The current effort addresses two issues important to the research conducted by the Thermal Radiation Group at Virginia Tech. The first research topic involves the development of a method which can properly model the diffraction of radiation as it enters an instrument aperture. The second topic involves the study of a potential next-generation space-borne radiometric instrument concept. Presented are multiple modeling efforts to describe the diffraction of monochromatic radiant energy passing through an aperture for use in the Monte-Carlo ray-trace environment. Described in detail is a deterministic model based upon Heisenberg's uncertainty principle and the particle theory of light. This method is applicable to either Fraunhofer or Fresnel diffraction situations, but is incapable of predicting the secondary fringes in a diffraction pattern. Also presented is a second diffraction model, based on the Huygens-Fresnel principle with a correcting obliquity factor. This model is useful for predicting Fraunhofer diffraction, and can predict the secondary fringes because it keeps track of phase. NASA is planning for the next-generation of instruments to follow CERES (Clouds and the Earth's Radiant Energy System), an instrument which measures components of the Earth's radiant energy budget in three spectral bands. A potential next-generation concept involves modification of the current CERES instrument to measure in a larger number of wavelength bands. This increased spectral partitioning would be achieved by the addition of filters and detectors to the current CERES geometry. The capacity of the CERES telescope to serve for this purpose is addressed in this thesis. / Master of Science

The survivability of U.S. warships has become a higher priority than ever before. Two ways to improve survivability are to either avoid damage, or to continue to operate after damage has been incurred. This thesis concentrates on the first line of defense, which involves the first of these two approaches. Specifically, this thesis evaluates the extent of threat due to optical contrast with the ocean background. As part of this effort, an MCRT tool was created that allows the user to vary the shape and surface properties of a ship. A reverse MCRT was performed in order to reduce the processing time required to get accurate results. Using this MCRT tool, the user can determine the theoretical contrast with the ocean surface that would be seen at any viewing angle with and without a polarization filter. The contrast due to differential polarization and a change in viewing angle is estimated to determine the extent of threat. These results can be determined for both daytime and nighttime conditions by specifying if the ray trace is in the infrared or visible light range. The location of the sun for daytime conditions, and the temperature of the surfaces for nighttime conditions, can all be adjusted by the user. In order to get an accurate estimation of the signal power coming from the ocean surface, a great deal of time and effort was spent modeling the ocean surface. Many studies have been done concerning the slope statistics of an ocean surface, some more informative than others. This thesis takes two of the most complete studies and brings them together to get accurate slope statistics in both along-wind and crosswind directions. An original idea by the author was used to give a typical shape to the waves of the simulated ocean surface. The surface properties of the ship were determined using Fresnel's equations and the complex index of refraction of water at the particular wavelengths of interest. / Master of Science

Since the early 70's chemistry and transport models (ChTMs) have been proposed and improved. Tropospheric ChTMs for trace species are detailed numerical formulations intended to represent the atmospheric system as a whole, accounting for all the individual processes and phenomena that influence climate changes. The development of computer resources and the retrieval of emission inventories and observational data of the species of interest have enhanced the model evolution towards three-dimensional global models that account for more complicated chemical mechanisms, wet and dry deposition phenomena, and interactions and feedback mechanisms between meteorology and atmospheric chemistry. The purpose of this study is to ascertain the sensitivity of the solar radiative field in the atmosphere to absorption and scattering by aerosols. This effort is preliminary to the study of feedback mechanisms between photolytic processes that create and destroy aerosols and the radiation field itself. In this study, a cloud of water-soluble aerosols, randomly distributed in space within hypothetical 1-cm cubes of atmosphere, is generated. A random radius is assigned to each aerosol according to a lognormal size distribution function. The radiative field characterization is analyzed using a Mie scattering code to determine the scattering phase function and the absorption and scattering coefficients of sulfate aerosols, and a Monte Carlo ray-trace code is used to evaluate the radiative exchange. The ultimate goal of the effort is to create a tool to analyze the vertical distribution of absorption by aerosols in order to determine whether or not feedback between photolytic processes and the radiation field needs to be included in a Third Generation Chemistry and Transport model. / Master of Science

As the planetary climate continues to evolve, it is important to build an accurate long-term climate record. State-of-the-art atmospheric science requires a variety of approaches to the measurement of the atmospheric structure and composition. This thesis supports the possibility of inferring cloud properties from sun photometer observations of the cloud solar aureole using an artificial neural network (ANN). Training of an ANN requires a large number of input and output parameter sets. A cloud radiance model is derived that takes into consideration the cloud depth, the mean size of the cloud water particles, and the cloud liquid water content. The cloud radiance model derived here is capable of considering the wavelength of the incident sunlight and the cloud lateral dimensions as parameters; however, here we consider only one wavelength—550 nm—and one lateral dimension—500 m—to demonstrate its performance. The cloud radiance model is then used to generate solar aureole profiles corresponding to the cloud parameters as they would be observed using a sun photometer. Coefficients representative of the solar aureole profiles may then be used as inputs to a trained ANN to infer the parameters used to generate the profile. This process is demonstrated through examples. A manuscript submitted for possible publication based on an early version of the cloud radiance model was deemed naïve by reviewers, ultimately leading to improvements documented here. / Master of Science / The Earth's climate is driven by heat from the sun and the exchange of heat between the Earth and space. The role of clouds is paramount in this process. One aspect of "cloud forcing" is cloud structure and composition. Required measures may be obtained by satellite or surface-based observations. Described here is the creation of a numerical model that calculates the disposition of individual bundles of light within water clouds. The clouds created in the model are all described by the mean size of the cloud water droplets, the amount of water in the cloud, and cloud depth. Changing these factors relative to each other changes the amount of light that traverses the cloud and the angle at which the individual bundles of light leave the cloud as measured using a device called a sun photometer. The measured amount and angle of bundles of light leaving the cloud are used to recover the parameters that characterize the cloud; i.e., the size of the cloud water droplets, the amount of water in the cloud, and the cloud depth. Two versions of the cloud radiance model are described.

A primary objective of the effort reported here is to develop a radiometric instrument modeling environment to provide complete end-to-end numerical models of radiometric instruments, integrating the optical, electro-thermal, and electronic systems. The modeling environment consists of a Monte Carlo ray-trace (MCRT) model of the optical system coupled to a transient, three-dimensional finite-difference electrothermal model of the detector assembly with an analytic model of the signal-conditioning circuitry. The environment provides a complete simulation of the dynamic optical and electrothermal behavior of the instrument. The modeling environment is used to create an end-to-end model of the CERES scanning radiometer, and its performance is compared to the performance of an operational CERES total channel as a benchmark. A further objective of this effort is to formulate an efficient design environment for radiometric instruments. To this end, the modeling environment is then combined with evolutionary search algorithms known as genetic algorithms (GA's) to develop a methodology for optimal instrument design using high-level radiometric instrument models. GA's are applied to the design of the optical system and detector system separately and to both as an aggregate function with positive results. / Ph. D.

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.

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