The primary objective of the work reported here is to develop a methodology to predict the uncertainty associated with radiation heat transfer problems solved using the Monte Carlo ray-trace method (MCRT). Four equations are developed to predict the uncertainty of the distribution factor from one surface to another, the global uncertainty of all the distribution factors in an enclosure, the uncertainty of the net heat flux from a surface, and the global uncertainty of the net heat flux from all the surfaces in an enclosure, respectively. Numerical experiments are performed to successfully validate these equations and to study the impact of various parameters such as the number of surfaces in an enclosure, the number of energy bundles traced in the MCRT model, the fractional uncertainty of emissivity and temperature, and the temperature distribution in the enclosure. Finally, the methodology is successfully applied to a detailed MCRT model of a CERES-like radiometer. / Ph. D.

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. / Doctor of Philosophy / This dissertation is intended to extend the research in the field of theoretical and experimental radiation heat transfer and applied optics. It is specifically focused on efforts for more precisely implementing the radiation heat transfer, predicting the temperature evolution of the Earth's ocean-atmosphere system and identifying the atmospheric properties of the water clouds using the tools of Machine learning and artificial neural networks (ANNs). The results of this dissertation can be applied to the conception of advanced radiation and optical modeling tools capable of significantly reducing the computer resources required to model global-scale atmospheric radiation problems. The materials of this thesis are organized for solving the following three problems using ANNs: 1: Application of artificial neural networks into radiation heat transfer: The application of artificial neural networks), which is the basis of AI methodologies, to a variety of real-world problems is an on-going active research area. Artificial intelligence, or machine learning, is a state-of-the-art technology that is ripe for applications in the field of remote sensing and applied optics. Here a deep-learning algorithm is developed for predicting the radiation heat transfer behavior as a function of the input parameters such as surface models and temperature of the enclosures of interest. ANN-based algorithms are very fast, so developing ANN-based algorithms to replace ray trace calculations, whose execution currently dominates the run-time of MCRT algorithms, is useful for speeding up the computational process. 2. Numerical focusing of a wide-field-angle Earth radiation budget imager using an Artificial Neural Network: Traditional Earth radiation budget (ERB) instruments consist of downward-looking telescopes in low earth orbit (LOE) which scan back and forth across the orbital path. While proven effective, such systems incur significant weight and power penalties and may be susceptible to eventual mechanical failure. This dissertation intends to support a novel approach using ANNs in which a wide-field-angle imager is placed in LOE and the resulting astigmatism is corrected algorithmically. The application of this technology is promising to improve the performance of freeform optical systems proposed by NASA for Earth radiation budget monitoring. 3: Recovering water cloud droplets properties from solar aureole photometry using an ANNs: Atmospheric aerosols are known to play an important role in determining the Earth's radiant energy budget and, by extension, the evolution of its climate. Data obtained during aerosol field studies have already been used in the vicarious calibration of space-based sensors, and they could also prove useful in refining the angular distribution models (ADMs) used to interpret the contribution of reflected solar radiation to the planetary energy budget. Atmospheric aerosol loading contributes to the variation in radiance with zenith angle in the circumsolar region of the sky. Measurements obtained using a sun-photometer have been interpreted in terms of the aerosol single-scattering phase function, droplet size distribution, and aerosol index of refraction, all of which are of fundamental importance in understanding the planetary weather and climate. While aerosol properties may also be recovered using lidar, this dissertation proposes to explore a novel approach for recovering them via sun-photometry. The atmospheric scattering model developed here can be used to produce the extensive dataset required to compose, train, and test an artificial neural network capable of recovering water cloud droplet properties from solar aureole observations.

