Efficient Markov Chain Monte Carlo Sampling Using Polynomial Chaos Expansion

2013 ◽  
Author(s):  
Hamid Bazargan ◽  
Mike Christie ◽  
Hamdi Tchelepi
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Polynomial Chaos ◽  
Monte Carlo Sampling ◽  
Polynomial Chaos Expansion ◽  
Chaos Expansion

scholarly journals Performance of non-intrusive uncertainty quantification in the aeroservoelastic simulation of wind turbines

2019 ◽  
Vol 4 (3) ◽  
pp. 397-406 ◽  
Author(s):  
Pietro Bortolotti ◽  
Helena Canet ◽  
Carlo L. Bottasso ◽  
Jaikumar Loganathan
Keyword(s):  
Monte Carlo ◽  
Wind Turbine ◽  
Uncertainty Quantification ◽  
Polynomial Chaos ◽  
Solution Space ◽  
Monte Carlo Sampling ◽  
Polynomial Chaos Expansion ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Chaos Expansion

Abstract. The present paper characterizes the performance of non-intrusive uncertainty quantification methods for aeroservoelastic wind turbine analysis. Two different methods are considered, namely non-intrusive polynomial chaos expansion and Kriging. Aleatory uncertainties are associated with the wind inflow characteristics and the blade surface state, on account of soiling and/or erosion, and propagated throughout the aeroservoelastic model of a large conceptual offshore wind turbine. Results are compared with a brute-force extensive Monte Carlo sampling, which is used as benchmark. Both methods require at least 1 order of magnitude less simulations than Monte Carlo, with a slight advantage of Kriging over polynomial chaos expansion. The analysis of the solution space clearly indicates the effects of uncertainties and their couplings, and highlights some possible shortcomings of current mostly deterministic approaches based on safety factors.

scholarly journals Stochastic Modeling of Electrical Field in Potato Tuber using Polynomial Chaos Expansion

2019 ◽  
Vol 29 ◽  
pp. 01008
Author(s):  
Bartosz Sawicki ◽  
Artur Krupa
Keyword(s):  
Monte Carlo ◽  
Potato Tuber ◽  
Polynomial Chaos ◽  
Ohmic Heating ◽  
Stochastic Approach ◽  
Polynomial Chaos Expansion ◽  
Chaos Expansion ◽  
Computational Power ◽  
Electrical Field ◽  
Stochastic Parameters

The paper deals with numerical modeling of objects with a natural origin. The stochastic approach based on description using random variables allows processing such challenges. The Monte-Carlo methods are known a tool for simulations containing stochastic parameters however, they require significant computational power to obtain stable results. Authors compare Monte- Carlo with more advanced Polynomial Chaos Expansion (PCE) method. Both statistical tools have been applied for simulation of the electric field used in ohmic heating of potato tuber probes. Results indicate that PCE is remarkably faster, however, it simplifies some probabilistic features of the solution.

scholarly journals Automated Parameter Blocking for Efficient Markov Chain Monte Carlo Sampling

2017 ◽  
Vol 12 (2) ◽  
pp. 465-490 ◽  
Author(s):  
Daniel Turek ◽  
Perry de Valpine ◽  
Christopher J. Paciorek ◽  
Clifford Anderson-Bergman
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Sampling
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