MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641083868 A) filed by Marri Laxman Reddy Institute Of Technology And Management; Guru Nanak University, Ibrahimpatnam; Sir M. Visvesvaraya Institute Of Technology; Dr. Naresh Kumar; Dr Mani Ramanuja; Dr. Supriya Kumar Paul; and Dr. V. Naga Radhika on July 08, 2026, for An Intelligent System And Method For Optimizing Fluid Flow In Superhydrophobic Channel Systems Using Predictive Modeling.
Inventors include Marri Laxman Reddy Institute Of Technology And; Guru Nanak University, Ibrahimpatnam; Sir M. Visvesvaraya Institute Of Technology; Dr. Naresh Kumar; Dr Mani Ramanuja; Dr. Supriya Kumar Paul; and Dr. V. Naga Radhika.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Abstract Super-hydrophobic channel systems have emerged as a promising solution for reducing drag and enhancing fluid transport efficiency in microfluidic and macro-scale applications. These surfaces, characterized by high contact angles and low adhesion, significantly alter flow behavior by introducing slip boundary conditions and minimizing viscous resistance. However, optimizing flow performance in such systems remains a complex challenge due to the nonlinear interactions between surface properties, channel geometry, and operating conditions. This study proposes a smart optimization framework based on Artificial Neural Network (ANN) techniques to model and predict fluid flow behavior in super-hydrophobic channels. The ANN is trained using a comprehensive dataset generated from computational fluid dynamics (CFD) simulations and experimental observations, capturing key parameters such as surface roughness, contact angle, pressure gradient, and flow velocity. The trained model demonstrates high accuracy in predicting flow characteristics and identifying optimal configurations for maximum efficiency and minimal energy loss. Furthermore, the integration of ANN enables rapid evaluation of design alternatives, significantly reducing computational cost compared to traditional simulation methods. The results highlight the potential of intelligent data-driven approaches in advancing the design of next-generation fluidic systems, with applications in lab-on-chip devices, biomedical engineering, and energy-efficient transport systems.
Disclaimer: Curated by HT Syndication.
