Book Chaptershttp://hdl.handle.net/20.500.14131/5482024-02-03T19:29:19Z2024-02-03T19:29:19Z10 - Other nanoparticles transport interactionsEl-Amin, Mohamed F.http://hdl.handle.net/20.500.14131/11252023-11-16T10:05:03Z2023-06-23T00:00:00Z10 - Other nanoparticles transport interactions
El-Amin, Mohamed F.
This chapter presents some the most special aspects of nanoparticles interactions, e.g., nanoparticle cotransport and/or interaction with nonaqueous phase liquids (NAPLs) and nanoparticles–polymers transport in porous media and nanoparticles associated with heat transfer. The concept of stability of nanoparticles suspensions is discussed in Section 10.2, while Section 10.3 presents the nanoparticles interaction with NAPL transport. After that Section 10.4 introduces the polymer transport under magnetic field in porous media with analytical solutions. The nanoparticles interactions with heat transfer are discussed in Section 10.5. Finally, the nanofluids in boundary layer flow are discussed in Section 10.6 and similarity solutions are introduced in both analytical and numerical modes.
2023-06-23T00:00:00Z8 - Magnetic nanoparticles transport in porous mediaEl-Amin, Mohamed F.http://hdl.handle.net/20.500.14131/11242023-11-16T09:57:22Z2023-06-23T00:00:00Z8 - Magnetic nanoparticles transport in porous media
El-Amin, Mohamed F.
This chapter presents the mathematical modeling of magnetic nanoparticle transport with single-phase flow in porous media under the effect of an external magnetic field. We developed a mathematical model for the transport of magnetic nanoparticles in a two-phase flow, followed by the development of a corresponding numerical solution. Finally, we introduce analytical solutions to the single-phase case. The rest of this chapter is organized as follows: Section 8.3 presents the mathematical modeling of the transport of magnetic nanoparticles; while Section 8.4 focuses on the single-phase case, the two-phase case is provided in Section 8.5 with numerical solutions. Finally, analytical solutions are presented in Section 8.6.
2023-06-23T00:00:00Z9 - Nano-ferrofluids transport in porous mediaEl-Amin, Mohamed F.http://hdl.handle.net/20.500.14131/11232023-11-16T09:56:33Z2023-06-23T00:00:00Z9 - Nano-ferrofluids transport in porous media
El-Amin, Mohamed F.
Nano-ferrofluids type is one of the prospective applications of nanoparticles that work with the magnetic field. This chapter will discuss nano-ferrofluids transport in porous media. This chapter presents the properties of ferrofluids without repeating the main equations that presented in Chapter 8. The ferrofluids transport in single-phase flow has been introduced and possible analytical solution for some cases. After that, the modeling of nonisothermal ferrofluids transport in porous media has been established, and an appropriate numerical algorithm has been developed. Finally, the model and numerical method of ferrofluids transport in two-phase flow are provided.
2023-06-23T00:00:00Z11 - Machine learning techniques for nanoparticles transportEl-Amin, Mohamed F.http://hdl.handle.net/20.500.14131/11222023-11-16T09:55:55Z2023-06-23T00:00:00Z11 - Machine learning techniques for nanoparticles transport
El-Amin, Mohamed F.
Machine learning is a branch of artificial intelligence concerned with creating and developing algorithms that enable computers to learn behaviors or patterns from empirical data. The aim of this chapter is the implementation of machine learning algorithms in predicting nanoparticle transport in the oil reservoir. We used Jupyter Notebook for the implementation, which utilizes Python programming language. Jupyter Notebook is an open-source web tool that allows you to write live code while creating statistics and machine learning models. This chapter contains selected machine learning techniques that can be used for nanoparticle transport in porous media. It starts with the fundamentals of a number of machine learning methods, followed by basic metrics that are frequently used. After that, we discuss datasets and their analysis. Finally, we explain how to implement machine learning techniques in the Jupyter Notebook environment using Python programming language.
2023-06-23T00:00:00Z