Recent Submissions

  • Chapter 1: Traditional Modeling of Fluid Flow and Transport Phenomena, in the Book: Fractional Modelling of Fluid Flow and Transport Phenomena

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elseiver, 2024-07-31)
    This chapter provides an introductory overview of the key concepts and principles in fluid mechanics. It begins by exploring the properties and classifications of fluids. We then present the fluid motion equations, covering the principles of mass, momentum, energy conservation, and solute transport. As common cases, the inviscid flow, Euler's equations, and Bernoulli's principle are included, illustrating fundamental concepts in fluid dynamics. The chapter also emphasizes the significance of dimensional analysis. This powerful tool simplifies complex fluid dynamics problems and helps identify parallels between disparate systems. Following this, we examine boundary layer theory, essential for understanding fluid behavior in proximity to solid surfaces. Additionally, the chapter introduces the concept of non-Newtonian fluids. Finally, we discuss the fundamentals of flow in porous media. This includes an overview of Darcy's law, various dispersion models, and the dynamics of two-phase and multiphase flows within porous structures.
  • 10 - Other nanoparticles transport interactions

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    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.
  • 8 - Magnetic nanoparticles transport in porous media

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    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.
  • 9 - Nano-ferrofluids transport in porous media

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    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.
  • 11 - Machine learning techniques for nanoparticles transport

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    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.
  • 12 - Applications of nanoparticles in porous media

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    Some applications of using nanoparticle transport in porous media are presented in this chapter. The applications include using nanoparticles in enhanced oil recovery in addition to the very wide area of application of nanoparticles in the field of heat transfer. Moreover, the combination of nanoparticles and surfactants is also presented. Another very recent application is the harvesting of atmosphere water with aid of using nanoparticles. Also, we discussed the carbon dioxide capture by nanoporous materials and its sequestration in the geological underground. Another important application presented of nanofluid in porous media is the metal hydride hydrogen storage.
  • 7 - Nanoparticles transport in anisotropic porous media

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    Anisotropy of porous media is an essential feature in subsurface formations. In this chapter, nanoparticle transport in anisotropic porous media will be discussed. The next section presents the nature of the anisotropic porous media. After that, we introduce the mathematical modeling of the flow in anisotropic porous media. Moreover, the model of nanoparticle transport in anisotropic porous media has been developed. Then, the numerical techniques that are appropriate for anisotropic porous media have been discussed, particularly the multipoint flux approximation, followed by a numerical example.
  • 6 - Nanoparticles transport in fractured porous media

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    This chapter discusses the modeling of flow fractured porous media. It covers the most common fundamental approaches and presents their physical, mathematical and numerical aspects. Several approaches are introduced including the dual-continuum, boundary conditions, shape factor, and discrete fracture model (DFM). After that, we focus on the DFM for nanoparticle transport in single-phase flow and two-phase flow. Numerical multiscale time-splitting scheme has been developed to solve the DFM model. Finally, the hybrid embedded fracture model has been discussed.
  • 5: Iterative schemes and convergence analysis

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    Iterative approaches are frequently used to solve intricate coupled and highly nonlinear systems. As stated in the previous chapters the mathematical model that governed the nanoparticles transport in porous media consists of equations of pressure, nanoparticles concentration, deposited nanoparticles concentration on the pore-walls, and entrapped nanoparticles concentration in pore-throats. In the case of the two-phase flow, the saturation equation is also considered. Iterative methods are often employed to solve such kinds of time-dependent complicated systems. The nonlinear iterative numerical scheme such as iterative Implicit Pressure Concentration, and the iterative Implicit Pressure Explicit Saturation–Implicit Concentration (IMPES-IMC) has been introduced to solve the model under consideration. The iterative IMP-IMC scheme is devoted to solving the problem of nanoparticles transport with single-phase flow in porous media, while the iterative IMPES-IMC treats the two-phase flow case. This chapter focuses on the iterative methods and investigates their theoretical and numerical convergence. Moreover, a theoretical foundation for the convergence of the iterative approach has been proved using the mathematical induction method.
  • 4 - Temporal numerical discretization schemes

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    This chapter is concerned with the numerical methods frequently used for temporal discretization in the problems of nanoparticles transport in porous media. The forward and backward Euler difference schemes are presented in the following section. Then, the Courant–Friedrichs–Lewy (CFL) stability condition was introduced. After that, we discussed the possibility of using a multiscale time-splitting scheme. Also, we defined the relaxation factor and how it can be used with the CFL condition. Then, we presented the Implicit Pressure Implicit Concentration scheme as well as the Implicit Pressure Explicit Saturation Implicit Concentration scheme. Finally, a stability analysis for the Implicit Pressure Explicit Saturation scheme has been provided.
  • 3 - Spatial numerical discretization methods for nanoparticles transport in porous media

