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62 results for “hydrodynamic model”
Fig. 3. Full 3D in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 3. Full 3D model of Baculites compressus with model components. A. Complete, digitally-reconstructed shell rendered in X-ray view to show internal structure. B. Three-dimensional model the soft body. C. Three-dimensional model of the cameral volumes within the phragmocone.
Fig. 4 in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 4. Generation of a 3D printed model of Nautilus pompilius with theoretically equal physical properties to the virtual counterparts. A. Original virtual model from Peterman et al. (2019: fig. 2.5). B. Modified virtual model with simplified internal geometry. The center of buoyancy remains the same because external geometry does not change. The total center of mass, however, is corrected by a bismuth counterweight of known volume, density, and mass. C. 3D printed posterior half of the physical model with bismuth counterweight in the computed position. D. Anterior half of the physical model showing the one-way valve for liquid to exit upon displacement by an air-filled balloon. E. Neutrally buoyant physical model with the required volume to liquid ratio for neutral buoyancy. This computed volume of air is inserted through a one-way entrance valve into the internal balloon. Tracking points are placed parallel to the aperture in order to analyze movement in a hydrodynamic setting.
Fig. 5 in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 5. Position of the ventral tracking point (V) and umbilical tracking point (U) as a function of time measured with the physics modeling software (Tracker 4.11.0; Brown 2017). Note that the rotation of the aperture is coupled with translational motion, resulting in complex movement.
Fig. 11 in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 11. Thrust required to change the Baculites compressus model orientation (θa). A. Thrust Scenario 1: A continuous thrust supplied to the venter with a similar thrust ratio to Nautilus (Table 1). The average change from a vertical resting orientation (Δ θpeak) is 22.7°. B. Thrust Scenario 2: Periodic pulses from a pump with a simulated mantle cavity of 20% soft body volume of B. compressus (Table 1). The average change from a vertical resting orientation ( Δ θpeak) is 25.6°. C. Thrust Scenario 3: Periodic pulses from a pump with a thrust ratio between Sepia officinalis and Loligo vulgaris (Table 1). The average change from a vertical resting orientation (Δ θpeak) is 72.2°. Average peak thrust (Fpeak) error bars represent one standard deviation calibrated from 30 second intervals of pumping.
Fig. 8 in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 8. Hydrodynamic restoration of the Nautilus pompilius 3D printed model following underdamped harmonic oscillation. Apertural angle (θa) measured in degrees as a function of time after rotating approximately 38° from the equilibrium orientation. An angle of zero represents a condition where the aperture is horizontally oriented. Open dots represent the peaks used to calculate decay in amplitude with time (grey dashed curves).
Fig. 2 in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 2. Shell and septum thickness measured from three specimens of Baculites compressus (WSU-1400, WSU-1401, and WSU-1405). Exponential curves were fit to these points to define thickness for the full 3D model as a function of whorl height.
Fig. 1 in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 1. Three-dimensional reconstruction of a fragmentary baculite Baculites compressus Say, 1820 (WSU-1400) from the late Campanian Pierre Shale of Meade County, South Dakota. A. Model of a fragmentary specimen generated by photogrammetry with the software (3DF Zephyr). B. Broken septum isolated from the photogrammetry model. C. Suture pattern. D. Complete septum created by reconstructing the higher-order frilling with the suture pattern as a template.
