scholarly journals Ground Subsidence Prediction Model and Parameter Analysis for Underground Gas Storage in Horizontal Salt Caverns

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Guimin Zhang ◽  
Yuxuan Liu ◽  
Tao Wang ◽  
Hao Zhang ◽  
Zhenshuo Wang
Keyword(s):  
Prediction Model ◽  
Operating Conditions ◽  
Parameter Analysis ◽  
Ground Subsidence ◽  
Subsidence Trough ◽  
Reduced Volume ◽  
Proposed Model ◽  
Maximum Subsidence ◽  
Bedded Salt Formations ◽  
Salt Caverns

Due to a great demand of natural gas or oil storage in these years, horizontal caverns were proposed to fully use bedded salt formations of China. Under the same geological and operating conditions, the horizontal cavern would shrink more than traditional pear-shaped cavern, which might bring larger ground subsidence and affect the safety of storage facilities. A new prediction model was proposed in this paper for the time-dependent ground subsidence above horizontal caverns. The proposed model considered the impurity of bedded salt formations and simplified the horizontal cavern to an ideal cylinder. The shape of the subsidence trough was determined by the probabilistic integration method, and corresponding calculation formulas for the tilt, curvature, horizontal displacement, and horizontal strain were derived. Based on the assumption that the subsidence volume at the ground was proportional to the reduced volume of horizontal cavern, a formula for the reduced volume over time was established. FLAC3D was introduced to simulate the ground subsidence, and the results show that the proposed prediction model agreed well with the simulation results. Finally, the proposed prediction model was used to analyze the impacts of different stratigraphic parameters and design parameters. The results mainly show that, as the draw angle increases, the subsidence trough becomes deeper and narrower; as the depth of the cavern increases, the maximum subsidence first increases and then decreases, and the subsidence trough gradually becomes round; with the increase of the purity, the subsidence gradually decreases; with the increase of the creep properties and the stress exponential constant, the maximum subsidence first increases rapidly and then slowly approaches the limit; increasing the brine extraction velocity can shorten the cavern construction period and then reduce excessive ground subsidence; the subsidence decreases nonlinearly with the increase of internal pressure; with the increase of the cross section diameter and length of the horizontal cavern, the subsidence presents a significant nonlinear increase. In addition, unlike the traditional pear-shaped cavern, under the same conditions, the ground subsidence above the horizontal cavern according to this newly proposed model is much larger, and the ground subsidence contour line is no longer a standard circle. The findings of this study can help for better understanding of the prediction of ground subsidence above salt caverns and also provide a reference for the design and construction. However, the proposed prediction method is ideal and theoretical and should be further improved by engineering practice in the future.

A Study of Transmission Error Modeling and Preload Compensation for the Cable-Driven Sheaves Used in Space Docking Locks

2020 ◽  
Vol 36 (6) ◽  
pp. N9-N20
Author(s):  
Chuntian Xu ◽  
Jianguang Li ◽  
Peng Wang ◽  
Zhengdong Xu
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Friction Coefficient ◽  
Prediction Model ◽  
Transmission Error ◽  
Error Modeling ◽  
Operating Conditions ◽  
Element Analysis ◽  
Proposed Model ◽  
Testing Efficiency

ABSTRACTThe transmission error of cable-driven sheaves (CDS) used in space docking locks directly affects the synchronous docking of two spacecraft, which is guaranteed mainly by the preload applied to their serial cables. But it is difficult controlled precisely because of the complicated cable deformation and operating conditions. The synchronous testing efficiency of the docking locks is inevitably influenced, correspondingly. This paper proposes a prediction model for the transmission error of CDS based on their cable deformation. In this model, the deformations of non- and free sectional cables are both modified on finite element analysis, which are respectively derived from classical Capstan equation and Hooke’s law for them without considering the effects of the friction coefficient between wire strands. Based on the proposed model, the relationships between the transmission error and dominating factors are analyzed. Then the preload compensation for transmission error is obtained at the engaging and locking angles of the docking locks, respectively. Experiments validate the model. This can provide a valuable reference in controlling the transmission error of CDS and improving the assembly efficiency of docking locks.

