Low observable technology is used in order to prevent detection, or to delay detection. Radar cross section is an important parameter in aircraft survivability since it measures how detectable an object is with radar. To find the radar cross section Maxwell's equations are solved numerically in the time-domain using a finite difference scheme. This numerical method called Finite Difference Time Domain is very suitable for structures including complex materials. However, this numerical method needs to be verified for large scale simulations, due to numerical dispersion errors. Therefore it is desirable to verify the accuracy of the numerical simulations. In this project, the analytical solution to the multiple scattering by two spheres is implemented using the T-matrix formulation. The analytical solution to the scattering problem is first validated with the analytical Mie-series solution then compared to the Finite Difference Time Domain implementation. The results imply that the difference between the numerical and analytical solution is larger for higher frequencies and larger computational volumes. / Smygteknik används för att förhindra detektering, eller för att fördröja detektion av ett flygplan. Radarmålarea är en viktig parameter för skyddsprestanda hos flygplan eftersom den mäter hur detekterbar ett föremål är med radar. För att hitta radarmålarean löses Maxwells ekvationer numeriskt i tidsdomänen med hjälp av ett finit differensschema. Den numeriska metoden som kallas Finita differensmetoden i tidsdomän, är mycket lämplig för strukturer med komplexa material. Den numeriska metoden behöver valideras för storskaliga simuleringar eftersom det förekommer felaktigheter på grund av den numeriska dispersionen. Därför är det önskvärt att kontrollera riktigheten av de numeriska simuleringarna. I detta projekt, är den analytiska lösningen till multipelspridning av två sfärer implementerad med hjälp av T-matrismetoden. Den analytiska lösningen på spridningsproblemet valideras först mot den analytiska Mie-serielösningen och sedan jämförs den med resultatet av simuleringarna med Finita differensmetoden i tidsdomän. Resultaten antyder att skillnaden mellan den numeriska och analytiska lösningen är större för högre frekvenser och större beräkningsvolymer.

The retrieval of an aerosol scattering phase function using a multi-satellite technique is proposed. A total of 33 phase functions were derived from 18 smoke cases and 15 dust cases. Each case was interrogated using four to nine satellite passes over the aerosol in a two to four hour time frame. The radiance values for the Red and Near-Infrared (NIR) channels were combined with backscattering angles to determine the shape of the scattering phase function. The radiance values were input into the Naval Postgraduate School (NPS) aerosol model to determine optical depths and sample phase functions. A comparison was made between the actual phase functions retrieved and the NPS model phase functions. It was found that the phase functions for the smoke cases more closely matched the model phase functions than in the dust cases. Some conclusions could be drawn about the actual aerosol size and density distribution based on how well it matched the model phase function. Further analysis is necessary to define the exact size and number of the aerosol particles. Fully understanding the aerosol composition is crucial in determining its effects on military sensors and impacts to operations.

Thesis (Ph. D.)--University of Washington, 1995. / Vita. Includes bibliographical references (leaves [187]-197).

Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2005. / Thesis Advisor(s): Philip A. Durkee. Includes bibliographical references (p. 77-78). Also available online.

