NUMERICAL SIMULATION OF GRAVITY INDUCED AGGREGATE MIGRATION IN LARGE IN-SITU SCC WALL CASTINGS

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1 NUMERICAL SIMULATION OF GRAVITY INDUCED AGGREGATE MIGRATION IN LARGE IN-SITU SCC WALL CASTINGS Jon Spangenberg 1*, Jesper H. Hattel 1, Nicolas Roussel 2 and Mette R. Geiker 3 1 Department of Mechanical Engineering, Technical University of Denmark, DENMARK. 2 IFSTTAR, Université Paris Est, FRANCE. 3 Department of Structural Engineering, Norwegian University of Science and Technology, NORWAY. *: corresponding author. josp@mek.dtu.dk ABSTRACT Self-compacting concrete (SCC) is in theory an obvious material choice when casting large in-situ walls. However, the great flow distances involved in these castings demand a segregation resistant SCC in order not to obtain heterogeneities which in worst cases can lead to a reduction in the structure s load carrying capacity. In this paper a numerical model is used to determine the aggregate distribution in a 20 meter long and 3 meter high wall casting. A finite volume based computational fluid dynamics model is developed and utilized to identify the specific flow patterns leading to heterogeneities and to analyze different casting scenarios in order to evaluate which casting strategies result in the most homogenous aggregate distributions. Keywords: Wall casting; numerical model; heterogeneities; casting strategy INTRODUCTION SCC is especially suitable for some casting applications such as in-situ wall castings, since the workers are not forced to perform the vibration process in inaccessible areas of the form [1]. However, one of the reasons for the still limited usages of SCC in these applications is the risk of obtaining heterogeneous aggregate distributions due to gravity induced aggregate migration [2]. One method to investigate this risk is to carry out a experiments where various mix designs and casting strategies are investigated, but this is time consuming and expensive, since it includes producing, casting and analysing several of tenths of cubic meters of SCC per. wall. An alternative method to analyse the risk of heterogeneities is to base the investigation on the predictions of a numerical model. 189

2 In literature, computational fluid dynamics (CFD) simulations have been used with success to investigate different physical phenomena related to the usage of SCC [3], e.g. analysing rheological behaviours [4-6], identifying minimum fluidity [7], and investigating aggregate blocking [8]. However, numerical models have also previously been used by the authors to investigate flow induced aggregate migration in a model fluid which resemble SCC [9]. In addition, recent results show that it is possible to predict the aggregate distribution in SCC beam castings with a finite volume based CFD model together with a continuous method for representation of the aggregates [10]. In this paper, the numerical approach presented by the authors in [10] is used to capture the aggregate distribution when simulating the casting of a 20 meter long and 3 meter high wall. In addition, the predictions of the numerical model are used to analyse which casting techniques are preferable from a homogenous aggregate distribution point of view. It should be emphasized that the models used in the present work have been thoroughly verified against proper experiments, however only at lab scale. In the first part of the paper, the numerical model and the assumptions needed to simulate the gravity induced aggregate migration phenomenon during casting of SCC are described. In the second part, a description of the simulated casting scenarios investigated in this study is presented. Finally, the results of the different casting strategies are illustrated and discussed. NUMERICAL MODEL The two dimensional CFD code used to simulate the gravity induced aggregate migration in this study is similar to the one used in [10]. The code was developed in the technical computing language MATLAB and the aggregate migration is modelled by taking the following three physical phenomena into account. 1. The global free surface non-newtonian SCC flow 2. The gravity induced aggregate migration 3. The effect of local aggregate volume fraction on local rheological parameters More details about the numerical method used to model the aggregate migration can be found in [10]. The geometry of the wall is a length of 20 meters, a height of 3 meters and an assumed width of 0.3 meters. Subsequently, the amount of SCC used for the wall is 18 m 3 and that amount is modelled as being casted in approximately 20 minutes. In this study it is assumed that in order to cast the wall three concrete trucks are delivering 6 m 3 each and that the SCC in one of the batches is prone to gravity induced aggregate migration. In the rest of the paper the batch prone to gravity induced aggregate migration is referred to as batch with aggregate migration, whereas the two unaffected batches are referred to as batches without aggregate migration. The material properties for the batch with aggregate migration are assumed to be similar to the ones used in [10], see Tab. 1-2 and Fig

