Prediction of Thrust and Torque for Multifacet Drills (MFD)

Ground anchors have been very practical in a wide range of geotechnical structures. Good bond properties at the anchor-mortar and mortar-rock interfaces can ensure transmitting an applied tensile load to a load bearing structure efficiently. The bond performance between the mortar and rock is necessary to be studied. A push-out test of mortar from rock block can be used to analyze the interfacial properties between the two materials. In this paper, an analytical model is proposed to determine the push-out capacity of mortar from rock block. Based on the deformation compatibility at the interface, the compressive stress in the mortar and the interfacial shear stress at the mortar-rock interface are formulated at different loading stages. By modeling interfacial debonding as an interfacial shear crack, the push-out load is then expressed as a function of the interfacial crack length. In virtue of the Lagrange Multiplier Method, the maximum push-out load is determined. The validity of the proposed model is verified with the experimental results. It can be concluded that if the interfacial parameters at the mortar-rock interface are obtained, the push-out capacity of mortar from rock block can be accurately determined using the proposed model. The proposed solution in this paper would provide a good theoretical basis in evaluating the stability of ground anchors in practice.

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Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽

10.3390/en14040930 ◽

2021 ◽

Vol 14 (4) ◽

pp. 930

Author(s):

Fahimeh Hadavimoghaddam ◽

Mehdi Ostadhassan ◽

Ehsan Heidaryan ◽

Mohammad Ali Sadri ◽

Inna Chapanova ◽

...

Keyword(s):

Machine Learning ◽

Viscosity Data ◽

Oil Viscosity ◽

Pvt Data ◽

Proposed Model ◽

Wide Range ◽

Engineering Problems ◽

Functional Forms ◽

Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

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Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Remote Sensing ◽

10.3390/rs11060608 ◽

2019 ◽

Vol 11 (6) ◽

pp. 608 ◽

Cited By ~ 2

Author(s):

Yun-Jia Sun ◽

Ting-Zhu Huang ◽

Tian-Hui Ma ◽

Yong Chen

Keyword(s):

Remote Sensing ◽

Real Data ◽

Total Variation Regularization ◽

Joint Sparsity ◽

Alternating Direction ◽

Proposed Model ◽

Comparison Results ◽

Wide Range ◽

Image Edges ◽

Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

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Multi-stable composite twisting structure for morphing applications

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2011.0631 ◽

2012 ◽

Vol 468 (2141) ◽

pp. 1230-1251 ◽

Cited By ~ 61

Author(s):

X. Lachenal ◽

P. M. Weaver ◽

S. Daynes

Keyword(s):

Analytical Model ◽

Material Properties ◽

Degrees Of Freedom ◽

Design Parameters ◽

Engineering Structures ◽

The Past ◽

Wide Range ◽

Morphing Structure ◽

Low Mass ◽

Unstable States

Conventional shape-changing engineering structures use discrete parts articulated around a number of linkages. Each part carries the loads, and the articulations provide the degrees of freedom of the system, leading to heavy and complex mechanisms. Consequently, there has been increased interest in morphing structures over the past decade owing to their potential to combine the conflicting requirements of strength, flexibility and low mass. This article presents a novel type of morphing structure capable of large deformations, simply consisting of two pre-stressed flanges joined to introduce two stable configurations. The bistability is analysed through a simple analytical model, predicting the positions of the stable and unstable states for different design parameters and material properties. Good correlation is found between experimental results, finite-element modelling and predictions from the analytical model for one particular example. A wide range of design parameters and material properties is also analytically investigated, yielding a remarkable structure with zero stiffness along the twisting axis.

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Analytical Model for Estimating the Impact of Changing the Nominal Power Parameter in LTE

Mobile Information Systems ◽

10.1155/2018/2458204 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7

Author(s):

A. B. Vallejo-Mora ◽

M. Toril ◽

S. Luna-Ramírez ◽

M. Regueira ◽

S. Pedraza

Keyword(s):

Analytical Model ◽

Mobile Networks ◽

Radio Resource Management ◽

Estimation Accuracy ◽

Model Assessment ◽

Power Parameter ◽

Management Procedure ◽

Proposed Model ◽

A Cell ◽

The Impact

UpLink Power Control (ULPC) is a key radio resource management procedure in mobile networks. In this paper, an analytical model for estimating the impact of increasing the nominal power parameter in the ULPC algorithm for the Physical Uplink Shared CHannel (PUSCH) in Long Term Evolution (LTE) is presented. The aim of the model is to predict the effect of changing the nominal power parameter in a cell on the interference and Signal-to-Interference-plus-Noise Ratio (SINR) of that cell and its neighbors from network statistics. Model assessment is carried out by means of a field trial where the nominal power parameter is increased in some cells of a live LTE network. Results show that the proposed model achieves reasonable estimation accuracy, provided uplink traffic does not change significantly.

