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Peng, C. (University of Science and Technology Beijing) | Guo, Q. S. (University of Science and Technology Beijing) | Zhang, Z. C. (University of Science and Technology Beijing) | Zhao, L. (University of Science and Technology Beijing) | Yan, Z. X. (University of Science and Technology Beijing)

ABSTRACT: With the increase of the slope height in open-pit mines, the contradiction between the mining safety and stripping quantity becomes progressively serious. According to the analysis and calculation, the original slope angle in Gaocun Iron mine is conservative. After engineering geological investigation and rock mechanical tests, three optimization schemes were proposed. FLAC^{3D} numerical simulation software was used to analyze the slope stability by several indexes such as displacement and plastic zone. In addition, the safety factors were obtained according to the limit equilibrium method by Geo-slope software. Eventually, the final slope angle of the mine was determined. It showed that the optimized slope angle is improved by 3° compared with that of the former design on the whole, and the slope stability well meets the requirement of production.

1 INTRODUCTION

There are a growing number of metal mines carrying on deep mining in our country, and the design of open pit slopes faces a dilemma in that situation: When the slope angle is too big, the steep slope will cause instability and failure, which is not conducive to the normal production of the mine; In contrast, the small angle will increase the stripped amount and the production costs significantly. To solve this problem, slope angle must be optimized on the premise of mining safety (Heok & Bray 1981, Duncan & Christopher 2005).

Gaocun pit of Nanshan Mining Co., Ltd., is a large open pit mine, whose ore production has reached 7 million tons per year with the total mining and stripping of 18 million tons. After entering the second phase of open pit mining, the north-south length of the stope expands from 780 m to 1500 m, and the east-west width expends from 575 m to 820 m. The highest level of open pit mining is up to +90 m and the bottom elevation is down to-186m. Under the conditions of high and steep slope mining, with mining depth increases, the contradiction between security and economic production is gradually highlighted. Therefore, slope design must be optimized to ensure the production safety and increase economic efficiency.

Engineering, equilibrium method, initial optimization scheme, Limit Equilibrium Method, metals & mining, Modeling & Simulation, optimization, optimization scheme, platform, relative safety factor, Reservoir Characterization, safety factor, security platform, slope angle, slope optimization scheme, stability analysis, stage range, ultimate slope angle optimization

SPE Disciplines:

Technology:

Zheng, Qiang (Wuhan University of Technology, Harbin Engineering University) | Chang, Hai-Chao (Wuhan University of Technology) | Liu, Zu-Yuan (Wuhan University of Technology) | Feng, Bai-Wei (Wuhan University of Technology)

Hull optimization design based on computational fluid dynamics (CFD) is a highly computationally intensive complex engineering problem. Because of reasons such as many variables, spatially complex design performance, and huge computational workload, hull optimization efficiency is low. To improve the efficiency of hull optimization, a dynamic space reduction method based on a partial correlation analysis is proposed in this study. The proposed method dynamically uses hull-form optimization data to analyze and reduce the range of values for relevant design variables and, thus, considerably improves the optimization efficiency. This method is used to optimize the wave-making resistance of an S60 hull, and its feasibility is verified through comparison.

In recent years, to promote the rapid development of green ships, hull optimization methods based on computational fluid dynamics (CFD) have been widely used by many researchers, such as Tahara et al. (2011), Peri and Diez (2013), Kim and Yang (2010), Yang and Huang (2016), Chang et al. (2012), and Feng et al. (2009). However, hull optimization design is a typically complex engineering problem. It requires many numerical simulation calculations, and the design performance space is complex, which has resulted in low optimization efficiency and difficulty in obtaining a global optimal solution. Commonly used solutions include 1) efficient optimization algorithms, 2) approximate model techniques, and 3) high-performance cluster computers. However, these methods still cannot satisfy the engineering application requirements in terms of efficiency and quality of the solution.