3 Table 1.Mix proportions of the SCC in the batch prone to gravity induced migration, from [10]. Components Kg/m 3 Norcem Standard FA Free water Sand 0/8 mm Gravel 8/16 mm Air entraining agent 1.81 High range water reducing admixture 2.56 Retardation agent 0.28 Viscosity agent 2.09 Density 2346 Figure 1. Particle size distribution for the sand and gravel of the SCC in the batch prone to gravity induced migration, from [10]. Table 2.Fresh properties measurements and computed physical parameters of the SCC in the batch prone to gravity induced migration, from [10]. Slump flow T500 LCPC box Yield stress Plastic viscosity 650 mm 1.4 s 72 cm 40 ± 10 Pa 100 ± 10 Pa.s The dense packing fraction of the two aggregate fractions seen in Fig. 1 was in [10] found to be The material properties used by the numerical model to simulate the batch with aggregate migration are the density, yield stress, plastic viscosity, and the dense packing fraction. In the simulations of this study, only the aggregates from mm which initially constituted a volume fraction of 16% were allowed to migrate, since [10] showed that aggregates less than 11 mm did not migrate during flow. The two batches without aggregate migration are modelled with a homogenous aggregate distribution during casting. In addition, in order to simulate these two batches, the numerical model used a density of 2346 kg/m 3, a yield stress of 60 Pa, and a plastic viscosity of 100 Pa.s. Note that in this study, the presence of any steel bars and the influence of two of the four lateral walls are neglected, since the simulation is carried out in two dimensions. Furthermore, it is assumed that the thixotropy of the utilized SCC is negligible. 191

4 CASTING SCENARIOS All the investigated casting scenarios are carried out with a strategy where either 6 or 9 inlets are utilized. The position of the inlets for the two casting strategies is illustrated in Figs. 2. The numbering of the inlets represents the casting sequence starting with inlet 1 (i1), then i2 and so forth. Only one inlet is filling the formwork at a time. The amount of SCC casted from each of the inlets in case of the 6 inlet casting strategy is 3 m 3, whereas the amount of SCC is 2 m 3 when using 9 inlets. Figure 2. (Top) Position of the inlets for the 6 inlet casting strategy (bottom) Position of the inlets for the 9 inlet casting strategy. Distances are in (m). In the rest of the paper the individual casting scenario is referred to by Casting #. The specifics of each of the casting scenarios are as follows: 1. The 6 inlet casting strategy is utilized. The batch with aggregate migration is filled from i1 and i2 (3 m 3 from each). 2. The 9 inlet casting strategy is utilized. The batch with aggregate migration is filled from i1, i2, and i3 (3 m 3 from each). 3. The 9 inlet casting strategy is utilized. The batch with aggregate migration is filled from i4, i5, and i6 (3 m 3 from each). 4. The 9 inlet casting strategy is utilized. The batch with aggregate migration is filled from i7, i8, and i9 (3 m 3 from each). The evaluation of the casting scenarios is carried out with two criteria. The first one refers to maximizing the minimum volume fraction of the mm aggregates in the wall. The other criterion consists of minimizing the standard deviation (STD) of the mm aggregates as calculated by Eqn. (1). n 1 cv 2 Minimize: ( φ φ ) (1) n cv i= 1 ini 192

5 Where φ is the local volume fraction scalar of the mm aggregates in a control volume, φini is the initial volume fraction of the 11-16mm aggregates, and ncv is the number of control volumes in the domain which is filled or partially filled with SCC. RESULTS Figs. 3-6 present the numerical results for each of the casting scenarios at the end of the mold filling. The four figures include two plots each. The first plot shows the position of the batches with and without aggregate migration at the end of the casting. The second plot illustrates the aggregate volume fraction for the mm aggregates. Figure 3. Casting 1: (Top) final position of batches. (bottom) volume fraction for 11-16mm aggregates. Distances are in (m). STD= and min. volume fraction= Figure 4. Casting 2: (Top) final position of batches. (bottom) volume fraction for 11-16mm aggregates. Distances are in (m). STD = and min. volume fraction =

