The Tribological Properties and Mathematical Analysis of Nanofluids

Dear Colleagues,

Nanoscience is the study of manipulating or engineering matter, particles, and structures on the nanoscale scale, which is the scale of atoms and molecules. Nanotechnology is a technology that is used in nanoscience research to create custom-made materials and products with improved qualities, new types of smart medicines and sensors, new nanoelectronic components and brain structures, and even interfaces between electronic components and biological and molecular components as well as neural networks.

Machine learning methods are one of the engineering tools used in data prediction and prediction. Thanks to their powerful algorithms, they have s higher predictive ability compared to traditional mathematical modeling tools. Machine learning algorithms are widely used in many fields, including energy, medicine, manufacturing, finance, and economics.

In the context of nanoscience and nanotechnology, mathematical modelling, coding, or the simulation of nanomaterials and nanosized neural networks play an important role in the study of various physical, biological, and chemical properties. Thus, the applications of mathematics in nano- and neuroscience are gaining momentum as the mutual benefits of this collaboration become increasingly obvious.

The aim of this Special Issue is to investigate the optimization of nanomaterials, which are applied in many fields, including the field of energy, via modeling and simulations using various machine learning algorithms. The investigation of the usability of machine learning algorithms in various energy applications made with nano-sized materials and the simulation models that have been developed will play a leading role in both scientific research and in industry. We encourage the presentation of numerical and applied studies conducted using machine learning algorithms.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• The thermophysical properties of nanofluids;
• The modeling of single-phase and multi-phase nanofluid flows;
• Energies of nanomaterials and neural networks;
• Heat transfer phenomena of nanofluids;
• The interaction between the biological organisms inside a cell;
• Mathematical modeling in neural network calculation;
• Mathematical calculations and neural networks;
• Computational intelligence and mathematical models;
• Neural computing, neural engineering, and artificial intelligence;
• Neural control and neural networks analysis;
• Modeling of single-phase and multi-phase nanofluid flows.

Dr. Anum Shafiq
Dr. Andaç Batur Çolak
Guest Editors

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