Research Article |
Corresponding author: Johann Waringer ( johann.waringer@univie.ac.at ) Academic editor: Astrid Schmidt-Kloiber
© 2023 Ariane Vieira, Hendrik C. Kuhlmann, Johann Waringer, Carina Zittra, Simon Vitecek, Stephan Handschuh.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Vieira A, Kuhlmann HC, Waringer J, Zittra C, Vitecek S, Handschuh S (2023) Hydraulic engineering of Drusinae larvae: head morphologies and their impact on surrounding flow fields. Contributions to Entomology 73(2): 269-278. https://doi.org/10.3897/contrib.entomol.73.e109206
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Body morphologies are significantly different amongst the members of the Drusinae subfamily. Aligned with such differences is the selective niche location chosen by many species from the subfamily. Typically, they live on the sediments of cold, well-oxygenated mountain streams from the Eurasian Region. However, each of the three evolutionary lineages (shredders, grazers and carnivorous filter feeders) inhabit different hydraulic locations according to their foraging behaviour. To investigate the relationship between the body morphology and the flow field near the body, we use Large Eddy Simulations to compute the flow past five different species of the subfamily. We selected species representing the three evolutionary lineages of the subfamily, Drusus alpinus Meyer-Dür 1875 from the shredders clade, D. bosnicus Klapálek 1899 and D. monticola McLachlan 1876 from the grazers clade and Cryptothrix nebulicola McLachlan 1867 and D. discolor (Rambur 1842) from the filter feeders clade. For the simulations, three-dimensional body shapes were reconstructed from X-ray micro CT data and exposed to a turbulent flow corresponding to water-depth and velocity data measured in the field. The total forces acting on each morphotype were found to be comparable. The lift coefficients computed and ranging from 0.07 to 0.17 are smaller than the drag coefficients which were found to range from 0.32 to 0.55. The local distribution of the skin-friction indicates flow-separation zones near the edges of the bodies, in particular, between the head and the pronotum, which are differently located according to each species. Moreover, we observe higher streamwise normal stresses upstream of the head of the filter feeder species. It is hypothesised that the upstream horseshoe vortex can lift up drifting food particles and transport these to the larvae’s filtering legs, thereby enhancing the encounter rates of particles with the filtering devices.
ecomorphology, Insecta, larva, numerical flow analysis, Trichoptera
We observe a great variety of life forms due to evolution. Through natural selection, different population members are more likely to survive and pass their characteristics to their offspring. As the natural environment is intrinsically transient, previously neutral or harmful characteristic properties may become beneficial, while helpful features may become unfavourable.
In this context, Drusinae is a highly endemic caddisfly subfamily restricted to Eurasian mountains, where cold running waters provide a well-oxygenated habitat. The subfamily includes three main clades connected to their foraging method: omnivorous shredders, grazers and filtering carnivores (
In this work, we simulate the flow around different species from the Drusinae subfamily. The goal is to investigate whether changes in body morphology yield significant modifications of the surrounding flow field and, thus, of the forces acting on the body. If such differences exist, they could possibly be favourable for the respective species. To that end, three-dimensional models of larvae are constructed from tomographic data and flow simulations are carried out for two different flow conditions.
To simulate the flow past different Drusinae species, we use realistic body shapes and resolve the problem numerically in time and space using OpenFOAM. To that end, we employed micro-computer tomography (µ-CT) data of different species to construct the body shapes. Based on the larvae’s natural setting, a rectangular computational domain in the form of an open channel was created. The reconstructed bodies were then placed in the channel and exposed to a turbulent flow, based on flow velocities measured in situ at the larvae locations. The workflow is illustrated in Fig.
The turbulent flow is fully described by the incompressible Navier-Stokes equations and suitable boundary conditions as indicated in Fig.
(1)
where Ub is the bulk (or mean) velocity and ν is the kinematic viscosity (ν = 1.31 × 10−6 m2/s for water at a temperature of 10.5 °C). At the outlet, we assume zero streamwise gradients of all flow velocities. The numerical solution yields the instantaneous velocity and pressure fields. After a transient time, these quantities are averaged to obtain temporal mean values.
