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

Wang, Jiaqi; Yousefzadi Nobakht, Ali; Blanks, James Dean; Shin, Dongwon; Lee, Sangkeun; Shyam, Amit; Rezayat, Hassan; Shin, Seungha

doi:10.1002/adts.201800196

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

Journal Article · Wed Jan 30 00:00:00 EST 2019 · Advanced Theory and Simulations

DOI:https://doi.org/10.1002/adts.201800196· OSTI ID:1493986

^[1];

^[1]; Blanks, James Dean ^[1];

^[2];

^[2]; Rezayat, Hassan ^[1];

^[1]

Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1493986

Alternate ID(s):: OSTI ID: 1492904

Journal Information:: Advanced Theory and Simulations, Vol. 2, Issue 4; ISSN 2513-0390

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 15 works

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References (39)

Phase stability and structure of spinel-based transition aluminas Wolverton, C.; Hass, K. C. Physical Review B, Vol. 63, Issue 2 https://doi.org/10.1103/PhysRevB.63.024102	journal	December 2000
Interflake thermal conductance of edge-passivated graphene Shin, Seungha; Kaviany, Massoud Physical Review B, Vol. 84, Issue 23 https://doi.org/10.1103/PhysRevB.84.235433	journal	December 2011
Thermal conductivity of liquid metals and metallic alloys Giordanengo, B.; Benazzi, N.; Vinckel, J. Journal of Non-Crystalline Solids, Vol. 250-252 https://doi.org/10.1016/S0022-3093(99)00268-9	journal	August 1999
Accelerating materials property predictions using machine learning Pilania, Ghanshyam; Wang, Chenchen; Jiang, Xun Scientific Reports, Vol. 3, Issue 1 https://doi.org/10.1038/srep02810	journal	September 2013
Detecting Novel Associations in Large Data Sets Reshef, D. N.; Reshef, Y. A.; Finucane, H. K. Science, Vol. 334, Issue 6062 https://doi.org/10.1126/science.1205438	journal	December 2011
Numerical simulation of thermal behavior and multicomponent mass transfer in direct laser deposition of Co-base alloy on steel Gan, Zhengtao; Yu, Gang; He, Xiuli International Journal of Heat and Mass Transfer, Vol. 104 https://doi.org/10.1016/j.ijheatmasstransfer.2016.08.049	journal	January 2017
Structural deformation and transformation of θ′-Al2Cu precipitate in Al matrix via interfacial diffusion Wang, Jiaqi; Shin, Seungha; Yousefzadi Nobakht, Ali Computational Materials Science, Vol. 156 https://doi.org/10.1016/j.commatsci.2018.09.024	journal	January 2019
Comparative Evaluation of Cast Aluminum Alloys for Automotive Cylinder Heads: Part II—Mechanical and Thermal Properties Roy, Shibayan; Allard, Lawrence F.; Rodriguez, Andres Metallurgical and Materials Transactions A, Vol. 48, Issue 5 https://doi.org/10.1007/s11661-017-3986-0	journal	March 2017
Comparative Evaluation of Cast Aluminum Alloys for Automotive Cylinder Heads: Part I—Microstructure Evolution Roy, Shibayan; Allard, Lawrence F.; Rodriguez, Andres Metallurgical and Materials Transactions A, Vol. 48, Issue 5 https://doi.org/10.1007/s11661-017-3985-1	journal	March 2017
Comparison of finite element techniques for solidification problems Dalhuijsen, A. J.; Segal, A. International Journal for Numerical Methods in Engineering, Vol. 23, Issue 10 https://doi.org/10.1002/nme.1620231003	journal	October 1986
Thermal conductivity of Si/SiGe superlattice nanowires Li, Deyu; Wu, Yiying; Fan, Rong Applied Physics Letters, Vol. 83, Issue 15 https://doi.org/10.1063/1.1619221	journal	October 2003
