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Computational Analysis of Structural Defects in Silica Aerogels

Published online by Cambridge University Press:  07 October 2019

Hunter Gore ,
Luis Caldera ,
Xiao Shen  and
Firouzeh Sabri
Hunter Gore
Affiliation:
Dept. of Physics and Materials Science, University of Memphis, Memphis, TN. 38152
Luis Caldera
Affiliation:
Dept. of Physics and Materials Science, University of Memphis, Memphis, TN. 38152
Xiao Shen
Affiliation:
Dept. of Physics and Materials Science, University of Memphis, Memphis, TN. 38152
Firouzeh Sabri*
Affiliation:
Dept. of Physics and Materials Science, University of Memphis, Memphis, TN. 38152
*
*(Email: [email protected])
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Abstract

Technological advances in synthesis and preparation of aerogels have resulted in formulations that have the mechanical integrity (while retaining flexibility) to be utilized in a broad range of applications and have overcome the initial brittleness that this class of materials was once known for. Both structural and functional aerogels show a drop in performance when subjected to certain cyclic thermal or impact loading due to the wear and formation of cracks, which reduces their lifespan. Here we present the proof-of-concept of a computational toolset that connects the change in thermal profile to structural failure and degradation. In combination with an appropriate finite element (FEM) solver, we have developed a genetic algorithm that can reconstruct the size and shape of the defective region in silica aerogels given the temperatures from a sensor grid. Results show that a heatmap can be used as the foundation for reconstructing faults and defects in thermally insulating materials. Furthermore, the model developed in this study can be expanded to accommodate other material types. Experimental setup can used to benchmark and refine the computational toolset.

Keywords

sol-gelthermal conductivitysimulation
Type
Articles
Information
MRS Advances , Volume 4 , Issue 46-47: Soft Materials and Biomaterials , 2019 , pp. 2479 - 2488
Copyright
Copyright © Materials Research Society 2019 

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References

REFERENCES

Allison, S.W., Baker, E.S., Lynch, K. J., Sabri, F., Radiat Phys Chem. 135, 88-93 (2017).CrossRefGoogle Scholar
Mitchell, K.E., Gardner, V., Allison, S.W., and Sabri, F., Opt. Mater. 60, 50-56 (2016)CrossRefGoogle Scholar
Parajuli, P., Allison, S.W., and Sabri, F., Meas. Sci. Technol. 28 (2017)CrossRefGoogle Scholar
Sabri, F., Allison, S.W., Aryal, M., Collins, J., and Bell, H., MRS Advances 3, 3489-3494 (2018).CrossRefGoogle Scholar
Brites, C.D.S., Millan, A., and Carlos, L.D. in Handbook on the Physics and Chemistry of Rare Earths (Book 49) edited by Bünzli, J-C.G. and Pecharsky, V.K. (North Holland, The Netherlands, 2016) p. 339Google Scholar
Marchetta, J.G., Sabri, F., Williams, D. S., Pumroy, D. W., Journal of Spacecraft and Rockets 55:1007-1013 (2018).CrossRefGoogle Scholar
Sabri, F., Marchetta, J., and Smith, K.M., Acta Astronautica, 91,173-179 (2013).CrossRefGoogle Scholar
Eiben, A. E., Smith, J. E., Introduction to Evolutionary Computing, 2nd Ed. Springer (2015)CrossRefGoogle Scholar
Li, Y., Udpa, L., and Udpa, S. S., IEEE Transactions on Magnetics, 40,410-417 (2004).CrossRefGoogle Scholar
Raudenský, M., Woodbury, K.A., Kral, J., Brezina, T. Numerical Heat Transfer, Part B Fundamentals. 1,293-306(1995).CrossRefGoogle Scholar