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.

This dissertation presents three different aspects of the incorporation of directionality into the Monte Carlo ray-trace (MCRT) environment: (1) the development of a methodology for using directional surface optical data, (2) the measurement of the bi-directional reflectivity functions for two different surfaces, and (3) MCRT simulations performed using these directional data sets. The methodology presented is based upon a rigorous analytical formulation and is capable of performing simulations of radiation exchange involving directional emission, absorption and reflection given the bi-directional reflectivity functions (BDRF) of the participating surfaces. A wavelet compression technique is presented for the management of extremely large directional data sets. The BDRFs of two different surfaces were acquired using a Surface Optics Corporation model SOC-250 bi-directional reflectometer. These data were processed according to the methodology presented and an MCRT code was used to simulate the action of the SOC-250 in measuring radiant energy reflected from the surfaces of the two samples when illuminated by the source of the SOC-250. Another MCRT code was used to simulate the radiant energy reflected into a plane at the exit of an open-ended rectangular box when the entrance to the box is illuminated by source of the SOC-250. The RMS error between the MCRT simulations of sampling using the SOC-250 and the measured data were determined and then divided by the mean BDRF level of the measured data (RMS/mean[rho]) to provide an estimate of convergence. The RMS/mean[rho] was observed to fall from as much as 138 to 0.84 for the aluminum substrate coated with Krylon Shortcuts Hunter Green Satin aerosol paint as the number of energy bundles emitted in the MCRT simulation went from 103 to 106 at an incident zenith angle of 40 deg. The RMS/mean[rho] was observed to fall from as much as 2.2 to 0.2 for the Norton (150 Fine grit) all-purpose sandpaper coated with Krylon Shortcuts Hunter Green Satin aerosol paint as the number of energy bundles emitted in the MCRT simulation went from 103 to 106 at an incident zenith angle of 40 deg. / Ph. D.

Two aspects of the study of the Earth radiation budget and the effects of clouds on our climate system are considered in this dissertation : instrumentation and data interpretation. Numerical models have been developed to characterize the optical/thermal-radiative behavior, the dynamic electrothermal response and the structural thermal transients of radiometric channels. These models, applied to a satellite-borne scanning radiometer, are used to determine the instrument point spread function and the potential for optical and thermal-radiative contamination of the signal due to out-of-field radiation and emission from the radiometer structure. The capabilities of the model are demonstrated by scanning realistic Earth scenes. In addition, the optical/thermal-radiative model is used for the development of an infrared field radiometer to interpret results from the experimental characterization of the instrument. The model allowed the sensitivity of the instrument response to assembly uncertainties to be determined. Data processing consists of converting radiometric data into estimates of the flux at the top of the atmosphere. Primary error sources are associated with the procedures used to compensate for unsampled data. The time interpolation algorithm applied to a limited number of observations can produce significantly biased estimates of monthly mean fluxes. A diurnal interpolation protocol using correlative ISCCP cloudiness data is developed to compensate for sparse temporal sampling of Earth radiation budget data. The bias is shown to be significantly reduced in regions where the variability of the cloud cover is well accounted for by ISCCP data. / Ph. D.

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

Basal cell carcinoma is a form of a non-melanoma skin tumour, that commonly forms over the sun exposed regions of the head and neck. Investigation of the rate of occurrence at different sites on face and neck shows considerable variation from site to site. The inner canthus has a disproportionate number when compared to more exposed sites. The eye brow ridge, cheek bone and nose limit the field of view of the inner canthus, thus it is expected to receive less radiation than other more exposed regions. To explain the disproportionate rate, it is hypothesised that a portion of radiation incident onto the eye is reflected to the inner canthus. The aim of this thesis is to investigate the contribution that the radiation reflected off the surface of the eye makes to the overall dose on the inner canthus. The inter reflections between the eye and inner canthus were studied through the use of the ray tracing program Zemax. Zemax was used to trace rays in a non sequential mode incident onto a model eye and periorbital region. To obtain the models of the eye and periorbital region, both magnetic resonance imaging and a casting process was investigated, with the later being superior for our uses. With the model obtained, it was used in a series of three dimensional ray tracing programs. On a macroscopic scale there is a small increase in the irradiance on the inner canthus (2 % over a 1 cm2 area). Peaks of high irradiance (19 % increase in irradiance above direct irradiance) were discovered over the surface when the detector was divided into 200 mm elements. It was concluded that these increases above the direct irradiance in these small regions, increases the possibility of the occurrence of a Basal cell carcinoma. Individual facial geometry, will greatly effect the location and size of these peaks and as a result an experimental method to measure the dose distribution across the inner canthus was proposed. Initially it was planned to use polysulphone film to measure the erythemal dose on the inner canthus. Results from the modelling indicated that any measurements made had to be at a high spatial resolution. Polysulphone film was found to be inadequate for this, due to its large uncertainties. An alternative method was investigated so that a population study could be performed in future studies using visible radiation and high dynamic range images gave a simple and effective clinical assessment tool. The high dynamic range images showed hot spots in the irradiance across the inner canthus agreeing with the model. The small spots of high relative irradiance may not be the only reason for the increased rate in this region. Greater skin sensitivity and absence of sun screen use at this site are other possibilities. It is believed however that the irradiance distribution across the inner canthus on a microscopic scale goes a long way to increasing the risk for certain people.

Approved for public release, distribution is unlimited / The purpose of this thesis is to determine the effect that the inclusion of satellite altimeter data has on weapon preset accuracy. GDEM data and MODAS data utilizing four satellite altimeters were used by the Weapon Acoustic Preset Program to determine the suggested presets for a Mk 48 torpedo. The acoustic coverage area generated by the program will be used as the metric to compare the two sets of outputs. The assumption is that the MODAS initialized presets will be more accurate, and, therefore, the difference between the two sets of presets can be attributed to inaccuracy on the part of the GDEM presets. Output presets were created for two different scenarios, an Anti-Surface Warfare (ASUW) scenario and an Anti-Submarine Warfare (ASW) scenario, and three different depth bands, shallow, mid, and deep. After analyzing the output, it became clear that the GDEM data predicted a weapon effectiveness that was far higher than the effectiveness predicted by the MODAS data. Also, while GDEM predicted a wide range of coverage percentages MODAS predicted a narrow range of coverage percentages. / Ensign, United States Navy

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