experiments were conducted at a fluid temperature of 20°C ± 0.2°C, and the error bars indicate the 95% uncer- tainty band of the measurement. The results of this fig- ure are in agreement with the previous reported observations for viscosity enhancement of nanofluids for different particle loadings . A closer observation of the results also shows that the nanofluids exhibited New- tonian characteristics for the range of shear rates investi- gated. This suggests that during near-wall fluid velocitymeasurement inside the microchannel, nanofluid does not show any shear thickening or thinning effects. The Newtonian behavior of SiO 2 -water nanofluids also falls
a pipeline that produces random disturbance signal. Two pairs of sensors positioned upstream and downstream, installed in an axial distance (L) with each other in parallel beam projection. The transmit time ( τ ) taken from the time the particle moving from the upstream to downstream. The velocitymeasurement obtained by dividing the time and the distance between upstream and downstream.
The velocity-measurement system depends on the interruption (or occultation) of laser beam by the projectile. As the projectile passes between a laser source and a detector, it casts a shadow on the detector. A laser and a detector are fitted near the entry and exit ports, i.e. top and bottom edges, on one of the channels. Each of the two vertical parallel columns constituting the channels contain a number of right-angled prisms. Without the presence of any obstruction, the laser beam is reflected back and forth by the retro reflecting right angle prisms while being laterally displaced till it falls on the light detector, thus, virtually forming a laser screen or a curtain. Any object, moving or stationary, passing through the laser screen obstructs the beam from reaching the detector as shown in Fig 2.
Chapter three covers the methodology of the project. All of the mechanism that involved in this project such as ;specific sensing mechanism; Visual Basic 6.0; cross-correlation method; parallel beam projection; arrangement of sensor for velocitymeasurement(jig &fixture); transmitter circuit; receiver circuit; and Data Acquisition System will also be discussed in this chapter.
However, the proposed PIV method reduces the number of velocity vectors in the x-y plane and significantly deforms the shape of the correlation area. We must increase the number of pixels of the camera to maintain the good spatial resolution found in traditional PIV velocity distributions. Recently, the number of pixels of digital single-lens reflex (SLR) cameras has increased, and their emerging large-scale commercial production has been accompanied by reduced cost. The number of pixels in a commercially available digital SLR camera is presently over 24,000,000, and the sensitivity exceeds the requirements of ISO 25,600. If necessary, however, the number of pixels can be easily increased by lining up multiple cameras. A two-color PIV system using an ND:YAG la- ser  or Ar-ion laser  has been previously proposed, but the wavelength of the lasers was not optimal for the wavelength range of red-green-blue (RGB) filters in color charge-coupled device (CCD) cameras. Recently, various diode lasers have been developed with wavelengths that cover the visible light range and have power in excess of thousands of milliwatts. These diode lasers can be used to create a color PIV system for measuring the velocity field of airflow by illuminating different colors of diode lasers at defined time intervals.
This paper aimed at the problem of super-speed maneuvering target tracking in the 3D space, proposes a self-adaption algorithm which using the radial velocity to track super-speed maneuvering target. Compared with VSMM algorithm, this algorithm has more simple structure, smaller amount of computations. The simulation results show that this algorithm has great improvement in tracking accuracy, maneuver detection speed and convergence rate. Tracking the super-speed maneuvering target more accurate, fast and stable. The new tracking algorithm has some value for project practice.
Since the impedance of the water-filled tube is similar to the water impedance, the wall cannot be regarded as the rigid boundary. The sound field in the tube will be influenced by the wall . Therefore, the sound velocity will be affected. Moreover, the standing wave field will be changed due to the sound velocity and the wave-number change. All these issues can affect the accuracy of the measurement. The sound velocity in the tube will be lower than the standard velocity from the empirical formula  in the open water. If the wall is thicker, the sound velocity will be closer to the standard value. In Ref. , the deviation from the empirical value would be 2.5% or 3.5% when the ratio (inner radius/tube thickness) is 1 or 0.5. Therefore, the measured value is much lower than the standard value in the tube.
