A Micro-Metal Inserts Based Microchannel Heat Sink for Thermal Management of Densely Packed Semiconductor Systems

The thermal management of high-heat-density devices is essential for reliable operation. In this work, a novel procedure is proposed and investigated for the efficient thermal management of such devices. The proposed procedure introduces different arrangements of metal inserts within a cooling channel heat sink. The objective of those inserts is to form boundary layers to prevent any hot spots from appearing within the flow and increase temperature uniformity. Five different arrangements are introduced and numerically investigated using the commercial software package ANSYS FLUENT 2021R1. The model was validated against previous findings and showed a good agreement with errors of less than 5.5%. The model was then used to study the heat transfer characteristics of the proposed cases compared to traditional straight channels under the same operating conditions. All the proposed arrangements displayed better heat transfer characteristics than the traditional configuration within the studied range. They also exhibited lower temperature nonuniformities, implying better temperature distribution. The temperature contours over the heat source top surface and the flow streamlines are also introduced. Among all the proposed arrangements cases, a microchannel with micro metal insert located at the top wall along with a second row of inserts covering two-thirds of the bottom wall is studied. This case achieved the best heat transfer characteristics and highest temperature uniformity, making it a viable candidate for high power density devices’ thermal management

Prediction-Based Lossless Image Compression

In this paper, a lossless image compression technique using prediction errors is proposed. To achieve better compression performance, a novel classifier which makes use of wavelet and Fourier descriptor features is employed. Artificial neural network (ANN) is used as a predictor. An optimum ANN configuration is determined for each class of the images. In the second stage, an entropy encoding is performed on the prediction errors which improve the compression performance further. The prediction process is made lossless by making the predicted values as integers both at the compression and decompression stages. The proposed method is tested using three types of datasets, namely CLEF med 2009, COREL1 k and standard benchmarking images. It is found that the proposed method yields good compression ratio values in all these cases and for standard images, the compression ratio values achieved are higher compared to those obtained by the known algorithms