Bulletin of the American Physical Society
75th Annual Meeting of the Division of Fluid Dynamics
Volume 67, Number 19
Sunday–Tuesday, November 20–22, 2022; Indiana Convention Center, Indianapolis, Indiana.
Session Q33: Geophysical Fluid Dynamics: General |
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Chair: Ching-Yao Lai, Princeton University Room: 241 |
Monday, November 21, 2022 1:25PM - 1:38PM |
Q33.00001: Fluid-driven fractures in layered hydrogels Emilie Dressaire, Marie C Sigallon, Sri Savya Tanikella The formation of fractures in low permeability rocks facilitates fluid transport and storage. Reservoir rocks, including shale exhibit strata or layers of varying composition and mechanical properties. To model how liquid-filled fractures propagate through layered media, we use laboratory-scale experiments. We study the geometry of the fracture that forms and propagates upon injection of a low-viscosity fluid in a block of hydrogel composed of two layers with different stiffness or Young's modulus. Our experiments show that the fracture geometry depends on the layer in which it is initiated. A fracture formed in the soft layer does not propagate in the stiff layer. A fracture formed in the stiff layer rapidly transfers into the soft layer when it reaches the interface between the two layers. Scaling arguments are provided to explain the experimental results and provide insights into the propagation of fractures in geological rock formations. |
Monday, November 21, 2022 1:38PM - 1:51PM |
Q33.00002: Using Fire Dynamic Simulator (FDS) to Augment Laboratory Scale Fires Sanika Ravindra Nishandar, Yucheng He, Marko Princevac, David Weise Control measures such as prescribed burns to prevent wildfires in Southern California, have to be conducted during summer and fall months when the risk of fire is high. A significant hindrance to the prescribed burn in these weather conditions is the presence of ladder fuels which facilitate transition of surface fires to unmanageable crown fires. Laboratory scaled study was conducted to understand the interaction between surface fires and ladder fires to supplement the current knowledge on the chapparal fire ecosystem. Thermal energy exchange, rate of spread and fire intensity is determined based on mass loss and temperatures obtained from thermocouples and IR camera. This climate dependent experimental study requires multiple recurrences over a long period of time. Hence, for a complete understanding of fire spread pattern we deployed fire dynamics simulator to augment the obtained experimental data. Results from these simulations would later be compared with the ongoing experiments during different weather conditions. This combination of cyber-physical study gives a comprehensive understanding of the transition from surface fires to crown fires via the ladder fuels. In this presentation we will discuss the experimental setup, laboratory results and comparable FDS simulations. |
Monday, November 21, 2022 1:51PM - 2:04PM |
Q33.00003: A data-driven prediction of atmospheric flow for urban air mobility using deep neural network Yedam Lee, Sang Lee A data-driven prediction of an atmospheric boundary layer in an urban environment modeled by a simplified set of cubes was performed. Gated recurrent unit (GRU) neural network was organized to predict the turbulent flow generated via large eddy simulation (LES). To avoid the curse of dimensionality, singular value decomposition and convolutional autoencoder were combined with the GRU neural network for data compression into latent space. A study under a gradual change of size of latent space was performed to optimize the size of latent space as a function of reconstruction capability and computational efficiency. The prediction model utilized a series of input latent vectors to predict the output latent vectors for the reconstruction procedure. A loss of physical properties originally captured in the LES data was backpropagated in the prediction phase. The prediction model reproduced the instantaneous flow structures and the turbulence statistics from the urban flow data. A computational cost comparison between the prediction model and LES data indicated that the present model has a potential to generate a real-time prediction of high-resolution turbulent flow in an urban environment. |
Monday, November 21, 2022 2:04PM - 2:17PM |
Q33.00004: Numerical investigation of Monin-Obukhov functions for CO2 and H2O in the Roughness Sublayer Einara Zahn, Khaled Ghannam, Nelson L Dias, Elie R Bou-Zeid The Monin-Obukhov Similarity Theory (MOST) is an extension of the logarithmic law of the wall to flows with buoyancy that allows the estimation of turbulent flow quantities in the Atmospheric Surface Layer. Its dimensionless functions are routinely used in field experiments and weather and climate models to estimate turbulent fluxes and provide lower boundary conditions to atmospheric circulation. Nonetheless, in the presence of tall vegetation MOST is known to break down in the Roughness Sublayer (RSL), a region that extends up to three times the canopy height. This poses a challenge to the use of MOST in forested ecosystems given that measurement towers usually lie inside the RSL. Many experimental studies have investigated the reasons behind this “failure”, the most common explanations being the mixed contribution (or “dissimilarity”) of sources and sinks of scalars inside the canopy; non-stationarity caused by external forcings; the active versus the passive role of scalars; and entrainment at the top of the Atmospheric Boundary Layer. Given the limitations of field measurements to study this problem in more detail, we designed a numerical study using Large Eddy Simulations. Our simulations replicate plant canopies with CO2 and H2O sinks and sources emitted (or absorbed) from soil (evaporation and respiration) and canopy (plant transpiration and photosynthesis). By simulating each component separately (canopy and soil), we can disentangle the role of boundary condition dissimilarity on the validity of MOST at various heights above the canopy. The impact of dissimilarity, buoyancy and stationarity are also addressed by investigating the budgets of scalar variances and covariances. Overall, the aim of this study is to examine the reasons behind MOST failure in the RSL, with focus on its nondimensional functions for scalars, as well as to provide a path to its improvement or the development of alternative theories. |
