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Turbulent Flow Visualization in Computational and Experimental HydraulicsA.E. Mynett I.A. Sadarjoen A.J.S. Hin

Delft Hydraulics Delft University of Technology University of GroningenStrategic R&D Dept. of Technical Informatics Dept. of Computing ScienceDelft Delft GroningenAbstract

Many practical problems in open channel hydraulicsthat were traditionally investigated in hydraulic model ex-periments, are nowadays being solved by using compu-tational fluid dynamics. However, in order to interpretcomputational results, there is a clear preference amongscientist and engineersfor visualization in analogy with ex-perimental techniques. One such technique, particle trac-ing, enables a dynamic (Lagrangian) interpretation of astatically (Eulerian) computed vectorfield. However, quiteoften the emphasis in particle tracing is only on the meanflow properties, while effects due to dispersion and mixingure often not accounted for. Hence turbulentjow charac-teristics have to be incorporated in a visualization systemfor practical hydraulic engineering problems. The parti-cle tracing technique presented in this case study has beenspecifically developed to combine both mean andjuctuat-ing velocity vectors, thus simulating stochastic perturba-tions around meanjow conditions. A number of cases arepresented in this paper that demonstrate the practical ap-plicabilityof advanced visualization techniques in realisticengineering studies.

in this respect, and considerable advanceshave been madein the field of numerical flow modeling. However, in allapplications of CFD, the huge amount of data generatedbynumerical flow simulations, are usually stored as a greatnumber of discrete - and often time-dependent - dataitems like Eulerian velocity vectors and turbulent flow pa-rameters on often extensivecomputational grids. Althoughcalculations can be performed quite rapidly using presentday computer technology, it does not help the researcheror the consulting engineer very much if he has to digestand interpret all this information from printouts or vectorplots of computed numbers. Their interest is primarily di-rected towards understanding the physical implications ofthe numerical simulations.

1 Problem StatementEnvironmental aspectsare becoming important consid-erations n the design of hydraulic structures. Spreading ofpollutants in rivers, lakes, estuariesand seasare of great n-terest, not only becauseof accumulation of toxic materialsin ecologically sensitive areas,but even more so in case ofcatastrophes ikeleakage of hazardous luids horn transporttankers or pipeline bursts. Traditionally, hydraulic modelexperiments were used in the above mentioned situations

to simulate the processesdetermining spreading and mix-ing of fluids. However, the effort associated with settingup scale models, calibrating them, and then performinga great number of experiments for all relevant parametercombinations, requires considerable ime and costs. Hencealternative methods have been developed to speed up in-vestigation time and reduce costs.

This is where scientific visualization has given consid-erable mpetus to the field of computational fluid dynamics.Initially the emphasis in fl ow visualization was either onaerodynamic applications of external flows around airfoilsand spacecraft,or on internal pipe flows for industrial pro-cesses. However, visualization techniques have also beendeveloped for applications of CFD in free surface flows asencountered n rivers, canals, estuariesand seas,commonlyreferred to as computational hydraulics. Most flow visu-alization techniques have been developed n analogy withexperimental techniques used n hydraulic scale models, soas to interpret the outcome of numerical computations interms of the same underlying physical phenomena. Thecasespresented n this paper demonstrate his analogy be-tween numerical and experimental simulations using parti-cles and coloured dye as tracer elements. Both applicationsincorporate and explicitly show the effect of turbulence onspreading and mixing in realistic configurations like naturalbays or manmade harbour inlets.

Computational fluid dynamics has greatly contributed

In general, the hydraulic design process can be charac-terized by the following stages:l establishing initial configurationl performing computational or experimental simulationl interpreting the resulting flow pattern0 modifying configurationl etc.

1070-2385/95$4.0001995 IEEE(See color plates, page CP-50)

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Upson [l] refers to this process as the computationalcycle. Although in principle this process s straightforwardand well-defined, there are quite a number of difficultiesthat are encountered on the way. Moreover, these difficul-ties often are case-dependent, s described below.

Figure 1: Bay of Gdahsk: mean and fluctuating particles

One particular case considered in this paper was thespreading of the outflow of the river Vistula in the Bayof Gdafisk, Poland. Due to the tidal motion, circulationpatterns are induced inside the bay area. Numerical sim-ulations and scientific visualization techniques were usedto detect the circulation pattern. Yet another case was thehydraulic design of a harbour entrance near Lith in the riverMeuse in The Netherlands . In order to eliminate siltationand associateddredging of the harbour entrance due to de-position of river sediment, he geometry of the harbour wasto be designed n such a way that the overall flow in the riverwas guided smoothly past the entrance. Flow visualizationtechniques were used both in experimental and numericalsimulations to optimize the design of the harbour layout.2 Case Study ApplicationsIn order to be able to comparecomputational results withexperimental simulations using coloured dye, particle trac-ing techniques were used. When doing so, the followingaspectshave to be dealt with:

l How to visualize the effect of turbulence? Most ofthe visualization packages or computational fluid dynam-ics present the computational results in terms of separate(vector) fields for mean velocity and (scalar) fields for tur-bulence intensity. Some packages allow particle tracingto display the mean flow pattern, but none of the known

