Experiments and numerical simulation of natural convection heat transfer with nanosuspensions are presented in this work. The investigations are carried out for three different types of nanosuspensions: namely, spherical-based (alumina/water), tubular-based (multi-walled carbon nanotube/water), and flake-based (graphene/water). A comparison with in-house experiments is made for all the three nanosuspensions at different volume fractions and for the Rayleigh numbers in the range of 7 × 105–1 × 107. Different models such as single component homogeneous, single component non-homogeneous, and multicomponent non-homogeneous are used in the present study. From the present numerical investigation, it is observed that for lower volume fractions (∼0.1%) of nanosuspensions considered, single component models are in close agreement with the experimental results. Single component models which are based on the effective properties of the nanosuspensions alone can predict heat transfer characteristics very well within the experimental uncertainty. Whereas for higher volume fractions (∼0.5%), the multi-component model predicts closer results to the experimental observation as it incorporates drag-based slip force which becomes prominent. The enhancement observed at lower volume fractions for non-spherical particles is attributed to the percolation chain formation, which perturbs the boundary layer and thereby increases the local Nusselt number values.
Lighting fixtures are finding a wide range of applications in Roadways, Sports lighting, Architectural lighting, Industries, etc. Lumen requirement for these applications is constantly increasing thereby augmenting the power needed and consequently the heat generation. To suffice these needs, high power density luminaries with lumens output in several thousand are used. Hitherto, thermal management of these luminaires was achieved through passive cooling with the help of heatsinks attached at the back of LEDs. Heatsinks utilized for this high-power density fixtures are relatively large to provide a higher surface area for heat transfer. With the larger heatsinks in the lighting system, the cost associated with packaging, mounting, manufacturing increases significantly. In addition, weight and EPA of these fixtures increases as well which has a negative impact on retrofit applications where existing infrastructure is designed for lighter weight products and replacing with higher weight and EPA product is not an optimal solution. To address these concerns, the present study focuses on utilizing an active cooling method with multiple fans placed in parallel to reduce the system size and weight. Several parameters such as fan speed, number of fins, fin height, input power, are varied to evaluate LED temperatures. Comparison is made with various design configurations and optimized design obtained through analysis is used for the final product development. Overall reduction in the weight and cost associated is then discussed in details in the summary.
Signify is focused on building a state-of-the-art, innovative and sustainable solutions for industries, homes, buildings, and communities. In the last decade or so there has been a considerable shift from using traditional incandescent luminaires to highly efficient, cheaper, and robust LED (Light Emitting Diode) based lighting fixtures. LEDs are semiconductor devices and thus their life depends largely on operating temperatures. Thermal management of the lighting fixture, therefore, becomes crucial for the overall performance. Heat sinks are designed for given operating conditions for better thermal management. With the improved LED efficiencies there are two alternatives that the product designer can opt for namely, to increase the lumen output for the present fixture or to reduce the overall heat sink size. To assist the product designer in this aspect, the present paper reports the thermal management of lighting luminaries using two different modelling techniques such as Finite Volume Method (FVM) and One Dimensional (1D) resistive network analysis. These two modelling techniques are employed to predict the temperature profiles on the luminaire and then compared them with the actual test results. The processing time, accuracy, and method of implementation for both these techniques are then discussed.
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