6 Figure 5. Casting 3: (Top) final position of batches. (bottom) volume fraction for 11-16mm aggregates. Distances are in (m). STD = and min. volume fraction = Figure 6. Casting 4: (Top) final position of batches. (bottom) volume fraction for 11-16mm aggregates. Distances are in (m). STD = and min. volume fraction = Fig. 3 illustrates as expected that it is only the batch with aggregate migration where a variation in the volume fraction is observed. In addition, the figures show that the aggregates are accumulating at the bottom of the formwork and that a zone arises in between the accumulated zone and the batch without aggregate migration from where the aggregates are depleted from. When comparing Figs. 3 and 4, the STD and the minimum volume fraction illustrate that for this specific case a more homogeneous aggregate distribution can be obtained when casting with 9 inlets instead of casting with 6 inlets. As expected, this comparison indicates that decreasing the flow distance of the SCC by increasing the number of inlets utilized has a positive effect on the homogeneity of the aggregate distribution in the wall. A comparison between the numerical results obtained for Casting 2-4, see Figs. 4-6, shows that the aggregate migration is primarily taking place when casting the batch with aggregate migration in the bottom of the formwork. Only a very limited aggregate migration is observed when casting the batch with aggregate migration as the second or third batch. These results indicate that the flow along and near the bottom of the formwork has a substantial effect on the inhomogeneities obtained at the end of the wall casting.a design of 194

7 experiments (DOE) is subsequently carried out to determine a casting scenario with 9 inlets which improves the homogeneity of the aggregate distribution when casting the batch with aggregate migration from i1, i2, and i3. The best result of the DOE is shown in Fig. 7. The casting is performed by placing the batch with aggregate migration in the following sequence: 0.35m 3 at i1, 0.35m 3 at i2, 0.9m 3 at i3, 1.2m 3 at i1, 1.5m 3 at i2, and 1.7m 3 at i3. Afterwards, the second batch without aggregate migration is placed by: 1m 3 at i4, 3m 3 at i6, and 2m 3 at i5. Finally, the third batch without aggregate migration is placed by: 2m 3 at i7, 2m 3 at i8, and 2m 3 at i9. When comparing the STD and the minimum volume fraction of the numerical results in Fig. 4 and 7, it is seen that it is possible to improve the homogeneity of the aggregate distribution by reducing these two quantities with 17% and 9%, respectively. Figure 7. Best result of DOE: (Top) final position of batches. (bottom) volume fraction for 11-16mm aggregates. Distances are in (m). STD= and min. volume fraction = CONCLUSION This paper illustrates how it is possible to use CFD calculations to extract information about aggregate distribution in large in-situ SCC wall castings. The results of the numerical model indicate that the aggregate migration during flow along and near the bottom of the formwork for an SCC (prone to gravity induced aggregate migration) is one of the primary factors resulting in an inhomogenous aggregate distribution at the end of the casting. Consequently, the risk of heterogeneities is less when placing an SCC prone to gravity induced aggregate migration further up the mold, but only in the case where the structural buildup of the previously placed SCC is not advanced enough to create a solid surface for the new SCC to be placed on. Finally, this paper provides an example of how this numerical approach can be utilized either as a basis of a DOE as in this paper or combined with an optimization algorithm to find the casting strategy which improves the most the homogeneity of the aggregate distribution. However, it has to be mentioned that in order for this numerical tool to be used for an arbitrary SCC much more material data still needs to be examined. 195

8 LIST OF REFERENCES 1. voscc.dk, Spangenberg, J., Numerical modelling of form filling with self-compacting concrete, Ph.D. Thesis, Technical University of Denmark, Roussel, N., Geiker, M. R., Dufour, F., Trane, L. N. and Szabo, P., Computational modeling of concrete flow: General overview, Cement and Concrete Research, 2007, Vol. 37(9), pp Mori, H. and Tanigawa, Y., Simulation methods for fluidity of fresh concrete, Memoirs of the School of Engineering, Nagoya University, 1992, Vol. 44(1), pp Thrane, L. N., Form filling with Self-Compacting Concrete, Ph.D. Thesis, Technical University of Denmark, Noor, M. and Uomoto, T., Three dimensional discrete element simulation of rheological test of self-compacting concrete. In Self-Compacting Concrete: Proceedings of the First International RILEM Symposium. pp Roussel, N., Staquet, S., Schwarzentruber, L., Le Roy, R. and Toutlemonde, F., SCC casting prediction for the realization of prototype vhpc-precambered composite beams, Materials and Structures, 2007, Vol. 40, pp Gram, A. and Silfwerbrand, J., Numerical simulation of fresh scc flow: applications. Materials and Structures, 2011, Vol. 44, pp Spangenberg, J., Roussel, N., Hattel, J., Stang, H., Skocek, J. and Geiker, M. R., Flow induced particle migration in fresh concrete: Theoretical frame, numerical simulations and experimental results. Cement and Concrete Research, Vol. 42(4), pp Spangenberg, J., Roussel, N., Hattel, J., Sarmiento, E., Zirgulis, G. and Geiker, M. R., Patterns of gravity induced aggregate migration during casting of fluid concretes. Cement and Concrete Research, Vol. 42(12), pp