A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics
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Navaneetha Krishnan Balakrishnan 1 , Jennifer Philip 2 , Hasan Amin 3 , Prince Brahma 4 , Aaron Borges 4 , Vrishin Chari 4 , C Prabhu 4 *
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1 Everild Scientific, Coimbatore, Tamil Nadu, INDIA2 Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, NETHERLANDS3 Ford Motors, Chennai, Tamil Nadu, INDIA4 Department of Automobile Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, INDIA* Corresponding Author
Abstract
Technical education requires regular upgrades in pedagogical methodologies to keep up student’s skill on par with ever demanding job market. This paves the way for creating newer e-learning concepts for classroom to replace or supplement established teaching protocols. In line with this motive, this study deals with the development of an educational software tool to understand the traits of an internal combustion engine. The core of this software tool consists of polynomial regression equations, which in turn was arrived from statistical models using real world experimental data. A MATLAB-based GUI allows the operator to effortlessly interact with the software tool. Upon installation, the software requires the user to define input variables for it to automatically compute data and represent the output data in both visual and tabulated form. The advantage of three-dimensional surface plots for visual representation allows for understating the interactive effect of multiple input parameters on any given output parameter. Overall, average relative error for the model is less than 6%, thus exhibiting a good statistical fit.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EUR J SUSTAIN DEV RES, Volume 8, Issue 2, 2024, Article No: em0252
https://doi.org/10.29333/ejosdr/14302
Publication date: 01 Apr 2024
Online publication date: 28 Feb 2024
Article Views: 10050
Article Downloads: 3371
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- Abdellatief, M., Sultan, A. B., Jabar, A., & Abdullah, R. (2011). A technique for quality evaluation of e-learning from developers perspective. American Journal of Economics and Business Administration, 3(1), 157-164. https://doi.org/10.3844/ajebasp.2011.157.164
- Acevedo, J. G., Ochoa, G. V., & Obregon, L. G. (2020). Development of a new educational package based on e-learning to study engineering thermodynamics process: Combustion, energy and entropy analysis. Heliyon, 6(6), e04269. https://doi.org/10.1016/j.heliyon.2020.e04269
- ANSYS. (2023). ANSYS. https://www.ansys.com/en-in/products#t=ProductsTab&sort=relevancy&layout=card
- Ardebili, S. M. S., Solmaz, H., Calam, A., & Ipci, D. (2021). Modelling of performance, emission, and combustion of an HCCI engine fueled with fusel oil-diethylether fuel blends as a renewable fuel. Fuel, 290, 120017. https://doi.org/10.1016/j.fuel.2020.120017
- Atmanli, A., Yuksel, B., Ileri, E., & Karaoglan, A. D. (2015). Response surface methodology based optimization of diesel–n-butanol–cotton oil ternary blend ratios to improve engine performance and exhaust emission characteristics. Energy Conversion and Management, 90, 383-394. https://doi.org/10.1016/j.enconman.2014.11.029
- Audie Technology. (2023). Audie technology. http://www.audietech.com
- AVL. (2023). AVL. http://www.avl.com/simulation
- Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., & Escaleira, L. A. (2008). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76(5), 965-977. https://doi.org/10.1016/j.talanta.2008.05.019
- Bharadwaz, Y. D., Rao, B. G., Rao, V. D., & Anusha, C. (2016). Improvement of biodiesel methanol blends performance in a variable compression ratio engine using response surface methodology. Alexandria Engineering Journal, 55(2), 1201-1209. https://doi.org/10.1016/j.aej.2016.04.006
- Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., & Ciganek, A. P. (2012). Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843-855. https://doi.org/10.1016/j.compedu.2011.10.010
- Billa, K. K., Deb, M., Sastry, G. R. K., & Dey, S. (2021). Experimental investigation on dispersing graphene-oxide in biodiesel/diesel/higher alcohol blends on diesel engine using response surface methodology. Environmental Technology, 43(20), 3131-3148. https://doi.org/10.1080/09593330.2021.1916091
- Borgnakke, C., & Sonntag, R. E. (2022). Fundamentals of thermodynamics. John Wiley & Sons.
