scholarly journals Prediction and analysis of penetration rate in drilling operation using deterministic and metaheuristic optimization methods

2021 ◽  
Author(s):  
Ibrahim Sobhi ◽  
Abdelmadjid Dobbi ◽  
Oussama Hachana
Keyword(s):  
High Performance ◽  
Mean Squared Error ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Computational Time ◽  
Objective Functions ◽  
Memory Space ◽  
Drilling Operation ◽  
Drilling Parameters ◽  
Selection Of

AbstractThe rate of penetration (ROP) optimization is one of the most important factors in improving drilling efficiency, especially in the downturn time of oil prices. This process is crucial in the well planning and exploration phases, where the selection of the drilling bits and parameters has a significant impact on the total cost and time of the drilling operation. Thus, the optimization and best selection of the drilling parameters are critical. Optimization of ROP is difficult due to the complexity of the relationship between the drilling variables and the ROP. For this reason, the development of high-performance computer systems, predictive models, and algorithms will be the best solution. In this study, a new investigation approach for ROP optimization has been done regarding different ROP models (Maurer, Bingham, Bourgoyne and Young models), algorithms (Multiple regression, ant colony optimization (ACO), fminunc, fminsearch, fsolve, lsqcurvefit, lsqnonlin), and different objective functions. The well-known data from the Louisiana field in an offshore well have been used to compare the used parameter estimation approach with other techniques. Indeed, datasets from an onshore well in the Hassi Messaoud Algerian field are explored. The results confirmed the superiority and the effectiveness of B&Y models compared to Bingham and Maurer models. Fminsearch, lsqcurvefit, ACO, and Excel (GRG) algorithms give the best results in ROP prediction while the application of the MNLR approach. Using the mean squared error (MSE) and the determination coefficient (R$$^{2}$$ 2 ) as objective functions significantly increases the accuracy prediction where the results given are ($$R=0.9522$$ R = 0.9522 , $$RMSE=2.85$$ R M S E = 2.85 ) and ($$R= 0.9811$$ R = 0.9811 , $$RMSE=4.08$$ R M S E = 4.08 ) for Wells 1 and 2, respectively. This study validates the application of B&Y model in both onshore and offshore wells. The findings reveal to deal with data limitation problems in ROP prediction. Simple and effective optimization techniques that require less memory space and computational time have been provided.

Optimization of Machining Parameters during Drilling of 7075 Aluminium Alloy

2012 ◽  
Vol 248 ◽  
pp. 20-25
Author(s):  
Abolfazl Golshan ◽  
Danial Ghodsiyeh ◽  
Soheil Gohari ◽  
Amran Bin Ayob ◽  
B.T. Hang Tuah Baharudin
Keyword(s):  
Aluminum Alloy ◽  
Performance Optimization ◽  
High Performance ◽  
Cutting Speed ◽  
Drilling Process ◽  
Machining Parameters ◽  
Algorithm Optimization ◽  
Drilling Parameters ◽  
Drill Diameter ◽  
Selection Of

Proper selection of drilling parameters is one of the significant challenges in drilling process. In this study, a new method for selection of optimal machining parameters during drilling operation is investigated. The present study deals with multiple-performance optimization of machining characteristics during drilling of 7075 aluminum alloy. The most commonly-used material in aerospace industry is aluminum alloy with zinc as the primary alloying element. The drilling parameters used for this experiment include cutting speed, feed rate and drill diameter while the two output parameters are surface roughness and dimension error. These outputs are specified to be optimized as a measure of process performance. The statistical model is generated from linear polynomial equations which are developed from different output responses when the machining parameters are changed. The Non-dominated Sorting Genetic Algorithm optimization results show high performance in solving the present problem.

scholarly journals Calibration of the modified Bartlett-Lewis model using global optimization techniques and alternative objective functions

2012 ◽  
Vol 16 (3) ◽  
pp. 873-891 ◽  
Author(s):  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
K. Scheerlinck ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Global Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Local Search Algorithms ◽  
Stochastic Point Process ◽  
Shuffled Complex Evolution ◽  
Global Optimization Methods ◽  
Downhill Simplex

Abstract. The calibration of stochastic point process rainfall models, such as of the Bartlett-Lewis type, suffers from the presence of multiple local minima which local search algorithms usually fail to avoid. To meet this shortcoming, four relatively new global optimization methods are presented and tested for their ability to calibrate the Modified Bartlett-Lewis Model. The list of tested methods consists of: the Downhill Simplex Method, Simplex-Simulated Annealing, Particle Swarm Optimization and Shuffled Complex Evolution. The parameters of these algorithms are first optimized to ensure optimal performance, after which they are used for calibration of the Modified Bartlett-Lewis model. Furthermore, this paper addresses the choice of weights in the objective function. Three alternative weighing methods are compared to determine whether or not simulation results (obtained after calibration with the best optimization method) are influenced by the choice of weights.

Orientation Optimization in Additive Manufacturing: Evaluation of Recent Trends

2021 ◽  
Author(s):  
Jannatul Bushra ◽  
Hannah D. Budinoff
Keyword(s):  
Additive Manufacturing ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Practical Implementation ◽  
Objective Functions ◽  
Build Orientation ◽  
Computational Optimization ◽  
Build Time ◽  
Orientation Optimization

Abstract Build orientation in additive manufacturing influences the mechanical properties, surface quality, build time, and cost of the product. Rather than relying on trial-and-error or prior experience, the choice of build orientation can be formulated as an optimization problem. Consequently, orientation optimization has been a popular research topic for several decades, with new optimization methods being proposed each year. However, despite the rapid pace of research in additive manufacturing, there has not been a critical comparison of different orientation optimization methods. In this study, we present a critical review of 50 articles published since 2015 that proposes a method for orientation optimization for additive manufacturing. We classify included papers by optimization methods used, AM process modeled, and objective functions considered. While the pace of research in recent years has been rapid, most approaches we identified utilized similar objective functions and computational optimization techniques to research from the early 2000s. The most common optimization method in the included research was exhaustive search. Most methods focused on broad applicability to all additive manufacturing processes, rather than a specific process, but a few works focused on powder bed fusion and material extrusion. We also identified several areas for future work including integration with other design and process planning tasks such as topology optimization, more focus on practical implementation with users, testing of computational efficiency, and experimental validation of utilized objective functions.

