An attempt to measure fluctuating local pressure in free turbulent flow in water

Kawata, Takuya; Maeda, Hoshito; Obi, Shinnosuke

doi:10.1299/jfst.2014jfst0014

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…The dynamic responses of the pressure-measurement set-up introduce additional challenges to load-estimation algorithms. The pressure tubes that connect the model pressure taps to pressure sensors smooth out high-frequency contents and produce second-order responses that are comparable with gust pressure readings in both magnitude and time scale [23], which significantly distort and mask true pressure readings. These complex data are invaluable to test the limits of the CBM approach.…”

Section: Description Of Datasetsmentioning

confidence: 99%

Cluster-based Bayesian approach for noisy and sparse data: application to flow-state estimation

Kaiser,

Iacobello,

Rival

2024

Proc. R. Soc. A.

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This study presents a cluster-based Bayesian methodology for state estimation under realistic conditions including noisy data from sparse sensors. The proposed approach is interpretable and, building upon previous work on transition networks, explicitly accounts for experimental noise within the data-driven framework by means of data clustering. Experimental measurements are exploited, beyond model training, to quantify the degree of uncertainty (noise) for each trained state. Such noise levels are eventually associated with probability distributions that, when combined with Bayes’ theorem, allow us to perform real-time state estimation. The proposed methodology is tested on two cases of challenging flows generated by an accelerating elliptical plate and also a delta wing experiencing gusts. Results specifically indicate that the proposed approach is robust against the number of clusters, enabling state estimation with a significant order reduction, notably decreasing the computational cost while preserving estimation accuracy. Based on the present findings, the proposed data-driven approach can be employed for realistic state estimation in nonlinear systems where noise, sensor sparsity and nonlinearities represent a challenging scenario.

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Section: Description Of Datasetsmentioning

confidence: 99%

Cluster-based Bayesian approach for noisy and sparse data: application to flow-state estimation

Kaiser,

Iacobello,

Rival

2024

Proc. R. Soc. A.

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Velocity–pressure correlation measurement based on planar PIV and miniature static pressure probes

Kawata

Obi

2014

Exp Fluids

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Sparse Pressure-Based Machine Learning Approach for Aerodynamic Loads Estimation During Gust Encounters

Chen,

Kaiser,

et al. 2024

AIAA Journal

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Estimation of aerodynamic loads is a significant challenge in complex gusty environments due to the associated complexities of flow separation and strong nonlinearities. In this study, we explore the practical feasibility of multilayer perceptron (MLP) for estimating aerodynamic loads in gusts, when confounded by noisy and spatially distributed sparse surface pressure measurements. As a demonstration, a nonslender delta wing experiencing various gusts with different initial and final conditions is considered. Time-resolved lift and drag, and spatially distributed sparse surface pressure measurements are collected in a towing-tank facility. The nonlinear MLP model is used to estimate gust scenarios that are unseen in training progress. A filtering process allows us to examine the fluctuation of the dynamic response from the pressure measurements on the MLP. Estimation results show that the MLP model is able to capture the relationship between surface pressure and aerodynamic loads with a minimum quantity of learning samples, delivering accurate estimations, despite the slightly large errors for the cases at the boundary of the datasets. The results also indicate that the dynamic response of the pressure measurements has an influence on the learning of MLP. We further utilize gradient maps to perform a sensitivity analysis, so as to evaluate the contribution of the pressure data to the estimation of gust loads. This study reveals the significant contribution of the sensors located near the leading edge and at the nose of the delta wing. Our findings suggest the potential for an efficient sensor deployment strategy in data-driven aerodynamic load estimation.

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An attempt to measure fluctuating local pressure in free turbulent flow in water

Cited by 4 publications

References 16 publications

Cluster-based Bayesian approach for noisy and sparse data: application to flow-state estimation

Cluster-based Bayesian approach for noisy and sparse data: application to flow-state estimation

Velocity–pressure correlation measurement based on planar PIV and miniature static pressure probes

Sparse Pressure-Based Machine Learning Approach for Aerodynamic Loads Estimation During Gust Encounters

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