Parametric studies of exhaust smoke–superstructure interaction on a naval ship using CFD

Computers & Fluids 36 (2007) 794–816 www.elsevier.com/locate/compfluid

Parametric studies of exhaust smoke–superstructure interaction on a naval ship using CFD P.R. Kulkarni a

a,*

, S.N. Singh b, V. Seshadri

b

Naval Construction Wing, Department of Applied Mechanics, IIT Delhi, New Delhi 110 016, India b Professor, Department of Applied Mechanics, IIT Delhi, India Received 20 December 2005; received in revised form 20 June 2006; accepted 27 July 2006 Available online 2 November 2006

Abstract The prediction of flow path of exhaust plume from the ship funnels is extremely complicated since the phenomenon is affected by a large number of parameters like wind velocity and direction, level of turbulence, geometry of the structures on ship’s deck, efflux velocity of smoke etc. To complicate the matters, the entire turbulent flow field is subject to abrupt changes as the yaw angle changes. In order to understand how the smoke is brought down to ship’s deck, it is necessary to have a knowledge of the funnel exhaust behavior very early in the design spiral of the ship by undertaking parametric investigation of the interaction effect between exhaust smoke and the ship superstructure. This paper presents such a parametric investigation on representative topside configurations of a generic frigate using computational fluid dynamics (CFD). The results presented have been analysed for a total of 112 different cases by varying velocity ratios and onset wind direction for four superstructure configurations. Use of both experimental and computational approaches has been made so that they become complementary to each other. The CFD simulation has been done using the computational code FLUENT version 6.0. Closure was achieved by using the standard k–e turbulence model. The parametric study has demonstrated that CFD is a powerful tool to study the problem of exhaust smoke–superstructure interaction on ships and is capable of providing a means of visualising the path of the exhaust under different operating conditions very early in the design spiral of a ship. Ó 2006 Elsevier Ltd. All rights reserved.

1. Introduction The downwash of exhaust causes funnel gases to disperse downward toward the deck more rapidly than upward. This has many adverse consequences like the sucking of hot exhaust into the GT intake and the ships ventilation system apart from high temperature contamination of topside electronic equipment and interference of the smoke with flight operations. Understanding of dispersion of exhaust smoke is therefore an important aspect of ship design that falls under the category of ship aerodynamics. However, very often, this application of aerodynamics is not recognised a priori in the design of ships. As a result, the smoke nuisance problem gets detected at a very late stage, often post construction, during the sea trials or even *

Corresponding author. E-mail address: [email protected] (P.R. Kulkarni).

0045-7930/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.compfluid.2006.07.001

post delivery. Modifications to the topside configuration and the imperative and costly troubleshooting between the launch and delivery of such a vessel become inevitable [1,2]. In order to take the smoke nuisance problem into account, the ship designer needs to be able to have a means of visualising the path of the exhaust under different conditions during the design phase, which will enable detection of shortfalls very early in the design spiral. This requires the ship designer to have knowledge of the funnel exhaust behavior, which shall enable him to find efficient means to eliminate the problem and also to avoid the costly post construction additions and alterations. Traditionally, the funnel performance has been investigated using scale models in wind tunnel at a relatively advanced stage of design, when many aspects of the design are frozen. Making changes at that stage may involve redesigning many aspects of the ship. Further, wind tunnel studies are very lengthy, time consuming and expensive.

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Nomenclature K velocity ratio = Ve/Vw Vx, Vy, Vz components of Vw in x, y and z directions on superstructure Vw relative velocity between the wind and ship, Vw = jVwodj Ve exhaust velocity (m/s) Vship cruising velocity of ship Vwind ambient wind velocity at sea Vwod velocity of wind over deck qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Vxy

Vyz Vxz  

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðV y Þ2 þ ðV z Þ2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðV x Þ2 þ ðV z Þ2

Vx

V wind cfd

Vx V wind exp

w

normalized Vx from CFD simulation normalized Vx from wind tunnel mapping

using three-hole probe angle of wind relative to the ship’s heading

ðV x Þ2 þ ðV y Þ2

The prediction of flow path of exhaust plume from the ship funnels is extremely complicated since the phenomenon is affected by a large number of parameters like wind velocity and direction, level of turbulence, geometry of the struc-

tures on the ship deck, efflux velocity of smoke etc. To complicate the matters, the entire turbulent flow field is subject to abrupt changes as the yaw angle changes. It is not always possible to cover the entire range of all param-

Fig. 1. Simplified topside of a generic frigate. (a) Generic frigate, (b) superstructure without the hull and (c) dimension of the 1:50 scale model superstructure. All dimensions in cm.

Fig. 2. Parametric studies on four configurations of superstructure: (a) configuration ‘A’, (b) configuration ‘A-1’, (c) configuration ‘B’ and (d) configuration ‘C’.

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eters to simulate every possible working condition in the wind tunnel. CFD has emerged as a serious alternative to wind tunnel studies and is capable of providing solutions very early in the design spiral. This paper presents the results of parametric investigation of the interaction effect between the exhaust smoke and the superstructure of a generic frigate (Fig. 1a) which were modelled without the hull (Fig. 1b) using CFD (Fluent version 6.0) by varying the velocity ratio (K), onset wind direction (yaw angle w) and superstructure configurations. Four variants of the simplified superstructure (Fig. 2) were investigated at four velocity ratios each and further, each of these combinations were investigated at seven different onset flow conditions, making a total of 112 cases. The results are from an experimental and numerical study undertaken with an aim of gaining an understanding of the typical flow field around the topside of naval ships and the interaction between the bluff body air wake (of the funnel and superstructure/mast located on ship’s topside) and the ship’s exhaust. 2. Review of literature Contamination from exhaust discharged from ship’s funnel is a problem for almost all vessels, regardless of type – be it the passenger liners, merchant ships or the naval ships. Investigation of exhaust smoke–superstructure interaction on ships has been reported in literature dating back to early 1940’s when the problem of smoke nuisance manifested itself on passenger ships. The problem of smoke nuisance in the context of naval ships continues to be a subject of research. Work on this problem has also been reported from the offshore industry. Study of exhaust smoke dispersion from the ship and its interaction with the rest of the superstructure have been through wind tunnel modelling [1–13], field measurements and observations [6–8], analytical methods [14], and lately, by CFD [15–34]. Recourse to some or all of these methods has been made by researchers to get an insight into the physics of the problem and to suggest the guidelines to avoid the problem of smoke nuisance on ships. The evolution of the funnel shape and topside configuration on passenger and naval ships over the last hundred years and a comprehensive review of the problem of smoke nuisance on ships by various researchers since 1930’s has been presented in a review paper by Kulkarni et al. [35]. The studies by various researchers (reviewed by Kulkarni et al. in [35]) have identified that the smoke nuisance problem on ships is dependent on the following parameters: 2.1. Turbulence The turbulence zone over the ship superstructure is an important factor in the smoke nuisance problem. The height of the turbulent zone is mainly a function of geometry and is practically independent of wind velocity. A number of studies have been reported in the literature [4–9] regarding the turbulence due to ship superstructure and ways to solve

