An ANN-based framework for rapid spectral fatigue analysis of steel catenary risers

Research output: Chapter in Book/Conference paperConference paper

2 Citations (Scopus)

Abstract

A simplified framework is presented in which an existing artificial neural network (ANN) based tool for critical stress range prediction is used in order to rapidly assess the fatigue life of a steel catenary riser (SCR). The simplified approach considers the first-order motions of the hosting floater (heave, pitch and roll motions) and irregular sea-states to assess the critical stress range within the touchdown zone (TDZ) of the SCR. Stress transfer functions are generated that approximate the SCR TDZ critical stress range due to vertical motion at the SCR hang-off point. The motion response amplitude operators (RAOs) and transfer functions are then combined to generate the SCR TDZ stress spectra and hence assess accumulated fatigue damage for all potential sea-states at the floater location. The fatigue lives of two large diameter SCRs subject to a sample irregular wave scatter diagram are calculated using the simplified framework. The results are then compared with those determined via a state of the art commercial software that uses a dynamic time-domain finite element (FE) analysis with rain-flow cycle (RFC) counting and shown to provide a good agreement. It is an important result as the time required to run the simplified analysis is an order of magnitude smaller than the more rigorous analysis (minutes versus hours). It demonstrates the usefulness of the simplified approach at the early stages of an SCR design where a large number of simulations are needed for sensitivity studies in order to select an optimized concept.

Original languageEnglish
Title of host publicationOffshore Technology Conference Asia 2018, OTCA 2018
Place of PublicationMalaysia
PublisherOffshore Technology Conference
ISBN (Print)9781510862159
Publication statusPublished - 1 Jan 2018
EventOffshore Technology Conference Asia 2018, OTCA 2018 - Kuala Lumpur, Malaysia
Duration: 20 Mar 201823 Mar 2018

Conference

ConferenceOffshore Technology Conference Asia 2018, OTCA 2018
CountryMalaysia
CityKuala Lumpur
Period20/03/1823/03/18

Fingerprint

Fatigue of materials
Neural networks
Steel
Transfer functions
Fatigue damage
Thyristors
Rain
Mathematical operators
Finite element method

Cite this

Hejazi, R., Grime, A., Randolph, M., & Efthymiou, M. (2018). An ANN-based framework for rapid spectral fatigue analysis of steel catenary risers. In Offshore Technology Conference Asia 2018, OTCA 2018 Malaysia: Offshore Technology Conference.
Hejazi, Rasoul ; Grime, Andrew ; Randolph, Mark ; Efthymiou, Mike. / An ANN-based framework for rapid spectral fatigue analysis of steel catenary risers. Offshore Technology Conference Asia 2018, OTCA 2018. Malaysia : Offshore Technology Conference, 2018.
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title = "An ANN-based framework for rapid spectral fatigue analysis of steel catenary risers",
abstract = "A simplified framework is presented in which an existing artificial neural network (ANN) based tool for critical stress range prediction is used in order to rapidly assess the fatigue life of a steel catenary riser (SCR). The simplified approach considers the first-order motions of the hosting floater (heave, pitch and roll motions) and irregular sea-states to assess the critical stress range within the touchdown zone (TDZ) of the SCR. Stress transfer functions are generated that approximate the SCR TDZ critical stress range due to vertical motion at the SCR hang-off point. The motion response amplitude operators (RAOs) and transfer functions are then combined to generate the SCR TDZ stress spectra and hence assess accumulated fatigue damage for all potential sea-states at the floater location. The fatigue lives of two large diameter SCRs subject to a sample irregular wave scatter diagram are calculated using the simplified framework. The results are then compared with those determined via a state of the art commercial software that uses a dynamic time-domain finite element (FE) analysis with rain-flow cycle (RFC) counting and shown to provide a good agreement. It is an important result as the time required to run the simplified analysis is an order of magnitude smaller than the more rigorous analysis (minutes versus hours). It demonstrates the usefulness of the simplified approach at the early stages of an SCR design where a large number of simulations are needed for sensitivity studies in order to select an optimized concept.",
keywords = "Artificial Neural Network (ANN), Deep-water risers, Spectral fatigue analysis, Steel catenary risers",
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Hejazi, R, Grime, A, Randolph, M & Efthymiou, M 2018, An ANN-based framework for rapid spectral fatigue analysis of steel catenary risers. in Offshore Technology Conference Asia 2018, OTCA 2018. Offshore Technology Conference, Malaysia, Offshore Technology Conference Asia 2018, OTCA 2018, Kuala Lumpur, Malaysia, 20/03/18.

