The integration of artificial neural networks (ANN) with incompressible smoothed particle hydrodynamics (ISPH) modeling offers new insights on heat and mass transfer effects from exothermic chemical reactions within complex geometries, such as rectangular annuli, especially concerning nano-enhanced phase change materials (NEPCM).
Recent advancements have highlighted the significant role of double diffusive processes driven by differing density gradients and diffusion. A groundbreaking study conducted by Allakany, Alsedias, and Aly outlines the impact of exothermic reactions alongside Soret-Dufour numbers on the behavior of NEPCM inside porous annuli. This innovative research aims to address pressing challenges within thermal systems, particularly those related to optimizing heat transfer to improve energy efficiency for applications including electronic device cooling and the performance of heat exchangers.
Utilizing the ISPH method, the research presents detailed numerical simulations to understand how various dimensionless parameters influence the thermosolutal convection of NEPCM. The study indicates key findings where increased values of the Rayleigh number and buoyancy ratio lead to enhanced double diffusion efficiencies. Notably, the dimensionless Frank-Kamenetskii number is identified as pivotal, substantially supporting the temperature distributions within the annulus setup.
The authors elaborate, “The ANN model introduced a precise agreement of the prediction values with the actual values of \\overline{Nu} and \\overline{Sh}.” This assertion demonstrates the reliability of the ANN approach as it collaborates with ISPH data to yield effective predictive modeling outcomes for these significant thermal parameters.
Detailed analysis reveals how variations of the Frank-Kamenetskii number propel temperature distributions, shifting concentration strengths during double diffusion processes. The delineated findings indicate: “Increasing Fk boosts temperature distributions, shifting the location of Cr from the center area of the annulus toward the right cold walls.” This fundamental insight signifies the potential of optimizing thermal management techniques through strategic domain configurations.
With wide-ranging industrial applications, including thermal control for automotive systems, chemical processing, and solar technology development, the introduction of NEPCM and ANN modeling within complex geometries promises significant advancements. The combination of these methodologies enhances the ability to develop precisely tuned systems for effective energy utilization.
This pivotal work not only opens new pathways for research but also establishes foundational knowledge for future studies. The authors intend to explore the integration of heat and mass flux models with magnetic fields, pushing the boundaries of current thermal engineering analyses.
The research culminates by underlining the necessity to thoroughly investigate double diffusion dynamics, proposing enhanced ANN models equipped to tackle forthcoming challenges within complex thermal systems.