scholarly journals Mechanism Analysis of Roadway Rockbursts Induced by Dynamic Mining Loading and Its Application

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2313 ◽  
Author(s):  
Zheng-yi Wang ◽  
Lin-ming Dou ◽  
Gui-feng Wang
Keyword(s):  
Real Time ◽  
Failure Process ◽  
Impact Resistance ◽  
Mechanism Analysis ◽  
Dynamic Failure ◽  
Analysis Model ◽  
Effective Prevention ◽  
Safety Production ◽  
Ultimate Resistance

Roadway rockbursts seriously restrict the safety production of coal mines; however, the interaction between dynamic loads and roadway surrounding rocks has not been fully considered in existing studies. A dynamic failure analysis model of anchoring supporting structures was established to analyze dynamic effects of stress waves. Taking a rockburst in LW402103 of the Hujiahe coal mine as the case, the theoretical model was well applied and verified. The static cumulative resistance Qs (210.5 kN) which was incurred by deformation of rocks provided the basis (81.93% in the overall real-time resistance Q) of dynamic failures. However, the additional impact resistance Qd (46.43 kN) brought about by the energy release in the failure process of elastic zones triggered impact failures. As a result, under conditions that the overall real-time resistance Q (256.93 kN) exceeded the ultimate resistance [Q] (250.8 kN), the dynamic failure of supports occurred. The in situ application was implemented by taking pressure-relief measures and parameter optimizations of roadway supports, which achieved an effective prevention of rockbursts.

A Real-Time Friction Prediction Model for in Service Drill String Based on Machine Learning Methods Coupling with Mechanical Mechanism Analysis

2021 ◽  
Author(s):  
Huijuan Guo ◽  
Huaidong Luo ◽  
Guodong Zhan ◽  
Baodong Wang ◽  
Shuo Zhu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Horizontal Wells ◽  
Drill String ◽  
Mechanism Analysis ◽  
Analysis Model ◽  
Machine Learning Methods ◽  
Weight On Bit ◽  
Friction Prediction ◽  
Independent Parameters

Abstract With highly deviated wells and horizontal wells are widely used in the oil industry. The large slope well sections and long horizontal well sections will lead to a sharp increase of the drill string torque and friction, which may reduce the drilling efficiency, and even lead to accidents. Therefore, real-time and accurate analysis of drill string’s torque and friction is an urgent problem facing by the modern drilling technology. The paper established a real-time friction prediction model that combines machine learning methods with drill string mechanical mechanism analysis model. Based on 84000 sets of field monitoring data obtained on-site, a regular data training set for weight on bit (WOB) and torque prediction was constructed with 23 types of time-series related parameters and 10 types of timing independent parameters. Relationships between time-series related parameters and timing independent parameters with the weight on bit and torque were trained to utilize long and short-term memory (LSTM) neural network and muti-layer back propagation (BP) network respectively. The new developed LSTM-BP neural network achieves high-precision prediction results of WOB and torque with a relative error of less than 14%. Based on derived WOB and torque prediction results, a theoretical mechanical analysis model of the entire drill string was adopted in this paper to develop the quantitative relation between WOB and torque with the friction coefficient of the drill string and oil casing. Suitable friction coefficients along the drill string can be finally obtained by solving the equilibrium function between predicted WOB, torque and measured hook load, rotary-table torque via an iteration algorithm. A case study was performed finally using the proposed intelligent analysis method to calculate the friction coefficients. This proposed methodology can be referenced to decrease the sticking risks and improve the drilling efficiency, which can finally increase the extension limit of horizontal wells in complex strata.

