Accuracy of bubble velocity measurement with a four-point optical fibre probe

For the operation of high void fraction bubbly flows in bubble\ud columns, insight in primary parameters such as bubble size,\ud shape and velocity as well as gas volume fraction is essential.\ud At high gas volume fractions the flow system becomes\ud opaque, ruling out non-intrusive optical techniques. As an\ud alternative optical fibre probes can be used, which have the\ud advantage of low cost, simplicity of setup and easy\ud interpretation of the results.\ud By using four-point optical fibre probe, properties of bubbles\ud can be studied, such as bubble velocity, bubble size, etc.\ud However, the effect of bubble wobbling behaviour and\ud physical properties of liquids on the accuracy of the velocity\ud measurements has not been investigated in detail.\ud In the present study, the performance of a four-point optical\ud fibre probe was evaluated for five different liquids. The probe\ud performance and causes of inaccuracies are discuss

Métodos de selección del profesorado. Algunos ejemplos europeos

La Universidad Tecnológica de Delft, en los Países Bajos, es uno de los centros académicos técnicos con más renombre en el mundo. Robert F. Mudde, catedrático de esa universidad, explica en el siguiente artículo uno de los pilares en los que se funda su calidad: la contratación del personal docent

Measuring the Gas-Solids Distribution in Fluidized Beds - A Review

This paper reviews techniques for measuring the voidage distribution in gas-solid fluidized beds, with a focus on the developments during the last ten years. Most attention is given to recent progress in tomography and pressure measurements, but visual observations, capacitance probes and optical probes are also covered

Effect of sieving and isopropanol on the fluidization behavior of TiO2 nanoparticles

The fluidization of ABF nanoparticles has gained the attention of many researchers due to its interesting applications but difficult fluidization. Typically, these particles are sieved to remove the large agglomerates that are formed during storage. Otherwise, the larger agglomerates stay near the distributor plate, hindering the proper fluidization of the nanoparticles due to the formation of channels throughout the bed. To solve that, several papers propose to improve the fluidization conditions using external assistance methods. Such methods impose an external force that can break up the agglomerates; examples are magnetic or electric fields, vibration or centrifugal beds [1]. A different approach is to change the surface properties of the nanoparticles, decreasing the cohesive forces. Tahmasebpooret al. [2] analysed the influence of the hydrogen bonds during the fluidization of nanoparticles. They showed that the use of isopropanol vapour (ISP) in the fluidizing gas can reduce the cohesive forces between nanoparticles increasing the bed aspect ratio. Sieving of nanoparticles and the use of ISP in the gas stream have been commonly used to improve the fluidization quality during the last years. However, the influence of both processes on the fluidization behaviour has not been studied in detail. For the former, the effect of the sieving size on the bed dynamics is still unknown. Regarding the ISP, its influence for long fluidization times has not been addressed yet. For instance, whether the ISP should be continuously on the gas stream or working with gas pulses to improve the fluidization has not been clarified. Therefore, the objective of this experimental work is to further understand the influence of the sieving size and the effect of ISP during the fluidization of TiO2 nanoparticles. The experiments are carried out in a 5 cm inner diameter column with a porous distributor. Nitrogen is used as fluidizing gas. The experiments are analysed using a 2D tomography setup. The attenuation of the X-rays are measured when they go through the fluidized by a plate detector, with a size of 30 cm x 30 cm and 1524x1548 pixels. The fluidization of TiO2 nanoparticles sieved with a 350 µm mesh shows higher bed expansion than the powder sieved with a 850 µm mesh. Considering the effect of time, the use of ISP initially increases the bed expansion, but after that the bed height decreases faster than for the situation without ISP. Please click Additional Files below to see the full abstract

Pressure and X-ray tomography characterization of the fluidization behavior of TiO2 nanoparticles

During recent years the fluidization of nanoparticles has been attracting the interest of the scientific community as the number of industrial applications has increased [1]. In these systems, the powder tends to agglomerate during the fluidization showing either bubble-less and smooth behavior for agglomerate particulate fluidization (APF), and bubbling behavior with little bed expansion for agglomerate bubbling fluidization (ABF) [2]. Regarding the ABF regime, relatively high gas velocities are required to fluidize these nanoparticles, which causes a large powder elutriation and the reduction of the fluidization quality. In this sense, it would be interesting to detect the changes in the fluidization behavior using an easy and reliable measurement technique. However, the literature commonly uses of the bed expansion ratio or the bed pressure drop [1], which are not able to detect maldistributions inside the fluidized bed. Therefore, in this work is proposed the use of the pressure fluctuation signals as a tool to describe the state of a fluidized bed of nanoparticles. The differential pressure signals will be analyzed in the time and frequency domain following earlier work for micron-sized particles [3, 4]. TiO2 nanoparticles (dp= 21nm) were fluidized in a Perpex column of 5 cm inner diameter at different gas velocities. The particles were sieved with a 350μm mesh to remove the large agglomerates and dry nitrogen was used during all experiments. To validate the pressure results, an X-ray tomography system is employed. This technique measures the attenuation of the X-rays through the fluidized bed, which is placed between the X-ray source and the detector. A square detector of 30 cm x 30 cm with a pixel resolution of 1524x1548 is employed. In this way, it is possible to obtain 2D pictures of the fluidization regime at a frame rate of 22 Hz (see Fig. 1). The results show that the main frequencies of the power spectrum are moved towards to higher frequencies as the gas velocity is increased (see Fig. 2). We will demonstrate that the pressure fluctuation data can indicate whether or not the nanoparticle bed is properly fluidized . Please click Additional Files below to see the full abstract

