Friday, March 29, 2019

Anysys Fluent Simulation Of Turbulent Flow Engineering Essay

Anysys still dissimulation Of Turbulent Flow Engineering EssayThe characteristics of peregrine commingle with sudden amplification in a 12 diameter proportionality subway system be investigated exploitation ANSYS silverish. Results show melted re-circulates just after elabo come outness, length of recirculation zone approximates to 0.35m. Velocity, uplift intensity and insistency vary on pipe length in accordance with Bernoullis principle. Influence of change in turbulence exemplifications on the true is also investigated with the Reynolds Stress mould providing the comparatively best fit although other turbulence feignings ( tangible k- and SST k-) provide reasonably stuffy fitting rides. Results were checked for enlist independence and validated.Computational Fluid dynamics (CFD) involves figuringal simulation of fluid lights in different situations employing numerical root word of basic issue equatings e.g. the continuity equation and other equations ov er a discretized social unit reference (Versteeg, and Malalasekera, 2007). The usage of CFD transcends the traditional electron orbit of chemical engineering profession into wider areas such as oceanography, biomedical engineering electrical circuitry, etc (Fairweather, 2011). choppy expansion in pipes involves fluid function from a smaller hydraulic diameter to a larger one. Flow separation usually chances in a sudden expansion scenario, where a part of the fluid flows in opposition to the main fluid flow. This are called eddies, and are strong contributors to the irreversibility of practical flows as energy is dissipated by this eddies. Thus it is of great consequence to be able to homunculus eddies in a sudden expansion flow adequately and observe the characteristics of this recirculation zone (efluids, 2011 Gharegbagi and Ali, 2011 Mahmud, 2011 Roy, et al 2010).Sudden expansion is a simple looking but intriguing side of fluid flow in pipes. Sanmiguel-Rojas (2010) implies that not m all significant studies have been make on instabilities en dealered in steady, dissolute, sudden expansion fluid flow with love to spatial structure of piping with D2/D1 = 2. However, previous remarkable work in this field includes Roy, et al (2010) and Mansoori and Bazargan-Lari (2007).Examples of scenarios in which the preceding(prenominal) phenomenon occurs include Flows into a tank, rock oil drilling and extraction, plug flow reactors, combustion engines, aerodynamics, etc.SoftwareANSYS still is a commercial CFD package that standards flow via the finite-volume method (a variation of the finite deflexion method) created by the company Fluent (now part of ANSYS Inc.). Pre-processing of the case translate (meshing) was done on Gambit which comes along with Fluent (now ICEM). The version of Fluent employed in this report is 12.1 (CFD-online, 2011 Weidner, 2011 ANSYS, 2009).This report covers the Reynolds Average Navier-Stokes (RANS) modelling of troubled flow with sudden expansion in a 1 2 diameter piping, victimization the pressure ground solver and the second come in upwind difference scheme in ANSYS Fluent. Effects of changes in turbulence models on numerational time, and accuracy would be examined, visual p sets would be apply to describe and analyse modelling results.SIMULATION METHODOLOGY build 1 diagrammatic representation of simulation process (Fairweather, 2011)Nature of Fluid flow under considerationCalculating the Reynolds number of the flow helps to determine the temperament of the flow. At Normal Temperature and pressure (P = 101.325 kgm-2, T = 288.16 K)Generally it is accepted that flows with a Reynolds number (Re) 4000 are turbulent in nature. Therefore it is established that the flow under consideration is a turbulent flowReynolds-Averaged Navier Stokes (RANS)RANS involves the time averaging of the equations that govern turbulent fluid flow to thugture information on variations that occur on a minute scale while a voiding horrendously lengthy computation times. RANS represents variations as a mean such that and PRANS is employed in obtaining the equations that were numerically solved in this report assuming constant swiftness and viscous flows (Fairweather, 2011).GeometryThe geometry consists of two pipes of diameter ratio 12 fall in together through which fluid flows with no bends as shown belownumber 2 geometry of pipe showing mesh grid/mesh discretizationGoverning EquationsContinuity equationMomentum equation (x-direction only)Where TURBULENCE MODELS manageable k- modelThe k- model is a two equation model that assumes a linear relationship between Reynolds stress and estimate of strain. It has the advantages of fast computation time, wide usage and extensive organisation. However, it predicts badly the length of eddies for complex flows. The realizable k- model is an update to the model based on observed strengths and weaknesses of the sample k- model (Fairweather, 2011 ANSYS, 2009).Below is a mathematical representation of the regular k- modelWhere = k or S=source term for k or Sk= G- (production rate of k-destruction rate of k) S= (C1G-C2)(/k) = (production rate of k-destruction rate of k)N.B. for this simulation andSST k- modelThe k- model is also a two equation model based on the Wilcox k- model. It is sui board for skirt bounded flows and free shear flows as it performs low Reynolds number corrections, computation time is relatively fast and accuracy is better than the k- model in most cases. is specific dissipation rate and is analogous to a ratio of /k. The SST k- model is an improved version of the standard k- model (ANYSYS, 2009).Reynolds Stress ModelThis is a very rigorous model, with heptad equations unlike the preceding 2-equation models. It provides more(prenominal) accuracy where other models are awry(p) e.g. impinging flows and can predict fluid flow for a lot of cases closely without any dedicated / individual adjustments. Howev er, computing woos are large (Fairweather, 2011)The first six equations of the RSM model can be condensed into the equation belowWhere The seventh equation (turbulence dissipation rate) isN.B. in this simulation andnumeric methodsThe discretization employed is the finite volume method. It is a variant of the finite difference method. This scheme splits up the domain into discrete correspond volumes over which the control equations are resolved using a truncated Taylor series expansion. impermanent volume method is the most established of Discretization schemes in CFD modelling. Convective fluxes were evaluated with the second lay upwind-difference scheme (Fairweather, 2011 Versteeg, and Malalasekera, 2007).Boundary conditions dishearten 1 enclosure conditions for numeric resultant role (adapted from Versteeg, and Malalasekera, 2007)Realizable k- modelSST k- modelReynold Stress modelInletk = 0.01148438 m2s-2 = 0.02888982 m2s-3k = 0.1148438 m2s-2= 27.95085Rij = =OutletInterio rk = 0 = 0k = 0 = 0Rij = 0 = 0Wallslaw of the wall righteousness of the wallWall functionsConvergence criteria and levelsFor all the equations solved by for each one model, a uniform convergence criterion of 1.0 x 10-4 was used for both equation solved. The measure represented an informed compromise between delightful accuracy and realistic computation time (ANYSYS, 2009). It is worthy of note that for the RSM model, this relatively stringent criterion caused the number of iterations to exceed 14,000 without any obvious procession in results as shown in fig 2. Therefore a cap of 4,000 iterations was placed on the RSM calculations. Results show there was no ensuing controvert impact on accuracy of numerical solution. figure of speech 3 grummet length for RSM model showing run Independence testThe table below shows that results from the modelling experiment are similar and essentially the comparable within three (3) decimal places of precision irrespective of mesh sizing employed. Also since assurance of mesh independence cannot be guaranteed by absolute reduction in cell surface (Sloan et al, 1986), an attempt was made at adaptive meshing to attenuate important flow variations and phenomenon with the same results obtained.Table 2 power system/Mesh independence of simulationGambit Mesh/Grid sizeVolume of unit cellsMass flow rate at inlet kgs-1Mass flow rate at Pressure-outlet kgs-1 faultPercentageDifference (%)5439,9930.0168099440.016809996-5.22E-083.09 x 10-47163,3110.016784670.0167845511.19E-077.08 x 10-41055,1820.0167289940.0167292042.1E-071.255 x 10-310b100,6930.0167289940.016728895-9.9E-085.9 x 10-41516,7500.0166090190.016608695-3.24E-071.95 x 10-3N.B. 10 b instrument mesh size 10 with boundary layer mesh added (adaptive meshing)Grid optimization (Mesh finesse Vs Time trade off)The greater the volume of unit cells in grid per geometry, the better the accuracy of numeric outline. However, within the scope of grid independence, results are relatively uniform irrespective of mesh size. The cost of finesse of grid is computation time could be noticed with the case of mesh size 5 (439,993 cells) which took almost forever to compute using the RSM model and had to be terminated. Thus mesh 10 (55,182 cells) and 10b (100,693 cells) were employed for analysis with other mesh sizes serving as validation checksRESULTS AND ANALYSIS straggle 1Taking a close look at flow close to the walls of the pipe, we see the effect of sudden expansion resulting in backflowing of fluid creating velocities in the opposite direction (red box). Recirculation zone is approximately 0.37m in length. We also can see how the fluid adjust to changes in geometry with a sharp rise stop number to fill the voids created by liquid locomote backwards then a gradual decrease as pressure pile us towards the exit of the pipeFig 4 stop number variation along length of pipe close to the walls showing personal effects of recirculationFig 6 shows the variation in turbulence intensity. It can be seen that the flow becomes more turbulent around the recirculation zone with dead (stagnant) flow occurring just at the corners of the pipe. Fig 7 displays the total