- Burke, R.D., De Jonge, N., Avola, C., & Forte, B. (2017). A virtual engine laboratory for teaching powertrain engineering. Computer Applications in Engineering Education, 25(6), 948-960. https://doi.org/10.1002/cae.21847
- Caton, J. A. (2001). Comparisons of instructional and complete versions of thermodynamic engine cycle simulations for spark-ignition engines. International Journal of Mechanical Engineering Education, 29(4), 283-306. https://doi.org/10.7227/IJMEE.29.4.1
- Caton, J. A. (2002). Illustration of the use of an instructional version of a thermodynamic cycle simulation for a commercial automotive spark-ignition engine. International Journal of Mechanical Engineering Education, 30(4), 283-297. https://doi.org/10.7227/IJMEE.30.4.1
- Cave, P. R. (1974). Computer modelling as an aid to teaching in an internal combustion engineering course. International Journal of Mathematical Educational in Science and Technology, 5(3-4), 555-559. https://doi.org/10.1080/0020739740050405
- Choudhury, P. K. (2019). Student assessment of quality of engineering education in India: Evidence from a field survey. Quality Assurance in Education, 27(1), 103-126. https://doi.org/10.1108/QAE-02-2015-0004
- Clarizia, F., Colace, F., De Santo, M., Lombardi, M., Pascale, F., & Pietrosanto, A. (2018). E-learning and sentiment analysis: A case study. In Proceedings of the 6th International Conference on Information and Education Technology (pp. 111-118). https://doi.org/10.1145/3178158.3178181
- Cruz-Peragón, F., Palomar, J. M., Torres-Jimenez, E., & Dorado, R. (2012). Spreadsheet for teaching reciprocating engine cycles. Computer Applications in Engineering Education, 20(4), 681-691. https://doi.org/10.1002/cae.20438
- Das, A. K., Mohapatra, T., Panda, A. K., & Sahoo, S. S. (2021a). Study on the performance and emission characteristics of pyrolytic waste plastic oil operated CI engine using response surface methodology. Journal of Cleaner Production, 328, 129646. https://doi.org/10.1016/j.jclepro.2021.129646
- Das, S., Kashyap, D., Bora, B. J., Kalita, P., & Kulkarni, V. (2021b). Thermo-economic optimization of a biogas-diesel dual fuel engine as remote power generating unit using response surface methodology. Thermal Science and Engineering Progress, 24, 100935. https://doi.org/10.1016/j.tsep.2021.100935
- Depcik, C., Jacobs, T., Hagena, J., & Assanis, D. N. (2007). Instructional use of a single-zone, premixed charge, spark-ignition engine heat release simulation. International Journal of Mechanical Engineering Education, 35(1), 1-31. https://doi.org/10.7227/IJMEE.35.1.1
- DIESEL-RK. (2023). Diesel RK. https://diesel-rk.bmstu.ru/
- Dimitrov, E. (2020). Computer program for indicator diagram processing of the internal combustion engine. IOP Conference Series: Materials Science and Engineering, 977, 012012. https://doi.org/10.1088/1757-899X/977/1/012012
- Dubey, A., Mehndiratta, A., Sagar, M., & Kashiramka, S. (2019). Reforms in technical education sector: Evidence from World Bank-assisted technical education quality improvement programme in India. Higher Education, 78, 273-299. https://doi.org/10.1007/s10734-018-0343-1
- Ekaterina, G., Anastasya, B., & Ksenya, G. (2015). Sociocultural competence training in higher engineering education: The role of gaming simulation. Procedia-Social and Behavioral Sciences, 166, 339-343. https://doi.org/10.1016/j.sbspro.2014.12.533
- Farid, S., Ahmad, R., Niaz, I. A., Itmazi, J., & Asghar, K. (2014). Identifying perceived challenges of e-learning implementation. In Proceedings of the 1st International Conference on Modern Communication & Computing Technologies.
- Filipi, Z. S., Zhang, G., & Assanis, D. N. (1997). Development of interactive graphical software tools in the context of teaching modeling of internal combustion engines in a multimedia classroom. ASEE PEER. https://doi.org/10.18260/1-2--6514
- Fowler, L., Armarego, J., & Allen, M. (2001). Case tools: Constructivism and its application to learning and usability of software engineering tools. Computer Science Education, 11(3), 261-272. https://doi.org/10.1076/csed.11.3.261.3835
- Gambhir, V., Wadhwa, N. C., & Grover, S. (2016). Quality concerns in technical education in India: A quantifiable quality enabled model. Quality Assurance in Education, 24(1), 2-25. https://doi.org/10.1108/QAE-07-2011-0040
- Gamma Technologies. (2023). Gamma technologies. http://www.gtisoft.com/gt-suite/product-options
- Ganji, P. R., Putta, K. B. C., Kattela, S. P., Raju, V. R. K., & Rao, S. S. (2021). Optimisation of EGR and SOI for better combustion characteristics using response surface methodology. International Journal of Ambient Energy, 42(14), 1660-1669. https://doi.org/10.1080/01430750.2019.1612782
- García, M. T., Aguilar, F. J. J.-S., Trujillo, E. C., & Villanueva, J. A. B. (2012). Educational software for diesel engine simulation performance and parametric analysis. International Journal of Engineering Education, 28(5), 1188-1198.
