Multifidelity data fusion via bayesian inference

S. Ashwin Renganathan, Kohei Harada, Dimitri N. Mavris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


We consider the fusion of two aerodynamic data sets originating from differing fidelity physical or computer experiments. We specifically address the fusion of: 1) noisy and incomplete field from wind tunnel measurements and 2) deterministic but biased fields from numerical simulations. These two data sources are fused in order to estimate the true field that best matches measured quantities of interest that are a function of the field itself. For example, two sources of pressure fields about an aircraft are fused based on measured forces and moments from a wind-tunnel experiment. We employ a Bayesian framework to infer the true fields conditioned on measured values of certain quantities of interest. Essentially we perform a statistical correction to the fields obtained from physical and computer experiments. Additionally, we also show how to propagate the uncertainty in the original data into the fused data. The formulation of the methodology and its demonstration on the flow past the RAE2822 airfoil and the Common Research Model wing at transonic conditions are discussed.

Original languageEnglish (US)
Title of host publicationAIAA Aviation 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Number of pages19
ISBN (Print)9781624105890
StatePublished - 2019
EventAIAA Aviation 2019 Forum - Dallas, United States
Duration: Jun 17 2019Jun 21 2019

Publication series

NameAIAA Aviation 2019 Forum


ConferenceAIAA Aviation 2019 Forum
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Aerospace Engineering


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