The Expanded Lever Rule on the Phase Transformation Rate Curve for Phase Volume Fraction Calculation in Thermal Simulations

2020 ◽  
Vol 51 (10) ◽  
pp. 5049-5055
Author(s):  
Xuan-Wei Lei ◽  
Rong-Bo Yang ◽  
Lin-Lin Xiao ◽  
Jia-Jun Ding ◽  
Xue-Hui Zhang ◽  
...  
Keyword(s):  
Phase Transformation ◽  
Volume Fraction ◽  
Rate Curve ◽  
Transformation Rate ◽  
Lever Rule ◽  
Phase Volume ◽  
Phase Volume Fraction ◽  
Thermal Simulations

scholarly journals Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Wook Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee
Keyword(s):  
High Performance ◽  
Materials Science ◽  
Volume Fraction ◽  
Training Data ◽  
Phase Fraction ◽  
Phase Volume ◽  
Generative Adversarial Network ◽  
Phase Volume Fraction ◽  
Original Dataset ◽  
Multi Phase

AbstractAdvanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.

The Influence of the Ferrite and Pearlite Grain Size on the S-N Fatigue Characteristics of Steel

2014 ◽  
Vol 224 ◽  
pp. 3-8 ◽  
Author(s):  
Sebastian Kamiński ◽  
Marcel Szymaniec ◽  
Tadeusz Łagoda
Keyword(s):  
Mechanical Properties ◽  
Grain Size ◽  
Volume Fraction ◽  
Fatigue Properties ◽  
Phase Volume ◽  
Bending Tests ◽  
Phase Volume Fraction ◽  
Similar Chemical ◽  
Fatigue Characteristics ◽  
Ferrite Grain Size

In this work an investigation of internal structure influence on mechanical and fatigue properties of ferritic-pearlitic steels is shown. Ferrite grain size and phase volume fraction of three grades of structural steel with similar chemical composition, but different mechanical properties, were examined. Afterwards, samples of the materials were subjected to cyclic bending tests. The results and conclusions are presented in this paper

A two-step approach for copper and nickel extracting and recovering by emulsion liquid membrane

2016 ◽  
Vol 74 (10) ◽  
pp. 2454-2461
Author(s):  
Qiang Bi ◽  
Juanqin Xue ◽  
Yingjuan Guo ◽  
Guoping Li ◽  
Haibin Cui
Keyword(s):  
Liquid Membrane ◽  
Volume Fraction ◽  
Process Variable ◽  
Phase Volume ◽  
Optimum Conditions ◽  
Application Technology ◽  
Emulsion Liquid Membrane ◽  
Phase Volume Fraction ◽  
Internal Phase ◽  
Valuable Metals

The recycling of copper and nickel from metallurgical wastewater using emulsion liquid membrane (ELM) was studied. P507 (2-ethylhexyl phosphonic acid-2-ethylhexyl ester) and TBP (tributyl phosphate) were used as carriers for the extraction of copper and nickel by ELMs, respectively. The influence of four emulsion composition variables, namely, the internal phase volume fraction (ϕ), surfactant concentration (Wsurf), internal phase stripping acid concentration (Cio) and the carrier concentration (Cc), and the process variable treat ratio on the extraction efficiencies of copper or nickel were studied. Under the optimum conditions, 98% copper and nickel were recycled by using ELM. The results indicated that ELM extraction is a promising industrial application technology to retrieve valuable metals in low concentration metallurgical wastewater.

Prediction of Frictional Pressure Gradient in Horizontal Oil/Water Dispersion Flow

SPE Journal ◽  
2010 ◽  
Vol 16 (01) ◽  
pp. 148-154 ◽  
Author(s):  
Jany Carolina Vielma ◽  
Ovadia Shoham ◽  
Ram S. Mohan ◽  
Luis E. Gomez
Keyword(s):  
Pressure Gradient ◽  
Volume Fraction ◽  
Average Error ◽  
Dispersed Phase ◽  
Phase Volume ◽  
Water Dispersion ◽  
Phase Volume Fraction ◽  
Law Of The Wall ◽  
Oil Water ◽  
The Law

Summary A novel model has been developed for the prediction of frictional pressure gradient in unstable turbulent oil/water dispersion flow in horizontal pipes. This model uses the friction-factor approach, based on the law of the wall, to predict the pressure gradient. Modification of both the von Karman coefficient κ' and the parameter B' have been carried out in the law of the wall to include the effect of the dispersed phase—namely, the dispersed-phase volume fraction and the characteristic-droplet-size diameters. The developed model applies to both dilute and dense flows, covering the entire range of water cuts. Model predictions have been compared with a comprehensive experimental database collected from literature, resulting in an absolute average error of 9.6%. Also, the comparisons demonstrate that the developed model properly represents the physical phenomena exhibited in unstable turbulent oil/water dispersions. These include drag reduction, increase in frictional pressure gradient with increasing dispersed-phase volume fraction, and the peak in the frictional pressure gradient at the oil/water phase-inversion region.

Sign in / Sign up

Export Citation Format

Share Document