Integrated intelligent computing for heat transfer and thermal radiation-based two-phase MHD nanofluid flow model View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04-01

AUTHORS

Muhammad Asif Zahoor Raja, Ammara Mehmood, Adeel Ahmad Khan, Aneela Zameer

ABSTRACT

In this work, novel application of integrated computational heuristics is presented for computational fluid mechanics problem arising in the study of heat transfer and thermal radiation in two-phase magnetohydrodynamic (MHD) fluid flow model involving nanoparticles using the accurate approximation ability of neural networks hybrid with global exploration of genetic algorithm aided with local search exploitation of sequential quadratic programming. The networks are designed and arbitrarily combined to formulate mean squared error-based objective function for solving and governing nonlinear nanofluidic system. The designed methodology is evaluated to study the dynamics of the system by means of velocities, temperature and concentration profiles for prevailing factors based on variation in Reynolds and Schmidt numbers, as well as, rotation, radiation, magnetic, thermophoretic and Brownian parameters. The pragmatic worth of the scheme is established through statistical inferences in terms of accuracy, convergence and complexity metrics. More... »

PAGES

1-33

References to SciGraph publications

  • 2017-09. Numerical investigation of magneto-nanoparticles for unsteady 3D generalized Newtonian liquid flow in THE EUROPEAN PHYSICAL JOURNAL PLUS
  • 2018-09. Computational intelligence methodology for the analysis of RC circuit modelled with nonlinear differential order system in NEURAL COMPUTING AND APPLICATIONS
  • 2018-04. Intelligent computing to solve fifth-order boundary value problem arising in induction motor models in NEURAL COMPUTING AND APPLICATIONS
  • 2017-12. Neural network methods to solve the Lane–Emden type equations arising in thermodynamic studies of the spherical gas cloud model in NEURAL COMPUTING AND APPLICATIONS
  • 2019-01. Fractional neural network models for nonlinear Riccati systems in NEURAL COMPUTING AND APPLICATIONS
  • 2016-12. A new numerical approach to solve Thomas–Fermi model of an atom using bio-inspired heuristics integrated with sequential quadratic programming in SPRINGERPLUS
  • 2017-09. Design of Bio-inspired Heuristic Techniques Hybridized with Sequential Quadratic Programming for Joint Parameters Estimation of Electromagnetic Plane Waves in WIRELESS PERSONAL COMMUNICATIONS
  • 2019-01. Bio-inspired heuristics hybrid with sequential quadratic programming and interior-point methods for reliable treatment of economic load dispatch problem in NEURAL COMPUTING AND APPLICATIONS
  • 2018-06. Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system in NEURAL COMPUTING AND APPLICATIONS
  • 1988-10. Genetic Algorithms and Machine Learning in MACHINE LEARNING
  • 2015-05. Artificial neural network method for solving the Navier–Stokes equations in NEURAL COMPUTING AND APPLICATIONS
  • 2015-12. Numerical analysis of natural convection for non-Newtonian fluid conveying nanoparticles between two vertical parallel plates in THE EUROPEAN PHYSICAL JOURNAL PLUS
  • 2018-03. Design of artificial neural network models optimized with sequential quadratic programming to study the dynamics of nonlinear Troesch’s problem arising in plasma physics in NEURAL COMPUTING AND APPLICATIONS
  • 2018-12. Intelligent computing approach to solve the nonlinear Van der Pol system for heartbeat model in NEURAL COMPUTING AND APPLICATIONS
  • 1992-07. Genetic Algorithms in SCIENTIFIC AMERICAN
  • 2017-01. An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch’s problem in NEURAL COMPUTING AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00521-019-04157-1

    DOI

    http://dx.doi.org/10.1007/s00521-019-04157-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1113172949