Prediction model of flow field in an isolator over various operating conditions

2021 ◽  
Vol 111 ◽  
pp. 106576
Author(s):  
Chen Kong ◽  
Juntao Chang ◽  
Ziao Wang ◽  
Yunfei Li
Keyword(s):  
Flow Field ◽  
Prediction Model ◽  
Operating Conditions

scholarly journals Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mingli Wang ◽  
Huikuan Gu ◽  
Jiang Hu ◽  
Jian Liang ◽  
Sisi Xu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Prediction Model ◽  
Volumetric Modulated Arc Therapy ◽  
Model Evolution ◽  
Prediction Ability ◽  
Knowledge Based ◽  
Proposed Model ◽  
Arc Therapy ◽  
Knowledge Based Planning

Abstract Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

2021 ◽  
pp. 1-18
Author(s):  
Zhang Zixian ◽  
Liu Xuning ◽  
Li Zhixiang ◽  
Hu Hongqiang
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Influencing Factors ◽  
Association Rules ◽  
Coal And Gas Outburst ◽  
Feature Subset ◽  
Gas Outburst ◽  
Proposed Model ◽  
Optimal Feature Subset ◽  
Feature Dimension

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.

Ground Motion Prediction Model for the Peak Inelastic Displacement of Single-Degree-of-Freedom Bilinear Systems

2018 ◽  
Vol 34 (3) ◽  
pp. 1177-1199 ◽  
Author(s):  
Pablo Heresi ◽  
Héctor Dávalos ◽  
Eduardo Miranda
Keyword(s):  
Prediction Model ◽  
Ground Motion ◽  
Degree Of Freedom ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Vibration Period ◽  
Single Degree Of Freedom ◽  
Inelastic Displacement ◽  
Single Degree ◽  
Proposed Model

This paper presents a ground motion prediction model (GMPM) for estimating medians and standard deviations of the random horizontal component of the peak inelastic displacement of 5% damped single-degree-of-freedom (SDOF) systems, with bilinear hysteretic behavior and 3% postelastic stiffness ratio, directly as a function of the earthquake magnitude and the distance to the source. The equations were developed using a mixed effects model, with 1,662 recorded ground motions from 63 seismic events. In the proposed model, the median is computed as a function of the vibration period and the normalized strength of the system, as well as the event magnitude and the Joyner-Boore distance to the source. The standard deviation of the model is computed as a function of the vibration period and the normalized strength of the system. The proposed model has the advantage of not requiring an auxiliary elastic GMPM to predict the median and dispersion of peak inelastic displacement.

scholarly journals Design and Analysis of High-Efficient Driver Model for LED Luminaires

2021 ◽  
pp. 96-106
Author(s):  
Onur Akalp ◽  
Harun Ozbay ◽  
Serhat Berat Efe
Keyword(s):  
Energy Efficient ◽  
Supply System ◽  
Operating Conditions ◽  
Driver Model ◽  
Zero Voltage Switching ◽  
Proposed Model ◽  
High Efficient ◽  
Driver Circuit ◽  
Low Volume ◽  
Zero Voltage

LED luminaires need a driver circuit for working properly. Most of the drivers have disadvantages such as losses during operation. This issue becomes more important while supplying with limited sources such as renewables. To overcome the problem, this study proposes a novel energy efficient driver for LED luminaires based on zero voltage switching (ZVS) single-ended primary inductance converter (SEPIC) technology. Driver and hence luminaires were designed to be fed from photovoltaic (PV) panels. In addition, an adaptive MPPT algorithm was developed to obtain optimum efficiency from supply system. SEPIC approach was preferred for MPPT application due to its advantages such as non-reversing polarity. This feature allows energy efficiency in corporation with ZVS. Proposed model was designed under PSIM platform with all components; PV panels, ZVS, SEPIC, and LED luminaires. A detailed analysis was performed by using system graphs under various operating conditions as different irradiance levels. Results show that proposed model is energy efficient and modular because of its low-volume structure. Therefore the model can lead smaller driver circuits with minimum losses.