visualization systems are capable of combining vector andscalar fields to display flow characteristics caused by tur-bulence. These features, however, are essential n studiesrelated to dispersion and mixing processescaused by e.g.river outflow in a bay or circulation near a harbour entrance.Hence, n order to study thesemixing processesn hydraulicengineering practice, visualization of the combined vectorand scalar fields is necessary. These techniques have re-cently been developed in a joint research project betweenDelft University and Delft Hydraulics, as described by Hin[2]. The methodology is based on particle tracing. Thisimplies that a Lagrangian representation is derived fromthe Eulerian computational data. Effects of turbulence aresimulated in the visualization technique by using not onlymean but also luctuating velocity components. Thereforerandom velocity fluctuations are added to the mean particlemotion, in accordance with the correct turbulence intensi-ties computed for the flow field. Of course accurateparticletracing methods are required, in particular for complicated3D curvilinear grids which often occur in computationalgeometries related to engineering practice, as ndicated bySadarjoen [33.

Figure 2: Bay of Gdafisk: particle concentrations

Figure 1 shows the location of the particles originatingfrom an ntermittent particle source n the mouth of the Vis-tulaRiver. The circulation pattern in the bay can clearly beobserved and nterpreted. An alternative way of visualizingthe flow pattern in the bay is presented n Figure 2, wherea volume method has been used o show the concentrationof partic.les n a number of control volumes. This lattertechnique s particularly useful to investigate the spreadingand mixing of dissolved substanceswhich is often subjectof interest in environmental studies.

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b How to determine the number of particles, startingpoint, and type of injection? These are all important pa-rameters hat may influence the visual effect considerably.For example, f an nstantaneoussource of particles is used,viz. particles are released at one particular instant of ti me,dispersion of the flow field will lead to a change in spa-tial concentration during the time of simulation. On theother hand, if a continuous source of particles is used, theeffect of dispersion becomes ess pronounced but the over-all structure of the flow field becomes better visible. Oneway to overcome these difficulties and combine the strongpoints of both methods is to use an intermittent source:continuous release of particles during limited time inter-vals. The effect of dispersion can clearly be made visiblein this way, while the overall flow dynamics are adequatelypresented as well. Several dynamic examplesand types ofparticle sourceswill be displayed from video at the confer-ence; some static mages are ncorporated in this paper (seecolour plates as well).

Figure 3: Lith harbour: mean and fluctuating particles

l How to compare numerical and experimental$ow sim-ulations? In the case study for Lith harbour, a comparisonwas made between numerical simulations and hydraulicscale model experiments. Video recordings were avail-able from the experimental set-up, containing a numberof runs using coloured dye injection for different harbourgeometries. Detailed investigatons of the spatial structureof the computed flow pattern were carried out dynamicallyby zooming and circulating around areas of interest. Inthe video presentation and in the Figures in this paper, theviewing angle for the numerical simulations was chosenin accordance with the hydraulic model study. Aspects ofhow to achieve feature detection from vector quantities in

numerically simulated flow fields in combination with ex-perimental flow visualization are described by Pagendarmand Walter [4].

Figure 4: Lith harbour: turbulent particle pathsFigure 3 shows one particular snapshotof particles, orig-inating from an intermittent injection source upstream, o-wards the upper right comer of the Figure. The main riveris situated on the upper i& t part, and the overall flow di-rection is from top to bottom. Lith harbour is located atthe bottom part, separated rom the main river channel bya guiding barrier. The harbour entrance and the connectionwih the main river is in the upper center part of the Figure.The mean flow pattern can be inferred from the white par-

ticles near the harbour entrance (seealso colour plates). Instationary flow conditions these mean particle paths can beinterpreted as dividing streamlines between the main riverflow and the relatively stagnant harbour basin. However,due to turbulence there is exchange of flow across thesedividing streamlines, as ndicated by the green particles inthe Figure. It can clearly be observed from the length ofthe particle paths, that the tlow velocity in the main river byfar exceeds he circulation speed nside the harbour. Thisimplies that dissolved matter, suspendedsedimen or toxicsubstances hat are transported with the river flow, are muchlonger detainde inside the harbour than in the main riverchannel.Figure 4 shows the turbulent particle paths originatingfrom a continuous injection source upstream. From thesepaths the effects of dispersion in the main river channeland circulation inside the harbour can be inferred. Also,the proportion of a possible source of pollution entering theharbour as well as the residence time of pollutants inside,can be estimated rom the relative density of particle paths,as presented n the Figure.

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3 Future DevelopmentsAlthough considerable improvement has already beenmade by using scientific visualization in combination withnumerical computat ion, the approach followed here is stilltraditional in the sense hat it very much follows a se-quential approach. First the schematization is carried out,

then the computations are performed and then the resultsare visually displayed and interpreted. If this leads o mod-ifications in the original design, a new schematization iscarried out, new computations are performed and the newresults are visually displayed and interpreted.This processcould again be mproved and accelerated, fthe computations could be visually monitored, interpreted