- Ghanbari, M., Mozafari-Vanani, L., Dehghani-Soufi, M., & Jahanbakhshi, A. (2021). Effect of alumina nanoparticles as additive with diesel–Biodiesel blends on performance and emission characteristic of a six-cylinder diesel engine using response surface methodology (RSM). Energy Conversion and Management: X, 11, 100091. https://doi.org/10.1016/j.ecmx.2021.100091
- Gokuladas, V. K. (2010). Technical and non‐technical education and the employability of engineering graduates: An Indian case study. International Journal of Training and Development, 14(2), 130-143 https://doi.org/10.1111/j.1468-2419.2010.00346.x
- Gómez-de la Cruz, F. J., Torres-Jimenez, E., Palomar-Carnicero, J. M., & Cruz-Peragon, F. (2021). On the spreadsheet in the learning of thermal engines in the undergraduate engineering education: Applications to study turbocharged reciprocating engines. Computer Applications in Engineering Education, 30(1), 106-116.
- Grigoraş, G., Dănciulescu, D., & Sitnikov, C. (2014). Assessment criteria of e-learning environments quality. Procedia Economics and Finance, 16, 40-46. https://doi.org/10.1016/S2212-5671(14)00772-2
- Gurses, A., Dogar, C., & Gunes, K. (2015). A new approach for learning: Interactive direct teaching based constructivist learning (IDTBCL). Procedia-Social and Behavioral Sciences, 197, 2384-2389. https://doi.org/10.1016/j.sbspro.2015.07.296
- Handoyo, E. (2007). The interesting of learning thermodynamics through daily life. In Proceedings of the Maranatha Teaching and Learning International Conference.
- Hinostroza, J. R., Rehbein, L. E., Mellar, H., & Preston, C. (2000). Developing educational software: A professional tool perspective. Education and Information Technologies, 5, 103-117. https://doi.org/10.1023/A:1009699417462
- Hirkude, J. B., & Padalkar, A. S. (2014). Performance optimization of CI engine fuelled with waste fried oil methyl ester-diesel blend using response surface methodology. Fuel, 119, 266-273. https://doi.org/10.1016/j.fuel.2013.11.039
- Huffman, G. D. (2000). Using the ideal gas law and heat release models to demonstrate timing in spark and compression ignition engines. International Journal of Mechanical Engineering Education, 28(4), 279-296. https://doi.org/10.7227/IJMEE.28.4.1
- Ibrahim, D. (2011). Engineering simulation with MATLAB: Improving teaching and learning effectiveness. Procedia Computer Science, 3, 853-858. https://doi.org/10.1016/j.procs.2010.12.140
- Inglis, A. (2008). Approaches to the validation of quality frameworks for e-learning. Quality Assurance in Education, 16(4), 347-362. https://doi.org/10.1108/09684880810906490
- Islam, N., Beer, M., & Slack, F. (2015). E-learning challenges faced by academics in higher education. Journal of Education and Training Studies, 3(5), 102-112. https://doi.org/10.11114/jets.v3i5.947
- Jatoth, R., Gugulothu, S. K., Sastry, G. R. K., & Surya, M. S. (2021). Statistical and experimental investigation of the influence of fuel injection strategies on gasoline/diesel RCCI combustion and emission characteristics in a diesel engine. International Journal of Green Energy, 18(12), 1229-1248. https://doi.org/10.1080/15435075.2021.1897829
- Kamarulzaman, M. K., & Abdullah, A. (2020). Multi-objective optimization of diesel engine performances and exhaust emissions characteristics of hermetia illucens larvae oil-diesel fuel blends using response surface methodology. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. https://doi.org/10.1080/15567036.2020.1849450
- Karabas, H., & Boran, S. (2019). Comparison of engine performance and exhaust emission properties of diesel and safflower biodiesel using multi-response surface methodology. Environmental Progress & Sustainable Energy, 38(3), e13034. https://doi.org/10.1002/ep.13034
