Abdalellah Omer Mohmmed

The interactions between subsea pipelines and the seabed generate complex vibratory responses that can compromise structural integrity, yet these interactions remain difficult to characterize with sufficient fidelity for design purposes. This study examines a numerical workflow for incorporating hydrodynamic forces and displacement-dependent pipe-soil interaction into the finite element analysis of a nominally 400 m subsea pipeline. The hydrodynamic formulation includes irregular wave kinematics, directional spreading, current effects, and force corrections related to pipeline movement, while the geotechnical representation uses prescribed pipe-soil contact and lateral resistance relationships. The workflow links local resistance characterization with the global structural response and investigates the sensitivity of calculated lateral displacement to applied stabilization weight and seabed strength assumptions. This approach enables computationally efficient structural analysis without sacrificing the fidelity needed to assess whole-system performance, directly addressing the central modeling challenge posed by the pipe-soil interface. Over a 100-year simulated service life, the maximum lateral displacement recorded across the 400 m pipeline reached 5.43 m, while displacement peaked at approximately 3.98 m under a chain weight of 18.37 kg. These results demonstrate the model’s sensitivity to key design parameters governing pipeline stability. The analysis provides an implementation-based case study for assessing the consequences of alternative seabed-resistance assumptions rather than establishing a new soil constitutive model or an independently validated through-life design procedure, and it provides a robust foundation for future subsea pipeline engineering practices.

Hydrogen plays a vital role as an energy carrier in the global effort to combat climate change, with significant applications in sectors such as transportation and ammonia production. However, traditional hydrogen production methods are heavily carbon-intensive, with over 98% of hydrogen derived from fossil fuels. This primarily occurs through steam methane reforming (76%) and coal gasification (22%). While steam methane reforming is cost-effective, it generates approximately 9 kg of CO₂ per kg of hydrogen. Consequently, advancing green hydrogen production methods has become a critical area of research. This study explores and compares various green hydrogen production techniques powered by renewable energy sources, including solar, wind, hydro, biomass, and hybrid systems. Production methods such as electrolysis, thermal, chemical, photonic, and biological processes are evaluated using a Pugh matrix, accounting for factors including efficiency, hydrogen yield, resource availability, operating conditions, cost, and greenhouse gas emissions. The findings indicate that alkaline electrolysis currently represents the most viable option for green hydrogen production. These findings affirm alkaline electrolysis as the most appropriate near-term technology for large-scale green hydrogen implementation in Oman and the GCC, while also advocating for the ongoing development of PEM and emerging pathways to ensure long-term diversification. Ultimately, this study provides a clear and practical decision-support framework for the strategic selection of hydrogen technologies in renewable-rich arid regions.

The intermittent passage of liquid slugs and gas pockets in slug flow generates substantial cyclic stress damage to piping systems and their supports. This issue poses significant challenges to the various industries in which this flow pattern is present. Despite their critical implications, the structural response to slug‐induced forces has not yet been thoroughly elucidated. This study addresses this gap through a comprehensive experimental investigation of the influence of slug length on the structural integrity of pipes. A non‐invasive image‐processing technique was employed to measure the slug length, while biaxial strain gauges captured the pipe wall strain, accounting for Poisson and friction fluid–structure interaction (FSI) coupling mechanisms. The findings revealed a reduction in the induced stresses with increasing superficial liquid velocity and slug length. Furthermore, a semi‐empirical model was developed by integrating slug length with the superficial gas and liquid velocities based on the slug unit concept to predict the structural stresses. The model provides a robust predictive framework for elucidating the relationship between slug length and induced stresses. However, its accuracy is influenced by the slug formation mechanism and various slug flow sub‐regimes. The model demonstrated exceptional predictive capability, achieving a mean error of 6.2% and coefficient of determination of 93%.

Accurate prediction of the slug frequency in horizontal pipe flow is essential for appropriate design and operation in various industrial processes. This study provides a comprehensive review of existing empirical correlations for slug frequency in horizontal pipes, highlighting their limitations and applicability. A total of 36 correlations were examined, and 1083 data points were collected from experiments using pipes with inner diameters ranging from 3.7 to 150 mm. The correlations were categorized based on pipe diameter and gas–liquid working fluids. The correlations based on the Froude number had the best performance for superficial liquid velocities within the range of v_SL = 0.502–1.505 m/s, with a maximum mean relative difference (MRD) of ±30% and a mean absolute relative difference (MARD) of 40% for superficial gas velocities v_SG greater than 1 m/s. The Strouhal number produced the most effective correlations for most of the datasets examined in slug frequency testing. In contrast, the air–oil slug frequency correlations were unable to accurately predict the air‐water experimental data, except for that of Al‐Safran (2016), which was limited to small‐diameter pipes. 909 data points were used to evaluate slug frequency for high viscous fluids; the results showed that the correlation of Baba et al. (2017) provided the best prediction performance, with a maximum MRD of 30%.

