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The visualization and analysis of a novel acoustic-particulate system is the objective of this study. The system is composed of rice-husk fired smoke particulates (36.7 nm–840 μm) and one annular resonant circular-tube waveguide contrarily coupled with two sound sources. The collective interaction behavior process of smoke particulates in an inhomogeneous acoustic field is displayed during an experiment and a simulation. The result shows that the aggregation and fragmentation of particles under a change in resonant frequencies and sound pressure amplitude is extremely complex. This complex process consists of dynamically tuning the particle characteristics to attain stripes shaped like thin-films/umbrellas and clusters with volume-change/fragmentation. The balanced modulation of the acoustic radiation force and secondary radiation force to alter the particle characteristics (size and stack density) is verified to be the control mechanism of the particle system. The intermediate variable of the process control is the acoustic contrast factor (Ф) related to the physical characteristics of the growing particulates. The value plus-minus alternation of Ф results in different particulate processes. This study can enhance the application of aerodynamic acoustic-particulate-fluid systems for environment protection, energy fuel conversion, and industrial production.

Aerosol control is a big revolution in production and use of submicronic particles. Developing a novel aerosol control technology in the area is the study objective of this paper. Attributing to the aerosol control potential in acoustics, a normal 16-sided aerosol aggregation chamber (pelletier dish) symmetrically installed with 16 poles of Helmholtz sound source (HSS) is successfully used to aggregate smog aerosols. The aerosols are generated by the combustion of chemical material. In the acoustic conditions of 2, 4, 8 and 16 beams of opposing waves, the shapes of aerosol distribution always display as the specific aerosol petals of number equaling to the operating number of HSS. With the increase of the operating number, the removal effectiveness rises gradually to 1.5 wavelengths, obviously larger than half wavelengths achieved in the previous study. The results are repeatability in experiment, and especially the aerosol aggregation can be pre-designed by acoustic field simulation.

Soil moisture is an important variable in many environmental researches and application areas as it affects the interactions between atmosphere and land surface by controlling the energy and water exchange. The current measurement techniques are insufficient to acquire accurate large-scale root zone soil moisture (RZSM) data at the spatial resolution of interest. Though assorted models have been successfully applied in relatively small areas to estimate RZSM, the large-scale estimation is still facing challenges as it requires the flexibility and practicality of the models for the applications under various conditions. Though physically based soil moisture models are widely used, the errors in model physics affect the flexibility of these models meanwhile their large demand of data and computational resources reduces the practicality. On the contrary, the statistical and data-driven methods have high potential but their applications for large-scale RZSM estimation have not been fully explored. To develop feasible models for large-scale RZSM estimation using the surface observations, artificial neural networks, specifically multilayer perceptrons (MLPs), were applied in this study to estimate RZSM at the depths of 20cm and 50cm, using the data of 557 stations in the United States. Two experiments including four models were developed and the input variables of the models were carefully selected. The sensitivity analysis found that surface soil moisture and the cumulative rainfall, snowfall, air temperature and surface soil temperature were important inputs. If given soil texture data as inputs, the models achieved better performance and were extremely sensitive to them. The results showed that the MLPs were effective and flexible for the estimation of soil moisture at 20cm under various climate types and were insensitive to the potential errors in soil moisture datasets. However, the results of the estimation at 50cm are not as good as that of the 20cm. Thesis Master of Science (MSc)