Data-driven study of surface expressions of underwater features in environmental flows
Abstract/Contents
- Abstract
- In shallow coastal systems such as estuaries and river mouths as well as other environmental systems such as rivers and lakes, flows are typically strongly dependent on depth and local bathymetry. Thus, the characteristics of the bottom—which may include bathymetry, benthic communities, sedimentary bedforms, and submerged vegetation—play a key role in determining the characteristics of the flow. Accurate knowledge of such nearshore bathymetry and bottom features is critical to the understanding of coastal processes, and is essential for models of environmental flows that determine the fate of policy and coastal restoration efforts. However, due to issues of cost, limited physical accessibility, and optical opacity of flow due to sediment or other particulate matter, many of the existing methods for in-situ and remote measurements of such bottom features remain prohibitive. So this important source of hydraulic control must be inferred from its effect on the observable flow features, often on the free surface. This thesis constitutes a novel investigation into whether and how surface flow signatures alone can be used to address this bathymetry inference problem. Through carefully designed experiments and deep learning based analysis, we first demonstrate that disturbances on the free surface of a flow driven over various kinds of model bathymetric features carry information that can help distinguish these bottom features, and that this identification of bottom features is still be possible in the presence of externally imposed surface disturbances, such as wind. Following this result, we demonstrate the development of methods that enable us to remotely and reliably measure these free surface signatures, and extract salient surface features of underwater disturbances without actually knowing a priori what their form, shape, or location is. Through an experimental demonstration using these methods, we illustrate that the mean velocity of these surface features is nearly equal to the mean surface-flow velocity which suggests that once underwater flow structures impact the surface or attach to it, they will follow the surface flow. Next, we measure and probe these surface expressions to develop an understanding of how the signatures are different for the various bed treatments. We find that in general, the surface can only respond in limited ways. In other words, due to a combination of the limitations imposed by gravity and surface tension forces, and secondary instabilities that lead to the decay of signatures into capillary ripples, the surface can only support a limited range of surface expressions. This amounts to saying that the surfaces are nearly isospectral (i.e. having the same spectra or "homophonic") in the mean sense because we find the mean spectral behavior of surface slopes to be similar. We find that sparsity of the bedform, in addition to its height, must also be a key parameter controlling the transport of momentum from the bed to the surface. While the mean response may itself take us only so far in distinguishing between submerged bed types, we know from our CNN study that the differences in signatures exist. This suggests that the distinguishing information about bedforms does not then necessarily lie in the surface shape or arrangement of the disturbance but possibly in how frequently or intermittently the information makes its way to the surface. We explore future directions for our work and show preliminary results that suggest that indeed higher order moments of surface slopes have the potential to highlight the differences in signatures across bedforms. We synthesize our findings, and suggest future experiments which concurrently measure the bulk flow velocity field and surface slopes so that more information about the governing mechanisms for information transport from the bed to the surface can be gleaned. Finally, to extend our work, we propose new lab experiments to better understand the role of sparsity, size, and arrangement of bedform units in influencing surface expressions, and also field experiments to explore the implications of our finding that naturally occurring surface features can be used for mean surface-flow velocity measurements in environmental flows.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Gakhar, Saksham |
|---|---|
| Degree supervisor | Koseff, Jeffrey Russell |
| Degree supervisor | Ouellette, Nicholas (Nicholas Testroet), 1980- |
| Thesis advisor | Koseff, Jeffrey Russell |
| Thesis advisor | Ouellette, Nicholas (Nicholas Testroet), 1980- |
| Thesis advisor | Fong, Derek |
| Degree committee member | Fong, Derek |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Saksham Gakhar. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/ns457cg7553 |
Access conditions
- Copyright
- © 2022 by Saksham Gakhar
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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