Tomographic X-ray particle tracking velocimetry: Proof-of-concept in a creeping flow
Abstract

We investigate the feasibility of in-laboratory tomographic X-ray particle tracking velocimetry (TXPTV) and consider creeping flows with nearly density matched flow tracers. Specifically, in these proof-of-concept experiments we examined a Poiseuille flow, flow through porous media and a multiphase flow with a Taylor bubble. For a full 360$$^\circ$$${}^{\circ }$computed tomography (CT) scan we show that the specially selected 60 micron tracer particles could be imaged in less than 3 seconds with a signal-to-noise ratio between the tracers and the fluid of 2.5, sufficient to achieve proper volumetric segmentation at each time step. In the pipe flow, continuous Lagrangian particle trajectories were obtained, after which all the standard techniques used for PTV or PIV (taken at visible wave lengths) could also be employed for TXPTV data. And, with TXPTV we can examine flows inaccessible with visible wave lengths due to opaque media or numerous refractive interfaces. In the case of opaque porous media we were able to observe material accumulation and pore clogging, and for flow with Taylor bubble we can trace the particles and hence obtain velocities in the liquid film between the wall and bubble, with thickness of liquid film itself also simultaneously obtained from the volumetric reconstruction after segmentation. more »

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Award ID(s):
Publication Date:
NSF-PAR ID:
10362696
Journal Name:
Experiments in Fluids
Volume:
63
Issue:
1
ISSN:
0723-4864
Publisher:
National Science Foundation
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1. Abstract

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2. Abstract

We report on a series of detailed Breit-Pauli and Dirac B-spline R-matrix (DBSR) differential cross section (DCS) calculations for excitation of the$$5\,^2\textrm{S}_{1/2} \rightarrow 5\,^2\textrm{P}_{1/2}$$$5{\phantom{\rule{0ex}{0ex}}}^{2}{\text{S}}_{1/2}\to 5{\phantom{\rule{0ex}{0ex}}}^{2}{\text{P}}_{1/2}$and$$5\,^2\textrm{S}_{1/2}\rightarrow 5\,^2\textrm{P}_{3/2}$$$5{\phantom{\rule{0ex}{0ex}}}^{2}{\text{S}}_{1/2}\to 5{\phantom{\rule{0ex}{0ex}}}^{2}{\text{P}}_{3/2}$states in rubidium by 40 eV incident electrons. The early BP computations shown here were carried out with both 5 states and 12 states, while the DBSR models coupled 150 and 325 states, respectively. We also report corresponding results from a limited set of DCS measurements on the unresolved$$5\,^2\textrm{P}_{1/2,3/2}$$$5{\phantom{\rule{0ex}{0ex}}}^{2}{\text{P}}_{1/2,3/2}$states, with the experimental data being restricted to the scattered electron angular range 2–$$10^\circ$$${10}^{\circ }$. Typically, good agreement is found between our calculated and measured DCS for excitation of the unresolved$$5\,^2\textrm{P}_{1/2,3/2}$$$5{\phantom{\rule{0ex}{0ex}}}^{2}{\text{P}}_{1/2,3/2}$states, with best accord being found between the DBSR predictions and the measured data. The present theoretical and experimental results are also compared with predictions from earlier 40 eV calculations using the nonrelativistic Distorted-Wave Born Approximation and a Relativistic Distorted-Wave model.

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3. Abstract

Micro-scale inorganic particles (d > 1 µm) have reduced surface area and higher density, making them negatively buoyant in most dip-coating mixtures. Their controlled delivery in hard-to-reach places through entrainment is possible but challenging due to the density mismatch between them and the liquid matrix called liquid carrier system (LCS). In this work, the particle transfer mechanism from the complex density mismatching mixture was investigated. The LCS solution was prepared and optimized using a polymer binder and an evaporating solvent. The inorganic particles were dispersed in the LCS by stirring at the just suspending speed to maintain the pseudo suspension characteristics for the heterogeneous mixture. The effect of solid loading and the binder volume fraction on solid transfer has been reported at room temperature. Two coating regimes are observed (i) heterogeneous coating where particle clusters are formed at a low capillary number and (ii) effective viscous regime, where full coverage can be observed on the substrate. ‘Zero’ particle entrainment was not observed even at a low capillary number of the mixture, which can be attributed to the presence of the binder and hydrodynamic flow of the particles due to the stirring of the mixture. The critical film thickness for particle entrainment is$${h}^{*}=0.16a$$${h}^{\ast }=0.16a$formore »

4. Abstract

Given a suitable solutionV(tx) to the Korteweg–de Vries equation on the real line, we prove global well-posedness for initial data$$u(0,x) \in V(0,x) + H^{-1}(\mathbb {R})$$$u\left(0,x\right)\in V\left(0,x\right)+{H}^{-1}\left(R\right)$. Our conditions onVdo include regularity but do not impose any assumptions on spatial asymptotics. We show that periodic profiles$$V(0,x)\in H^5(\mathbb {R}/\mathbb {Z})$$$V\left(0,x\right)\in {H}^{5}\left(R/Z\right)$satisfy our hypotheses. In particular, we can treat localized perturbations of the much-studied periodic traveling wave solutions (cnoidal waves) of KdV. In the companion paper Laurens (Nonlinearity. 35(1):343–387, 2022.https://doi.org/10.1088/1361-6544/ac37f5) we show that smooth step-like initial data also satisfy our hypotheses. We employ the method of commuting flows introduced in Killip and Vişan (Ann. Math. (2) 190(1):249–305, 2019.https://doi.org/10.4007/annals.2019.190.1.4) where$$V\equiv 0$$$V\equiv 0$. In that setting, it is known that$$H^{-1}(\mathbb {R})$$${H}^{-1}\left(R\right)$is sharp in the class of$$H^s(\mathbb {R})$$${H}^{s}\left(R\right)$spaces.

5. Abstract Background

Protein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming.

Results

In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database withMproteins can be transformed into a much more simpler problem: to find a number inside a sorted array of lengthM. This pre-screening process narrows down themore »

Conclusions

The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from$$O(M^2)$$$O\left({M}^{2}\right)$to$$O(M\log M)$$$O\left(MlogM\right)$for performing an all-against-all PPI prediction for a database withMproteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.