the problem of smoke nuisance. For avoiding the smoke nuisance, knowledge of the probable height of the turbulence boundary and the height of funnel that should project above the turbulence boundary for a given efflux speed would be necessary to ensure that no smoke is drawn into the turbulent zone. Better understanding of the phenomenon of turbulence, improved power of present day computers and the development of commercial CFD codes are enabling the simulation of exhaust smoke problem onboard ships. There are reviews/studies by specialists on the available turbulence models, some of which focus specifically on the performance of CFD in the area of wind engineering [36,37]. These would be applicable in the study of the problem of smoke nuisance on ships too. The k–e model is probably the simplest type that is practically useful [36]. The more complex models tend to better represent the effects of turbulent anisotropy, which can be important in some applications, including turbulent dispersion and buoyancy effects. However, they usually offer insufficient benefits in return for substantial extra effort required to solve them. Murakami [37] reports that the k–e model does predict the mean wind speed well, but overestimates the turbulent kinetic energy around sharp edges. Further, he concludes from his study that the k–e models do give large reductions in convergence times and make parametric studies possible. 2.2. Downwash The phenomenon of the exhaust gases emitted from the funnel being pulled into the its wake and caused to come down on the deck as they mix with vortices behind the funnel is referred to as ‘‘downwash’’. When this occurs, the plume rise may be diminished and the effluent may be trapped in the wake of the funnel and nearby superstructure on the ship. The shape of the funnel and the yaw angle also influence the amount of downwash. The funnel generates a vortex trail that depends on its shape and the extent of its streamlining. The strength of these trailing vortices is a major contributor to the downwash. Early wind tunnel tests of Sherlock and Stalker [3,4] proposed the well accepted rule that downwash would not occur or would be very slight if the velocity ratio at the top of the funnel was 1.5 or greater. They have also reported that the exit temperature or buoyancy had little effect on downwash. 2.3. Velocity ratio The velocity ratio (K) defined as Ve/Vw, varies widely from one ship to another and also on the same ship on changing condition of power output of the prime mover and wind speed. Based on the wind tunnel studies, Third and Ower [8] report that plume heights vary greatly between one design and another at the same K values. They report an extensive investigation of the effect of K on the path of the exhaust. Kulkarni et al. [12] have concluded from their flow visualisation studies that higher K values (i.e. K > 2) are not the critical design conditions on ships.

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Fig. 3. Frame of reference and funnels offset in configuration ‘A-1’: (a) model of simplified superstructure, (b) superstructure configuration ‘A’ and (c) superstructure configuration ‘A-1’.

It is the lower values of K (which can be encountered during part load operations of the prime mover) that are the critical design conditions for the smoke nuisance problem. Baham and Mc Cullum [14] have reported that K is the determining factor for the downwash problem. 2.4. Effect of angle of yaw Yaw angle (w) is the angle of wind relative to the ship’s heading. For a ship cruising at a velocity Vship in an ambient wind velocity Vwind, the wind over deck is defined as Vwod the vector sum Vwod = Vwind  Vship. It is assumed that Vwind and Vship are coplanar with the ocean surface, and the wind speed is defined as Vw = jVwodj and its direction by yaw angle w as defined in Fig. 4. Based on wind tunnel studies, Ower and Third [7] and Third and Ower [8] report that when the relative wind moved off the bow, the plume had a rapidly shortening length of deck to pass over. But any advantage from this could be lost when the angle reached the critical value for the flow around the funnel casing, which was usually around 16°. Burge and Ower [9] report that wind tunnel experiments and field studies on ships which specifically generated smoke for observations have shown that a critical condition in the funnel wake is reached when the relative wind is in the region of 17–25° off the bow. Here, the lower boundary of the plume is reported to have suddenly descended over the leeward face of the funnel, even when the funnel top was above the turbulent boundary.

3. Approach adopted The analysis of the flow of exhaust fumes from the ship funnels is extremely complicated. The flow is three dimensional, highly turbulent and could some times be unsteady. The interaction between the funnels and the structures on ship’s deck makes prediction that much more difficult. The present state of knowledge in CFD is such that no single turbulence model can accurately predict the flows in all situations. Moreover, practicing engineers do not accept the results of CFD without some form of validation that demonstrates computational fidelity to reality, which relies on comparison of computational results to experimental data. It is essential therefore, to have experimental data to validate the predictions from CFD analysis at least for few conditions, and only then a detailed prediction for varied conditions can accurately be made. Hence, the use of both experimental and computational approaches has been made so that they become complementary to each other. The analysis was carried out on a 1:50 scale model of a typical topside configuration of a generic frigate (Fig. 1a). The dimensions of the topside of the frigate are shown in Fig. 1c. Flow around the simplified superstructure of the ship without the hull was modelled to study the exhaust smoke–superstructure interaction. Four variants of the superstructure configuration with progressive introduction of the structures on the topside (i.e. the bridge block/mast

Fig. 4. Definition of the velocities and direction of ship and wind.

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upstream and downstream of the funnel) were investigated in order to understand how each of these structures on the deck affect the exhaust plume path, These variants were named as configurations ‘A’, ‘A-1’, ‘B’, and ‘C’ as shown in Fig. 2. The coordinate system used on the model super-

structure is shown in Fig. 3a. The funnels in the superstructure configuration ‘A’ (Fig. 3b) are offset by half a funnel width on either side (along the y-axis) to obtain the superstructure configuration ‘A-1’ as shown in Fig. 3c.

Fig. 5. Qualitative comparison of flow visualisation by CFD simulation and wind tunnel studies for superstructure configurations ‘A’, ‘B’ and ‘C’ at K = 1 and 2. (a) K = 1 for superstructure configuration ‘A’; (b) K = 2 for superstructure configuration ‘A’; (c) K = 1 for superstructure configuration ‘B’; (d) K = 2 for superstructure configuration ‘B’; (e) K = 1 for superstructure configuration ‘B’; (f) K = 2 for superstructure configuration ‘B’; (g) K = 1 for superstructure configuration ‘C’; (h) K = 2 for superstructure configuration ‘C’.

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The plume behavior in cross-flow is usually modelled into two separate regions – the initial plume rise region and the far field region. In the context of dispersion of exhaust plume from ship’s funnel, it is only the initial plume rise phase that needs to be modelled. Further, in the near field, the plume path is inertia dominated rather than buoyancy dominated. Therefore, in the present study, the buoyancy is neglected in the simulations and accordingly, air at ambient temperature is injected through the funnels of the model superstructure to represent the ejection of ship’s exhaust smoke. Even though the buoyancy has been neglected, useful data for the types of local flow patterns that are typically of interest is still obtained. The experimental study comprised of mapping of the flow field as well as the exhaust smoke trajectories around scale models of different variants of simplified superstructures of a generic frigate in the wind tunnel by velocity measurements [11] and flow visualisation photographs [12], which yielded a large body of benchmark data. These data provided the physical quantities that could directly be correlated to the results of the numerical simulations by CFD code FLUENT version 6.0, wherein closure was achieved by using the standard k–e turbulence model along with grid refinement and grid adaptation techniques. The results of the numerical simulations were directly correlated to the data from the experimental studies by comparing the iso-velocity contours of normalized Vx and the plume trajectory from flow visualisation using path lines. The qualitative comparison between the flow visualisation photographs from wind tunnel studies and the flow visualisation from path lines obtained from post processing the converged results of FLUENT for superstructure configurations ‘A’, ‘B’ and ‘C’ at velocity ratios of 1 and 2 are shown in Fig. 5. For quantitative comparison, the velocity measurements of the flow characteristics from the CFD

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simulation were extracted at the 12 planes of superstructure configuration ‘A’ (shown in Fig. 6a) in the computational domain and were compared with the benchmark data from wind tunnel studies at the corresponding planes. The percentage of error between the wind tunnel measurements and the CFD simulation was calculated at each plane. The results of qualitative and quantitative comparison have been presented in [23], which show a reasonably good agreement of the experimental data with the results from CFD simulation. Having established the capability of the CFD code FLUENT version 6.0 to predict the flow and performance characteristics around the ship’s superstructures by demonstrating a reasonably good agreement between the predicted and the experimental results [23], the same turbulence model and solution techniques were used for undertaking parametric investigation of the exhaust smoke–superstructure interaction on the ship’s topside. The results of the parametric investigation using CFD have been presented in this paper. The wind tunnel studies were limited to yaw angle (w) of 0° and K of 1 and 2 [11,12]. The CFD results presented in this paper have analysed a total of 112 different cases by studying four velocity ratios (K = 1, 2, 3 and 4), seven yaw angles (w) (0–30° in steps of 5°) and four superstructure configurations. 4. Numerical considerations of CFD studies 4.1. Computational domain The computational domain shown in Fig. 7 extended up to 450 cm (between upstream and down stream) in the ship moving direction (Global x-axis in this analysis), 75 cm transverse (y-direction) and 95 cm vertically (z-direction). The upstream edge of the computational domain extended

Fig. 6. Location of planes on superstructure configurations ‘A’ (a) and ‘C’ (b), and grid points on a representative plane.