for all of the flow variables, it implies that the flow length scale (and structure) is also not well scaled by the bed roughness length scale. This agrees with the laboratory ob- servations of Cooper and Tait , who over the same two gravel beds observed that the spatial organization of the time ‐ averaged flow is not well associated with the bed surface topography. It also supports the findings of Lamarre and Roy  and Legleiter et al. , who carried out velocity measurements in gravel‐bed rivers at the reach scale. Lamarre and Roy  found that roughness elements had surprisingly little impact on the spatial organization of the flow at the reach scale, despite a topographically complex channel boundary. They observed that the complexity of the bed was not reflected in the spatial variability of vertical profiles of time‐averaged streamwise velocity. As a result, they concluded that the distribution of the mean flow prop- erties displayed a well‐organized, coherent spatial pattern that was controlled by flow depth rather than by abrupt, isolated changes associated with individual clasts. Legleiter et al.  also discovered results which supported these claims, even in flows where the flow depth was of the same order as the D 84 of the bed. Individual roughness elements
Combustion occurs after spark ignition in the engine chamber and its burning fuel is chemiluminescent. The flame front spreads following the explosion path. Thus, the flame front speed can be measured using optical diagnostics techniques to trace the combustion process quantitatively. The combustion within an IC engine is difficult to observe because of the turbulence and quenching. Many scientists recognize the importance of these phenomena in flame propagation analysis [93, 94]. The movement of piston in the cylinder causes the flame to swirl therefore, the analysis starts from a vortex model. During the intake stroke, the gas is let into the chamber in a vortex flow. When piston is moving down during the explosion stroke, combustion causes a similar swirl but turbulence is more complex and different in each engine type (Fig. 5.15). These engine photos (Fig 5.11) shows that combustion starts nearby the spark ignition. It then spreads to the boundary of chamber bore by crossing intake valve and exhaust valve. The propagation can be seen as a vortex model (Fig. 5.15). The flame velocity increases rapidly in the intake valve area because of the high density of compressed gas mixture. Simultaneously, the flame radiates through the exhaust valves with lower speed. The averaged intensities first increase rapidly and then gradually fall (Fig. 5.14).
Image velocimetry is an optics-based approach for stream flow measurement using commercially available near-infrared digital cameras to acquire video footage in full HD (30fps). Video footage is then subjected to optical flow tracking techniques based on cross- correlation, and feature-based tracking, enabling the displacement rates of detected features (for example natural foam, seeds, woody debris, and turbulent structures) to be computed. First, we extract video frames from the footage, then, georeference them to convert image pixels to real-world coordinates. Second, we extract the start and end position of selected surface water features, then convert them to real-word coordinates to, finally, generate vectors of water velocities. The computation of water velocity vectors are achieved through application of several methodological approaches including large scale particle image velocimetry (LSPIV) (Muste et al. 2008, LeCoz et al. 2010), particle tracking velocimetry (PTV) (Tauro and Grimaldi 2017), and Kanade-Lucas-Tomasi (KLT) flow tracking (Perks et al. 2016). Following the determination of the surface velocity, a site-specific velocity coefficient can be calculated to translate surface velocities to depth-averaged velocities. To calibrate the relationship between surface velocity and discharge or water level, site-specific flow data (e.g. ADCP data) is necessary.
The infra red emitters and detectors are linked to the measurement section. Light passing through the pipe passes through the optical fibre and is then converted into an electrical signal by the IR receiver. The signal is amplified and filtered. Data is then passed through a Keithley KUSB-3100 Series data acquisition system before it enters a PC. It was then processed offline using several algorithms such as cross correlation. The algorithm was developed using the Visual Basic 6.0 software to display the velocity.
Currently, the available technologies that are capable of monitoring pulse wave velocity (PWV) in a patient are uncomfortable and obstructive. Recently, it has been hypothesized the use of photoplethysmographic (PPG) for this purpose and, therefore, the need to capture and understand the hemodynamic variables used in the PPG signal acquirement process, such as the local pulse transit time (PTT) and local PWV. This work aims to verify the feasibility of the PPG technique in the construction of local PTT and PWV monitor, using PPG sensors and low-cost integrated circuits. In this paper, the low-cost term is used as a synonym for retail sensors, available commercially and commonly used in academic projects for the Arduino platform. It is important for the development of wearable technologies that can be used in a future project to monitor PTT and PWV using a minimally obstructive approach.
Acquisition of real-time hydraulic data is an essential component for flood forecasting. However, we frequently face difficulties obtaining discharge data using classical contact methods during high magnitude floods and for systems experiencing rapid hydro- geomorphological adjustment. Therefore, we developed low-cost, non-contact sensors and platforms that are designed to overcome these difficulties. These advances enable flood flow properties to be monitored at multiple locations across a river catchment, at low-cost, and communicated in near real-time by using an image velocimetry method. This is an optics-based approach for stream flow measurement using commercially available near- infrared digital cameras to acquire video footage in full HD (30fps). Video footages are then subjected to optical flow tracking techniques based on cross-correlation, and feature-based tracking, enabling the displacement rates of detected features (for example natural foam, seeds, woody debris, and turbulent structures) to be computed. This manual provides a step by step guidance to install an image-based gauging station. It contains the list of necessary components, the calibration process of a new camera and the assembly procedure of the system.