Improvement in strength and electrical conductivity of Cu–Ni–Si alloys by aging and cold rolling Suzuki, S.; Shibutani, N.; Mimura, K. Journal of Alloys and Compounds, Vol. 417, Issue 1-2 https://doi.org/10.1016/j.jallcom.2005.09.037	journal	June 2006
Mechanical stress as a function of temperature for aluminum alloy films Gardner, Donald S.; Flinn, Paul A. Journal of Applied Physics, Vol. 67, Issue 4 https://doi.org/10.1063/1.345611	journal	February 1990
Thermal-conductivity measurements of GaAs/AlAs superlattices using a picosecond optical pump-and-probe technique Capinski, W. S.; Maris, H. J.; Ruf, T. Physical Review B, Vol. 59, Issue 12 https://doi.org/10.1103/PhysRevB.59.8105	journal	March 1999
Determination of thermal diffusivity and thermal conductivity of Mg–Al alloys Rudajevová, A.; Staněk, M.; Lukáč, P. Materials Science and Engineering: A, Vol. 341, Issue 1-2 https://doi.org/10.1016/S0921-5093(02)00233-2	journal	January 2003
The application of machine learning to structural health monitoring Worden, Keith; Manson, Graeme Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 365, Issue 1851 https://doi.org/10.1098/rsta.2006.1938	journal	December 2006
Comparison of microstructure and mechanical properties of A356 aluminum alloy/Al2O3 composites fabricated by stir and compo-casting processes Sajjadi, S. A.; Ezatpour, H. R.; Torabi Parizi, M. Materials & Design, Vol. 34 https://doi.org/10.1016/j.matdes.2011.07.037	journal	February 2012
The effects of microstructural stability on the compressive response of two cast aluminum alloys up to 300 °C Shower, Patrick; Roy, Shibayan; Hawkins, Charles Shane Materials Science and Engineering: A, Vol. 700 https://doi.org/10.1016/j.msea.2017.06.024	journal	July 2017
Thermal conductivity of bulk and nanowire Mg $_{2}$ Si $_{x}$ Sn $_{1 - x}$ alloys from first principles Li, Wu; Lindsay, L.; Broido, D. A. Physical Review B, Vol. 86, Issue 17 https://doi.org/10.1103/PhysRevB.86.174307	journal	November 2012
Petascale supercomputing to accelerate the design of high-temperature alloys Shin, Dongwon; Lee, Sangkeun; Shyam, Amit Science and Technology of Advanced Materials, Vol. 18, Issue 1 https://doi.org/10.1080/14686996.2017.1371559	journal	January 2017
Local decomposition induced by dislocation motions inside precipitates in an Al-alloy Yang, B.; Zhou, Y. T.; Chen, D. Scientific Reports, Vol. 3, Issue 1 https://doi.org/10.1038/srep01039	journal	January 2013
Thermal Conductivity and Thermoelectric Power of Germanium‐Silicon Alloys Steele, M. C.; Rosi, F. D. Journal of Applied Physics, Vol. 29, Issue 11 https://doi.org/10.1063/1.1722984	journal	November 1958
Thermal conductance of metal-metal interfaces Gundrum, Bryan C.; Cahill, David G.; Averback, Robert S. Physical Review B, Vol. 72, Issue 24 https://doi.org/10.1103/PhysRevB.72.245426	journal	December 2005
Thermal Flow Simulation on Twin Roll Casting Process for Thin Strip Production of Magnesium Alloy Ju, Dong Ying; Zhao, Hong Yang; Hu, Xiao Dong Materials Science Forum, Vol. 488-489 https://doi.org/10.4028/www.scientific.net/MSF.488-489.439	journal	July 2005
Electrical resistivity and thermal conductivity of liquid Fe alloys at high P and T, and heat flux in Earth's core de Koker, N.; Steinle-Neumann, G.; Vlcek, V. Proceedings of the National Academy of Sciences, Vol. 109, Issue 11 https://doi.org/10.1073/pnas.1111841109	journal	February 2012