- Kashyap, D., Das, S., & Kalita, P. (2021). Exploring the efficiency and pollutant emission of a dual fuel CI engine using biodiesel and producer gas: An optimization approach using response surface methodology. Science of the Total Environment, 773, 145633. https://doi.org/10.1016/j.scitotenv.2021.145633
- Katekaew, S., Suiuay, C., Senawong, K., Seithtanbutara, V., Intravised, K., & Laloon, K. (2021). Optimization of performance and exhaust emissions of single-cylinder diesel engines fueled by blending diesel-like fuel from Yang-hard resin with waste cooking oil biodiesel via response surface methodology. Fuel, 304, 121434. https://doi.org/10.1016/j.fuel.2021.121434
- Khanjani, A., & Sobati, M. A. (2021). Performance and emission of a diesel engine using different water/waste fish oil (WFO) biodiesel/diesel emulsion fuels: Optimization of fuel formulation via response surface methodology (RSM). Fuel, 288, 119662. https://doi.org/10.1016/j.fuel.2020.119662
- Khoobbakht, G., Karimi, M., & Kheiralipour, K. (2019). Effects of biodiesel-ethanol-diesel blends on the performance indicators of a diesel engine: A study by response surface modeling. Applied Thermal Engineering, 148, 1385-1394. https://doi.org/10.1016/j.applthermaleng.2018.08.025
- Khoobbakht, G., Najafi, G., Karimi, M., & Akram, A. (2016). Optimization of operating factors and blended levels of diesel, biodiesel and ethanol fuels to minimize exhaust emissions of diesel engine using response surface methodology. Applied Thermal Engineering, 99, 1006-1017. https://doi.org/10.1016/j.applthermaleng.2015.12.143
- Kirkpatrick, A., & Willson, B. (1998). Computation and experimentation on the web with application to internal combustion engines. Journal of Engineering Education, 87(S5), 529-537. https://doi.org/10.1002/j.2168-9830.1998.tb00389.x
- Kirkpatrick, A., Lee, A., & Willson, B. (1997). The engine in engineering-development of thermal/fluids web based applications. In Proceedings of 27th Annual Conference. Teaching and Learning in an Era of Change (pp. 744-747). IEEE. https://doi.org/10.1109/FIE.1997.635929
- Krishnamoorthy, V., Dhanasekaran, R., Rana, D., Saravanan,, S., & Kumar, B. R. (2018). A comparative assessment of ternary blends of three bio-alcohols with waste cooking oil and diesel for optimum emissions and performance in a CI engine using response surface methodology. Energy Conversion and Management, 156, 337-357. https://doi.org/10.1016/j.enconman.2017.10.087
- Kumar, B. R., Saravanan, S., Rana, D., & Nagendran, A. (2016). Combined effect of injection timing and exhaust gas recirculation (EGR) on performance and emissions of a DI diesel engine fuelled with next-generation advanced biofuel–Diesel blends using response surface methodology. Energy Conversion and Management, 123, 470-486. https://doi.org/10.1016/j.enconman.2016.06.064
- Kumar, S., & Dinesha, P. (2018). Optimization of engine parameters in a bio diesel engine run with honge methyl ester using response surface methodology. Measurement, 125, 224-231. https://doi.org/10.1016/j.measurement.2018.04.091
- Laborda, J., Caroli, M., & Sagone, E. (2014). Generalized self-efficacy and well-being in adolescents with high vs. low scholastic self-efficacy. Procedia-Social and Behavioral Sciences, 141, 867-874. https://doi.org/10.1016/j.sbspro.2014.05.152
- Lan, Q., Bai, Y., Fan, L., Gu, Y., Wen, L., & Yang, L. (2020). Investigation on fuel injection quantity of low-speed diesel engine fuel system based on response surface prediction model. Energy, 211, 118946. https://doi.org/10.1016/j.energy.2020.118946
- Li, J., Han, Y., Mao, G., & Wang, P. (2020). Optimization of exhaust emissions from marine engine fueled with LNG/diesel using response surface methodology. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(12), 1436-1448. https://doi.org/10.1080/15567036.2019.1604859
