RSS 1.0Natural Science, Math and Tech Unit (NSMTU)
Recent Submissions
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Chapter 5: Numerical Methods for Solving Fractional PDEs in the Book: Fractional Modelling of Fluid Flow and Transport PhenomenaThis chapter provides an extensive review of numerical methods tailored for solving fractional-order ordinary differential equations (ODEs), fractional partial differential equations (FPDEs), and systems of such equations. We discuss several numerical schemes, including explicit and implicit finite difference, Galerkin and mixed finite element, spectral element methods, and meshless methods, highlighting their application to both time and space fractional-order PDEs. Starting with the adaptation of classical numerical techniques, such as the Euler method and Runge-Kutta methods, to the fractional calculus framework, demonstrating their effectiveness through the introduction of fractional versions like the Fractional Forward Euler Method and Explicit Fractional Order Runge-Kutta (EFORK) methods, through examples and pseudocodes.
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Chapter 4: Analytical Solutions of Fractional PDEs, in the Book: Fractional Modelling of Fluid Flow and Transport PhenomenaThis chapter introduces the analytical solutions of fractional partial differential equations (PDEs), focusing on their significance in modeling transport phenomena that exhibit anomalous diffusion or non-local dynamics. The chapter begins by exploring power-series methods for solving fractional differential equations (FDEs), illustrating the technique through examples such as gas flow in porous media and boundary-layer flow. It then transitions to the Adomian Decomposition Method (ADM), a semi-analytical approach that simplifies the solution of nonlinear, fractional-order differential equations. Through detailed examples, including the time-fractional convection-conduction equation, the time-fractional diffusion-reaction equation, time and time-space fractional advection-diffusion equation, the chapter showcases the versatility and efficiency of ADM in handling complex fractional PDEs. Finally, the diffusion equation with time Caputo-Fabrizio fractional derivative has been solved using Laplace transform method.
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Chapter 3: Fundamentals of Fractional Modeling of Fluid Flow, of the Book: Fractional Modelling of Fluid Flow and Transport PhenomenaAs the primary focus of this book is employing fractional modeling in the study of fluid dynamics and transport phenomena, this chapter presents the basics of fractional modeling of fluid flow. It begins with exploring fractional differential equations and discusses their advantages and challenges. Subsequent sections focus on the derivations of fractional-order formulations for conserving mass and momentum. The chapter also introduces the derivation of the fractional energy conservation equation, including models for heat conduction, heat convection-conduction, and general transport phenomena. Additionally, the discussion extends to fluid flow in porous media, featuring adaptations of Darcy’s Law that incorporate time and space memory effects and address anomalous diffusion processes.
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Chapter 12: Fractional Models in Renewable Energy Systems, in the Book: Fractional Modelling of Fluid Flow and Transport PhenomenaThis chapter explores the application of fractional calculus in modeling and controlling renewable energy systems, including wind energy, solar energy, and biochemical reactions. For wind turbines, it has been used in the modeling of turbulent flows and improves control systems through fractional-order PID controllers. In solar energy, it refines the thermal dynamics models of solar heating systems, while in biochemical processes, it offers a detailed analysis of reaction kinetics in anaerobic digestion.
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Chapter 2: Fundamentals of Fractional CalculusThis chapter presents the concepts of fractional calculus used in the field of fluid mechanics required in the rest of the book. The chapter begins with an overview and then introduces preliminary concepts crucial for understanding fractional calculus, including the Gamma and Beta functions, the Mittag-Leffler function, and various fractional operators. These foundational elements are essential for grasping the more complex aspects of fractional calculus in fluid mechanics. Moreover, the chapter examines different fractional derivative models, providing the basic definition of several key types. These include the Riemann-Liouville, Caputo, Grünwald-Letnikov, Caputo-Fabrizio, and Atangana-Baleanu fractional derivatives. Each model is explored, offering insights into their unique characteristics and applications. Also, a significant portion of the chapter is dedicated to the Laplace transform, which covers its definition, basic principles, and properties, along with a list of common Laplace transforms and techniques for applying the inverse Laplace transform. This comprehensive coverage equips readers with the tools to use the Laplace transform in various contexts of fractional calculus. The chapter ends with exercises designed to reinforce the concepts covered.
