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Author

Asif Afzal

Other affiliations: Glocal University
Bio: 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.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
15 Mar 2021-Energy
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.

209 citations

Journal ArticleDOI
15 Jan 2021-Energy
TL;DR: In this paper, the performance and emission characteristics of a modified common rail direct injection (CRDI) diesel engine fueled by Ricinus communis biodiesel (RCME20), diesel (80%), and their blends with strontium-zinc oxide (Sr@ZnO) nanoparticle additives were evaluated.

139 citations

Journal ArticleDOI
01 Dec 2019-Fuel
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.

137 citations

Journal ArticleDOI
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.
Abstract: Preparation of nanofluid is of prime importance to obtain better thermal and physical properties. Different preparation parameters used in nanofluid preparation sometimes perform contrarily even if prepared with same nanoparticles and base fluid. Stability, thermal conductivity, and viscosity of the nanofluid are significantly affected by the cluster (agglomerate) size of nanoparticles in the base fluid which deteriorate thermal performance. In order to break the agglomerates and improve the dispersion of nanoparticles, ultrasonication is a more prevalent method. Nanofluids react differently for different sonication time and the reaction of the nanofluid with the change in sonication time varies for different nanofluids, which is dependent on various factors. In this regard, research works pertinent to the effect of ultrasonication on different properties of nanofluids are confined. In this paper, review of investigations carried out on experimentally evaluated ultrasonication effects on thermal properties and various physical properties of nanofluid. It is found that with an increased sonication time/energy, reduces the particle size and thus aids in obtaining a better dispersion leading to enhancement of stability, thermal conductivity and reducing viscosity. However, the longer ultrasonication duration was not found to be better in all cases where best performance was obtained for an optimum duration of ultrasonication.

114 citations

Journal ArticleDOI
13 Jun 2022
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.
Abstract: Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

114 citations


Cited by
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Journal ArticleDOI
01 Jul 1968-Nature
TL;DR: The Thermophysical Properties Research Literature Retrieval Guide as discussed by the authors was published by Y. S. Touloukian, J. K. Gerritsen and N. Y. Moore.
Abstract: Thermophysical Properties Research Literature Retrieval Guide Edited by Y. S. Touloukian, J. K. Gerritsen and N. Y. Moore Second edition, revised and expanded. Book 1: Pp. xxi + 819. Book 2: Pp.621. Book 3: Pp. ix + 1315. (New York: Plenum Press, 1967.) n.p.

1,240 citations

Journal ArticleDOI

1,011 citations

01 Jan 2016
TL;DR: The principles of enhanced heat transfer is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for reading principles of enhanced heat transfer. As you may know, people have look numerous times for their chosen books like this principles of enhanced heat transfer, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their desktop computer. principles of enhanced heat transfer is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the principles of enhanced heat transfer is universally compatible with any devices to read.

553 citations

01 Jan 2010
TL;DR: In this article, the authors present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices, which is low-cost, wireless, and incrementally deployable within existing buildings.
Abstract: Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

489 citations

01 Jan 2014
TL;DR: In this paper, the authors investigated the engine performance and emissions produced from Jatropha curcas, Ceiba pentandra and Calophyllum inophyllus biodiesel in compressed ignition engine.
Abstract: Biodiesel is a recognized replacement for diesel fuel in compressed ignition engines due to its significant environmental benefits. The purpose of this study is to investigate the engine performance and emissions produced from Jatropha curcas, Ceiba pentandra and Calophyllum inophyllum biodiesel in compressed ignition engine. The biodiesel production process and properties are discussed and a comparison of the three biodiesels as well as diesel fuel is undertaken. After that, engine performance and emissions testing was conducted using biodiesel blends 10%, 20%, 30% and 50% in a diesel engine at full throttle load. The engine performance shows that those biodiesel blends are suitable for use in diesel engines. A 10% biodiesel blend shows the best engine performance in terms of engine torque, engine power, fuel consumption and brake thermal efficiency among the all blending ratios for the three biodiesel blends. Biodiesel blends have also shown a significant reduction in CO2, CO and smoke opacity with a slight increase in NOx emissions.

254 citations