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This study investigates heat transfer enhancement in magneto-nanofluidic systems through the strategic placement of adiabatic blocks in grooved channels. Using CuO–H2O nanofluid in a bottom-heated channel with circular expansion, we examine the complex interactions between forced convection, magnetic fields, and uoyancy effects. Through systematic numerical analysis, we explore the combined influences of Rayleigh, Reynolds, and Hartmann numbers on thermal performance. Our findings reveal significant heat transfer enhancement (up to 137 %) under optimal conditions, particularly with vertical magnetic field orientation at Re = 100 and Ha = 30. The results demonstrate how adiabatic blocks modify flow structures, with larger blocks diminishing vortex intensity while elevated Ra generates secondary vortices that interact with primary circulations. Magnetic field effects show notable dependence on orientation, with vertical fields generally promoting better heat transfer than horizontal configurations. To complement the numerical analysis, we develop a predictive model using Artificial Neural Network (ANN) for Nusselt numbers across various operating conditions, achieving over 99 % accuracy. The integrated computational-ANN approach offers significant advancements in optimizing thermal systems in various areas, ranging from electronics cooling to microfluidic devices.
This paper is about the gas turbine cooling model using the extended jet array. The aim of the present study is to explain the effect of the impinging jet configuration of elongated jet holes on the heat transfer distribution with a deep Convolutional Neural Network (CNN). CNNs preserve spatial relationships through convolutional operations—unlike traditional Artificial Neural Networks (ANNs), which require flattening of input data—allowing for higher accuracy with smaller datasets and faster training due to fewer trainable parameters. The goal is to understand how accurate and fast deep learning models with limited data can deliver predictions for complex systems such as jet impingement cooling. The proposed CNN model was compared with the numerical and experimental data. Jet impingement cooling was examined with four different Reynolds numbers (16250, 21700, 27100, 36250) and six dimensionless gaps between the jet and the target surface (G/D = 1, 2, 3, 4, 5, and 6). Mean and local Nusselt number (Nu) distributions were investigated in particular. G/D = 2 was selected as the test data for the model among the configurations. While the CFD model failed to identify this peak performance point, the CNN model accurately captured this maximum behavior despite being trained only on other configurations. Moreover, the CNN model demonstrated more suitable prediction accuracy in the first two jet impingement regions than the CFD data, especially in capturing peak Nusselt numbers and localized hot spots. It has been determined that the CNN model provided more accurate predictions of the local Nusselt numbers within the measurement region compared to the Computational Fluid Dynamics (CFD) study for the average Nu, the proposed CNN model showed a closer agreement to experimental data than numerical simulation by 4.15 % at the Re = 32500 and G/D = 2.
In this work, thermomagnetic convection and irreversibility production in a hybrid nanofluid-filled wavy-walled porous thermal system containing a semi-circular heated bottom is presented. Both the sidewalls of the enclosure are cooled and undulated with varying undulation numbers. The lower wall is partially undulated following a semi-circular-shaped object and is heated isothermally. The horizontal walls are insulated. The cavity is occupied with Cu-Al2O3/water-based hybrid nanofluid and porous substances under the impact of the evenly applied horizontal magnetic field. This work significantly contributes to the existing research rendering an exhaustive understanding of the hydrothermal flow-physics as well as irreversibility production of a hybrid nanofluid in the cavity having surface undulation. The Galerkin weighted finite element method is utilized to solve the mathematical model. The hydrothermal performance of the thermal system is considerably influenced by various pertinent factors such as Darcy-Rayleigh number, Darcy number, Hartmann number, and number of undulations. The wall undulations have a critical role in altering the hydrothermal performance. Heatlines are used to analyse heat transport dynamics from the protruded hot surface to the heat sink. The protruded heater wall induces the formation of a hot upward plume in the nearest fluid layers. The flow divides into two parts forming a pair of circulations due to symmetrical cooling at the sidewalls. The flow behaviours are significantly dampened by increasing the Hartmann number. The associated total entropy generation is also demonstrated. This study contributes to the existing domain knowledge and provides insights for designing and optimizing similar thermal systems.
Lens systems have a major influence on the image due to effects such as depth of field or optical aberrations. The only method to simulate these effects precisely is to trace rays through an actual lens system. This provides accurate results, but only with high computational effort. To speed up the ray tracing through the lens system, various acceleration methods have been developed, requiring considerable precomputations. We present a new method based on the Realistic Camera by Kolb et. al.. Instead of tracing each ray through the lens system, the rays are precomputed once and stored in a lookup table. In contrast to other methods, our method is simple, and does not require substantial preprocessing upfront. We can simulate complex effects such as chromatic aberrations accurately in real-time, regardless the number of lens surfaces in the system. Our method achieves the same performance as state-of-the-art methods like Polynomial Optics, while maintaining the same number of samples per pixel.