Determination of thermal conductivity of eutectic Al-Cu compounds utilizing experiments, molecular dynamics simulations and machine learning

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • A. Nazarahari
  • A. C. Fromm
  • H. C. Ozdemir
  • C. Klose
  • H. J. Maier
  • D. Canadinc

Organisationseinheiten

Externe Organisationen

  • Koc University
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Details

OriginalspracheEnglisch
Aufsatznummer045001
FachzeitschriftModelling and Simulation in Materials Science and Engineering
Jahrgang31
Ausgabenummer4
PublikationsstatusVeröffentlicht - 14 Apr. 2023

Abstract

In this study, the thermal conductivity ( κ ) of Al-Cu eutectics were investigated by experimental and computational methods to shed light on the role of these compounds in thermal properties of Al-Cu connections in compound casting. Specifically, the nonequilibrium molecular dynamics (MD) method was utilized to simulate the lattice thermal conductivity ( κ l ) of six compositions of Al-Cu with 5-30 at.% Cu. To extend the results of the MD simulations to bulk materials, instead of using conventional linear extrapolation methods, a machine learning approach was developed for the dataset acquired from the MD simulations. The bootstrapping approach was utilized to find the most suitable method among the support vector machine (SVM) with polynomial and radial basis function (RBF) kernels and the random forest method. The results showed that the SVM model with RBF kernel performed the best, and thus was used to predict the bulk κ l . Subsequently, the chosen compositions were produced by induction casting and their electrical conductivities were measured via eddy current method for calculating the electronic contribution of κ using the Wiedemann-Franz law. Finally, the actual κ of the alloys were measured using the xenon flash method and the results were compared with the computational values. It was shown that the MD method is capable of successfully simulating the thermal conductivity of this system. In addition, the experimental results demonstrated that the κ of Al-Cu eutectics decreases almost linearly with formation of the Al2Cu phase due to increase in the Cu content. Overall, the current findings can be used to enhance the κ of cooling devices made via Al-Cu compound casting.

ASJC Scopus Sachgebiete

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Determination of thermal conductivity of eutectic Al-Cu compounds utilizing experiments, molecular dynamics simulations and machine learning. / Nazarahari, A.; Fromm, A. C.; Ozdemir, H. C. et al.
in: Modelling and Simulation in Materials Science and Engineering, Jahrgang 31, Nr. 4, 045001, 14.04.2023.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

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title = "Determination of thermal conductivity of eutectic Al-Cu compounds utilizing experiments, molecular dynamics simulations and machine learning",
abstract = "In this study, the thermal conductivity ( κ ) of Al-Cu eutectics were investigated by experimental and computational methods to shed light on the role of these compounds in thermal properties of Al-Cu connections in compound casting. Specifically, the nonequilibrium molecular dynamics (MD) method was utilized to simulate the lattice thermal conductivity ( κ l ) of six compositions of Al-Cu with 5-30 at.% Cu. To extend the results of the MD simulations to bulk materials, instead of using conventional linear extrapolation methods, a machine learning approach was developed for the dataset acquired from the MD simulations. The bootstrapping approach was utilized to find the most suitable method among the support vector machine (SVM) with polynomial and radial basis function (RBF) kernels and the random forest method. The results showed that the SVM model with RBF kernel performed the best, and thus was used to predict the bulk κ l . Subsequently, the chosen compositions were produced by induction casting and their electrical conductivities were measured via eddy current method for calculating the electronic contribution of κ using the Wiedemann-Franz law. Finally, the actual κ of the alloys were measured using the xenon flash method and the results were compared with the computational values. It was shown that the MD method is capable of successfully simulating the thermal conductivity of this system. In addition, the experimental results demonstrated that the κ of Al-Cu eutectics decreases almost linearly with formation of the Al2Cu phase due to increase in the Cu content. Overall, the current findings can be used to enhance the κ of cooling devices made via Al-Cu compound casting.",
keywords = "Al-Cu eutectics, machine learning, molecular dynamics, thermal conductivity, Wiedemann-Franz law",
author = "A. Nazarahari and Fromm, {A. C.} and Ozdemir, {H. C.} and C. Klose and Maier, {H. J.} and D. Canadinc",
note = "Funding Information: This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 394563137—SFB 1368. D Canadinc acknowledges the financial support provided by the Alexander von Humboldt Foundation within the Humboldt Research Award program.",
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TY - JOUR

T1 - Determination of thermal conductivity of eutectic Al-Cu compounds utilizing experiments, molecular dynamics simulations and machine learning

AU - Nazarahari, A.