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Traditional Modeling of Fluid Flow and Transport PhenomenaThis 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.
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Chapter 2: Fundamentals of Fractional Calculus, of the book: Fractional Modelling of Fluid Flow and Transport PhenomenaThis chapter presents the concepts of fractional calculus used in the field of fluid mechanics required in the rest of the book. The chapter begins with an overview and then introduces preliminary concepts crucial for understanding fractional calculus, including the Gamma and Beta functions, the Mittag-Leffler function, and various fractional operators. These foundational elements are essential for grasping the more complex aspects of fractional calculus in fluid mechanics. Moreover, the chapter examines different fractional derivative models, providing the basic definition of several key types. These include the Riemann-Liouville, Caputo, Grünwald-Letnikov, Caputo-Fabrizio, and Atangana-Baleanu fractional derivatives. Each model is explored, offering insights into their unique characteristics and applications. Also, a significant portion of the chapter is dedicated to the Laplace transform, which covers its definition, basic principles, and properties, along with a list of common Laplace transforms and techniques for applying the inverse Laplace transform. This comprehensive coverage equips readers with the tools to use the Laplace transform in various contexts of fractional calculus. The chapter ends with exercises designed to reinforce the concepts covered.
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Traditional Modeling of Fluid Flow and Transport Phenom- enaThis 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 equa- tions, 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.
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Prediction of Adsorption and Desorption Isotherms for Atmospheric Water HarvestingThis study improves the mathematical modeling for the potential of atmospheric water harvesting (AWH) using desiccant materials. This research is crucial in highlighting the sustainable water management strategies of AWH systems in converting atmospheric moisture into a vital water source. The primary focus of our research methodology was the analytical derivation of sorption isotherms, essential for the hygrothermal simulation of desiccant materials. This was accomplished through the application of two established models, namely, the Guggenheim-Anderson-de Boer (GAB) and Van Genuchten (VG). Experimental data on various anhydrous salts from existing literature have been used. An in-depth comparative analysis of these models reveals that the VG model aligns more closely with the experimental data, thus asserting its superiority in enhancing the selection and efficiency of desiccant materials in AWH systems. By confirming the VG model’s superiority in accurately modeling sorption isotherms, our research not only improves the model of AWH systems but also, importantly, contributes to the development of advanced water harvesting technologies.
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Turbulent Reynolds Stresses Prediction using Stochastic Gradient Boosting RegressionPredicting turbulent Reynolds stresses (TRS) accurately is crucial for the advancement of fluid dynamics and engineering applications. This study presents an application of stochastic gradient boosting regression (GBR) to predict TRS within a turbulent vertical axisymmetric jet. Leveraging a comprehensive dataset encompassing flow rate, spatial development ratios, velocity profiles, and root mean square of velocity fluctuations, we engaged in a thorough analysis to capture the intricate dynamics governing TRS. Our approach involved the application of GBR, a machine learning algorithm renowned for its precision and flexibility. GBR’s capability to optimize any differentiable loss function was utilized to minimize predictive errors iteratively. The model’s performance was scrutinized using metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination. Intriguingly, our findings revealed that feature scaling, a common preprocessing technique, did not enhance the model’s performance. The unscaled model exhibited superior accuracy, challenging the notion that feature scaling is universally beneficial. The study’s findings underscore the importance of empirical validation of preprocessing techniques and spotlight the effectiveness of GBR in modeling TRS. The insights from this research could have significant implications for turbulence modeling practices and machine learning methodologies within fluid dynamics and other related fields.
