Numerical Flow Modeling of Artificial Ocean Upwelling

Artificial Upwelling (AU) of nutrient-rich Deep Ocean Water (DOW) to the ocean's sunlit surface layer has recently been put forward as a means of increasing marine CO2 sequestration and fish production. AU and its possible benefits have been studied in the context of climate change mitigation as well as food security for a growing human population. However, extensive research still needs to be done into the feasibility, effectiveness and potential risks, and side effects associated with AU to be able to better predict its potential. Fluid dynamic modeling of the AU process and the corresponding inorganic nutrient transport can provide necessary information for a better quantification of the environmental impacts of specific AU devices and represents a valuable tool for their optimization. Yet, appropriate capture of all flow phenomena relevant to the AU process remains a challenging task that only few models are able to accomplish. In this paper, simulation results obtained with a newly developed numerical solution method are presented. The method is based on the open-source modeling environment OpenFOAM. It solves the unsteady Reynolds-Averaged Navier-Stokes (RANS) equations with additional transport equations for energy, salinity, and inorganic nutrients. The method aims to be widely applicable to oceanic flow problems including temperature- and salinity-induced density stratification and passive scalar transport. The studies presented in this paper concentrate on the direct effects of the AU process on nutrient spread and concentration in the ocean's mixed surface layer. Expected flow phenomena are found to be captured well by the new method. While it is a known problem that cold DOW that is upwelled to the surface tends to sink down again due to its high density, the simulations presented in this paper show that the upwelled DOW settles at the lower boundary of the oceans mixed surface layer, thus keeping a considerable portion of the upwelled nutrients available for primary production. Comparative studies of several design variants, with the aim of maximizing the amount of nutrients that is retained inside the mixed surface layer, are also presented and analyzed.

The <i>M</i>_w 7.5 Tadine (Maré, Loyalty Islands) earthquake and related tsunami of 5 December 2018: seismotectonic context and numerical modeling

On 5 December 2018, a magnitude Mw 7.5 earthquake occurred southeast of Maré, an island of the Loyalty Islands archipelago, New Caledonia. This earthquake is located at the junction between the plunging Loyalty Ridge and the southern part of the Vanuatu Arc, in a tectonically complex and very active area regularly subjected to strong seismic crises and earthquakes higher than magnitude 7 and up to 8. Widely felt in New Caledonia, it was immediately followed by a tsunami warning, confirmed shortly after by a first wave arrival at the Loyalty Islands tide gauges (Maré and Lifou), and then along the east coast of Grande Terre of New Caledonia and in several islands of the Vanuatu Archipelago. Two solutions of the seafloor initial deformation are considered for tsunami generation modeling, one using a non-uniform finite-source model from USGS and the other being a uniform slip model built from the Global Centroid Moment Tensor (GCMT) solution, with the geological knowledge of the region and empirical laws establishing relationships between the moment magnitude and the fault plane geometry. Both tsunami generation and propagation are simulated using the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), an open-source modeling code solving the shallow-water equations on an unstructured grid allowing refinement in many critical areas. The results of numerical simulations are compared to tide gauge records, field observations and testimonials from 2018. Careful inspection of wave amplitude and wave energy maps for the two simulated scenarios shows clearly that the heterogeneous deformation model is inappropriate, while it raises the importance of the fault plane geometry and azimuth for tsunami amplitude and directivity. The arrival times, wave amplitude and polarities obtained with the uniform slip model are globally coherent, especially in far-field locations (Hienghène, Poindimié and Port Vila). Due to interactions between the tsunami waves and the numerous bathymetric structures like the Loyalty and Norfolk ridges in the neighborhood of the source, the tsunami propagating toward the south of Grande Terre and the Isle of Pines is captured by these structures acting like waveguides, allowing it to propagate to the north-northwest, especially in the Loyalty Islands and along the east coast of Grande Terre. A similar observation results from the propagation in the Vanuatu islands, from Aneityum to Efate.

Fundamentals of the Thermal Analysis of Complex Arrangements of Underground Heat Sources

This paper introduces a new analytical method for the 3-dimensional analysis of heat sources installed underground. Such sources include primarily electric power cables and steam pipes in urban areas. For complex arrangements of the heat sources, the heat transfer and cable rating calculations require the application of numerical methods, which call for high level of expertise and are generally difficult to use. The computational algorithm presented in this paper uses a point sources approach and is an extension of the work published by the authors based on line source modeling, with the goal of simplifying the numerical calculations. The proposed approach is applicable for all heat sources, which are directly buried in a uniform or a partially dried out soil. The method is illustrated by several numerical examples. These examples are used for comparison with the existing standard approach.

