Ain Shams Engineering Journal最新文献

A new controllable rotary bending cracking cropping method, based on V-notch stress concentration, is proposed to address the drawbacks of the metal bar separation process, such as poor cross-section quality and low material utilization. The controlled rotary bending cracking cropping experimental device is developed to implement this method. Subsequently, a mechanical model for the precision cropping process is established using material strength theory, and a load control strategy based on fracture mechanics theory is applied to predict cropping efficiency. Experimental verification is then conducted, with results indicating that increasing notch depth improves cropping efficiency and cross-section quality. Additionally, maintaining constant stress intensity factor amplitude (ΔK = 0.4K_c) and notch depth of 5 mm yields optimal cropping effects. The precision cropping process leverages the fatigue crack propagation mechanism to explain the cropping process, offering theoretical guidance for selecting appropriate parameters in subsequent precision cropping processes.

A well-designed exhaust manifold has a positive effect on the efficiency of an engine and exhaust emissions. If the dimensions and geometric structure of the exhaust manifold are not designed in accordance with the pressure fluctuations of the fluid, this will have a negative effect on the velocity, temperature, density and pressure of the flow. In view of this and the high production costs of locomotive diesel engines, the pressure and velocity distributions in the exhaust manifold of a six-cylinder locomotive engine are investigated numerically in this study. Two different designs for the diesel engine are studied, taking into consideration the area in which the exhaust manifold will be mounted and the other engine parts. The pressure and velocity variations of the exhaust manifolds are compared via a computational fluid dynamics analysis, and analyses are performed using test data from a heavy-duty diesel engine at different power values (225, 450, 675, and 900 HP) and 1500 rpm, with the aim of finding the optimal design. Since the diameters at the cylinder outlets cannot be changed, the designs are created to fit within the existing area of the engine area The exhaust outlet is located in the middle of the manifold in the first model examined here, and is positioned close to the right-hand side of the manifold in the second model (the existing configuration). It is found that the flow becomes more efficient in the model in which the outlet is in the middle of the exhaust manifold.

In this theoretical paper, an investigation is conducted into the peristaltic transition of a hyperbolic tangent nanofluid that contains mobile gyrotactic microorganisms. This study examines the entropy generation resulting from magnetohydrodynamic (MHD) flow and heat transport. The analysis encompasses an anisotropically stenosed endoscope, which is influenced by Ion-slip, activation energy, viscous dissipation, Hall efficacy, Joule heating and entropy generation. The impacts of nonlinear thermal radiation and chemical processes with Soret and Dufour schemes are studied. The porous medium is described using a modified form of Darcy's principle involving a Forchheimer framework. The assumptions involve the extended wavelength besdes reduced Reynolds numeral. The homotopy perturbation strategy is employed to solve the resulting equations. The results show that the critical velocity rises as the local temperature Grashof numeral increases. Moreover, the study offers insights into the movement of digestive gastric fluid within the small intestine as the endoscope moves through.

Nowadays, Internet technology is developing very quickly, because of which webpages are generated exponentially. Web content categorization is mandatory to explore and search related webpages based on queries of users and becomes a dreary task. Most web content categorization methods ignore the contextual knowledge and semantic features of the web page. Pornographic webpage–filtering system does not deliver perfect extraction of advantageous datasets in unstructured web content. Such mechanisms take no reasoning ability to intellectually filter web content to categorize medical websites in adult content webpages. This study introduces a Type-2 Fuzzy Ontology with Dendritic Neural Network Based Semantic Feature Extraction for Web Content Classification (TFODNN-SFEWCC) method. The presented method mainly focused on the detection of different types of web content and blocking pornographic content. It uses the DNN model for the extraction of useful keywords from web pages and eliminates unwanted ones. In addition, the proposed technique employs type 2 fuzzy ontology for the automated classification of web content into multiple classes. The pigeon swarm optimization algorithm is applied to optimize the performance of the Dendritic Neural Network approach for hyperparameter tuning. The experimental evaluation of the proposed method occurs utilizing a web database, and the outcomes are studied under various aspects. The comprehensive comparison study highlighted the betterment of the proposed technique over other existing approaches.

Micropolar fluids have received a lot of interest for their industrial uses due to their distinctive microstructures. The diversified utilization of heat and mass transport process of micropolar material induced by shrinkable curved surface in raising the efficiency of numerous industrial processes has been found. For instance, extrusion procedure, fiber technology, polymer extraction, etc. The primary motive for conducting this disquisition is to explore the transmission of heat and mass in the flow of a micropolar (non-Newtonian) fluid processing vortex viscosity and micro-inertial aspects over a curved stretching/shrinking sheet. Physical factors of irregular heat generation/absorption rate along with linear radiative heat flux are included in thermal transport whereas mass diffusion involves the impact of Arrhenius kinetics and chemically reactive species. The basic constitutive equation is modeled in curvilinear coordinates after obliging conservation laws. A set of similar variables is implemented to change the governing formulation into the dimensionless format. Multiple branch solutions are attained via the bvp4c procedure. Associated distributions (velocity, micro rotation, temperature, and concentration) are organized against the sundry parameters for both branches and interpreted through sketches. Relevant quantities versus different parameters are also encountered through tabular data. The credibility of computed results is assumed by making agreement with previous studies. After a thorough insight into this work, it is inferred that multiple solutions are noted for distinct values of suction and material parameters.

