Variable thrust and high efficiency propulsion with oscillating foils at high Reynolds numbers

Dave, Mukul; Spaulding, Arianne; Franck, Jennifer A.

doi:10.1016/j.oceaneng.2020.107833

Cited by 12 publications

(8 citation statements)

References 33 publications

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“…Due to the changing relative velocity vector, the relative angle of attack of the flow with respect to the foil, α, varies over the cycle as demonstrated by figure 3. As noted in previous work [12,6,7], the relative angle of attack can be used as an indicator of propulsive performance. Over the span of the high heave and high pitch kinematics tested, the shape of α throughout the cycle can take various forms, and thus two critical values are reported for each set of kinematics: the maximum value, α max , and value at mid-downstroke, α mid .…”

Section: Prelabeled Datasetmentioning

confidence: 93%

“…To demonstrate the mesh structure and resolution in close vicinity of the foil, figure 2 illustrates the foil position and deformed mesh configuration during upstroke. A mesh resolution study has been previously performed [12].…”

Section: Cfd Methodsmentioning

confidence: 99%

“…This dataset is performed at a high, turbulent Reynolds number of 10 6 using a Reynolds-averaged Navier-Stokes (RANS) solver with k-ω SST turbulence closure. More details on this dataset including the simulation set-up, validation, mesh refinement, and comparison against experimental data, are outlined in Dave et al [12].…”

Section: Prelabeled Datasetmentioning

confidence: 99%

“…Subsequently, the model is applied to vorticity wake images from a CFD database of thrustgenerating oscillating foils with two degrees-of-freedom, pitch and heave, as shown in figure 1b. This larger and unlabeled dataset is comprised of more complex vortex wakes (see Dave et al [12]) represented in figure 1b. Using the autoencoder and clustering pipeline, the wake images are automatically sorted into groups.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Clustering and Performance Prediction of Vortex Wakes from Bio-inspired Propulsors

Calvet,

Dave,

Franck

2020

Preprint

Self Cite

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An unsupervised machine learning strategy is developed to automatically cluster the vortex wakes of bio-inspired propulsors into groups of similar propulsive thrust and efficiency metrics. A pitching and heaving foil is simulated via computational fluid dynamics with over 121 unique kinematics by varying the frequency, heaving amplitude and pitching amplitude. A Reynolds averaged Navier-Stokes (RANS) model is employed to simulate the flow over the oscillating foils at Re = 10 6 , computing the propulsive efficiency, thrust coefficient and the unsteady vorticity wake signature that is unique to each kinematics. Using a pairwise Pearson correlation it is found that the Strouhal number, St, most strongly influences the thrust coefficient, whereas the relative angle of attack, defined by both the mid-stroke, α mid , and maximum, αmax, have the most significant impact on propulsive efficiency. Next, the various kinematics are automatically clustered into distinct groups exclusively using the vorticity footprint in the wake. A convolutional autoencoder is developed to reduce vortex wake images to their most significant features, and a k-means++ algorithm performs the clustering. The results are assessed by comparing clusters to a thrust versus propulsive efficiency map, which confirms that wakes of similar performance metrics are successfully clustered together. This automated clustering has the potential to identify complex vorticity patterns in the wake and modes of propulsion not easily discerned from traditional classification methods.

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Section: Prelabeled Datasetmentioning

confidence: 93%

Section: Cfd Methodsmentioning

confidence: 99%

Section: Prelabeled Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Clustering and Performance Prediction of Vortex Wakes from Bio-inspired Propulsors

Calvet,

Dave,

Franck

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Subsequently, the tuned model is applied to vorticity wake images from a CFD database of thrust-generating oscillating foils with two degreesof-freedom, pitch and heave, as shown in figure 1(b). This larger and unlabeled dataset is comprised of more complex vortex wakes (see Dave et al [14]) represented in figure 1(b).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised clustering and performance prediction of vortex wakes from bio-inspired propulsors

Calvet¹,

Dave²,

Franck³

2021

Bioinspir. Biomim.

Self Cite

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An unsupervised machine learning strategy is developed to automatically cluster the vortex wakes of bio-inspired propulsors into groups of similar propulsive thrust and efficiency metrics. A pitching and heaving foil is simulated via computational fluid dynamics with 121 unique kinematics by varying the frequency, heaving amplitude, and pitching amplitude. A Reynolds averaged Navier-Stokes model is employed to simulate the flow over the oscillating foils at Re = 10 6 , computing the propulsive efficiency, thrust coefficient and the unsteady vorticity wake signature. Using a pairwise Pearson correlation it is found that the Strouhal number most strongly influences the thrust coefficient, whereas the relative angle of attack, defined by both the mid-stroke and maximum have the most significant impact on propulsive efficiency. Next, the various kinematics are automatically clustered into distinct groups exclusively using the vorticity footprint in the wake. A convolutional autoencoder is developed to reduce vortex wake images to their most significant features, and a k-means++ algorithm performs the clustering. The results are assessed by comparing clusters to a thrust versus propulsive efficiency map, which confirms that wakes of similar performance metrics are successfully clustered together. This automated clustering has the potential to identify complex vorticity patterns in the wake and modes of propulsion not easily discerned from traditional classification methods.

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On the kinematics and dynamics parameters governing the flow in oscillating foils

Cimarelli

Franciolini

Crivellini

2021

Journal of Fluids and Structures

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Variable thrust and high efficiency propulsion with oscillating foils at high Reynolds numbers

Cited by 12 publications

References 33 publications

Unsupervised Clustering and Performance Prediction of Vortex Wakes from Bio-inspired Propulsors

Unsupervised Clustering and Performance Prediction of Vortex Wakes from Bio-inspired Propulsors

Unsupervised clustering and performance prediction of vortex wakes from bio-inspired propulsors

On the kinematics and dynamics parameters governing the flow in oscillating foils

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