Monday, November 21, 2022 2:17PM - 2:30PM |
Q33.00005: Probabilistic learning for predictive modeling of climate variability Balu Nadiga Reduced-order dynamical models play a central role in developing our understanding of predictability of climate. In this context, the Linear Inverse Modeling (LIM) approach (closely related to Dynamic Mode Decomposition DMD), by helping capture a few essential interactions between dynamical components of the full system, has proven valuable in being able to give insights into the dynamical behavior of the full system. While nonlinear extensions of the LIM approach have been attempted, none have gained widespread acceptance. We demonstrate that Reservoir Computing (RC), a form of machine learning suited for learning in the context of chaotic dynamics, by exploiting the phenomenon of generalized synchronization, provides an alternative nonlinear approach that comprehensively outperforms the LIM approach. Additionally, the potential of the RC approach to capture the structure of the climatological attractor and to continue the evolution of the system on the attractor in a realistic fashion long after the ensemble average has stopped tracking the reference trajectory is highlighted. Next, a Bayesian Neural Network approach based on Stein Variational Gradient Descent is presented. Finally, a broader perspective on the use of data-driven methods in understanding climate predictability is offered. |
Monday, November 21, 2022 2:30PM - 2:43PM |
Q33.00006: Spatio-temporal interactions between large-scale climate oscillations and extreme-temperature events Manuel Fossa, Luminita Danaila, Michael Ghil Understanding, modeling, and predicting complex systems such as the climate system requires coupling distinct subsystems and processes that act at different space and time scales. For the atmosphere and oceans, wavelike features interact with turbulent cascades and give rise to distinct regimes crucial for mixing and dissipation. |
Monday, November 21, 2022 2:43PM - 2:56PM |
Q33.00007: The role of fluid mechanics in building a resilient, food secure future Christopher Dougherty, Chris Roh, David Kremers Concepts such as food security and resilience, not traditionally connected to the field of fluid mechanics, fundamentally rely upon flows of information carried in the medium of a fluid to form the basic contextual environment of agroecosystems. According to the United States Department of Agriculture (USDA), a resilient system is one that remains or becomes robust against threat and in this way resilience can be thought of as a measure of a system’s ability to survive, cope, or recover from damage due to disruption. In the ever-expanding domain of threats to food security (i.e. the availability of nutritionally adequate food), one of the first to be encountered in the crop growing cycle is that of pests and disease. Various measurable parameters contribute to this, but it is wind, moisture, temperature (i.e. weather) and its effect on the transport of the so-called ‘chemical landscape’ that would be of particular interest to the fluid dynamicist. Examples of the role of fluid mechanics in determining system-level viability (in vivo at field scale) as well as some specific engineered efforts to better inform robust agricultural practices pertaining to the particular threat of pests will be discussed. |
Monday, November 21, 2022 2:56PM - 3:09PM |
Q33.00008: A toy model of plate tectonics Jinzi M Huang Observing the presence of marine fossils in the mountains, Leonardo da Vinci was one of the first scientists who noticed the incessant geological movements of our planet. We now know that the continents do not stay in place and instead undergo tectonic motions, and thermal convection in Earth's mantle is believed to be the driving force of these motions. How does mantle convection couple to the continental drift? Does the moving continent affect the mantle motion beneath it? We address these questions through a simple fluid-structure interaction model, where a simple and efficient spectral solver reveals many geological-like events such as the divergence and convergence of continents and the periodic motion of Wilson cycles. These results are consistent with the lab-scale experiments of Zhang and Zhong (2007), and we hope that such models can shed light on the fluid mechanical origin of plate tectonics. |
Monday, November 21, 2022 3:09PM - 3:22PM Author not Attending |
Q33.00009: Simulation and modeling of settling in polydisperse gas-solid flows Emily Foster, Sarah Beetham, Eric Breard, Ph.D. Sedimenting flows occur in a wide range of industrial and natural systems, such as circulating fluidized bed reactors and pyroclastic density currents (PDCs), the most hazardous volcanic process. In systems with sufficiently high mass loading, momentum coupling between the phases gives rise to mesoscale behavior, such as clustering. These structures are then capable of generating and sustaining turbulence in the carrier phase and directly impact large-scale quantities of interest, such as settling time. As an added complexity, many flows of interest consist of a polydisperse particulate phase. In this talk, we characterize the sedimentation behavior of a range of polydisperse gas-solid flows, sampled from a parameter space typically associated with PDCs. Highly resolved data is collected using an Euler-Lagrange framework and polydisperse settling behavior is contrasted with Stokes settling and analogous ensembles of monodisperse particles. Finally, a settling law is proposed which takes into account polydispersity by leveraging the first four moments of the particulate phase distribution. |
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