To solve the problem of low optimization efficiency and difficulty in obtaining an optimal solution in engineering optimization problems, many scholars have conducted research on design space reduction technology. Reungsinkonkarn and Apirukvorapinit (2014) applied the search space reduction (SSR) algorithm to the particle swarm optimization (PSO) algorithm, eliminating areas in which optimal solutions may not be found through SSR to improve the optimization efficiency of the algorithm. Chen et al. (2015) and Diez et al. (2014, 2015) used the Karhunen–Loeve expansion to evaluate the hull, eliminating the less influential factors to achieve space reduction modeling with fewer design variables. Further extensions to nonlinear dimensionality reduction methods can be found in D'Agostino et al. (2017) and Serani et al. (2019). Jeong et al. (2005) applied space reduction techniques to the aerodynamic shape optimization of the vane wheel, using the rough set theory and decision trees to extract aerofoil design rules to improve each target. Gao et al. (2009) and Wang et al. (2014) solved the problem of low optimization efficiency in the aerodynamic shape optimization design of an aircraft, by using analysis results of partial correlation, which reduced the range of values of relevant design variables to reconstruct the optimized design space. Li et al. (2013) divided the design space into several smaller cluster spaces using the clustering method, which is a global optimization method based on an approximation model, thus achieving design space reduction. Chu (2010) combined the rough set theory and the clustering method for application to the concept design stage of bulk carriers, thus realizing the exploration and reduction of design space. Feng et al. (2015) applied the rough set theory and the sequential space reduction method to the resistance optimization of typical ship hulls to achieve the reduction of design space. Wu et al. (2016) used partial correlation analysis to reduce the design space of variables of a KCS container ship to improve optimization efficiency. Most of the above space reduction methods need to sample and calculate the original design space in the early stage of optimization and then obtain the reduced design space through data mining. This process increases the computational cost of sampling, making it difficult to control optimization efficiency.

SNAME-JSR-04190019

Artificial Intelligence, design space, dynamic space reduction method, freight & logistics services, fuzzy logic, hull, iteration, machine learning, marine transportation, optimal solution, optimization, optimization efficiency, Optimization Method, optimization problem, Optimization Result, partial correlation analysis, partial correlation coefficient, reduction, Reduction Method, space reduction method, spatial reduction, Upstream Oil & Gas

Industry:

- Transportation > Marine (1.00)
- Energy > Oil & Gas > Upstream (0.74)
- Transportation > Freight & Logistics Services > Shipping (0.54)

SPE Disciplines: Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)

Technology:

- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Fuzzy Logic (0.88)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (0.54)

**Abstract**

A new Parametric Mesh Transformation Method (PMTM), which is used for structure shape optimization of the hull, is proposed in this paper. The purpose of PMTM is to realize dimension-driven of the FEM model for hull structure, without calling mesh generator as the traditional parametric method doing. In this method, the plate is split into quadrilaterals with the concept of N-Sided region modeling technology. Then create a Coons surface that interpolates four edges for each quadrilateral. Get the dimensionless parameters for all nodes of the mesh within each Coons patch by surface reverse calculation method. A combined reverse calculation method, which takes into account of both efficiency and generality, is developed and used in calculation of dimensionless parameters. When the structure is changed, all the Coon surfaces are changed accordingly. Then substitute the dimensionless parameters of each node into the surface equations of the corresponding Coons surface, and a new point is obtained. The new created point is the new location of the node. Calculate new location for each node with the above procedure, and the new finite element model according to the new structure is obtained. This method is applied to the shape optimization of a 300 ft. jack-up rig compared with the traditional method. The result shows that PMTM is able to realize dimension-driven of FEM model. It is also proved that when the parameters of the parametric structure model changed gradually, the structure stresses change smoothly with PMTM. That is an important advantage which the tradition methods do not have, and it could improve the efficiency as well as quality of hull structure shape optimization.

Artificial Intelligence, contour, finite element model, hull structure, hull structure shape optimization, jack-up rig, mesh, mesh transformation, mesh transformation method, optimization, Optimization Method, PMTM, procedure, quadrilateral, shape optimization, sketch, structure shape optimization, traditional method, transformation, Transformation Method, Upstream Oil & Gas

SPE Disciplines: Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.50)

Zhao, Min (Shanghai Jiao Tong University) | Yuan, Qingqing (Shanghai Jiao Tong University) | Wang, Tao (Shanghai Jiao Tong University) | Ge, Tong (Shanghai Jiao Tong University) | Cui, Weicheng (Shanghai Ocean University)

A heavier-than-water underwater vehicle (HUV) is unlike an unmanned underwater vehicle. The most notable characteristic is its ability to balance its weight with the hydrodynamic lift of water even though the weight is much greater than the buoyancy. Since the hydrostatic equilibrium is not based on the balance between the gravity and the buoyancy, the vehicle has a smaller volume, a larger payload, and better maneuverability. However, the design of the vehicle relies heavily on the abilities of naval architects. In order to ease the reliance on naval architects and to improve the prototype design, we propose a multidisciplinary system model using a semi-empirical model and three multidisciplinary design optimization methods. We applied all-in-one method, analytical target cascading method, and bi-level integrated system collaborative optimization method to the conceptual design of the vehicle in order to attain an optimal multidisciplinary design characterized by minimal total weight, long-range cruising capabilities, and high maneuverability. The results from the three different methods show that the general performance of optimized HUV was significantly better than the performances of prototype design, which suggests the feasibility and superiority of model and optimization methods.

