인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Offshore riser systems are fundamental components in subsea oil and gas production, yet their design and performance assessment remain challenging due to nonlinear dynamics and uncertain environmental conditions. This study presents a comprehensive framework that integrates high-fidelity finite element simulations with advanced machine learning (ML) techniques to evaluate the nonlinear behavior of fixed risers under coupled hydrodynamic, current, and wind loads. A three-dimensional finite element model developed in ABAQUS/Standard captures the structural response, quantified through a von Mises stress-based performance function. To improve computational efficiency, three surrogate models—Deep Neural Network (DNN), Gaussian Process Regression (GPR), and Support Vector Regression (SVR)—were trained on data generated via a hybrid sampling strategy that combines copula-based methods with adaptive sampling. Additionally, a Polynomial Chaos-Kriging (PCK) model was employed for robust uncertainty quantification, while Sobol sensitivity analysis was conducted to identify dominant environmental and structural parameters. Results show that the DNN model achieved the highest predictive accuracy (R² = 0.987, MSE = 0.021) compared to GPR and SVR, with current velocity and buoy dimensions emerging as the most influential parameters. The adaptive sampling strategy reduced data requirements by approximately 40% while maintaining accuracy, and the PCK model provided reliable uncertainty estimates with narrower prediction bounds compared to pure GPR. The proposed framework demonstrates the potential of combining physics-based simulations with ML surrogates to enable efficient performance evaluation, enhance reliability assessment, and support resilient design optimization of offshore riser systems under uncertainty.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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