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A Thermo-Mechanical Multi-Scale Simulation for the Compaction Process of the Oxide-Coated Aluminum Nano-Powders

Orvati Movaffagh, Amir Mohammad | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56250 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Khoei, Amir Reza
  7. Abstract:
  8. This research introduces a novel thermo-mechanical multiscale technique, utilizing machine learning, for simulating the compaction process of aluminum nanopowders with surface oxidation at various temperatures. The methodology employed involves the utilization of nonlinear thermo-mechanical Finite Element Method (FEM) for macro scale analysis, while employing the Molecular Dynamics (MD) method to calculate the mechanical and thermal characteristics of aluminum nanopowders at the nano-scale. The first part of the research presents a comprehensive study on the thermal conductivity of alumina-coated aluminum nanopowders, which is a crucial property for their application in powder metallurgy, particularly in multi-scale numerical modeling of warm compaction. We utilize the molecular dynamics method to predict the thermal conductivity of these nanopowders, with a focus on exploring the effects of temperature and density on thermal conductivity. In our study, the Müller-Plathe's reverse non-equilibrium molecular dynamics (rNEMD) method is employed to calculate the thermal conductivity using the well-known ReaxFF force field, and hydrostatic compression tests at different temperatures are used to incorporate the effects of density and temperature. Our results are consistent with the findings of relevant experimental and numerical studies in the literature. This study includes a thorough evaluation of the impact of temperature and density on the thermal conductivity of alumina-coated aluminum nanopowders as well as the details of the thermal conductivity calculation method, such as the importance of considering the size dependency of the rNEMD method. Following the investigation of thermal properties, the mechanical properties of these nanopowders are explored using the MD method. Hydrostatic and triaxial tests are conducted to obtain stress-strain curves, which play a crucial role in estimating the material property matrix and the vector of nodal forces in the finite element formulation. The stress-strain curves and mechanical simulations carried out at different temperatures demonstrate good agreement with related findings in other articles, supporting the notion that hot compaction is more favorable compared to compaction at low temperatures. To utilize the obtained curves in this section, machine learning is employed to estimate stresses, thereby eliminating the need for constitutive models. Finally, the research presents a comprehensive mechanical and thermal finite element formulation at the macro scale, along with essential details of neural network modeling to harness information obtained from the molecular dynamics method. To showcase the capability and effectiveness of the proposed multiscale modeling technique, two thermo-mechanical examples are solved and presented in the final chapter of this work
  9. Keywords:
  10. Multiscale Modeling ; Thermal Conductivity ; Artificial Neural Network ; Nonlinear Finite Element Method ; Molecular Dynamics ; Thermal Finite Element Method ; Aluminum Nanopowders

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