- Magnani, F. S., de Andrade, G. M., & Willmersdorf, R. B. (2018). Influence of mathematical simplifications on the dynamic and energetic performance of an engine/motorcycle integrated model. International Journal of Mechanical Engineering Education, 46(2), 138-157. https://doi.org/10.1177/0306419017720425
- Mahla, S. K., Ardebili, S. M. S., Mostafaei, M., Dhir, A., Goga, G., & Chauhan, B. S. (2020). Multi-objective optimization of performance and emissions characteristics of a variable compression ratio diesel engine running with biogas-diesel fuel using response surface techniques. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. https://doi.org/10.1080/15567036.2020.1813847
- Mahwish, W., & Farooq, H. (2009). Empirical study of learner contentment towards e-learning: Influential role of key factors. International Islamic University. https://linc.mit.edu/linc2010/proceedings/session11Waheed.pdf
- Masoumi, D., & Lindström, B. (2012). Quality in e-learning: A framework for promoting and assuring quality in virtual institutions. Journal of Computer Assisted Learning, 28(1), 27-41. https://doi.org/10.1111/j.1365-2729.2011.00440.x
- MathWorks. (2023). MATLAB. https://in.mathworks.com/products/matlab.html
- McMasters, R. (2011). A student-built internal combustion engine simulation using Excel. In Proceedings of the ASEE Annual Conference & Exposition. https://doi.org/10.18260/1-2--17387
- Mecware. (2023). Mecware. http://www2.wbs.ne.jp/~mec/
- Misut, M., & Pribilova, K. (2015). Measuring of quality in the context of e-learning. Procedia-Social and Behavioral Sciences, 177, 312-319. https://doi.org/10.1016/j.sbspro.2015.02.347
- Mulop, N., Yusof, K. M., & Tasir, Z. (2012). A review on enhancing the teaching and learning of thermodynamics. Procedia-Social and Behavioral Sciences, 56, 703-712. https://doi.org/10.1016/j.sbspro.2012.09.706
- Navimipour, N. J., & Zareie, B. (2015). A model for assessing the impact of e-learning systems on employees’ satisfaction. Computers in Human Behavior, 53, 475-485. https://doi.org/10.1016/j.chb.2015.07.026
- Niederhauser, D. S., & Stoddart, T. (2001). Teachers’ instructional perspectives and use of educational software. Teaching and Teacher Education, 17(1), 15-31. https://doi.org/10.1016/S0742-051X(00)00036-6
- Optimum Power Technologies. (2023). Power technologies. http://www.optimum-power.com/virtualenginesproducts.html
- Ozgur, C. (2021). Optimization of biodiesel yield and diesel engine performance from waste cooking oil by response surface method (RSM). Petroleum Science and Technology, 39(17-18), 683-703. https://doi.org/10.1080/10916466.2021.1954019
- Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285-1296. https://doi.org/10.1016/j.compedu.2009.06.011
- Pâmîntaş, E. (2015). Higher technical education–Research vs. education. Technique of teaching, between classical and modern. Acta Universitatis Cibiniensis Technical Series [Journal of the University of Cibinese Technical Series], 66(1), 125-130. https://doi.org/10.1515/aucts-2015-0040
- Pandian, M., Sivapirakasam, S., & Udayakumar, M. (2011). Investigation on the effect of injection system parameters on performance and emission characteristics of a twin cylinder compression ignition direct injection engine fuelled with pongamia biodiesel–Diesel blend using response surface methodology. Applied Energy, 88(8), 2663-2676. https://doi.org/10.1016/j.apenergy.2011.01.069
- Parida, M., Joardar, H., Rout, A. K., Routaray, I., & Mishra, B. P. (2019). Multiple response optimizations to improve performance and reduce emissions of Argemone Mexicana biodiesel-diesel blends in a VCR engine. Applied Thermal Engineering, 148, 1454-1466. https://doi.org/10.1016/j.applthermaleng.2018.11.061
- Partington, J. R. (1989). A short history of chemistry. Courier Corporation.
