scholarly journals Prediction of smoke filling in large volumes by means of data assimilation–based numerical simulations

2012 ◽  
Vol 30 (4) ◽  
pp. 300-317 ◽  
Author(s):  
Tarek Beji ◽  
Steven Verstockt ◽  
Rik Van de Walle ◽  
Bart Merci
2003 ◽  
Vol 331 (12) ◽  
pp. 843-848 ◽  
Author(s):  
Martin Galmiche ◽  
Joël Sommeria ◽  
Emmanuelle Thivolle-Cazat ◽  
Jacques Verron

2007 ◽  
Vol 163 (1-4) ◽  
pp. 149-162 ◽  
Author(s):  
Jordan Van Aalsburg ◽  
Lisa B. Grant ◽  
Gleb Yakovlev ◽  
Paul. B. Rundle ◽  
John B. Rundle ◽  
...  

2016 ◽  
Vol 144 (10) ◽  
pp. 3631-3649 ◽  
Author(s):  
Andrew B. Penny ◽  
Joshua P. Hacker ◽  
Patrick A. Harr

A nondeveloping tropical disturbance, identified as TCS025, was observed during three intensive observing periods during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC)/Tropical Cyclone Structure-2008 (TCS-08) field experiment. The low-level circulation of the disturbance was relatively weak, asymmetric, and displaced a considerable distance from the midlevel circulation. An ensemble of high-resolution numerical simulations initialized from global model analyses was used to further examine TCS025. These simulations tended to unrealistically overdevelop the TCS025 disturbance. This study extends that work by examining the impact of assimilating in situ observations of TCS025 and dual-Doppler radial velocities from the airborne Electra Doppler Radar (ELDORA) using the Data Assimilation Research Testbed (DART) ensemble data assimilation system. The assimilation of observations results in a more accurate vortex structure that is consistent with the observational analysis. In addition, forecasts initialized from the state of the ensemble after data assimilation exhibit less development than both the control simulation and an ensemble of forecasts without prior data assimilation. A composite analysis of developing and nondeveloping forecasts from the ensemble reveals that convection was more active in developing simulations, especially near the low-level circulation center. This led to larger diabatic heating rates, spinup of the low-level circulation from vorticity stretching, and greater alignment of the low- and midlevel vorticity centers. In contrast, nondeveloping simulations exhibited less convection, and the circulation was more heavily impacted by vertical wind shear.


Author(s):  
Benoit Tremblay ◽  
Jean-François Cossette ◽  
Maria D. Kazachenko ◽  
Paul Charbonneau ◽  
Alain Patrick Vincent

Coverage of plasma motions is limited to the line-of-sight component at the Sun's surface. Multiple tracking and inversion methods were developed to infer the transverse motions from observational data. Recently, the DeepVel neural network was trained with computations performed by numerical simulations of the solar photosphere to recover the missing transverse component at surface and at two additional optical depths simultaneously from the surface white light intensity in the Quiet Sun. We argue that deep learning could provide additional spatial coverage to existing observations in the form of depth-dependent synthetic observations, i.e. estimates generated through the emulation of numerical simulations. We trained different versions of DeepVel using slices from numerical simulations of both the Quiet Sun and Active Region at various optical and geometrical depths in the solar atmosphere, photosphere and upper convection zone to establish the upper and lower limits at which the neural network can generate reliable synthetic observations of plasma motions from surface intensitygrams. Flow fields inferred in the photosphere and low chromosphere $\tau \in [0.1, 1)$ are comparable to inversions performed at the surface ($\tau \approx 1$) and are deemed to be suitable for use as synthetic observations in data assimilation processes and data-driven simulations. This upper limit extends closer to the transition region ($\tau \approx 0.01$) in the Quiet Sun, but not for Active Regions. Subsurface flows inferred from surface intensitygrams fail to capture the small-scale features of turbulent convective motions as depth crosses a few hundred kilometers. We suggest that these reconstructions could be used as first estimates of a model's velocity vector in data assimilation processes to nowcast and forecast short term solar activity and space weather.


2018 ◽  
Vol 35 (7) ◽  
pp. 2612-2628 ◽  
Author(s):  
Fumiya Togashi ◽  
Takashi Misaka ◽  
Rainald Löhner ◽  
Shigeru Obayashi

Purpose It is of paramount importance to ensure safe and fast evacuation routes in cities in case of natural disasters, environmental accidents or acts of terrorism. The same applies to large-scale events such as concerts, sport events and religious pilgrimages as airports and to traffic hubs such as airports and train stations. The prediction of pedestrian is notoriously difficult because it varies depending on circumstances (age group, cultural characteristics, etc.). In this study, the Ensemble Kalman Filter (EnKF) data assimilation technique, which uses the updated observation data to improve the accuracy of the simulation, was applied to improve the accuracy of numerical simulations of pedestrian flow. Design/methodology/approach The EnKF, one of the data assimilation techniques, was applied to the in-house numerical simulation code for pedestrian flow. Two cases were studied in this study. One was the simplified one-directional experimental pedestrian flow. The other was the real pedestrian flow at the Kaaba in Mecca. First, numerical simulations were conducted using the empirical input parameter sets. Then, using the observation data, the EnKF estimated the appropriate input parameter sets. Finally, the numerical simulations using the estimated parameter sets were conducted. Findings The EnKF worked on the numerical simulations of pedestrian flow very effectively. In both cases: simplified experiment and real pedestrian flow, the EnKF estimated the proper input parameter sets which greatly improved the accuracy of the numerical simulation. The authors believe that the technique such as EnKF could also be used effectively in other fields of computational engineering where simulations and data have to be merged. Practical implications This technique can be used to improve both design and operational implementations of pedestrian and crowd dynamics predictions. It should be of high interest to command and control centers for large crowd events such as concerts, airports, train stations and pilgrimage centers. Originality/value To the authors’ knowledge, the data assimilation technique has not been applied to a numerical simulation of pedestrian flow, especially to the real pedestrian flow handling millions pedestrian such as the Mataf at the Kaaba. This study validated the capability and the usefulness of the data assimilation technique to numerical simulations for pedestrian flow.


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