Abstract

This paper presents a comparison of two multi-fidelity methods for the forward uncertainty quantification of a naval engineering problem. Specifically, we consider the problem of quantifying the uncertainty of the hydrodynamic resistance of a roll-on/roll-off passenger ferry advancing in calm water and subject to two operational uncertainties (ship speed and payload). The first four statistical moments (mean, variance, skewness, and kurtosis), and the probability density function for such quantity of interest (QoI) are computed with two multi-fidelity methods, i.e., the Multi-Index Stochastic Collocation (MISC) and an adaptive multi-fidelity Stochastic Radial Basis Functions (SRBF). The QoI is evaluated via computational fluid dynamics simulations, which are performed with the in-house unsteady Reynolds-Averaged Navier–Stokes (RANS) multi-grid solver chinavis. The different fidelities employed by both methods are obtained by stopping the RANS solver at different grid levels of the multi-grid cycle. The performance of both methods are presented and discussed: in a nutshell, the findings suggest that, at least for the current implementation of both methods, MISC could be preferred whenever a limited computational budget is available, whereas for a larger computational budget SRBF seems to be preferable, thanks to its robustness to the numerical noise in the evaluations of the QoI.

Highlights

  • Aerial, ground, and water-born vehicles must perform, in general, under a variety of environmental and operating conditions and, their design analysis and optimization processes cannot avoid taking into account the stochasticity associated with environmental and operational parameters

  • The performance of the Multi-Index Stochastic Collocation (MISC) and adaptive multi-fidelity Stochastic Radial Basis Functions (SRBF) methods is compared on: (i) an analytical test function and (ii) the forward uncertainty quantification (UQ) analysis of a roll-on/roll-off passenger (RoPax) ferry sailing in calm water with two operational uncertainties, ship speed and draught, the latter being directly linked to the payload

  • Where range[S, Gref] is the largest common range of values taken by S and Gref, CDFS and CDFGref are the empirical cumulative density function (CDF) obtained by the set used before of 10000 random samples of the MISC/SRBF surrogate models and reference model, respectively

Read more

Summary

Introduction

Ground, and water-born vehicles must perform, in general, under a variety of environmental and operating conditions and, their design analysis and optimization processes cannot avoid taking into account the stochasticity associated with environmental and operational parameters. A URANS-based statistically significant evaluation of ship maneuvering performance in irregular waves may require up to 1M CPU hours on HPC systems [2] In this context, the use of efficient uncertainty quantification (UQ) methods is essential to make the design analysis and optimization processes affordable. The performance of the Multi-Index Stochastic Collocation (MISC) and adaptive multi-fidelity Stochastic Radial Basis Functions (SRBF) methods is compared on: (i) an analytical test function and (ii) the forward UQ analysis of a roll-on/roll-off passenger (RoPax) ferry sailing in calm water with two operational uncertainties, ship speed and draught, the latter being directly linked to the payload.

Forward uncertainty quantification methods
Multi‐index stochastic collocation (MISC)
Tensorized Lagrangian interpolant operators
MISC surrogate model
MISC quadrature
An adaptive algorithm for the multi‐index set 3
Adaptive multi‐fidelity stochastic radial basis functions (SRBF)
SRBF surrogate model
Multi‐fidelity approach
Adaptive sampling approach
Numerical tests
Error metrics
Formulation
Numerical results
Formulation and CFD method
Findings
Conclusions and future work
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call