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0915", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Interdisciplinary Engineering", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Engineering", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "COMSATS Institute of Information Technology", 
              "id": "https://www.grid.ac/institutes/grid.418920.6", 
              "name": [
                "Department of Electrical Engineering, COMSATS Institute of Information Technology, 43600, Attock, Pakistan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Raja", 
            "givenName": "Muhammad Asif Zahoor", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Pakistan Institute of Engineering and Applied Sciences", 
              "id": "https://www.grid.ac/institutes/grid.420112.4", 
              "name": [
                "Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Mehmood", 
            "givenName": "Ammara", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Allama Iqbal Open University", 
              "id": "https://www.grid.ac/institutes/grid.445214.2", 
              "name": [
                "Department of Physics, Allama Iqbal Open University, Islamabad, Pakistan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Khan", 
            "givenName": "Adeel Ahmad", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Pakistan Institute of Engineering and Applied Sciences", 
              "id": "https://www.grid.ac/institutes/grid.420112.4", 
              "name": [
                "Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), 45650, Nilore, Islamabad, Pakistan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zameer", 
            "givenName": "Aneela", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1080/09540091.2014.907555", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002634454"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cma.2016.11.021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005966756"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-015-2046-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006238847", 
              "https://doi.org/10.1007/s00521-015-2046-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1140/epjp/i2015-15238-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006516279", 
              "https://doi.org/10.1140/epjp/i2015-15238-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1108/hff-12-2014-0387", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009055074"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1022602019183", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009899114", 
              "https://doi.org/10.1023/a:1022602019183"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2015.10.015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014727621"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/oca.2228", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015581205"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2015.10.017", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017890860"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40064-016-3093-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018893238", 
              "https://doi.org/10.1186/s40064-016-3093-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s40064-016-3093-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018893238", 
              "https://doi.org/10.1186/s40064-016-3093-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2014.07.036", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019298799"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2016.08.079", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019452042"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.enconman.2016.12.032", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019846178"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2016.10.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020432662"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.molliq.2014.03.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020654942"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cma.2015.06.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021332614"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jtice.2014.10.018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024493488"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijheatmasstransfer.2014.08.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024679782"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-014-1762-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032317647", 
              "https://doi.org/10.1007/s00521-014-1762-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2014.08.055", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032612153"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2015.09.123", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034788959"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2677-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035029099", 
              "https://doi.org/10.1007/s00521-016-2677-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2677-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035029099", 
              "https://doi.org/10.1007/s00521-016-2677-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/09540091.2015.1092499", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035957286"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2400-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036489540", 
              "https://doi.org/10.1007/s00521-016-2400-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2400-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036489540", 
              "https://doi.org/10.1007/s00521-016-2400-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2014.10.036", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037694622"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijheatmasstransfer.2016.04.121", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040123999"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.apm.2014.11.024", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041146209"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2806-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042383188", 
              "https://doi.org/10.1007/s00521-016-2806-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.camwa.2016.06.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044162063"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ijthermalsci.2016.08.018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044500355"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.matcom.2016.08.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045162820"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.apm.2016.01.034", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047739158"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2016.09.032", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050578430"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jmmm.2014.08.021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051634845"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2547-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051905517", 
              "https://doi.org/10.1007/s00521-016-2547-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2547-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051905517", 