INTRODUCTION

Unmanned underwater vehicles (UUVs) used for ocean exploration can be divided into two types: (1) Remotely Operated Vehicle (ROV) (Barry and Hashimoto, 2009), which is remotely operated via a tether or umbilical, and (2) Autonomous Underwater Vehicle (AUV) (Wynn et al., 2014), which is operated by implemented programming. Over the past decades, thousands of unmanned vehicles have been made for industrial and scientific purposes. In recent years, the demand for increased range, payload, and intelligent control have led to a new type of conceptual AUV. For instance, the Underwater Engineering Research Institute at Shanghai Jiao Tong University (Wu et al., 2010; Yan et al., 2012; Li et al., 2014) proposed a heavier-than-water underwater vehicle (HUV). The idea is based on the theory of aircraft, which is in turn based on the balance between gravity and lift. Commonly, AUV has a state of neutral buoyancy or they are lighter than water, as is true for the Autosub, Thesus, REMUS and Hugin AUV that are being developed in European and North American countries. Therefore, buoyant material or equipment is used and the vehicle becomes larger and heavier. However, the HUV works with negative buoyancy during underwater cruising, which is completely different from the way that an AUV works. Consequently, the hydrostatic equilibrium of the HUV is not based on a balance between gravity and buoyancy because the lift generated by the wings at a high speed is significantly greater than the gravity. Therefore, more payloads, such as batteries, can be carried by the HUV to ensure the long-range capabilities of the vehicles. Moreover, the dimensions of the HUV are smaller than those of a traditional AUV, which, combined with the addition of wings and fins, improves maneuverability.

Artificial Intelligence, battery, CFD method, coefficient, diameter, displacement, electrical industrial apparatus, equilibrium module, ground transportation, heavier-than-water underwater vehicle, huv, module, motion control module, multidisciplinary design optimization, optimization, Optimization Method, optimization problem, pressure hull, structure module, underwater vehicle

Country:

- North America > United States (1.00)
- Asia > China > Shanghai (0.35)

Industry:

- Electrical Industrial Apparatus (0.68)
- Energy (0.46)
- Transportation > Ground > Road (0.34)

SPE Disciplines: Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)

Technology:

Summary Model-based production optimization has become a promising technique in recent years for improving reservoir management and asset development in the petroleum industry. A variety of methods have been developed to address production-optimization problems. However, many solutions resulting from these methods are not ready to be accepted by (operations) engineers because they are difficult to understand and implement in practice. A typical example is the erratically oscillatory, bang-bang-type solution of well-control optimization problems. For this challenge, a regularized optimization problem is formulated in this work with two purposes: to create smooth solutions and to improve the convergence speed. Furthermore, the original, high-dimensional optimization problems can be reduced to low-dimensional ones by a principal-component-analysis (PCA) -based regularization method such that some population-based methods, including genetic algorithm (GA) and particle-swarm optimization (PSO), can be used as search engines to find optimal or improved solutions that tend to have less chance of trapping in local optima. Examples show that the methodology proposed can ensure a continuous transition over time between variables (e.g., well controls) such that the generated solution is more acceptable to the (operations) engineers. Moreover, it significantly speeds up the convergence of the optimization process, allowing large-scale problems to be addressed efficiently. Introduction From the perspective of reservoir engineering, a reservoir-production strategy is usually not optimal (nor robust) if it purely relies on experience or passively reacts to reservoir feedback. Production optimization with the aid of reservoir-simulation models offers a considerable opportunity for us to improve the efficiency of reservoir management and to maximize the profit of an asset (van den Berg 2007; Jansen et al. 2008; van Essen et al. 2013). This opportunity is unique because reservoir models provide a means for us to test alternative production strategies before an optimal or improved action is actually undertaken in the field. Moreover, the simulation models allow us to integrate various data and information through inverse modeling (Fu and Gómez-Hernández 2009a, 2009b) or history matching (Foss and Jansen 2011), thereby reducing uncertainties and improving our knowledge of the subsurface.

april 2018, Artificial Intelligence, base case, constraint, Control step, convergence speed, correlation range, dimensionality, evolutionary algorithm, gradient-based method, machine learning, objective function, optimization, optimization problem, optimization process, production optimization, PSO, regularization method, Scenario, Simulation, smoothness, Upstream Oil & Gas

Thank you!