Optimization of Flexible Pipes Dynamic Analysis Using Artificial Neural Networks

2016 ◽  
Author(s):  
Victor Chaves ◽  
Luis V. S. Sagrilo ◽  
Vinícius Ribeiro Machado da Silva
Keyword(s):  
Dynamic Analysis ◽  
Fem Simulation ◽  
Training Data ◽  
Local Analysis ◽  
Flexible Pipe ◽  
Nonlinear Fem ◽  
Irregular Wave ◽  
Flexible Pipes ◽  
Artificial Neural ◽  
Hybrid Methodology

Irregular wave dynamic analysis is an extremely computational expensive process on flexible pipes design. One emerging method that aims to reduce these computational costs is the hybrid methodology that combines Finite Element Analyses (FEA) and Artificial Neural Network (ANN). The proposed hybrid methodology aims to predict flexible pipe tension and curvatures in the bend stiffener region. Firstly using short FEA simulations to train the ANN, and then using only the ANN and the prescribed floater motions to get the rest of the response histories. Two approaches are developed with respect to the training data. One uses an ANN for each sea state in the wave scatter diagram and the other develops an ANN for each wave incidence direction. In order to evaluate the accuracy of the proposed approaches, a local analysis is applied, based on the predicted tension and curvatures, to calculate stresses in tension armour wires and the corresponding flexible pipe fatigue lifes. The results are compared to those from full nonlinear FEM simulation.

Artificial Neural Networks Applied to Flexible Pipes Fatigue Calculations

2015 ◽  
Author(s):  
Victor Chaves ◽  
Luis V. S. Sagrilo ◽  
Vinícius Ribeiro Machado da Silva ◽  
Mario Alfredo Vignoles
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Cost ◽  
Local Analysis ◽  
Flexible Pipe ◽  
Training Time ◽  
Flexible Pipes ◽  
Dynamic Analyses ◽  
Artificial Neural ◽  
Hybrid Methodology

Flexible pipes play an important role in offshore oil exploitation activities nowadays. However, time-domain flexible pipe irregular wave dynamic analyses are extremely computational expensive. One of the various existing methods to reduce computational costs in dynamic analyses is the hybrid methodology that combines dynamic Finite Element Analyses (FEA) and Artificial Neural Networks (ANN). This paper presents a novel application of this methodology for flexible pipes fatigue calculations. In order to decrease computational cost involved in these analyses the proposed hybrid methodology aims to predict tension and curvatures in the bend stiffener region. Firstly using short FEA simulations to train the ANN, and then using only the ANN and the prescribed floater motions to get the rest of the response histories. With the predicted tension and curvatures, a local analysis is applied to calculate stresses in tensile armour wires and the corresponding fatigue lives. To evaluate the optimal ANN a sensibility study is developed for some key parameters as: training time length, neurons on hidden layer and delay length. A full FEA is also performed in order to evaluate the accuracy of the proposed hybrid methodology, comparing both full FEA flexible pipe fatigue results and those obtained using the hybrid methodology.

Parallelized Element-by-Element Architecture for Structural Analysis of Flexible Pipes Using Macro Finite Elements

2017 ◽  
Author(s):  
Fernando Geremias Toni ◽  
Clóvis de Arruda Martins
Keyword(s):  
Structural Analysis ◽  
Finite Elements ◽  
Degrees Of Freedom ◽  
Local Analysis ◽  
Present Moment ◽  
Flexible Pipe ◽  
Computational Performance ◽  
Flexible Pipes ◽  
Oil And Natural Gas ◽  
Global Stiffness Matrix

Flexible pipes are employed to transport oil and natural gas from the seabed to the floating units, and vice versa. These pipes are made of concentric layers of different geometries, materials and structural functions in order to withstand a series of static and dynamic loads from its adverse operating environment. The local analysis is an important stage in the design of a flexible pipe and consists in determining the stresses and strains distributions along its layers. Multipurpose finite element packages, such as ANSYS and ABAQUS, are commonly used in this task, but present many limitations for their generic nature, varying from the absence of specific tools for model creation to heavy restrictions of the number of degrees-of-freedom to make computational processing feasible. Over the past years, several macro finite elements were formulated by PROVASI & MARTINS specifically for modeling a flexible pipe, allowing a reduction in the total number of degrees-of-freedom. However, until the present moment, there is no parallel processing software that efficiently implements these elements for large model applications. Aiming greater computational performance, the macro elements can be combined with the element-by-element (EBE) method, which is characterized by the global stiffness matrix elimination, is highly parallelizable, scalable and shows a memory consumption that grows linearly with the number of elements in the model. In this context, a parallelized architecture for structural analysis of flexible pipes that explores the EBE method and macro finite elements has been developed, being of great interest for design applications in the industry.