Hydrodynamic daily climatology of South Eastern Australia based on a 22 year run of the SEA-COFS model
<p>This dataset is a daily climatology created based on a 22 year free run of the SEA-COFS model full domain (EAC 25.25 - 45.55 °S) forced with BARRA-R winds and tides, spanning from January 1994 to September 2016. The model has 30 sigma-stretch vertical levels and an across-shore resolution of 2.5 km (on shelf) – 6 km (off shelf) and 5 km along-shore. The original dataset can be access here:</p> <p>Li, J & Kerry C. (2021). A high-resolution, 22-year, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System (Version 2.0). <a href="https://doi.org/10.26190/TT1Q-NP46">https://doi.org/10.26190/TT1Q-NP46</a></p> <p>For a full description of this version of the model and its validation refer to the following papers:</p> <ul> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2021). <a href="https://doi.org/10.1029/2021GL094115%20">Dynamics of interannual eddy kinetic energy modulations in a Western Boundary Current</a>. <em>Geophysical Research Letters,</em>Vol 48, October 2021.DOI: <a href="https://doi.org/10.1029/2021GL094115">https://doi.org/10.1029/2021GL094115</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2021/2021GL094115.pdf">[PDF file]</a></li> <li> </li> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2022). <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current</a>. <em>Journal Of Climate,</em> February 2022. DOI: <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">https://doi.org/10.1175/JCLI-D-21-0622.1</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2022/Li_2022.pdf">[PDF file]</a></li> </ul> <p>This daily climatology of 365 days was created with NCO tools by taking the mean of the 22 ROMS output files (one for each year) containing daily averages, to generate each climatological day. Specific commands used are recorded in the NetCDF file history. Leap year days were excluded since its climatology was computed with only 5 instances.</p> <p>A sample file with the climatological data for January 1st is provided here as an example of the NetCDF format of the dataset. The entire dataset is one file of approximately 62GB and can be provided upon request. </p> <p><strong>NOTE: </strong>The ocean_time variable reflects the dates of the year 1994, but the values of the variables correspond to climatological values computed as described. </p> <p>The ROMS variables below are present in this daily climatology, as well as the S-coordinate stretching curves, grid defining variables and other time independent parameters:</p> <p>AKs = "time-averaged salinity vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKt = "time-averaged temperature vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKv = "time-averaged vertical viscosity coefficient" [meter2 seconds-1]</p> <p>bustr = "time-averaged bottom u-momentum stress" [newton meter-2]</p> <p>bvstr = "time-averaged bottom v-momentum stress" [newton meter-2]</p> <p>omega = "time-averaged S-coordinate vertical momentum component" [meter3 second-1]</p> <p>pvorticity = "time-averaged potential vorticity" [meter-1 second-1]</p> <p>pvorticity = "time-averaged 2D potential vorticity" [meter-1 second-1]</p> <p>rho = "time-averaged density anomaly" [kilogram meter-3]</p> <p>rvorticity = "time-averaged relative vorticity, vertical component" [second-1]</p> <p>rvorticity_bar = "time-averaged 2D relative vorticity" [second-1]</p> <p>salt: = "time-averaged salinity" [PSU]</p> <p>shflux = "time-averaged surface net heat flux" [watt meter-2]</p> <p>ssflux = "time-averaged surface net salt flux, (E-P)*SALT" [meter second-1]</p> <p>sustr = "time-averaged surface u-momentum stress" [newton meter-2]</p> <p>svstr = "time-averaged surface v-momentum stress" [newton meter-2]</p> <p>temp = "time-averaged potential temperature" [Celsius]</p> <p>u = "time-averaged u-momentum component" [meter second-1]</p> <p>u_eastward = "time-averaged eastward momentum component at RHO-points" [meter second-1]</p> <p>ubar = "time-averaged vertically integrated u-momentum component" [meter second-1]</p> <p>ubar_eastward = "time-averaged eastward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>uu = "time-averaged u-momentum times u-momentum" [meter2 second-2]</p> <p>uv = "time-averaged u-momentum times v-momentum" [meter2 second-2]</p> <p>v = "time-averaged v-momentum component" [meter second-1]</p> <p>v_northward = "time-averaged northward momentum component at RHO-points" [meter second-1]</p> <p>vbar = "time-averaged vertically integrated v-momentum component" [meter second-1]</p> <p>vbar_northward = "time-averaged northward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>vv = "time-averaged v-momentum times v-momentum" [meter2 second-2]</p> <p>w = "time-averaged vertical momentum component" [meter second-1]</p>
The computation results of coupled hydrological and hydrodynamic modelling application for the Nemunas River watershed – Curonian Lagoon – South-Eastern Baltic Sea continuum