Source Model and Simulated Strong Ground Motion of the 2021 Yangbi, China Shallow Earthquake Constrained by InSAR Observations

On 21 May 2021, an Mw 6.1 earthquake, causing considerable seismic damage, occurred in Yangbi County, Yunnan Province of China. To better understand the surface deformation pattern, source characteristics, seismic effect on nearby faults, and strong ground motion, we processed the ascending and descending SAR images using the interferometric synthetic aperture radar (InSAR) technique to capture the radar line-of-sight (LOS) directional and 2.5-dimensional deformation. The source model was inverted from the LOS deformation observations. We further analyzed the Coulomb failure stress (CFS) transfer and peak ground acceleration (PGA) simulation based on the preferred source model. The results suggest that the 2021 Yangbi earthquake was dextral faulting with the maximum slip of 0.9 m on an unknown blind shallow fault, and the total geodetic moment was 1.4 × 1018 Nm (Mw 6.06). Comprehensive analysis of the CFS transfer and geological tectonics suggests that the Dian–Xibei pull-apart basin is still suffering high seismic hazards. The PGA result demonstrates that the seismic intensity of this event reached up to VIII. The entire process from InSAR deformation to source modeling and strong ground motion simulation suggests that the InSAR technique will play an important role in the assessment of earthquake disasters in the case of the shortening of the SAR imaging interval.

A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms

Effective noise reduction and speech enhancement algorithms have great potential to enhance lives of hearing aid users by restoring speech intelligibility. An open problem in today’s commercial hearing aids is how to take into account users’ preferences, indicating which acoustic sources should be suppressed or enhanced, since they are not only user-specific but also depend on many situational factors. In this paper, we develop a fully probabilistic approach to “situated soundscaping”, which aims at enabling users to make on-the-spot (“situated”) decisions about the enhancement or suppression of individual acoustic sources. The approach rests on a compact generative probabilistic model for acoustic signals. In this framework, all signal processing tasks (source modeling, source separation and soundscaping) are framed as automatable probabilistic inference tasks. These tasks can be efficiently executed using message passing-based inference on factor graphs. Since all signal processing tasks are automatable, the approach supports fast future model design cycles in an effort to reach commercializable performance levels. The presented results show promising performance in terms of SNR, PESQ and STOI improvements in a situated setting.

Near source modeling of pollutant emissions from an elevated source over an urban area under cross high ventilation

Due to the rapid urbanization of many cities around the world, industrial manufacturing plants have grown rapidly, thus leading to the release of large amounts of pollutants into the environment. This is a main reason for the degradation of the local air quality, resulting in an increasing risk of unfavorable sanitary conditions for city dwellers. Understanding the dispersion of pollutants in local population environments, meteorological conditions and other physical characteristics is fundamental for predicting and evaluating air quality. This paper provides comprehensive details on the study of flow patterns and pollutant dispersion processes in urban areas. Several factors which include building geometry, local atmospheric effects, structural obstructions, and velocity of exhaust pollutants, are examined considering field data, wind tunnel tests, operational simulation techniques, and computational fluid dynamics. Good agreements are noticeable. Simultaneous evolutions of the velocity, thermal and scalar mass fraction fields of the pollutant emitting from a three-dimensional elevated source around a rectangular obstacle placed on a turbulent boundary layer wall, and also downstream the obstacle have been successfully carried out. The most serious pollutant levels in urban areas under various high wind velocities are identified.

De-bubbling Seismic Data using a Generalized Neural Network

Estimating the far-field source signature has always been an important part of seismic processing. However, estimating the source signature from an air gun array is difficult because of the complex interaction between the air bubble oscillations from each air gun, the state of the sea surface, variations in air pressure, the air guns geometry, etc. Removing the bubble noise is important since proper seismic imaging requires a zero-phased, spiky wavelet. De-bubbling has conventionally been done by deconvolution using an (assumed) known source signature. Several methods to estimate the signature and de-bubble the data have been implemented, for instance, source modeling or using near-field hydrophone measurements. We describe an alternative approach using a convolutional neural network for de-bubbling. The network is trained on real data containing a large range of source signatures to make the network robust and adaptive to signature variations. If the signature in the test data is equal to one of the signatures used in the training, the network performs well. Also, if the signature changes in the middle of a sail line, the network can adapt to this change. Moreover, we investigate the network’s sensitivity to changing geology within a survey and on two different surveys on the Norwegian Continental Shelf. If the test data are from similar geology as the training data, the network performs better than if not. Even when applied to a different part of the Norwegian Continental Shelf, the network is still able to remove most of the bubble noise.

Magma Migration at Shallower Levels and Lava Fountains Sequence as Revealed by Borehole Dilatometers on Etna Volcano

A main challenge in open conduit volcanoes is to detect and interpret the ultra-small strain (&lt;10–6) associated with minor but critical eruptions such as the lava fountains. Two years after the flank eruption of December 2018, Etna generated a violent and spectacular eruptive sequence of lava fountains. There were 23 episodes from December 13, 2020 to March 31, 2021, 17 of which in the brief period 16 February to 31 March with an intensified occurrence rate. The high-precision borehole dilatometer network recorded significant strain changes in the forerunning phase of December 2020 accompanying the final magma migration at the shallower levels, and also during the single lava fountains and during the entire sequence. The source modeling provided further information on the shallow plumbing system. Moreover, the strain signals also gave useful information both on the explosive efficiency of the lava fountains sequence and the estimate of erupted volume. The high precision borehole dilatometers confirm to be strategic and very useful tool, also to detect and interpret ultra-small strain changes associated with explosive eruptions, such as lava fountains, in open conduit volcanoes.