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<span style="font-size: 14pt;"><b><i>"A Data-Driven Approach to Deflagration-to-Detonation Modeling in Thermonuclear Supernovae"
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<div><span style="font-size: 16pt;"><b>Brandon Gusto</b></span><b><br>
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<div>Graduate Student, Dept. of Scientific Computing, Florida State University</div>
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<div>NOTE: Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic.
<b>All are welcome to attend.<br>
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<div><b>https://fsu.zoom.us/j/94273595552<br>
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<div>Meeting # <b>942 7359 5552 </b></div>
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<div><b>Sep 21st, 2022, Schedule: </b></div>
<div>* <b><span style="color: inherit; font-family: inherit; font-size: inherit; font-style: inherit; font-variant-ligatures: inherit; font-variant-caps: inherit;">3:30</span> to 4:30 PM</b> Eastern Time (US and Canada)<br>
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<div><b style="color: inherit; font-family: inherit; font-size: inherit; font-style: inherit; font-variant-ligatures: inherit; font-variant-caps: inherit;"> Colloquium</b> - Attend F2F (in
<b>499 DSL</b>) or Virtually (via <b>Zoom</b>)<br>
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<div><b style="color: inherit; font-family: inherit; font-size: inherit; font-style: inherit; font-variant-ligatures: inherit; font-variant-caps: inherit;">Abstract:</b><br>
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<div><span style="color: rgb(0, 0, 0); font-family: Tahoma, Geneva, sans-serif; font-size: 12pt;">We aim to construct a physics-motivated model of deflagration-to-detonation transition (DDT) in application to explosions of thermonuclear supernovae (SN Ia).
The DDT mechanism has been postulated as the necessary physics process to obtain qualitative agreement between SN Ia observations and computational explosion models. This work builds upon a series of studies of turbulent combustion that develops during the
final stages of the SN explosion. These studies indicate that DDT in SN Ia is possible via the Zel'dovich reactivity gradient mechanism.</span><br>
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<div class="elementToProof"><span style="font-family: Tahoma, Geneva, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"><br>
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<div class="elementToProof"><span style="font-family: Tahoma, Geneva, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">We investigate the Zel'dovich mechanism with a series of direct numerical simulations (DNS) for a range of conditions expected to exist
in the dense white dwarf plasma. We use the results of these well resolved simulations to construct a data-driven sub-grid scale model (SGSM) of DDT for the large-eddy simulation (LES) scales in SN Ia. The SGSM is implemented using a Keras/Tensorflow-based
artificial neural network (ANN), trained on the aforementioned DNS database, and integrated into our supernova simulation code, FLASH/Proteus.</span></div>
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<div class="elementToProof"><span style="font-family: Tahoma, Geneva, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">The model is evaluated both in the training environment as well as in a series of reactive large-eddy simulations. We analyze the performance
of the SGSM in terms of computational efficiency and accuracy of the classification of prospective DDT kernels.</span></div>
<div class="elementToProof"><span style="font-family: Tahoma, Geneva, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"><br>
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<div class="elementToProof"><span style="font-family: Tahoma, Geneva, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">In the future, the SGSM model will be applied to multi-dimensional simulations of reactive turbulence, RTI-unstable deflagrations, and integrated
SN Ia explosion models. The integrated SN Ia explosion models will require the multi-scale extension of the SGSM to reach the large spatial scales present in those models.</span></div>
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