              "https://doi.org/10.1007/s00521-016-2547-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2530-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053716585", 
              "https://doi.org/10.1007/s00521-016-2530-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-016-2530-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053716585", 
              "https://doi.org/10.1007/s00521-016-2530-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/scientificamerican0792-66", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056571710", 
              "https://doi.org/10.1038/scientificamerican0792-66"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tnano.2015.2416318", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061713318"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1364/oe.24.024297", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1065209061"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2017.01.053", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1074242619"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.dsp.2017.02.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083699488"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-2949-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084022534", 
              "https://doi.org/10.1007/s00521-017-2949-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2017.03.028", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084060156"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-2991-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084517937", 
              "https://doi.org/10.1007/s00521-017-2991-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-2991-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084517937", 
              "https://doi.org/10.1007/s00521-017-2991-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-2991-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084517937", 
              "https://doi.org/10.1007/s00521-017-2991-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-2991-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084517937", 
              "https://doi.org/10.1007/s00521-017-2991-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-2991-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084517937", 
              "https://doi.org/10.1007/s00521-017-2991-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-3019-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085103769", 
              "https://doi.org/10.1007/s00521-017-3019-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-3019-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085103769", 
              "https://doi.org/10.1007/s00521-017-3019-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-3019-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085103769", 
              "https://doi.org/10.1007/s00521-017-3019-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-3019-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085103769", 
              "https://doi.org/10.1007/s00521-017-3019-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-3019-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085103769", 
              "https://doi.org/10.1007/s00521-017-3019-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-017-3019-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085103769", 
              "https://doi.org/10.1007/s00521-017-3019-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11277-017-4251-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085107665", 
              "https://doi.org/10.1007/s11277-017-4251-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11277-017-4251-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085107665", 
              "https://doi.org/10.1007/s11277-017-4251-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/01457632.2017.1320172", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085248120"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.icheatmasstransfer.2017.04.016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085398979"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1140/epjp/i2017-11658-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091402285", 
              "https://doi.org/10.1140/epjp/i2017-11658-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1140/epjp/i2017-11658-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091402285", 
              "https://doi.org/10.1140/epjp/i2017-11658-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2018.01.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100745472"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jtice.2018.05.046", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105225541"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-04-01", 
        "datePublishedReg": "2019-04-01", 
        "description": "In this work, novel application of integrated computational heuristics is presented for computational fluid mechanics problem arising in the study of heat transfer and thermal radiation in two-phase magnetohydrodynamic (MHD) fluid flow model involving nanoparticles using the accurate approximation ability of neural networks hybrid with global exploration of genetic algorithm aided with local search exploitation of sequential quadratic programming. The networks are designed and arbitrarily combined to formulate mean squared error-based objective function for solving and governing nonlinear nanofluidic system. The designed methodology is evaluated to study the dynamics of the system by means of velocities, temperature and concentration profiles for prevailing factors based on variation in Reynolds and Schmidt numbers, as well as, rotation, radiation, magnetic, thermophoretic and Brownian parameters. The pragmatic worth of the scheme is established through statistical inferences in terms of accuracy, convergence and complexity metrics.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00521-019-04157-1", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1104357", 
            "issn": [
              "0941-0643", 
              "1433-3058"
            ], 
            "name": "Neural Computing and Applications", 
            "type": "Periodical"
          }
        ], 
        "name": "Integrated intelligent computing for heat transfer and thermal radiation-based two-phase MHD nanofluid flow model", 
        "pagination": "1-33", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "5f2abc8b0c92c3d7b43ba074ee4a130c60aaa2fb3b9bf7a70bf5d0e38df06a0a"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00521-019-04157-1"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1113172949"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00521-019-04157-1", 
          "https://app.dimensions.ai/details/publication/pub.1113172949"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T14:03", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000371_0000000371/records_130831_00000006.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs00521-019-04157-1"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00521-019-04157-1'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00521-019-04157-1'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00521-019-04157-1'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00521-019-04157-1'


