ME 697R: Computation Methods for Nanoscale Energy Transport

By Xiulin Ruan

Mechanical Engineering, Purdue University, West Lafayette, IN

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Abstract

Fall 2019 This Course is in production

This course provides a detailed presentation of the computational methods used to treat energy transport and conversion in the atomic and nanoscales. The methods include lattice dynamics, molecular dynamics, first principles calculations, Boltzmann transport equation, Monte Carlo methods, and machine learning. Energy transport by four energy carriers, i.e., phonons, electrons, photons, and molecules, will be covered. Thermal, mechanical, electrical, and optical properties will be predicted, and the effects of spatial confinement on these properties will be introduced. Relevant applications such as thermal management, thermoelectrics, laser-matter interaction, and energy storage will be included. The learning objectives of the course are listed below:

  • Provide an introductory understanding of numerical methods for solving energy transport problems in bulk materials and nanostructures.
  • Develop ability to simulate energy transport in the atomistic scale with ab initio calculations, lattice dynamics, and molecular dynamics (MD).
  • Develop ability to simulate energy transport in mesoscopic regime with Boltzmann transport equation and Monte Carlo (MC) methods.
  • Provide an introductory understanding of machine learning techniques in nanoscale energy transport.

Topics Covered:

  • Lattice dynamics: 2 weeks
  • Molecular dynamics simulations: 4 weeks
  • Ab initio calculations: 3 weeks
  • Boltzmann transport equation: 3 weeks
  • Monte Carlo method: 0.5 week
  • Multiscale multiphysics modeling: 0.5 week
  • Introduction to machine learning in energy transport: 1 week
  • Project presentations: 1 week

Bio

Xuilin Ruan Professor Ruan received his B.S. and M.S. in Engineering Thermophysics from Tsinghua University in 2000 and 2002, respectively. He received an M.S. in Electrical Engineering and Ph.D. in Mechanical Engineering from the University of Michigan at Ann Arbor, in 2006 and 2007 respectively. He then joined Purdue as an assistant professor. He was promoted to associate professor with tenure in 2013 and to full professor in 2017. Dr. Ruan received several awards, including the NSF CAREER Award in 2012, the ASME Heat Transfer Division Best Paper Award in 2015, the College of Engineering Early Career Research Excellence Award in 2016, the School of Mechanical Engineering Outstanding Graduate Student Mentor Award in 2016, the B.F.S. Schaefer Award in 2017, and was named a University Faculty Scholar in 2017. He was an Air Force Summer Faculty Fellow at the Wright Patterson Air Force Base at Dayton, Ohio in 2010, 2011, and 2013. He currently serves as an associate editor for the ASME Journal of Heat Transfer, and an editorial board member for Scientific Reports, a journal published by the Nature Publishing Group.

References

  • Charles Kittel, “Introduction to Solid State Physics”, 7th edition, John Wiley, 1996.
  • Martin T. Dove, “Introduction to Lattice Dynamics”, Cambridge University Press, 1993.
  • Daan Frenkel and Berend Smit, “Understanding molecular simulation, from algorithms to applications”, Academic Press, 2002.
  • Michael Springborg, “Methods of electronic-structure calculations from molecules to solids”, John Wiley, 2000.

Cite this work

Researchers should cite this work as follows:

  • Xiulin Ruan (2019), "ME 697R: Computation Methods for Nanoscale Energy Transport," https://nanohub.org/resources/31093.

