scholarly journals Aerodynamic Studies on Non-Premixed Oxy-Methane Flames and Separated Oxy-Methane Cold Jets

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 429 ◽  
Author(s):  
Tamal Jana ◽  
Mrinal Kaushik ◽  
Dipankar Deb ◽  
Vlad Mureşan ◽  
Mihaela Ungureşan
Keyword(s):  
Kinetic Energy ◽  
Radial Velocity ◽  
Turbulent Kinetic Energy ◽  
Large Scale ◽  
Inlet Temperature ◽  
Centerline Velocity ◽  
Inlet Velocity ◽  
Jet Mixing ◽  
Mixing Characteristics ◽  
Jet Velocity

Both cold and flame jets find numerous applications in different fields, ranging from domestic applications to aerospace and space technology. Indeed, the applications of isothermal and non-isothermal jets in the flame heating industry fascinated the researchers to gain an in-depth understanding. Nevertheless, these benefits are not standalone, rather, they are associated with major disadvantages such as improper jet mixing and flame instabilities that require careful remedies. In the present investigation, three-inline jets, having methane jet at the center and two oxygen jets at the periphery, are studied computationally in a three-dimensional domain, with and without considering the effects of combustion. To study the mixing characteristics of cold jets, the radial velocity distributions at different streamwise locations have been analyzed at the jet inlet velocity of 27 m/s. However, for oxygen and methane flame jets, inlet velocities are varied as 27 m/s and 54 m/s. Moreover, to investigate the effects of temperature variation on mixing characteristics at a particular jet velocity, the inlet temperatures of reactants are varied as 300 K, 500 K, and 700 K, at the jet inlet velocity of 27 m/s. Combustion is found to have a profound impact on the mixing characteristics. At the inlet temperature of 300 K, a higher centerline velocity decay is observed for non-reactive jets as compared to reactive flame jets. Moreover, the turbulent kinetic energy distribution is relatively higher in the case of non-reactive jets, which is the direct evidence of an augmented mixing. As is obvious, the turbulent kinetic energy at the jet shear layer is maximum due to the formation of large-scale coherent eddies. The decay in centerline velocity is found to be increasing with an increase of inlet temperature. Additionally, with an increase of jet velocity, the radial velocity profiles become more asymmetrical, consequently yielding an unstable flame.

Turbulent Jet Mixing Enhancement and Control Using Self-Excited Nozzles

2007 ◽  
Vol 129 (7) ◽  
pp. 842-851 ◽  
Author(s):  
Uri Vandsburger ◽  
Yiqing Yuan
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Nozzle Exit ◽  
Large Scale ◽  
Rectangular Cross Section ◽  
Near Field ◽  
Excitation Frequency ◽  
Mixing Enhancement ◽  
Mixing Rate ◽  
Jet Mixing

A new self-excited jet methodology was developed for the mixing enhancement of jet fluid with its surrounding, quiescent, stagnant, or coflowing fluid. The nozzles, of a square or rectangular cross section, featured two flexible side walls that could go into aerodynamically-induced vibration. The mixing of nozzle fluid was measured using planar laser-induced fluorescence (PLIF) from acetone seeded into the nozzle fluid. Overall, the self-excited jet showed enhanced mixing with the ambient fluid; for example, at 390Hz excitation a mixing rate enhancement of 400% at x∕D=4 and 200% at x∕D=20 over the unexcited jet. The mixing rate was sensitive to the excitation frequency, increasing by 60% with the frequency changing from 200 to 390Hz (corresponding to a Strouhal number from 0.052 to 0.1). It was also observed that the mixing rate increased with the coflow velocity. To explain the observed mixing enhancement, the flow field was studied in detail using four-element hot wire probes. This led to the observation of two pairs of counter rotating large-scale streamwise vortices as the dominant structures in the excited flow. Shedding right from the nozzle exit, these inviscid vortices provided a rapid transport of the momentum and mass between the jet and the surrounding fluid at a length scale comparable to half-nozzle diameter. Moreover, the excited jet gained as much as six times the turbulent kinetic energy at the nozzle exit over the unexcited jet. Most of the turbulent kinetic energy is concentrated within five diameters from the nozzle exit, distributed across the entire jet width, explaining the increased mixing in the near field.

scholarly journals Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Population Size ◽  
Turbulent Kinetic Energy ◽  
Forced Convection ◽  
Inlet Temperature ◽  
Fluid Temperature ◽  
Interface System

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

Large-scale modes of turbulent channel flow: transport and structure

2001 ◽  
Vol 448 ◽  
pp. 53-80 ◽  
Author(s):  
Z. LIU ◽  
R. J. ADRIAN ◽  
T. J. HANRATTY
Keyword(s):  
Shear Stress ◽  
Kinetic Energy ◽  
Shear Layer ◽  
Turbulent Kinetic Energy ◽  
Large Scale ◽  
Reynolds Shear Stress ◽  
Reynolds Numbers ◽  
Upstream Region ◽  
Two Dimensional ◽  
Normal Plane