<p>The datasets provided here were used to analyse the cumulative impacts of climate change in a Nemunas River watershed – Curonian Lagoon – South‑Eastern Baltic Sea continuum by applying a state-of-the-art coupled modelling system, which consists of hydrological and hydrodynamic models.</p> <p>Meteorological data used for running the models were acquired from CORDEX (Coordinated Regional Downscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model (RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models:</p> <table> <tbody> <tr> <th>Abbreviation in datasets</th> <th>Model</th> <th><strong>Institution</strong></th> </tr> </tbody> <tbody> <tr> <td>ICHEC</td> <td>EC-Earth</td> <td>Irish Centre for High-End Computing</td> </tr> <tr> <td>IPSL</td> <td>IPSL-CM 5A-MR</td> <td>The Institut Pierre-Simon Laplace</td> </tr> <tr> <td>MOHC</td> <td>HadGEM2-ES</td> <td>Met Office Hadley Centre</td> </tr> <tr> <td>MPI</td> <td>MPI-ESM-LR</td> <td>Max Planck Institute for Meteorology</td> </tr> </tbody> </table> <p> </p> <p>Climate change scenarios and periods:</p> <ul> <li>Historical/reference (1970-2005);</li> <li>RCP4.5 (2005-2100);</li> <li>RCP8.5 (2005-2100).</li> </ul> <p>The datasets consist of time series for the parameters of:</p> <ul> <li><strong>Ice thickness</strong> - average ice thickness in the Curonian Lagoon;</li> <li><strong>Meteorological data</strong> - bias-corrected temperature and precipitation data for the marine and terrestrial areas;</li> <li><strong>Nemunas River discharge</strong> - simulated average daily values for the discharge and water temperature;</li> <li><strong>Salinity</strong> - selected points in the south-eastern Baltic Sea and one point next to Juodkrantė (in the Curonian Lagoon);</li> <li><strong>Water fluxes</strong> - through four predefined cross-sections in the Curonian Lagoon;</li> <li><strong>Water level</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea;</li> <li><strong>Water residence time</strong> - in the total Curonian Lagoon area, as well as its northern and southern parts;</li> <li><strong>Water temperature</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea.</li> </ul> <p>Some of the datasets (zip files) have additional information (coordinates, data column explanations, units, etc.) in READ_ME.txt files.</p>
Data from: Hydrodynamic analysis of bioinspired vortical cross-step filtration by computational modelling
<p><span><span>Research on the suspension-feeding apparatus of fishes has led recently to the identification of novel filtration mechanisms involving vortices. Structures inside fish mouths form a series of 'backward-facing steps' by protruding medially into the mouth cavity. In paddlefish and basking shark mouths, porous gill rakers lie inside 'slots' between the protruding branchial arches. Vortical flows inside the slots of physical models have been shown to be important for the filtration process, but the complex flow patterns have not been visualized fully. Here we resolve the three-dimensional hydrodynamics by computational fluid dynamics simulation of a simplified mouth cavity including realistic flow dynamics at the porous layer. We developed and validated a modelling protocol in ANSYS Fluent software that combines a porous media model and permeability direction vector mapping. We found that vortex shape and confinement to the medial side of the gill rakers result from flow resistance by the porous gill raker surfaces. Anteriorly directed vortical flow shears the porous layer in the centre of slots. Flow patterns also indicate that slot entrances should remain unblocked, except for the posterior-most slot. This new modelling approach will enable future design exploration of fish-inspired filters.</span></span></p>
UltraScan Solution Modeler (US-SOMO) hydrodynamic parameter, structural small angle scattering and SESCA circular dichroism (CD) calculations on AlphaFold predicted structures
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Data from: Hydrodynamic analysis of bioinspired vortical cross-step filtration by computational modelling
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Data from: Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modeling
<p>Molecular analysis of DNA left in the environment, known as environmental DNA (eDNA), has proven to be a powerful and cost-effective approach to infer occurrence of species. Nonetheless, relating measurements of eDNA concentration to population abundance remains difficult because detailed knowledge on the processes that govern spatial and temporal distribution of eDNA should be integrated to reconstruct the underlying distribution and abundance of a target species. In this study, we propose a general framework of abundance estimation for aquatic systems on the basis of spatially replicated measurements of eDNA. The proposed method explicitly accounts for production, transport, and degradation of eDNA by utilizing numerical hydrodynamic models that can simulate the distribution of eDNA concentrations within an aquatic area. It turns out that, under certain assumptions, population abundance can be estimated via a Bayesian inference of a generalized linear model. Application to a Japanese jack mackerel (<em>Trachurus japonicus</em>) population in Maizuru Bay revealed that the proposed method gives an estimate of population abundance comparable to that of a quantitative echo sounder method. Furthermore, the method successfully identified a source of exogenous input of eDNA (a fish market), which may render a quantitative application of eDNA difficult to interpret unless its effect is taken into account. These findings indicate the ability of eDNA to reliably reflect population abundance of aquatic macroorganisms; when the "ecology of eDNA" is adequately accounted for, population abundance can be quantified on the basis of measurements of eDNA concentration.</p>
Supplement for "Mesoscopic hydrodynamic model for spreading, sliding and coarsening compound drops"
<p>This repository contains the<strong> data sets</strong> and <strong>source code</strong> corresponding to the following publication</p> <p> </p> <p>Diekmann, J. and Thiele, U.<br>“Mesoscopic hydrodynamic model for spreading, sliding and coarsening compound drops”<br>Phys. Rev. Fluids <strong>10</strong>, 024002, 2025<br>DOI: <a href="https://doi.org/10.1103/PhysRevFluids.10.024002">https://doi.org/10.1103/PhysRevFluids.10.024002</a></p> <p> </p> <p>Please consider the README for more details.</p>
