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Naut your everyday jellyfish model: Exploring how tentacles and oral arms impact locomotion

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Jellyfish tentacles and oral arms suppress the vortex wake that drives swimming, cutting speed by up to ~400 percent.

desk verdict First systematic sweep of tentacle effects in a jellyfish IB model; the central trend is credible, though the vortex-suppression mechanism is under-quantified and the 2D setup likely exaggerates the effect. read the letter →

arxiv 1908.04202 v1 pith:GU3ZVZMH submitted 2019-08-09 physics.flu-dyn q-bio.QM

Jason G. Miles , Nicholas A. Battista This is my paper · ORCID
classification physics.flu-dynq-bio.QM
keywords jellyfishlocomotiontentaclesandoralarmsporoelasticappendagesimmersedboundarymethodvortexwakesuppressionReynoldsnumbercostoftransportfluidmixing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether a jellyfish's tentacles and oral arms, usually studied for feeding and stinging, also set how fast it can swim. Using a two-dimensional fluid-structure model of an idealized flexible bell with poroelastic appendages, the authors find that appendages inhibit forward swimming by suppressing the vortices that generate thrust. The effect is large: at the same contraction, a bell with eight tentacles/oral arms swims about 400 percent slower than the same bell with none, and adding symmetric appendages monotonically lowers speed. Length, placement, and density enter nonlinearly, so small morphological changes can produce sharp drops in swimming performance. The paper reads these results as a constraint that helps explain why some jellyfish actively hunt while others drift and filter-feed.

What carries the argument

The central object is an idealized two-dimensional semi-elliptical jellyfish bell with poroelastic tentacles/oral arms hanging inside, solved by the immersed boundary method in a viscous incompressible fluid. The bell is made of virtual springs and beams; the tentacles/oral arms are modeled as poroelastic structures with a Brinkman-type slip velocity controlled by a permeability coefficient; and contraction is driven by sinusoidally varying muscle springs. This machinery allows a systematic sweep over Reynolds number, appendage number, length, density, and placement, with output metrics of average forward speed, Strouhal number, cost of transport, and flow-mixing fields. The key work it does is to isolate the appendages' wake effect by holding bell geometry and kinematics fixed while varying only tentacle morphology.

What would settle it

A three-dimensional immersed-boundary simulation, or a laboratory particle-image-velocimetry study of a tethered or swimming medusa with and without oral arms at $\mathrm{Re}\approx 150$, could check whether the starting vortex is actually suppressed and whether the ~400 percent speed gap survives outside two dimensions.

Watch

Extended reading notes

Core claim

The central claim is that tentacles and oral arms are not passive drag: in the idealized model, adding poroelastic appendages monotonically decreases forward swimming speed, and the apparent mechanism is that the appendages suppress vortex formation and alter the vortex ring wake. The paper quantifies this as a fraction of bodylengths per bell contraction, noting that the no-appendage case is about 400 percent faster than the eight-appendage case at the same contraction kinematics. It further claims nonlinear relationships between appendage length, number, density, and placement and swimming speed, with three regimes for length (negligible effect, sharp drop, plateau), and ties the differences to changes in wake topology and flow-mixing structures rather than to a simple drag penalty.

Load-bearing premise

The results rest on the assumption that a two-dimensional fluid model, with no out-of-plane motion or vortex stretching, faithfully reproduces how poroelastic tentacles/oral arms suppress the vortex wake; if two-dimensional confinement artificially enhances the appendage-vortex interaction, the reported speed reductions would be too large.

