Numerical algorithm for the time dependent strongly-out-of-equilibrium Boltzmann equation: high dimensional Gauss integration
This thesis focuses on substituting the current Monte Carlo method with Gauss integration, which adapts to the geometry of the problem, to solve the collision term of Boltzmann equation. The scattering integral is first derived from the time dependent Boltzmann equation for the study of electroni...
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2022
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sg-ntu-dr.10356-1570102023-02-28T23:15:15Z Numerical algorithm for the time dependent strongly-out-of-equilibrium Boltzmann equation: high dimensional Gauss integration Chua, Jia Yi Marco Battiato School of Physical and Mathematical Sciences marco.battiato@ntu.edu.sg Science::Physics This thesis focuses on substituting the current Monte Carlo method with Gauss integration, which adapts to the geometry of the problem, to solve the collision term of Boltzmann equation. The scattering integral is first derived from the time dependent Boltzmann equation for the study of electronic population in stronglyout- of-equilibrium solids. Next, Gauss points of the Cartesian product of simplexes are constructed using C++ to solve the scattering integral with Gauss integration. Comparing the new Gauss integration with the Monte Carlo method, it was found that Gauss integration can produce an accurate result in a shorter period of time, which is sufficient to replace the Monte Carlo method in solving the problem. Lastly, this new method was used in application to graphene to compute the thermalisation of electrons in scattering. Bachelor of Science in Applied Physics 2022-05-06T05:58:28Z 2022-05-06T05:58:28Z 2022 Final Year Project (FYP) Chua, J. Y. (2022). Numerical algorithm for the time dependent strongly-out-of-equilibrium Boltzmann equation: high dimensional Gauss integration. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157010 https://hdl.handle.net/10356/157010 en application/pdf Nanyang Technological University |
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Science::Physics Chua, Jia Yi Numerical algorithm for the time dependent strongly-out-of-equilibrium Boltzmann equation: high dimensional Gauss integration |
description |
This thesis focuses on substituting the current Monte Carlo method with Gauss
integration, which adapts to the geometry of the problem, to solve the collision
term of Boltzmann equation. The scattering integral is first derived from the time
dependent Boltzmann equation for the study of electronic population in stronglyout-
of-equilibrium solids. Next, Gauss points of the Cartesian product of simplexes
are constructed using C++ to solve the scattering integral with Gauss integration.
Comparing the new Gauss integration with the Monte Carlo method, it was found
that Gauss integration can produce an accurate result in a shorter period of time,
which is sufficient to replace the Monte Carlo method in solving the problem.
Lastly, this new method was used in application to graphene to compute the thermalisation
of electrons in scattering. |
author2 |
Marco Battiato |
author_facet |
Marco Battiato Chua, Jia Yi |
format |
Final Year Project |
author |
Chua, Jia Yi |
author_sort |
Chua, Jia Yi |
title |
Numerical algorithm for the time dependent strongly-out-of-equilibrium Boltzmann equation: high dimensional Gauss integration |
title_short |
Numerical algorithm for the time dependent strongly-out-of-equilibrium Boltzmann equation: high dimensional Gauss integration |
title_full |
Numerical algorithm for the time dependent strongly-out-of-equilibrium Boltzmann equation: high dimensional Gauss integration |
title_fullStr |
Numerical algorithm for the time dependent strongly-out-of-equilibrium Boltzmann equation: high dimensional Gauss integration |
title_full_unstemmed |
Numerical algorithm for the time dependent strongly-out-of-equilibrium Boltzmann equation: high dimensional Gauss integration |
title_sort |
numerical algorithm for the time dependent strongly-out-of-equilibrium boltzmann equation: high dimensional gauss integration |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/157010 |
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1759855750121783296 |