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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 study investigates aluminum heat sinks integrated with GR42 phase change material (PCM), aiming to enhance thermal inertia and stabilize device temperatures. This is important, since the increasing miniaturization and performance demands for electronic devices have led in a growing need for advanced thermal management systems capable of handling rapid transient loads. The novelty of the work lies in examining different heat sink layouts that optimize the heat dissipation capabilities of GR42 PCMs while minimizing size and weight for electronic cooling. The experimental testing was conducted using three configurations, namely no fin, circular fin and hexagonal fin, with three input powers (4 W, 8 W, and 12 W). The experimental results show that the hexagonal-fin heat sink, surface area is 25 % more than circular fins, exhibited superior thermal performance without significant variation in input power. Notably, the hexagonal-fin heat sink achieved the desired set point temperature of 55 ◦C in 33 % less time than the circular-fin design and 45 % less time than the heat sink without fins. Furthermore, the peak temperatures increased up to 21 % for circular fins. During the charging cycles, the enhancement ratios vary from 72 % and begin to narrow to 25 % during discharge cycles. The hexagonal-fin configuration also exhibited superior melting dynamics, completing the phase transition 44 % faster at higher input power than the circular fins, and established a thermal deviation of nearly 51 % less than finless heat sinks. These findings underscore the critical role of fin geometry and PCM integration in achieving uniform temperature distribution and improved energy storage efficiency. Overall, the hexagonal-fin heat sink with GR42 PCM shows strong potential as a passive cooling solution for low-power portable electronic devices.
Optimization of hybrid solar chimney power plants (HSCPPs): A review of multi-objective approaches
(2025)
The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 minutes to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.
A numerical study is carried out meticulously to scrutinize the impact of different shapes of chimneys like circular (outer dia, dc), convergent (outer dia, 0.5dc), divergent (outer dia, 1.5dc), sudden contraction (outer dia, 0.5dc), and sudden expansion (outer dia, 1.5dc) on the performance of an SCPP. Furthermore, the parametric impact with different chimney divergence angles (CDA, ϕ), and ground absorber slope angle (GSA, γ) on the SCPP performance is also scrutinized. Optimum divergence angle (ϕ=+0.75◦) enhances the power generation up to ~ 47% (76 kW) with a horizontal ground absorber surface. An increase or decrease in CDA lessens the power generation. With a sloped ground absorber angle γ=0.6◦, the gain in power generation is 60% (82 kW). The study of combination of ground sloped absorber (γ=0.6◦) and divergent chimney (ϕ=+0.75◦) shows enhancement of the power generation upto 80% (92 kW) more than the classical Manzaranes plant.