Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session A26: Focus Session: Fluid Dynamics of Atmospheric and Oceanic Extreme Events |
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Sponsoring Units: GPC Chair: Pedram Hassanzadeh, Rice University; Mohammad Farazmand, MIT Room: Georgia World Congress Center B314 |
Sunday, November 18, 2018 8:00AM - 8:13AM |
A26.00001: Are extreme dissipation events predictable in turbulent fluid flows? Mohammad M Farazmand, Patrick J Blonigan, Themistoklis Sapsis We derive precursors of extreme dissipation events in a turbulent channel flow. Combining dynamics and statistics for the underlying attractor, we extract a characteristic state that precedes laminarization events that subsequently lead to extreme dissipation episodes. Our approach utilizes coarse statistical information for the turbulent attractor to identify high-likelihood regions in the state space. We then search within this high probability set for the state that leads to the most finite-time growth of the flow kinetic energy. This state has both high probability of occurrence and leads to extreme values of dissipation. We use the alignment between a given turbulent state and this critical state as a precursor for extreme events and demonstrate its favorable properties for prediction of extreme dissipation events. Finally, we analyze the physical relevance of the derived precursor and show its robust character for different Reynolds numbers. |
Sunday, November 18, 2018 8:13AM - 8:26AM |
A26.00002: Sea level rise threat from submarine melting of glaciers Claudia Cenedese, Fiammetta Straneo Rising global air and ocean temperatures have been identified as drivers of the observed increase in the discharge of ice from the Antarctic and Greenland ice sheets. For the Greenland Ice Sheet (GrIS), this mass flux to the ocean includes: surface melt, leaving the ice sheets as runoff and subglacial discharge; subsurface melt of glaciers; and calving (and eventually melt) of icebergs. At present, GrIS mass loss accounts for one quarter of the observed global sea level rise (7.5 ± 1.8 mm from 1992 to 2011) and it is crucial to understand the mechanisms and drivers of this loss to improve our ability to predict future sea-level rise and prepare global societies for its consequences. Submarine melting of glaciers are affected both by the fjord stratification and subglacial discharge. In particular, the latter generates buoyancy driven plumes rising vertically and mixing with the submarine meltwater and the entrained ambient waters. The distribution of the subglacial discharge, i.e. single or multiple plumes and/or line vs. point source plumes, has been observed to influence the magnitude and distribution of submarine melting of glaciers. Furthermore, plumes’ sediment loads influence the entrainment of ambient waters and consequently submarine melting. |
Sunday, November 18, 2018 8:26AM - 8:39AM |
A26.00003: Scalable diagnostics, forecasting and reduced-order model discovery for global atmospheric chemistry data Nathan Kutz We introduce a new set of algorithmic tools capable of producing scalable decompositions for the diagnostics, forecasting, and model reduction of global atmospheric chemistry data. By exploiting emerging randomized linear algebra algorithms, a suite of decompositions are proposed that extract low-rank features from atmospheric data with improved interpretability. Importantly, our proposed algorithms scale with the intrinsic rank rather than the ever increasing spatio-temporal measurement space. In addition to scalability, three additional innovations are proposed for improved interpretability: (i) a non-negative decomposition of the data is demonstrated, improving interpretability by constraining the chemical space to have only positive expression values, and (ii) sparse matrix decompositions, which thresholds low-correlations to zero, thus highlighting dominant spatial activity, and (iii) a model discovery technique for building reduced order models of global chemistry. Our methods are demonstrated on global chemistry data, showing improvements in computational speed and interpretability. Such technologies are critically enabling for real-time global monitoring. |
Sunday, November 18, 2018 8:39AM - 8:52AM |
A26.00004: Data-driven forecasting of high-dimensional 3D Rayleigh-Benard turbulence using Hankel-DMD Mohammad Amin Khodkar, Pedram Hassanzadeh, Athanasios Antoulas Dynamic Mode Decomposition (DMD) with Takens’ delay-embedding (Hankel-DMD) is used to forecast the spatio-temporal evolution of the fully turbulent flow in a high-dimensional 3D Rayleigh-Benard convection system at the Rayleigh number of one million. A long dataset is produced using DNS, which is then employed to build a reduced-order model for the horizontally averaged temperature anomaly (which is a function of height z and time t) using Hankel-DMD, which is data-driven and model-free. Compared with the DNS data, the reduced model can fairly accurately predict the spatio-temporal evolution of the temperature anomaly and the leading principal components for a few hundred advective time scale before the prediction diverges from DNS and decays to zero. The impact of the length of the dataset used to build the reduced model, the embedded delay, and the reduced dimension on the prediction accuracy and prediction limit are investigated in detail to find the optimal range for these parameters and understand the capabilities and limitations of this approach. |
Sunday, November 18, 2018 8:52AM - 9:05AM |