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Location

2004 Mechanical Engineering, Purdue University, West Lafayette, IN

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Lecture Number/Topic Online Lecture Video Lecture Notes Supplemental Material Suggested Exercises
ME 697R Course Overview View HTML
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ME 697R Lecture 1: Introduction View HTML
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ME 697R Lecture 2.1: Lattice Dynamics - Lattice Structure View HTML
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ME 697R Lecture 2.2: Lattice Dynamics - Reciprocal Lattice View HTML
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ME 697R Lecture 2.3A: Lattice Dynamics - Crystal Binding and Interatomic Potentials View HTML
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ME 697R Lecture 2.3B: Lattice Dynamics - Crystal Binding and Interatomic Potentials View HTML
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ME 697R Lecture 2.4: Lattice Dynamics - Dynamical Matrix and Phonon Dispersion View HTML
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ME 697R Lecture 2.5: Lattice Dynamics - Introduction to GULP View HTML
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ME 697R Lecture 3.1: Molecular Dynamics - Introduction View HTML
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ME 697R Lecture 3.2: Molecular Dynamics - Integration Algorithms View HTML
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ME 697R Lecture 3.3: Molecular Dynamics - Temperature Control View HTML
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ME 697R Lecture 3.4: Molecular Dynamics - Boundary and Initial Conditions View HTML
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ME 697R Lecture 3.5: Molecular Dynamics - Autocorrelation Function and Transport Properties View HTML
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ME 697R Lecture 3.6: Molecular Dynamics - Non-equilibrium MD View HTML
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ME 697R Lecture 3.7: Molecular Dynamics - Argon Thermal Conductivity: A Case Study View HTML
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ME 697R Lecture 3.8: Molecular Dynamics - Introduction to LAMMPS View HTML
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ME 697R Lecture 3.9: Molecular Dynamics - Modal Methods View HTML
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ME 697R Lecture 5.1: First Principles Method - Electronic Structure of Atoms and Molecules View HTML
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ME 697R Lecture 5.2: First Principles Method - Electronic Structure of Solids View HTML
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ME 697R Lecture 5.3A: First Principles Method - Density Functional Theory I View HTML
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ME 697R Lecture 5.3B: First Principles Method - Density Functional Theory II View HTML
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ME 697R Lecture 5.4: First Principles Method - Lattice Dynamces and Moleular Dynamics View HTML
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ME 697R Lecture 5.5A: First Principles Method - Development of Empirical Interatomic Potentials using DFT I View HTML
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ME 697R Lecture 5.5B: First Principles Method - Development of Empirical Interatomic Potentials using DFT II View HTML
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ME 697R Lecture 5.6: First Principles Method - First Principles Calculations of Electron-Phonon Coupling and Electrical Conductivity View HTML
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ME 697R Lecture 5.7A: First Principles Method - First Principles Calculations of Thermal Conductivity I View HTML
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ME 697R Lecture 5.7B: First Principles Method - First Principles Calculations of Thermal Conductivity II View HTML
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ME 697R Lecture 5.8: First Principles Method - First Principles Calculations of Optical Properties View HTML
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ME 697R Lecture 5.9A: First Principles Method - First Principles Prediction of Local Thermal Non-equilibrium I View HTML
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ME 697R Lecture 6: Monte Carlo Method View HTML
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ME 697R Lecture 7.1: Multiscale Multiphysics Simulations - Overview View HTML
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ME 697R Lecture 7.2: Multiscale Multiphysics Simulations - Thermoelectric Transport in PbTe View HTML
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ME 697R Lecture 7.3: Multiscale Multiphysics Simulations - Thermal Transport Across Metal-Nonmetal Interfaces View HTML
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ME 697R Lecture 7.4: Multiscale Multiphysics Simulations - Optical and Radiative Transport View HTML
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ME 697R Lecture 7.5: Multiscale Multiphysics Simulations - Electron-Phonon Coupling in Quantum Dots View HTML
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ME 697R Lecture 8.1: Machine Learning Techniques - Overview View HTML
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ME 697R Lecture 8.2: Machine Learning Techniques - Machine Learning Based Interatomic Potentials (MLIPs) View HTML
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ME 697R Lecture 8.3A: Machine Learning Techniques - Machine Learning Based Nanostructure Optimization I View HTML
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ME 697R Lecture 8.3B: Machine Learning Techniques - Machine Learning Based Nanostructure Optimization II View HTML
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ME 697R Lecture 8.4: Machine Learning Techniques - High Throughput Computation-Driven Material Discovery View HTML
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ME 697R Lecture 8.5: Machine Learning Techniques - Prediction of Thermal Properties View HTML
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