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Chapter 1: Traditional Modeling of Fluid Flow and Transport Phenomena, in the Book: Fractional Modelling of Fluid Flow and Transport PhenomenaThis 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.
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Physics‐based and data‐driven approaches for lifetime estimation under variable conditions: Application to organic light‐emitting diodesThe prognosis of organic light-emitting diodes (OLEDs) not only requires early detection of a bearing defect, but also the capability to predict their life data under all operational scenarios. The use of sophisticated machine learning (ML) algorithms is undoubtedly becoming an increasingly exciting research direction, as these algorithms can yield high predictive models with minimal domain expertise. The central question of this perspective is: how well can ML models advance our ability to forecast the lifetime of OLEDs compared to the physics-based models? In this paper, data-driven methods, feed-forward neural networks (FFNN), support vector machines (SVMs), k-nearest neighbors (KNNs), partial least squares regression (PLSR), and decision trees (DTs), are used to predict the lifetime and reliability of OLEDs through analyzing the lumen degradation data collected from the accelerated lifetime test. The final predicted results indicate that both the data-driven and our physics-based OLED lifetime models fit well the experimental data. The main drawback of the former method is that their efficacy is highly contingent on the quantity and quality of the operational dataset. Among all these methods, much more reliability information (time to failure) and the highest prediction accuracy can be achieved by FFNN.
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Digital Twin Integration for Hydrogen Leakage Modeling and AnalysisDigital twin technology is revolutionizing the modeling and simulation of complex systems, such as hydrogen leakage. This paper focuses on integrating digital twin components, starting with experimental measurements. The collected data is then used to calibrate the corresponding physics-based model, improving the accuracy of predictions. A numerical simulator is developed to enable virtual experiments and performance analysis. Comprehensive datasets are generated from real and virtual experiments, enhancing the capabilities of the twin by providing diverse training data. Machine learning prediction utilizes real and artificial datasets to gain insights into system behavior. The bidirectional relationship between the numerical simulator and machine learning prediction enhances accuracy and enriches the overall process. Viewed through the lens of science fiction, the case study on hydrogen leakage can be sculpted into a compelling narrative intertwining advanced technology, human resilience, and the ever-present challenge of harnessing and managing powerful, futuristic energy sources deep within planets.
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The Generalized Average Entropy with Applications to some Satellite Image ThresholdingThis paper suggests a generalization to the average entropy that was introduced by Kittaneh et. al. [The American Statistician 70 (2016) 1]. Some properties of this generalization are studied and a new fully automated entropy-based multi-level image thresholding method with a solid theoretical background is proposed. The method is applied to several real satellite images and compared with the so-called Otsu’s method. The comparative study shows that the proposed method is comparable to Otsu’s method.
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Metabolomic profiling reveals altered phenylalanine metabolism in Parkinson’s disease in an Egyptian cohortParkinson’s disease (PD) is the most common motor neurodegenerative disease worldwide. Given the complexity of PD etiology and the different metabolic derangements correlated to the disease, metabolomics profiling of patients is a helpful tool to identify patho-mechanistic pathways for the disease development. Dopamine metabolism has been the target of several previous studies, of which some have reported lower phenylalanine and tyrosine levels in PD patients compared to controls.