Constructing stable 3D hydrodynamical models of giant stars
<p>MESA inlist associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017A&A...599A...5O">Ohlmann et al. (2017)</a>. MESA version 6208.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201629692">10.1051/0004-6361/201629692</a></p>
Impact of nonlinear hydrodynamic modelling on geometric optimisation of a spherical heaving point absorber
<p>Due to the amount of iterative computation involved, researchers involved in geometric optimisation of wave energy devices typically employ linear hydrodynamic models. However, the exaggerated motion of wave energy devices, aided by energy maximising control action, challenges the assumptions upon which linear hydrodynamic modelling relies. Furthermore, the optimal device geometry is also sensitive to the nature of the energy-maximisation controller employed, and to the set of wave conditions over which the optimisation is carried out.<br> <br> In order to focus on the essential issues, this study takes the simplest possible device for optimisation, a heaving sphere (with just one free parameter), but one which exhibits nonlinear hydrodynamic characteristics, due to the non-uniform cross-sectional area. The study examines the sensitivity to the inclusion of nonlinear Froude-Krylov forces. In addition, the sensitivity of the optimal device size to differences in the applied control algorithm is also studied, as are effects due to different representative sea state representations and performance evaluation criteria.</p>
Data from: Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modeling
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Modelling coastal connectivity in the Mediterranean Sea: Similar effects of changes in hydrodynamics and reduction in planktonic duration
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Data from: Seeds in motion: genetic assignment and hydrodynamic models demonstrate concordant patterns of seagrass dispersal
Movement is fundamental to the ecology and evolutionary dynamics within species. Understanding movement through seed dispersal in the marine environment can be difficult due to the high spatial and temporal variability of ocean currents. We employed a mutually enriching approach of population genetic assignment procedures and dispersal predictions from a hydrodynamic model to overcome this difficulty and quantify the movement of dispersing floating fruit of the temperate seagrass Posidonia australis Hook.f. across coastal waters in southwestern Australia. Dispersing fruit cohorts were collected from the water surface over two consecutive years and seeds were genotyped using microsatellite DNA markers. Likelihood-based genetic assignment tests were used to infer the meadow of origin for seed cohorts and individuals. A three-dimensional hydrodynamic model was coupled with a particle transport model to simulate the movement of fruit at the water surface. Floating fruit cohorts were mainly assigned genetically to the nearest meadow, but significant genetic differentiation between cohort and most-likely meadow of origin suggested a mixed origin. This was confirmed by genetic assignment of individual seeds from the same cohort to multiple meadows. The hydrodynamic model predicted 60% of fruit dispersed within 20 km, but that fruit were physically capable of dispersing beyond the study region. Concordance between these two independent measures of dispersal provide insight into the role of physical transport for long distance dispersal (LDD) of fruit and the consequences for spatial genetic structuring of seagrass meadows.
Data from: Understanding age-specific dispersal in fishes through hydrodynamic modelling, genetic simulations and microsatellite DNA analysis
Many marine species have vastly different capacities for dispersal during larval, juvenile and adult life stages, and this has the potential to complicate the identification of population boundaries and the implementation of effective management strategies such as marine protected areas. Genetic studies of population structure and dispersal rarely disentangle these differences and usually provide only lifetime-averaged information that can be considered by managers. We address this limitation by combining age-specific autocorrelation analysis of microsatellite genotypes, hydrodynamic modelling and genetic simulations to reveal changes in the extent of dispersal during the lifetime of a marine fish. We focus on an exploited coral reef species, Lethrinus nebulosus, which has a circum-tropical distribution and is a key component of a multispecies fishery in northwestern Australia. Conventional population genetic analyses revealed extensive gene flow in this species over vast distances (up to 1500 km). Yet, when realistic adult dispersal behaviours were modelled, they could not account for these observations, implying adult dispersal does not dominate gene flow. Instead, hydrodynamic modelling showed that larval L. nebulosus are likely to be transported hundreds of kilometres, easily accounting for the observed gene flow. Despite the vast scale of larval transport, juvenile L. nebulosus exhibited fine-scale genetic autocorrelation, which declined with age. This implies both larval cohesion and extremely limited juvenile dispersal prior to maturity. The multidisciplinary approach adopted in this study provides a uniquely comprehensive insight into spatial processes in this marine fish.
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Allen Brain Atlas
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OpenNeuro
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