second sensor can, therefore, be determined from the local phase between both filtered signals. This time interval is then used in equation (2), and the particle velocity between the sensors S1 and S2 is calculated. The final result of processing a 2-seconds period of the raw signals is shown in Fig. 11. Velocity scatter is relatively high. The average particle velocity is 82.6 m/ s and the RMS (Root Mean Square) is 3.87 m/s. The standard measurement uncertainty is 0.15 m/s. This value is low due to the large number of measurements. The average measured velocity is 8.2 m/s, i.e. 11 % higher than the particle velocity measured by the me- chanical device. It is interesting that similar differences were observed for other turbines as well, although they have different designs and a different number and curvature of blades. It may be assumed, therefore, that this difference is not the result of a measurement error, but is conditioned by the different measurement approaches applied in both velocitymeasurement methods. The average particle velocity is measured by a mechanical device, whilst the maximum particle speed is measured by an electronic system that reacts to the first hit of the membranes caused by the fastest particles. An explanation for the high velocity scatter was also found. The composition of shotblasting par- ticles is not uniform. The particle diameter may vary from 0.05 mm to 1 mm. This causes a very high varia- tion in the particles impact force intensity and, there- fore, causes less distinctive magnitude changes in the measured signal and some phase shift of the filtered signal. The next reason for the large scatter of the measured velocities is a poorly tuned, prototype tur- bine. This is primarily due to different degrees of tur- bine-blade wear and non-optimised matching of the cast blades with the grooves of the cast rotor ring. An attempt was made, therefore, to establish the degree of non-tunableness in the shotblasting turbines op- eration. The filtered signal from the first sensor was analysed. The time delays between the successive membrane hits were determined first. The results were then split into eight rows, each of them corresponds
Abstract. To measure a flow in a closed duct, one of the available methods is to explore the velocity field. With this method, the quality of the flow measurement is very dependent on the location of the velocitymeasurement points in the duct section. Recommendations about velocity schemes are proposed in international standards (ISO 3966, ISO 7145, EN 12599 …) for circular and rectangular ducts. These recommendations assume that a turbulent flow profile is established. This requires flow profiler and/or long straight lengths upstream and downstream the measurement section. On site, these recommendations are difficult to apply strictly because conditions of straight lengths are often not available. Secondly, the velocitymeasurement schemes proposed in standards are often time consuming and users prefer sometimes to simplify them. In this case, the estimation of the measurement error is not known. A numerical study has been carried out to investigate the influence of the velocitymeasurement scheme on the flow measurement when the distance between disturbances and the measurement section is small in the case of circular and rectangular ducts. The results are presented in term of measurement error according to the shape of the duct, velocity scheme, number of velocity measurements, distance between disturbances and measurement section.
Maximum shear wave velocity is one of the most important dynamic parameters of soils which contributes in estimating the dynamic behaviour of soils through the maximum shear modulus. There are different methods for measuring shear wave velocity either in laboratory or in the field. One of the laboratory methods that recently has become popular due to its simplicity, is use of Bender Elements in soil samples. Contrary to its simplicity, it has several uncertainties in its data interpretation. Frequency at which the shear wave velocity is measured is one of the effective parameters that significantly affects the clearance of received wave. This article presents results from a laboratory investigation into the shear wave velocitymeasurement of remolded specimens of Firoozkooh sand. Specimens were subjected to 13 different levels of frequency and 11 different levels of confining pressure. Results shows that by increasing the confining pressure, the frequency at which the received wave has the best clearance, increases. It also shows that frequency dependence of soils increases by increasing the confining pressure.
The data from the OCT is the backscattered intensity of light in the depth and in time. This light is fluctuating over time as it is backscattered by many quickly moving nanoparticles in the light beam, it is therefore impossible to obtain information about the motion of a single particles, because this signal is an average of all positions of the particles. To find a relationship in time at a specific depth a method commonly known as autocorrelation is used. This method multiplies the fluctuating signal measured in time with the same signal and this second signal is shifted a bit forward in time. The signal is correlated to the signal with a small time-shift because the particles move by diffusion or by the velocity of the fluid, which scatters the light slightly different . However as the time-shift gets larger the original particles move through the beam until they are no longer inside the light and no correlation can be found between the original signal and the shifted signal. This results in a autocorrelated curve with a decay in the correlation as the time-shift becomes larger. The autocorrelation function is often normalized between zero and one, which mean there is no correlation or the signal is exactly the same respectively.