     

    This table displays all metadata directly associated to this object as RDF triples.

    248 TRIPLES      21 PREDICATES      75 URIs      16 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00521-019-04157-1 schema:about anzsrc-for:09
    2 anzsrc-for:0915
    3 schema:author N2157e0aa8aef402a90cec80ab3cfb8d6
    4 schema:citation sg:pub.10.1007/s00521-014-1762-2
    5 sg:pub.10.1007/s00521-015-2046-1
    6 sg:pub.10.1007/s00521-016-2400-y
    7 sg:pub.10.1007/s00521-016-2530-2
    8 sg:pub.10.1007/s00521-016-2547-6
    9 sg:pub.10.1007/s00521-016-2677-x
    10 sg:pub.10.1007/s00521-016-2806-6
    11 sg:pub.10.1007/s00521-017-2949-0
    12 sg:pub.10.1007/s00521-017-2991-y
    13 sg:pub.10.1007/s00521-017-3019-3
    14 sg:pub.10.1007/s11277-017-4251-y
    15 sg:pub.10.1023/a:1022602019183
    16 sg:pub.10.1038/scientificamerican0792-66
    17 sg:pub.10.1140/epjp/i2015-15238-6
    18 sg:pub.10.1140/epjp/i2017-11658-6
    19 sg:pub.10.1186/s40064-016-3093-5
    20 https://doi.org/10.1002/oca.2228
    21 https://doi.org/10.1016/j.apm.2014.11.024
    22 https://doi.org/10.1016/j.apm.2016.01.034
    23 https://doi.org/10.1016/j.asoc.2014.08.055
    24 https://doi.org/10.1016/j.asoc.2014.10.036
    25 https://doi.org/10.1016/j.asoc.2015.10.015
    26 https://doi.org/10.1016/j.asoc.2015.10.017
    27 https://doi.org/10.1016/j.asoc.2016.10.009
    28 https://doi.org/10.1016/j.asoc.2017.03.028
    29 https://doi.org/10.1016/j.asoc.2018.01.009
    30 https://doi.org/10.1016/j.camwa.2016.06.014
    31 https://doi.org/10.1016/j.cma.2015.06.010
    32 https://doi.org/10.1016/j.cma.2016.11.021
    33 https://doi.org/10.1016/j.dsp.2017.02.001
    34 https://doi.org/10.1016/j.enconman.2016.12.032
    35 https://doi.org/10.1016/j.icheatmasstransfer.2017.04.016
    36 https://doi.org/10.1016/j.ijheatmasstransfer.2014.08.004
    37 https://doi.org/10.1016/j.ijheatmasstransfer.2016.04.121
    38 https://doi.org/10.1016/j.ijthermalsci.2016.08.018
    39 https://doi.org/10.1016/j.jmmm.2014.08.021
    40 https://doi.org/10.1016/j.jtice.2014.10.018
    41 https://doi.org/10.1016/j.jtice.2018.05.046
    42 https://doi.org/10.1016/j.matcom.2016.08.002
    43 https://doi.org/10.1016/j.molliq.2014.03.002
    44 https://doi.org/10.1016/j.neucom.2014.07.036
    45 https://doi.org/10.1016/j.neucom.2015.09.123
    46 https://doi.org/10.1016/j.neucom.2016.08.079
    47 https://doi.org/10.1016/j.neucom.2016.09.032
    48 https://doi.org/10.1016/j.neucom.2017.01.053
    49 https://doi.org/10.1080/01457632.2017.1320172
    50 https://doi.org/10.1080/09540091.2014.907555
    51 https://doi.org/10.1080/09540091.2015.1092499
    52 https://doi.org/10.1108/hff-12-2014-0387
    53 https://doi.org/10.1109/tnano.2015.2416318
    54 https://doi.org/10.1364/oe.24.024297
    55 schema:datePublished 2019-04-01
    56 schema:datePublishedReg 2019-04-01
    57 schema:description In this work, novel application of integrated computational heuristics is presented for computational fluid mechanics problem arising in the study of heat transfer and thermal radiation in two-phase magnetohydrodynamic (MHD) fluid flow model involving nanoparticles using the accurate approximation ability of neural networks hybrid with global exploration of genetic algorithm aided with local search exploitation of sequential quadratic programming. The networks are designed and arbitrarily combined to formulate mean squared error-based objective function for solving and governing nonlinear nanofluidic system. The designed methodology is evaluated to study the dynamics of the system by means of velocities, temperature and concentration profiles for prevailing factors based on variation in Reynolds and Schmidt numbers, as well as, rotation, radiation, magnetic, thermophoretic and Brownian parameters. The pragmatic worth of the scheme is established through statistical inferences in terms of accuracy, convergence and complexity metrics.
    58 schema:genre research_article
    59 schema:inLanguage en
    60 schema:isAccessibleForFree false
    61 schema:isPartOf sg:journal.1104357