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    Nowadays, computational methods are becoming increasingly more of a third science research means, parallel with experimental and theoretical methods. Especially in oil engineering and the research of groundwater flow and transport phenomena, numerical simulation is turning the most essential method, due to the fast development of computers. Generally, when adopting numerical simulation to research problems, the first step is establishing a mathematical model according to some physical laws of the problems. The second procedure is discretizing the mathematical model, followed by the third step, which is to code and run it on the computer to get the results. Finally, we can understand the original problem through simulation results. In this chapter, we introduce numerical methods that will be used for spatial discretization in nanoparticle transport in porous media. This chapter starts with mesh generation using MATLAB, including uniform and nonuniform 1D/2D/3D grids. After that, we introduce the cell-centered finite difference method (CCFD), including the discretization of the pressure equation, Darcy's law, and how to treat the boundary conditions. Then, the vectored implementation (shifting-matrix) of the CCFD method was presented. Therefore, the harmonic mean of permeability and transmissibility matrices has been listed. Moreover, the finite element method (FEM) is discussed by highlighting its discretization and weak formulation. The theoretical foundation of the FEM requires presenting Raviart–Thomas space. Also, the mixed FEM has been discussed with some numerical examples as it is essential in solving partial differential equations (PDEs) that govern the flow and transport in porous media.
  • 2 - Dimensional analysis and analytical solutions

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-23)
    An analytical solution helps in the validation of numerical methods/solutions as well as the comprehension of mechanisms and physical effects. Analytical solutions of the problem of flow in porous media, magnetic flow in porous media, nanoparticles transport in porous media, and magnetic nanoparticles transport in porous media are addressed in this chapter. Also, dimensional analysis is vital in handling problems regardless of their actual dimensions, which is helpful in large-scale problems such as hydrocarbon reservoirs. Therefore, this chapter also introduces a simplified one-dimensional model of nanoparticle transport in porous media and its generalized nondimensional form that will be solved analytically and numerically to obtain physical insight.
  • 1 - Basic concepts and modeling aspects

    El-Amin, Mohamed F.; No Collaboration; Energy Lab; 0; 0; NSMTU; 0; El-Amin, Mohamed F. (Elsevier, 2023-06-01)
    This chapter presents basic concepts related to the mathematical modeling of nanoparticle transport in porous media that will be used throughout this book. The next section of the introduction includes the continuum theory of fluid flow, followed by the flow in porous media. Then, the rock and fluid physical properties are presented. The mathematical modeling of single-phase and two-phase flow in porous media is discussed. Finally, the modeling of nanoparticles in porous media, which is mainly used in this book, with all aspects and MATLAB code for validation against experimental data is stated.
  • A bibliometric analysis of GCC healthcare digital transformation

    Brahimi, Tayeb; Department Collaboration; Energy Lab; 0; 0; NSMTU; 0; brahimi, Tayeb (Elsevier Inc, 2023-05-24)
    From diagnosing to preventing the spread of coronavirus, digital transformation and innovative technology have demonstrated their ability to play a key role in every aspect of the COVID-19 pandemic. Today, digital transformation goes beyond the application of artificial intelligence (AI) to increase productivity; it is currently reaching the large population in the Gulf Cooperation Council (GCC) and has a significant impact on both work and daily life. This study aims to evaluate the GCC’s various contributions to scientific publications on digital transformation, focusing on the methods used to combat the COVID-19 pandemic and protect community well-being, including the most recent AI applications for COVID-19 safety measures, symptom detection, and remote healthcare. The research methodology used in this study is based on bibliometric analysis, a collection of strategies for analyzing vast amounts of bibliographic data by combining mathematical, statistical, and computer techniques. A set of publications is retrieved from three databases, Scopus, Web of Science (WoS), and Then, VOS viewer is used to extract quantitative publication metrics and visualize coexisting networks of key terms extracted during the last 5years. This study focuses on the Scopus database while the WoS and the Lens databases are left for the user as an active learning process with some research directions in exploring bibliometric analysis in healthcare and digital transformation. From 2017 to 2021, 1520 healthcare, AI, and digital transformation documents were retrieved from the Scopus database using the journals’ abstract, title, and keywords “TILE-ABSKEY” sections. Results show that the total number of published documents in the GCC in healthcare and AI increased from 107 papers in 2017 to 720 papers in 2021. Furthermore, the number of citations jumped from 44 in 2017 to more than 4600 in 2021. The most active country was Saudi Arabia, followed by United Arab Emirates (UAE), Qatar, Oman, Kuwait, and Bahrain. Three of the top five most active institutions were from Saudi Arabia—King Saud University, King Abdulaziz University, and Imam Abdulrahman University—followed by the University of Sharjah from the UAE and Qatar University. Out of the 1520 documents retrieved, 20.6% were published in medicine, 18.6% in computer science, and 9.4% in engineering. Our findings indicate that AI and healthcare research are generally well established within each country, with more advancement in Saudi Arabia and UAE, but need more collaborative research between the GCC. This study provides a comprehensive overview of the bibliometric analysis of GCC healthcare digital transformation and AI, which may help researchers, policymakers, and practitioners better understand healthcare needs and development within the GCC
  • Powerful Mathematica Codes for Goodness-of-Fit Tests for Censored Data

