Dynamics of rising CO\u3csub\u3e2\u3c/sub\u3e bubble plumes in the QICS field experiment: Part 1 - The experiment

© 2015 The Authors. Published by Elsevier Ltd. The dynamic characteristics of CO2 bubbles in Scottish seawater are investigated through observational data obtained from the QICS project. Images of the leaked CO2 bubble plume rising in the seawater were captured. This observation made it possible to discuss the dynamics of the CO2 bubbles in plumes leaked in seawater from the sediments. Utilising ImageJ, an image processing program, the underwater recorded videos were analysed to measure the size and velocity of the CO2 bubbles individually. It was found that most of the bubbles deform to non-spherical bubbles and the measured equivalent diameters of the CO2 bubbles observed near the sea bed are to be between 2 and 12 mm. The data processed from the videos showed that the velocities of 75% of the leaked CO2 bubbles in the plume are in the interval 25-40 cm/s with Reynolds numbers (Re) 500-3500, which are relatively higher than those of an individual bubble in quiescent water. The drag coefficient C d is compared with numerous laboratory investigations, where agreement was found between the laboratory and the QICS experimental results with variations mainly due to the plume induced vertical velocity component of the seawater current and the interactions between the CO2 bubbles (breakup and coalescence). The breakup of the CO2 bubbles has been characterised and defined by Eötvös number, Eo, and Re

Frequency effect on streaming phenomenon induced by Rayleigh surface acoustic wave in microdroplets

Acoustic streaming of ink particles inside a water microdroplet generated by a surface acoustic wave(SAW) has been studied numerically using a finite volume numerical method and these results have been verified using experimental measurements. Effects of SAW excitation frequency, droplet volume, and radio-frequency (RF) power are investigated, and it has been shown that SAW excitation frequency influences the SAWattenuation length, lSAW , and hence the acoustic energy absorbed by liquid. It has also been observed that an increase of excitation frequency generally enhances the SAW streaming behavior. However, when the frequency exceeds a critical value that depends on the RF power applied to the SAW device, weaker acoustic streaming is observed resulting in less effective acoustic mixing inside the droplet. This critical value is characterised by a dimensionless ratio of droplet radius to SAWattenuation length, i.e., Rd/lSAW . With a mean value of Rd/lSAW  ≈ 1, a fast and efficient mixing can be induced, even at the lowest RF power of 0.05 mW studied in this paper. On the other hand, for the Rd/lSAW ratios much larger than ∼1, significant decreases in streaming velocities were observed, resulting in a transition from regular (strong) to irregular (weak) mixing/flow. This is attributed to an increased absorption rate of acoustic wave energy that leaks into the liquid, resulting in a reduction of the acoustic energy radiated away from the SAW interaction region towards the droplet free surface. It has been demonstrated in this study that a fast and efficient mixing process with a smaller RF power could be achieved if the ratio of Rd/lSAW  ≤ 1 in the SAW-droplet based microfluidics

Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settings

Reconstructing 3D human heads in low-view settings presents technical challenges, mainly due to the pronounced risk of overfitting with limited views and high-frequency signals. To address this, we propose geometry decomposition and adopt a two-stage, coarse-to-fine training strategy, allowing for progressively capturing high-frequency geometric details. We represent 3D human heads using the zero level-set of a combined signed distance field, comprising a smooth template, a non-rigid deformation, and a high-frequency displacement field. The template captures features that are independent of both identity and expression and is co-trained with the deformation network across multiple individuals with sparse and randomly selected views. The displacement field, capturing individual-specific details, undergoes separate training for each person. Our network training does not require 3D supervision or object masks. Experimental results demonstrate the effectiveness and robustness of our geometry decomposition and two-stage training strategy. Our method outperforms existing neural rendering approaches in terms of reconstruction accuracy and novel view synthesis under low-view settings. Moreover, the pre-trained template serves a good initialization for our model when encountering unseen individuals.Comment: Accepted by ICCV2023. Visit our project page at https://github.com/xubaixinxbx/3dhead

Dynamics of rising CO2 bubble plumes in the QICS field experiment: Part 2 – Modelling

An oceanic two-phase plume model is developed to include bubble size distribution and bubble interactions, applied to the prediction of CO2 bubble plume and CO2 solution dynamics observed from the recent QICS field experiment in the Scottish sea at Ardmucknish Bay. Observations show bubbles form at between 2 and 12 mm in diameter, where the inclusion of the interactions within the simulations brings results of bubble plumes closer to that of the experiment. Under a given leakage flux, simulations show that the bubble size affects the maximum pCO2 dissolved in the water column, while the bubble interactions affect the vertical bubble distribution. The maximum modelled pCO2 increases from a background 360 μatm to 400, 427 and 443 μatm as CO2 injection rates increase from 80, 170 to 208 kg/day respectively at low tide. An increase of the leakage rate to 100% of the injection rate shows the maximum pCO2 could be 713 μatm, approaching the mean pCO2 observed of 740 μatm during the high leakage component of the experiment, suggesting that the flux may be greater than estimated due to the varied flux and activity across the pockmarks during the leakages

Using Bayes Theorem to Quantify and Reduce Uncertainties when Monitoring Varying Marine Environments for Indications of a Leak

Monitoring the marine environment for leaks from geological storage projects is a challenge due to the variability of the environment and the extent of the area that migrating CO2 might seep through the seafloor. Due to the environmental risk associated leaks should not be allowed to continue undetected. There is also a cost issue since marine operations are expensive, so false alarms should be avoided. The main question is then: how large a deviation in the monitoring data should cause mobilization of confirmation and localization procedures? Here Baye’s theorem and Bayesian decision theory is suggested as a tool for quantifying certainties and to implement costs for false positives (false alarms) and false negatives (undetected leaks) in the decision procedure. The procedure is exemplified using modeled natural CO2 content variability and the predicted CO2 signal from a simulated leak