Alexandru Crivoi

Viscosity affected by nanoparticle aggregation in Al2O3-water nanofluids

An investigation on viscosity was conducted 2 weeks after the Al2O3-water nanofluids having dispersants were prepared at the volume concentration of 1-5%. The shear stress was observed with a non-Newtonian behavior. On further ultrasonic agitation treatment, the nanofluids resumed as a Newtonian fluids. The relative viscosity increases as the volume concentrations increases. At 5% volume concentration, an increment was about 60% in the re-ultrasonication nanofluids in comparison with the base fluid. The microstructure analysis indicates that a higher nanoparticle aggregation had been observed in the nanofluids before re-ultrasonication.

Sessile nanofluid droplet drying.

Nanofluid droplet evaporation has gained much audience nowadays due to its wide applications in painting, coating, surface patterning, particle deposition, etc. This paper reviews the drying progress and deposition formation from the evaporative sessile droplets with the suspended insoluble solutes, especially nanoparticles. The main content covers the evaporation fundamental, the particle self-assembly, and deposition patterns in sessile nanofluid droplet. Both experimental and theoretical studies are presented. The effects of the type, concentration and size of nanoparticles on the spreading and evaporative dynamics are elucidated at first, serving the basis for the understanding of particle motion and deposition process which are introduced afterward. Stressing on particle assembly and production of desirable residue patterns, we express abundant experimental interventions, various types of deposits, and the effects on nanoparticle deposition. The review ends with the introduction of theoretical investigations, including the Navier-Stokes equations in terms of solutions, the Diffusion Limited Aggregation approach, the Kinetic Monte Carlo method, and the Dynamical Density Functional Theory. Nanoparticles have shown great influences in spreading, evaporation rate, evaporation regime, fluid flow and pattern formation of sessile droplets. Under different experimental conditions, various deposition patterns can be formed. The existing theoretical approaches are able to predict fluid dynamics, particle motion and deposition patterns in the particular cases. On the basis of further understanding of the effects of fluid dynamics and particle motion, the desirable patterns can be obtained with appropriate experimental regulations.

Dynamic viscosity measurement in non-Newtonian graphite nanofluids

The effective dynamic viscosity was measured in the graphite water-based nanofluids. The shear thinning non-Newtonian behavior is observed in the measurement. On the basis of the best fitting of the experimental data, the viscosity at zero shear rate or at infinite shear rate is determined for each of the fluids. It is found that increases of the particle volume concentration and the holding time period of the nanofluids result in an enhancement of the effective dynamic viscosity. The maximum enhancement of the effective dynamic viscosity at infinite rate of shear is more than 24 times in the nanofluids held for 3 days with the volume concentration of 4% in comparison with the base fluid. A transmission electron microscope is applied to reveal the morphology of aggregated nanoparticles qualitatively. The large and irregular aggregation of the particles is found in the 3-day fluids in the drying samples. The Raman spectra are extended to characterize the D and G peaks of the graphite structure in the nanofluids. The increasing intensity of the D peak indicates the nanoparticle aggregation growing with the higher concentration and the longer holding time of the nanofluids. The experimental results suggest that the increase on effective dynamic viscosity of nanofluids is related to the graphite nanoparticle aggregation in the fluids.

Fractal speckle image analysis for surface characterization of aerospace structures

Surface characterization of the working components has always been a subject of interest among researchers and industry specialists. Especially in the aerospace industry where the aerodynamic capabilities are largely altered by the surface quality of the component of interest, there remains an extensive need for developing systems for effectively characterizing the surface quality. To realize an optical based non-contact and an in-line surface roughness measurement system, it is essential to understand the relationship between the quality of the surface and statistical parameter of the reflected speckles. The range of the measurement system being proportional to the wavelength of light used makes the analysis fundamentally important in order to understand the properties of speckles at a different wavelength. In this context, this paper examines the nature of the formed IR speckles from three different diffusers by analyzing their raw structure. Image processing algorithms that are developed study the different parameters of the 8-bit binary speckles, namely, the fractal property and number of connecting components. The paper also discusses the future work direction on relating the proposed analysis to derive the algorithm required for evaluating the surface finish parameters.

Fractal Patterns in the Nanofluidic Sessile Droplet Drying

The two-dimensional lattice-gas kinetic Monte Carlo model is used to simulate the process of a nanofluidic sessile droplet drying with a moving contact line in this study. A new modeling approach is implemented by introducing the two-dimensional circular simulation domain in order to operate with the top view of spherical cap of a drying droplet. The non-uniform effective chemical potential function is applied to the model, taking into account the thickness profile of a droplet. Although our simulation is two-dimensional, this modification mimics to some extent the three-dimensional nature of a mesoscopic sessile droplet. Hence we introduce a new, pseudo-3D modification of Monte Carlo simulation model, i.e., a 2D model with additional dependence of the chemical potential on the local droplet thickness. In result, the evaporation-induced nanoparticle self-assembly led to the formation of a fractal-like structure, which is observed in simulation runs. Interestingly, the fractal-shaped patterns obtained in numerous simulations are comparable to the experimentally recorded images of dried nanoparticle structures. The difference in average fractal dimensions of simulated and experimental images is estimated to be less than 5%. It is suggested that the lattice-gas model provides an easy way to reproduce the formation of branched structures from nanoparticles during the drying of spherical sessile droplets.Copyright

A fractal image analysis methodology for heat damage inspection in carbon fiber reinforced composites

The use of carbon fiber-reinforced polymer (CFRP) composite materials in the aerospace industry have far improved the load carrying properties and the design flexibility of aircraft structures. A high strength to weight ratio, low thermal conductivity, and a low thermal expansion coefficient gives it an edge for applications demanding stringent loading conditions. Specifically, this paper focuses on the behavior of CFRP composites under stringent thermal loads. The properties of composites are largely affected by external thermal loads, especially when the loads are beyond the glass temperature, Tg, of the composite. Beyond this, the composites are subject to prominent changes in mechanical and thermal properties which may further lead to material decomposition. Furthermore, thermal damage formation being chaotic, a strict dimension cannot be associated with the formed damage. In this context, this paper focuses on comparing multiple speckle image analysis algorithms to effectively characterize the formed thermal damages on the CFRP specimen. This would provide us with a fast method for quantifying the extent of heat damage in carbon composites, thus reducing the required time for inspection. The image analysis methods used for the comparison include fractal dimensional analysis of the formed speckle pattern and analysis of number and size of various connecting elements in the binary image.

Switching On and Off the Coffee-Ring Effect in Drying Sessile Nanofluid Droplets

The diffusion-limited cluster aggregation (DLCA) model has been implemented in a three-dimensional (3D) domain with a shape of an approximately spherical cap for simulating the drying process of a sessile nanofluid droplet. The droplet evaporation is investigated with the pinned three-phase line, resulting in shrinking contact angle and outward capillary flow. The cluster-cluster aggregation between the particles is taken into account in the model, and the transition from the uniform deposition to the coffee-ring pattern is established by altering the sticking probability parameter. The results of the simulation turn to be consistent with the experimental observation. The influence of the parameters, such as particle volumetric concentration and relative domain size, are studied.Copyright