    62 schema:name Integrated intelligent computing for heat transfer and thermal radiation-based two-phase MHD nanofluid flow model
    63 schema:pagination 1-33
    64 schema:productId N17f063a5b8f74db5a0e58177b5f0cea3
    65 Nddaa4b418b9a486d94e13e76f291d0ef
    66 Ned2ec2d55a9c4cbe8d77b25112c01cd3
    67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113172949
    68 https://doi.org/10.1007/s00521-019-04157-1
    69 schema:sdDatePublished 2019-04-11T14:03
    70 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    71 schema:sdPublisher N315c6da6f44d4a379a71abb4dd3f0ab6
    72 schema:url https://link.springer.com/10.1007%2Fs00521-019-04157-1
    73 sgo:license sg:explorer/license/
    74 sgo:sdDataset articles
    75 rdf:type schema:ScholarlyArticle
    76 N1540d2486d414a708deaf8288a837e31 schema:affiliation https://www.grid.ac/institutes/grid.418920.6
    77 schema:familyName Raja
    78 schema:givenName Muhammad Asif Zahoor
    79 rdf:type schema:Person
    80 N17f063a5b8f74db5a0e58177b5f0cea3 schema:name readcube_id
    81 schema:value 5f2abc8b0c92c3d7b43ba074ee4a130c60aaa2fb3b9bf7a70bf5d0e38df06a0a
    82 rdf:type schema:PropertyValue
    83 N2157e0aa8aef402a90cec80ab3cfb8d6 rdf:first N1540d2486d414a708deaf8288a837e31
    84 rdf:rest N7272b2076a884ac88abe99cf7d253573
    85 N315c6da6f44d4a379a71abb4dd3f0ab6 schema:name Springer Nature - SN SciGraph project
    86 rdf:type schema:Organization
    87 N573369b71d5d41399949e0b30060a9de schema:affiliation https://www.grid.ac/institutes/grid.420112.4
    88 schema:familyName Zameer
    89 schema:givenName Aneela
    90 rdf:type schema:Person
    91 N7272b2076a884ac88abe99cf7d253573 rdf:first N73f2763c43284442824b3453ac02ddfa
    92 rdf:rest Ncf8aeeed0cf3423d8a9472a10156d052
    93 N73f2763c43284442824b3453ac02ddfa schema:affiliation https://www.grid.ac/institutes/grid.420112.4
    94 schema:familyName Mehmood
    95 schema:givenName Ammara
    96 rdf:type schema:Person
    97 N90c8f592519a451687fd7d811778807a rdf:first N573369b71d5d41399949e0b30060a9de
    98 rdf:rest rdf:nil
    99 Nc36c6e195dbf46dab19cab6a8ab32e4f schema:affiliation https://www.grid.ac/institutes/grid.445214.2
    100 schema:familyName Khan
    101 schema:givenName Adeel Ahmad
    102 rdf:type schema:Person
    103 Ncf8aeeed0cf3423d8a9472a10156d052 rdf:first Nc36c6e195dbf46dab19cab6a8ab32e4f
    104 rdf:rest N90c8f592519a451687fd7d811778807a
    105 Nddaa4b418b9a486d94e13e76f291d0ef schema:name doi
    106 schema:value 10.1007/s00521-019-04157-1
    107 rdf:type schema:PropertyValue
    108 Ned2ec2d55a9c4cbe8d77b25112c01cd3 schema:name dimensions_id
    109 schema:value pub.1113172949
    110 rdf:type schema:PropertyValue
    111 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    112 schema:name Engineering
    113 rdf:type schema:DefinedTerm
    114 anzsrc-for:0915 schema:inDefinedTermSet anzsrc-for:
    115 schema:name Interdisciplinary Engineering
    116 rdf:type schema:DefinedTerm
    117 sg:journal.1104357 schema:issn 0941-0643
    118 1433-3058
    119 schema:name Neural Computing and Applications
    120 rdf:type schema:Periodical
    121 sg:pub.10.1007/s00521-014-1762-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032317647
    122 https://doi.org/10.1007/s00521-014-1762-2
    123 rdf:type schema:CreativeWork
    124 sg:pub.10.1007/s00521-015-2046-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006238847
    125 https://doi.org/10.1007/s00521-015-2046-1
    126 rdf:type schema:CreativeWork
    127 sg:pub.10.1007/s00521-016-2400-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1036489540
    128 https://doi.org/10.1007/s00521-016-2400-y
    129 rdf:type schema:CreativeWork
    130 sg:pub.10.1007/s00521-016-2530-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053716585
    131 https://doi.org/10.1007/s00521-016-2530-2
    132 rdf:type schema:CreativeWork
    133 sg:pub.10.1007/s00521-016-2547-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051905517
    134 https://doi.org/10.1007/s00521-016-2547-6
    135 rdf:type schema:CreativeWork