Editorial extensions

If this is right

  • Adding more symmetric tentacles/oral arms monotonically reduces forward swimming speed; the eight-appendage bell swims roughly four times slower than the appendage-free bell at the same contraction.
  • Appendage length acts in three regimes: very short appendages barely change speed, moderate lengths cause a sharp decline, and very long appendages plateau so that further lengthening no longer matters.
  • When placement and density vary, fewer appendages do not always mean faster swimming; clustered or uneven configurations can outperform uniform ones, so morphology and fluid scale couple nonlinearly.
  • The appendages redirect the wake: instead of a vertically advected vortex ring, vortices bounce laterally off the appendages, increasing horizontal mixing near the bell and reducing mixing downstream.
  • Because the appendage-free bell is the only case that falls in the presumed efficient Strouhal band ($0.2 < \mathrm{St} < 0.4$) for $\mathrm{Re}\gtrsim 50$, tentacle/oral arm load shifts the jellyfish outside the efficient cruising regime.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the two-dimensional mechanism carries to three dimensions, appendage morphology should be treated as a first-order determinant of medusan ecology: species with many long, dense tentacles/oral arms are paying a large swimming-speed tax that favors passive foraging over active hunting.
  • The two-dimensional geometry has no vortex stretching or out-of-plane motion, so the observed inelastic-wall effect may be stronger or weaker in a real three-dimensional wake; a three-dimensional simulation at the same $\mathrm{Re}$ and appendage count would test whether the ~400 percent gap survives.
  • A natural experimental extension would be to track wake vorticity and swimming speed in a live medusa before and after temporarily removing or folding back its oral arms, checking whether suppression of the starting vortex is visible in particle-image velocimetry.
  • Because the paper reports that varying the poroelasticity coefficient did not strongly change speeds over the range tested, the model's insensitivity to appendix permeability may not hold at higher densities or longer lengths; this is a testable parameter-space point, not a claim the paper makes.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents a two-dimensional immersed-boundary study of an idealized flexible jellyfish bell with poroelastic tentacles/oral arms, using the open-source IB2d code. The authors vary Reynolds number, tentacle/oral arm number, length, placement, and density, and report forward swimming speeds, Strouhal number, cost of transport, vorticity fields, and FTLE-based mixing diagnostics. The central claim is that tentacles and oral arms inhibit forward swimming, by suppressing vortex formation, and that the relationship between morphology and swimming performance is nonlinear. The results are presented as a comparative parameter sweep, with percentage reductions in swimming speed relative to a no-tentacle case, and the paper includes an appendix on the sensitivity to the poroelasticity coefficient alpha.

Significance. If the central claim holds, the paper identifies tentacle and oral arm morphology as a first-order determinant of jellyfish swimming performance, moving beyond the common treatment of appendages as passive drag elements. The work also provides a reusable open-source modeling framework for poroelastic appendages in IB2d, and the main qualitative result—that appendages slow forward swimming—is consistent with the single prior observation by Katija (2015). The parameter sweeps are systematic and the alpha-robustness check in Appendix B, although limited, supports the comparability of the different sections. The main weakness is that the proposed mechanism, vortex suppression, is supported almost entirely by qualitative visualizations rather than quantitative wake diagnostics, and the 2D geometry may strengthen the appendage–vortex interaction relative to real three-dimensional jellyfish. The paper is a useful contribution if the mechanism claim is backed by quantitative measures or explicitly softened.

major comments (4)
  1. [Section 3.1, Figures 7 and 12] The central mechanistic claim that tentacles/oral arms inhibit swimming by suppressing vortex formation is inferred from vorticity colormaps and FTLE fields, but no quantitative wake diagnostic is reported. A direct drag force on the appendages, or a change in bell deformation due to the added elastic load, could also produce the observed speed reductions. Please compute a quantitative measure such as circulation of the leading vortex ring, total enstrophy in the wake, vortex impulse, or wake momentum for the no-tentacle and tentacle cases, and show that it correlates with the speed changes.
  2. [Section 2.1 and Section 2.2] The grid and domain convergence checks cited from Miles et al. 2019 and Battista et al. 2019 were performed on jellyfish models without tentacles, so they do not validate the tentacle–vortex interaction itself. In the present 2D geometry every poroelastic tentacle is effectively an infinite cylinder spanning the out-of-plane direction, which may exaggerate its blocking effect on the vortex wake compared with finite, separated tentacles in a real jellyfish. Please add a sensitivity test for the tentacle resolution or Lagrangian mesh spacing, discuss the expected 3D effects quantitatively, or explicitly state that the magnitude of the speed reductions is a 2D result that requires 3D confirmation.
  3. [Appendix B and Section 2.2] The poroelasticity coefficient alpha is set to 500,000 in Section 3.1, 10,000 in Section 3.2, and 25,000 in Section 3.3, with the justification that varying alpha does not significantly affect speeds. However, the supporting Figure A1 is presented only for Re = 150, and the text notes numerical stability issues for alpha below 10^4. Since cross-section comparisons are used in Sections 3.3.1–3.3.3, please report alpha sensitivity at additional Reynolds numbers, or restrict the cross-section claims to the range where alpha robustness has been demonstrated.
  4. [Section 3.1 and Tables 3–5] All percentage differences and qualitative rankings are based on a single simulation per configuration; no error bars, repeated trials, or variability estimates are reported. Some comparisons are close enough that run-to-run variation could change the ranking, for example the Re = 300 entries in Table 4 (49.3% vs 49.5%) and the Re = 37.5 entries in Table 5. Please provide at least a small number of repeated simulations for selected cases, or a convergence-based uncertainty estimate, to establish that the reported ordering of configurations is robust.
minor comments (5)
  1. [Abstract and Section 3.1] The phrase 'downwards of 400%' in the abstract and '~400% faster' in Section 3.1 is not consistent with the tabulated percentage decreases of roughly 40–80% in Tables 3–5; please report the comparison as a speed ratio or correct the percentage wording.
  2. [Figure 1 caption and Section 2.2] There are several typos, including 'tenatcles' in the Figure 1 caption, 'oral hands' in Section 2.2, 'poroelastsic' in Section 3, and repeated 'Lyanpunov' in figure captions for FTLE; these should be corrected.
  3. [Figure A5 caption] The caption for Figure A5 is missing a closing parenthesis: '... of varying lengths (in multiples of the bell radius, a, between the 4th and 5th contraction cycle.' should be completed.
  4. [Equations (8) and (9)] The definitions of N and dS in the cost-of-transport formulas are slightly ambiguous; please state explicitly which time interval and which distance are used, and whether the same normalization is applied in both the work-based and power-based definitions.
  5. [Section 3.3.1] The discussion of why ABCDEF can be faster than ACDF for Re < 75 is speculative ('rigid wall' versus 'cushioned' interactions) without supporting quantitative evidence; either add a diagnostic that distinguishes these scenarios or present the explanation as a hypothesis.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: swimming speeds are simulation outputs, not fits; the tentacle effect is not constructed from its own conclusion.