A26.00005: Interpretable nonlinear models of unsteady flow physics Steven Brunton, Nathan Kutz, Jean-Christophe Loiseau, Bernd R. Noack Accurate and efficient reduced-order models are essential to understand, predict, estimate, and control unsteady fluid flows. These models should ideally be generalizable, interpretable, and based on limited training data. This talk will explore the sparse identification of nonlinear dynamics (SINDy) approach to uncover interpretable models for unsteady flow physics. First, we will discuss how it is possible to enforce known constraints, such as energy conserving quadratic nonlinearities, to essentially “bake in” known physics. Next, we will demonstrate that higher-order nonlinearities can approximate the effect of truncated modes, resulting in more accurate models of lower order than Galerkin projection. Finally, we will discuss the use of intrinsic measurement coordinates, such as lift, drag, and pressure, to build nonlinear models, circumventing the well-known issue of continuous mode deformation associated with methods based on the proper orthogonal decomposition. This approach will be demonstrated on several relevant flow configurations with low-dimensional dynamics. |
Sunday, November 18, 2018 9:05AM - 9:18AM |
A26.00006: Learning from Smart Lagrangian particles Luca Biferale, Michele Buzzicotti, Patricio Clark Di Leoni, Andrea Mazzino We present a series of innovative ideas on how to track and/or reconstruct extreme events in turbulent and complex flows. These ideas are based on the instrumentation of small objects/probes that are able to sense the flow and learn from these measurements how to navigate inside it. We discuss a proof-of-concept using inertial drifters able to change their buoyancy in order to optimise vorticity tracking in 2d and 3d flows. We also argue how to use the information gathered by the particles to reconstruct the underlying flow via Lagrangian nudging of the equations of motion. |
Sunday, November 18, 2018 9:18AM - 9:31AM |
A26.00007: Complementing Imperfect Models with Data for the Prediction of Extreme Events in Turbulent Systems Zhong Yi Wan, Themistoklis Sapsis A major challenge in projection-based order reduction methods for nonlinear dynamical systems lies in choosing a set of modes that can faithfully represent the overall dynamics. Modes lacking in number or dynamical importance may lead to significant compromise in accuracy, or worse, completely different dynamical behaviors in the model. In this work, we present a framework for using data-driven models to assist dynamical models, obtained through projection, when the reduced set of modes are not necessarily optimal. We make use of the long short-term memory (LSTM), a recurrent neural network architecture, to extract latent information from the reduced-order time series data and derive dynamics not explicitly accounted for by the projection. We apply the framework to projected dynamical models of differing fidelities for prediction of intermittent events in turbulent systems such as the Kolmogorov flow. |
Sunday, November 18, 2018 9:31AM - 9:44AM |
A26.00008: Severe Storm Infrasound Observations during Spring 2018 Christopher E Petrin, Matthew S Van Den Broeke, Brian R Elbing Infrasound, sound at frequencies below human hearing, from severe storms has been associated with reports of severe weather events. Due to weak atmospheric attenuation, infrasound can be detected 100 km from its source and has been observed from tornado-producing storms well before tornadogenesis, which makes it an appealing method for long-range passive monitoring to improve tornado forecasting. However, the infrasound signature observed during tornadoes has a similar structure to that of some non-tornadic hail-producing storms. Studies of severe hail-producing storms with no evidence of concentrated vorticity have shown no acoustic energy. Recent simulations suggest that tornado-like vortices produce infrasound from near the melting level, where diabatic processes involving hail are active, suggesting that hail may have a connection with tornado infrasound. A station was established at Oklahoma State University to monitor atmospheric infrasound during severe storms that produce tornadoes and/or hail. Recent observations during the spring of 2018 along with available radar data will be presented and compared with observations from the same station during a small tornado on 11 May 2017. |
Sunday, November 18, 2018 9:44AM - 9:57AM |
A26.00009: Theoretical and Numerical Study of Electrohydrodynamic Generation of Atmospheric Turbulence Yuan Yao, Jesse S Capecelatro Ionization produced by cosmic rays and atmospheric radioactivity creates charged short life-time aerosols. The high variability of these aerosol particles leads to time varying electric fields and space charge in the atmosphere, which can often be amplified by more than three orders of magnitude in extreme conditions, such as thunderstorms. The nonlinear coupling between ionized particles, cloud droplets, and the background electric field can result in a body force due to electrohydrodynamic (EHD) interactions. Although the EHD effect has been widely shown to induce turbulence in various engineering systems, its effect on atmospheric turbulence remain elusive. In this presentation, we investigate the effect of EHD on atmospheric turbulence under both fair-weather and thunderstorm conditions. A linear stability analysis is performed to understand the stability criteria, relevant non-dimensional numbers, and time scales for EHD-induced turbulence. Then simulations are carried out with relevant atmospheric parameters at 6 km altitude to understand the effect of non-linearity on turbulence generation. The turbulent kinetic energy budget and growth rate are reported to assess the significance of EHD for a range of particle charges and background electric fields. |
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