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Asif Afzal

Researcher at P A College of Engineering

Publications -  234
Citations -  5070

Asif Afzal is an academic researcher from P A College of Engineering. The author has contributed to research in topics: Diesel fuel & Biodiesel. The author has an hindex of 23, co-authored 156 publications receiving 1653 citations. Previous affiliations of Asif Afzal include Glocal University.

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A novel battery thermal management system using nano-enhanced phase change materials

TL;DR: In this article, the authors presented a novel modified battery module configuration employing two-layer nanoparticle enhanced phase change materials (nePCM), and compared the cooling performance of proposed battery thermal management systems (BTMS) at an ambient temperature ranging from 30°C to 40°C with external natural convection conditions.
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The effects of graphene oxide nanoparticle additive stably dispersed in dairy scum oil biodiesel-diesel fuel blend on CI engine: performance, emission and combustion characteristics

TL;DR: In this article, the effects of graphene oxide nanoparticles on performance and emissions of a CI engine fueled with dairy scum oil biodiesel was studied, and an ideal graphene-to-surfactant ratio was defined, highest absolute value UV-absorbency was seen for a mass fraction of 1:4.
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An overview on the effect of ultrasonication duration on different properties of nanofluids

TL;DR: In this article, a review of the effect of ultrasonication on different properties of nanofluids are confined. But, the authors did not find that the longer ultrasonation duration was not better in all cases where best performance was obtained for an optimum duration of Ultrasonication, and they found that with an increased sonication time/energy, reduces the particle size and aids in obtaining a better dispersion leading to enhancement of stability, thermal conductivity and reducing viscosity.
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Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

TL;DR: In this paper , a review of machine learning techniques employed in the nanofluid-based renewable energy system, as well as new developments in machine learning research, is presented.