Abstract---Now days it's very important to increase the efficiency of Internal Combustion (IC) engines. Due to many factors such as fuel economy, fuel crisis and ...
International Conference on Computing and Control Engineering (ICCCE 2012), 12 & 13 April, 2012
LabVIEW Implementation of an Automated Cooling Technique for Internal Combustion Engine using ANN K.V. Santhosh and Nalin Kumar Sharma Abstract---Now days it’s very important to increase the efficiency of Internal Combustion (IC) engines. Due to many factors such as fuel economy, fuel crisis and ultimately to increase the output. This paper proposes an effective cooling system in IC engine. During the process of combustion a large portion of heat is transferred to various engine components and the engine may be damaged unless the excess heat is carried away and these parts are adequately cooled. Adequate cooling is then a fundamental problem associated with internal combustion engines. In the present paper, efforts have been made to design anintelligent cooling technique using Artificial Neural Network (ANN). The basic principle behind this is to control the flow rate of coolant and speed of thermo fan by regulating the valve controlled using ANN. The design was modelled and simulated using the LabVIEW platform. Keywords--- IC Engine, Artificial Neural Network, Cooling System, LabVIEW. I.
INTRODUCTION
Heat engines generate mechanical power by extracting energy from heat flows, much as a water wheel extracts mechanical power from a flow of mass falling through a distance. Engines are inefficient, so more heat energy enters the engine than comes out as mechanical power; the difference is waste heat which must be removed. Internal combustion engines remove waste heat through cool intake air, hot exhaust gases, and explicit engine cooling. Engines with higher efficiency have more energy leave as mechanical motion and less as waste heat. Some waste heat is essential: it guides heat through the engine, much as a water wheel works only if there is some exit velocity in the waste water to carry it away and make room for more water. Thus, all heat engines need cooling to operate. Did you know that up to a third of the heat energy produced by an internal combustion engine ends up as waste heat in the cooling system? A gallon of gasoline produces about 19, 000 to 20,000 BTUs of heat energy when it is burned, which is enough to boil over 120 gallons of water!So the two or so gallons of coolant that circulate within the typical automotive cooling system have to carry away a lot of heat. The radiator also has to be fairly efficient at getting rid of the heat, too, otherwise the BTUs will start to back up and make the engine overheat. An efficient cooling system,
K.V. Santhosh, Department of Electrical Engineering, National Institute of Technology, Silchar, India. Nalin Kumar Sharma, Department of Electrical Engineering, National Institute of Technology, Silchar, India
therefore, requires several things: an adequate supply of coolant, an efficient heat exchanger, a fan to pull air through the radiator at low speeds, a water pump to keep the coolant moving, and a thermostat to regulate the operating temperature of the engine for good performance, fuel economy and emissions. The coolant must also have the right mix of water and antifreeze to provide adequate freezing and boiling protection, and the proper amount of corrosion inhibitors to protect against rust, oxidation and electrolysis. To keep the cooling system in good operating condition, it is important to check the level, strength and condition of the coolant on a regular basis - and to replace or recycle the coolant before the protective additives are entirely depleted. According to the U.S. Department of Transportation, cooling system failure is the leading cause of mechanical breakdowns on the highway. And according to numerous aftermarket surveys that have been performed over the years, coolant neglect is one of the leading causes of cooling system breakdowns. Cooling is also needed because high temperatures damage engine materials and lubricants. Internal combustion engines burn fuel hotter than the melting temperature of engine materials, and hot enough to set fire to lubricants. Engine cooling removes energy fast enough to keep temperatures low so the engine can survive. Most internal combustion engines are fluid cooled using either air (a gaseous fluid) or a liquid coolant run through a heat exchanger (radiator) cooled by air. There are many demands on a cooling system. One key requirement is that an engine fails if just one part overheats. Therefore, it is vital that the cooling system keep all parts at suitably low temperatures. Liquid-cooled engines are able to vary the size of their passageways through the engine block so that coolant flow may be tailored to the needs of each area. Locations with either high peak temperatures (narrow islands around the combustion chamber) or high heat flow (around exhaust ports) may require generous cooling [1]-[4]. However, there exists another problem that excessive cooling will also decrease the power of IC engines. For this purpose optimized cooling systems in IC engine is required. At present, the traditional control of temperature of coolant can’t satisfy the needs of its dynamic characters. The problems with traditional cooling system and dynamic characters are (1) if the coolant level is less than required, the engine characteristics are affected.(2) If the coolant flow is not sufficient or speed of thermo fan is not sufficient than heat production is very high i.e. cooling process is not exact, the engine characteristics are affected. (3) If the coolant flow and thermo fan speed is more than required then it is also loss of power. Because of these drawbacks the paper
ISBN 978-1-4675-2248-9 © 2012 Published by Coimbatore Institute of Information Technology
International Conference on Computing and Control Engineering (ICCCE 2012), 12 & 13 April, 2012 presents an automated cooling technique using the neural network. Here the neural network is used to control the speed of fan and coolant speed based on the inputs of IC engines like the temperature, level of coolant and so on. Simulation results show that the smart proposed control system has good quality and strong adaptive ability, and control method is simple, reliable and easy to implement, achieving a satisfactory control effect in the Advanced Optimized Cooling of coolant. The paper is organised as follows: after introduction in Section-I, a brief description on block diagram is given in Section-II. Section-III discusses on the problem statement followed by proposed solution in Section-IV. Finally, result and conclusion is given in Section-V II.