Improvement of mechanical properties for Al alloys using equal-channel angular pressing Horita, Z.; Fujinami, T.; Nemoto, M. Journal of Materials Processing Technology, Vol. 117, Issue 3 https://doi.org/10.1016/S0924-0136(01)00783-X	journal	November 2001
A review of shape memory alloy research, applications and opportunities Mohd Jani, Jaronie; Leary, Martin; Subic, Aleksandar Materials & Design (1980-2015), Vol. 56 https://doi.org/10.1016/j.matdes.2013.11.084	journal	April 2014
Simultaneously Increasing the Ductility and Strength of Nanostructured Alloys Zhao, Y. H.; Liao, X. Z.; Cheng, S. Advanced Materials, Vol. 18, Issue 17 https://doi.org/10.1002/adma.200600310	journal	September 2006
Size and Interface Effects on Thermal Conductivity of Superlattices and Periodic Thin-Film Structures Chen, G. Journal of Heat Transfer, Vol. 119, Issue 2 https://doi.org/10.1115/1.2824212	journal	May 1997
Thermal transport and fire retardance properties of cellular aluminium alloys Lu, T. J.; Chen, C. Acta Materialia, Vol. 47, Issue 5 https://doi.org/10.1016/S1359-6454(99)00037-3	journal	March 1999
Thermal conductivity in strain symmetrized Si/Ge superlattices on Si(111) Chakraborty, S.; Kleint, C. A.; Heinrich, A. Applied Physics Letters, Vol. 83, Issue 20 https://doi.org/10.1063/1.1628819	journal	November 2003
Heat flow diversion in supported graphene nanomesh Yousefzadi Nobakht, Ali; Shin, Seungha; Kihm, Kenneth D. Carbon, Vol. 123 https://doi.org/10.1016/j.carbon.2017.07.025	journal	October 2017
Least Squares Support Vector Machine Classifiers Suykens, J. A. K.; Vandewalle, J. Neural Processing Letters, Vol. 9, Issue 3, p. 293-300 https://doi.org/10.1023/A:1018628609742	journal	June 1999
Nanostructures versus Solid Solutions: Low Lattice Thermal Conductivity and Enhanced Thermoelectric Figure of Merit in Pb _9.6 Sb _0.2 Te ₁₀ _- _x Se _x Bulk Materials Poudeu, Pierre F. P.; D'Angelo, Jonathan; Kong, Huijun Journal of the American Chemical Society, Vol. 128, Issue 44 https://doi.org/10.1021/ja0647811	journal	November 2006
Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory Hautier, Geoffroy; Fischer, Christopher C.; Jain, Anubhav Chemistry of Materials, Vol. 22, Issue 12 https://doi.org/10.1021/cm100795d	journal	June 2010
Comparison of Model Selection for Regression Cherkassky, Vladimir; Ma, Yunqian Neural Computation, Vol. 15, Issue 7 https://doi.org/10.1162/089976603321891864	journal	July 2003
First-principles calculations of the electronic structure and spectra of strongly correlated systems: the LDA + U method Anisimov, Vladimir I.; Aryasetiawan, F.; Lichtenstein, A. I. Journal of Physics: Condensed Matter, Vol. 9, Issue 4, p. 767-808 https://doi.org/10.1088/0953-8984/9/4/002	journal	January 1997
Petascale supercomputing to accelerate the design of high-temperature alloys Shin, Dongwon; Lee, Sangkeun; Shyam, Amit Figshare, 39.77 kB https://doi.org/10.6084/m9.figshare.5537344.v1	dataset	October 2017
Petascale supercomputing to accelerate the design of high-temperature alloys [Supplemental Data] Shin, Dongwon; Lee, Sangkeun; Shyam, Amit figshare-Supplementary information for journal article at DOI: 10.1080/14686996.2017.1371559, 1 DOCX file (39.77 kB) https://doi.org/10.6084/m9.figshare.5537344	figure	October 2017

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