{"title":"预测火花持续时间对丙烷直喷式大缸径压燃发动机缸内性能影响的神经网络方法","authors":"Cahyani Windarto , Ocktaeck Lim","doi":"10.1016/j.fuproc.2024.108088","DOIUrl":null,"url":null,"abstract":"<div><p>In the current study, we examined the impact of spark duration strategy on a large bore compression ignition engine fueled with propane direct injection. An artificial neural network also was used to forecast engine in-cylinder performance characteristics. A rapid compression and expansion machine (RCEM) with a spark plug was tested with a high-pressure direct injection propane of 200 bar. While the timing of the injection was set to 20 °CA bTDC, the spark duration can range from 0.7 to 5.0 milliseconds. Crank angle degree, pressure, ignition coil number and spark duration were used as input parameters in the ANN model to predict in-cylinder performance, while engine performance parameters such as heat release rate (HRR), turbulent kinetic energy (TKE), tumble ratio, indicated power, and combustion efficiency (<span><math><msub><mi>η</mi><mi>c</mi></msub></math></span>) were used as output parameters. The ANN model was created using the neural network toolbox and standard backpropagation with the Levenberg-Marquardt training algorithm was used with the learning rate and training epochs of the ANN model set to 0.001 and 1000, respectively. The accuracy of the model was validated by comparing the predicted datasets with the experimental data. The five projected parameters of heat release rate (HRR), turbulent kinetic energy (TKE), tumble ratio, indicated power, and combustion efficiency (<span><math><msub><mi>η</mi><mi>c</mi></msub></math></span>) showed <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> values of 0.9833, 0.9860, 0.9728, 0.9807, 0.9052, and 0.9999, respectively, and <span><math><mi>MSE</mi></math></span> values of 0.1419, 0.0023, 0.6428, 0.0106, 0.0050, and 0.0134. The <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> of the validation dataset was nearly 0.98, which is close to that of the training dataset. The coefficients of determination (<span><math><msup><mi>R</mi><mn>2</mn></msup></math></span>) were greater than 0.9 in the projected results, and the <span><math><mi>MSE</mi></math></span> was reasonably low, indicating that a predictive model based on ANN model could predict in-cylinder performance of a large bore compression ignition engine.</p></div>","PeriodicalId":326,"journal":{"name":"Fuel Processing Technology","volume":"257 ","pages":"Article 108088"},"PeriodicalIF":7.2000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378382024000584/pdfft?md5=c972f3c6162025038b85108075a98a03&pid=1-s2.0-S0378382024000584-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A neural network approach on forecasting spark duration effect on in-cylinder performance of a large bore compression ignition engine fueled with propane direct injection\",\"authors\":\"Cahyani Windarto , Ocktaeck Lim\",\"doi\":\"10.1016/j.fuproc.2024.108088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In the current study, we examined the impact of spark duration strategy on a large bore compression ignition engine fueled with propane direct injection. An artificial neural network also was used to forecast engine in-cylinder performance characteristics. A rapid compression and expansion machine (RCEM) with a spark plug was tested with a high-pressure direct injection propane of 200 bar. While the timing of the injection was set to 20 °CA bTDC, the spark duration can range from 0.7 to 5.0 milliseconds. Crank angle degree, pressure, ignition coil number and spark duration were used as input parameters in the ANN model to predict in-cylinder performance, while engine performance parameters such as heat release rate (HRR), turbulent kinetic energy (TKE), tumble ratio, indicated power, and combustion efficiency (<span><math><msub><mi>η</mi><mi>c</mi></msub></math></span>) were used as output parameters. The ANN model was created using the neural network toolbox and standard backpropagation with the Levenberg-Marquardt training algorithm was used with the learning rate and training epochs of the ANN model set to 0.001 and 1000, respectively. The accuracy of the model was validated by comparing the predicted datasets with the experimental data. The five projected parameters of heat release rate (HRR), turbulent kinetic energy (TKE), tumble ratio, indicated power, and combustion efficiency (<span><math><msub><mi>η</mi><mi>c</mi></msub></math></span>) showed <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> values of 0.9833, 0.9860, 0.9728, 0.9807, 0.9052, and 0.9999, respectively, and <span><math><mi>MSE</mi></math></span> values of 0.1419, 0.0023, 0.6428, 0.0106, 0.0050, and 0.0134. The <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> of the validation dataset was nearly 0.98, which is close to that of the training dataset. The coefficients of determination (<span><math><msup><mi>R</mi><mn>2</mn></msup></math></span>) were greater than 0.9 in the projected results, and the <span><math><mi>MSE</mi></math></span> was reasonably low, indicating that a predictive model based on ANN model could predict in-cylinder performance of a large bore compression ignition engine.</p></div>\",\"PeriodicalId\":326,\"journal\":{\"name\":\"Fuel Processing Technology\",\"volume\":\"257 \",\"pages\":\"Article 108088\"},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0378382024000584/pdfft?md5=c972f3c6162025038b85108075a98a03&pid=1-s2.0-S0378382024000584-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fuel Processing Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378382024000584\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fuel Processing Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378382024000584","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
A neural network approach on forecasting spark duration effect on in-cylinder performance of a large bore compression ignition engine fueled with propane direct injection
In the current study, we examined the impact of spark duration strategy on a large bore compression ignition engine fueled with propane direct injection. An artificial neural network also was used to forecast engine in-cylinder performance characteristics. A rapid compression and expansion machine (RCEM) with a spark plug was tested with a high-pressure direct injection propane of 200 bar. While the timing of the injection was set to 20 °CA bTDC, the spark duration can range from 0.7 to 5.0 milliseconds. Crank angle degree, pressure, ignition coil number and spark duration were used as input parameters in the ANN model to predict in-cylinder performance, while engine performance parameters such as heat release rate (HRR), turbulent kinetic energy (TKE), tumble ratio, indicated power, and combustion efficiency () were used as output parameters. The ANN model was created using the neural network toolbox and standard backpropagation with the Levenberg-Marquardt training algorithm was used with the learning rate and training epochs of the ANN model set to 0.001 and 1000, respectively. The accuracy of the model was validated by comparing the predicted datasets with the experimental data. The five projected parameters of heat release rate (HRR), turbulent kinetic energy (TKE), tumble ratio, indicated power, and combustion efficiency () showed values of 0.9833, 0.9860, 0.9728, 0.9807, 0.9052, and 0.9999, respectively, and values of 0.1419, 0.0023, 0.6428, 0.0106, 0.0050, and 0.0134. The of the validation dataset was nearly 0.98, which is close to that of the training dataset. The coefficients of determination () were greater than 0.9 in the projected results, and the was reasonably low, indicating that a predictive model based on ANN model could predict in-cylinder performance of a large bore compression ignition engine.
期刊介绍:
Fuel Processing Technology (FPT) deals with the scientific and technological aspects of converting fossil and renewable resources to clean fuels, value-added chemicals, fuel-related advanced carbon materials and by-products. In addition to the traditional non-nuclear fossil fuels, biomass and wastes, papers on the integration of renewables such as solar and wind energy and energy storage into the fuel processing processes, as well as papers on the production and conversion of non-carbon-containing fuels such as hydrogen and ammonia, are also welcome. While chemical conversion is emphasized, papers on advanced physical conversion processes are also considered for publication in FPT. Papers on the fundamental aspects of fuel structure and properties will also be considered.