The effect of internal pipe wall roughness on the accuracy of clamp-on ultrasonic flow meters

Clamp-on transit-time ultrasonic flowmeters (UFMs) suffer from poor accuracy compared with spool-piece UFMs due to uncertainties that result from the in-field installation process. One of the important sources of uncertainties is internal pipe wall roughness which affects the flow profile and also causes significant scattering of ultrasound. This paper purely focuses on the parametric study to quantify the uncertainties (related to internal pipe wall roughness) induced by scattering of ultrasound and it shows that these effects are large even without taking into account the associated flow disturbances. The flowmeter signals for a reference clamp-on flowmeter setup were simulated using 2-D finite element analysis including simplifying assumptions (to simulate the effect of flow) that were deemed appropriate. The validity of the simulations was indirectly verified by carrying out experiments with different separation distances between ultrasonic probes. The error predicted by the simulations and the experimentally observed errors were in good agreement. Then, this simulation method was applied on pipe walls with rough internal surfaces. For ultrasonic waves at 1 MHz, it was found that compared with smooth pipes, pipes with only a moderately rough internal surface (with 0.2-mm rms and 5-mm correlation length) can exhibit systematic errors of 2 in the flow velocity measurement. This demonstrates that pipe internal surface roughness is a very important factor that limits the accuracy of clamp on UFMs

Stochastic stability of viscoelastic systems under Gaussian and Poisson white noise excitations

As the use of viscoelastic materials becomes increasingly popular, stability of viscoelastic structures under random loads becomes increasingly important. This paper aims at studying the asymptotic stability of viscoelastic systems under Gaussian and Poisson white noise excitations with Lyapunov functions. The viscoelastic force is approximated as equivalent stiffness and damping terms. A stochastic differential equation is set up to represent randomly excited viscoelastic systems, from which a Lyapunov function is determined by intuition. The time derivative of this Lyapunov function is then obtained by stochastic averaging. Approximate conditions are derived for asymptotic Lyapunov stability with probability one of the viscoelastic system. Validity and utility of this approach are illustrated by a Duffing-type oscillator possessing viscoelastic forces, and the influence of different parameters on the stability region is delineated

Fast Monte Carlo Simulation for Patient-specific CT/CBCT Imaging Dose Calculation

Recently, X-ray imaging dose from computed tomography (CT) or cone beam CT (CBCT) scans has become a serious concern. Patient-specific imaging dose calculation has been proposed for the purpose of dose management. While Monte Carlo (MC) dose calculation can be quite accurate for this purpose, it suffers from low computational efficiency. In response to this problem, we have successfully developed a MC dose calculation package, gCTD, on GPU architecture under the NVIDIA CUDA platform for fast and accurate estimation of the x-ray imaging dose received by a patient during a CT or CBCT scan. Techniques have been developed particularly for the GPU architecture to achieve high computational efficiency. Dose calculations using CBCT scanning geometry in a homogeneous water phantom and a heterogeneous Zubal head phantom have shown good agreement between gCTD and EGSnrc, indicating the accuracy of our code. In terms of improved efficiency, it is found that gCTD attains a speed-up of ~400 times in the homogeneous water phantom and ~76.6 times in the Zubal phantom compared to EGSnrc. As for absolute computation time, imaging dose calculation for the Zubal phantom can be accomplished in ~17 sec with the average relative standard deviation of 0.4%. Though our gCTD code has been developed and tested in the context of CBCT scans, with simple modification of geometry it can be used for assessing imaging dose in CT scans as well.Comment: 18 pages, 7 figures, and 1 tabl

Efficient electronic entanglement concentration assisted with single mobile electron

We present an efficient entanglement concentration protocol (ECP) for mobile electrons with charge detection. This protocol is quite different from other ECPs for one can obtain a maximally entangled pair from a pair of less-entangled state and a single mobile electron with a certain probability. With the help of charge detection, it can be repeated to reach a higher success probability. It also does not need to know the coefficient of the original less-entangled states. All these advantages may make this protocol useful in current distributed quantum information processing.Comment: 6pages, 3figure

Relativistic description of J/\psi dissociation in hot matter

The mass spectra and binding radii of heavy quark bound states are studied on the basis of the reduced Bethe-Salpeter equation. The critical values of screening masses for $c\bar{c}$ and $b\bar{b}$ bound states at a finite temperature are obtained and compared with the previous results given by non-relativistic models.Comment: 13 latex pages, 2 figure

Value encoding in the globus pallidus: fMRI reveals an interaction effect between reward and dopamine drive

The external part of the globus pallidus (GPe) is a core nucleus of the basal ganglia (BG) whose activity is disrupted under conditions of low dopamine release, as in Parkinson's disease. Current models assume decreased dopamine release in the dorsal striatum results in deactivation of dorsal GPe, which in turn affects motor expression via a regulatory effect on other nuclei of the BG. However, recent studies in healthy and pathological animal models have reported neural dynamics that do not match with this view of the GPe as a relay in the BG circuit. Thus, the computational role of the GPe in the BG is still to be determined. We previously proposed a neural model that revisits the functions of the nuclei of the BG, and this model predicts that GPe encodes values which are amplified under a condition of low striatal dopaminergic drive. To test this prediction, we used an fMRI paradigm involving a within-subject placebo-controlled design, using the dopamine antagonist risperidone, wherein healthy volunteers performed a motor selection and maintenance task under low and high reward conditions. ROI-based fMRI analysis revealed an interaction between reward and dopamine drive manipulations, with increased BOLD activity in GPe in a high compared to low reward condition, and under risperidone compared to placebo. These results confirm the core prediction of our computational model, and provide a new perspective on neural dynamics in the BG and their effects on motor selection and cognitive disorders