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Title: Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology

Abstract

A large number of microstructural parameters and a wide range of transport physics impose challenges on thermal transport analysis of alloy. Herein, modern data science techniques are employed to overcome the challenges, pursuing effective calculation of thermal transport properties. This emerging approach is tested for precipitate-hardened aluminum (Al) alloy with consideration of precipitate morphology. The finite element method (FEM) is employed to create a database of effective thermal conductivity of hypothetical Al alloys with varying precipitate morphological and thermal transport features. Using the FEM-generated data sets, the correlation analysis is conducted to qualitatively evaluate the importance of various precipitate features. The correlation analysis identifies the surface area, average diameter, and volume fraction of precipitates as the most descriptive features for determining the thermal conductivity of alloys. Afterward machine learning (ML) models are trained to accurately predict the effective thermal conductivity. Comparing the ML predictions with effective thermal conductivity and microstructural information from experiments, precipitate thermal transport properties can be calculated, such as interfacial conductance between Al matrix and precipitate, without atomistic simulations. This research demonstrates the feasibility of data-driven approaches for effective thermal transport calculation and the promise of the FEM-generated data analysis for more comprehensive evaluation of metallic alloys.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2];  [1]; ORCiD logo [1]
  1. Univ. of Tennessee, Knoxville, TN (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1493986
Alternate Identifier(s):
OSTI ID: 1492904
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Advanced Theory and Simulations
Additional Journal Information:
Journal Volume: 2; Journal Issue: 4; Journal ID: ISSN 2513-0390
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Wang, Jiaqi, Yousefzadi Nobakht, Ali, Blanks, James Dean, Shin, Dongwon, Lee, Sangkeun, Shyam, Amit, Rezayat, Hassan, and Shin, Seungha. Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology. United States: N. p., 2019. Web. doi:10.1002/adts.201800196.
Wang, Jiaqi, Yousefzadi Nobakht, Ali, Blanks, James Dean, Shin, Dongwon, Lee, Sangkeun, Shyam, Amit, Rezayat, Hassan, & Shin, Seungha. Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology. United States. doi:10.1002/adts.201800196.
Wang, Jiaqi, Yousefzadi Nobakht, Ali, Blanks, James Dean, Shin, Dongwon, Lee, Sangkeun, Shyam, Amit, Rezayat, Hassan, and Shin, Seungha. Wed . "Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology". United States. doi:10.1002/adts.201800196. https://www.osti.gov/servlets/purl/1493986.
@article{osti_1493986,
title = {Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology},
author = {Wang, Jiaqi and Yousefzadi Nobakht, Ali and Blanks, James Dean and Shin, Dongwon and Lee, Sangkeun and Shyam, Amit and Rezayat, Hassan and Shin, Seungha},
abstractNote = {A large number of microstructural parameters and a wide range of transport physics impose challenges on thermal transport analysis of alloy. Herein, modern data science techniques are employed to overcome the challenges, pursuing effective calculation of thermal transport properties. This emerging approach is tested for precipitate-hardened aluminum (Al) alloy with consideration of precipitate morphology. The finite element method (FEM) is employed to create a database of effective thermal conductivity of hypothetical Al alloys with varying precipitate morphological and thermal transport features. Using the FEM-generated data sets, the correlation analysis is conducted to qualitatively evaluate the importance of various precipitate features. The correlation analysis identifies the surface area, average diameter, and volume fraction of precipitates as the most descriptive features for determining the thermal conductivity of alloys. Afterward machine learning (ML) models are trained to accurately predict the effective thermal conductivity. Comparing the ML predictions with effective thermal conductivity and microstructural information from experiments, precipitate thermal transport properties can be calculated, such as interfacial conductance between Al matrix and precipitate, without atomistic simulations. This research demonstrates the feasibility of data-driven approaches for effective thermal transport calculation and the promise of the FEM-generated data analysis for more comprehensive evaluation of metallic alloys.},
doi = {10.1002/adts.201800196},
journal = {Advanced Theory and Simulations},
number = 4,
volume = 2,
place = {United States},
year = {2019},
month = {1}
}

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