OPTIMAL SENSOR PLACEMENT FOR THE ESTIMATION OF TURBULENCE MODEL PARAMETERS IN CFD

@article{Papadimitriou2015OPTIMALSP,
  title={OPTIMAL SENSOR PLACEMENT FOR THE ESTIMATION OF TURBULENCE MODEL PARAMETERS IN CFD},
  author={Dimitri Papadimitriou and Costas Papadimitriou},
  journal={International Journal for Uncertainty Quantification},
  year={2015},
  volume={5},
  pages={545-568}
}
The optimal placement of sensors for the estimation of turbulence model parameters in computational fluid dynamics is presented. The information entropy (IE), applied on the posterior uncertainty of the model parameters inferred from Bayesian analysis, is used as a scalar measure of uncertainty. Using an asymptotic approximation, the IE depends on nominal values of the CFD model and prediction error model parameters. It is derived from the sensitivities of the flow quantities predicted by the… 
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13 Citations
Background Citations
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Methods Citations
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13 Citations

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58 References

Bayesian uncertainty quantification of turbulence models based on high-order adjoint

Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error

A sensor placement strategy using joint entropy is able to lead to predictions of wind characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy.

The effect of prediction error correlation on optimal sensor placement in structural dynamics

Entropy-Based Optimal Sensor Location for Structural Model Updating

The proposed entropy-based measure of uncertainty is well-suited for making quantitative evaluations and comparisons of the quality of the parameter estimates that can be achieved using sensor configurations with different numbers of sensors in each configuration.

Predictive RANS simulations via Bayesian Model-Scenario Averaging

Optimal sensor placement methodology for parametric identification of structural systems

Bayesian uncertainty quantification applied to RANS turbulence models

A Bayesian uncertainty quantification approach is developed and applied to RANS turbulence models of fully-developed channel flow. The approach aims to capture uncertainty due to both uncertain…

Bayesian estimates of parameter variability in the k-ε turbulence model

Bayesian uncertainty analysis with applications to turbulence modeling

Optimal measurement locations for parameter estimation of non linear distributed parameter systems

This study shows that the measurement location plays an essential role in the parameter estimation procedure and an efficient computational optimum experiment design on discrete domains is formulated.
...