AU - Fromm, A. C.

AU - Ozdemir, H. C.

AU - Klose, C.

AU - Maier, H. J.

AU - Canadinc, D.

N1 - Funding Information: This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 394563137—SFB 1368. D Canadinc acknowledges the financial support provided by the Alexander von Humboldt Foundation within the Humboldt Research Award program.

PY - 2023/4/14

Y1 - 2023/4/14

N2 - In this study, the thermal conductivity ( κ ) of Al-Cu eutectics were investigated by experimental and computational methods to shed light on the role of these compounds in thermal properties of Al-Cu connections in compound casting. Specifically, the nonequilibrium molecular dynamics (MD) method was utilized to simulate the lattice thermal conductivity ( κ l ) of six compositions of Al-Cu with 5-30 at.% Cu. To extend the results of the MD simulations to bulk materials, instead of using conventional linear extrapolation methods, a machine learning approach was developed for the dataset acquired from the MD simulations. The bootstrapping approach was utilized to find the most suitable method among the support vector machine (SVM) with polynomial and radial basis function (RBF) kernels and the random forest method. The results showed that the SVM model with RBF kernel performed the best, and thus was used to predict the bulk κ l . Subsequently, the chosen compositions were produced by induction casting and their electrical conductivities were measured via eddy current method for calculating the electronic contribution of κ using the Wiedemann-Franz law. Finally, the actual κ of the alloys were measured using the xenon flash method and the results were compared with the computational values. It was shown that the MD method is capable of successfully simulating the thermal conductivity of this system. In addition, the experimental results demonstrated that the κ of Al-Cu eutectics decreases almost linearly with formation of the Al2Cu phase due to increase in the Cu content. Overall, the current findings can be used to enhance the κ of cooling devices made via Al-Cu compound casting.

AB - In this study, the thermal conductivity ( κ ) of Al-Cu eutectics were investigated by experimental and computational methods to shed light on the role of these compounds in thermal properties of Al-Cu connections in compound casting. Specifically, the nonequilibrium molecular dynamics (MD) method was utilized to simulate the lattice thermal conductivity ( κ l ) of six compositions of Al-Cu with 5-30 at.% Cu. To extend the results of the MD simulations to bulk materials, instead of using conventional linear extrapolation methods, a machine learning approach was developed for the dataset acquired from the MD simulations. The bootstrapping approach was utilized to find the most suitable method among the support vector machine (SVM) with polynomial and radial basis function (RBF) kernels and the random forest method. The results showed that the SVM model with RBF kernel performed the best, and thus was used to predict the bulk κ l . Subsequently, the chosen compositions were produced by induction casting and their electrical conductivities were measured via eddy current method for calculating the electronic contribution of κ using the Wiedemann-Franz law. Finally, the actual κ of the alloys were measured using the xenon flash method and the results were compared with the computational values. It was shown that the MD method is capable of successfully simulating the thermal conductivity of this system. In addition, the experimental results demonstrated that the κ of Al-Cu eutectics decreases almost linearly with formation of the Al2Cu phase due to increase in the Cu content. Overall, the current findings can be used to enhance the κ of cooling devices made via Al-Cu compound casting.

KW - Al-Cu eutectics

KW - machine learning

KW - molecular dynamics

KW - thermal conductivity

KW - Wiedemann-Franz law

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U2 - 10.1088/1361-651X/acc960

DO - 10.1088/1361-651X/acc960

M3 - Article

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VL - 31

JO - Modelling and Simulation in Materials Science and Engineering

JF - Modelling and Simulation in Materials Science and Engineering

SN - 0965-0393

IS - 4

M1 - 045001

ER -

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