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Prediction of Maximum Combustion Efficiency and Thrust Force of DLR Scramjet Engine Using ANN Models
DLR scramjet engine is one of the well-known strut base parallel fuel injection engine, with significant thrust force due to low pressure loss but with slight decrease in combustion performance. The performance parameters such as combustion efficiency and thrust force are generally affected by desig...
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| Published in: | SN computer science 2023-09, Vol.4 (5), p.508, Article 508 |
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| cites | cdi_FETCH-LOGICAL-c2348-1e2733032dac651861c017c279cdb5522409eb23ca56857b5102ff91b7083dbc3 |
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| container_title | SN computer science |
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| creator | Debnath, Anupam Roy, Bidesh |
| description | DLR scramjet engine is one of the well-known strut base parallel fuel injection engine, with significant thrust force due to low pressure loss but with slight decrease in combustion performance. The performance parameters such as combustion efficiency and thrust force are generally affected by design variables such as strut angle and divergent angle of the engine. In this context, ANN models were developed to foretell the performance parameters of DLR scramjet engine for varying strut angle and divergent angle. The results predicted by the developed ANN models are comparable to the computational results. In addition, it is seen that the statistical indicators like
R
2
, RMSE and MAPE are in the range of 0.997–0.999, 0.038–0.016 and 3.38–1.26 for prediction of combustion efficiency and thrust force. Therefore, it can be finally concluded that the developed ANN models are competent to predict the performance parameters such as combustion efficiency and thrust force of the DLR scramjet engine. |
| doi_str_mv | 10.1007/s42979-023-02020-8 |
| format | article |
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R
2
, RMSE and MAPE are in the range of 0.997–0.999, 0.038–0.016 and 3.38–1.26 for prediction of combustion efficiency and thrust force. Therefore, it can be finally concluded that the developed ANN models are competent to predict the performance parameters such as combustion efficiency and thrust force of the DLR scramjet engine.</description><identifier>ISSN: 2661-8907</identifier><identifier>ISSN: 2662-995X</identifier><identifier>EISSN: 2661-8907</identifier><identifier>DOI: 10.1007/s42979-023-02020-8</identifier><language>eng</language><publisher>Singapore: Springer Nature Singapore</publisher><subject>Algorithms ; Combustion efficiency ; Computer Imaging ; Computer Science ; Computer Systems Organization and Communication Networks ; Data Structures and Information Theory ; Efficiency ; Enabling Innovative Computational Intelligence Technologies for IOT ; Finite volume method ; Fuel injection ; Geometry ; Hydrogen ; Information Systems and Communication Service ; Low pressure ; Machine learning ; Mathematical models ; Original Research ; Parameters ; Pattern Recognition and Graphics ; Performance evaluation ; Pressure loss ; Qualitative research ; Simulation ; Software Engineering/Programming and Operating Systems ; Struts ; Supersonic aircraft ; Supersonic combustion ramjet engines ; Thrust ; Turbulence models ; Vision</subject><ispartof>SN computer science, 2023-09, Vol.4 (5), p.508, Article 508</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2348-1e2733032dac651861c017c279cdb5522409eb23ca56857b5102ff91b7083dbc3</citedby><cites>FETCH-LOGICAL-c2348-1e2733032dac651861c017c279cdb5522409eb23ca56857b5102ff91b7083dbc3</cites><orcidid>0000-0002-1122-2034</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Debnath, Anupam</creatorcontrib><creatorcontrib>Roy, Bidesh</creatorcontrib><title>Prediction of Maximum Combustion Efficiency and Thrust Force of DLR Scramjet Engine Using ANN Models</title><title>SN computer science</title><addtitle>SN COMPUT. SCI</addtitle><description>DLR scramjet engine is one of the well-known strut base parallel fuel injection engine, with significant thrust force due to low pressure loss but with slight decrease in combustion performance. The performance parameters such as combustion efficiency and thrust force are generally affected by design variables such as strut angle and divergent angle of the engine. In this context, ANN models were developed to foretell the performance parameters of DLR scramjet engine for varying strut angle and divergent angle. The results predicted by the developed ANN models are comparable to the computational results. In addition, it is seen that the statistical indicators like
R
2