- Patel, H., Rajai, V., Das, P., Charola, S., Mugdal, A., & Maiti, S. (2018). Study of jatropha curcas shell bio-oil-diesel blend in VCR CI engine using RSM. Renewable Energy, 122, 310-322. https://doi.org/10.1016/j.renene.2018.01.071
- Patel, P. D., Lakdawala, A., & Patel, R. N. (2016). Box-Behnken response surface methodology for optimization of operational parameters of compression ignition engine fuelled with a blend of diesel, biodiesel and diethyl ether. Biofuels, 7(2), 87-95. https://doi.org/10.1080/17597269.2015.1118784
- Poompipatpong, C., & Kengpol, A. (2015). Design of a decision support methodology using response surface for torque comparison: An empirical study on an engine fueled with waste plastic pyrolysis oil. Energy, 82, 850-856. https://doi.org/10.1016/j.energy.2015.01.095
- Pote, R., Patil, R., & Badadhe, A. (2020). Optimisation of performance and emission parameters of diesel engine using tyre pyrolysis oil. Australian Journal of Mechanical Engineering, 20(4), 1172-1184. https://doi.org/10.1080/14484846.2020.1785187
- Prasad, G. A., Murugan, P. C., Wincy, W. B., & Sekhar, S. J. (2021). Response surface methodology to predict the performance and emission characteristics of gas-diesel engine working on producer gases of non-uniform calorific values. Energy, 234, 121225. https://doi.org/10.1016/j.energy.2021.121225
- Prensky, M. (2004). Proposal for educational software development sites: An open source tool to create the learning software we need. On the Horizon, 12, 41-44. https://doi.org/10.1108/10748120410699585
- Prof. Blair and Associates. (2023). Prof. Blair and associates. http://www.profblairandassociates.com
- Ramachander, J., Gugulothu, S. K., Sastry, G. R. K., Panda, J. K., & Surya, M. S. (2021a). Performance and emission predictions of a CRDI engine powered with diesel fuel: A combined study of injection parameters variation and Box-Behnken response surface methodology based optimization. Fuel, 290, 120069. https://doi.org/10.1016/j.fuel.2020.120069
- Ramachander, J., Gugulothu, S. K., Sastry, G. R., & Surya, M. S. (2021b). Statistical and experimental investigation of the influence of fuel injection strategies on CRDI engine assisted CNG dual fuel diesel engine. International Journal of Hydrogen Energy, 46(42), 22149-22164. https://doi.org/10.1016/j.ijhydene.2021.04.010
- Ricardo PLC. (2023). Ricardo PLC. https://software.ricardo.com/product-families
- Rüütmann, T., & Kipper, H. (2012). Rethinking effective teaching and learning for the design of efficient curriculum for technical teachers. In Proceedings of the 15th International Conference on Interactive Collaborative Learning. IEEE. https://doi.org/10.1109/ICL.2012.6402030
- Saidur, R., Jahirul, M. I., Hasanuzzaman, M., & Masjuki, H. H. (2008). Analysis of exhaust emissions of natural gas engine by using response surface methodology. Journal of Applied Sciences, 8(19), 3328-3339. https://doi.org/10.3923/jas.2008.3328.3339
- Sakthivel, R., Ramesh, K., Marshal, S. J. J., & Sadasivuni, K. K. (2019). Prediction of performance and emission characteristics of diesel engine fuelled with waste biomass pyrolysis oil using response surface methodology. Renewable Energy, 136, 91-103. https://doi.org/10.1016/j.renene.2018.12.109
- Saravanan, S., Kumar, B. R., Varadharajan, A., Rana, D., Sethuramasamyraja, B., & Rao, G. L. N. (2017). Optimization of DI diesel engine parameters fueled with iso-butanol/diesel blends–Response surface methodology approach. Fuel, 203, 658-670. https://doi.org/10.1016/j.fuel.2017.04.083
- Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & education, 49(2), 396-413. https://doi.org/10.1016/j.compedu.2005.09.004
- Sharma, N. (2014). Expansion of engineering education in India: Issues, challenges and achievable suggestions. Journal of Academia and Industrial Research, 3(3), 118-122.