    136 sg:pub.10.1007/s00521-016-2677-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1035029099
    137 https://doi.org/10.1007/s00521-016-2677-x
    138 rdf:type schema:CreativeWork
    139 sg:pub.10.1007/s00521-016-2806-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042383188
    140 https://doi.org/10.1007/s00521-016-2806-6
    141 rdf:type schema:CreativeWork
    142 sg:pub.10.1007/s00521-017-2949-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084022534
    143 https://doi.org/10.1007/s00521-017-2949-0
    144 rdf:type schema:CreativeWork
    145 sg:pub.10.1007/s00521-017-2991-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1084517937
    146 https://doi.org/10.1007/s00521-017-2991-y
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/s00521-017-3019-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085103769
    149 https://doi.org/10.1007/s00521-017-3019-3
    150 rdf:type schema:CreativeWork
    151 sg:pub.10.1007/s11277-017-4251-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1085107665
    152 https://doi.org/10.1007/s11277-017-4251-y
    153 rdf:type schema:CreativeWork
    154 sg:pub.10.1023/a:1022602019183 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009899114
    155 https://doi.org/10.1023/a:1022602019183
    156 rdf:type schema:CreativeWork
    157 sg:pub.10.1038/scientificamerican0792-66 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056571710
    158 https://doi.org/10.1038/scientificamerican0792-66
    159 rdf:type schema:CreativeWork
    160 sg:pub.10.1140/epjp/i2015-15238-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006516279
    161 https://doi.org/10.1140/epjp/i2015-15238-6
    162 rdf:type schema:CreativeWork
    163 sg:pub.10.1140/epjp/i2017-11658-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091402285
    164 https://doi.org/10.1140/epjp/i2017-11658-6
    165 rdf:type schema:CreativeWork
    166 sg:pub.10.1186/s40064-016-3093-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018893238
    167 https://doi.org/10.1186/s40064-016-3093-5
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1002/oca.2228 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015581205
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1016/j.apm.2014.11.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041146209
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1016/j.apm.2016.01.034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047739158
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1016/j.asoc.2014.08.055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032612153
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1016/j.asoc.2014.10.036 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037694622
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.1016/j.asoc.2015.10.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014727621
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.1016/j.asoc.2015.10.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017890860
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.1016/j.asoc.2016.10.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020432662
    184 rdf:type schema:CreativeWork
    185 https://doi.org/10.1016/j.asoc.2017.03.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084060156
    186 rdf:type schema:CreativeWork
    187 https://doi.org/10.1016/j.asoc.2018.01.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100745472
    188 rdf:type schema:CreativeWork
    189 https://doi.org/10.1016/j.camwa.2016.06.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044162063
    190 rdf:type schema:CreativeWork
    191 https://doi.org/10.1016/j.cma.2015.06.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021332614
    192 rdf:type schema:CreativeWork
    193 https://doi.org/10.1016/j.cma.2016.11.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005966756
    194 rdf:type schema:CreativeWork
    195 https://doi.org/10.1016/j.dsp.2017.02.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083699488
    196 rdf:type schema:CreativeWork