For the flow measurements, nine final instar larvae from Drusinae were selected in their characteristic habitats, representing the three clades of the subfamily (two shredders, four grazers, three filter feeders). The measurements were taken directly in front of the larvae using a tripod mounted Schiltknecht MiniWater 20 Micro velocimeter probe (resolution: 0.01 m/s, probe diameter Hp = 11 mm) with a sampling rate of 1 Hz. The measured velocities ranged from 0 to 0.93 m/s. The total water depths were between 10 mm and 30 mm. Typically, larvae were sitting on top of flattened sediment pebbles, aligned with the flow direction and head upstream. Further information and an analysis of the measured data can be found in
We employed micro-computer tomography (µ-CT) to create the external surface of five specimens according to the three different feeding modes prevailing in Drusinae: Drusus alpinus Meyer-Dür, 1875 (representing the shredder clade), D. bosnicus Klapálek, 1899 and D. monticola McLachlan, 1876 (representing the grazer clade) and D. discolor (Rambur, 1842) and C. nebulicola McLachlan, 1867 (representing the filtering carnivore clade). For µ-CT scans, the merged volume was exported as *.TXM file into Amira 2019.1 (FEI SAS, Mérignac, France, part of Thermo Fisher ScientificTM). Image segmentation was achieved in Amira 6.5.0 (Visage Imaging, Inc., San Diego, CA, USA). Then we used the Amira Surface Generate tool to create the three-dimensional surface renderings. Further information regarding the technical aspects of tomographic scans and a discussion of the three-dimensional tomography reconstruction of the external surface can be found in
The surface renderings were further processed using Blender to prepare 3D geometries for the numerical simulations. In Blender, the original finely triangularised geometry was smoothed and transformed into a symmetric quadrilateral surface mesh. One example of the re-topology is shown in Fig.
After re-topology of the torso, the legs were moved to a position which resembles the clinging position of the larvae observed in the field. An example is shown in Fig.
Based on Ub and H from above, the simulation is carried out for bulk Reynolds numbers Reb = 7634 and Reb = 10496. Since the flow is expected to be turbulent at these Reynolds numbers, we use Large Eddy Simulation (LES). The turbulent inlet flow is generated by the divergence-free synthetic eddy method (DFSEM) (
The simulations are carried out using OpenFOAM. This open-source software employs cell-centred finite volumes to discretise the governing equations. The mesh was generated with the tool snappyHexMesh, which requires a cubic hexahedral background mesh (without the body). This background mesh was created with cells of size ∆x+ = ∆y+ = ∆z+ = ∆+max = 11.32, where the superscript ‘+’ indicates non-dimensional quantities (in wall units). They are based on the friction velocity , where is the mean wall stress of the turbulent flow (here acquired from the auxiliary RANS) and ρ the fluid density. Hence, the non-dimensional velocity, length and time are , , and t+ = t / (H / uτ), respectively. The pressure is scaled differently (see below). In a final step, the mesh was refined locally near the channel floor and the larva’s surface reaching ∆y+min = 0.36. The maximum number of grid points was 6.49 × 106 for D. monticola and the minimum was 5.03 × 106 for C. nebulicola. A non-dimensional time of t+ = 0.44 was adequate for all field variables to reach a statistically steady state. After that, mean quantities were calculated by averaging over t+ ∈ [0.44,1.75].
All flow fields are decomposed into a temporal mean (indicated by an overbar) and a fluctuation (indicated by a prime). For the pressure field , for example, we have
(2)
where and the mean of the pressure fluctuation vanishes. For the present calculations, we use t0+ = 0.44 and t1+ = 1.75. Characteristic velocities and bulk Reynolds numbers are provided in Table
Ub | Reb | uτ | UL |
---|---|---|---|
0.40 m/s | 7634 | 0.022 m/s | 0.35 m/s |
0.55 m/s | 10496 | 0.030 m/s | 0.48 m/s |
To analyse the flow past the larva, it is useful to define a local velocity
, (3)
to which the larva is exposed. The velocity UL is defined as the mean over the area [0,HL] × [−0.7H,0.7H] at the position x = x0 upstream of the larva. Here, we use the height HL = 10 mm (0.4H) which is comparable to the height HP = 11 mm of the velocity probe used to measure the flow velocity in front of the larvae, and x0 = −HL = 0.4H. Note that the mean elevation of the larvae from the ground is h = 2.86 mm which is only a fraction of HL (Table
Elevation from the ground h of different larvae in metric units and in wall units (h+) for each bulk velocity Ub.