An ANN-based framework for rapid spectral fatigue analysis of steel catenary risers. / Hejazi, Rasoul; Grime, Andrew; Randolph, Mark; Efthymiou, Mike.

Offshore Technology Conference Asia 2018, OTCA 2018. Malaysia : Offshore Technology Conference, 2018.

Research output: Chapter in Book/Conference paperConference paper

TY - GEN

T1 - An ANN-based framework for rapid spectral fatigue analysis of steel catenary risers

AU - Hejazi, Rasoul

AU - Grime, Andrew

AU - Randolph, Mark

AU - Efthymiou, Mike

PY - 2018/1/1

Y1 - 2018/1/1

N2 - A simplified framework is presented in which an existing artificial neural network (ANN) based tool for critical stress range prediction is used in order to rapidly assess the fatigue life of a steel catenary riser (SCR). The simplified approach considers the first-order motions of the hosting floater (heave, pitch and roll motions) and irregular sea-states to assess the critical stress range within the touchdown zone (TDZ) of the SCR. Stress transfer functions are generated that approximate the SCR TDZ critical stress range due to vertical motion at the SCR hang-off point. The motion response amplitude operators (RAOs) and transfer functions are then combined to generate the SCR TDZ stress spectra and hence assess accumulated fatigue damage for all potential sea-states at the floater location. The fatigue lives of two large diameter SCRs subject to a sample irregular wave scatter diagram are calculated using the simplified framework. The results are then compared with those determined via a state of the art commercial software that uses a dynamic time-domain finite element (FE) analysis with rain-flow cycle (RFC) counting and shown to provide a good agreement. It is an important result as the time required to run the simplified analysis is an order of magnitude smaller than the more rigorous analysis (minutes versus hours). It demonstrates the usefulness of the simplified approach at the early stages of an SCR design where a large number of simulations are needed for sensitivity studies in order to select an optimized concept.

AB - A simplified framework is presented in which an existing artificial neural network (ANN) based tool for critical stress range prediction is used in order to rapidly assess the fatigue life of a steel catenary riser (SCR). The simplified approach considers the first-order motions of the hosting floater (heave, pitch and roll motions) and irregular sea-states to assess the critical stress range within the touchdown zone (TDZ) of the SCR. Stress transfer functions are generated that approximate the SCR TDZ critical stress range due to vertical motion at the SCR hang-off point. The motion response amplitude operators (RAOs) and transfer functions are then combined to generate the SCR TDZ stress spectra and hence assess accumulated fatigue damage for all potential sea-states at the floater location. The fatigue lives of two large diameter SCRs subject to a sample irregular wave scatter diagram are calculated using the simplified framework. The results are then compared with those determined via a state of the art commercial software that uses a dynamic time-domain finite element (FE) analysis with rain-flow cycle (RFC) counting and shown to provide a good agreement. It is an important result as the time required to run the simplified analysis is an order of magnitude smaller than the more rigorous analysis (minutes versus hours). It demonstrates the usefulness of the simplified approach at the early stages of an SCR design where a large number of simulations are needed for sensitivity studies in order to select an optimized concept.

KW - Artificial Neural Network (ANN)

KW - Deep-water risers

KW - Spectral fatigue analysis

KW - Steel catenary risers

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SN - 9781510862159

BT - Offshore Technology Conference Asia 2018, OTCA 2018

PB - Offshore Technology Conference

CY - Malaysia

ER -

Hejazi R, Grime A, Randolph M, Efthymiou M. An ANN-based framework for rapid spectral fatigue analysis of steel catenary risers. In Offshore Technology Conference Asia 2018, OTCA 2018. Malaysia: Offshore Technology Conference. 2018