Hybrid nanofluid flow along a conical gap geometry considering nanoparticle shapes and Riga disk

Abstract

The disk-cone geometry has significant applications in bioengineering and medical technologies, including Rheometers, conical diffusers, viscometers, and biomedical devices. This study investigates the influence of nanoparticle shapes on the flow dynamics and thermal performance of a hybrid nanofluid composed of iron oxide ( F e 3 O 4 ) and titanium oxide ( T i O 2 ) nanoparticles suspended in ethylene glycol ( C 2 H 6 O 2 ) as the base fluid. The analysis is conducted within a conical gap formed by a Riga disk-cone geometry (RDCG) under varying rotational and contra-rotational conditions. The Riga disk's electromagnetic properties are incorporated to examine their impact on fluid behavior. Five distinct nanoparticle shapes namely, spheres, bricks, blades, cylinders, and platelets are considered to evaluate their effects on velocity and heat transfer profiles. The governing equations of the system are transformed into a self-similar form using appropriate similarity mappings and numerically solved with the b v p 5 c method in MATLAB R2023a. The solution datasets are subsequently utilized to train a Levenberg-Marquardt backpropagation neural network (LMB-NN) to model and validate the system's behavior. The model demonstrates high reliability, with minimal absolute errors for critical parameters, confirmed through regression analysis, error histograms, and mean squared error (MSE) evaluations. Results reveal that increased rotational speeds of the Riga disk and cone significantly enhance the convective heat transfer, optimizing the cooling efficiency. Moreover, the contra-rotational motion maximizes velocity profiles for brick-shaped nanoparticles, whereas blade-shaped nanoparticles yield the highest peaks in heat transfer rates. These results highlight the critical role of nanoparticle shapes in optimizing hybrid nanofluid performance for advanced bioengineering applications. • The study highlights that nanoparticle shapes significantly influence the velocity and heat transfer in hybrid nanofluids within a Riga disk-cone configuration. • Sphere-shaped nanoparticles achieve the highest convective heat transfer rates in small rotations, while blade-shaped nanoparticles perform best under large rotational conditions. • The Levenberg-Marquardt backpropagation neural network (LMB-NN) accurately models the system's behavior, validating the numerical results with minimal errors. • The findings support advanced cooling systems and biomedical applications, emphasizing the Riga disk's utility in medical devices and drug delivery technologies

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