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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Published on: May 15, 2017

Deep learning based analysis of microstructured materials for thermal radiation control.

Jonathan Sullivan¹, Arman Mirhashemi², Jaeho Lee³

¹Department of Mechanical and Aerospace Engineering, University of California, Irvine, USA.

Scientific Reports

|June 13, 2022

Summary

We developed a deep neural network to rapidly optimize microstructured materials for thermal management. This machine learning approach accelerates the design of advanced cooling and heating systems by simulating millions of possibilities in seconds.

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Area of Science:

Materials Science
Optical Engineering
Aerospace Engineering

Background:

Microstructured materials are vital for thermal management in aerospace and space applications.
Optimizing microstructure design for thermal radiation is computationally intensive due to vast design spaces.
Current methods yield results for limited conditions, hindering broad application.

Purpose of the Study:

To develop a deep neural network (DNN) that emulates finite-difference time-domain (FDTD) simulations for optical properties of microstructured materials.
To establish a machine learning-based approach for efficient microstructure design optimization in thermal radiation control.
To enable rapid exploration of the design space for advanced thermal management systems.

Main Methods:

Developed a DNN trained on FDTD simulation outputs.
Utilized discrete inputs derived from the complex refractive index to represent materials.
Enabled the network to learn relationships between microtexture geometry, wavelength, and material properties.

Main Results:

The DNN accurately predicts optical properties for microstructures, extrapolating to materials not in the training set.
The surrogate DNN simulates over 1,000,000 material/geometry/wavelength combinations in under a minute.
Achieved a speed increase of over 8 orders of magnitude compared to traditional FDTD simulations.

Conclusions:

The deep learning approach significantly accelerates the design optimization of microstructured materials for thermal radiation control.
This method enables rapid, sweeping thermal-optical optimizations for passive cooling and heating systems.
The DNN can serve as a replacement for conventional optical simulations in microstructure design.