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A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

Evolutionary Computation ◽

10.1162/evco_a_00107 ◽

2014 ◽

Vol 22 (1) ◽

pp. 159-188 ◽

Cited By ~ 5

Author(s):

Mikdam Turkey ◽

Riccardo Poli

Keyword(s):

Population Dynamics ◽

Dynamic Behaviour ◽

Fitness Landscape ◽

Population Based ◽

Levels Of Abstraction ◽

Collective Dynamic ◽

Model Studies ◽

Proposed Model ◽

Wide Range ◽

Multiple Levels

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

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Evaluation of Belleville Springs and Damping Through Static Cyclic Loading “Hysteresis” for pVARD Optimization: Part-I

10.1115/omae2021-61985 ◽

2021 ◽

Author(s):

Abdelsalam Abugharara ◽

Stephen Butt

Keyword(s):

Data Analysis ◽

Cyclic Loading ◽

Dynamic Compression ◽

Range Data ◽

Compression Tests ◽

Rotary Drilling ◽

Static Compression ◽

Drilling Performance ◽

Wide Range ◽

Compression Hysteresis

Abstract One unconventional application that researchers have been investigating for enhancing drilling performance, has been implemented through improving and stabilizing the most effective downhole drilling parameters including (i) increasing downhole dynamic weight on bit (DDWOB), (ii) stabilizing revolution per minutes (rpm), (iii) minimizing destructive downhole vibrations, among many others. As one portion of a three-part-research that consists of a comprehensive data analysis and evaluation of a static compression hysteresis, dynamic compression hysteresis, and corresponding drilling tests, this research investigates through static cyclic loading “Hysteresis” of individual and combined springs and damping the functionality of the passive Vibration Assisted Rotary Drilling (pVARD) tool that could be utilized towards enhancing the drilling performance. Tests are conducted on the two main pVARD tool sections that include (i) Belleville springs, which represent the elasticity portion and (ii) the damping section, which represents the viscous portion. Firstly, tests were conducted through static cyclic loading “Hysteresis” of (i) a mono elastic, (ii) a mono viscus, and (iii) dual elastic-viscus cyclic loading scenarios for the purpose of further examining pVARD functionality. For performing static compression tests, a calibrated geomechanics loading frame was utilized, and various spring stacking of different durometer damping were tested to seek a wide-range data and to provide a multi-angle analysis. Results involved analyzing loading and displacement relationships of individual and combined springs and damping are presented with detailed report of data analysis, discussion, and conclusions.

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An analytical model for predicting the shear strength of unsaturated soils

Proceedings of the Institution of Civil Engineers - Geotechnical Engineering ◽

10.1680/jgeen.21.00135 ◽

2022 ◽

pp. 1-57

Author(s):

Tuan A. Pham ◽

Melis Sutman

Keyword(s):

Shear Strength ◽

Analytical Model ◽

Unsaturated Soils ◽

High Performance ◽

Micromechanical Model ◽

Experimental Studies ◽

Stress Equilibrium ◽

Proposed Model ◽

New Equation ◽

Multi Phase

The prediction of shear strength for unsaturated soils remains to be a significant challenge due to their complex multi-phase nature. In this paper, a review of prior experimental studies is firstly carried out to present important pieces of evidence, limitations, and some design considerations. Next, an overview of the existing shear strength equations is summarized with a brief discussion. Then, a micromechanical model with stress equilibrium conditions and multi-phase interaction considerations is presented to provide a new equation for predicting the shear strength of unsaturated soils. The validity of the proposed model is examined for several published shear strength data of different soil types. It is observed that the shear strength predicted by the analytical model is in good agreement with the experimental data, and get high performance compared to the existing models. The evaluation of the outcomes with two criteria, using average relative error and the normalized sum of squared error, proved the effectiveness and validity of the proposed equation. Using the proposed equation, the nonlinear relationship between shear strength, saturation degree, volumetric water content, and matric suction are observed.

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Prediction of High Viscosity Liquid/Gas Two-Phase Slug Length in Horizontal and Slightly Inclined Pipelines

10.2118/206280-ms ◽

2021 ◽

Author(s):

Omar Shaaban ◽

Eissa Al-Safran

Keyword(s):

Numerical Optimization ◽

Flow Behavior ◽

Mechanistic Model ◽

High Viscosity ◽

Oil Well ◽

Liquid Slug ◽

Two Phase ◽

Proposed Model ◽

Wide Range ◽

Flow Closure

Abstract The production and transportation of high viscosity liquid/gas two-phase along petroleum production system is a challenging operation due to the lack of understanding the flow behavior and characteristics. In particular, accurate prediction of two-phase slug length in pipes is crucial to efficiently operate and safely design oil well and separation facilities. The objective of this study is to develop a mechanistic model to predict high viscosity liquid slug length in pipelines and to optimize the proper set of closure relationships required to ensure high accuracy prediction. A large high viscosity liquid slug length database is collected and presented in this study, against which the proposed model is validated and compared with other models. A mechanistic slug length model is derived based on the first principles of mass and momentum balances over a two-phase slug unit, which requires a set of closure relationships of other slug characteristics. To select the proper set of closure relationships, a numerical optimization is carried out using a large slug length dataset to minimize the prediction error. Thousands of combinations of various slug flow closure relationships were evaluated to identify the most appropriate relationships for the proposed slug length model under high viscosity slug length condition. Results show that the proposed slug length mechanistic model is applicable for a wide range of liquid viscosities and is sensitive to the selected closure relationships. Results revealed that the optimum closure relationships combination is Archibong-Eso et al. (2018) for slug frequency, Malnes (1983) for slug liquid holdup, Jeyachandra et al. (2012) for drift velocity, and Nicklin et al. (1962) for the distribution coefficient. Using the above set of closure relationships, model validation yields 37.8% absolute average percent error, outperforming all existing slug length models.

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Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

Journal of Energy Resources Technology ◽

10.1115/1.4041724 ◽

2018 ◽

Vol 141 (4) ◽

Cited By ~ 4

Author(s):

Qihong Feng ◽

Ronghao Cui ◽

Sen Wang ◽

Jin Zhang ◽

Zhe Jiang

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Diffusion Coefficient ◽

Co2 Storage ◽

Co2 Injection ◽

Support Vector ◽

Hybrid Technique ◽

Machine Model ◽

Proposed Model ◽

Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

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