Probing the Dynamic Self-Assembly Behaviour of Photoswitchable Wormlike Micelles in Real Time

2018 ◽  
Author(s):  
Elaine A. Kelly ◽  
Judith E. Houston ◽  
Rachel Evans
Keyword(s):  
Real Time ◽  
Absorption Spectroscopy ◽  
Self Assembly ◽  
Uv Light ◽  
Wormlike Micelles ◽  
Tetraethylene Glycol ◽  
Light Illumination ◽  
First Time ◽  
Dependent Processes

Understanding the dynamic self-assembly behaviour of azobenzene photosurfactants (AzoPS) is crucial to advance their use in controlled release applications such as<i></i>drug delivery and micellar catalysis. Currently, their behaviour in the equilibrium <i>cis-</i>and <i>trans</i>-photostationary states is more widely understood than during the photoisomerisation process itself. Here, we investigate the time-dependent self-assembly of the different photoisomers of a model neutral AzoPS, <a>tetraethylene glycol mono(4′,4-octyloxy,octyl-azobenzene) </a>(C<sub>8</sub>AzoOC<sub>8</sub>E<sub>4</sub>) using small-angle neutron scattering (SANS). We show that the incorporation of <i>in-situ</i>UV-Vis absorption spectroscopy with SANS allows the scattering profile, and hence micelle shape, to be correlated with the extent of photoisomerisation in real-time. It was observed that C<sub>8</sub>AzoOC<sub>8</sub>E<sub>4</sub>could switch between wormlike micelles (<i>trans</i>native state) and fractal aggregates (under UV light), with changes in the self-assembled structure arising concurrently with changes in the absorption spectrum. Wormlike micelles could be recovered within 60 seconds of blue light illumination. To the best of our knowledge, this is the first time the degree of AzoPS photoisomerisation has been tracked <i>in</i><i>-situ</i>through combined UV-Vis absorption spectroscopy-SANS measurements. This technique could be widely used to gain mechanistic and kinetic insights into light-dependent processes that are reliant on self-assembly.

Flat Flexible Thin Milli-electrode Array for Real-time in situ Water Quality Monitoring in Distribution Systems

2017 ◽  
Vol 2017 (4) ◽  
pp. 5598-5617
Author(s):  
Zhiheng Xu ◽  
Wangchi Zhou ◽  
Qiuchen Dong ◽  
Yan Li ◽  
Dingyi Cai ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Distribution Systems ◽  
Electrode Array ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Underground Microseismic Event Monitoring and Localization within Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2830
Author(s):  
Sili Wang ◽  
Mark P. Panning ◽  
Steven D. Vance ◽  
Wenzhan Song
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Information Needs ◽  
Localization Algorithm ◽  
Distributed Sensors ◽  
Distributed Sensor ◽  
Microseismic Events ◽  
The Stability ◽  
Stability And Accuracy

Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests.

scholarly journals An Autonomous Platform for Near Real-Time Surveillance of Harmful Algae and Their Toxins in Dynamic Coastal Shelf Environments

2021 ◽  
Vol 9 (3) ◽  
pp. 336
Author(s):  
Stephanie K. Moore ◽  
John B. Mickett ◽  
Gregory J. Doucette ◽  
Nicolaus G. Adams ◽  
Christina M. Mikulski ◽  
...  
Keyword(s):  
Real Time ◽  
Domoic Acid ◽  
Harmful Algae ◽  
Coastal Trapped Waves ◽  
Resource Managers ◽  
Bloom Formation ◽  
High Concentrations ◽  
Data Gap ◽  
Autonomous Technology

Efforts to identify in situ the mechanisms underpinning the response of harmful algae to climate change demand frequent observations in dynamic and often difficult to access marine and freshwater environments. Increasingly, resource managers and researchers are looking to fill this data gap using unmanned systems. In this study we integrated the Environmental Sample Processor (ESP) into an autonomous platform to provide near real-time surveillance of harmful algae and the toxin domoic acid on the Washington State continental shelf over a three-year period (2016–2018). The ESP mooring design accommodated the necessary subsystems to sustain ESP operations, supporting deployment durations of up to 7.5 weeks. The combination of ESP observations and a suite of contextual measurements from the ESP mooring and a nearby surface buoy permitted an investigation into toxic Pseudo-nitzschia spp. bloom dynamics. Preliminary findings suggest a connection between bloom formation and nutrient availability that is modulated by wind-forced coastal-trapped waves. In addition, high concentrations of Pseudo-nitzschia spp. and elevated levels of domoic acid observed at the ESP mooring location were not necessarily associated with the advection of water from known bloom initiation sites. Such insights, made possible by this autonomous technology, enable the formulation of testable hypotheses on climate-driven changes in HAB dynamics that can be investigated during future deployments.

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