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Fig. 7. Computational domain with boundary conditions.

up to 10 times the height of the superstructure of 5 cm. The downstream edge of the computational domain extended up to two times the length of the superstructure from the aft end of the superstructure to provide for the path of the plume to fully develop. The height of the computational domain was 10 times the height of the funnel to allow the plume to develop properly. This presented a blockage of about 3–4% for superstructure configurations ‘A’ and ‘A-1’. However, the introduction of the superstructure/mast blocks in configurations ‘B’ and ‘C’ coupled with the requirement of the superstructure to be modelled at a yaw angle with respect to the incident wind resulted in an increase of cross-section of the superstructure. The width of the computational domain was accordingly increased to ensure that the corresponding maximum blockage ratio of the frontal area of the geometry to the area of the tunnel inlet was of order 8% and thus, the free stream boundary condition could be specified as per FLUENT. 4.2. Boundary conditions In general, for a ship, the only known flow is that of the approaching natural wind. The flow around the superstructure is a secondary process resulting from the interaction of the external wind with the total structure of the platform. The following boundary conditions were applied to the computational domain (Fig. 7). (a) In the present study, no attempt has been made to simulate the atmospheric boundary layer and all studies have been carried out for uniform wind velocity. The flow domain was bounded at the entry by this specified upstream boundary. At the entry of the ambient air, the INLET boundary condition was specified with a velocity of wind Vw of 10 m/s. (b) At the exit of the plume from the funnel, the INLET boundary condition was imposed for the air with a velocity Ve suitably chosen so as to achieve velocity ratios (K) of 1, 2, 3 and 4.

(c) At the exit of the domain, the OUTLET boundary condition was applied. The outflow boundary models the flow exit where the details of flow velocity and pressure are not known prior to the solution of the flow problem. Therefore, no conditions at the outflow boundary were defined and the code extrapolated the required information from the interior. (d) In the bottom of the domain and on the superstructure model, no-slip, adiabatic WALL boundary condition was applied. (e) For the exterior, i.e. the sides and the top of the computational domain, symmetric boundary condition was applied. (f) The phenomenon of interest is near field dispersion of jet in the disturbed flow field created by bluff bodies of the superstructure. Though the GT exhaust from the funnel of a ship has both momentum and buoyancy, in the near field, the plume path is inertia dominated rather than buoyancy dominated [12]. Therefore, in the entire analysis, isothermal jet has been used. 4.3. Computational mesh, grid adaptation and validation The computational domain was discretised using the unstructured 3D tetrahedral mesh. Since the flow and heat transfer were decoupled in the simulation (i.e. there are no temperature dependent properties or buoyancy forces) the equations were solved for iso-thermal flow (by turning ‘off’ the energy equation) to yield a converged flow field solution which was carefully post processed and the cells having the high level of gradients were refined. The mesh was generated such that its quality criteria (in terms of the skewness, orthogonality and warpage) specified in FLUENT was satisfied. This was achieved in two stages. In the first stage, the computational domain was meshed using a coarse grid to solve for the flow pattern and plume path. In the second stage, solution-adaptive refinement was undertaken and the mesh was adapted based on the plume gradients as well as the gradients of flow parameters in the

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wake region. Solution-adaptive refinement was limited to those regions where greater mesh resolution was needed. The flow around the superstructure contains flow features that are easy to identify. The wakes represent a total pressure deficit, and jets are identifiable by a region of relatively high-velocity fluid. The iso-value grid adaptation feature of FLUENT using the parameters of pressure and velocity was used to adapt the grid. The grid was thereafter improved by using volume adaptation wherein the refining was based on either the cell volume or the change in volume between the cell and its neighbours. One such grid adaptation undertaken for superstructure configuration ‘B’ is shown in Fig. 8. A coarse mesh with 461,073 tetrahedral cells (Fig. 8a) was used to obtain the initial solution. Thereafter, the regions of pressure deficit were identified from the plot of total pressure (Fig. 8c) and the regions of increased velocity in the region of the jet were identified from the plot of total velocity (Fig. 8d). The iso-values of pressure and velocity thus identified were used to refine the mesh in the wake region as well as in the region of the smoke jet. The mesh was further refined using volume adaptation with the criterion that the maximum cell volume change should be less than 50%. The minimum cell volume for adaptation was also limited. The resulting mesh, after smoothing and swapping, on the centerline plane of symmetry of the computational domain is shown in Fig. 8b. It can be seen that the interface between the

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refined region within the jet and the wake and the surrounding mesh is not sharp. The adapted mesh now had 811,291 tetrahedral cells, an increase of 350,218 cells in the region of interest and almost a 2-fold increase in the total number of cells. The resulting mesh is optimal for the flow solution because the solution based on a coarse mesh was used to determine where additional cells were added. The solution from the previous (coarse) mesh was mapped on to this new (adapted) mesh and the calculations were re-started. All simulations were run on Intel Xeon dual 2.8 GHz processor on Windows XP workstation. The solution was converged to a normalized residual level of 106. Simulations using coarse mesh took up to 2 days to converge. Simulations with adapted and refined mesh with two to three times the number of cells used in the early computations took up to 10 days to converge. The converged solution was carefully post processed for analysis and the results are presented in the subsequent sections. The process of finding a grid independent solution can be a complex one, especially when 3-D grids are considered. The difference between a fully grid independent solution and an error of 1% in the solution can often be as a result of a 10-fold increase in the number of cells. Accepting a possible 1% error can therefore save both time and money when extreme accuracy is not necessary. The approach adopted for defining the criteria for accepting the level of grid refinement and the grid independence in

Fig. 8. Coarse and refined mesh and the contours of total pressure and velocity magnitude on centerline plane of superstructure configuration ‘B’, at K = 3 and w = 0°. (a) Coarse mesh with 461,037 cells; (b) adapted and refined mesh with 811,219 cells; (c) contours of total pressure (Pa); (d) contours of vel. magnitude (m/s).