full rationale

The central claim — that tentacles/oral arms inhibit forward swimming by suppressing vortex formation — is read directly from immersed-boundary simulations in which tentacle morphology is an input and the swimming speed is an output. Nothing in the method sets the speed or the speed ratios to the morphology by construction: U_avg emerges from the coupled Navier-Stokes/poroelastic-IB solve (Eqs. A1-A4 and Eq. 5), and the reported ~400% comparison is a ratio of independently measured simulated speeds. The Katija 2015 comparison is external, not a fitted target. The self-citations to IB2d and to the prior no-tentacle models are not load-bearing in a circular way: IB2d is open source and externally documented, and the convergence checks cited from Miles et al. 2019 and Battista et al. 2019 are explicitly for the base no-tentacle model, so they cannot by construction manufacture the tentacle effect. The alpha-insensitivity test in Appendix B is an internal parameter check, and Appendix B itself notes numerical stability limits rather than tuning to a desired outcome. The paper's own stated limitations — that convergence studies were not rerun with tentacles (Section 2.1) and that tentacle/oral arm stiffness was not thoroughly investigated (Section 4) — are quantitative-confidence concerns, not evidence that any prediction reduces to an input. No equation is equivalent to another by definition, and no fitted parameter is renamed as a prediction. Thus there is no significant circularity.

Assumptions & free parameters 10 free parameters · 8 assumptions · 0 invented entities

The central claim rests on a 2D immersed-boundary model with prescribed bell kinematics and poroelastic appendages. The main free choices are the porosity coefficient alpha, which changes between the three parameter studies, and the idealized geometry and stiffness parameters. No new physical entities are introduced; the appendages are modeled with existing IB2d elasticity and porosity machinery.

