Journal of Science and Transport Technology

Analysing temperature distributions in turbine first-stage rotor blades of a helicopter turboshaft engine

2024-05-24T09:10:58+00:00

In helicopter turboshaft engines, turbine blades operate under extreme conditions. With increasing engine power, the gas temperature following the combustion chamber can reach approximately 1300 K. The turbine rotors endure significant centrifugal forces due to their high rotational speeds. Additionally, they experience thermal and aerodynamic loads from the flow of combustible gases, which non-uniformly impact the turbine blades at high temperatures. Furthermore, the mechanical properties of turbine blade materials are limited and strongly influenced by operating temperatures. This article presents a numerical investigation focusing on the temperature distribution of first-stage turbine rotor blades that do not feature internal cooling channels. The results indicate the regions of peak temperatures and evaluate rotor blade strength. Comparative analysis between theoretical and numerical calculations of blade temperature distribution reveals minor disparities: approximately 30 degrees at the blade shroud, 8 degrees at the mid-span section, and 15 degrees at the hub. These variations amount to less than 3% at the shroud, 1% at the mid-span section, and 1.5% at the hub.

Preparation and characterization of calcium oxide from snail shell

2024-08-06T09:58:38+00:00

This paper studied the synthesis of calcium oxide (CaO) from snail shells by calcination, crushing, and grinding. Then, the sample was analyzed by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), specific surface area (BET), and particle size. The XRD result showed obtained CaO content of 98.7%. FTIR spectrum analysis revealed a sharp band appeared at 3647 cm^-1 due to the formation of Ca(OH)₂ and the band at 1419 cm^-1 was the characteristic peak of CaO. SEM images supplied a fairly uniform crystal structure and high porosity capillary. TGA analysis also gave a clear mass loss in two temperature ranges from 320-500^oC and 515-780^oC. At the same time, our results are compared with previous related studies.

Geotechnical Forensic Investigations of a Gravity Dam: Addressing Seepage and Sliding Problems in the Basalt Foundations of Karjan Dam, Gujarat, India

2024-06-25T15:22:58+00:00

Geotechnical forensic engineering plays a crucial role in identifying and mitigating potential issues in large gravity dams. The Karjan Dam in Gujarat, India, a 100-meter-high gravity dam built 38 years ago on Deccan basalt, encountered significant geotechnical challenges during construction due to early investigation oversights. These oversights failed to detect sub-horizontal weathered rock seams within the basalt during the pre-construction investigations, which could have posed significant seepage and sliding risks during the dam's operation. Subsequent detailed investigations during the construction phase revealed that these seams extended throughout the foundations of the dam blocks.

In-situ shear tests were conducted to determine the shear parameters, namely cohesion (C) and the angle of internal friction (ϕ) of the seams. The test results indicated low shear strength parameters, necessitating a re-evaluation of the dam's safety. Stability analysis revealed that the spillway and certain non-overflow blocks were at risk of sliding and seepage after reservoir filling. To address these geotechnical challenges, a combination of treatments—including concrete shear keys, grouting, and design enhancements—was implemented to prevent sliding and control seepage. The timely forensic investigation and treatment during the construction stage ensured the dam's safety, which has operated without issues since 1986.

This study underscores the critical importance of integrating engineering and geological assessments at various stages of dam construction. Re-evaluating and addressing evolving foundation conditions, particularly during construction, is essential for applying effective treatments to prevent dam failure during operation. The findings from this case study provide valuable geotechnical insights required for enhancing the safety and resilience of dam infrastructure globally.

Enhancing concrete structure maintenance through automated crack detection: A computer vision approach

2024-07-24T02:02:37+00:00

This paper presents the development of an Artificial Intelligence (AI) and Machine Learning (ML) model designed to detect cracks on concrete surfaces. The objective is to enhance the automation, precision, and performance of crack detection using the computer vision algorithm. Employing a ML approach and the YOLOv9 algorithm, this study developed a system to accurately identify concrete cracks from a diverse dataset. A total of 16,301 images of concrete surfaces, balanced between those with and without cracks, were utilized. The dataset was split into various sets with different ratios to ensure comprehensive model training. A transfer-learning methodology was employed to optimize the model's performance. The accuracy of the model was measured in each experiment to determine the optimal result. The most successful experiment resulted in a model with a mean Average Precision (mAP) of 94.6%, a Precision of 94.1%, and a Recall of 88.4%. These results demonstrate the effectiveness of AI and ML in concrete crack detection.

Enhancing Inland Waterway Safety and Management through Machine Learning-Based Ship Detection

2024-08-22T09:31:16+00:00

Efficient ship detection is essential for inland waterway management. Recent advances in artificial intelligence have prompted research in this field. This study introduces a real-time ship detection model utilizing computer vision and the YOLO object detection framework. The model is designed to identify and locate common inland waterway vessels, such as container ships, passenger vessels, barges, ferries, canoes, fishing boats, and sailboats. Data augmentation techniques were employed to enhance the model's ability to handle variations in ship appearance, weather, and image quality. The system achieved a mean Average Precision (mAP) of 98.4%, with precision and recall rates of 96.6% and 95.0%, respectively. These results demonstrate the model's effectiveness in practical applications. Its ability to generalize across diverse vessel types and environmental conditions suggests its potential integration into video surveillance for improved maritime safety, traffic control, and search and rescue operations.