    197 https://doi.org/10.1016/j.enconman.2016.12.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019846178
    198 rdf:type schema:CreativeWork
    199 https://doi.org/10.1016/j.icheatmasstransfer.2017.04.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085398979
    200 rdf:type schema:CreativeWork
    201 https://doi.org/10.1016/j.ijheatmasstransfer.2014.08.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024679782
    202 rdf:type schema:CreativeWork
    203 https://doi.org/10.1016/j.ijheatmasstransfer.2016.04.121 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040123999
    204 rdf:type schema:CreativeWork
    205 https://doi.org/10.1016/j.ijthermalsci.2016.08.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044500355
    206 rdf:type schema:CreativeWork
    207 https://doi.org/10.1016/j.jmmm.2014.08.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051634845
    208 rdf:type schema:CreativeWork
    209 https://doi.org/10.1016/j.jtice.2014.10.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024493488
    210 rdf:type schema:CreativeWork
    211 https://doi.org/10.1016/j.jtice.2018.05.046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105225541
    212 rdf:type schema:CreativeWork
    213 https://doi.org/10.1016/j.matcom.2016.08.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045162820
    214 rdf:type schema:CreativeWork
    215 https://doi.org/10.1016/j.molliq.2014.03.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020654942
    216 rdf:type schema:CreativeWork
    217 https://doi.org/10.1016/j.neucom.2014.07.036 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019298799
    218 rdf:type schema:CreativeWork
    219 https://doi.org/10.1016/j.neucom.2015.09.123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034788959
    220 rdf:type schema:CreativeWork
    221 https://doi.org/10.1016/j.neucom.2016.08.079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019452042
    222 rdf:type schema:CreativeWork
    223 https://doi.org/10.1016/j.neucom.2016.09.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050578430
    224 rdf:type schema:CreativeWork
    225 https://doi.org/10.1016/j.neucom.2017.01.053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074242619
    226 rdf:type schema:CreativeWork
    227 https://doi.org/10.1080/01457632.2017.1320172 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085248120
    228 rdf:type schema:CreativeWork
    229 https://doi.org/10.1080/09540091.2014.907555 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002634454
    230 rdf:type schema:CreativeWork
    231 https://doi.org/10.1080/09540091.2015.1092499 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035957286
    232 rdf:type schema:CreativeWork
    233 https://doi.org/10.1108/hff-12-2014-0387 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009055074
    234 rdf:type schema:CreativeWork
    235 https://doi.org/10.1109/tnano.2015.2416318 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061713318
    236 rdf:type schema:CreativeWork
    237 https://doi.org/10.1364/oe.24.024297 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065209061
    238 rdf:type schema:CreativeWork
    239 https://www.grid.ac/institutes/grid.418920.6 schema:alternateName COMSATS Institute of Information Technology
    240 schema:name Department of Electrical Engineering, COMSATS Institute of Information Technology, 43600, Attock, Pakistan
    241 rdf:type schema:Organization
    242 https://www.grid.ac/institutes/grid.420112.4 schema:alternateName Pakistan Institute of Engineering and Applied Sciences
    243 schema:name Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), 45650, Nilore, Islamabad, Pakistan
    244 Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan
    245 rdf:type schema:Organization
    246 https://www.grid.ac/institutes/grid.445214.2 schema:alternateName Allama Iqbal Open University
    247 schema:name Department of Physics, Allama Iqbal Open University, Islamabad, Pakistan
    248 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...