Flexible Pipe Systems Configurations for the Pre-Salt Area

2010 ◽  
Author(s):  
Judimar Clevelario ◽  
Fabio Pires ◽  
Claudio Barros ◽  
Terry Sheldrake
Keyword(s):  
Water Injection ◽  
High Strength Steels ◽  
High Strength ◽  
Pilot Project ◽  
Local Analysis ◽  
Flexible Pipe ◽  
Pipe System ◽  
Flexible Pipes ◽  
Unbonded Flexible Pipes ◽  
Global And Local

Unbonded flexible pipes are being considered as an actual solution for the following developments for the Brazilian Pre-Salt area. This technology is already being successfully used in the first EWT installed in the Brazilian Pre-salt and being qualified for the first Pre-salt Pilot Project development. However, unlikely the current project developments in water depths around 1500m, the free catenary configuration is not always an applicable option not only due to the 2500m water depth but also to the presence of contaminants such as CO2 and H2S in the conveyed fluids which in certain applications make the use of conventional high strength steels unfeasible, making the use of sour service armour wires mandatory. This paper presents the result of the global and local analysis performed for different applications such as 4″ gas lift, 6″ water injection, 6″ production and 9.13″ Gas export structures designed specifically for the ultra deep water in Brazilian Pre-Salt area. The aim of this study was to verify the feasibility of the free hanging catenary configuration and determine the most suitable flexible pipe system configuration for different applications, confirming that the flexible pipes are an adequate solution for the Pre-Salt even when the service life requirements exceeds 20 years and associated safety factors.

scholarly journals Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

2021 ◽  
Author(s):  
Haitham Baomar ◽  
Peter J. Bentley
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Training Data ◽  
Aviation Industry ◽  
Control Models ◽  
Novel Approach ◽  
Network Modules ◽  
Artificial Neural

AbstractWe describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

scholarly journals Estimation of electrical resistivity using artificial neural networks: a case study from Lublin Basin, SE Poland

2021 ◽  
Author(s):  
Jakub Ważny ◽  
Michał Stefaniuk ◽  
Adam Cygal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Data ◽  
Multilayer Perceptrons ◽  
Borehole Data ◽  
Geological Interpretation ◽  
Se Poland ◽  
Geological Conditions ◽  
Artificial Neural ◽  
Reliable Solution

AbstractArtificial neural networks method (ANNs) is a common estimation tool used for geophysical applications. Considering borehole data, when the need arises to supplement a missing well log interval or whole logging—ANNs provide a reliable solution. Supervised training of the network on a reliable set of borehole data values with further application of this network on unknown wells allows creation of synthetic values of missing geophysical parameters, e.g., resistivity. The main assumptions for boreholes are: representation of similar geological conditions and the use of similar techniques of well data collection. In the analyzed case, a set of Multilayer Perceptrons were trained on five separate chronostratigraphic intervals of borehole, considered as training data. The task was to predict missing deep laterolog (LLD) logging in a borehole representing the same sequence of layers within the Lublin Basin area. Correlation between well logs data exceeded 0.8. Subsequently, magnetotelluric parametric soundings were modeled and inverted on both boreholes. Analysis showed that congenial Occam 1D models had better fitting of TM mode of MT data in each case. Ipso facto, synthetic LLD log could be considered as a basis for geophysical and geological interpretation. ANNs provided solution for supplementing datasets based on this analytical approach.

scholarly journals Burst pressure prediction of fiber-reinforced flexible pipes with arbitrary generatrix

2021 ◽  
Vol 16 ◽  
pp. 155892502199081
Author(s):  
Guo-min Xu ◽  
Chang-geng Shuai
Keyword(s):  
Transfer Matrix ◽  
Linear Equations ◽  
Burst Pressure ◽  
Fiber Reinforced ◽  
Flexible Pipe ◽  
Theoretical Derivation ◽  
Design And Optimization ◽  
Flexible Pipes ◽  
Novel Method ◽  
Winding Angle

Fiber-reinforced flexible pipes are widely used to transport the fluid at locations requiring flexible connection in pipeline systems. It is important to predict the burst pressure to guarantee the reliability of the flexible pipes. Based on the composite shell theory and the transfer-matrix method, the burst pressure of flexible pipes with arbitrary generatrix under internal pressure is investigated. Firstly, a novel method is proposed to simplify the theoretical derivation of the transfer matrix by solving symbolic linear equations. The method is accurate and much faster than the manual derivation of the transfer matrix. The anisotropy dependency on the circumferential radius of the pipe is considered in the theoretical approach, along with the nonlinear stretch of the unidirectional fabric in the reinforced layer. Secondly, the burst pressure is predicted with the Tsai-Hill failure criterion and verified by burst tests of six different prototypes of the flexible pipe. It is found that the burst pressure is increased significantly with an optimal winding angle of the unidirectional fabric. The optimal result is determined by the geometric parameters of the pipe. The investigation method and results presented in this paper will guide the design and optimization of novel fiber-reinforced flexible pipes.

