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This meter makes use of a fascinating effect which occurs when gas flows at very high velocity through a nozzle. As the gas is sucked through the nozzle the velocity increases as the cross-section of the nozzle passage decreases towards the throat. At the throat the maximum velocity which can be achieved is sonic – the speed of sound. Downstream of the throat, the velocity will either fall again returning to subsonic, or will rise and become supersonic. In normal operation of the nozzle, the supersonic region is likely to be small, and to be followed by a shock wave which stands across the divergent portion of the nozzle, and causes the gas velocity to drop, very suddenly, from supersonic to subsonic. The existence of the shock wave does not mean that one will hear a “sonic boom”! Such booms are usually caused by moving shock waves carried forward by high-speed aircraft.
The fascinating effect of sonic conditions at the throat is that changes in the flow downstream of the throat have no effect on conditions upstream. The sonic or critical or choked condition, as it is called, appears to block any information which is trying to penetrate upstream. A simple picture of this is that the messengers carrying such information travel at the speed of sound, and so they are unable to make any headway over the fast-flowing gas stream.
Two important effects of this are that:
i) the mass flow rate is a function of the gas properties, the upstream stagnation temperature and pressure and the area of the throat, provided that the nozzle is actually running at critical conditions (which can be ascertained by checking the downstream pressure);
ii) the nozzle acts as a flow controller, creating steady conditions upstream even though conditions downstream are unsteady.
Figure 2.6(a) shows a simple illustration of a convergent-divergent nozzle with a throttle valve downstream. Figure 2.6(b) shows the variation of pressure through the nozzle, and the critical flow conditions are those bounded by curves c and i for which the flow becomes sonic at the throat.
The object of calibration is to benchmark a flowmeter to an absolute datum. Just as a benchmark tells us how a particular geographical point compares in height with sea level datum, so a calibration of a flowmeter tells us how the signal from the flowmeter compares with the absolute standard of a national laboratory. The analogy is not perfect at this stage. The national laboratory standard must also be compared with other more fundamental measures of time and mass, and it will be essential to check and compare different national standards in different countries from time to time.
There is a desire to reference back to fundamental measurements such as mass, length and time. Thus if we can measure mass flow on a calibration facility by using fundamental measurements of mass and time, this will bring us nearer to the absolute values than, say, obtaining the volume of a calibration vessel by using weighed volumes of water and deducing the volume from the density. The first is more correctly termed primary calibration, whereas the second fails strictly to achieve this. It is likely that the final accuracy will reflect this. Liquid calibration facilities can achieve a rather higher accuracy than is possible for most of the gas calibration facilities. In part, this difference will result from the lower density and the increased difficulties of handling a gas.
The result of a calibration will be both a comparison with the national standard and also a range within which the reading is likely to lie.
Calibration Considerations
Figure 4.1 shows three different approaches to obtaining the data. Although the selection of calibration points and their optimum distribution over the range is not obvious, most of the reference books seem to omit any comment on this. Figure 4.1(a) records data at one flow rate. This might be appropriate if the flowmeter were expected to operate at one rate. It shows the fact that there is variation with time to which the repeatability refers. Such a plot might be used to compare the calibration facility itself with other facilities or to test the consistency of diverter operation. Figure 4.1(b) is probably the most usual plot. Here a number of points have been taken over the full range of the flowmeter.
Flow Measurement Handbook is a reference for engineers on flow measurement techniques and instruments. It strikes a balance between laboratory ideas and the realities of field experience and provides practical advice on design, operation and performance of flowmeters. It begins with a review of essentials: accuracy, flow, selection and calibration methods. Each chapter is then devoted to a flowmeter class and includes information on design, application installation, calibration and operation. Among the flowmeters discussed are differential pressure devices such as orifice and Venturi, volumetric flowmeters such as positive displacement, turbine, vortex, electromagnetic, magnetic resonance, ultrasonic, acoustic, multiphase flowmeters and mass meters, such as thermal and Coriolis. There are also chapters on probes, verification and remote data access.
We generate an algebra on blood phenotypes with multiplication based on the human ABO-blood group inheritance pattern. We assume that gametes are not chosen randomly during meiosis. We investigate some of the properties of this algebra, namely, the set of idempotents, lattice of ideals and the associative enveloping algebra.
