Bayesian optimization of riser configurations

Abstract Optimizing the configuration of risers is a challenging task. It requires numerous nonlinear dynamic finite element analyses to evaluate each candidate configuration regarding its structural behavior. In such an optimization procedure, the computational time is commonly dominated by the structural analysis step. Therefore, reducing the number of simulations required to find feasible candidates is paramount to reduce the overall computational cost. In this work, we propose applying the Bayesian Optimization (BO) algorithm to optimize steel risers’ initial configuration efficiently. The performance of BO, measured as the number of objective function evaluations, is shown to be competitive in selected problems of steel lazy-wave risers and catenary risers with hydrodynamic dampers, compared to other optimization methods found in the literature (i.e., MIDACO commercial code, globalized bounded Nelder–Mead and genetic algorithms). In particular, we demonstrate the superior performance of BO compared to genetic algorithms, the most commonly found method in riser literature. Representative examples, which illustrate the capabilities of the proposed strategy, are presented and discussed.

[1]  Peter I. Frazier,et al.  Parallel Bayesian Global Optimization of Expensive Functions , 2016, Oper. Res..

[2]  Harold J. Kushner,et al.  A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise , 1964 .

[3]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[4]  Matthias Poloczek,et al.  Advances in Bayesian Optimization with Applications in Aerospace Engineering , 2018 .

[5]  Jasper Snoek,et al.  Bayesian Optimization with Unknown Constraints , 2014, UAI.

[6]  Peter I. Frazier,et al.  Bayesian optimization for materials design , 2015, 1506.01349.

[7]  Hans-Martin Gutmann,et al.  A Radial Basis Function Method for Global Optimization , 2001, J. Glob. Optim..

[8]  Baskar Ganapathysubramanian,et al.  Algorithm 1025: PARyOpt: A Software for Parallel Asynchronous Remote Bayesian Optimization , 2018, ACM Trans. Math. Softw..

[9]  I. Sobol.  On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .

[10]  Atharv Bhosekar,et al.  Advances in surrogate based modeling, feasibility analysis, and optimization: A review , 2018, Comput. Chem. Eng..

[11]  William J. Welch,et al.  Computer experiments and global optimization , 1997 .

[12]  Ruben Martinez-Cantin,et al.  Funneled Bayesian Optimization for Design, Tuning and Control of Autonomous Systems , 2016, IEEE Transactions on Cybernetics.

[13]  Peter I. Frazier,et al.  A Tutorial on Bayesian Optimization , 2018, ArXiv.

[14]  Nando de Freitas,et al.  Portfolio Allocation for Bayesian Optimization , 2010, UAI.

[15]  Aaron Klein,et al.  Towards Automatically-Tuned Neural Networks , 2016, AutoML@ICML.

[16]  J. Mockus.  Bayesian Approach to Global Optimization: Theory and Applications , 1989 .

[17]  Nando de Freitas,et al.  A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.

[18]  Matt J. Kusner,et al.  Bayesian Optimization with Inequality Constraints , 2014, ICML.

[19]  D. Ginsbourger,et al.  Contaminant source localization via Bayesian global optimization , 2017, Hydrology and Earth System Sciences.

[20]  Prabhat,et al.  Scalable Bayesian Optimization Using Deep Neural Networks , 2015, ICML.

[21]  Kirthevasan Kandasamy,et al.  High Dimensional Bayesian Optimisation and Bandits via Additive Models , 2015, ICML.

[22]  Mario A. Storti,et al.  MPI for Python , 2005, J. Parallel Distributed Comput..

[23]  Timothy W. Simpson,et al.  Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not , 2008 .

[24]  Jialei Wang,et al.  Bayesian Optimization with Parallel Function Evaluations and Multiple Information Sources: Methodology with Applications in Biochemistry, Aerospace Engineering, and Machine Learning , 2017 .

