Weld Microstructure and Hardness Prediction for In-Service Hot-Tap Welds

Author(s):  
Wentao Cheng ◽  
Yong-Yi Wang ◽  
William Amend ◽  
Jim Swatzel

Welding onto an in-service pipeline is frequently required to repair damaged areas and for system modifications. There are often significant economic and environmental incentives to perform in-service welding, including the ability to maintain operations during welding and to avoid venting the contents to the atmosphere. Welds made onto in-service pipelines tend to cool at an accelerated rate. These welds are likely to have high heat-affected zone (HAZ) hardness which increases their susceptibility to hydrogen cracking. Accurate prediction of HAZ hardness is critical in developing successful welding procedures for in-service hot-tap welds. The present PRCI thermal analysis software for hot-tap welding uses an empirical-formula-based HAZ hardness prediction procedure. This paper describes an effort funded by PRCI to produce a significantly improved HAZ hardness prediction procedure over the procedure in the current PRCI thermal analysis software. A markedly improved hardness prediction procedure was developed and systematically validated using extensive experimental data of actual welds. The underlying hardness calculation algorithms were based on the proven state-of-the-art phase transformation models. Although on the average the procedure under-predicts the measured hardness by a small amount, the new hardness prediction procedure is a significant improvement in overall accuracy over the procedure in the current PRCI thermal analysis software. The procedure developed here lays the foundation for a much more accurate hardness prediction module in the future version of the PRCI thermal analysis software.

Author(s):  
Yaoshan Chen ◽  
Yong-Yi Wang ◽  
David Horsley

This paper describes an improved numerical model for the predictions of cooling rate and heat-affected-zone (HAZ) hardness for welding onto an in-service pipeline. Compared to the current PRCI thermal analysis software, the improvements in this new model include a new mesh generator for the heat transfer finite element procedure and a dynamically-coupled microstructure model that features a state-of-the-art phase transformation and hardness calculation algorithms. The new mesh generator is capable of producing finer mesh than that in the current PRCI thermal analysis software, particularly in the HAZ region so more accurate temperature field can be captured for the hardness calculation. To validate the implementation of these improvements in the model, previous measurements by Battelle and EWI have been collected and compared to the predicted results by the new model. These measurements include cooling times from 800°C to 500°C (t8/5) for both sleeve and branch configurations, and hardness in the HAZ for some of the sleeve configurations.


2007 ◽  
Vol 28 (4) ◽  
pp. 258-281 ◽  
Author(s):  
Bruno Agostini ◽  
Matteo Fabbri ◽  
Jung E. Park ◽  
Leszek Wojtan ◽  
John R. Thome ◽  
...  

2011 ◽  
Vol 130-134 ◽  
pp. 1753-1757
Author(s):  
J.F. Ruan ◽  
J. Yang ◽  
G.Q. Lv ◽  
G.S. Deng ◽  
L. Liu

The main components of a space helix TWT (traveling wave tube) are electron gun, helix slow-wave system and collector. Thermal issue is of great importance for space helix TWTs. High heat efficiency of cathode is required for electron gun, as well as high heat transmission capacity for slow-wave system and collector. Some structure optimization for the electron gun, slow-wave system and the collector of some type of space helix TWT has been proposed aiming the above purpose. To evaluate the structural optimization means, the related thermal analysis has been carried out using ANSYS software. The simulation results demonstrate that the structure optimization is effective. And the actual effect needs to be further studied.


Author(s):  
P.J. van der Wel ◽  
J.A. Bielen ◽  
T. Henderson ◽  
J. Middleton

Author(s):  
Luca Aurelio ◽  
Paolo Battagli ◽  
Dino Bianchi ◽  
Arlie R. Martin ◽  
Leonardo Tognarelli

In mid-’98 it was decided to develop a new high efficiency version of the very successful MS5002 (GE Frame 5 two-shaft), to satisfy the most recent Customer requirements in terms of fuel consumption and environmental impact. The machine was conceived considering different markets, primarily mechanical drive, but also non-Oil&Gas power generation. Power class is 30 MW, pressure ratio is 17:1, simple cycle efficiency is over 36% and combined cycle efficiency approximately 51%. The new model retains features that contributed to the success of its predecessors. The main ones are the full heavy-duty concept for on-site maintenance, the moderate firing temperature (compared with state of the art) for highest reliability, the two-shaft design with free power turbine for mechanical drive use, the high heat recovery capability. Achievement of high cycle efficiency with low firing temperature is possible thanks the advanced tools used for the definition, design and optimization of airfoils, clearances, leakages and distribution of cooling flows. Aero-thermal design was largely based on state of the art 3D CFD and on sophisticated airfoil cooling techniques of the same type extensively used in aircraft engine development. The dry-low-emissions combustion system design is derived from the GEPS DLN2.6. A thorough testing program, including the full-scale test of the axial compressor and a full load prototype test, is planned to support development and to validate the design.


1994 ◽  
Author(s):  
Hans Peter de Koning ◽  
Hans de Wolf ◽  
Pau Planas Almazan ◽  
Reinier van Oosten

2021 ◽  
Vol 13 (23) ◽  
pp. 4941
Author(s):  
Rukhshanda Hussain ◽  
Yash Karbhari ◽  
Muhammad Fazal Ijaz ◽  
Marcin Woźniak ◽  
Pawan Kumar Singh ◽  
...  

Recently, deep learning-based methods, especially utilizing fully convolutional neural networks, have shown extraordinary performance in salient object detection. Despite its success, the clean boundary detection of the saliency objects is still a challenging task. Most of the contemporary methods focus on exclusive edge detection modules in order to avoid noisy boundaries. In this work, we propose leveraging on the extraction of finer semantic features from multiple encoding layers and attentively re-utilize it in the generation of the final segmentation result. The proposed Revise-Net model is divided into three parts: (a) the prediction module, (b) a residual enhancement module, and (c) reverse attention modules. Firstly, we generate the coarse saliency map through the prediction modules, which are fine-tuned in the enhancement module. Finally, multiple reverse attention modules at varying scales are cascaded between the two networks to guide the prediction module by employing the intermediate segmentation maps generated at each downsampling level of the REM. Our method efficiently classifies the boundary pixels using a combination of binary cross-entropy, similarity index, and intersection over union losses at the pixel, patch, and map levels, thereby effectively segmenting the saliency objects in an image. In comparison with several state-of-the-art frameworks, our proposed Revise-Net model outperforms them with a significant margin on three publicly available datasets, DUTS-TE, ECSSD, and HKU-IS, both on regional and boundary estimation measures.


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