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<p>The importance of nanoparticle aggregation extends to numerous technical applications, including microfluidic systems, thermal control, and thermal exchangers’ devices using nanofluids. The focus of the proposed research is to explore the impact of nanoparticles agglomerations with the significance of thermal radiation on titania–ethylene glycol nanofluid flow past a Riga wedge. Moreover, the effects of homogeneous-heterogeneous chemical reactions are also vital aspects of this investigation. Further, the adjusted Krieger–Dougarty and Maxwell–Bruggeman models applied to access aggregation of nanoparticles. The Runge–Kutta scheme combined with the shooting method is operated to analyze the characteristics of the flow field followed by the suitable transformation used for the conversion of dimensional governing equations to their corresponding non-dimensional form. However, the main focus of this work is enhancing rate coefficients through artificial neural network (ANN)-based regression analysis, supported by the Levenberg–Marquardt algorithm. The variation in rate coefficients with different parameters is presented in tabular form, comparing the presence of nanoparticles in aggregated and non-aggregated environments. The main outcomes are the velocity profile decreases with a higher solid volume fraction and increased porosity parameter results in a higher velocity. Also, nanoparticles without aggregation demonstrate a stronger influence than those that undergo aggregation.</p>

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Neural computing analysis of rate coefficients in nanofluid flow over a wedge with nanoparticle aggregation effect: a machine learning approach

  • 期刊论文

  • 发表日期：

    2025年6月14日

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  • P. K. Pattnaik,
  • S. R. Mishra,
  • Abhishek Sharma,
  • Ram Prakash Sharma

摘要

The importance of nanoparticle aggregation extends to numerous technical applications, including microfluidic systems, thermal control, and thermal exchangers’ devices using nanofluids. The focus of the proposed research is to explore the impact of nanoparticles agglomerations with the significance of thermal radiation on titania–ethylene glycol nanofluid flow past a Riga wedge. Moreover, the effects of homogeneous-heterogeneous chemical reactions are also vital aspects of this investigation. Further, the adjusted Krieger–Dougarty and Maxwell–Bruggeman models applied to access aggregation of nanoparticles. The Runge–Kutta scheme combined with the shooting method is operated to analyze the characteristics of the flow field followed by the suitable transformation used for the conversion of dimensional governing equations to their corresponding non-dimensional form. However, the main focus of this work is enhancing rate coefficients through artificial neural network (ANN)-based regression analysis, supported by the Levenberg–Marquardt algorithm. The variation in rate coefficients with different parameters is presented in tabular form, comparing the presence of nanoparticles in aggregated and non-aggregated environments. The main outcomes are the velocity profile decreases with a higher solid volume fraction and increased porosity parameter results in a higher velocity. Also, nanoparticles without aggregation demonstrate a stronger influence than those that undergo aggregation.