- Sharma, P., & Pandher, J. S. (2018). Quality of teachers in technical higher education institutions in India. Higher Education, Skills and Work-Based Learning, 8(4), 511-526. https://doi.org/10.1108/HESWBL-10-2017-0080
- Sharma, P., & Sharma, A. K. (2021). Application of response surface methodology for optimization of fuel injection parameters of a dual fuel engine fuelled with producer gas-biodiesel blends. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. https://doi.org/10.1080/15567036.2021.1892883
- Sharma, P., Chhillar, A., Said, Z., & Memon, S. (2021). Exploring the exhaust emission and efficiency of algal biodiesel powered compression ignition engine: Application of Box-Behnken and desirability based multi-objective response surface methodology. Energies, 14(18), 5968. https://doi.org/10.3390/en14185968
- Shatrov, M. G., Krichevskaya, T. Y., Yakovenko, A. L., & Solovyev, A. (2020). The it based internal combustion engines integrated teaching complex. In M. Auer, & T. Tsiatsos (Eds.), The challenges of the digital transformation in education (pp. 333-343). Springer. https://doi.org/10.1007/978-3-030-11932-4_32
- Simsek, S., & Uslu, S. (2020). Determination of a diesel engine operating parameters powered with canola, safflower and waste vegetable oil based biodiesel combination using response surface methodology (RSM). Fuel, 270, 117496. https://doi.org/10.1016/j.fuel.2020.117496
- Simsek, S., Uslu, S., & Simsek, H. (2022). Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine. Energy, 239, 122389. https://doi.org/10.1016/j.energy.2021.122389
- Singh, A., Sinha, S., Choudhary, A. K., & Chelladurai, H. (2022). Biodiesel production using heterogeneous catalyst, application of Taguchi robust design and response surface methodology to optimise diesel engine performance fuelled with Jatropha biodiesel blends. International Journal of Ambient Energy, 43(1), 2976-2987. https://doi.org/10.1080/01430750.2020.1789741
- Singh, D. K., & Tirkey, J. V. (2022). Performance optimization through response surface methodology of an integrated coal gasification and CI engine fuelled with diesel and low-grade coal-based producer gas. Energy, 238, 121982. https://doi.org/10.1016/j.energy.2021.121982
- Singh, T. S., Rajak, U., Samuel, O. D., Chaurasiya, P. K., Natarajan, K., Verma, T. N., & Nashine, P. (2021). Optimization of performance and emission parameters of direct injection diesel engine fuelled with microalgae spirulina (L.)–Response surface methodology and full factorial method approach. Fuel, 285, 119103. https://doi.org/10.1016/j.fuel.2020.119103
- Solmaz, H., Ardebili, S. M. S., Calam, A., Yilmaz, E., & Ipci, D. (2021). Prediction of performance and exhaust emissions of a CI engine fueled with multi-wall carbon nanotube doped biodiesel-diesel blends using response surface method. Energy, 227, 120518. https://doi.org/10.1016/j.energy.2021.120518
- Srinidhi, C., Madhusudhan, A., Channapattana, S. V., Gawali, S. V., & Aithal, K. (2021). RSM based parameter optimization of CI engine fuelled with nickel oxide dosed Azadirachta indica methyl ester. Energy, 34, 121282. https://doi.org/10.1016/j.energy.2021.121282
- Stanisavljević-Petrović, Z. Stankovic, Z., & Jevtić, B. (2015). Implementation of educational software in classrooms–Pupils’ perspective. Procedia-Social and Behavioral Sciences, 186, 549-559. https://doi.org/10.1016/j.sbspro.2015.04.131
- Statease. (2023). Convert coded response surface model to actual. https://www.statease.com/docs/v11/contents/advanced-topics/convert-coded-response-surface-model-to-actual/
- Sun, P.-C., Tsai, R. J., Finger, G., Chen, Y.-Y., & Yeh, D. (2006). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. https://doi.org/10.1016/j.compedu.2006.11.007
- Teoh, Y. H., How, H. G., Sher, F., Le, T. D., Ong, H. C., Nguyen, H. T., & Yaqoob, H. (2021). Optimization of fuel injection parameters of moringa oleifera biodiesel-diesel blend for engine-out-responses improvements. Symmetry, 13(6), 982. https://doi.org/10.3390/sym13060982
- Toshniwal, O., & Yammiyavar, P. (2013). Intelligent interactive tutor for rural Indian education system. In A. Agrawal, R. C.Tripathi, E. Y. L. Do, & M. D. Tiwari (Eds.), Proceedings of the Intelligent Interactive Technologies and Multimedia (pp. 186-199). Springer. https://doi.org/10.1007/978-3-642-37463-0_17
- Tretinjak, M. F., Bednjanec, A., & Tretinjak, M. (2014). Application of modern teaching techniques in the educational process. In Proceedings of the 37th International Convention on Information and Communication Technology. IEEE. https://doi.org/10.1109/MIPRO.2014.6859643
- Tulsi, P. K., & Poonia, M. P. (2015). Building excellence in engineering education in India. In Proceedings of the 2015 IEEE Global Engineering Education Conference (pp. 624-629). IEEE. https://doi.org/10.1109/EDUCON.2015.7096035
- Tzur, S., Katz, A., & Davidovich, N. (2021). Learning supported by technology: Effectiveness with educational software. European Journal of Educational Research, 10(3), 1137-1156. https://doi.org/10.12973/eu-jer.10.3.1139
- Unni, J. (2016). Skill gaps and employability: Higher education in India. Journal of Development Policy and Practice, 1(1), 18-34. https://doi.org/10.1177/2455133315612310
- Venkatram, R. (2016). (Technical) colleges: Technical education in India–The strengths and challenges. In M. Pilz (Ed.), Preparation for the world of work (pp. 81-102). Springer. https://doi.org/10.1007/978-3-658-08502-5_6
- Vijayashree, P., Kumar, V. J., & Ganesan, V. (2006). GANESH: A GUI approach to SI engine simulation. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition. https://doi.org/10.1115/IMECE2006-14332
- Vinay, Singh, B., Yadav, A. K. (2018). Optimisation of performance and emission characteristics of CI engine fuelled with mahua oil methyl ester–diesel blend using response surface methodology. International Journal of Ambient Energy, 41(6), 674-685. https://doi.org/10.1080/01430750.2018.1484804
- Wong, D. (2007). A critical literature review on e-learning limitations. Journal for the Advancement of Science and Arts, 2(1), 55-62.