The construction industry is shifting towards sustainability, emphasizing the need for innovative materials. Recycled Aggregate Concrete (RAC), utilizing recycled aggregates, emerges as a promising eco-friendly solution to minimize waste and resource utilization. However, accurately predicting its compressive strength (CS) is challenging due to varying composition and properties. This study addresses this issue by employing machine learning models, specifically five gradient boosting algorithms: Gradient Boosting Machine (GBM), LightGBM, XGBoost, Categorical Gradient Boost (CGB), and HistGradientBoosting (HGB). A total of 314 mixes from relevant published literature were aggregated to train the models. These models are meticulously fine-tuned through hyperparameter optimization for optimal predictive performance. The study also introduces SHAP (SHapley Additive exPlanations) algorithms for model interpretability, elucidating feature contributions to predictions. The results revealed that among the five gradient boosting models, CGB demonstrated the highest R2 value of 92% on the testing set, while LightGBM exhibited the lowest Coefficient of Determination (R2) value of 88%. Additionally, CGB achieved the lowest Root Mean Square Error (RMSE) of approximately 4.05, whereas XGBoost showed the highest RMSE of around 4.8. Furthermore, for Mean Absolute Error (MAE), LightGBM recorded the lowest value of approximately 3.16, while HGB yielded the highest MAE of about 3.8. The SHAP analyses reveal influential features impacting RAC strength, highlighting the significance of cement, water, sand, and recycled aggregate water absorption in predicting RAC compressive strength.

This research deals with the economic-environmental dispatch problem (EEDP) in multi-terminal high-voltage direct-current (MT-HVDC) systems by proposing a convex approximation based on semi-definite programming (SDP). The exact formulation of the EEDP corresponds to a non-convex, nonlinear programming problem due to the presence of a nonlinear quadratic constraint regarding the products between voltage variables. The thermal plants' economic and objective functions are modeled using typical quadratic functions. The SDP approach allows reaching a convex approximation that ensures the global optimal solution for each objective function independently or the construction of the optimal Pareto front via the weighting-factor optimization methodology. The proposed SDP approach also considers uncertainties in the demand and in the available power of renewable sources, which makes it robust. The main contribution of this research is a multi-period analysis that includes large-scale renewable generation sources and a robust analysis regarding demand and variations in renewable generation. Numerical results in two MT-HVDC systems demonstrate the effectiveness of the proposed SDP approach when compared to combinatorial optimization algorithms. All numerical simulations were carried out using the CVX convex disciplined tool, with the help of the SEDUMI and SDPT solvers, in the MATLAB programming environment.

This article explores the performance of interconnected queueing systems and queueing-inventory systems (IQSQIS) in stochastic modeling. IQSQIS features two types of service facilities with dual service stations: one providing non-commodity service with a multi-server, and another handling commodity sales with a single server. As directed, a customer type $i,i=1,2$ can approach the station $i,i=1,2$ whose arrival pattern follows the marked Markovian arrival process (MMAP) since we assume that both service stations have an equal arrival phase. The IQSQIS gives an offer to the type-2 customer to choose type-1 service at the end of their service completion. Numerical results suggest that this option reduces customer wait times, orbit size, and overall system costs. The comparative analysis of heterogeneous and homogeneous servers in station-1 and queue-dependent and non-queue-dependent service facilities in station-2 is presented and investigated using the numerical outputs.

The major aim of the current study is to develop a data-driven methodology based on green processing for estimation of drug solubility in supercritical carbon dioxide as the solvent. Several machine learning algorithms were utilized to simulate Capecitabine solubility in supercritical carbon dioxide for green pharmaceutical manufacturing applications which can enhance the solubility of drugs by this method of processing. In the models, the inputs are pressure (P) and temperature (T), and the target output (Y) is solubility. Tree-based ensemble models of RF (Random Forest), ET (Extra Tree), and GB (Gradient Boosting) were selected for modeling in this research in combination with the optimizer to model the process. The hyper-parameters of models were optimized to reduce the error in the fitting. The coefficient of determination (R² score) values obtained more than 0.96 and RMSE (root mean square error) for ET, GB, and RF models are 2.91, 2.37, and 4.45, respectively. Based on accurate analyses of results Gradient Boosting selected for primary model in this research. The models were able to estimate the drug solubility which can be used to estimate solubility for a wide range, thereby saving time and costs of measurements.