Turbulent flow in a rectangular channel is investigated to determine the scale and pattern of the eddies that contribute most to the total turbulent kinetic energy and the Reynolds shear stress. Instantaneous, two-dimensional particle image velocimeter measurements in the streamwise-wall-normal plane at Reynolds numbers Reh = 5378 and 29 935 are used to form two-point spatial correlation functions, from which the proper orthogonal modes are determined. Large-scale motions – having length scales of the order of the channel width and represented by a small set of low-order eigenmodes – contain a large fraction of the kinetic energy of the streamwise velocity component and a small fraction of the kinetic energy of the wall-normal velocities. Surprisingly, the set of large-scale modes that contains half of the total turbulent kinetic energy in the channel, also contains two-thirds to three-quarters of the total Reynolds shear stress in the outer region. Thus, it is the large-scale motions, rather than the main turbulent motions, that dominate turbulent transport in all parts of the channel except the buffer layer. Samples of the large-scale structures associated with the dominant eigenfunctions are found by projecting individual realizations onto the dominant modes. In the streamwise wall-normal plane their patterns often consist of an inclined region of second quadrant vectors separated from an upstream region of fourth quadrant vectors by a stagnation point/shear layer. The inclined Q4/shear layer/Q2 region of the largest motions extends beyond the centreline of the channel and lies under a region of fluid that rotates about the spanwise direction. This pattern is very similar to the signature of a hairpin vortex. Reynolds number similarity of the large structures is demonstrated, approximately, by comparing the two-dimensional correlation coefficients and the eigenvalues of the different modes at the two Reynolds numbers.

Development and Validation of a Porous Medium Computational Fluid Dynamics Model for a Jet

2008 ◽  
Author(s):  
Timothy J. Drzewiecki ◽  
Brian L. Mount ◽  
Martin Lopez de Bertodano
Keyword(s):  
Porous Medium ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Transport Model ◽  
Constitutive Relations ◽  
Symmetric Domain ◽  
Jet Mixing ◽  
Negative Reactivity ◽  
An Array Of Cylinders ◽  
Development And Validation

The fast boron shutdown injection in a PHWR consists of a jet flowing through a very large moderator tank that contains an array of cylindrical coolant channels. The accurate prediction of the turbulent jet mixing is required to determine an accurate distribution of boron inside the moderator tank to model the insertion of negative reactivity into the reactor during fast shutdown. A CFD code is used to determine the distribution of boron in the moderator tank. The flow is analyzed with a porous medium model based on volume averaged momentum, turbulent kinetic energy, and turbulence dissipation equations. The additional source terms arise due to the averaging must be constituted. The constitutive relations that are implemented in the present model are: (i) the drag force on an array of cylinders for the momentum equations and (ii) the additional mixing effect of the cylinders which results in the sources of turbulent kinetic energy and turbulence dissipation transport model. The CFD analysis is performed on a porous, axis symmetric domain. The CFD results are finally compared with data for the boron concentration distribution obtained in a scaled geometrically similar experiment, demonstrating the validity of the approach.

scholarly journals Experimental Analysis of Horizontal Turbulence of Flow over Flat and Deformed Beds

2015 ◽  
Vol 62 (3-4) ◽  
pp. 77-99 ◽  
Author(s):  
Donatella Termini
Keyword(s):  
Shear Stress ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Experimental Analysis ◽  
Large Scale ◽  
Laboratory Experiments ◽  
Reynolds Shear Stress ◽  
Turbulent Structures ◽  
Quadrant Method ◽  
Initial Sequence

AbstractLaboratory experiments in a straight flume were carried out to examine the evolution of large-scale horizontal turbulent structures under flat-bed and deformed-bed conditions. In this paper, the horizontal turbulence of flow under these conditions is analyzed and compared. The conditioned quadrant method is applied to verify the occurrence of turbulent events. The distributions of horizontal Reynolds shear stress and turbulent kinetic energy are also presented and discussed. Results show the occurrence of an “initial” sequence of horizontal vortices whose average spatial length scales with the channel width. Under deformed-bed conditions, this spatial length does not change.

scholarly journals A CFD-based procedure for airspace integration of small unmanned aircraft within congested areas

2017 ◽  
Vol 9 (4) ◽  
pp. 235-252 ◽  
Author(s):  
Craig WA Murray ◽  
David Anderson
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Flow Characteristics ◽  
Unmanned Aircraft ◽  
Prevailing Wind ◽  
A Posteriori ◽  
Vehicle Performance ◽  
Inlet Velocity ◽  
Congested Area ◽  
The City