free parameters (10)
  • Bell semi-minor axis a = 0.5 m
    Geometric input for the idealized bell, inherited from Hoover et al. 2015; not varied in this study.
  • Bell semi-major axis b = 0.75 m
    Geometric input for the idealized bell, inherited from Hoover et al. 2015.
  • Spring stiffness kspr = 1e7 kg*m/s^2
    Chosen to keep the bell nearly inextensible; not varied.
  • Beam stiffness kbeam = 2.5e5 kg*m/s^2
    Controls bell bending; not varied.
  • Muscle spring stiffness kmuscle = 1e5 kg*m/s^2
    Sets contraction strength; not varied.
  • Contraction frequency f = 0.8 Hz
    Kinematic driving frequency; fixed for all simulations.
  • Poroelasticity coefficient alpha, Section 3.1 = 500000 m^-2
    Chosen by hand; controls fluid slip through tentacles. Robustness checked only at Re=150 in Appendix B.
  • Poroelasticity coefficient alpha, Section 3.2 = 10000 m^-2
    Different value used for length sweeps; comparison across sections relies on the Appendix B robustness claim.
  • Poroelasticity coefficient alpha, Section 3.3 = 25000 m^-2
    Third value used for density and placement sweeps.
  • Tentacle morphology parameters = varied
    Number, length, density, and placement of tentacles are the independent variables of the parameter sweep, not fitted to data, but chosen by hand.
assumptions (8)
  • standard math Incompressible Navier-Stokes with immersed-boundary delta-function coupling describes jellyfish locomotion.
    Assumed physical model for fluid-structure interaction, stated in Appendix A.
  • domain assumption A 2D semi-elliptical model represents the 3D jellyfish bell and its wake.
    Used throughout; 2D vortex dynamics can differ from 3D, and the paper does not validate tentacle cases against 3D simulations.
  • domain assumption Tentacles and oral arms are poroelastic structures with slip velocity U_b = u + f_elastic/(alpha*mu), Eq. (5).
    Brinkman-based model for porous structures; alpha is a free parameter, and real tentacles are flexible permeable tissues, not necessarily captured at this level.
  • domain assumption Bell contraction is prescribed by the sinusoidal muscle rest length RL(t) = 2a(1 - |sin(pi f t)|), Eq. (6).
    Kinematics are imposed rather than emergent from neural or muscle dynamics; swimming speed is a response to this imposed motion.
  • domain assumption Tentacles and oral arms are structurally identical; no distinction is made between them.
    Stated in Section 2.2; real oral arms may differ in porosity and stiffness.
  • ad hoc to paper Varying alpha does not significantly affect speeds, so sections with different alpha can be compared.
    Appendix B tests this only at Re=150 and for specific tentacle numbers; it is assumed to hold across all Re and morphologies compared in Sections 3.1 to 3.3.
  • ad hoc to paper Prior grid and domain convergence results for the no-tentacle jellyfish apply to the tentacle cases.
    The paper cites convergence studies by Miles et al. 2019 and Battista et al. 2019 for the base model but does not present new convergence data for the poroelastic tentacle geometry.
  • domain assumption Tentacles are initially placed within the bell interior and interact with the fluid only through the immersed-boundary coupling.
    Geometry in Figure 3; real tentacles trail outside the bell, and the model's dynamics depend on this initial placement.

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Pith. "Pith review of Naut your everyday jellyfish model: Exploring how tentacles and oral arms impact locomotion." pith.science (2026). https://pith.science/paper/GU3ZVZMH

@misc{pith2026190804202,
  author       = {Pith},
  title        = {Pith review of: Naut your everyday jellyfish model: Exploring how tentacles and oral arms impact locomotion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GU3ZVZMH}},
  note         = {Machine review of arXiv:1908.04202}
}
read the original abstract

Jellyfish - majestic, energy efficient, and one of the oldest species that inhabits the oceans. It is perhaps the second item, their efficiency, that has captivated scientists for decades into investigating their locomotive behavior. Yet, no one has specifically explored the role that their tentacles and oral arms may have on their potential swimming performance, arguably the very features that give jellyfish their beauty while instilling fear into their prey (and beach-goers). We perform comparative in silico experiments to study how tentacle/oral arm number, length, placement, and density affect forward swimming speeds, cost of transport, and fluid mixing. An open source implementation of the immersed boundary method was used (IB2d) to solve the fully coupled fluid-structure interaction problem of an idealized flexible jellyfish bell with poroelastic tentacles/oral arms in a viscous, incompressible fluid. Overall tentacles/oral arms inhibit forward swimming speeds, by appearing to suppress vortex formation. Non-linear relationships between length and fluid scale (Reynolds Number) as well as tentacle/oral arm number, density, and placement are observed, illustrating that small changes in morphology could result in significant decreases in swimming speeds, in some cases by downwards of 400% between cases with to without tentacles/oral arms.

Figures

Figures reproduced from arXiv: 1908.04202 by the authors.

