3D particle concentration measurement in turbulent flows using magnetic resonance imaging

Turbulent, dispersed multiphase flows are of critical importance in a wide range of application areas. Experimental investigations are indispensable in the study of such flows, as experiments can provide reliable domain-specific knowledge and/or validation for computational fluid dynamics (CFD) tools. However, only a limited set of experimental techniques currently are available for studying particle-laden flows: pointwise measurements provide high temporal resolution but poor spatial coverage, while laser-based techniques can allow for 2D or 3D measurements, but only in geometrically simple flows. Magnetic Resonance Imaging (MRI) is a powerful tool that can provide fully quantitative, 3D experimental data without the need for optical access. Currently, MRI can provide the time-averaged, 3-component velocity and/or scalar concentration fields in turbulent single-phase flows of arbitrary geometric complexity. In recent years MRI has been applied to the study of single-phase flows across a broad range of problems from the engineering, environmental, and medical arenas. MRI data sets are particularly well suited for validating CFD simulations of complex 3D flows because comprehensive data coverage can be obtained in a relatively short time. The present work describes development, validation, and application of a new diagnostic, wherein MRI is used to obtain the 3D mean volume fraction field for solid microparticles dispersed in a turbulent water flow. The new method is referred to as Magnetic Resonance Particle concentration, or MRP. This technique was designed to maintain the same advantages of existing MRI-based techniques: quantitative data can be obtained in 3D for fully turbulent flow in arbitrarily complex geometries. MRP is based on a linear relationship between the MRI signal decay rate and particle volume fraction (Yablonskiy and Haacke, 1994). The MRP method and underlying physics were validated through several studies, increasing in complexity from a single particle suspended in a gel to a fully turbulent channel flow seeded uniformly with particles. The channel flow case showed that the signal decay rate varied linearly with particle volume fraction, and that the measured proportionality constant was within 5% of the value predicted by the theory of Yablonskiy and Haacke (1994). This good agreement was observed for two fully turbulent Reynolds numbers, 6300 and 12,200, and over most of the measurement domain. However, the measured proportionality constant was lower than expected in the the furthest upstream portion of the channel; several potential reasons for this discrepancy were identified, but none could be proven conclusively at this stage. Following the validation experiments, MRP was applied to three application cases drawn from real-world flows of interest. First, the dispersion of two particle streaks in a model human nasal passage was studied. The results showed that almost all particles reaching the upper portions of the nasal passage (e.g., the olfactory region) entered the nose near the nostril tip, even at high breathing rates where the flow was not laminar. The second case involved MRP concentration measurements for a particle streak in a generic gas turbine blade internal cooling passage. Results in this case provided evidence that small dust-like particles ingested into a cooling passage may behave inertially in the presence of fine flow features, such as the recirculation regions behind ribbed flow turbulators. In the final case, the performance of a particle separator device proposed by Musgrove et al. (2009) was quantified using both MRP and a sample-based analysis performed outside the MRI environment. The two techniques were in agreement regarding the poor overall effectiveness of the separator, and the 3D MRP data were used to examine the particle transport physics and suggest potential design improvements. Taken together, results from the three test cases showed that MRP can provide quantitative, 3D particle concentration data in application-relevant flows, leading to unique insights that would not be possible with existing measurement techniques.

Keywords: Mechanical Engineering.
Advisor: Eaton John K. 
Dabiri John O. (John Oluseun)
Elkins Christopher J.