    Kittaneh, Omar; No Collaboration; NA; 0; 0; NSMTU; 0; Kittaneh, Omar (Springer Nature, 2022)
    In reliability studies of energy and electrical systems life data are often censored, because life tests are terminated, and life data are analyzed before the failure of all sample units. The most important task to accomplish a successful reliability analysis is to choose, through statistical goodness-of-fit tests, the correct or nearly correct probability distribution to describe the failure mechanism of given experimental data. However, due to censoring, this task would not be as easy as testing complete samples. Unfortunately, the built-in functions and codes of the available computation programs are not valid to test for incomplete or censored samples and give completely wrong results if they are used for that purpose, even on the most sophisticated ones like Mathematica and MATLAB. On the other hand, there is a high chance to slip up when trying to perform this type of tests by someone with humble probabilistic and mathematical background. Correct performance of such tests requires a deep knowledge in how to treat the estimating equations of the candidate distribution’s parameters from a censored sample. This type of equations is usually implicit, which often needs a careful numerical treatment to be successfully solved. Also, we should keep in mind that the test statistics formulas of censored samples are different from those of complete samples. The corresponding critical value of the test must be modified according to the type of the distribution nominated, the degree of censoring, and the complete sample size. Therefore, there is a crucial need to have codes that safely run the tests and give reliable results. This book chapter is devoted to introducing efficient Mathematica codes for two of the best goodness-of-fit tests for censored data, the Cramér–von Mises and Anderson-Darling tests for Weibull and lognormal distributions, which are useful in a great variety of applications in energy studies, particularly as models for product life. The codes are presented together with some practical examples extracted from the literature in various topics of energy systems and related fields.
  • Stress, anxiety, and depression among students and employees during the pandemic

    Brahimi, Tayeb; Bathallath, Joudi; No Collaboration; Energy Lab; 1; NSMTU; Bathallath, Joudi (Routledge, 2021-09-30)
    Psychological and social implications due to COVID-19 pandemic are particularly relevant in Higher Education Institutions (HEI). The objective of this chapter is to examine and analyze the level of depression, anxiety, and stress between university students and employees at the HEI in Saudi Arabia due to COVID-19. The method used in this study is based on a narrative review of recent literature on related mental health symptoms and interventions due to the pandemic and a survey conducted on 51 students and 72 employees using the Patient Health Questionnaire for depression, Generalized Anxiety Disorder (GAD), and Perceived Stress Scale (PSS). Results show that psychological well-being is crucial to overcoming COVID-19 and avoiding mental illness and emotional coping. Higher levels of stress, anxiety, and depression were found to be higher in adults than adolescents. The study concluded that there is a need for mental health awareness regarding COVID-19, and it is suggested that there should be an online therapy session with people who have severe levels of stress, anxiety, and depression.
  • Aerodynamic Analysis and Performance Prediction of VAWT and HAWT Using CARDAAV and Qblade Computer Codes

    Brahimi, Tayeb; Praschivoiu, Ion; External Collaboration; Energy Lab; NSMTU; Brahimi, Tayeb (IntechOpen, 2021-04-21)
    Wind energy researchers have recently invited the scientific community to tackle three significant wind energy challenges to transform wind power into one of the more substantial, low-cost energy sources. The first challenge is to understand the physics behind wind energy resources better. The second challenge is to study and investigate the aerodynamics, structural, and dynamics of large-scale wind turbine machines. The third challenge is to enhance grid integration, network stability, and optimization. This chapter book attempts to tackle the second challenge by detailing the physics and mathematical modeling of wind turbine aerodynamic loads and the performance of horizontal and vertical axis wind turbines (HAWT & VAWT). This work underlines success in the development of the aerodynamic codes CARDAAV and Qbalde, with a focus on Blade Element Method (BEM) for studying the aerodynamic of wind turbines rotor blades, calculating the induced velocity fields, the aerodynamic normal and tangential forces, and the generated power as a function of a tip speed ration including dynamic stall and atmospheric turbulence. The codes have been successfully applied in HAWT and VAWT machines, and results show good agreement compared to experimental data. The strength of the BEM modeling lies in its simplicity and ability to include secondary effects and dynamic stall phenomena and require less computer time than vortex or CFD models. More work is now needed for the simulation of wind farms, the influence of the wake, the atmospheric wind flow, the structure and dynamics of large-scale machines, and the enhancement of energy capture, control, stability, optimization, and reliability.