In-cylinder pressure profile used in the model was obtained experimentally to increase the accuracy of the model. Pressure profiles were expressed mathematically using the Fourier series. The motion equations were solved by using the Taylor series method. The solution of the mathematical model was performed by coding in the MATLAB interface. Cyclic speed fluctuations obtained from the model were compared with experimental results and found compitable. A validated model was used to analyze the effects of in-cylinder pressure, mass moment of inertia of crankshaft and connecting rod, friction, and piston mass. In experiments for 1500, 1800, 2400, and 2700 rpm engine speeds, crankshaft speed fluctuations were observed as 12.84%, 8.04%, 5.02%, and 4.44%, respectively. In simulations performed for the same speeds, crankshaft speed fluctuations were calculated as 10.45%, 7.56%, 4.49%, and 3.65%. Besides, it was observed that the speed fluctuations decreased as the average crankshaft speed value increased. In the simulation for 157.07, 188.49, 219.91, 251.32, and 282.74 rad/s crankshaft speeds, crankshaft speed fluctuations occurred at rates of 10.45%, 7.56%, 5.84%, 4.49%, and 3.65%, respectively. The effective engine power was achieved as 5.25 kW at an average crankshaft angular speed of 219.91 rad/s. The power of friction loss in the engine was determined as 0.68 kW.

scholarly journals Wind Speed Modeling by Nested ARIMA Processes

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 69 ◽  
Author(s):  
So-Kumneth Sim ◽  
Philipp Maass ◽  
Pedro Lind
Keyword(s):  
Wind Speed ◽  
Power Plants ◽  
Time Windows ◽  
Basic Research ◽  
Random Variable ◽  
The North ◽  
Wind Power Plants ◽  
The North Sea ◽  
Speed Fluctuations ◽  
Large Wind

Wind speed modelling is of increasing interest, both for basic research and for applications, as, e.g., for wind turbine development and strategies to construct large wind power plants. Generally, such modelling is hampered by the non-stationary features of wind speed data that, to a large extent, reflect the turbulent dynamics in the atmosphere. We study how these features can be captured by nested ARIMA models. In this approach, wind speed fluctuations in given time windows are modelled by one stochastic process, and the parameter variation between successive windows by another one. For deriving the wind speed model, we use 20 months of data collected at the FINO1 platform at the North Sea and use a variable transformation that best maps the wind speed onto a Gaussian random variable. We find that wind speed increments can be well reproduced for up to four standard deviations. The distributions of extreme variations, however, strongly deviate from the model predictions.

Low Cost Engine Management System (EMS) for the Cost Sensitive Two-Wheeler Application: Idle Speed and A/F Ratio Control Using PID and Fuzzy Logic Control Algorithms

2007 ◽  
Author(s):  
P. V. Manivannan ◽  
A. Ramesh
Keyword(s):  
Fuzzy Logic ◽  
Fuel Consumption ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Engine Management System ◽  
Position Sensor ◽  
Idle Speed ◽  
The Cost ◽  
Two Wheeler ◽  
Speed Fluctuations

In this work an Engine Management System (EMS) using a low cost 8-bit microcontroller specifically for the cost sensitive small two-wheeler application was designed and developed. Only the Throttle Position Sensor (TPS) and the cam position sensor (also used for speed measurement) were used. A small capacity 125CC four stroke two-wheeler was converted into a Port Fuel Injected (PFI) engine and was coupled to a fully instrumented Eddy Current Dynamometer. Air-fuel ratio was controlled using the open loop, lookup-table [speed (N) and throttle (α)] based technique. Spark Time was controlled using a proportional / fuzzy logic based close loop control algorithm for the idle speed control to reduce fuel consumption and emissions. Test results show a significant improvement in engine performance over the original carbureted engine, in terms of fuel consumption, emissions and idle speed fluctuations. The Proportional controller resulted in significantly lower speed fluctuations and HC / CO emissions than the fuzzy logic controller. Though the fuzzy logic controller resulted in low cycle by cycle variations than the original carbureted engine, it leads to significantly higher HC levels. The performance fuzzy logic can be improved by modifying the membership function shapes with more engine test data.

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