Metallurgical and Materials Engineering

Effects Of Engine Nacelle Dimension On Steam Turbine Cooling Performance And Optimization Of Dimension To Improve Cooling Performance

2025-08-08

Considering the importance of increasing the performance of steam turbines and the effect of dimensional optimization on them, this paper aims to investigate the impacts of the nozzle's optimum axial distance to the engine nacelle and the engine nacelle length on the engine’s performance. In order to provide a close simulation of the actual condition, the results were obtained by solving the 2D steady-state compressible Reynolds-averaged Navier-Stokes (RANS) equations using the Finite Volume Method and the Shear Stress Transport (SST) turbulence model (FVM). The findings demonstrated that the mass flow rate of the coolant fluid rose by more than 50% as the nacelle length increased but was not considered when the nozzle-to-nacelle distance increased.

Synthesis Of Nanoparticles Used Plant Extract

Hawraa H. Esmael, Zahraa A. Rasheed and Rafal Shakeeb alanee — 2025-07-07

Numerous potential applications in the biological and environmental fields are presented by the development of green nanoparticles. In particular, green synthesis aims to employ less toxic chemicals. For instance, using organic resources, such as plants, is usually permissible. Plants also contain lowering and capping agents. Here, we discuss the basics of green chemistry and look at new applications for plant-mediated synthesis-produced nanoparticles. Nanoparticles include platinum, palladium, copper, silver, gold, zinc oxide, and titanium dioxide.

A Sustainable Shift: Replacing Conventional Aggregates WITH Mswi Bottom Ash IN Concrete

M. Rajender Reddy & Dr. G. B. Ramesh Kumar — 2025-07-07

The construction industry faces immense pressure to reduce its environmental footprint, particularly regarding resource depletion and waste generation. Municipal Solid Waste Incineration (MSWI) Bottom Ash (BA), a significant residue from waste-to-energy plants, presents a potential solution as a substitute for conventional natural aggregates (NA) in concrete. With increasing emphasis on sustainable construction materials, this study investigates the feasibility of using Municipal Solid Waste Incinerator (MSWI) bottom ash as a substitute for conventional coarse aggregates in concrete. The paper concludes that MSWI BA, when properly processed and incorporated, can contribute to sustainable concrete production, reducing reliance on virgin aggregates and diverting waste from landfills, aligning with circular economy principles. However, standardized regulations, robust quality control protocols, and further research into long-term durability and leaching under various conditions are crucial for wider adoption.. This research demonstrates that MSWI BA is a technically viable and environmentally beneficial alternative aggregate, paving the way for more sustainable concrete production and effective waste management strategies within a circular economy framework. This study checks MSWI bottom ash can safely and effectively replace sand or gravel in concrete. In this paper, how much replacement of natural coarse aggregate is done is also told with the result, and what benefit will be there to the environment. Also, how fine aggregate and coarse aggregate are replaced is explained.

Handling Imbalance Noisy Dataset By A Hybrid SMOTE-LOF-Transforms Model

Shadan Taha Rashid, Mohammad-Reza Feizi-Derakhshi, Pedram Salehpour — 2025-07-15

In In this study, we aim to evaluate the effectiveness of various machine learning models for fake news detection on PolitiFact and GossipCop datasets obtained from FakeNewsNet, focusing on identifying the most accurate and reliable model. We focused on using state-of-the-art methods combining deep learning transformers and SMOTE resampling techniques for class imbalance, LOF as outlying point detection. The proposed method in this paper, which combines the transformer attention mechanism with SMOTE and LOF resampling techniques, achieved the highest performance metrics on both datasets. The novelty of this model lies in its ability to train on a noisy imbalanced dataset in a short period of time while achieving high accuracy. It recorded an accuracy of 91.5% in PolitiFact and 87.2% in GossipCop, outperforming other models in accuracy, recall and F1 scores. Compared to models such as BERT, BERT + LSTM, and SAFE (multi-faceted), the attention mechanism stands out due to its ability to dynamically focus on relevant items. Overall, this work demonstrates the necessity of retraining models to accommodate the distinctive features of different datasets. It also demonstrates the effectiveness of attention mechanisms in understanding complex narratives and highlights the benefits of a multi-faceted perspective. This indicates that for realistic applications, choosing or designing models with high performance on multiple datasets is necessary, which may pave the way for future work on improving fake news detection using advanced NLP techniques and multimodal approaches.

