Steel Structure Prediction Model for Fixed Roof Oil Tanks

ce/papers ◽  
2021 ◽  
Vol 4 (2-4) ◽  
pp. 2375-2381
Author(s):  
Uros Denic ◽  
Milan Spremic
Author(s):  
Lubomír Klimeš ◽  
Josef Štětina ◽  
Tomáš Mauder

Continuous casting of steel is currently a predominant production method of steel, which is used for more than 95% of the total world steel production. An effort of steelmakers is to cast high-quality steel with a desired structure and with a minimum number of defects, which reduce the productivity. The paper presents our developed GPU-based heat transfer and solidification model for continuous casting, which is coupled with a submodel used for the prediction of the steel micro-structure. The model is implemented in CUDA/C++, which allows for rapid computing on NVIDIA GPUs. The time-dependent temperature distribution calculated by the thermal model is iteratively passed to the submodel for the steel micro-structure prediction. The structural submodel determines the spatially-dependent rates of temperature change in the strand, for which the interdendritic solidification model IDS predicts the micro-structure of steel. The paper presents preliminary simulation results for the steel grade used for pressure vessel plates, which is sensitive to rapid cooling rates.


2012 ◽  
Vol 155-156 ◽  
pp. 1061-1065
Author(s):  
Gang Wang ◽  
Li Na Feng ◽  
Xiao Ming Zhang

Basing on the original mechanical structure of arc-submerging welder for flat fillet seams, this article designs an arc-submerging welder control system for large steel structures flat fillet seams, such as blast furnaces, oil tanks, and so on, and expatiates on the working principle by circuit designing and testing in detail. This control system has been used greatly in DongYing, DaGang and other construction site for large steel structure.


2020 ◽  
Author(s):  
Kun Tian ◽  
Xin Zhao ◽  
Xiaogeng Wan ◽  
Stephen Yau

Abstract Background Protein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins, such as X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy, are complex and may require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction.Results Here we describe a new approach that uses integration and analysis of torsion angle information from the Protein Data Bank to enable prediction of protein structures from amino acid sequences. Our prediction model performed well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. This new prediction model performs well with an average of 92.5% accuracy for structure classification, which is higher than the previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence.Conclusions The results show that our method provides a new effective and reliable tool for protein structure prediction research.


2005 ◽  
Vol 173 (4S) ◽  
pp. 427-427
Author(s):  
Sijo J. Parekattil ◽  
Udaya Kumar ◽  
Nicholas J. Hegarty ◽  
Clay Williams ◽  
Tara Allen ◽  
...  

Author(s):  
Vivek D. Bhise ◽  
Thomas F. Swigart ◽  
Eugene I. Farber
Keyword(s):  

2009 ◽  
Author(s):  
Christina Campbell ◽  
Eyitayo Onifade ◽  
William Davidson ◽  
Jodie Petersen

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