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LBM-MHD Data-Driven Approach to Predict Rayleigh–Bénard Convective Heat Transfer by Levenberg–Marquardt Algorithm

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Název: LBM-MHD Data-Driven Approach to Predict Rayleigh–Bénard Convective Heat Transfer by Levenberg–Marquardt Algorithm
Autoři: Taasnim Ahmed Himika, MD Farhad Hasan, Md Mamun Molla, Md Amirul Islam Khan
Rok vydání: 2023
Sbírka: La Trobe University (Melbourne): Figshare
Témata: Mathematical sciences, lattice Boltzmann, Rayleigh–Bénard convection, magnetohydrodynamics, Levenberg– Marquardt algorithm, data-driven analysis, Nusselt number, Hartmann number, porosity, rectangular cavity
Popis: This study aims to consider lattice Boltzmann method (LBM)–magnetohydrodynamics (MHD) data to develop equations to predict the average rate of heat transfer quantitatively. The present approach considers a 2D rectangular cavity with adiabatic side walls, and the bottom wall is heated while the top wall is kept cold. Rayleigh–Bénard (RB) convection was considered a heat-transfer phenomenon within the cavity. The Hartmann (Ha) number, by varying the inclination angle (θ), was considered in developing the equations by considering the input parameters, namely, the Rayleigh (Ra) numbers, Darcy (Da) numbers, and porosity (ϵ) of the cavity in different segments. Each segment considers a data-driven approach to calibrate the Levenberg–Marquardt (LM) algorithm, which is highly linked with the artificial neural network (ANN) machine learning method. Separate validations have been conducted in corresponding sections to showcase the accuracy of the equations. Overall, coefficients of determination (R2) were found to be within 0.85 to 0.99. The significant findings of this study present mathematical equations to predict the average Nusselt number (Nu¯). The equations can be used to quantitatively predict the heat transfer without directly simulating LBM. In other words, the equations can be considered validations methods for any LBM-MHD model, which considers RB convection within the range of the parameters in each equation.
Druh dokumentu: article in journal/newspaper
Jazyk: unknown
DOI: 10.26181/22144511.v1
Dostupnost: https://doi.org/10.26181/22144511.v1
https://figshare.com/articles/journal_contribution/LBM-MHD_Data-Driven_Approach_to_Predict_Rayleigh_B_nard_Convective_Heat_Transfer_by_Levenberg_Marquardt_Algorithm/22144511
Rights: CC BY 4.0
Přístupové číslo: edsbas.D6D3A4AE
Databáze: BASE
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: LBM-MHD Data-Driven Approach to Predict Rayleigh–Bénard Convective Heat Transfer by Levenberg–Marquardt Algorithm
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Taasnim+Ahmed+Himika%22">Taasnim Ahmed Himika</searchLink><br /><searchLink fieldCode="AR" term="%22MD+Farhad+Hasan%22">MD Farhad Hasan</searchLink><br /><searchLink fieldCode="AR" term="%22Md+Mamun+Molla%22">Md Mamun Molla</searchLink><br /><searchLink fieldCode="AR" term="%22Md+Amirul+Islam+Khan%22">Md Amirul Islam Khan</searchLink>
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2023
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: La Trobe University (Melbourne): Figshare
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  Data: <searchLink fieldCode="DE" term="%22Mathematical+sciences%22">Mathematical sciences</searchLink><br /><searchLink fieldCode="DE" term="%22lattice+Boltzmann%22">lattice Boltzmann</searchLink><br /><searchLink fieldCode="DE" term="%22Rayleigh–Bénard+convection%22">Rayleigh–Bénard convection</searchLink><br /><searchLink fieldCode="DE" term="%22magnetohydrodynamics%22">magnetohydrodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Levenberg–+Marquardt+algorithm%22">Levenberg– Marquardt algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22data-driven+analysis%22">data-driven analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Nusselt+number%22">Nusselt number</searchLink><br /><searchLink fieldCode="DE" term="%22Hartmann+number%22">Hartmann number</searchLink><br /><searchLink fieldCode="DE" term="%22porosity%22">porosity</searchLink><br /><searchLink fieldCode="DE" term="%22rectangular+cavity%22">rectangular cavity</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This study aims to consider lattice Boltzmann method (LBM)–magnetohydrodynamics (MHD) data to develop equations to predict the average rate of heat transfer quantitatively. The present approach considers a 2D rectangular cavity with adiabatic side walls, and the bottom wall is heated while the top wall is kept cold. Rayleigh–Bénard (RB) convection was considered a heat-transfer phenomenon within the cavity. The Hartmann (Ha) number, by varying the inclination angle (θ), was considered in developing the equations by considering the input parameters, namely, the Rayleigh (Ra) numbers, Darcy (Da) numbers, and porosity (ϵ) of the cavity in different segments. Each segment considers a data-driven approach to calibrate the Levenberg–Marquardt (LM) algorithm, which is highly linked with the artificial neural network (ANN) machine learning method. Separate validations have been conducted in corresponding sections to showcase the accuracy of the equations. Overall, coefficients of determination (R2) were found to be within 0.85 to 0.99. The significant findings of this study present mathematical equations to predict the average Nusselt number (Nu¯). The equations can be used to quantitatively predict the heat transfer without directly simulating LBM. In other words, the equations can be considered validations methods for any LBM-MHD model, which considers RB convection within the range of the parameters in each equation.
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  Data: article in journal/newspaper
– Name: Language
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  Data: unknown
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.26181/22144511.v1
– Name: URL
  Label: Availability
  Group: URL
  Data: https://doi.org/10.26181/22144511.v1<br />https://figshare.com/articles/journal_contribution/LBM-MHD_Data-Driven_Approach_to_Predict_Rayleigh_B_nard_Convective_Heat_Transfer_by_Levenberg_Marquardt_Algorithm/22144511
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  Data: edsbas.D6D3A4AE
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      – Type: doi
        Value: 10.26181/22144511.v1
    Languages:
      – Text: unknown
    Subjects:
      – SubjectFull: Mathematical sciences
        Type: general
      – SubjectFull: lattice Boltzmann
        Type: general
      – SubjectFull: Rayleigh–Bénard convection
        Type: general
      – SubjectFull: magnetohydrodynamics
        Type: general
      – SubjectFull: Levenberg– Marquardt algorithm
        Type: general
      – SubjectFull: data-driven analysis
        Type: general
      – SubjectFull: Nusselt number
        Type: general
      – SubjectFull: Hartmann number
        Type: general
      – SubjectFull: porosity
        Type: general
      – SubjectFull: rectangular cavity
        Type: general
    Titles:
      – TitleFull: LBM-MHD Data-Driven Approach to Predict Rayleigh–Bénard Convective Heat Transfer by Levenberg–Marquardt Algorithm
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            NameFull: Taasnim Ahmed Himika
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            NameFull: MD Farhad Hasan
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            NameFull: Md Mamun Molla
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            NameFull: Md Amirul Islam Khan
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              M: 01
              Type: published
              Y: 2023
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