Paper Info
Title
Multilevel Control Variates for Uncertainty Quantification in Simulations of Cloud Cavitation
Abstract
We quantify uncertainties in the location and magnitude of extreme pressure spots revealed from large scale multiphase flow simulations of cloud cavitation collapse. We examine clouds containing 500 cavities and quantify uncertainties related to their initial spatial arrangement. The resulting 2,000-dimensional space is sampled using a nonintrusive and computationally efficient multilevel Monte Carlo (MLMC) methodology. We introduce novel empirically optimal control variate coefficients to enhance the variance reduction in MLMC. The proposed multilevel control variates Monte Carlo leads to more than two orders of magnitude speedup when compared to standard Monte Carlo methods. We identify large uncertainties in the location and magnitude of the peak pressure pulse and present its statistical correlations and joint probability density functions with the geometrical characteristics of the cloud. Characteristic properties of spatial cloud structure are identified as potential causes of significant uncertainties in exerted collapse pressures.
Year
DOI
Venue
2018
10.1137/17M1129684
SIAM JOURNAL ON SCIENTIFIC COMPUTING
Keywords
Field
DocType
compressible multiphase flow,high performance computing,diffuse interface method,bubble collapse,cloud cavitation,uncertainty quantification,multilevel Monte Carlo,control variates,fault tolerance
Magnitude (mathematics),Mathematical optimization,Uncertainty quantification,Supercomputer,Control variates,Fault tolerance,Mechanics,Multiphase flow,Mathematics,Cavitation,Cloud computing
Journal
Volume
Issue
ISSN
40
5
1064-8275
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Jonas Sukys1213.76
Ursula Rasthofer211.06
Fabian Wermelinger311.06
Panagiotis E. Hadjidoukas410918.24
Petros Koumoutsakos5106584.99