author={Dimitri Papadimitriou and Costas Papadimitriou},
  journal={International Journal for Uncertainty Quantification},
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… 

Bayesian Optimal Sensor Placement for Modal Identification of Civil Infrastructures

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Optimization of hydrogen sensor placement for hydrogen leakage monitoring in the fuel cell truck

  • Shujie LiuR. He
  • Engineering
    Journal of the Brazilian Society of Mechanical Sciences and Engineering
  • 2023
Hydrogen safety is one of the most important issues for fuel cell vehicles due to the leakage and wide flammability of hydrogen. It is essential to detect the hydrogen leak to support hydrogen

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Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for

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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.

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

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