BLOCK DIAGRAM
Fig.1 shows the block diagram of the proposed cooling system. The objective of the proposed technique is to control the temperature of the IC engine. The control of temperature is achieved by two ways (i) By a thermofan and (ii) By the flow of coolant. The temperature of the IC engine is controlled by the speed of thermofan or by controlling the flow rate of coolant. The control action is achieved by using ANN. Coolant level
ANN Controller
3. Off the engine when the temperature is high and uncontrolled by thermofan and coolant 4. Decrease in flow rate and thermofan speed when the temperature of engine is not high IV.
PROBLEM SOLUTION
To achieve the objective mentioned in the previous section anANN is implemented [5]-[8]. This model is designed on the LabVIEW platform. A. Front panel of LabVIEW The front panel of LabVIEW is the window through which the user can interact with the system. The front panel of the LabVIEW is designed consisting of control where the user can give the inputs and the indicators which will show the output. The proposed system consists of an ON button which indicates the function is operational. A numerical indicator which can be used by the user to set the desired temperature set point. The actual temperature can also be read using a numerical indicator. Apart from these indicators and controls, there are numerical indicators which will display the flow rate of the coolant and the speed of the thermofan. An indicator is also given to indicate the level of coolant in the tank. There is also a gauge which indicates the fuzzy values of the temperature of IC engine. There is an OFF indicator which indicates the engine cannot switch ON because of the reasons like coolant level is below desired or engine is overheat, the indicator will glow with a red indication. The front view of the LabVIEW program is given in Fig. 2
Thermofan I C Engine
Temperature sensor
Coolant flow
Fig.1 Overview schematic of a classic control loop. At present the cooling of IC engine is done using the flow of coolant and a fan. The major drawbacks of the present technique is that the flow of coolant is constant and also the fan also rotates at a constant speed which means that whatever is the temperature of IC engine, the cooling system works the same yielding reduction in engine efficiency. The following are incorporated in the proposed system: 1) A thermocouple which senses the temperature of the IC engine 2) An ultrasonic sensor is placed in the coolant tank to measure the liquid level. 3) Incorporate a Artificial Neural Network (ANN) controller to control the flowrate and control the speed of thermofan. III.
PROBLEM STATEMENT
Given an arrangement of system as shown in Fig.1, design an intelligent cooling technique having the following properties 1. Control the flow rate of coolant and speed of thermofan proportionally to the temperature of IC engine. 2. Off the engine when the level of coolant in the storage tank is below the operating condition
Fig. 2 Front panel view of the proposed cooling technique
B. Block Diagram of LabVIEW The block diagram vi consists of the palates used to write the program for the working of the proposed system. The block diagram consists of palates to accept the inputs like the level of the coolant, desired engine temperature, actual engine temperature, fan speed and flow rate of coolant. Then we have the neural network block. Then neural network is trained to produce output to control the speed of fan and flow of coolant based on the input of the temperature of IC engine. Fig 3 shows the block diagram view of the proposed cooling technique.The neural vi is designed using the backpropagation algorithm to control the parameters as per the set point given by the user. The control is algorithm is designed to perform as:
ISBN 978-1-4675-2248-9 © 2012 Published by Coimbatore Institute of Information Technology
International Conference on Computing and Control Engineering (ICCCE 2012), 12 & 13 April, 2012 1. The ON indicator is glowed as soon as the process is active. 2. The level guage shows the actual level of the coolant present in the storage tank. 3. The temperature indicates the actual temperature of the IC engine which should be maintained at the temperature set by manufacturer. 4. Indicate the flow rate of the coolant, and control input for pump 5. Indicate the speed of the thermofan and control input to thermofan. 6. Switch OFF the engine when the temperature of IC engine is higher than the setpoint or coolant level is very low. It is also indicated using the OFF indicator.