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the present investigation has been the comparison of the predictions from the CFD simulation with the experimental results that were used as benchmark data. The grid refinement that gives a reasonably good agreement with the experimental results was considered acceptable and sufficient. The velocity measurements from three-hole probe available at the 250 grid points on each plane (shown in Fig. 6b) for superstructure configuration ‘A’ [11] were used to initially quantitatively validate the CFD results. From the converged solution of the CFD simulation, the velocity values were extracted at the corresponding 250 grid points from the computational domain for all the 12 planes [23]. The difference between the measured and the computed values was calculated for all the corresponding 250 grid points. The percentage of error between the wind tunnel measurements and the CFD simulation was thereafter calculated at each plane by using the equation:

ment. It is therefore concluded from the qualitative as well as the quantitative comparison between the experimental data and the CFD simulation that the grid refinement and grid adaptation techniques adopted provide satisfactory results. In view of the good agreement with experimental results, the degree of grid refinement may be considered to be sufficient. 5. Analysis of the data Pathlines were used to provide the flow visualisation of the smoke from CFD simulation. Massless particles were introduced in the mean flow, which were released from the funnel exit. The kinematic information available from the converged solution of the numerical simulation enabled the tracing of the plume trajectory. Flow was analysed at three horizontal planes at a height of 0.4 h, 0.9 h and 1.4 h (‘h’ is the height of the funnel). However, analysis of the results presented in this paper are confined to horizontal plane ‘Q’, located at a height of 0.9 h, shown in Fig. 9 and at the centerline plane, which is the plane of symmetry of the superstructure shown in Fig. 10. Further, 12 transverse planes (numbered 1–12) (Fig. 6a) were defined to study the flow structure along the length of the superstructures. The reference co-ordinate system employed is shown in Fig. 3a. Apart from the flow visualisation photographs from wind tunnel studies and the pathlines from CFD simulation, the airflow around the superstructures was analysed by obtaining the following vector plots:

Percentage of error on each plane vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n "    #2 u1 X Vx Vx t ¼  n i¼1 V wind exp V wind cfd The difference between the measured and the computed values of normalized Vx (Table 1) shows a reasonably good comparison, with the error varying between 3.22% and 5.67% in the regions down stream of the funnel, where the wake is relatively weakened and the dominant mean flow is in the x-direction. Least error is found at planes 11 and 12 (3.88% and 3.23%). However, in the wake regions (planes 3 and 9), the measurements from three-hole probe are subject to uncertainties in view of significant Vz component at these planes. This is reflected by the larger errors of 8.63% and 10.37% respectively at these planes between the measured and the computed values of normalized Vx. From the study of the capability of CFD to predict airflow over topsides of cruise vessels and other maritime structures, Jensen et al. [34] conclude that the differences between CFD and model-test results are not generally larger than that between full-scale and model-scale results. Further, the differences are not much larger than often found when the same vessel is tested in different wind tunnels. In the present study, the average value of the error (excluding planes 3 and 9) is about 4.76% and is considered to be a reasonably good agreement. Even if large discrepancies are indeed seen at some planes, the comparison of CFD and wind tunnel tests show an overall good agree-

(a) Vector plots of velocity magnitudes of Vyz (defined as qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðV y Þ þ ðV z Þ2 ) at the 12 transverse planes. (b) q Vector plots of ffivelocity magnitudes of Vxy (defined as ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 ðV x Þ þ ðV y Þ ) at the horizontal plane ‘Q’.

Fig. 9. Location of plane ‘Q’ on superstructure configurations – (a) ‘B’ and (b) ‘C’.

Table 1 Percentage error between the measured and the computed values of normalized Vx Plane

% Error

1

2

3

4

5

6

7

8

9

10

11

12

4.88

4.67

8.63

5.67

5.68

4.41

5.61

5.76

10.37

4.42

3.88

3.23

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Fig. 10. Location of centerline plane on superstructure configuration ‘B’.

(c) q Vector plots of velocity magnitudes of Vxz (defined as ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 ðV x Þ þ ðV z Þ ) on the centerline plane. 6. Results and discussion 6.1. Superstructure configuration ‘A’ The funnels in the superstructure configuration ‘A’ are inline as shown in Figs. 1b and 5a. The results for this superstructure configuration are presented in Figs. 11–18. 6.1.1. Analysis of the flow structure at w = 0° The study of the secondary flows i.e. Vyz, Vxy and Vxz was not possible from the experimental data that were generated using a three-hole probe [11]. A more complete analysis of the flow field from CFD studies compliments the findings from wind tunnel studies [11,12]. The vector plot of cross-flow velocities of Vyz, Vxy and Vxz for superstructure configuration ‘A’ at K = 1, w = 0° obtained from CFD indicates there are two contra-rotating vortices in the region immediately behind the funnel at planes 3 and 4 and 9 and 10 (Fig. 11a, b, e and f). However, at increasing downstream distances, they appear to dissipate and become weak and are less apparent at planes 6 and 7 (Fig. 11c and d). The vortices cause a significant region of recirculation in the wake of the funnels, which has been noticed in the vector plot of Vxy at planes ‘P’, and ‘Q’ as well as vector plot of Vxz on the centerline plane (though not presented in the paper). Further, Fig. 11 shows that the structure of the vortices is symmetrical about the centerline plane which indicates that the airflow pattern is symmetrical to port and starboard of the centerline plane of symmetry of the superstructure at w = 0°, as expected. 6.1.2. Effect of yaw angle on superstructure configuration ‘A’ The flow visualisation of the plume trajectory by pathlines at different yaw angles (0–30° in steps of 5°) at K = 1 are shown in Fig. 12. At 0° angle of yaw, the bending of the plume as well as its trajectory as visualised from the pathlines from CFD predictions for superstructure configuration ‘A’ at K = 1 is very similar to that observed by flow visualisation studies (Fig. 5a). It is seen from photographs of flow visualisation in Fig. 5a that the smoke does not come down on the deck. However, as the yaw angle increases, it is found that the plume rise tends to be diminished and further, the effluents are trapped in the wake of the funnel (Fig. 12). At w = 10°, the tendency of the plume to bend downwards after its exit

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from the funnel is observed (Fig. 12b). This tendency increases at w = 15°. At w = 20° (Fig. 12d), the plume is found to get trapped in the wake of the funnels which manifests as a downwash. It is observed that the plume rise tends to be diminished and further, the effluents are trapped in the wake of both the funnels thus resulting in high deck level concentrations immediately downwind of the funnels. The magnitude of trapping of the plume behind the funnel at w = 25° and 30° shows an increasing trend as seen in Fig. 12e and f. The performance of the funnel in the wind is found to degenerate as the yaw increases from 15° to 30°. A closer view of the pathlines showing the trapping of the plume in the wake of the funnels at w = 20°, 25° and 30° is shown in Fig. 13. These predictions by CFD conform to the findings of Ower and Third [7,8] and Burge and Ower [9] who, on the basis of wind tunnel tests and field observation on ships, have reported in the decade of 1950’s that when the relative wind is in the region of 17–25° off the bow, the lower boundary of the plume is reported to have suddenly descended over the leeward face of the funnel, even when the funnel top was above the turbulent boundary. This conformance of the CFD predicted plume trajectory at yaw angles between 20° and 30° to the findings of Ower and Third [7,8] and Burge and Ower [9] reinforces the point that CFD is a powerful tool in the study of the exhaust smoke–superstructure interaction. 6.1.3. Analysis of the flow structure causing the downwash At w = 0°, the airflow pattern is symmetrical to port and starboard of the centerline plane of symmetry of the superstructure. When the funnel gases are discharged clear of the local aerodynamic interference of the funnel itself, the plume is found to disperse symmetrically as shown in Figs. 12a and 5a. However, the symmetry of the airflow pattern is destroyed as soon as the ship begins to turn across the wind as confirmed by the vector plot of Vxy on plane ‘Q’ at w = 20° shown in Fig. 14. The yaw angle of 20° is chosen as a representative yaw angle for analysis at which the downwash is found to manifest. Unlike at w = 0° (Fig. 11a and b), the vector plot on planes 3 and 4 at w = 20° (Fig. 15a and b) clearly shows the absence of symmetry in the flow structure about the centerline plane of the superstructure. Further, the vector plot shows the cross-flow due to the yaw angle. This cross-flow causes the airflow around the funnel from the windward side to mix with both the airflow along the leeward side of the funnel as well as the airstream crossing the deck from the windward face of the funnel. Fig. 15c and d presents pathlines showing two different views of trajectory of the plume from the forward funnel at w = 20°. The comparison of the vector plot of Vyz at plane 3 (Fig. 15a) and the pathlines of the smoke trajectory (Fig. 15c) confirm that the airflow over the top of the funnel mixes with the eddies shed from the windward side of the funnel. This produces a large eddy or a sheet of small eddies into which the lower boundary of the plume descends.