Future integration of small unmanned aircraft within an urban airspace requires an a posteriori understanding of the building-induced aerodynamics which could negatively impact on vehicle performance. Moving away from generalised building formations, we model the centre of the city of Glasgow using Star-CCM+, a commercial CFD package. After establishing a critical turbulent kinetic energy for our vehicle, we analyse the CFD results to determine how best to operate a small unmanned aircraft within this environment. As discovered in a previous study, the spatial distribution of turbulence increases with altitude. It was recommended then that UAVs operate at the minimal allowable altitude within a congested area. As the flow characteristics in an environment are similar, regardless of inlet velocity, we can determine areas within a city which will have consistently low or high values of turbulent kinetic energy. As the distribution of turbulence is dependent on prevailing wind directions, some directions are more favourable than others, even if the wind speed is unchanging. Moving forward we should aim to gather more information about integrated aircraft and how they respond to turbulence in a congested area.

Large Eddy Simulation of a Turbulent Non-Reacting Spray Jet

2015 ◽  
Author(s):  
B. Hu ◽  
S. Banerjee ◽  
K. Liu ◽  
D. Rajamohan ◽  
J. M. Deur ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Large Eddy Simulation ◽  
Turbulent Kinetic Energy ◽  
Cell Size ◽  
Adaptive Mesh Refinement ◽  
Large Scale ◽  
Flow Behavior ◽  
Similarity Index ◽  
Eddy Simulation ◽  
Large Eddy

We performed Large Eddy Simulation (LES) of a turbulent non-reacting n-Heptane spray jet, referred to as Spray H in the Engine Combustion Network (ECN), and executed a data analysis focused on key LES metrics such as fraction of resolved turbulent kinetic energy and similarity index. In the simulation, we used the dynamic structure model for the sub-grid stress, and the Lagrangian-based spray-parcel models coupled with the blob-injection model. The finest mesh-cell size used was characterized by an Adaptive Mesh Refinement (AMR) cell size of 0.0625 mm. To obtain ensemble statistics, we performed 28 numerical realizations of the simulation. Demonstrated by the comparison with experimental data in a previous study [7], this LES has accurately predicted global quantities, such as liquid and vapor penetrations. The analysis in this work shows that 14 realizations of LES are sufficient to provide a reasonable representation of the average flow behavior that is benchmarked against the 28-realization ensemble. With the current mesh, numerical schemes, and sub-grid scale turbulence model, more than 95% of the turbulent kinetic energy is directly resolved in the flow regions of interest. The large-scale flow structures inferred from a statistical analysis reveal a region of disorganized flow around the peripheral region of the spray jet, which appears to be linked to the entrainment process.

scholarly journals Turbulent transport mechanisms in oscillating bubble plumes

2009 ◽  
Vol 633 ◽  
pp. 191-231 ◽  
Author(s):  
MARCO SIMIANO ◽  
D. LAKEHAL ◽  
M. LANCE ◽  
G. YADIGAROGLU
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Large Scale ◽  
Small Scale ◽  
Equivalent Diameter ◽  
Minor Effect ◽  
Reynolds Stresses ◽  
Bubble Plume ◽  
Low Velocity ◽  
Cross Sectional

The detailed investigation of an unstable meandering bubble plume created in a 2-m-diameter vessel with a water depth of 1.5 m is reported for void fractions up to 4% and bubble size of the order of 2.5 mm. Simultaneous particle image velocity (PIV) measurements of bubble and liquid velocities and video recordings of the projection of the plume on two vertical perpendicular planes were produced in order to characterize the state of the plume by the location of its centreline and its equivalent diameter. The data were conditionally ensemble averaged using only PIV sets corresponding to plume states in a range as narrow as possible, separating the small-scale fluctuations of the flow from the large-scale motions, namely plume meandering and instantaneous cross-sectional area fluctuations. Meandering produces an apparent spreading of the average plume velocity and void fraction profiles that were shown to remain self-similar in the instantaneous plume cross-section. Differences between the true local time-average relative velocities and the difference of the averaged phase velocities were measured; the complex variation of the relative velocity was explained by the effects of passing vortices and by the fact that the bubbles do not reach an equilibrium velocity as they migrate radially, producing momentum exchanges between high- and low-velocity regions. Local entrainment effects decrease with larger plume diameters, contradicting the classical dependence of entrainment on the time-averaged plume diameter. Small plume diameters tend to trigger ‘entrainment eddies’ that promote the inward-flow motion. The global turbulent kinetic energy was found to be dominated by the vertical stresses. Conditional averages according to the plume diameter showed that the large-scale motions did not affect the instantaneous turbulent kinetic energy distribution in the plume, suggesting that large scales and small scales are not correlated. With conditional averaging, meandering was a minor effect on the global kinetic energy and the Reynolds stresses. In contrast, plume diameter fluctuations produce a substantial effect on these quantities.

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