Numerical Study on Effects of Drilling Mud Rheological Properties on the Transport of Drilling Cuttings

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
E. GhasemiKafrudi ◽  
S. H. Hashemabadi
Keyword(s):  
Pressure Drop ◽  
Velocity Profile ◽  
Volume Fraction ◽  
Solid Phase ◽  
Numerical Study ◽  
Drilling Mud ◽  
Phase Volume ◽  
Phase Volume Fraction ◽  
Pressure Fields ◽  
New Correlation

Inaccurate prediction of the required pressures can lead to a number of costly drilling problems. In this study, the hydrodynamics of mud-cuttings were numerically studied using the Mixture Model. To this end, an in-house code was developed to calculate the velocity and pressure fields. The mud velocity profile using of Herschel–Bulkley model and solid phase volume fraction were locally calculated; moreover, pressure drop through the annulus was taken into account. The effects of velocity, mud properties, and solid phase volume fraction on pressure drop were discussed and a new correlation was proposed for calculating friction factor based on corresponding parameters.

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.

Numerical Study of Forced Convection of Nanofluids in a Microchannel Heat Sink

2008 ◽  
Author(s):  
Parisa Vaziee ◽  
Omid Abouali
Keyword(s):  
Heat Transfer ◽  
Pressure Drop ◽  
Heat Sink ◽  
Volume Fraction ◽  
Numerical Study ◽  
Heat Removal ◽  
Al2o3 Nanoparticles ◽  
Microchannel Heat Sink ◽  
Particle Volume ◽  
Volume Fractions

Effectiveness of the microchannel heat sink cooled by nanofluids with various particle volume fractions is investigated numerically using the latest theoretical models for conductivity and viscosity of the nanofluids. Both laminar and turbulent flows are considered in this research. The model of conductivity used in this research accounts for the fundamental role of Brownian motion of the nanoparticles which is in good agreement with the experimental data. The changes in viscosity of the nanofluid due to temperature variation are considered also. Final results are compared with the experimental measurements for heat transfer coefficient and pressure drop in microchannel. Enhancement in heat transfer is achieved for laminar flow with increasing of volume fraction of Al2O3 nanoparticles. But for turbulent flow an enhancement of heat removal was not seen and using higher volume fractions of nanoparticles increases the maximum substrate temperature. Pressure drop is increased with using nanofluids because of the augmentation in the viscosity and this increase is more noticeable in higher Reynolds numbers.

Sign in / Sign up

Export Citation Format

Share Document