Black coatings essentially absorb incident light at all wavelengths from all directions. They are used when minimal reflection or maximum absorption is desired and therefore are effective in applications that require control of stray light. Our motivation stems from the use of black coating Lord Aeroglaze® Z302 in aerospace and remote sensing applications and the desire to support the development of bidirectional spectral models that can be used successfully to predict the performance of optical instruments such as telescopes. The bidirectional reflectance distribution function (BRDF) is an indispensable parameter in the optical characterization of such coatings. The current effort involves investigation of the BRDF of the commercial black coating Aeroglaze® Z302. An automated goniometer reflectometer has been designed, fabricated and successfully used for performing the BRDF measurements of Z302 at visible and ultraviolet wavelengths and at both polarizations. The current contribution involves study of Z302 samples prepared at different thicknesses and by different methods, which provides insight about influence of surface roughness on BRDF of Z302. / Master of Science / When light falls on different materials it undergoes various phenomenon such as reflection, refraction, absorption and scattering. The amount of each phenomenon varies with the optical nature of a material as well as the wavelength and direction of the light. Therefore, understanding the optical properties of materials at various wavelengths of light is necessary for effectively using those materialsin specific applications which require light to be efficiently reflected or absorbed. This research studies an optical property known as Bidirectional Reflectance Distribution Function (BRDF) of a black coating called Lord Aeroglaze Z302. Black coatings are materials that ideally absorb almost all light that falls on them irrespective of the light’s direction and wavelength. They are used in applications where maximum absorption of light is required. One such application which relates to the motivation for this research is absorbing unwanted light in instruments used in space such as telescopes and radiometers. Z302 is used in the Clouds and the Earth’s Radiant Energy System (CERES) instruments developed by NASA. BRDF is an important parameter which gives information about all other optical properties of a surface and can be used to know optical performance of that surface. The current work describes the experiments and an automated device developed, called reflectometer, to measure the BRDF of Z302 at different angles and wavelengths of light. The results are reported for different thickness samples of Z302 coating, and two different wavelengths of light that belong to the visible and ultraviolet spectrum of light.

The Thermal Radiation Group, a laboratory in the Department of Mechanical Engineering at Virginia Polytechnic Institute and State University, is currently working to develop a new technology for thermal radiation detectors. The Group is also studying the viability of replacing current Earth Radiation Budget radiometers with this new concept. This next-generation detector consists of a thermopile linear array thermal radiation detector. The principal objective of this research is to develop an optical model for the detector and its cavity. The model based on the Monte-Carlo ray-trace (MCRT) method, permits parametric studies to optimize the design of the detector cavity and the specification of surface optical properties. The model is realized as a FORTRAN program which permits the calculation of quantities related to the cross-talk among pixels of the detector and radiation exchange among surfaces of the cavity. An important capability of the tool is that it provides estimates of the discrete Green's function that permits partial correction for optical cross-talk among pixels of the array. / Master of Science

The study whose results are presented here was carried out in support of an ongoing larger effort to investigate and understand the impact of coherence and polarization on the performance of instruments intended to monitor the Earth's radiant energy budget. The visibility of fringes produced by a Michelson interferometer is known to be sensitive to the degree to which the incident light beam is monochromatic. Therefore, the Michelson interferometer has significant potential as a tool for quantifying the degree of temporal coherence of a quasi-monochromatic light beam. Simulation of the performance of an optical instrument using the Monte-Carlo ray-trace (MCRT) method has been shown to be an efficient method for transferring knowledge of the coherence state of a beam of light from one instrument to another. The goal of the effort reported here is to create and experimentally validate an MCRT model for the optical performance of a Michelson interferometer. The effort is motivated by the need to consolidate the knowledge and skills of the investigator in the realm of physical optics, and by the need to make a useful analytical tool available to other investigators in the larger effort. / Master of Science / The purpose of this study is to investigate and understand the effects of coherence and polarization on the performance of instruments used to monitor and measure the Earth’s radiant energy budget. Coherence and polarization effects need to be understood because they have the potential to produce erroneous radiant energy budget data. Coherence is a measurable parameter describing the correlation between the electrical field phase of a single wave, or between several waves. Polarization is a measurable parameter that describes the orientation of the oscillating electric field of a propagating wave. One of the simplest ways to measure the effects of coherence and polarization is through the use of a Michelson Interferometer. Michelson Interferometers are sensitive machines that are able to produce interference patterns using a single beam of light. The clarity of the produced interference pattern is directly related to the amount of coherence and polarization present in the beam of light under examination. This is why a Michelson Interferometer is perfect for this application. A Michelson Interferometer created in a virtual workspace that utilized the Monte-Carlo ray-trace (MCRT) method has been shown to be an efficient method for transferring knowledge of the coherence state of a beam of light from one instrument to another. The Monte-Carlo ray-trace is an algorithm that facilitates the creation of virtual light rays that behave like natural light rays. The goal in using MCRT is to create and experimentally validate the level of accuracy of the virtual Michelson interferometer. The effort is motivated by the need to consolidate the knowledge and skills of the investigator in the realm of physical optics, and by the need to make a useful analytical tool available to other investigators in the larger effort.