"A Data-Driven Approach to Deflagration-to-Detonation Modeling in Thermonuclear Supernovae"
Sep 21, 2022, Schedule:
- Nespresso & Teatime ( 417 DSL - Commons )
- 03:00 to 03:30 PM Eastern Time (US and Canada)
- Colloquium - F2F ( 499 DSL ) / Virtual ( Zoom )
- 03:30 to 04:30 PM Eastern Time (US and Canada)
Meeting # 942 7359 5552
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.
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.
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.
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.