Identification of nozzle parameters in 3S device using artificial intelligence and meta-heuristic optimization scheme
The supersonic separator is a device used to separate the unwanted components in a natural gas. The separated components are drained through the condensate drain which is placed before the normal shockwave in the convergent divergent nozzle. In this work, optimal area ratio and operating pressure ra...
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| creator | Venkata Ramana, P. Gangadhar Prabhu, L. |
| description | The supersonic separator is a device used to separate the unwanted components in a natural gas. The separated components are drained through the condensate drain which is placed before the normal shockwave in the convergent divergent nozzle. In this work, optimal area ratio and operating pressure ratio of the nozzle are obtained to develop the shock at a specific location with an acceptable pressure recovery coefficient. Here, the non-conventional optimization scheme genetic algorithm is employed to identify the optimal parameters. Further, the mathematical modelling is replaced by a neural network in the optimization scheme to reduce the computational cost. The complete work is carried out in the Matlab platform. The optimal parameters obtained using neural network are well in agreement with the mathematical model and the computational cost is drastically reduced. |
| doi_str_mv | 10.1063/1.5141567 |
| format | Conference Proceeding |
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Gangadhar ; Prabhu, L.</creator><contributor>Reddy, Krishna R. ; Mahesh, Vinyas ; Loja, M. A. R.</contributor><creatorcontrib>Venkata Ramana, P. Gangadhar ; Prabhu, L. ; Reddy, Krishna R. ; Mahesh, Vinyas ; Loja, M. A. R.</creatorcontrib><description>The supersonic separator is a device used to separate the unwanted components in a natural gas. The separated components are drained through the condensate drain which is placed before the normal shockwave in the convergent divergent nozzle. In this work, optimal area ratio and operating pressure ratio of the nozzle are obtained to develop the shock at a specific location with an acceptable pressure recovery coefficient. Here, the non-conventional optimization scheme genetic algorithm is employed to identify the optimal parameters. Further, the mathematical modelling is replaced by a neural network in the optimization scheme to reduce the computational cost. The complete work is carried out in the Matlab platform. 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Gangadhar</creatorcontrib><creatorcontrib>Prabhu, L.</creatorcontrib><title>Identification of nozzle parameters in 3S device using artificial intelligence and meta-heuristic optimization scheme</title><title>AIP conference proceedings</title><description>The supersonic separator is a device used to separate the unwanted components in a natural gas. The separated components are drained through the condensate drain which is placed before the normal shockwave in the convergent divergent nozzle. In this work, optimal area ratio and operating pressure ratio of the nozzle are obtained to develop the shock at a specific location with an acceptable pressure recovery coefficient. Here, the non-conventional optimization scheme genetic algorithm is employed to identify the optimal parameters. Further, the mathematical modelling is replaced by a neural network in the optimization scheme to reduce the computational cost. The complete work is carried out in the Matlab platform. The optimal parameters obtained using neural network are well in agreement with the mathematical model and the computational cost is drastically reduced.</description><subject>Artificial intelligence</subject><subject>Computational efficiency</subject><subject>Computing costs</subject><subject>Divergent nozzles</subject><subject>Genetic algorithms</subject><subject>Heuristic methods</subject><subject>Mathematical models</subject><subject>Natural gas</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Parameter identification</subject><subject>Pressure ratio</subject><subject>Pressure recovery</subject><subject>Separators</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2020</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kUtLAzEUhYMoWKsL_0HAnTA1j0kys5Tio1BwoYK7IU3utCkzmTHJFOyvd7QFd67u4pzv3MO9CF1TMqNE8js6EzSnQqoTNKFC0ExJKk_RhJAyz1jOP87RRYxbQlipVDFBw8KCT652RifXedzV2Hf7fQO410G3kCBE7Dzmr9jCzhnAQ3R-jXX4hZxuRjVB07g1-FHV3uKR0tkGhuBicgZ3fXKt2x_yo9lAC5forNZNhKvjnKL3x4e3-XO2fHlazO-XWU9lkTJjCbMMFICkWpKxvszrWltelLrg3Oa1NcqUsCqKFVXABAVptGI5NVwwDXyKbg65feg-B4ip2nZD8OPKinEuOC-lFKPr9uCKxqXfmlUfXKvDV7XrQkWr40mr3tb_mSmpfn7wB_Bv6_57PA</recordid><startdate>20200110</startdate><enddate>20200110</enddate><creator>Venkata Ramana, P. 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Gangadhar ; Prabhu, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p168t-cd02d2e7ee61a6024364ffad389a833d4fdc7c9eb88b17e251e6ca7241c352ae3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial intelligence</topic><topic>Computational efficiency</topic><topic>Computing costs</topic><topic>Divergent nozzles</topic><topic>Genetic algorithms</topic><topic>Heuristic methods</topic><topic>Mathematical models</topic><topic>Natural gas</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Parameter identification</topic><topic>Pressure ratio</topic><topic>Pressure recovery</topic><topic>Separators</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Venkata Ramana, P. Gangadhar</creatorcontrib><creatorcontrib>Prabhu, L.</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Venkata Ramana, P. Gangadhar</au><au>Prabhu, L.</au><au>Reddy, Krishna R.</au><au>Mahesh, Vinyas</au><au>Loja, M. A. R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Identification of nozzle parameters in 3S device using artificial intelligence and meta-heuristic optimization scheme</atitle><btitle>AIP conference proceedings</btitle><date>2020-01-10</date><risdate>2020</risdate><volume>2204</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>The supersonic separator is a device used to separate the unwanted components in a natural gas. The separated components are drained through the condensate drain which is placed before the normal shockwave in the convergent divergent nozzle. In this work, optimal area ratio and operating pressure ratio of the nozzle are obtained to develop the shock at a specific location with an acceptable pressure recovery coefficient. Here, the non-conventional optimization scheme genetic algorithm is employed to identify the optimal parameters. Further, the mathematical modelling is replaced by a neural network in the optimization scheme to reduce the computational cost. The complete work is carried out in the Matlab platform. The optimal parameters obtained using neural network are well in agreement with the mathematical model and the computational cost is drastically reduced.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/1.5141567</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2020, Vol.2204 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
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| source | AIP Journals Complete |
| subjects | Artificial intelligence Computational efficiency Computing costs Divergent nozzles Genetic algorithms Heuristic methods Mathematical models Natural gas Neural networks Optimization Parameter identification Pressure ratio Pressure recovery Separators |
| title | Identification of nozzle parameters in 3S device using artificial intelligence and meta-heuristic optimization scheme |
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