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Fates of a Nonwetting Slug in Tapered Microcapillaries under Gravity and Zero Gravity Conditions: Dynamics, Asymptotic Equilibrium Analysis, and Computational Fluid Dynamics VerificationsIt has been determined experimentally and numerically that a nonwetting slug in a tapered capillary tube, under the sole action of capillary force, self-propels itself toward the wider end of the tube until an equilibrium state is reached. The aim of this work is to highlight the state of the slug at equilibrium in terms of configuration and location. Furthermore, it turns out that gravity adds richness to this phenomenon, and more fates become possible. A modified Bond number is developed that determines the relative importance of gravity and capillarity for this system. According to the magnitude of the Bond number, three more fates are possible. Therefore, in a tapered capillary tube held vertically upward with its wider end at the top, in the absence of gravity or under microgravity conditions, the nonwetting slug moves upward toward the wider end of the tube until it reaches equilibrium with the two menisci part of a single sphere. The location of the slug at equilibrium in this case represents the farthest fate among the other fates. When gravity exists yet capillarity dominates, the slug still moves upward toward the wider end. However, in this case, the two menisci become parts of two different spheres of different curvatures. For this scenario, the slug climbs upward but reaches a lower level compared to the previous scenario. On the other hand, when gravity dominates, the slug experiences a net downward pull toward the narrower end of the tube and starts to move in the direction of gravity until capillary force establishes a balance, then it stops. When gravity sufficiently dominates, it pulls the slug downward until it completely drains off the tube. A computational fluid dynamics (CFD) analysis is conducted in order to build a framework for verification exercises. Excellent agreements between the results of the developed model and the CFD analysis are obtained. A fate map and a scheme are developed to identify these four fates based on two Bond numbers; namely, the initial Bond number and that associated with the slug when it is at the exit.
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Mapping the Research Landscape of Organizational Climate and Performance Using Bibliometric AnalysisThis study aims to address the limited understanding of rganizational climate and performance by conducting a omprehensive bibliometric analysis of scholarly publications. The methodology involves analyzing publications using bibliometric techniques and VOSviewer. The results indicate that organizational performance, employee engagement, job satisfaction, leadership, and leadership culture are prominent topics within the field. The top five countries in terms of published documents and citations are the USA, India, the UK, Australia, and Malaysia. Recent publications have prioritized topics such as quality of work life, innovation, productivity, well-being, organizational commitment, work engagement, and corporate social responsibility. This study provides valuable insights for researchers, practitioners, and organizations to improve employee performance and productivity. The significance of this work lies in its ability to inform future research directions and guide collaboration efforts. Ultimately, this study advances the understanding of organizational climate and performance with practical implications for various organizational settings.
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Analytical solutions for harvesting atmospheric water using desiccant materialsAtmospheric water generation using desiccant materials is a promising technology for producing clean drinking water in water-scarce regions. While experimental research on this topic has been extensive, modeling and simulation research are still in their nascent stages. The development of accurate models and simulations is crucial for predicting performance and refining system design. This paper presents analytical solutions for predicting and improving the behavior of water absorption and desorption by the calcium chloride (CaCl2) desiccant, which is commonly used in atmospheric water generation systems. The model considers several physical effects, such as mass transfer, and diffusion. The model considers a linear relationship between the collected water content and relative humidity. Based on this assumption the model has been solved analytically for different cases of boundary conditions including, Dirichlet boundary conditions and Dirichlet–Neumann boundary conditions. Several physical scenarios have been calculated and the results have been discussed.
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Mapping the Research Landscape of Social and Cultural Impacts on Smart CitiesThe integration of digital technology and innovation in the creation of smart cities has significantly improved the quality of life for citizens. However, while there have been extensive studies on the technological capabilities of smart cities, there is a notable gap in research concerning their cultural and social aspects. To address this concern, this study aims to comprehensively examine the social and cultural impacts of smart cities through bibliometric analysis. By analyzing 1160 published articles from the Scopus database, the study highlights the importance of prioritizing the creation of inclusive, safe, resilient, and sustainable cities, aligning with the United Nations Sustainable Development Goal 11. The study identifies China, the United States, Italy, India, and the United Kingdom as the top contributing countries, with the Norwegian University of Science and Technology being the most active institution in this area. Moreover, this research explores the intersection of social and cultural impacts within the broader context of Innovation 5.0 and Industry 5.0, providing valuable insights for future researchers and practitioners. Nevertheless, it is crucial to acknowledge certain limitations, such as the reliance on Scopus data, which may exclude relevant publications from other sources. Additionally, the analysis based on bibliometric data may not capture the full extent of social and cultural impacts associated with smart cities.
