CHARACTERIZATION OF RADIATION DAMAGE TO A NOVEL PHOTONIC CRYSTAL SENSOR

New methods of nuclear fuel and cladding characterization must be developed and implemented to enhance the safety and reliability of nuclear power plants. One class of such advanced methods is aimed at the characterization of fuel performance by performing minimally intrusive in-core, real time measurements on nuclear fuel on the nanometer scale. Nuclear power plants depend on instrumentation and control systems for monitoring, control and protection. Traditionally, methods for fuel characterization under irradiation are performed using a “cook and look” method. These methods are very expensive and labor-intensive since they require removal, inspection and return of irradiated samples for each measurement. Such fuel cladding inspection methods investigate oxide layer thickness, wear, dimensional changes, ovality, nuclear fuel growth and nuclear fuel defect identification. These methods are also not suitable for all commercial nuclear power applications as they are not always available to the operator when needed. Additionally, such techniques often provide limited data and may exacerbate the phenomena being investigated. This thesis investigates a novel, nanostructured sensor based on a photonic crystal design that is implemented in a nuclear reactor environment. The aim of this work is to produce an in-situ radiation-tolerant sensor capable of measuring the deformation of a nuclear material during nuclear reactor operations. The sensor was fabricated on the surface of nuclear reactor materials (specifically, steel and zirconium based alloys). Charged-particle and mixed-field irradiations were both performed on a newly-developed “pelletron” beamline at Idaho State University's Research and Innovation in Science and Engineering (RISE) complex and at the University of Maryland's 250 kW Training Reactor (MUTR). The sensors were irradiated to 6 different fluences (ranging from 1 to 100 dpa), followed by intensive characterization using focused ion beam (FIB), transmission electron microscopy (TEM) and scanning electron microscopy (SEM) to investigate the physical deformation and microstructural changes between different fluence levels, to provide high-resolution information regarding the material performance. Computer modeling (SRIM/TRIM) was employed to simulate damage to the sensor as well as to provide significant information concerning the penetration depth of the ions into the material.

Multi-sensor Cloud and Aerosol Retrieval Simulator and Its Applications

Executing a cloud or aerosol physical properties retrieval algorithm from controlled synthetic data is an important step in retrieval algorithm development. Synthetic data can help answer questions about the sensitivity and performance of the algorithm or aid in determining how an existing retrieval algorithm may perform with a planned sensor. Synthetic data can also help in solving issues that may have surfaced in the retrieval results. Synthetic data become very important when other validation methods, such as field campaigns,are of limited scope. These tend to be of relatively short duration and often are costly. Ground stations have limited spatial coverage whilesynthetic data can cover large spatial and temporal scales and a wide variety of conditions at a low cost. In this work I develop an advanced cloud and aerosol retrieval simulator for the MODIS instrument, also known as Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS). In a close collaboration with the modeling community I have seamlessly combined the GEOS-5 global climate model with the DISORT radiative transfer code, widely used by the remote sensing community, with the observations from the MODIS instrument to create the simulator. With the MCARS simulator it was then possible to solve the long standing issue with the MODIS aerosol optical depth retrievals that had a low bias for smoke aerosols. MODIS aerosol retrieval did not account for effects of humidity on smoke aerosols. The MCARS simulator also revealed an issue that has not been recognized previously, namely,the value of fine mode fraction could create a linear dependence between retrieved aerosol optical depth and land surface reflectance. MCARS provided the ability to examine aerosol retrievals against “ground truth” for hundreds of thousands of simultaneous samples for an area covered by only three AERONET ground stations. Findings from MCARS are already being used to improve the performance of operational MODIS aerosol properties retrieval algorithms. The modeling community will use the MCARS data to create new parameterizations for aerosol properties as a function of properties of the atmospheric column and gain the ability to correct any assimilated retrieval data that may display similar dependencies in comparisons with ground measurements.