[25]  Jan Peters,et al.  An experimental comparison of Bayesian optimization for bipedal locomotion , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Joaquim R. R. A. Martins,et al.  Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design , 2019, Aerospace Science and Technology.

[27]  A. G. Zhilinskas,et al.  Single-step Bayesian search method for an extremum of functions of a single variable , 1975 .

[28]  Miaojing Shi,et al.  A Selective Biogeography-Based Optimizer Considering Resource Allocation for Large-Scale Global Optimization , 2019, Comput. Intell. Neurosci..

[29]  Matthias Gerdts,et al.  The oracle penalty method , 2010, J. Glob. Optim..

[30]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[31]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[32]  Ludimar Lima de Aguiar,et al.  Technical Feasibility Study of Steel Catenary Risers for Pre-Salt Field Developments , 2017 .

[33]  Kirthevasan Kandasamy,et al.  Parallelised Bayesian Optimisation via Thompson Sampling , 2018, AISTATS.

[34]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[35]  Shuhua Wang,et al.  A Novel Bayesian Optimization Framework for Computationally Expensive Optimization Problem in Tight Oil Reservoirs , 2017 .

[36]  Marco Antonio Luersen,et al.  Globalized Nelder-Mead method for engineering optimization , 2002 .

[37]  Beatriz S. L. P. de Lima,et al.  Optimization of Steel Catenary Risers for Offshore Oil Production Using Artificial Immune System , 2008, ICARIS.

[38]  Edmundo Q. de Andrade,et al.  Optimization Procedure of Steel Lazy Wave Riser Configuration for Spread Moored FPSOs in Deepwater Offshore Brazil , 2010 .

[39]  et al.,et al.  Jupyter Notebooks - a publishing format for reproducible computational workflows , 2016, ELPUB.

[40]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007 .

[41]  P.H.S. Cardoso,et al.  Optimization of catenary risers with hydrodynamic dampers , 2019, Ocean Engineering.

[42]  Nando de Freitas,et al.  Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.

[43]  Alán Aspuru-Guzik,et al.  Phoenics: A Bayesian Optimizer for Chemistry , 2018, ACS central science.

[44]  Jonas Mockus,et al.  On Bayesian Methods for Seeking the Extremum , 1974, Optimization Techniques.

[45]  Stefano Ermon,et al.  Sparse Gaussian Processes for Bayesian Optimization , 2016, UAI.

[46]  Lisandro Dalcin,et al.  Parallel distributed computing using Python , 2011 .

[47]  Marc Parizeau,et al.  DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..

[48]  Beatriz Souza Leite Pires de Lima,et al.  Bio‐inspired algorithms for the optimization of offshore oil production systems , 2012 .

[49]  Chun-Liang Li,et al.  High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models , 2016, AISTATS.

[50]  Julio R. Banga,et al.  Extended ant colony optimization for non-convex mixed integer nonlinear programming , 2009, Comput. Oper. Res..

[51]  M. J. Inman,et al.  BAYESIAN OPTIMIZATION TECHNIQUES FOR ANTENNA DESIGN , 2004 .

[52]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[53]  Beatriz Souza Leite Pires de Lima,et al.  A COMPARATIVE STUDY APPLIED TO RISERS OPTIMIZATION USING BIO-INSPIRED ALGORITHMS , 2009 .

[54]  Tom Dhaene,et al.  Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization , 2014, J. Glob. Optim..

[55]  Nikolaos Doulamis,et al.  Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction , 2019, Comput. Intell. Neurosci..

[56]  Bobak Shahriari,et al.  Practical Bayesian optimization with application to tuning machine learning algorithms , 2016 .

[57]  Stephen Kemble,et al.  MIDACO on MINLP space applications , 2013 .

[58]  Mario A. Storti,et al.  MPI for Python: Performance improvements and MPI-2 extensions , 2008, J. Parallel Distributed Comput..

[59]  D Zhu,et al.  Training of quantum circuits on a hybrid quantum computer , 2018, Science Advances.