×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerThe aim of this study is to optimize ESP performance by evaluating the current conditions and the performance optimization of the electrical submersible pump (ESP) for six oil wells in the Rmelan oil field. fluid and reservoir properties (API = 23, T = 78 C°, pressure of reservoir = 160 atm and the WC is 70%). This paper presents a sensitivity assay conducted by Nodal Analysis (Using PIPESIM Software) on the pump frequency and wellhead pressure. The outflow tubing performance and inflow performance relationship were generated and plotted for each well. The curves are investigated, indicating problems in some wells (W-12R, W-21KH, and W-21SH). The results of this study show that we can increase the flow rate by optimizing the ESP performance by decreasing the wellhead pressure to 71.58 psi and raising the frequency of ESP to a specific value of about 65 Hz based on the limites of production of each types pump capacity . Increasing the frequency from 55 to 65 Hz resulted in increasing the production from 634 to 1092 bb/day for W-12R, from 1928 to 2806 bbl/day for W-21KH, and from 1722 to 2279 bbl/day for W-21SH.
The objective of the current study is to determine the accuracy of a computational model that has been developed to simulate polyurethane foaming reactions by comparing its results with experimental findings on the system using both physical and chemical blowing agents. There was high concordance between the model outputs and the laboratory results in regard to the temporal development of reaction temperature as well as the resulting foam density, both of which were highly faithful recreations. The discussion provided further information about the optimization of the performance of cyclohexane, particularly when used in synergy with chemically active blowing agents, which speed up foaming. Besides, the polymerization dynamics were contained in the simulation, thus providing rich information on the structural changes that occur during the foaming process. Taken together, the results present a strong basis for the process performance optimization, as well as the predictive modeling of the blowing agent behavior. In the future, it will involve expanding the simulation model to include a wider range of agents, reaction mechanisms, and kinetics.
Polyurethane (PU) products enjoy remarkable versatility due to their tunable chemistry, segmented structure, and a wide range of mechanical properties, making them useful in flexible foam products, structural systems, and biomedical applications. However, the complex multiphase morphology and the strong interaction between reaction and processing processes make experimental characterization incomprehensible on its own. In turn, computational studies have become essential to study and design PU systems at a range of spatial and temporal scales. The current review provides an overview of simulation methodologies that are relevant to polyurethane, including atomistic molecular dynamics (MD), coarse-grained (CG), and mesoscale simulations, including dissipative particle dynamics (DPD), finite element method (FEM) modeling, and computational fluid dynamics (CFD) simulations. Atomistic models provide data on molecular interactions, hydrogen bonding, and thermomechanical behavior, and CG and mesoscale methods on phase separation and morphological evolution. At the bigger length scale, nonlinear mechanical response can be predicted using FEM, whereas foaming and mold-filling processes can be predicted using CFD that is coupled with reaction kinetics and population balance equations. Its focus is on multiscale modeling strategies, which combine these apparently different approaches, hence allowing the explanation of structure-property-process links. New trends and modern issues, including the integration of machine learning and tool models of digital twins, are also mentioned, highlighting new opportunities in predictive design, based on simulations, of polyurethane materials.
50W monocrystalline silicon solar module performance is tested with experimental measurements conducted at Baghdad city /Al-Jaderia (33.26 N, 44.21E). Solar irradiance striking is subjected to more losses which after the experiments conducted resulted approximately in 15% of the total energy which is converted into electric power energy. To study the effect of temperature variations on solar performance, solar irradiance must be kept constant and vice versa. Therefore, to have of the temperature range and for more accuracy, the measurements was done for tested module with three solar radiations levels; 500, 750 and 1000 W/m2. The maximum value of power (Pmax) at solar radiation intensity 1000W/m² was 46.34 W on January 2025 at cell temperature 24.1 oC, with the corresponding the maximum open voltage, and open circuit current 18.28 V, and 2.944 A respectively. The highest value of efficiency was 13.5 % January 2025 at solar radiation 500W/m². Consequently, The minimum value of power (Pmax) at solar radiation intensity 500W/m² was 27.54 W on October 2024 at cell temperature 40.5 oC, with the corresponding the maximum open voltage, and open circuit current 18.01 V, and 1.752 A respectively. The lowest value of efficiency was 6.9 % October 2024 at solar radiation 1000W/m². In general, the results showed slightly decrease in short circuit current with temperature increasing. With temperatures change great influence on the output voltage especially on open circuit voltage while very small decrease in the output current has been noticed.
