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Heat Transfer Analysis of Nanofluid Flow in a Rotating System with Magnetic Field Using an Intelligent Strength

Kamsing Nonlaopon1, Naveed Ahmad Khan2, Muhammad Sulaiman2

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Summary

This study explores heat transfer in rotating two-phase nanofluid flow under magnetic fields. A novel neural network approach accurately models nanofluid behavior, validating its robustness.

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Nusselt numberartificial intelligenceheat transferhorizontal platesmagnetic fieldskin-friction coefficientsoft computingtwo-phase nanofluid flow

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Area of Science:

  • Fluid Dynamics
  • Heat Transfer
  • Nanotechnology

Background:

  • Investigating heat transfer in nanofluids is crucial for advanced cooling systems.
  • Rotating systems with magnetic fields present complex fluid dynamics challenges.
  • Understanding two-phase flow behavior is essential for optimizing thermal performance.

Purpose of the Study:

  • To model and analyze heat transfer in a two-phase nanofluid flow between horizontal plates.
  • To incorporate the effects of rotation, magnetic fields, and external forces.
  • To develop and validate an efficient computational technique for analyzing nanofluid dynamics.

Main Methods:

  • Formulation of governing continuity and momentum equations.
  • Application of similarity transformations to reduce partial differential equations (PDEs) to ordinary differential equations (ODEs).
  • Development of a feed-forward neural network (FFNN) with a back-propagated Levenberg-Marquardt (BLM) algorithm.

Main Results:

  • The FFNN-BLM algorithm accurately predicts velocity, temperature, and concentration profiles.
  • Validation metrics (MAD, ENSE, TIC) show near-zero errors, confirming algorithm accuracy.
  • Comparison with LSM, NARX-LM, and RKFM demonstrates the proposed method's robustness and efficiency.

Conclusions:

  • The FFNN-BLM algorithm is a robust and accurate method for analyzing complex nanofluid heat transfer.
  • The study provides valuable insights into nanofluid behavior under combined external forces.
  • This research contributes to the development of advanced thermal management solutions.