Srinivasa REDDY KUNDURU, Hanumantha Rao YARRAPATHRUNI VENKATA, D. Vallapudi, Narmatha Deenadayalan, A. Kumaravel
{"title":"应用人工神经网络方法预测棉籽油、亚麻籽油和马化油生物燃料提取物对CI柴油机性能的影响","authors":"Srinivasa REDDY KUNDURU, Hanumantha Rao YARRAPATHRUNI VENKATA, D. Vallapudi, Narmatha Deenadayalan, A. Kumaravel","doi":"10.18186/thermal.1284626","DOIUrl":null,"url":null,"abstract":"In the wide survey, it is explored that the potential of artificial neural network is used to foretell the recital (performance) characteristics of a four stroke single cylinder diesel engine using the biofuel obtained from cottonseed, linseed and Mahua seed. The test engine was powered with diesel and biofuel with its blends from cotton seed, linseed and Mahua seed separately. Experimental results of the cotton seed oil, linseed oil and mahua oil as a substitute for diesel revealed that linseed oil provides the better engine performance nearly equal to diesel. The ANN is used to compute the performance characteristics such as Indicated power, Brake power, Friction power, Thermal efficiency, brake mean effective pressure, brake thermal efficiency, Brake specific fuel consumption, Indicated thermal efficiency, indicated mean effective pressure, Mechanical efficiency, Indicated specific fuel consumption, volumetric efficiency and combustion characteristics as compression ratio at different conditions of torque, speed, water flow , air rate and fuel rate. An ANN sculpt was developed with 80% of training data and 20% of testing data from experimental values. In this model, back propagation feed forward neural network with five inputs and eleven outputs has been used. The ANN model result accuracy was found to agree nearly with the experimental results with the regression coefficient value approximately equal to one and low mean square error value. Thus, the proposed ANN model was legitimate tool for predicting the combustion and performance of diesel engine.","PeriodicalId":45841,"journal":{"name":"Journal of Thermal Engineering","volume":" ","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of recital characteristics of a CI diesel engine operated by bio-fuel extracts from cotton seed oil, linseed oil and mahua seed oil using ANN metho\",\"authors\":\"Srinivasa REDDY KUNDURU, Hanumantha Rao YARRAPATHRUNI VENKATA, D. Vallapudi, Narmatha Deenadayalan, A. Kumaravel\",\"doi\":\"10.18186/thermal.1284626\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the wide survey, it is explored that the potential of artificial neural network is used to foretell the recital (performance) characteristics of a four stroke single cylinder diesel engine using the biofuel obtained from cottonseed, linseed and Mahua seed. The test engine was powered with diesel and biofuel with its blends from cotton seed, linseed and Mahua seed separately. Experimental results of the cotton seed oil, linseed oil and mahua oil as a substitute for diesel revealed that linseed oil provides the better engine performance nearly equal to diesel. The ANN is used to compute the performance characteristics such as Indicated power, Brake power, Friction power, Thermal efficiency, brake mean effective pressure, brake thermal efficiency, Brake specific fuel consumption, Indicated thermal efficiency, indicated mean effective pressure, Mechanical efficiency, Indicated specific fuel consumption, volumetric efficiency and combustion characteristics as compression ratio at different conditions of torque, speed, water flow , air rate and fuel rate. An ANN sculpt was developed with 80% of training data and 20% of testing data from experimental values. In this model, back propagation feed forward neural network with five inputs and eleven outputs has been used. The ANN model result accuracy was found to agree nearly with the experimental results with the regression coefficient value approximately equal to one and low mean square error value. 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Prediction of recital characteristics of a CI diesel engine operated by bio-fuel extracts from cotton seed oil, linseed oil and mahua seed oil using ANN metho
In the wide survey, it is explored that the potential of artificial neural network is used to foretell the recital (performance) characteristics of a four stroke single cylinder diesel engine using the biofuel obtained from cottonseed, linseed and Mahua seed. The test engine was powered with diesel and biofuel with its blends from cotton seed, linseed and Mahua seed separately. Experimental results of the cotton seed oil, linseed oil and mahua oil as a substitute for diesel revealed that linseed oil provides the better engine performance nearly equal to diesel. The ANN is used to compute the performance characteristics such as Indicated power, Brake power, Friction power, Thermal efficiency, brake mean effective pressure, brake thermal efficiency, Brake specific fuel consumption, Indicated thermal efficiency, indicated mean effective pressure, Mechanical efficiency, Indicated specific fuel consumption, volumetric efficiency and combustion characteristics as compression ratio at different conditions of torque, speed, water flow , air rate and fuel rate. An ANN sculpt was developed with 80% of training data and 20% of testing data from experimental values. In this model, back propagation feed forward neural network with five inputs and eleven outputs has been used. The ANN model result accuracy was found to agree nearly with the experimental results with the regression coefficient value approximately equal to one and low mean square error value. Thus, the proposed ANN model was legitimate tool for predicting the combustion and performance of diesel engine.
期刊介绍:
Journal of Thermal Enginering is aimed at giving a recognized platform to students, researchers, research scholars, teachers, authors and other professionals in the field of research in Thermal Engineering subjects, to publish their original and current research work to a wide, international audience. In order to achieve this goal, we will have applied for SCI-Expanded Index in 2021 after having an Impact Factor in 2020. The aim of the journal, published on behalf of Yildiz Technical University in Istanbul-Turkey, is to not only include actual, original and applied studies prepared on the sciences of heat transfer and thermodynamics, and contribute to the literature of engineering sciences on the national and international areas but also help the development of Mechanical Engineering. Engineers and academicians from disciplines of Power Plant Engineering, Energy Engineering, Building Services Engineering, HVAC Engineering, Solar Engineering, Wind Engineering, Nanoengineering, surface engineering, thin film technologies, and Computer Aided Engineering will be expected to benefit from this journal’s outputs.