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

Cathodic Protection Design Consideration for an Offshore Flexible Riser Connected to an Impressed Current System

2021 ◽  
Author(s):  
Thierry Dequin ◽  
Clark Weldon ◽  
Matthew Hense
Keyword(s):  
Cathodic Protection ◽  
Sacrificial Anode ◽  
Flexible Riser ◽  
Flexible Pipe ◽  
Linear Resistance ◽  
Flexible Pipes ◽  
Flexible Risers ◽  
Impressed Current ◽  
Host Structure ◽  
Impressed Current Cathodic Protection

Abstract Flexible risers are regularly used to produce oil and gas in subsea production systems and by nature interconnect the subsea production system to the floating or fixed host facilities. Unbonded flexible pipes are made of a combination of metallic and non-metallic layers, each layer being individually terminated at each extremity by complex end fittings. Mostly submerged in seawater, the metallic parts require careful material selection and cathodic protection (CP) to survive the expected service life. Design engineers must determine whether the flexible pipe risers should be electrically connected to the host in order to receive cathodic protection current or be electrically isolated. If the host structure is equipped with a sacrificial anode system, then electrical continuity between the riser and the host structure is generally preferred. The exception is often when the riser and host structure are operated by separate organizations, in which case electrical isolation may be preferred simply to provide delineation of ownership between the two CP systems. The paper discusses these interface issues between hull and subsea where the hull is equipped with an impressed current cathodic protection (ICCP) system, and provides guidance for addressing them during flexible pipe CP design, operation, and monitoring. Specifically, CP design philosophies for flexible risers will be addressed with respect to manufacturing, installation and interface with the host structure’s Impressed Current Cathodic Protection (ICCP) system. The discussion will emphasize the importance of early coordination between the host structure ICCP system designers and the subsea SACP system designers, and will include recommendations for CP system computer modeling, CP system design operation and CP system monitoring. One of the challenges is to understand what to consider for the exposed surfaces in the flexible pipes and its multiple layers, and also the evaluation of the linear resistance of each riser segment. The linear resistance of the riser is a major determinant with respect to potential attenuation, which in turn largely determines the extent of current drain between the subsea sacrificial anode system and the hull ICCP system. To model the flexible riser CP system behavior for self-protection, linear resistance may be maximized, however the use of a realistic linear resistance is recommended for evaluation of the interaction between the host structure and subsea system. Realistic flexible linear resistance would also reduce conservatism in the CP design, potentially save time during the offshore campaign by reducing anode quantities, and also providing correct evaluation of drain current and stray currents.

Investigation on Mechanical Properties of Fibreglass Reinforced Flexible Pipes Under Torsion

2018 ◽  
Author(s):  
Pan Fang ◽  
Yuxin Xu ◽  
Shuai Yuan ◽  
Yong Bai ◽  
Peng Cheng
Keyword(s):  
Mechanical Behavior ◽  
Torsion Angle ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Load Condition ◽  
Element Model ◽  
Flexible Pipe ◽  
Torsional Load ◽  
Flexible Pipes ◽  
The Impact

Fibreglass reinforced flexible pipe (FRFP) is regarded as a great alternative to many bonded flexible pipes in the field of oil or gas transportation in shallow water. This paper describes an analysis of the mechanical behavior of FRFP under torsion. The mechanical behavior of FRFP subjected to pure torsion was investigated by experimental, analytical and numerical methods. Firstly, this paper presents experimental studies of three 10-layer FRFP subjected to torsional load. Torque-torsion angle relations were recorded during this test. Then, a theoretical model based on three-dimensional (3D) anisotropic elasticity theory was proposed to study the mechanical behavior of FRFP. In addition, a finite element model (FEM) including reinforced layers and PE layers was used to simulate the torsional load condition in ABAQUS. Torque-torsion angle relations obtained from these three methods agree well with each other, which illustrates the accuracy and reliability of the analytical model and FEM. The impact of fibreglass winding angle, thickness of reinforced layers and radius-thickness ratio were also studied. Conclusions obtained from this research may be of great practicality to manufacturing engineers.

Sign in / Sign up

Export Citation Format

Share Document