Authors: Alireza Nazarahari | Andreas Fromm | Huseyin Can Ozdemir | Christian Klose | Hans Jürgen Maier | D. Canadinç
Source: Modelling and Simulation in Materials Science and Engineering (article, 2023-03-31)
Field: Materials Science — Materials Chemistry

Determination of thermal conductivity of eutectic Al–Cu compounds utilizing experiments, molecular dynamics simulations and machine learning Abstract In this study, the thermal conductivity ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>κ</mml:mi> </mml:math> ) 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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>κ</mml:mi> <mml:mrow> <mml:mtext>l</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>κ</mml:mi> <mml:mrow> <mml:mtext>l</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> . 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>κ</mml:mi> </mml:math> using the Wiedemann–Franz law. Finally, the actual <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>κ</mml:mi> </mml:math> 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>κ</mml:mi> </mml:math> of Al–Cu eutectics decreases almost linearly with formation of the Al 2 Cu phase due to increase in the Cu content. Overall, the current findings can be used to enhance the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>κ</mml:mi> </mml:math> of cooling devices made via Al–Cu compound casting. Support vector machine ; Algorithm ; Eutectic system ; Artificial intelligence ; Materials science ; Thermal conductivity ; Machine learning ; Extrapolation ; Thermodynamics ; Computer science ; Physics ; Mathematics ; Statistics ; Microstructure Thermal properties of materials ; Aluminum Alloy Microstructure Properties ; Thermodynamic and Structural Properties of Metals and Alloys