scholarly journals Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Christina Ng ◽  
Susilawati Susilawati ◽  
Md Abdus Samad Kamal ◽  
Irene Mei Leng Chew
Keyword(s):  
Prediction Model ◽  
Transmission Model ◽  
Lane Change ◽  
Traffic Data ◽  
Cell Transmission Model ◽  
Traffic State ◽  
Proposed Model ◽  
Significant Difference ◽  
The Mean ◽  
Change Prediction

This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).

scholarly journals Machine Learning and Prediction-Based Resource Management in IoT Considering Qos

2019 ◽  
Vol 8 (2) ◽  
pp. 687-694
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Real Data ◽  
Computing Methods ◽  
Energy Utilization ◽  
Real Field ◽  
Proposed Model ◽  
Improved Performance ◽  
Video Sensors

Internet of Things (IoT) is one of the fast-growing technology paradigms used in every sectors, where in the Quality of Service (QoS) is a critical component in such systems and usage perspective with respect to ProSumers (producer and consumers). Most of the recent research works on QoS in IoT have used Machine Learning (ML) techniques as one of the computing methods for improved performance and solutions. The adoption of Machine Learning and its methodologies have become a common trend and need in every technologies and domain areas, such as open source frameworks, task specific algorithms and using AI and ML techniques. In this work we propose an ML based prediction model for resource optimization in the IoT environment for QoS provisioning. The proposed methodology is implemented by using a multi-layer neural network (MNN) for Long Short Term Memory (LSTM) learning in layered IoT environment. Here the model considers the resources like bandwidth and energy as QoS parameters and provides the required QoS by efficient utilization of the resources in the IoT environment. The performance of the proposed model is evaluated in a real field implementation by considering a civil construction project, where in the real data is collected by using video sensors and mobile devices as edge nodes. Performance of the prediction model is observed that there is an improved bandwidth and energy utilization in turn providing the required QoS in the IoT environment.

A Nitrogen Oxides Emission Prediction Model for Gas Turbines Based on Interpretable Multilayer Perceptron Neural Networks

2020 ◽  
Author(s):  
Dawen Huang ◽  
Shanhua Tang ◽  
Dengji Zhou
Keyword(s):  
Neural Networks ◽  
Nitrogen Oxides ◽  
Prediction Model ◽  
Working Conditions ◽  
Gas Turbines ◽  
Middle Layer ◽  
Operating Conditions ◽  
Nox Emission ◽  
Outlet Temperature ◽  
Output Layer

Abstract Gas turbines, an important energy conversion equipment, produce Nitrogen Oxides (NOx) emissions, endangering human health and forming air pollution. With the increasingly stringent NOx emission standards, it is more significant to ascertain NOx emission characteristics to reduce pollutant emissions. Establishing an emission prediction model is an effective way for real-time and accurate monitoring of the NOx discharge amount. Based on the multi-layer perceptron neural networks, an interpretable emission prediction model with a monitorable middle layer is designed to monitor NOx emission by taking the ambient parameters and boundary parameters as the network inputs. The outlet temperature of the compressor is selected as the monitorable measuring parameters of the middle layer. The emission prediction model is trained by historical operation data under different working conditions. According to the errors between the predicted values and measured values of the middle layer and output layer, the weights of the emission prediction model are optimized by the back-propagation algorithm, and the optimal NOx emission prediction model is established for gas turbines under the various working conditions. Furthermore, the mechanism of predicting NOx emission value is explained based on known parameter influence laws between the input layer, middle layer and output layer, which helps to reveal the main measurement parameters affecting NOx emission value, adjust the model parameters and obtain more accurate prediction results. Compared with the traditional emission monitoring methods, the emission prediction model has higher accuracy and faster calculation efficiency and can obtain believable NOx emission prediction results for various operating conditions of gas turbines.

scholarly journals An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Yuanzhe Yao ◽  
Zeheng Wang ◽  
Liang Li ◽  
Kun Lu ◽  
Runyu Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Prediction Model ◽  
Side Effect ◽  
Treatment Model ◽  
Proposed Model ◽  
Main Components

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.

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