Tailoring surface acoustic wave atomisation for cryo-electron microscopy sample preparation

Lab on a Chip ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1378-1385 ◽  
Author(s):  
Dariush Ashtiani ◽  
Alex de Marco ◽  
Adrian Neild
Keyword(s):  
Electron Microscopy ◽  
Fluid Flow ◽  
Sample Preparation ◽  
Acoustic Wave ◽  
Surface Acoustic Wave ◽  
Flow Rate ◽  
Fluid Flow Rate ◽  
Cryo Electron Microscopy ◽  
Electron Microscopy Grid

Surface acoustic wave (SAW) atomisation is investigated in the context of cryo electron microscopy grid preparation. Here, the primary requirements are a reproducible and narrow plume of droplets delivering a low fluid flow rate.

Application of Cross-correlation Analysis Method for Measurement of the Fluid Flow Rate Based on X-ray Radiation

2019 ◽  
Vol 11 (1) ◽  
pp. 01025-1-01025-5 ◽  
Author(s):  
N. A. Borodulya ◽  
◽  
R. O. Rezaev ◽  
S. G. Chistyakov ◽  
E. I. Smirnova ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Correlation Analysis ◽  
Flow Rate ◽  
Cross Correlation ◽  
Fluid Flow Rate ◽  
Analysis Method ◽  
X Ray ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

A Suction Device Using Air Under Pressure

1956 ◽  
Vol 23 (2) ◽  
pp. 269-272
Author(s):  
L. F. Welanetz
Keyword(s):  
Fluid Flow ◽  
Flow Rate ◽  
Fluid Flows ◽  
Central Hole ◽  
Fluid Flow Rate ◽  
Circular Plates ◽  
Suction Device ◽  
Holding Power ◽  
Plate Separation

Abstract An analysis is made of the suction holding power of a device in which a fluid flows radially outward from a central hole between two parallel circular plates. The holding power and the fluid flow rate are determined as functions of the plate separation. The effect of changing the proportions of the device is investigated. Experiments were made to check the analysis.

scholarly journals Estimation of Flow Rate Through Analysis of Pipe Vibration

2018 ◽  
Vol 12 (4) ◽  
pp. 294-300 ◽  
Author(s):  
Santhosh K. Venkata ◽  
Bhagya R. Navada
Keyword(s):  
Neural Network ◽  
Fluid Flow ◽  
Flow Rate ◽  
Back Propagation ◽  
Vibration Signal ◽  
Vibration Sensor ◽  
Percentage Error ◽  
Fluid Flow Rate ◽  
Bee Colony ◽  
Neural Network Algorithm

Abstract In this paper, implementation of soft sensing technique for measurement of fluid flow rate is reported. The objective of the paper is to design an estimator to physically measure the flow in pipe by analysing the vibration on the walls of the pipe. Commonly used head type flow meter causes obstruction to the flow and measurement would depend on the placement of these sensors. In the proposed technique vibration sensor is bonded on the pipe of liquid flow. It is observed that vibration in the pipe varies with the control action of stem. Single axis accelerometer is used to acquire vibration signal from pipe, signal is passed from the sensor to the system for processing. Basic techniques like filtering, amplification, and Fourier transform are used to process the signal. The obtained transform is trained using neural network algorithm to estimate the fluid flow rate. Artificial neural network is designed using back propagation with artificial bee colony algorithm. Designed estimator after being incorporated in practical setup is subjected to test and the result obtained shows successful estimation of flow rate with the root mean square percentage error of 0.667.

Mechanical Impact Effects of Fluid Hammer Effects on Drag Reduction of Coiled Tubing

2021 ◽  
pp. 1-10
Author(s):  
Yongsheng Liu ◽  
Xing Qin ◽  
Yuchen Sun ◽  
Zijun Dou ◽  
Jiansong Zhang ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Drag Reduction ◽  
Flow Rate ◽  
Fluid Velocity ◽  
Fluid Pressure ◽  
Great Influence ◽  
Radial Force ◽  
Fluid Flow Rate ◽  
Normal Contact ◽  
Coiled Tubing