A fast reconstruction algorithm for time-resolved X-ray tomography in bubbling fluidized beds

A new tomographic reconstruction algorithm is proposed for fast image reconstruction. The results are based on a high speed X-ray tomography system, consisting of 3 X-ray sources and 32 detectors for each source. The proposed algorithm combines void measurements of each X-ray beam into a triangular mesh, which is formed by the intersection points of all the beams. Simulations and real fluidized bed data are utilized to assess the quality of the proposed algorithm compared to the Simultaneous Algebraic Reconstruction Technique (SART). The influence of the number, position and diameter of the phantoms on the proposed reconstruction method is studied. The new method provides images with similar quality to SART reconstructions, although obtaining smaller bubble sizes. The low computing time needed to reconstruct each image with the new method, which is more than 5000 times faster than SART for a 40 × 40 mesh, encourages the use of the new method for the online image reconstruction of X-ray measurements

CFD-DEM simulation of nanoparticle agglomerates fluidization with a micro- jet

Nanoparticles can be fluidized as agglomerates, but for some materials this is cumbersome due to the cohesive nature. Micro-jets are shown to be effective for improving the fluidization in such cases (1). In this study, the mechanisms of micro-jet assistance are investigated by using an adhesive CFD-DEM (Computational Fluid Dynamics – Discrete Element Modelling) model. In previous studies, the complex agglomerates found in a fluidized bed are treated as the discrete elements (2). Here we use the simple agglomerates as the discrete elements, which are the building blocks of the larger complex agglomerates. The collision of the simple agglomerates are modeled by including collision mechanisms of elastic-plastic, cohesive and viscoelastic forces. Particles with =40 and =250 are used to represent the simple agglomerates. The cohesive force is expressed by the non-dimensional parameter , definded by the ratio of der Waals force over the particle gravity. A fluidized bed with dimension of 3 mm × 0.4 mm × 12 mm containing ~120,000 particles is simulated. At different cases, a micro-jet with horizontal cross-section size of 20 x 20 pointing downwards is turned ON or OFF (36 m/s) while the gas velocity to the bed is set as 2.8 cm/s or 4 cm/s, respectively. The schematic of the microjet in the bed is shown in Figure 1. In this way, like in our previous study, we keep the total amount of gas provided to the bed equal (2). Please click Additional Files below to see the full abstract

Fast X-ray tomography for the quantification of the bubbling-, turbulent- and fast fluidization-flow regimes and void structures

Multiple fluidization regimes were studied using X-ray tomography. Geldart B sand particles were used in a 14 cm (ID) column with a dual cyclone return system. Cross sectional solids concentration (/) was measured and the time averaged / ð /Þ decreased with velocity and axial height except in the turbulent regime where / remained constant. Radial profiles of / decreased to the centre, while all turbulent regime velocities resulted in similar radial / profiles. Results confirm the bubbling-turbulent transition velocity (Uc) determined from pressure fluctuations is a reliable quantification technique. The system exhibited slugging behaviour at higher bubbling regime velocities with voids taking on cylindrical shapes. Turbulent regime voids were characterised by elongated cylinders with diameters slightly less than the bubbling regime’s slugs or fast fluidization regime’s core annulus. Distribution curves of the / signal indicated a distinct dense phase in the bubbling and turbulent regime with a velocity independent solid concentration. Void velocity analysis suggested that the bubble linking algorithm was unable to detect fast rising voids at higher velocities.http://http://www.elsevier.com/locate/cejhb2013ai201

Modern American populism: Analyzing the economics behind the Silent Majority, the Tea Party and Trumpism

This article researches populism, more specifically, Modern American Populism (MAP), constructed of white, rural, and economically oppressed reactionarianism, which was borne out of the political upheaval of the 1960’s Civil Rights movement. The research looks to explain the causes of populism and what leads voters to support populist movements and politicians. The research focuses on economic anxiety as the main cause but also examines an alternative theory of racial resentment. In an effort to answer the question, what causes populist movements and motivations, I apply a research approach that utilizes qualitative and quantitative methods. There is an examination of literature that defines populism, its causes and a detailed discussion of the case studies, including the 1972 election of Richard Nixon; the Tea Party election of 2010; and the 2016 election of Donald Trump. In addition, statistical data analysis was run using American National Election Studies (ANES) surveys associated with each specific case study. These case studies were chosen because they most represent forms of populist movements in modern American history. While ample qualitative evidence suggested support for the hypothesis that economic anxiety is a necessary condition for populist voting patterns that elected Nixon, the Tea Party and Trump, the statistical data only supported the hypothesis in two cases, 2010 and 2016, with 1972 coming back inconclusive. The data also suggested that both economic anxiety and racial resentment played a role in 2010 and 2016, while having no significant effect in 1972 in either case. This suggests that further research needs to be conducted into additional populist case studies, as well as an examination into the role economic anxiety and economic crises play on racial resentment and racially motivated voting behavior