Validation Test Training Validation Test
R
Mean Squared Error (MSE) is the average squared difference between outputs and targets. Lower values are better. Zero means no error. Regression (R) values measure the correlation between outputs and targets. An R value of 1 means a close relationship, 0 a random relationship. With these details the network is trained. V.
RESULT AND CONCLUSION
The system after being trained by neural network using back-propagation was subjected to test and following results were tabulated. Table 2 summary of the result. Actual Indicated Status
Parameter 1 2 3
Table 1 summarizes the require data for training. OPTIMIZED PARAMETERS OF THE NEURAL NETWORKS MODEL 1st layer 6 No of neurons in 2nd layer 8 1st layer Logsig Transfer function of 2nd layer Logsig Output layer linear MSE Training 4.42E-09
Coolant level Coolant level Coolant level Temperature 180 oC 300 oC 800 oC
Fig. 3 block diagram view of the proposed cooling technique
C: BACK-PROPOGATION ALGORITHM The back-propagation learning algorithm can be divided into two phases: propagation and weight update [9]-[12]. Phase 1: Propagation - Each propagation involves the following steps: 1. Forward propagation of a training pattern's input through the neural network in order to generate the propagation's output activations. 2. Backward propagation of the propagation's output activations through the neural network using the training pattern's target in order to generate the deltas of all output and hidden neurons. Phase 2: Weight update - For each weight-synapse: 1. Multiply its output delta and input activation to get the gradient of the weight. 2. Bring the weight in the opposite direction of the gradient by subtracting a ratio of it from the weight. This ratio influences the speed and quality of learning; it is called the learning rate. The sign of the gradient of a weight indicates where the error is increasing; this is why the weight must be updated in the opposite direction. Repeat the phase 1 and 2 until the performance of the network is good enough. The training data for the network would be the set point data given by the user.
3.265E-08 2.993E-08 0.9999900 0.9999960 0.9999990
60% 10% 2% Flow rate 0.6 1.1 Max
60% 10% 2%
Engine ON Engine ON, Signal ON Engine OFF, Signal ON
Thermofan speed 170 280 Max
Status Engine ON Engine ON Engine OFF
The result show that the parameters are under control and the process is automated and has incorporated intelligence to produce efficient results. Thus increase the IC engine efficiency. VI.
REFERENCES
[1]
Singer, Charles Joseph; Raper, Richard, A History of Technology: The Internal Combustion Engine, Clarendon Press, 1954-1978. pp. 157–176 [2] Horst O. Hardenberg, The Middle Ages of the Internal Combustion Engine, 1999, Society of Automotive Engineers (SAE) [3] Haynes, Opel Omega & Senator Service and Repair Manual.. 1996. ISBN 1-85960-342-4. [4] Rankin Kennedy C.E. The Book of the Motor Car. Caxton. 1912. [5] Mahesh.L.Chugani, LabVlEW SignalProcessing, Prentice-Hall India, 1998. [6] National Instruments, LabVIEW Help Manual. [7] J. Fernandez de Canete, S. Gonzalez-Perez, and P. del Saz-Orozco, “Artificial Neural Networks for Identification and Control of a LabScale Distillation Column using LabVIEW”, World Academy of Science, Engineering and Technology, vol 47, pp 64-69, 2008. [8] R. Bishop, Learning with LabVIEW 7 Express, New Jersey, Prentice Hall, 2004. [9] T Poggio, F Girosi, “Networks for approximation and learning,” Proc. IEEE 78(9), pp. 1484-1487, 1990. [10] R Rojas, Neural networks, Springer-Verlag, Berlin, 1996. [11] Stuart Russell and Peter Norvig. Artificial Intelligence A Modern Approach. p. 578. "The most popular method for learning in multilayer networks is called Back-propagation”. [12] M.A. Hussain, “Review of the applications of neural networks inchemical process control. Simulation and on-line implementations”, Artificial Intelligence in Engineering, Vol. 13, pp. 55-68, 1999.
ISBN 978-1-4675-2248-9 © 2012 Published by Coimbatore Institute of Information Technology