The solubility of asphaltenes in crude oils is predominantly influenced by variations in temperature, pressure, and oil composition. These alterations can precipitate asphaltene deposition, resulting in diminished permeability, obstruction of wells and auxiliary surface facilities, and ultimately, a reduction or cessation of production. Therefore, it is imperative for upstream and downstream processing engineers to comprehend and predict asphaltene phase behavior to implement effective preventative and remedial strategies and minimize costs. Asphaltene precipitation can be predicted through the application of solubility and colloidal theories. In this study, cubic equations of state and cubic-plus-association equations of state are utilized as solubility theory-based methodologies. The advanced versions of the Peng-Robinson (APR78) and Soave-Redlich-Kwong (ASRK) cubic equations of state are compared with cubic-plus-association (CPA) equations of state using Multiflash software to predict fluid and asphaltene phase behavior. The simulation results demonstrate a strong correlation between the ASRK model and the CPA model, with a minor deviation from the results of the APR78 model. This observation suggests that these models can effectively predict asphaltene behavior and yield acceptable results when compared to experimental data for fluid and asphaltene. Considering the likelihood of asphaltene deposition within wells, hence, it is recommended to develop a model to determine the locations and quantities of deposition.

The recent advancement in sophisticated AI technologies in numerous sectors, including education and research, poses several challenges. The utilization of AI chatbot systems, particularly ChatGPT, has become increasingly prevalent and exhibits heightened capabilities in various aspects. However, ChatGPT poses various challenges to the conventional educational and research framework such as the potential for academic dishonesty. Consequently, Plagiarism has emerged as a pressing issue in academic writing and publishing, prompting the creation of numerous plagiarism-detection mechanisms. Thus, this chapter explores the challenges of plagiarism in the AI-empowered era by examining several written essays from the fields of engineering and medicine. The plagiarism in these essays was assessed using various free AI plagiarism-detection tools. The outcomes demonstrated different similarity levels for the same content, which may confuse educators during the assessment process. It delves into the potential measures that academic institutions can implement to preserve academic integrity.

Rising population levels exert significant pressure on available freshwater resources. Scientists and researchers from various countries are diligently seeking a long-lasting solution using solar-powered desalination. This research paper investigates the current advancements in solar desalination research by utilizing the method of “scientometrics”. Scientometrics employs traditional methodologies, including bibliometrics, which entails quantifying the number of research papers published, and citation analysis, which involves examining the frequency with which other researchers cite these papers. By integrating these two approaches, scientometrics provides invaluable information about the most influential countries, institutions, and individual researchers in the field. Utilizing the software program VOSviewer, a comprehensive analysis was conducted on 1855 research papers published between 2010 and 2024. These papers were selected based on a predetermined set of ten key search terms. The results of the analysis indicate that China is the leading country in this field, as it boasts the highest number of published papers and the most citations received overall. Notably, Egyptian research institutions have been identified as the most influential in this area. Moreover, a single author has notably amassed 3419 citations for their 54 published works on solar desalination. This analysis unveiled past and contemporary advancements in the field and identified current trends through keyword analysis. It also offers recommendations based on bibliometric findings, including suggestions for addressing the challenges faced by solar-derived systems and addressing research area saturation.

In the Deepwater Horizon oil spill, optical plume velocimetry (OPV), a flow measurement technique for use in seafloor hydrothermal systems, was found to have the least uncertainty in estimating the rate at which oil was escaping from the well in the deep sea. However, OPV still had a high uncertainty of 21%, partly due to the limited accuracy of the temporal cross-correlation algorithm used. In this work, the accuracy of several in-situ optical velocimetries, namely wavelet-based optical velocimetry (WOV), OPV, and two classical correlation-based algorithms, namely fast Fourier transform (FFT) and normalized cross-correlation (NCC), for a plume flow with Reynolds numbers varying from 1847 to 11,656 was investigated. WOV, FFT, and NCC resulted in flow rates closer to the expected turbulent plume flow rate as compared to OPV. Moreover, a noisy velocity field was found using OPV. The accuracy of wavelet-based algorithm outperformed all cross-correlation based algorithms. The flow rate was measured with an error of 8.5% using WOV, whereas errors of 18.2%, 19.7%, to 21.1% were obtained when applying FFT, OPV, and NCC, respectively. There was a statistically significant difference between wavelet-based and correlation-based algorithms, but no statistically significant difference between the estimation of the three cross-correlation based velocimetries. WOV outperformed the other velocimetries and estimated flow rates with an error of 8.5%, whereas the OPV, FFT, and NCC were estimated with errors of 19.7%, 18.2%, and 50.8%, respectively.

Due to COVID-19 outbreaks worldwide, the academic institutions have been enforced to entirely cancel face-to-face teaching including laboratories and other learning experiences as a mitigation step against the risk posed by the Coronavirus. Accordingly, various measures by the higher education providers have been initiated to implement social isolation strategies, and online teaching is followed with rapid curriculum transformation. The online delivery is more convenient, as it can provide vibrant and dynamic teaching and learning environment. However, due to time constraint, the curriculum transformation is anticipated to occur rapidly without sufficient preparation. Therefore, in this study, the concept of the emergency remote teaching (ERT) including its application and evaluation is thoroughly discussed. The application of the ERT in the Middle East College Oman has been considered as case study. This study draws on CIPP evaluation model to assess the effectiveness of the adopted model, and qualitative data were collected online taking random samples of students and educators. Besides, interview and questionnaire responses, experiences, beliefs, and challenges encountered by the educators and students on the emergency remote teaching were used and analyzed. In addition, students’ weekly attendance segregated with session modes, levels and module nature were considered to evaluate the students’ participation to the online classes. The collected information were analyzed, and based on the analysis outcomes, recommendations were forwarded to serve as an input for future strategies and policies and to improve the performance of teaching learning activities during similar circumstances.