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Fig. 11. Vector plot of Vyz on superstructure configuration ‘A’ at K = 1, w = 0°: (a) plane 3, (b) plane 4, (c) plane 6, (d) plane 7, (e) plane 9 and (f) plane 10.

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Fig. 12. Plume trajectory at K = 1 at different yaw angles for superstructure configuration ‘A’: (a) w = 0°, (b) w = 10°, (c) w = 15°, (d) w = 20°, (e) w = 25° and (f) w = 30°.

Fig. 13. Trapping of exhaust in the wake of the funnels at K = 1 for superstructure configuration ‘A’ at (a) w = 20°, (b) 25° and (c) 30°. (d) Downwash from forward and aft funnels at w = 30°.

As seen from Figs. 12 and 13, yaw angles from 20° to 30° can be termed as adverse range of yaw angles. Over the range of these adverse wind directions, the airflow results in the occurrence of downwash. 6.1.4. Effect of yaw angle at K = 2 The photographs of flow visualisation at a yaw angle of 0° and K = 2 (Fig. 5b), show that the trajectory of smoke is different from that at velocity ratio of 1 (Fig. 5a) as the smoke now exits with a greater momentum. As a result, the plume tends to travel straighter up before being

deflected by the cross-flow. After bending, the plume again becomes principally horizontal, as is the case when the value of K is 1. However, as the plume has risen vertically to a greater height before bending, it is found that the lower boundary of the smoke clears the deck at a greater height as compared to the case of K = 1. The pathlines from CFD analysis compare very well with the flow visualisation photographs (Fig. 5b). The visualisation of the plume by pathlines at K = 2 as the angle of yaw (w) is increased from 0° to 30° in steps of 5° for superstructure configuration ‘A’, is shown in

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Fig. 14. Vector plot of Vxy on superstructure configuration ‘A’ at K = 1, w = 20° at plane ‘Q’.

Fig. 15. Vector plot of Vyz and different views of trajectory of the plume in superstructure configuration ‘A’ at K = 1, w = 20°. (a) Plane 3. (b) Plane 4. (c) Pathlines showing the trapping of smoke in the vortices in the wake of the funnel. (d) Pathlines on the profile. Plane 3 is also shown.

Fig. 16. The plume trajectory is found to be well behaved at almost all the angles of yaw between 0° and 30° (Fig. 16). It is found that the plume neither bends downwards after its exit from the funnel nor does it get trapped in the wake of the funnel even over adverse range of yaw angles. However, at w = 25°, it is found that there is a tendency for the

smoke to be trapped in the wake of the forward funnel as shown in Figs. 16e and 17. This trend, however, is not found either at w = 20° or 30°. This shows that the increased momentum of the exhaust is able to overcome the trapping in the unsymmetrical eddies and vortices shed due to incident wind at non-zero yaw angles.

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Fig. 16. Plume trajectory at K = 2 at different yaw angles for superstructure configuration ‘A’: (a) w = 0°, (b) w = 10°, (c) w = 15°, (d) w = 20°, (e) w = 25° and (f) w = 30°.

come by increasing the momentum of the exhaust from the funnels. 6.2. Superstructure configuration ‘A-1’ The funnels in the superstructure configuration ‘A-1’ are offset by half a funnel width on either side (along the y-axis) (Fig. 3c), which enables the study of the effect of the funnels being located unsymmetrical to the centerline plane of symmetry on the flow structure, wake as well as the exhaust smoke–superstructure interaction. Fig. 17. Tendency of the plume to be trapped behind the funnel at K = 2 and w = 25°.

6.1.5. Effect of velocity ratio on downwash As K is increased to 3 and 4, no downwash is observed at any of the yaw angles. The pathlines of plume trajectory at K = 4 at different yaw angles is shown in Fig. 18. The findings at K = 2, 3 and 4 indicate that the problem of downwash over the range of adverse yaw angles is over-

6.2.1. Details of flow structure and effect of yaw angle A detailed analysis of the vector plot of the cross-flow velocities of Vyz, Vxy and Vxz for superstructure configuration ‘A-1’ at K = 1, w = 0° is not being presented in the paper because it is found that the flow structure is similar to that around configuration ‘A’ in that there are two contra-rotating vortices downstream of the funnel. However, these vortices are offset (as are the funnels) and are not symmetrical about the centerline plane of symmetry of

Fig. 18. Plume trajectory at K = 4 at different yaw angles for superstructure configuration ‘A’: (a) w = 15°, (b) w = 20°, (c) w = 25° and (d) w = 30°.

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the superstructure at w = 0°. Further, as observed in superstructure configuration ‘A’, here too, the vortices are very strong in the region immediately behind the funnel. Similarly, studies at different yaw angles and velocity ratios also show that the flow structure for superstructure configuration ‘A’ discussed in the previous section are found to be applicable to superstructure configuration ‘A-1’ as well. The airflow results in the occurrence of downwash (Fig. 19) over the range of adverse wind directions (i.e. yaw angles from 20° to 30°), just as it did in superstructure configuration ‘A’ (Fig. 12). Further, as K is increased, the plume comfortably clears the deck.

6.3. Superstructure configuration ‘B’ In this configuration of the superstructure, the funnels are inline and the mast is located between the two funnels. The results for this configuration of the superstructure are presented from Figs. 20–23. The impinging of the smoke on the mast located between the two funnels in superstructure configuration ‘B’ as predicted by CFD simulation is qualitatively similar to the flow visualisation from wind tunnel studies at K = 1 and 2 as seen in Fig. 5c and d respectively, as is the predic-

tion of the trajectory of the smoke from the aft funnel compared in Fig. 5e and f. 6.3.1. Details of flow structure The strong recirculation zone in the wake of the mast is confirmed from the vector plot of Vxy on plane ‘Q’ (Fig. 20a) as well as Vxz at the centerline plane (Fig. 21). Comparing Fig. 21 with the flow visualisation photographs at Fig. 5c and (d) enables the identification and location of recirculation zone in the photographs. Further, the vector plot also shows that the wake of the mast shields the exhaust jet from the aft funnel. 6.3.2. Effect of yaw angle on superstructure configuration ‘B’ At non-zero yaw angle the structure of the wake-affected region of the mast is altered as seen in the vector plot of Vxy on the plane ‘Q’, at w = 20° is shown in Fig. 20b. Two contra-rotating vortices in the x–y plane in the wake region of the mast as well as the forward funnel are seen. The structure of the vortices in the wake of the aft funnel is different from that of the forward funnel. The altering of the wake of aft funnel due to the presence of the mast upstream is clearly evident. Further, the loss of symmetry of the flow structure about the centerline plane results in the setting up of cross-flow, an unsymmetrical recirculation zone

Fig. 19. Plume trajectory from superstructure configuration ‘A-1’ at various yaw angles: (a) w = 10°, K = 1; (b) w = 20°, K = 1; (c) w = 30°, K = 1.

Fig. 20. Vector plot of Vxy at plane ‘Q’ of superstructure configuration ‘B’ K = 1: (a) w = 0° and (b) w = 20°.

