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Controlled agglomeration during spray drying offers several advantages for both powder manufacturers and consumers, and thus it is commonly implemented by industry. The implementation, however is largely based on experience, given the scarcity of comprehensive prediction tools. A resource-efficient approach to numerically treat agglomerates and yet provide an indication of their structures is desired to perform realistic simulations without the need for high-performance computing. In this work, a new numerical model for the treatment of coalescence and agglomeration was implemented and evaluated at two distinct scales with significantly different particle number densities within a Eulerian-Lagrangian CFD framework. The model could accurately predict the trends in the final particle size distributions and distinguish realistic agglomerate structures occurring under different conditions. Challenges were encountered as a result of how the underlying collision detection routine handles high particle number density. Several strategies are proposed to overcome these challenges. This work constitutes significant progress towards achieving an efficient prediction tool to estimate final powder properties and will prove useful in performing large-scale simulations to design and control agglomeration. Unlabelled Image • A rigorous modelling approach was developed for agglomeration in spray drying. • The proposed model was found to be resource efficient and yet useful and informative. • Coalescence and agglomeration of particles were distinguished via penetration depth. • Surface area reduction coined as a parameter indicated agglomerate compactness. • Experimental data obtained from lab- and large-scale dryer supported the assessment. [ABSTRACT FROM AUTHOR]

Highlights • Fourteen optimal vortex finders have been obtained using genetic algorithms. • The least Euler number obtained is 54.25% lower than that of the Stairmand cyclone. • The highest collection efficiency obtained is 5% higher than that of the Stairmand cyclone. • Optimal shape of the vortex finders obtained is stepped divergent and convergent-divergent. • Optimization using genetic algorithms provide better solutions than response surface methodology. Abstract Multi-objective optimization of vortex finders is performed using Response Surface Methodology (RSM) and Genetic Algorithms (GA) for enhancing the performance of the standard Stairmand cyclone separator. Three diameters at different axial locations and the distance between these locations are optimized for enhancing the performance of the cyclone separator. This approach provides flexibility to the optimization process as the vortex finder is free to assume any shape (convergent or divergent) according to changes in the three diameters at different locations of the vortex finder. Optimization is carried out using five independent parameters and two dependent variables, Euler number and collection efficiency. The optimization results confirm that the present strategy is capable of optimizing the vortex finder for better performance than the standard Stairmand model. Fourteen optimal vortex finders have been obtained in the proposed work to select the required conflicting performance parameters. Genetic algorithms provide better solutions than RSM. Optimal vortex finders are stepped divergent or convergent-divergent in nature. [ABSTRACT FROM AUTHOR]

Abstract: The hydrodynamic study of nanofluids in microchannel is carried out. For this, three microchannels of hydraulic diameters of 130, 211 and 300μm are fabricated by photolithographic and wet etching processes on silicon wafers. Alumina nanofluids with concentrations 0.25vol.%, 0.5vol.% and 1.0vol.% with particle sizes 45nm and 150nm are prepared, stabilized and characterized by standard methods like sonication, pH variation, Transmission Electron Microscope (TEM) and Dynamic Light Scattering (DLS) measurements. The base fluids used are water and Ethylene Glycol. The effect of volume fraction, channel size, particle size and base fluids are presented and analyzed. It is shown that there is an early transition to turbulence for 211 and 301μm channels. In addition, numerical modeling is carried out by using mixture rule and discrete phase modeling approach. This approach is found to be more suitable at higher Reynolds numbers whereas at lower Reynolds numbers the conventional mixture model can be used. Shear induced particle migration is identified to be an important phenomenon at higher Reynolds number and hence the reason for difference between these two models. [Copyright &y& Elsevier]

Abstract: Computational fluid dynamics (CFD) is used to model the shock-wave induced spray process (SISP). SISP is a method of applying coatings of various metallic-based materials onto a substrate. It utilizes the kinetic and thermal energy induced by a moving shock-wave to accelerate and heat powder particles. This process is a new cold gas-dynamic spraying (CGDS or simply cold spray) material deposition technique. The basic principles related to the coating formation and bonding mechanism in this process are hypothesized to be similar to those in traditional Cold Spray processes. The distinguishing feature of SISP is a sequence of moving shock-waves created by the fast opening and closing of a valve. A rate of 30 to 50 pulses per second is presently conceivable. When the valve is rapidly opened and closed, a shock-wave propagates into the spraying gun, accelerating and heating the powder present in the gun. Similar to the cold spray process, the particles then impact the substrate and deform plastically to produce a coating. Individual powder particles may reach the substrate at different velocities and temperatures depending on their location within the unsteady flow regime. An existing correlation for the “critical velocity” for bonding and a CFD model are used to predict whether a particle traveling within this unsteady flow regime will adhere to the substrate upon impact. This information is then used to predict if a coating can be formed under a specific set of spray conditions. [Copyright &y& Elsevier]

Abstract: Computational Fluid Dynamics (CFD) analysis of the effects of co-firing biomass with coal is presented in this study. Coal/biomass co-firing is a complex problem that involves gas and particle phases, along with the effect of the turbulence on the chemical reactions. The CFD analysis includes the prediction of volatile evolution and char burnout from the co-pulverized coal/biomass particles along with the simulation of the combustion chemistry occurring in the gas phase. The mathematical models consist of models for turbulent flow (RNG k–ε model); gas phase combustion (two-mixture fractions/PDF model); particles dispersion by turbulent flow (stochastic tracking model); coal/biomass particles devolatilization (two competing rates Kobayashi model); heterogeneous char reaction (kinetics/diffusion-limited rate model); and radiation (P-1 radiation model). The coal used is a Canadian high sulfur bituminous coal. The coal was blended with 5–20% wheat straw (thermal basis) for co-firing. The effect of the percentage of biomass blended with coal on the flow field, gas and particle temperature distribution, particles trajectories and gas emissions (CO2 and NO x ) are presented. One important result is the reduction of NO x and CO2 emissions when using co-combustion. This reduction depends on the proportion of biomass (wheat straw) blended with coal. [Copyright &y& Elsevier]