Infrared (IR) spectroscopy has shown potential to quickly and non-destructively measure the chemical signatures of biomedical samples such as single biological cells, and tissue from biopsy. The size of a single cell (diameter ~10-50 µm) are of a similar magnitude to the mid-IR wavelengths of light (~1-10 µm) giving rise to Mie-type scattering. The result of this scattering is that chemical information is significantly distorted in the IR spectrum.Distortions in biomedical IR spectra are often observed as a broad oscillating baseline on which the absorbance spectrum is superimposed. A spectral feature commonly observed is the sharp decrease in intensity at approximately 1700 cm-1, next to the Amide I band (~1655 cm-1), which pre-2009 was called the 'dispersion artefact'. The first contributing factor towards the 'dispersion artefact' investigated was the reflection signal arising from the air to sample interface entering the collection optics during transflection experiments. This was theoretically modelled, and then experimentally verified. It was shown that IR mapping could be done using reflection mode, yielding information from the optically dense nucleus which previously caused extinction of light in transmission mode.The most important contribution to the spectral distortions was due to resonant Mie scattering (RMieS) which occurs when the scattering particle is strongly absorbing such as biomedical samples. RMieS was shown to explain both the baselines in IR spectra, and the 'dispersion artefact' and was validated using a model system of poly(methyl methacrylate) (PMMA) of varying sizes from 5 to 15 µm. Theoretical simulations and experimental data had an excellent match thus proving the theory proposed. With an understanding of the physics/mathematics of the spectral distortions, a correction algorithm was written, the RMieS extended multiplicative signal correction (RMieS-EMSC). This algorithm modelled the measured spectrum as superposition of a first guess (the reference spectrum) which was of a similar biochemical composition to the pure absorbance spectrum of the sample, and a scattering curve. The scattering curve was estimated as the linear combination of a database of a large number of scattering curves covering a range of feasible physical parameters. Simulated and measured data verified that the RMieS-EMSC increased IR spectral quality.

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.

Increased efficiency and reduced emissions from gas turbine (GT) engines are of consistently growing concern for the current gas turbine community and for the political environment. GT engines commonly produce undesirable emissions such as Carbon Monoxide (CO), Carbon Dioxide (CO₂), Nitric Oxides (NO_x), and Unburned Hydrocarbons (UHC), which all pose various threats to the environment. Lean premixed combustion of hydrogen provides a potential solution to these concerns. A key component of successful lean hydrogen combustion is the fuel-air mixer. A facility and methodology for the evaluation of such a hydrogen-air mixer is developed and discussed in this thesis. The facility developed utilizes three experimental techniques: Mie scattering flow visualization, schlieren flow visualization, and Laser Doppler Velocimetry (LDV) to characterize and evaluate mixer performance. Results from the two flow visualization experiments illustrate the effectiveness of the established facility. The results from the Mie scattering experiment are post processed and overlaid on CFD predictions of mixer performance and many similarities are found. Capability of the LDV to measure two components of mean velocity is also demonstrated. / Master of Science

Remote sensing is the collection of information about an object from a distance without physically being in contact with it. The type of remote sensing of interest here is in the form of digital images of water bodies acquired by satellite. The advantage over traditional sampling techniques is that data can be gathered quickly over large ranges, and be available for immediate analysis. Remote sensing is a powerful technique for the monitoring of water bodies. To interpret the remotely sensed data, however, knowledge of the optical properties of the water constituents is needed. One of the most important of these is the volume scattering function, which describes the angular distribution of light scattered by a sample. This thesis presents the first measurements of volume scattering functions for Australian waters. Measurements were made on around 40 different samples taken from several locations in the Gippsland lakes and the Great Barrier Reef. The measurements were made by modifying an existing static light scattering spectrometer in order to accurately measure the volume scattering functions. The development of the apparatus, its calibration and automation, and the application of a complex series of post-acquisition data corrections, are all discussed. In order to extrapolate the data over the full angular range, the data was analysed using theoretical curves calculated for multi-modal size distributions using Mie light scattering theory applied to each data set. From the Mie fits the scattering and backscattering coefficients were calculated. These were compared with scattering coefficients measured using in situ sensors ac-9 and Hydroscat-6, and with values from the literature. The effect of chlorophyll a concentrations on the scattering coefficients was examined, and a brief investigation of the polarisation properties of the samples was also undertaken. Finally the angular effects on the relationship between the backscattering coefficient and the volume scattering function were investigated. This is important as in situ backscattering sensors often assume that measuring at a single fixed-angle is a good approximation for calculating the backscattering coefficient. This assumption is tested, and the optimal measurement angle determined.

No description available.

Microparticle experiments conducted using a newly commissioned quadrupole ion trap (QIT) are reported. Single polystyrene microparticles are confined using three dimensional electrodynamic quadrupole fields and characterised by their fluorescence emission and secular frequency measurements. The advantages of this confinement technique are that single particle properties can be measured free from ensemble averaging effects and unperturbed by solvents and (or) substrates. (For complete abstract open document)