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Fig. 21. Vector plot of Vxz on centerline plane of superstructure configuration ‘B’ at K = 1, w = 0°.

Fig. 22. Plume trajectory at K = 2 at different yaw angles for superstructure configuration ‘B’: (a) w = 0°, (b) w = 20° and (c) w = 30°.

and the associated shedding of vortices, which bring into effect the phenomenon of downwash that has been discussed in case of superstructure configuration ‘A’. The path lines of the plume trajectory at different yaw angles at K = 1 (Fig. 22) shows that the effect of the yaw angle for this configuration of the superstructure is similar to that of superstructure configuration ‘A’. The yaw angles in the region of 20–30° continue to be adverse for superstructure configuration ‘B’ as well, as far as downwash is concerned. 6.3.3. Effect of velocity ratio on superstructure configuration ‘B’ The presence of the mast which is located downstream of the forward funnel enables the comparison of the plume trajectory at different velocity ratios. This is done by studying the impinging of the smoke on the mast or the margin by which it clears the mast at different values of K. The smoke from the forward funnel has a very high probability of impinging on the mast (and the electronic sensors

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located on it) in this configuration. At K = 1, the flow visualisation photograph (Fig. 5c) shows that almost the entire smoke impinges the mast while the top edge of the smoke only just clears it. Thus the smoke would certainly contaminate the electronic equipment mounted on top of the mast. The flow visualisation by pathlines from CFD simulation also predicts a similar trajectory of the smoke at K = 1 (Figs. 5c and 23a). As K is increased to 2, it is found that the smoke traces a higher trajectory due to the greater momentum and as a result, the top edge of the smoke clears the mast almost completely as shown in Figs. 23b and 5d. Though the lower boundary of the smoke clears the deck at a greater height as compared to the case of K = 1, the lower edge of the smoke still impinges on the mast. However, the impinging of the lower edge of the smoke on the mast though is at a greater height as compared to the case of K = 1. As a result, though the top edge of the smoke clears the top of the mast comfortably, the masthead (i.e. the top of the mast) is still impinged by the smoke. The electronic equipment mounted on top of the mast would continue to be contaminated by smoke even after increasing K to 2 (Fig. 23b). These pathlines predicted by CFD compare very well with the flow visualisation photographs in Fig. 5d. As K is increased to 3, the lower edge of the plume is found to just clear the masthead (Fig. 23c). Further, at K = 4 (Fig. 23d), the plume comfortably clears the mast and the electronic equipment mounted on top would be free from any contamination. The mast is free from smoke contamination at K = 3 and 4. The pathlines of the smoke trajectory in Fig. 23 show that as the velocity ratio is increased from 1 to 4, the increasing momentum results in the plume tracing a higher trajectory, thus clearing the mast as well as the electronic equipment mounted on top. 6.4. Superstructure configuration ‘C’ The results for this configuration of the superstructure are presented from Figs. 24–30. Superstructure configuration ‘C’ has the superstructure/mast block located upstream of the funnel (Fig. 2d), which represents the typical cluttered topside of a naval ship featuring short funnels that are located in the vicinity of taller structures that are aerodynamically bluff bodies. The flow around this configuration of superstructure as well as the plume trajectory is expected to be significantly affected by the presence of this superstructure/mast block as the exhaust smoke exits into the wake of the bluff bodies located upstream of the funnel. The photographs from flow visualisation study (Fig. 5g) at K = 1 and w = 0°, show that the smoke gets trapped in the strong recirculation zone immediately downstream of the superstructure/mast block and comes down on the deck, thus exhibiting a severe downwash. Close-up photograph of the trajectory of the plume in the wake of the bluff body at K = 1 (Fig. 24a) shows that the plume path bends backwards from the vertical. The trajectory of the exhaust smoke as K is increased to 2 is shown in

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Fig. 23. Plume trajectory at w = 0° at different velocity ratios for superstructure configuration ‘B’: (a) K = 1, (b) K = 2, (c) K = 3 and (d) K = 4.

Figs. 5h and 24c. The smoke does exhibit a tendency to get sucked into the wake of the mast as captured in the photograph shown in Fig. 24c. However, the momentum is sufficient to enable it to overcome the wake region and avoid the downwash. The qualitative comparison between the flow visualisation by CFD simulation and the flow visualisation photographs from wind tunnel studies at K = 1 and 2 (Figs. 5g and h and 24) shows a very good prediction by CFD.

6.4.1. Details of flow structure The vector plot of Vxy for K = 1 at w = 0° for this configuration of the superstructure at plane ‘Q’ (Fig. 25), indicate the presence two counter rotating vortices behind the superstructure/mast block. The vortices are found behind the forward funnel as well. An enlarged view of these vortices of Vxy on plane ‘Q’ in Fig. 25b clearly shows the interaction of the mast wake with the wake of the forward funnel.

Fig. 24. Qualitative comparison of plume trajectory from forward funnel through flow visualisation by CFD simulation and wind tunnel studies for superstructure configuration ‘C’ at (a, b) K = 1 and (c, d) K = 2.

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Fig. 25. Vector plot of Vxy on plane ‘Q’ at K = 1.

Fig. 26. Vector plot of Vyz at K = 1 on planes 1 and 2. (a) Plane M and (b) plane 3.

In order to establish the process of shielding, and to study the flow structure immediately downstream of the superstructure/mast block in superstructure configuration

‘C’, an additional plane ‘M’ (Fig. 6b) is defined between the forward funnel and the superstructure/mast block. Two pairs of symmetrical contra-rotating vortices appear

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Fig. 27. Plume trajectory at w = 0° and (a) K = 1, (b) K = 2, (c) K = 3 and (d) K = 4 for superstructure configuration ‘C’.

Fig. 28. Plume trajectory at w = 10° and K = 2, 3 and 4 for superstructure configuration ‘C’. (a) K = 1, w = 10°; (b) K = 2, w = 10°; (b) K = 3, w = 10° and (d) K = 4, w = 10°.

behind the superstructure/mast block on plane ‘M’ (Fig. 26a). The first pair of the vortices is attributed to the mast and are present up to the height of the mast. The second pair of vortices is due to the presence of the superstructure block. The region of strong intensity of these two pair of vortices coincides with the shape of the

superstructure/mast block, indicating that the bluff body shape affects the size and the location of the vortices. As indicated in the vector plots of Vxy at plane ‘Q’ (Fig. 25), as well as the vector plots of Vyz at planes ‘M’ and 3 (Fig. 26), the vortices cause a significant recirculation zone in the wake of the superstructure/mast block as well

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Fig. 29. Vector plot of Vxy on plane ‘Q’ of superstructure configuration ‘C’ at w = 10°, K = 2.