Figure 1
Figure 1. Anatomy of a “True" Jellyfish (class Scyphozoa). Courtesy of the National Science Foundation [5]. A “true jellyfish" is one of a specific class of jellyfish - Scyphozoa. Scyphozoans tend to be the jellyfish that are familiar to aquarium-goers, identifiable by the cup shape of their bell. Another class of Medusozoa are Cubozoa (box jellyfish), denoted by their cube-shaped medusae. Both of these jellyfish classes are … view at source ↗
Figure 2
Figure 2. Illustrating the diversity of tentacles/oral arms among different jellyfish species, including: (a) Moon jellyfish courtesy of the Two Oceans Aquarium [10] (left) and Audubon Aquarium of the Americas [11] (right) (b) Australian Spotted Jellyfish courtesy of the Aquarium of Niagara [12] (c) Blue Blubber Jellyfish courtesy of H. Steiger [13], (d) Flame Jellyfish courtesy of B. Abbott (juvenile, top) [14] and the Osaka… view at source ↗
Figure 3
Figure 3. Jellyfish model geometry composed of discrete points is a semi-elliptical configuration with tentacles/oral arms. The points along the bell are connected by virtual springs and virtual beams and the tentacles/oral arms are modeled as poroelastic structures, which include virtual springs and beams tethering adjacent points in the IB2d software. Although we view the jellyfish as being immersed in the fluid, the jellyf… view at source ↗
Figures from the paper (27 more)
Figure 4
Figure 4. Figure 4: A snapshot of a jellyfish simulation with 8 tentacles/oral arms swimming at Re = 150 during its 5 th contraction cycle, illustrating some of the simulation data obtained at each time-step, e.g., positions of Lagrangian points as well as forces on them, magnitude of vel…
Figure 5
Figure 5. Figure 5: Geometric model considered in Section 3.1 to determine how the presence of tentacles/oral arms affects forward swimming speed. This same geometry is used in Section 3.2 but with different tentacle/oral arms lengths, given in multiples of the bell radius, a. We then exp…
Figure 6
Figure 6. Figure 6: Visualization comparing jellyfish swimming for a variety of different number of symmetric tentacles/oral arms, for Re = 150 with a contraction frequency of f = 0.8 Hz. As the number of tentacles increases, forward swimming progress is more limited. Previous studies of …
Figure 7
Figure 7. Figure 7: Visualization comparing a jellyfish with no tentacles/oral arms to the case with 6 tentacles/oral arms (3 symmetric per side) at Re = 150. The colormap represents vorticity [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Plots detailing (a) distance swam and (b) velocity over 8 bell contraction periods at Re = 150 for differing numbers of symmetric tentacles. Moreover, as viscosity decreases (and Re increases) forward swimming performance, e.g., swimming speed, increases for 10 . Re . …
Figure 9
Figure 9. Figure 9: Illustrating average forward swimming speed against Reynolds Number, Re, for different number of symmetric tentacles/oral arms. Swimming speed is measured in non-dimensional units (bodylengths/contraction) in normal form (a) and logarithmic form (b). It is clear that t…
Figure 10
Figure 10. Figure 10: Plot depicting the relationship between Strouhal Number, St, and Reynolds Number, Re, for different numbers of symmetric tentacles/oral arms. St is the inverse of non-dimensional swimming speed. Experimental studies of jellyfish have concluded that the cost of transpo…
Figure 11
Figure 11. Figure 11: Illustrating the relationship between cost of transport (COT) and Reynolds Number, Re, for different numbers of symmetric tentacles/oral arms, when COT is computed using (a) average power and (b) average work. We then performed Lagrangian Coherent Structure (LCS) anal…
Figure 12
Figure 12. Figure 12: Visualization comparing Lagrangian Coherent Structures (LCS) using finite-time Lyanpunov exponents (FTLE) for the case with 6 total tentacles/oral arms (3 symmetrically placed per side) and Re = {37.5, 75, 150, 300} at the beginning of the 4 th contraction cycle. Note…
Figure 13
Figure 13. Figure 13: Visualization comparing Lagrangian Coherent Structures (LCS) using finite-time Lyanpunov exponents (FTLE) for cases with either 0, 1, 2, 3 or 4 symmetrically-placed tentacles/oral arms per side for Re = 150 at the start of the 4th contraction cycle [PITH_FULL_IMAGE:f…
Figure 14
Figure 14. Figure 14: Plot detailing distance swam against bell contractions performed for differing tentacle/oral arm lengths at Re = 150. Tentacle/oral arm length is given in multiples of the bell radius, a. First we observed that longer tentacle/oral arms leads to decreased forward dist…
Figure 15