Optimization Of GTAW Process Parameters For Joining Dissimilar Alloys, SS 202 And SS 304

Thanveer Ahamed S, Siva Priya V, Umar Mohamed J, Palaniappan PL. K — 2025-07-07

This research investigates the joining of two dissimilar stainless-steel alloys, SS 304 and SS 202, using Gas Tungsten Arc Welding (GTAW). SS 304 is widely recognized for its excellent intergranular corrosion resistance, making it suitable for applications such as pressure vessels and automotive components. In contrast, SS 202 is a cost-effective alternative with good tensile strength and toughness, commonly employed in structural applications and kitchenware. TIG welding, known for its precision and control, enhances productivity while reducing time and cost. The study utilizes Taguchi’s Design of Experiments (DOE) methodology to determine the optimal welding parameters—including welding current, root gap, gas flow rate, groove angle, and filler material—to achieve uniform tensile strength and hardness in the dissimilar weld joint. Taguchi’s orthogonal array design, combined with Analysis of Variance (ANOVA), was employed to analyze weld characteristics and optimize process parameters for improved joint performance. The optimal tensile strength (553.543 MPa) was achieved with a welding current of 160 A, gas flow rate of 3 l/min, groove angle of 60°, root gap of 1 mm, and filler material 308 L. The optimal hardness (284.99 BHN) was obtained with a current of 60 A, gas flow rate of 9 l/min, groove angle of 40°, root gap of 1 mm, and filler material 304 L. These results were further validated and compared using predictive modeling techniques, including Linear Regression, Random Forest, Artificial Neural Networks (ANN), and a Genetic Algorithm, which served as a confirmatory approach for the optimal parameter combinations. The predicted outcomes were then compared with experimental values to assess the accuracy and reliability of the models in enhancing weld joint performance

Comparative Analysis Of Print Ink Density Of Conventional And Soya-Based Ink On Paper Board Using Offset Printing Process

Ramesh Panwar — 2025-08-08

The shift towards environmentally sustainable materials in the printing industry has led to increased interest in alternative ink formulations, such as soya-based inks, which offer reduced environmental impact compared to conventional petroleum-based inks. This study presents a comparative analysis of print ink density achieved using conventional and soya-based inks on paperboard substrates through the offset printing process. Ink density plays a critical role in determining print quality, influencing factors such as colour strength, image sharpness, and overall visual consistency. In this research, controlled printing trials were conducted using standard offset press conditions to ensure uniformity. Both ink types were applied to identical paperboard substrates, and ink density measurements were taken using a densitometer at designated control points. The aim is to evaluate the printing performance and visual strength of soya-based ink in relation to conventional ink, contributing to the understanding of its viability in commercial printing applications. The study offers insights into the print behaviour of sustainable inks and supports the industry's movement toward eco-friendly practices without compromising essential print quality parameters.

AI-Enabled Predictive Maintenance Framework For Connected Vehicles Using Cloud-Based Web Interfaces

Ravi Shankar Garapati, Dr Suresh Babu Daram — 2025-08-08

A predictive maintenance framework based on arti- ficial intelligence (AI) is presented. Connected vehicles transmit vehicle-generated data to a cloud server from multiple vehicles. The data is processed with the AI model to forecast vehicle main- tenance requirements and issues and for scheduling maintenance operations in advance. The results—displayed on an easy-to-use, cloud-based web interface—consist of multiple-choice dropdowns to select the desired query for the AI model for processing and forecasting. The web interface facilitates smooth access to the AI process results and allows users to analyze data and identify the maintenance needs of individual connected vehicles.