Fluidization of biomass particles is an important process in the gasification, pyrolysis and combustion in order to extract energy from biomass. Studies on the fluidization of biomass particles (corn cob and walnut shell), inert particles (sand, glass bead, and alumina), which are added to facilitate fluidization of biomass, and biomass/sand mixture were performed. Experiments were carried out in a 14.5 cm internal diameter cold flow fluidization bed to determine minimum fluidization velocities with air as fluidizing medium. On the of basis of experimental data from both present work and those found in the literature, new correlations were developed to predict minimum fluidization velocity for inert particles as well as biomass particles. It was found that the proposed correlations satisfactorily predict minimum fluidization velocities and was in well agreement with experimental data. Furthermore, effect of weight percentage of biomass in the biomass/sand mixtures was studied. The weight fraction of biomass particles in the mixture was chosen in the range of 0 ~ 100 wt. %. The results show that minimum fluidization velocity of the mixtures increases with an increase in biomass content. Using the present experimental data, a new correlation was developed in terms of mass ratio for predicting values of minimum fluidization velocity of these mixtures. However, the validity of the proposed correlation should be further studied by conducting more experiments using the biomass/sand mixtures of different particle size, shape, and density.

A rotor immersed in a thick turbulent boundary layer produces unsteady loading on the blades which generates unwanted noise and vibration. Two point velocity fluctuations were measured in detail to determine the full four-dimensional correlation function of a boundary layer generated over a smooth wall in the Virginia Tech Stability Wind Tunnel. The correlation function reveals anisotropy in the flow dominated by a large scale correlation structure elongated in the streamwise direction and inclined relative to the wall. This correlation function was then evaluated in the blade frame of reference of an idealized 10 bladed rotor partially immersed in the flow. Blade to blade upwash coherence shows significant asymmetry which is a direct result of the anisotropy of the flow. Using a newly developed theory, the correlation function was used to predict the far-field radiated noise from the rotor at various operating and flow conditions. Predictions show the sound field is dominated by the effects of "haystacking" which is further increased with the inclusion of the presence of the wall. Directivity predictions suggest the far-field sound field acts like a monopole/dipole combination. / Master of Science

Most flows in nature and engineering are turbulent, and many are wall-bounded. Further, in turbulent flows, the turbulence generally has a large impact on the behavior of the flow. It is therefore important to be able to predict the effects of turbulence in such flows. The Navier-Stokes equations are known to be an excellent model of the turbulence phenomenon. In simple geometries and low Reynolds numbers, very accurate numerical solutions of the Navier-Stokes equations (direct numerical simulation, or DNS) have been used to study the details of turbulent flows. However, DNS of high Reynolds number turbulent flows in complex geometries is impractical because of the escalation of computational cost with Reynolds number, due to the increasing range of spatial and temporal scales. In Large Eddy Simulation (LES), only the large-scale turbulence is simulated, while the effects of the small scales are modeled (subgrid models). LES therefore reduces computational expense, allowing flows of higher Reynolds number and more complexity to be simulated. However, this is at the cost of the subgrid modeling problem. The goal of the current research is then to develop new subgrid models consistent with the statistical properties of turbulence. The modeling approach pursued here is that of "Optimal LES". Optimal LES is a framework for constructing models with minimum error relative to an ideal LES model. The multi-point statistics used as input to the optimal LES procedure can be gathered from DNS of the same flow. However, for an optimal LES to be truly predictive, we must free ourselves from dependence on existing DNS data. We have done this by obtaining the required statistics from theoretical models which we have developed. We derived a theoretical model for the three-point third-order velocity correlation for homogeneous, isotropic turbulence in the inertial range. This model is shown be a good representation of DNS data, and it is used to construct optimal quadratic subgrid models for LES of forced isotropic turbulence with results which agree well with theory and DNS. The model can also be filtered to determine the filtered two-point third-order correlation, which describes energy transfer among filtered (large) scales in LES. LES of wall-bounded flows with unresolved wall layers commonly exhibit good prediction of mean velocities and significant over-prediction of streamwise component energies in the near-wall region. We developed improved models for the nonlinear term in the filtered Navier-Stokes equation which result in better predicted streamwise component energies. These models involve (1) Reynolds decomposition of the nonlinear term and (2) evaluation of the pressure term, which removes the divergent part of the nonlinear models. These considerations significantly improved the performance of our optimal models, and we expect them to apply to other subgrid models as well. / text

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