Figure 15. Figure 15: Illustrating average forward swimming speed for different lengths of symmetrically placed tentacles/oral arms at Re = 150. Swimming speed is measured in non-dimensional units (bodylengths/contraction) and tentacle length is measured in multiples of the bell radius, a.…
Figure 16
Figure 16. Figure 16: Illustrating the relationship between cost of transport (COT) and tentacle/oral arm length for different numbers of symmetric tentacles/oral arms at Re = 150, when COT is computed using (a) average power and (b) average work. Tentacle/oral arm length is given in multi…
Figure 17
Figure 17. Figure 17: Visualization of jellyfish position and a colormap of vorticity at the end of the 5 th contraction cycle for each case of differing number of tentacles/oral arms of specified length, at Re = 150. Note that length is given in multiples of the bell radius, a. These idea…
Figure 18
Figure 18. Figure 18: Visualization comparing Lagrangian Coherent Structures (LCS) using finite-time Lyanpunov exponents (FTLE) for the case with 6 total tentacles/oral arms (3 symmetrically placed per side) of varying lengths (in multiples of the bell radius, a, at the start of the 4th co…
Figure 19
Figure 19. Figure 19: Geometric setup for all cases considered in Section 3.3.1 to determine if the placement of the outermost tentacles/oral arms dictates forward swimming speed. Qualitative analysis of forward swimming performance is given in [PITH_FULL_IMAGE:figures/full_fig_p022_19.png]
Figure 20
Figure 20. Figure 20: Visualization comparing the positions of the jellyfish across the first 5 contraction cycles for all cases considered in Section 3.3.1 for Re = 150. Upon computing the forward swimming speed for each case of Re and tentacle/oral arm configuration considered, the case …
Figure 21
Figure 21. Figure 21: (a) Forward swimming speed and (b) power-based cost of transport for each simulation in Section 3.3.1. A nonlinear relationship between forward swimming speed, tentacle/oral arm number density and placement emerges [PITH_FULL_IMAGE:figures/full_fig_p024_21.png]
Figure 22
Figure 22. Figure 22: Visualization of jellyfish position and a colormap of vorticity across the 4 th to 5 th contraction cycle for each case considered at Re = 150. 3.3.2. How does density of tentacles affect swimming performance? For this study, we will use the same placement of the oute…
Figure 23
Figure 23. Figure 23: Geometric setup for all cases considered in Section 3.3.2 to determine how density of the tentacles/oral arms affects forward swimming speed. A qualitative analysis of forward swimming progress is given in [PITH_FULL_IMAGE:figures/full_fig_p026_23.png]
Figure 24
Figure 24. Figure 24: Visualization comparing the positions of the jellyfish across the first 5 contraction cycles for all cases considered in Section 3.3.2 for Re = 150 [PITH_FULL_IMAGE:figures/full_fig_p027_24.png]
Figure 25
Figure 25. Figure 25: (a) Forward swimming speed and (b) power-based cost of transport for each simulation in Section 3.3.2. A nonlinear relationship between forward swimming speed, tentacle/oral arm density and placement is observed again. From Sections 3.3.1 and 3.3.2, it is evident that…
Figure 26
Figure 26. Figure 26: Visualization of jellyfish position and a colormap of vorticity across its 5 th contraction cycle for each case considered at Re = 150. 3.3.3. How does stacking tentacle/oral arms towards the outermost ones affect swimming performance? For this study, we will use the …
Figure 27
Figure 27. Figure 27: Geometric setup for all cases considered in Section 3.3.3 to determine how placing more tentacles/oral arms towards the outermost ones affect forward swimming speed. A qualitative analysis of forward swimming progress is given in [PITH_FULL_IMAGE:figures/full_fig_p02…
Figure 28
Figure 28. Figure 28: Visualization comparing the positions of the jellyfish across the first 5 contraction cycles for all cases considered in Section 3.3.3 for Re = 150. Swimming speeds and cost of transport for Re = 37.5, 75, 150, and 300, are given in Figures 29a and 29b, respectively. …
Figure 29
Figure 29. Figure 29: (a) Forward swimming speed and (b) power-based cost of transport for each simulation in Section 3.3.3. A nonlinear relationship between number and density of tentacles/oral arms and forward swimming speed is observed. Similar to Sections 3.3.1 and 3.3.2, Section 3.3.3…
Figure 30
Figure 30. Figure 30: Visualization of jellyfish position and a colormap of vorticity across its 5 th contraction cycle for each case considered at Re = 150. 4. Discussion and Conclusion Previous fluid-structure interaction models have shown that a jellyfish’s bell morphology, material pro…

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.