Fig. 30. Vector plot of Vyz on superstructure configuration ‘C’ at K = 1, w = 10°: (a) plane 3 and (b) plane 4.

as the funnel. This recirculation zone causes the exhaust from the forward funnel to be sucked into this region as observed during the flow visualisation studies (Figs. 24a and 5g). These vortices behind the superstructure/mast block explain the phenomenon of the plume bending backwards from the vertical (Fig. 24a) as well as severe downwash at K = 1 (Figs. 5g and 24a). At K = 2, the increased momentum of the plume is able to overcome the eddies and vortices in the wake of the superstructure/mast block. The plume escapes the wake region without significant downwash, though there is a tendency for the smoke to be sucked in, as indicated by the

pathlines in Figs. 24d and 27b and the photograph from flow visualisation (Figs. 5h and 24c). Further increase in momentum at K = 3 and 4 (Fig. 27c and d) shows that there is absolutely no trapping of the smoke or downwash. This confirms that the increasing momentum of the exhaust is necessary to overcome the strong recirculation zone in the wake of the bluff bodies on the topside of the ship, particularly those in the vicinity of the funnel. 6.4.2. Effect of yaw angle on superstructure configuration ‘C’ At non-zero yaw angles, a significantly different plume trajectory of the exhaust from the forward funnel is

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observed. The exhaust seems to get divided into two parts after exit as shown in Fig. 28, unlike what was observed in superstructure configuration ‘A’ (Fig. 12) or ‘B’ (Fig. 22). The vector plots of Vxy at plane ‘Q’ for superstructure configuration ‘C’ at w = 10° and K = 2 (Fig. 29), indicate the presence of the two counter rotating vortices behind the superstructure/mast block. However, these vortices are now no longer symmetrical about the centerline plane, unlike the symmetrical flow structure in the case when w = 0° (Fig. 25). The vector plot of Vyz at planes 3 and 4 (Fig. 30) confirms the absence of symmetry of the vortices about the centerline plane. Further, the vector plot of Vyz (Fig. 30) shows that the strength of the two vortices is not equal unlike the equal strength of the two symmetrical vortices in the case when w = 0° at the planes M and 3 (Fig. 26). The unequal strength of the two contra-rotating vortices at planes 3 and 4 at w = 10° (Fig. 30) is explained from the vector plot of Vxy at the planes ‘Q’ (Fig. 29). The wind is incident at an angle of w = 10°. The vector plots of Vxy show the unsymmetrical region of low pressure on the leeward side due to the wake of the superstructure/mast block. The flow on plane ‘Q’ (Fig. 29) shows that as the wind is flowing past the superstructure/mast block located upstream of the forward funnel, the airflow from the windward side is found to sweep inwards into the region down stream of the forward funnel. The ‘‘sweeping inwards’’ of the airflow is aided by a region of low pressure due to the wake of the superstructure/mast block on the leeward side as indicated in Fig. 29. This sweeping action of the flow as it negotiates the superstructure/mast block at non-zero yaw angles causes the two vortices of Vyz at planes down stream of the forward funnel (such as those at planes 3 and 4 (Fig. 30)) to be of non-equal strength or intensity. As the exhaust jet rises from the funnel, it encounters these unequal vortices. A bulk of the exhaust jet is deflected by the cross-flow and the plume bends until its flow becomes principally horizontal as happens at w = 0°. However, the remainder of the jet, which is on the leeward side that encounters the stronger of the two vortices, curls, as seen in Fig. 28a. The analysis of the vector plots of Vxy (Fig. 29) and Vyz (Fig. 30) shows that the airflow from the windward side that is ‘‘sweeping inwards’’ causes the exhaust plume to curl. However, at exit of the funnel, the exhaust smoke has momentum, which resists this curling behavior. Therefore, the plume gets divided into two distinctly different parts as shown in Fig. 28a. The visualisation of the plume trajectory from pathlines at K = 2, 3 and 4 for w = 10° (Fig. 28b–d) indicate that the curling phenomenon manifests at higher velocity ratio’s as well. The comparison of Fig. 28a–d shows that the component of the plume that curls as well as its height above the deck remains more or less the same. However, on the remaining component of the plume (which does not curl), the effect of increasing momentum, as K is increased from 2 to 4, is evident in the form of

increasing height of the plume trajectory above the deck (Fig. 28). 7. Conclusions from parametric studies The prediction of flow path of the exhaust plume from the ship funnels is extremely complicated since the phenomenon is affected by a large number of parameters like wind velocity and direction, level of turbulence, geometry of the structures on the ship’s deck, efflux velocity of smoke, ships heading etc. Varying these parameters and investigating a total of 112 cases using CFD have explored the features of the exhaust smoke–superstructure interaction. Though the instantaneous velocity in the wake region of the bluff body due to unsteady vortices is not captured by the present CFD analysis as it assumes the flow to be quasi-steady, the following conclusions are drawn from the parametric investigation: (a) It is clear that the flow in the funnel region as well as behind the bluff bodies like the superstructure block/ mast is charecterised by recirculation zones, strong vortex fields and large velocity gradients. The analysis of the flow structure downstream of the bluff body such as the funnel or the mast indicates that it generates a vortex trail that depends on the shape of the bluff body and the degree of its streamlining. The strength of these trailing vortices is a major contributor of the downwash. (b) The performance of the funnel exit flow in the wind is found to deteriorate at the yaw angles between 20° and 30° and therefore, these may be termed as ‘‘adverse range of yaw angles’’ as far as downwash from the funnels is concerned. These yaw angles are found to be adverse in all the configurations of the superstructure investigated. It is strongly recommended that the ship designer should specially study the flow conditions with relative winds over the adverse range of yaw angles. (c) The conformance of the CFD predicted plume trajectory at yaw angles between 20° and 30° to the findings of Ower and Third [7,8] as well as Burge and Ower [9] based on their wind tunnel experiments and field observations reinforces the point that CFD is a powerful tool in the study of the exhaust smoke–superstructure interaction. (d) The major reason for the downwash at these ‘adverse’ yaw angles is loss of symmetry of the flow structure. The symmetry of the airflow pattern is destroyed as soon as the ship begins to turn across the wind. The absence of symmetry in the flow about the centerline plane of the superstructure causes cross-flow which results in the airflow around the funnel from the windward side to mix with the airflow along the leeward side of the funnel, thus producing eddies. The lower boundary of the plume gets trapped into these eddies and descends on the deck, resulting in downwash.

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(e) The location as well as the size of the bluff body upstream of the funnel (as in case of superstructure configuration ‘C’) results in the funnel ejecting the exhaust gases into a strong recirculation zone. Apart from causing the smoke to come down, the wake of the bluff body upstream causes the structure of the plume that is ejected from the downstream funnel to be adversely altered at non-zero yaw angles. (f) The presence of a bluff body induces a modification in the undisturbed velocity field, originating vortical structures downstream of the body whose form, dimension and persistence are a function of the size and location of the bluff body. Turbulent eddies caused by the interaction of the wind with bluff body superstructure as well as the smoke funnel itself are found to cause a downwash of the smoke. (g) It is concluded from the study of all the configurations of the superstructure taken up for investigation that a velocity ratio of at least 2 should be maintained to avoid the problem of downwash. At velocity ratio greater than 2 (i.e. at 3 and 4), the increased momentum ensures that the smoke stays well clear of the deck. However, apart from preventing the downwash, avoiding smoke nuisance on naval ships also needs ensuring that the smoke clears the mast that is located between the funnels, which is necessary to avoid the contamination of the electronic equipment located on top. (h) The analysis of the superstructure configuration ‘B’ shows that the momentum of the exhaust at velocity ratio of 2 is not sufficient to completely clear the mast. The plume requires greater momentum and therefore, a velocity ratio of at least 3 is required to ensure that the smoke does not contaminate the topside electronics located on the mast. However, an optimisation study is required to arrive at the economic velocity ratio, which falls within narrow limits – if the velocity ratio is too low, the smoke descends to the deck level. But on the other hand, if the velocity ratio were higher than necessary, then there would be a waste of power throughout the life of the ship. (i) This study has demonstrated that CFD is a powerful tool capable of predicting the larger scale features of the exhaust smoke–superstructure interaction. As a flow simulation tool, CFD can predict exhaust smoke–superstructure interaction around full-scale ships, which is particularly advantageous for the investigation of modern naval ships with complex superstructures. Further, the results of the study show that level of detail and the realistic physical foundations of the CFD simulation of the flow pattern and dispersal of exhaust plume smoke over the superstructure of naval ships gives unique opportunities to naval ship designers that were not available before. The advantages offered are:

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(i)

CFD methods allow complex flow patterns around the superstructure to be identified and problems about smoke nuisance can now be addressed during the early stages of ship design. (ii) They allow the detection of shortfalls in design and to find efficient means to eliminate them. (iii) CFD also provides capabilities for problem solving and troubleshooting of the smoke nuisance problem on the already constructed and operational ships. Acknowledgements The present study was sponsored by the Naval Research Board (NRB) of the DRDO (Defence Research and Development Organisation) and the authors are grateful to them for their support. References [1] Seshadri V, Singh SN. Wind tunnel studies to obviate the problem of unwarranted rise in air intake temperatures of gas turbines in naval ship. Technical Report, Applied Mechanics Department, IIT Delhi, 2000. [2] Seshadri V, Singh SN, Kulkarni PR. A study of the problem of ingress of exhaust smoke into the GT intakes in naval ships. J Ship Technol 2006;2(1):22–35. [3] Sherlock RH, Stalker EA. Control of gases in the wake of smoke stacks. Mech Eng 1940;62:455–8. [4] Sherlock RH, Stalker EA. A study of flow phenomenon in the wake of smoke stacks. Eng Res Bull, Univ Michigan 1941:44. [5] Nolan RW. Design of stacks to minimise smoke nuisance. Trans SNAME 1946;54:42–82. [6] Acker HG. Stack design to avoid smoke nuisance. Trans SNAME 1952;60:566. [7] Ower E, Third AD. Superstructure design in relation to the descent of funnel smoke. Trans Inst Marine Eng (London) 1959;1:109–38. [8] Third AD, Ower E. Funnel design and smoke plume. Trans Inst Marine Eng (London) 1962;72:245–72. [9] Burge CH, Ower E. Funnel design and smoke abatement. Trans Inst Marine Eng (London) 1950;62:119. [10] Micheal K Johns, Val Healy J. The airwake of a DD 963 class destroyer. Naval Eng J, ASNE 1989(May):36–42. [11] Kulkarni PR, Singh SN, Seshadri V. Experimental study of the flow field over simplified superstructure of a ship. Int J Maritime Eng, IJME Part A3 2005:19–42. [12] Kulkarni PR, Singh SN, Seshadri V. Flow visualisation studies of exhaust smoke–superstructure interaction on naval ships. Naval Eng J, ASNE 2005;117(1):41–56. [13] Davies ME, Cole LR, O’Neill PGG. Wind tunnel investigation of the temperature field due to the hot exhaust of power generation plants on offshore platforms. UK: National Maritime Institute; 1979. [14] Baham GJ, Mc Cullum D. Stack design technology for naval and merchant ships. Trans SNAME 1977;85:324–49. [15] Taylor K, Smith AG. CFD prediction of exhaust plumes and interaction with superstructures. Application of fluid dynamics in the safe design of topsides and superstructures. London: Institute of Marine Engineers; 1997. p. 56–61. [16] Jin E, Yoon J, Kim Y. A CFD based parametric study on the smoke behaviour of a typical merchant ship. PRADS’01, Shanghai, 2001. p. 459–65.

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P.R. Kulkarni et al. / Computers & Fluids 36 (2007) 794–816

[17] Camelli F, Lo¨hner R. Sandberg WC, Ramamurti R. VLES study of ship stack gas dynamics. AIAA paper 2004-0072. In: 42nd AIAA aerospace sciences meeting and exhibition, Reno (NV), January 2004. [18] Sandberg WC, Camelli F, Ramamurti R, Lohner R. Ship topside air contamination analysis: unsteady computations and experimental validation. In: ASNE annual meeting, 2004. [19] Moctar El, Gatchell Scott, Bertram Volker. RANSE simulations for aerodynamic flows around ship superstructures, 2001. In: 4th Num. towing tank symposium, Hamburg. [20] Kulkarni PR, Singh SN, Seshadri V. Behaviour of a ship funnel in the wake of a bluff body MARINE CFD-2005. In: RINA’s 4th int conference on marine hydrodynamics, Southampton, UK, 30–31 March 2005. p. 115–24. [21] Ramamurti R, Sandberg WC. Unstructured grids for ship unsteady airwake on the LPD-17: a successful validation. Naval Eng J, ASNE 2002;114(4):41–53. [22] Docton MKR, Underhill Richard, Morrison Elizabeth. Improving carrier operation through the application of CFD to the CVF design process. In: MARINE CFD-2005, RINA’s 4th int conference on marine hydrodynamics, Southampton, 30–31 March 2005. p. 125–31. [23] Kulkarni PR, Singh SN, Seshadri V. Comparison of CFD simulation of exhaust smoke–superstructure interaction on a ship with experimental data. In: Proc of naval platform technology seminar – 2005 (NPTS-05), Singapore, 17–18 May 2005. p. 150–72. [24] Kulkarni PR. An aerodynamic study of exhaust smoke–superstructure interaction on naval ships. PhD Thesis, IIT Delhi, India, 2004. [25] Tai TC, Carico D. Simulation of DD-963 ship airwake by Navier– Stokes method. J Aircraft 1995;32(6):1399–401. [26] Tai TC. Effect of ship motion on DD-963 ship airwake simulated by multizone Navier–Stokes solution. In: 21st Symposium on naval hydrodynamics 2001. p. 1007–17. [27] Landsberg AM, Sandberg WC, Young TR. DDG 51 Flight II A airwake study: hangar interior flow. Naval Research Laboratory Memorandum Report 96-7898, 1996.

[28] Landsberg AM, Sandberg WC. DDG 51 Flight II A airwake study: temperature field analysis for baseline and upgrade configurations. Naval Research Laboratory Memorandum Report 6401-00-8432, 2000. [29] Reddy KR, Toffoletto R, Jones KRW. Numerical simulation of ship airwake. Comput Fluids 2000;29:451–65. [30] Landsberg AM, Boris J, Sandberg WC, Young TR. Analysis of the nonlinear coupling of a helicopter downwash with an unsteady airwake. AIAA Paper, American Institute of Aeronanautics and Astronautics 1995-0047, Washington, DC, 1995. [31] Ramamurti R, Sandberg WC. LPD-17 topside aerodynamic study: FEFLO. NRL Memorandum Report NRL/MR/6410-00-8498, Center for Reactive Flow and Dynamical Systems, Laboratory for Computational Physics and Fluid Dynamics – Naval Research Laboratory, October 2000. [32] Guillot MJ, Walker MA, Reader K, Ramamurti R, Sandberg WC. LPD-17 topside aerodynamic study. Final Technical Report, Naval Research Laboratory, 2000. [33] Kulkarni PR, Singh SN, Seshadri V. Study of exhaust smoke ingress into GT intake on ships MAHY-06. In: Int conference on marine hydrodynamics, NSTL, Visakhapatnam (India), January 2006. [34] Jensen AG, Sondergaard T, Livesey F. Wind comfort for cruise/ passenger vessels. Cruise and Ferry; 1997. [35] Kulkarni PR, Singh SN, Seshadri V. Study of smoke nuisance problem on ships – a review. Int J Maritime Eng, IJME Part A2 2005:27–50. [36] Gosman AD. Developments in CFD for industrial and environmental applications in wind engineering. J Wind Eng Ind Aerodyn 1999;81:21–39. [37] Murakami S. Comparison of various turbulence models applied to a bluff body. J Wind Eng Ind Aerodyn 1993;46–47:21–36.