Abstract Aiming at the oscillation drag reduction tool that improves the extension limit of coiled tubing downhole operations, the fluid hammer equation of the oscillation drag reducer is established based on the fluid hammer effect. The fluid hammer equation is solved by the asymptotic method, and the distribution of fluid pressure and flow velocity in coiled tubing with oscillation drag reducers is obtained. At the same time, the axial force and radial force of the coiled tubing caused by the fluid hammer oscillator are calculated according to the momentum theorem. The radial force will change the normal contact force of the coiled tubing which has a great influence on frictional drag. The results show that the fluid flow rate and pressure decrease stepwise from the oscillator position to the wellhead position, and the fluid flow rate and pressure will change abruptly during each valve opening and closing time. When the fluid passes through the oscillator, the unit mass fluid will generate an instantaneous axial tension due to the change in the fluid velocity, thereby converting the static friction into dynamic friction, which is conducive to the extend limit of coiled tubing.

Effect of Cutting Parameters on the Surface Residual Stress of Face-Milled Pearlitic Ductile Iron

2017 ◽  
Vol 4 (1) ◽  
pp. 38-52
Author(s):  
Olutosin Olufisayo Ilori ◽  
Dare A. Adetan ◽  
Lasisi E. Umoru
Keyword(s):  
Residual Stress ◽  
Fluid Flow ◽  
Flow Rate ◽  
Ductile Iron ◽  
Cutting Parameters ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Fluid Flow Rate ◽  
Surface Residual Stress ◽  
Average Surface

The study determined the effect of cutting parameters on the surface residual stress of face-milled pearlitic ductile iron with a view to enhancing surface integrity of machined parts in the manufacturing industries. The pearlitic ductile iron used for this study was prepared and four cutting parameters were considered. The results obtained showed that the average surface residual stress of the machined surfaces was tensile and increased significantly with increase in depth of cut. Feed rate and cutting speed exhibited some effect, though not statistically significant, on average surface residual stress. The average residual stress was found to decrease significantly and drastically from 605.39 MPa to 101.72 MPa as cutting fluid flow rate increased from 0 ?/min to 4 ?/min. The study concluded that out of all four cutting parameters investigated, the cutting fluid flow rate has most considerable influence on the surface residual stress of the machined pearlitic ductile iron.

scholarly journals Nonlinear Autoregressive Neural Networks to Predict Hydraulic Fracturing Fluid Leakage into Shallow Groundwater

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 841 ◽  
Author(s):  
Reza Taherdangkoo ◽  
Alexandru Tatomir ◽  
Mohammad Taherdangkoo ◽  
Pengxiang Qiu ◽  
Martin Sauter
Keyword(s):  
Neural Networks ◽  
Fluid Flow ◽  
Hydraulic Fracturing ◽  
Flow Rate ◽  
Shallow Groundwater ◽  
Fluid Flow Rate ◽  
Shallow Aquifers ◽  
Fracturing Fluid ◽  
Upward Migration ◽  
Nonlinear Autoregressive

Hydraulic fracturing of horizontal wells is an essential technology for the exploitation of unconventional resources, but led to environmental concerns. Fracturing fluid upward migration from deep gas reservoirs along abandoned wells may pose contamination threats to shallow groundwater. This study describes the novel application of a nonlinear autoregressive (NAR) neural network to estimate fracturing fluid flow rate to shallow aquifers in the presence of an abandoned well. The NAR network is trained using the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithms and the results were compared to identify the optimal network architecture. For NAR-LM model, the coefficient of determination (R2) between measured and predicted values is 0.923 and the mean squared error (MSE) is 4.2 × 10−4, and the values of R2 = 0.944 and MSE = 2.4 × 10−4 were obtained for the NAR-BR model. The results indicate the robustness and compatibility of NAR-LM and NAR-BR models in predicting fracturing fluid flow rate to shallow aquifers. This study shows that NAR neural networks can be useful and hold considerable potential for assessing the groundwater impacts of unconventional gas development.

scholarly journals Development of a deep learning-based method to identify “good” regions of a cryo-electron microscopy grid

2020 ◽  
Vol 12 (2) ◽  
pp. 349-354 ◽  
Author(s):  
Yuichi Yokoyama ◽  
Tohru Terada ◽  
Kentaro Shimizu ◽  
Kouki Nishikawa ◽  
Daisuke Kozai ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Deep Learning ◽  
Cryo Electron Microscopy ◽  
Electron Microscopy Grid
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