, RMSE and MAPE are in the range of 0.997–0.999, 0.038–0.016 and 3.38–1.26 for prediction of combustion efficiency and thrust force. Therefore, it can be finally concluded that the developed ANN models are competent to predict the performance parameters such as combustion efficiency and thrust force of the DLR scramjet engine.</description><subject>Algorithms</subject><subject>Combustion efficiency</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer Systems Organization and Communication Networks</subject><subject>Data Structures and Information Theory</subject><subject>Efficiency</subject><subject>Enabling Innovative Computational Intelligence Technologies for IOT</subject><subject>Finite volume method</subject><subject>Fuel injection</subject><subject>Geometry</subject><subject>Hydrogen</subject><subject>Information Systems and Communication Service</subject><subject>Low pressure</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Original Research</subject><subject>Parameters</subject><subject>Pattern Recognition and Graphics</subject><subject>Performance evaluation</subject><subject>Pressure loss</subject><subject>Qualitative research</subject><subject>Simulation</subject><subject>Software Engineering/Programming and Operating Systems</subject><subject>Struts</subject><subject>Supersonic aircraft</subject><subject>Supersonic combustion ramjet engines</subject><subject>Thrust</subject><subject>Turbulence models</subject><subject>Vision</subject><issn>2661-8907</issn><issn>2662-995X</issn><issn>2661-8907</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kFFLwzAQx4MoOHRfwKeAz9VL0rTp45ibCtsU3Z5Dm6YzY01msoL79maroE9yHHfc_X938EfohsAdAcjvQ0qLvEiAspgxEnGGBjTLSCIKyM__9JdoGMIGACiHNM34ANWvXtdG7Y2z2DV4Xn6Ztmvx2LVVF07TSdMYZbRVB1zaGi8_fFzgqfNKH4mH2Rt-V75sN3qPJ3ZtrMarYOwajxYLPHe13oZrdNGU26CHP_UKraaT5fgpmb08Po9Hs0RRloqEaJozBozWpco4ERlRQHJF80LVFeeUplDoijJV8kzwvOIEaNMUpMpBsLpS7Ard9nd33n12OuzlxnXexpeSFpRQwQB4VNFepbwLwetG7rxpS3-QBOTRUNkbKqOh8mSoFBFiPRSi2K61_z39D_UNg7x2aA</recordid><startdate>20230901</startdate><enddate>20230901</enddate><creator>Debnath, Anupam</creator><creator>Roy, Bidesh</creator><general>Springer Nature Singapore</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-1122-2034</orcidid></search><sort><creationdate>20230901</creationdate><title>Prediction of Maximum Combustion Efficiency and Thrust Force of DLR Scramjet Engine Using ANN Models</title><author>Debnath, Anupam ; Roy, Bidesh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2348-1e2733032dac651861c017c279cdb5522409eb23ca56857b5102ff91b7083dbc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Combustion efficiency</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Computer Systems Organization and Communication Networks</topic><topic>Data Structures and Information Theory</topic><topic>Efficiency</topic><topic>Enabling Innovative Computational Intelligence Technologies for IOT</topic><topic>Finite volume method</topic><topic>Fuel injection</topic><topic>Geometry</topic><topic>Hydrogen</topic><topic>Information Systems and Communication Service</topic><topic>Low pressure</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Original Research</topic><topic>Parameters</topic><topic>Pattern Recognition and Graphics</topic><topic>Performance evaluation</topic><topic>Pressure loss</topic><topic>Qualitative research</topic><topic>Simulation</topic><topic>Software Engineering/Programming and Operating Systems</topic><topic>Struts</topic><topic>Supersonic aircraft</topic><topic>Supersonic combustion ramjet engines</topic><topic>Thrust</topic><topic>Turbulence models</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Debnath, Anupam</creatorcontrib><creatorcontrib>Roy, Bidesh</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer science database</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>SN computer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Debnath, Anupam</au><au>Roy, Bidesh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of Maximum Combustion Efficiency and Thrust Force of DLR Scramjet Engine Using ANN Models</atitle><jtitle>SN computer science</jtitle><stitle>SN COMPUT. 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R
2
, RMSE and MAPE are in the range of 0.997–0.999, 0.038–0.016 and 3.38–1.26 for prediction of combustion efficiency and thrust force. Therefore, it can be finally concluded that the developed ANN models are competent to predict the performance parameters such as combustion efficiency and thrust force of the DLR scramjet engine.</abstract><cop>Singapore</cop><pub>Springer Nature Singapore</pub><doi>10.1007/s42979-023-02020-8</doi><orcidid>https://orcid.org/0000-0002-1122-2034</orcidid></addata></record> |
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| subjects | Algorithms Combustion efficiency Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Efficiency Enabling Innovative Computational Intelligence Technologies for IOT Finite volume method Fuel injection Geometry Hydrogen Information Systems and Communication Service Low pressure Machine learning Mathematical models Original Research Parameters Pattern Recognition and Graphics Performance evaluation Pressure loss Qualitative research Simulation Software Engineering/Programming and Operating Systems Struts Supersonic aircraft Supersonic combustion ramjet engines Thrust Turbulence models Vision |
| title | Prediction of Maximum Combustion Efficiency and Thrust Force of DLR Scramjet Engine Using ANN Models |
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