Analysis and Improvement of the Hot Disk Transient Plane Source Method for Low Thermal Conductivity Materials

The hot disk transient plane source (TPS) method is a widely used standard technique (ISO 22007-2) for the characterization of thermal properties of materials, especially the thermal conductivity, k. Despite its well-established reliability for a wide variety of common materials, the hot disk TPS method is also known to suffer from a substantial systematic errors when applied to low-k thermal insulation materials. Here, we present a combined numerical and experimental study on the influence of the geometry of hot disk sensor on measured value of low-k materials. We demonstrate that the error is strongly affected by the finite thickness and thermal mass of the sensor's insulation layer was well as the corresponding increase of the effective heater size beyond the radius of the embedded metal heater itself. We also numerically investigate the dependence of the error on the sample thermal properties, confirming that the errors are worse in low-k samples. A simple correction function is also provided, which converts the apparent (erroneous) result from a standard hot disk TPS measurement to a more accurate value. A standard polyimide sensor was also optimized using both wet and dry etching to provide more accurate measurement directly. Experimentally corrected value of k for Airloy x56 aerogel and a commercial silica aerogel using the numerical correction factor derived based on the standard TPS sensor is in excellent agreement with the directly measured value from the TPS sensor using the optimized polyimide sensor. Both of these methods can reduce the errors to less than 4% as compared to around 40% error of overestimation from raw values measured with the pristine sensor. Such results show that both the numerical correction to a pristine senor or an optimized sensor are capable of providing highly accurate value of thermal conductivity for such materials.Comment: 76 pages, 11 figure

ASME International Mechanical Engineering Congress and Exposition

ABSTRACT Two-phase microchannel heat sinks are promising for the cooling of high power VLSI chips, in part because they can alleviate spatial temperature variations, or hotspots. Hotspots increase the maximum junction temperature for a given total chip power, thereby degrading electromigration reliability of interconnects and inducing strong variations in the signal delay on the chip. This work develops a modeling approach to determine the impact of conduction and convection on hotspot cooling for a VLSI chip attached to a microchannel heat sink. The calculation approach solves the steady-state twodimensional heat conduction equations with boundary conditions of spatially varying heat transfer coefficient and water temperature profile. These boundary conditions are obtained from a one-dimensional homogeneous two-phase model developed in previous work, which has been experimentally verified through temperature distribution and total pressure drop measurements. The new simulation explores the effect of microchannels on hotspot alleviation for 20 mm × 20 mm silicon chips subjected to spatially varying heat generation totaling 150 W. The results indicate that a microchannel heat sink of thickness near 500 µm can yield far better temperature uniformity than a copper spreader of thickness 1.5 mm

Predicting supercooling of phase change materials in arbitrarily varying conditions

Phase change materials are promising for thermal energy storage; however, one major bottleneck for their practical implementation is their unclear supercooling behaviors. In this work, we introduce a framework to predict the degree of supercooling for a phase change material subject to arbitrary geometrical and thermal conditions by analyzing the phase change material's intrinsic nucleation characteristics with a statistical model. The prediction capability of our framework is successfully validated with experiments using magnesium chloride hexahydrate as a phase change material. For a system with a uniform temperature distribution, our framework can predict the average degree of supercooling. For a general case such as phase change materials embedded in a heat sink, the framework can accurately predict the expected time, with less than 8% deviation, for nucleation under given conditions. This work provides important insights in understanding and predicting supercooling behavior, thereby providing guidelines for the optimal design of phase change material-based thermal energy storage applications