Numerical analysis of the effect of hydrogen doping ratio on gas transmission in low-pressure pipeline network

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 73, P. 868 - 884

Published: June 14, 2024

Language: Английский

Deep Learning-Based Fatigue Strength Prediction for Ferrous Alloy

Processes, Journal Year: 2024, Volume and Issue: 12(10), P. 2214 - 2214

Published: Oct. 11, 2024

As industrial development drives the increasing demand for steel, accurate estimation of material’s fatigue strength has become crucial. Fatigue strength, a critical mechanical property is primary factor in component failure within engineering applications. Traditional testing both costly and time-consuming, can lead to severe consequences. Therefore, need develop faster more efficient methods predicting evident. In this paper, dataset was established, incorporating data on material element composition, physical properties, performance parameters that influence strength. A machine learning regression model then applied facilitate rapid prediction ferrous alloys. Twenty characteristic parameters, selected their practical relevance applications, were used as input variables, with output. Multiple algorithms trained dataset, deep employed The models evaluated using metrics such MAE, RMSE, R2, MAPE. results demonstrated superiority proposed effectiveness methodologies.

Language: Английский

Method for identifying the leakage of buried natural gas pipeline by soil vibration signals

Gas Science and Engineering, Journal Year: 2024, Volume and Issue: 132, P. 205487 - 205487

Published: Nov. 8, 2024

Language: Английский