COMPARATIVE STUDY OF THERMAL CHARACTERISTICS OF METALLIC AND NONMETALLIC WATER-BASED NANOFLUIDS IN A SQUARE CAVITY

Heat transfer fluids are a dynamic factor that affects the costs and size of heat exchangers. However, low thermal properties of accessible coolants like water and oils place a setback on the growth of heat transfer to attain high-performance cooling. The paper presents a numerical analysis of a comparative study on thermal characteristics of Al2O3, CuO, AlN, and SiC water-based nanofluids in a square cavity. The cavity is surrounded by a hot moving horizontal plate, an adiabatic vertical wall on the right, and the left vertical and lower horizontal sides by cold isothermal walls. The governing equations were solved using finite approximation techniques to assess the thermal characteristics of the four different nanofluids in the enclosure with varying sizes of nanoparticles in the range of 1% ≤ φ ≤ 10%. The results reveal that CuO has a different pattern of heat characteristics compared to other nanofluids. CuO has the highest Nusselt number of 58.4715, and Al2O3 has the least value of 58.4634 at a 10 % volume fraction. Nanoparticle size has a substantial influence on the thermal attributes of the four nanofluids. This work indicates that different nanofluids have satisfactory thermal characteristics than based fluid water, which determines its applications. Keywords Cavity, Nanofluids, Natural convection, Heat transfer enhancement DOI: 10.7176/JIEA/11-2-08 Publication date: September 30th 202

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end