Larvae | D. alpinus | D. bosnicus | D. discolor | D. monticola | C. nebulicola |
---|---|---|---|---|---|
h | 3.30 mm | 2.92 mm | 2.86 mm | 3.05 mm | 2.20 mm |
h/HL | 0.33 | 0.292 | 0.286 | 0.305 | 0.22 |
Ub | h + | h + | h + | h + | h + |
0.40 m/s | 55.6 | 49.2 | 48.2 | 51.4 | 37.0 |
0.55 m/s | 74.7 | 66.1 | 64.8 | 69.1 | 49.8 |
Fig.
Profiles of the mean velocity in the mid-plane z as a function of the wall normal distance y/HL upstream of the larva body at x0 = − HL. Different species are distinguished by colour and type of symbol. The horizontal dashed line marks the average height of the larva models (h = 2.86 mm). Channel with Reb = 10496.
Fig.
For a 3D visualisation, we show in Fig.
An important question is whether different species perceive different mean forces due to the flow. In the symmetric arrangement considered, only drag and lift forces arise. They are usually expressed in terms of the drag and lift coefficients
, (4)
, (5)
which are just the scaled forces. As the scale, we employ the pressure rise (ρ / 2)U2L in the forward stagnation point of a body in a homogeneous flow with velocity UL multiplied by the streamwise projected area of the body (Table
D. alpinus | D. bosnicus | D. discolor | D. monticola | C. nebulicola |
---|---|---|---|---|
7.30 × 10−6 | 6.15 × 10−6 | 5.90 × 10−6 | 6.86 × 10−6 | 3.59 × 10−6 |
, (6)
, (7)
which are distinguished by the subscripts p (pressure part) and ν (viscous part). Note that the viscous normal stress is taken care of by the pressure part.
Fig.
Fig.
The smallest overall Reynolds stresses are found for Drusus bosnicus. A reason could be Drusus bosnicus has the smallest length-to-height ratio amongst the species. Furthermore, the tapered shape of the body may create a streamlined silhouette, which may be responsible for stress reduction. Regarding D. alpinus, the highest stresses arise past the dorsal line.
Fig.
LES was employed to simulate the flow past five species from Drusinae larvae for two Reynolds numbers representative of their habitats. Body shapes for the numerical simulations were generated from X-ray micro CT data using Blender. Mean flow properties, such as drag and lift coefficients and fluctuating properties, such as the streamwise Reynolds stress, were computed and evaluated for flow velocities determined beforehand during field excursions. The larvae are found to be situated in the buffer layer of the turbulent flow where they are subjected to a mean flow as well as to turbulent flow fluctuations. Amongst the fluctuations, the streamwise component is most prominent.
We found that the integral properties, such as the mean drag and the lift coefficients are almost independent of the species. However, local properties like Reynolds stress, skin friction and regions of recirculation vary amongst the species. We speculate the differences are due to the variability in the location of sharp body edges within the species, for example, the characteristically pronounced pronotum of D. bosnicus, as well as the shape of their cases. Therefore, body morphology modifications could alter the local flow which could aid their foraging behaviour. For example, the horseshoe vortex created ahead of a larva from the filter-feeders’ clade may lift up small food particles drifting on the ground and transport these to the filtering devices (Fig.
Indeed, the results presented here are the first numerical simulations of flow around caddisfly larvae. As such, they are prototypes in this research field and will undergo significant development in the coming years. For instance, we did not incorporate all possible body postures and were not able to account for variation in case shape or material. There is evidence that different body postures are used, for example, for feeding, but body posture and position relative to the direction of flow will also change substantially during larval movement. Moreover, a full-scale analysis of the Drusinae radiation could be conducted, aiming to test whether evolutionary trends can be detected amongst species exposed to similar flow regimes or whether each species has a unique flow niche. Thereby, the evolutionary significance of flow could be explored. To this end, it will be relevant to collect the full flow spectrum to which a Drusinae species’ larvae may be exposed through thorough field work.
On behalf of all authors, the corresponding author states that there is no conflict of interest.
This paper is part of the project “Intricate bodies in the boundary layer” (project number P31258-B29, PIs: J. Waringer, H. C. Kuhlmann) funded by the Austrian Science Fund (FWF).