This comprehensive study undertakes a two-tiered comparative analysis to systematically evaluate the fatigue and cracking performance of a 40-50 penetration grade asphalt binder and its corresponding asphalt concrete mixtures, modified with varying dosages (2%, 4%, and 6% by binder weight) of Nano-Alumina (NA) and Nano-Silica (NS). The experimental methodology involved extensive binder-level testing, including the evaluation of physical properties (penetration, softening point, ductility), rheological behavior (Rotational Viscosity (RV)), and fatigue characteristics using the Superpave parameter G* sin δ and the advanced Linear Amplitude Sweep (LAS) test. Furthermore, compatibility was assessed via storage stability and Scanning Electron Microscopy (SEM). The research culminated in mixture-level performance evaluation using the Indirect Tensile Cracking Test (IDEAL-CT) to derive the Cracking Tolerance Index (CT-Index), Flexibility Index (FI), and Crack Resistance Index (CRI). The results confirmed that both nanomaterials significantly enhance binder stiffness and thermal stability. Nano-Alumina (NA) consistently induced the most profound stiffening effect, reflected by a major reduction in penetration. Rheological and LAS testing indicated that NA provides a stable and progressive, dose-proportional enhancement in fatigue life from 2% to 6%, attributed to the formation of a sustained nanoscale reinforcement network. Conversely, Nano-Silica (NS) exhibited a potent viscosity-building effect due to its high surface area, achieving superior initial cracking tolerance and fatigue life at low concentrations (2% to 4%). Crucially, the study identified a narrow optimal range for NS; concentrations at 6% led to an adverse reduction in fatigue resistance (G* sin δ increase) and diminished flexibility, suggesting a constraint imposed by excessive stiffening and potential particle agglomeration. Mixture-level IDEAL-CT results further validated these trends: NA offered a balanced overall contribution, maximizing the CT-Index at 6% and CRI at 4%, while NS yielded an exceptionally high CT- Index value at 2% but showed a decline in performance at higher contents. The overall findings recommend an optimal practical dosage of 2-4% for NS and 4-6% for NA, underscoring the necessity of material-specific optimization for achieving enhanced durability and fatigue life under repeated loading.
Federated learning (FL) offers a robust and privacy-preserving approach for developing collaborative intrusion detection systems (IDS). However, statistical variance severely hinders its practical application. Although privacy-preserving federated learning models have been used to develop intrusion detection systems for cyberattacks, problems arise when statistical variance is present. In practice, the performance of the FedAvg algorithm is significantly affected by the heterogeneous distribution of customer data in a real-world network. This distribution causes skewness among customer data, resulting in poor detection accuracy, delayed convergence, and model instability. In this paper, presents conduct a comprehensive comparison of the Scaffold algorithm with the FedAvg baseline using the CICIDS2017 datasets. Because the Scaffold algorithm addresses the client skew problem using control variables, it is considered a state-of-the-art federated optimization technique under the heterogeneous partitioning approach. This paper documents the importance of using the Scaffold algorithm as a reliable and essential tool for building high-performance detection systems in a variety of scientific settings. Therefore, our results demonstrate that Scaffold achieved more stable convergence and outperformed FedAvg, with a 15.1% increase in F1-score and a 13.6% higher overall accuracy under highly skewed data distributions. The present evaluation process operates through simulation testing, but physical testbed implementation remains essential for future work to evaluate real-world deployment challenges.