A theoretical study of an unsteady two-layered blood flow through a stenosed artery is presented in this article. The geometry of a rigid stenosed artery is assumed to be $w$-shaped. The flow regime is assumed to be laminar, unsteady and uni-directional. The characteristics of blood are modelled by the generalized Oldroyd-B non-Newtonian fluid model in the core region and a Newtonian fluid model in the periphery region. The governing partial differential equations are derived for each region by using mass and momentum conservation equations. In order to facilitate numerical solutions, the derived differential equations are nondimensionalized. A well-tested explicit finite-difference method (FDM) which is forward in time and central in space is employed for the solution of a nonlinear initial boundary value problem corresponding to each region. Validation of the FDM computations is achieved with a variational finite element method algorithm. The influences of the emerging geometric and rheological parameters on axial velocity, resistance impedance and wall shear stress are displayed graphically. The instantaneous patterns of streamlines are also presented to illustrate the global behaviour of the blood flow. The simulations are relevant to haemodynamics of small blood vessels and capillary transport, wherein rheological effects are dominant.
We would like to present a method to compute the incompatibility operator in any system of curvilinear coordinates (components). The procedure is independent of the metric in the sense that the expression can be obtained by means of the basis vectors only, which are first defined as normal or tangential to the domain boundary, and then extended to the whole domain. It is an intrinsic method, to some extent, since the chosen curvilinear system depends solely on the geometry of the domain boundary. As an application, the in-extenso expression of incompatibility in a spherical system is given.
In oscillatory shear experiments, the values of the storage and loss moduli, $G^{\prime }(\unicode[STIX]{x1D714})$ and $G^{\prime \prime }(\unicode[STIX]{x1D714})$, respectively, are only measured and recorded for a number of values of the frequency $\unicode[STIX]{x1D714}$ in some well-defined finite range $[\unicode[STIX]{x1D714}_{\text{min}},\unicode[STIX]{x1D714}_{\text{max}}]$. In many practical situations, when the range $[\unicode[STIX]{x1D714}_{\text{min}},\unicode[STIX]{x1D714}_{\text{max}}]$ is sufficiently large, information about the associated polymer dynamics can be assessed by simply comparing the interrelationship between the frequency dependence of $G^{\prime }(\unicode[STIX]{x1D714})$ and $G^{\prime \prime }(\unicode[STIX]{x1D714})$. For other situations, the required rheological insight can only be obtained once explicit knowledge about the structure of the relaxation time spectrum $H(\unicode[STIX]{x1D70F})$ has been determined through the inversion of the measured storage and loss moduli $G^{\prime }(\unicode[STIX]{x1D714})$ and $G^{\prime \prime }(\unicode[STIX]{x1D714})$. For the recovery of an approximation to $H(\unicode[STIX]{x1D70F})$, in order to cope with the limited range $[\unicode[STIX]{x1D714}_{\text{min}},\unicode[STIX]{x1D714}_{\text{max}}]$ of the measurements, some form of localization algorithm is required. A popular strategy for achieving this is to assume that $H(\unicode[STIX]{x1D70F})$ has a separated discrete point mass (Dirac delta function) structure. However, this expedient overlooks the potential information contained in the structure of a possibly continuous $H(\unicode[STIX]{x1D70F})$. In this paper, simple localization algorithms and, in particular, a joint inversion least squares procedure, are proposed for the rapid recovery of accurate approximations to continuous $H(\unicode[STIX]{x1D70F})$ from limited measurements of $G^{\prime }(\unicode[STIX]{x1D714})$ and $G^{\prime \prime }(\unicode[STIX]{x1D714})$.
In this paper, we show that the cost of an optimal train journey on level track over a fixed distance is a strictly decreasing and strictly convex function of journey time. The precise structure of the cost–time curves for individual trains is an important consideration in the design of energy-efficient timetables on complex rail networks. The development of optimal timetables for busy metropolitan lines can be considered as a two-stage process. The first stage seeks to find optimal transit times for each individual journey segment subject to the usual trip-time, dwell-time, headway and connection constraints in such a way that the total energy consumption over all proposed journeys is minimized. The second stage adjusts the arrival and departure times for each journey while preserving the individual segment times and the overall journey times, in order to best synchronize the collective movement of trains through the network and thereby maximize recovery of energy from regenerative braking. The precise nature of the cost–time curve is a critical component in the first stage of the optimization.
This paper deals with the flux identification problem for scalar conservation laws. The problem is formulated as an optimization problem, where the objective function compares the solution of the direct problem with observed profiles at a fixed time. A finite volume scheme solves the direct problem and a continuous genetic algorithm solves the inverse problem. The numerical method is tested with synthetic experimental data. Simulation parameters are recovered approximately. The tested heuristic optimization technique turns out to be more robust than classical optimization techniques.