Abstract Monte Carlo simulations for photon transport are commonly used to predict the spectral response, including reflectance, absorptance, and transmittance in nanoparticle laden media, while the computational cost could be high. In this study, we demonstrate a general purpose fully connected neural network approach, trained with Monte Carlo simulations, to accurately predict the spectral response while dramatically accelerating the computational speed. Monte Carlo simulations are first used to generate a training set with a wide range of optical properties covering dielectrics, semiconductors, and metals. Each input is normalized, with the scattering and absorption coefficients normalized on a logarithmic scale to accelerate the training process and reduce error. A deep neural network with ReLU activation is trained on this dataset with the optical properties and medium thickness as the inputs, and diffuse reflectance, absorptance, and transmittance as the outputs. The neural network is validated on a validation set with randomized optical properties, as well as nanoparticle medium examples including barium sulfate, aluminum, and silicon. The error in the spectral response predictions is within 1% which is sufficient for many applications, while the speedup is 1–3 orders of magnitude. This machine learning accelerated approach can allow for high throughput screening, optimization, or real-time monitoring of nanoparticle media's spectral response.
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Piston-Type Optical Modulator for Dynamic Thermal Radiation Tuning Applications
This study introduces a movable piston-like structure that provides a simple and cost-effective avenue for dynamically tuning thermal radiation. This structure leverages two materials with dissimilar optical responses—graphite and aluminum—to modulate from a state of high reflectance to a state of high absorptance. A cavity is created in the graphite to house an aluminum cylinder, which is displaced to actuate the device. In its raised state, the large aluminum surface area promotes a highly reflective response, while in its lowered state, the expanded graphite surface area and blackbody cavity-like interactions significantly enhance absorptance. By optimizing the area ratio, reflectance tunability of over 30% is achieved for nearly the entire ultraviolet, visible, and near-infrared wavelength regions. Furthermore, a theoretical analysis postulates wavelength-dependent effectivenesses as high as 0.70 for this method, indicating that tunabilities approaching 70% can be achieved by exploiting near-ideal absorbers and reflectors. The analog nature of this control method allows for an infinitely variable optical response between the upper and lower bounds of the device. These valuable characteristics would enable this material structure to serve practical applications, such as reducing cost and energy requirements for environmental temperature management operations.
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- Award ID(s):
- 1941743
- PAR ID:
- 10323765
- Date Published:
- Journal Name:
- Materials
- Volume:
- 14
- Issue:
- 16
- ISSN:
- 1996-1944
- Page Range / eLocation ID:
- 4372
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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