Browsing by Author "Yarahmadi, Mehran"
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- Advances in Radiation Heat Transfer and Applied Optics, Including Application of Machine LearningYarahmadi, Mehran (Virginia Tech, 2021-01-14)Artificial neural networks (ANNs) have been widely used in many engineering applications. This dissertation applies ANNs in the field of radiation heat transfer and applied optics. The topics of interest in this dissertation include both forward and inverse problems. Forward problems involve applications in which numerical simulation is expensive in terms of time consummation and resource utilization. Artificial neural networks can be applied in these problems for speeding up the process and reducing the required resources. The Monte Carlo ray-trace (MCRT) method is the state-of-the-art approach for modeling radiation heat transfer. It has the disadvantage of being a complex and computationally expensive process. In this dissertation, after first identifying the uncertainties associated with the MCRT method, artificial neural networks are proposed as an alternative whose computational cost is greatly reduced compared to traditional MCRT method. Inverse problems are concerned with situations in which the effects of a phenomenon are known but the cause is unknown. In such problems, available data in conjunction with ANNs provide an effective tool to derive an inverse model for recovering the cause of the phenomenon. Two problems are studied in this context. The first is concerned with an imager for which the readout power distribution is available and the viewed scene is of interest. Absorbed power distributions on a microbolometer array making up the imager is produced by discretized scenes using a high-fidelity Monte Carlo ray-trace model. The resulting readout array/scene pairs are then used to train an inverse ANN. It is demonstrated that a properly trained ANN can be utilized to convert the readout power distribution into an accurate image of the corresponding discretized scene. The recovered scene of the imager is helpful for monitoring the Earth's radiant energy budget. In the second problem, the collection of scattered radiation by a sun-photometer, or aureolemeter, is simulated using the MCRT method. The angular distribution of this radiation is summarized using the probability density function (PDF) of the incident angles on a detector. Atmospheric water cloud droplets are known to play an important role in determining the Earth's radiant energy budget and, by extension, the evolution of its climate. An extensive dataset is produced using an improved atmospheric scattering model. This dataset is then used to train and test an inverse ANN capable of recovering water cloud droplets properties from solar aureole observations.
- Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural NetworkYarahmadi, Mehran; Mahan, J. Robert; McFall, Kevin; Ashraf, Anum Barki (MDPI, 2020-01-03)Narrow field-of-view scanning thermistor bolometer radiometers have traditionally been used to monitor the earth’s radiant energy budget from low earth orbit (LEO). Such instruments use a combination of cross-path scanning and along-path spacecraft motion to obtain a patchwork of punctual observations which are ultimately assembled into a mosaic. Monitoring has also been achieved using non-scanning instruments operating in a push-broom mode in LOE and imagers operating in geostationary orbit. The current contribution considers a fourth possibility, that of an imager operating in LEO. The system under consideration consists of a Ritchey-Chrétien telescope illuminating a plane two-dimensional microbolometer array. At large field angles, the focal length of the candidate instrument is field-angle dependent, resulting in a blurred image in the readout plane. Presented is a full-field focusing algorithm based on an artificial neural network (ANN). Absorbed power distributions on the microbolometer array produced by discretized scenes are obtained using a high-fidelity Monte Carlo ray-trace (MCRT) model of the imager. The resulting readout array/scene pairs are then used to train an ANN. We demonstrate that a properly trained ANN can be used to convert the readout power distribution into an accurate image of the corresponding discretized scene. This opens the possibility of using an ANN based on a high-fidelity imager model for numerical focusing of an actual imager.