Milad Sadeghzadeh

Bio: Milad Sadeghzadeh is an academic researcher from University of Tehran. The author has contributed to research in topics: Exergy & Nanofluid. The author has an hindex of 24, co-authored 62 publications receiving 1576 citations.

A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids

Abstract: Nanofluids are broadly applied in energy systems such as solar collector, heat exchanger and heat pipes. Dynamic viscosity of the nanofluids is among the most important features affecting their thermal behavior and heat transfer ability. Several predictive models, by employing various methods such as Artificial Neural Network, Support Vector Machine and mathematical correlations, have been proposed for estimating dynamic viscosity based on the influential factors such as size, type and volume fraction of nano particles and their temperature. The precision of the models depends on different elements such as the employed approach for modeling, input variables and the structure of the model. In order to have an accurate model for estimating the dynamic viscosity, it is necessary to consider all of the affecting factor. In this regard, the current study aim to review the researches concerns the applications of machine learning methods for dynamic viscosity modeling of nanofluids in order to provide deeper insight for the scientists. According to the reviewed scientific sources, the structure of model, such as number of neurons and layers in artificial neural network (ANN), the applied activation function, and utilized algorithm are the most influential factors on the accuracy of the model. Moreover, based on the studies considered both ANN and mathematical correlations, ANNs are more accurate and confident for estimating the nanofluids’ dynamic viscosity. The majority of the studies in this field used temperature and concentration of nanofluids as input data for their models, while size of nanostructures and shear rate are considered in some researches in addition to mentioned variables.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

The Audit Explosion

Abstract: Offers a comprehensive critique of the spread of auditing in both the public and private sectors and shows how to achieve a better balance between audits and other forms of accountability. Michael Power is Professor of Accounting at the LSE. ‘A rare attempt to stand back and question the very notion of auditing and the place it has assumed in our society.’ Financial Times

Smart Textiles for Electricity Generation.

Abstract: Textiles have been concomitant of human civilization for thousands of years. With the advances in chemistry and materials, integrating textiles with energy harvesters will provide a sustainable, environmentally friendly, pervasive, and wearable energy solution for distributed on-body electronics in the era of Internet of Things. This article comprehensively and thoughtfully reviews research activities regarding the utilization of smart textiles for harvesting energy from renewable energy sources on the human body and its surroundings. Specifically, we start with a brief introduction to contextualize the significance of smart textiles in light of the emerging energy crisis, environmental pollution, and public health. Next, we systematically review smart textiles according to their abilities to harvest biomechanical energy, body heat energy, biochemical energy, solar energy as well as hybrid forms of energy. Finally, we provide a critical analysis of smart textiles and insights into remaining challenges and future directions. With worldwide efforts, innovations in chemistry and materials elaborated in this review will push forward the frontiers of smart textiles, which will soon revolutionize our lives in the era of Internet of Things.

Fundamentals Of Engineering Thermodynamics

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