Non-invasive estimation of the powder size distribution from a single speckle image.

Non-invasive characterization of powders may take one of two approaches: imaging and counting individual particles; or relying on scattered light to estimate the particle size distribution (PSD) of the ensemble. The former approach runs into practical difficulties, as the system must conform to the working distance and other restrictions of the imaging optics. The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. The principle relies on the pupil function determining the basic sidelobe shape, whereas the particle size spread modulates the sidelobe intensity. We recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network, trained efficiently through a physics-informed semi-generative approach. In this work, we eliminate one of the most time-consuming steps of our previous method by engineering the pupil function. By judiciously blocking portions of the pupil, we sacrifice some photons but in return we achieve much enhanced sidelobes and, hence, higher sensitivity to the change of the size distribution. The result is a 60 × reduction in total acquisition and processing time, or 0.25 seconds per frame in our implementation. Almost real-time operation in our system is not only more appealing toward rapid industrial adoption, it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying, blending, and other chemical and pharmaceutical manufacturing processes.

Flow physics of planar bistable fluidic oscillator with backflow limbs.

Fluidic oscillators (FOs) are used in a variety of applications, including process control and process intensification. Despite the simple design and operation of FOs, the fluid dynamics of FOs exhibit rich complexities. The inherently unstable flow, jet oscillations, and resulting vortices influence mixing and other transport processes. In this work, we computationally investigated the fluid dynamics of a new design of a planar FO with backflow limbs. The design comprised of two symmetric backflow limbs leading to bistable flow. The unsteady flow dynamics, internal recirculation, jet oscillations, secondary flow vortices were computationally studied over a range of inlet Reynolds numbers (2400-12,000). The nature and frequency of the jet oscillations were quantified. The computed jet oscillation frequency was compared with the experimentally measured (using imaging techniques) jet oscillation frequency. The flow model was then used to quantitatively understand mixing, heat transfer, and residence time distribution. The approach and the results presented in this work will provide a basis for designing FO's with desired flow and transport characteristics for various engineering applications.

Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE).

Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.

Estimation of chemical and physical effects of cavitation by analysis of cavitating single bubble dynamics.

Cavitation events create extreme conditions in a localized 'bubble collapse' region, leading to the formation of hydroxyl radicals, shockwaves and microscopic high-speed jets, which are useful for many chemical and physical transformation processes. Single bubble dynamics equations have been used previously to investigate the chemical and physical effects of cavitation. In the present study, the state of the art of the single bubble dynamics equations was reviewed and certain noteworthy modifications were implemented. Simulations reaffirmed previously reported collapse temperatures of the order ~5,000 K and collapse pressures well over ~1,000 bar under varying operating conditions. The chemical effects were assessed in terms of the hydroxyl radical generation rate (OHG), calculated by applying the minimization of the Gibb's Free Energy method using simulated collapse conditions. OHG values as high as 1x1012OH molecules per collapse event were found under certain operating conditions. A new equation was proposed to assess the physical effects, in terms of the impact pressure of the water jet - termed as the jet hammer pressure (JHP), formed due to the asymmetrical collapse of a bubble near a wall. The predicted JHP were found to be within a range of ~100 to 1000 bar under varying operating conditions. Important issues such as the onset of cavitation and chaotic solutions, for a cavitating single bubble dynamics were discussed. The Blake threshold pressure was found to be a sufficient criterion to capture the onset of cavitation. The impact of key operating parameters on the chemical and physical effects of cavitation were investigated exhaustively through simulations, over the parameter ranges relevant to acoustic and hydrodynamic cavitation processes. Presented methodology and results will be useful for optimisation and further investigations of a broad range of acoustic and hydrodynamic cavitation-based applications.