pressure variations in the pipe. It can be noted that sudden expansion causes a expel in total fluid pressure. Fig 8 shows the radial tire swiftness and visibleness. It can be noted that stop number variation in the radial direction is minimal, which is typical of plug fluid flow depicted by fig 5. Fig 9 is a streamline plot of axile velocity, velocity variation along the axial direction is more rife than in the radial direction, also worthy of note is the length of the recirculation zone (black box) and the reattachment zone.Fig 5 stages of flow development at different positions on pipe lengthFig 6 Turbulence intensity profile of fluid along length of pipeFig 7 amount pressure profile of fluid along length of pipeFig 8 Radial velocity profile of fluidFig 9 streamline plot of a xial velocity of fluidPart 2Fig10(a-c) shows axial velocity profiles for different turbulent models in order of change magnitude complexity (realizable k- SST k- RSM). Curves get smother showing a more gradual response of the fluid to changes and also approach exact solution, as model complexity summations. However, all the essential features of the fluid flow are hale represented by all models.Fig 11(a-c) displays turbulence intensity variations, more variation details are captured as model increases in complexity. exemplary of note is that the SST k- model provides a more detailed picture of turbulent intensity variation in reference to the other models picking up intensities as low as 5.42 x 10-5 %, while the realizable k- picks up a minimum of 0.336% and RSM 1.45%Fig 12(a-c) shows streamline plot of axial velocity, though length of recirculation zone remains approximately the same the representation of velocity magnitude in recirculation zone varies visibly for each model. Fig 13(a-c) is the radial velocity profile the SST model indicates larger radial velocities along pipe length than both than both the realizable k- and the RSM models. For all models radial velocity variation is dominated by axial velocity variationsFig 10a k- modelFig 10b SST modelFig 10c RSM modelFig 11a k- modelFig 11c RSM modelFig 11b SST modelFig 12a k- modelFig 12b SST modelFig 12c RSM modelFig 13a k- modelFig 13b SST modelFig 13c RSM modelVALIDATION OF RESULTSFor CFD, convergence of numerical iterations does not really count for much as Versteeg and Malalasekra (2007) put it results are at best as good as the physics embodied in it, or at worst as good as the skill of the operator. Thus, validation of results becomes extremely important. The results obtained herein would be validated thusBernoullis equationFor an ideal fluid flow Bernoullis equation enables us to calculate the velocity at any point in the pipe (assuming constant flow rate, and negligible corrasion losses). Therefore we can validate output velocity from smooth-spoken using this principle (Roymech, 2011).Where vin = 1.73855 ms-1, P1= 101.325 kgm-2, P2= 101.325 kgm-2, g = 9.81 ms-2 =1.225 kgm-3 z1 = 0.1m z2 = 0.1mThereforeMass flux variation results from FluentThe third mechanism for validation will be the CFD package fluent itself. Analysis of the computation results as presented in table 4.0, show that value of delusions resulting residuals is very low (less than 0.0095%) indicating conservation of mass during numerical calculations which lend recognise to suitability and accuracy of model.Table 3 comparison of percentage error of each modelMODEL/mesh volumeK-EPSILON (%)SST K-OMEGA (%)REYNOLD STRESS (%)50.0003090.00352N/A70.0007080.0044683630.000673233100.0012550.0078670.00112410 b0.0001530.002580.001488150.001950.0007830.00927N.B. 10 b means mesh size 10 with boundary layer mesh addedResearch journalsIn addition to the above validation processes, the results of modelling experimen t reported in this work were compared with previous inquiry works such as (Roy, et al 2010), (Mansoori and Bazargan-Lai 2007) and (Teyssandiert, 1973). Results obtained corroborated foregoing analysis and results obtained it the above mentioned papers.CONCLUSIONIn summary, CFD modelling of sudden expansion flow in a 12 diameter ratio piping posses the pastime characteristics.Sudden expansion in pipe flow results in local anesthetic pressure lossesFlow fully develops into plug flow earlier exit at outlet and majority of the variations occur axially along reactor lengthRecirculation of fluid occurs after sudden expansion for a lengthspan of approximately 0.35m along pipeViscous effects along wall boundaries help dissipate energy of turbulent eddiesThe realizable k- model predicts the size and strength of recirculation zone poorly, but as flow develops into plug flow, the models accuracy remarkably improves with reference to the other models tested.Turbulence models become better wi th increase in complexity of model from k- to SST k- to RSM. Ability of other models to better the k- model in recirculation zone prediction can be attributed to embedded corrections for boundary layer flow, turbulent kinetic energy and dissipation rates.

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