Is Hydraulics Over-Used or Under-Used in Storm Drainage?

1994 ◽  
Vol 29 (1-2) ◽  
pp. 53-61
Author(s):  
Ben Chie Yen
Keyword(s):  
Unsteady Flow ◽  
Computational Time ◽  
Time Step ◽  
Flow Routing ◽  
Kinematic Wave Equation ◽  
Storm Drainage ◽  
Saint Venant Equations ◽  
Unsteady Flow Routing ◽  
Selection Of

Urban drainage models utilize hydraulics of different levels. Developing or selecting a model appropriate to a particular project is not an easy task. Not knowing the hydraulic principles and numerical techniques used in an existing model, users often misuse and abuse the model. Hydraulically, the use of the Saint-Venant equations is not always necessary. In many cases the kinematic wave equation is inadequate because of the backwater effect, whereas in designing sewers, often Manning's formula is adequate. The flow travel time provides a guide in selecting the computational time step At, which in turn, together with flow unsteadiness, helps in the selection of steady or unsteady flow routing. Often the noninertia model is the appropriate model for unsteady flow routing, whereas delivery curves are very useful for stepwise steady nonuniform flow routing and for determination of channel capacity.

Scalable Computing Architecture for Time-Dependent Transportation Optimization Problems Based on High-Performance Computing Techniques

2019 ◽  
Vol 2673 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Pengfei (Taylor) Li ◽  
Peirong (Slade) Wang ◽  
Farzana Chowdhury ◽  
Li Zhang
Keyword(s):  
Objective Function ◽  
High Performance Computing ◽  
High Performance ◽  
Optimization Problems ◽  
Dynamic Network ◽  
Alternative Formulation ◽  
High Fidelity ◽  
Objective Functions ◽  
Performance Computing ◽  
Transportation Optimization

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.

Selection of Insulating Elastomers for High-Performance Intrinsically Stretchable Transistors

2021 ◽  
Author(s):  
Hang Ren ◽  
Junmo Zhang ◽  
Yanhong Tong ◽  
Mingxin Zhang ◽  
Xiaoli Zhao ◽  
...  
Keyword(s):  
High Performance ◽  
Selection Of

scholarly journals Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 223
Author(s):  
Yen-Ling Tai ◽  
Shin-Jhe Huang ◽  
Chien-Chang Chen ◽  
Henry Horng-Shing Lu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Computational Complexity ◽  
High Performance ◽  
Low Cost ◽  
Structural Complexity ◽  
Correction Function ◽  
Computational Time ◽  
Learning Methods ◽  
Band Theory

Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi–Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi–Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi–Dirac correction function exhibits better capabilities of image augmentation and segmentation.

Realizing Stable High‐Performance and Low‐Energy‐Loss Ternary Photovoltaics through Judicious Selection of the Third Component

Solar RRL ◽  
2021 ◽  
pp. 2100450
Author(s):  
Bing-Huang Jiang ◽  
Yi-Peng Wang ◽  
Yu-Wei Su ◽  
Jia-Fu Chang ◽  
Chu-Chen Chueh ◽  
...  
Keyword(s):  
Energy Loss ◽  
High Performance ◽  
Low Energy ◽  
The Third ◽  
Selection Of

scholarly journals An Overview of Molecular Dynamic Simulation for Corrosion Inhibition of Ferrous Metals

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 46
Author(s):  
Nur Izzah Nabilah Haris ◽  
Shafreeza Sobri ◽  
Yus Aniza Yusof ◽  
Nur Kartinee Kassim
Keyword(s):  
Molecular Dynamic ◽  
Corrosion Inhibition ◽  
High Performance ◽  
Md Simulation ◽  
Ferrous Metal ◽  
Working Mechanism ◽  
Inhibitor Molecule ◽  
As Adsorption ◽  
Inhibition Studies ◽  
Selection Of

Molecular dynamics (MD) simulation is a powerful tool to study the molecular level working mechanism of corrosion inhibitors in mitigating corrosion. In the past decades, MD simulation has emerged as an instrument to investigate the interactions at the interface between the inhibitor molecule and the metal surface. Combined with experimental measurement, theoretical examination from MD simulation delivers useful information on the adsorption ability and orientation of the molecule on the surface. It relates the microscopic characteristics to the macroscopic properties which enables researchers to develop high performance inhibitors. Although there has been vast growth in the number of studies that use molecular dynamic evaluation, there is still lack of comprehensive review specifically for corrosion inhibition of organic inhibitors on ferrous metal in acidic solution. Much uncertainty still exists on the approaches and steps in performing MD simulation for corrosion system. This paper reviews the basic principle of MD simulation along with methods, selection of parameters, expected result such as adsorption energy, binding energy and inhibitor orientation, and recent publications in corrosion inhibition studies.

scholarly journals Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm

2021 ◽  
Vol 4 (3) ◽  
pp. 50
Author(s):  
Preeti Warrier ◽  
Pritesh Shah
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Power Converters ◽  
Buck Converter ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Computational Time ◽  
Optimization Approach ◽  
Response Characteristics ◽  
Fractional Order Controller

The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.

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