- Yaliwal, V., Banapurmath, N. R., Gaitonde, V. N., & Malipatil, M. D. (2019). Simultaneous optimization of multiple operating engine parameters of a biodiesel-producer gas operated compression ignition (CI) engine coupled with hydrogen using response surface methodology. Renewable Energy, 139, 944-959. https://doi.org/10.1016/j.renene.2019.02.104
- Yaman, H., Yesilyurt, M. K., & Uslu, S. (2022). Simultaneous optimization of multiple engine parameters of a 1-heptanol/gasoline fuel blends operated a port-fuel injection spark-ignition engine using response surface methodology approach. Energy, 238, 122019. https://doi.org/10.1016/j.energy.2021.122019
- Yanuschik, O. V., Pakhomova, E. G., & Batbold, K. (2015). E-learning as a way to improve the quality of educational for international students. Procedia-Social and Behavioral Sciences, 215, 147-155. https://doi.org/10.1016/j.sbspro.2015.11.607
- Yusri, I. M., Mamat, R., Azmi, W. H., Omar, A. I., Obed, M. A., & Sahiful, A. I. M. (2017). Application of response surface methodology in optimization of performance and exhaust emissions of secondary butyl alcohol-gasoline blends in SI engine. Energy Conversion and Management, 133, 178-195. https://doi.org/10.1016/j.enconman.2016.12.001
- Zeng, P., & Assanis, D. N. (2004). The development of a computer-based teaching tool for internal combustion engine courses. In Proceedings of the ASME International Mechanical Engineering Congress and Exposition. https://doi.org/10.1115/IMECE2004-61998
- Zueco, J. (2011). Educational software to study alternative internal combustion engine cycles. International Journal of Mechanical Engineering Education, 39(2), 101-113. https://doi.org/10.7227/IJMEE.39.2.2
How to cite this article
APA
Balakrishnan, N. K., Philip, J., Amin, H., Brahma, P., Borges, A., Chari, V., & Prabhu, C. (2024). A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics. European Journal of Sustainable Development Research, 8(2), em0252. https://doi.org/10.29333/ejosdr/14302
Vancouver
Balakrishnan NK, Philip J, Amin H, Brahma P, Borges A, Chari V, et al. A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics. EUR J SUSTAIN DEV RES. 2024;8(2):em0252. https://doi.org/10.29333/ejosdr/14302
AMA
Balakrishnan NK, Philip J, Amin H, et al. A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics. EUR J SUSTAIN DEV RES. 2024;8(2), em0252. https://doi.org/10.29333/ejosdr/14302
Chicago
Balakrishnan, Navaneetha Krishnan, Jennifer Philip, Hasan Amin, Prince Brahma, Aaron Borges, Vrishin Chari, and C Prabhu. "A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics". European Journal of Sustainable Development Research 2024 8 no. 2 (2024): em0252. https://doi.org/10.29333/ejosdr/14302
Harvard
Balakrishnan, N. K., Philip, J., Amin, H., Brahma, P., Borges, A., Chari, V., and Prabhu, C. (2024). A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics. European Journal of Sustainable Development Research, 8(2), em0252. https://doi.org/10.29333/ejosdr/14302
MLA
Balakrishnan, Navaneetha Krishnan et al. "A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics". European Journal of Sustainable Development Research, vol. 8, no. 2, 2024, em0252. https://doi.org/10.29333/ejosdr/14302