Details
| Original language | English |
|---|---|
| Article number | 045001 |
| Journal | Modelling and Simulation in Materials Science and Engineering |
| Volume | 31 |
| Issue number | 4 |
| Publication status | Published - 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.
Keywords
- Al-Cu eutectics, machine learning, molecular dynamics, thermal conductivity, Wiedemann-Franz law
ASJC Scopus subject areas
- Mathematics(all)
- Modelling and Simulation
- Materials Science(all)
- Physics and Astronomy(all)
- Condensed Matter Physics
- Engineering(all)
- Mechanics of Materials
- Computer Science(all)
- Computer Science Applications
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In: Modelling and Simulation in Materials Science and Engineering, Vol. 31, No. 4, 045001, 14.04.2023.
Research output: Contribution to journal › Article › Research › peer review
}
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
UR - http://www.scopus.com/inward/record.url?scp=85152767539&partnerID=8YFLogxK
U2 - 10.1088/1361-651X/acc960
DO - 10.1088/1361-651X/acc960
M3 - Article
AN - SCOPUS:85152767539
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 -