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Research Papers: Fundamental Issues and Canonical Flows

# Investigation in the Near-Field of a Row of Interacting Jets

[+] Author and Article Information
Shahriar Ghahremanian

Department of Building, Energy
and Environmental Engineering,
Faculty of Engineering and
Sustainable Development,
University of Gävle,
Gävle 801 76, Sweden
Division of Energy Systems,
Department of Management and Engineering,
e-mail: shahriar.ghahremanian@liu.se

Bahram Moshfegh

Department of Building, Energy
and Environmental Engineering,
Faculty of Engineering and
Sustainable Development,
University of Gävle,
Gävle 801 76, Sweden
Division of Energy Systems,
Department of Management and Engineering,
e-mail: bahram.moshfegh@hig.se

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received December 9, 2014; final manuscript received June 4, 2015; published online August 6, 2015. Assoc. Editor: Samuel Paolucci.

J. Fluids Eng 137(12), 121202 (Aug 06, 2015) (18 pages) Paper No: FE-14-1738; doi: 10.1115/1.4031014 History: Received December 09, 2014

## Abstract

Multiple interacting jets (confluent jets) are employed in many engineering applications, and the significant design factors must be investigated. Computational fluid dynamics (CFD) is used to numerically predict the flow field in the proximal region of a single row of round jets. The numerical results that are obtained when using the low Reynolds $k-ε$ are validated with the experimental data that are acquired by particle image velocimetry (PIV). PIV was used to measure mean velocity and turbulence properties in the proximal region of a row of six parallel coplanar round air jets with equidistant spacing at low Reynolds number (Re = 3290). The low Reynolds $k-ε$ underpredicts the streamwise velocity in the onset of the jets' decay. The characteristic points are determined for various regions between two neighboring jets. The comparison of the merging point (MP) and the combined point (CP) computed from measurements and simulations shows good agreement in the different regions between the jets. In this study, a computational parametric study is also conducted to determine the main effects of three design factors and the interactions between them on the flow field development using response surface method (RSM). The influences of the inlet velocity, the spacing between the nozzles, and the diameter of the nozzles on the locations of the characteristic points are presented in the form of correlations (regression equations). CFD is used to numerically predict the characteristic points for a set of required studies, for which the design values of the simulation cases are determined by the Box–Behnken method. The results indicate that the spacing between the nozzles has a major impact on the flow characteristics in the near-field region of multiple interacting jets. The RSM shows that the inlet velocity has a marginal effect on the merging and CPs. All of the square terms are removed from the response equations of MP, and only one two-way interaction term between inlet velocity and spacing remains in the regression model with a marginal effect. The square of the nozzle diameter contributes in the regression equations of CP in some regions between the jets.

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## Figures

Fig. 2

Location of the PIV measurement and the nozzle shape geometry

Fig. 1

Top-view sketch of the wind tunnel, test chamber, and instrumentation

Fig. 3

Computational domain and nozzle shape of the validation case

Fig. 4

Decay of the streamwise velocity along the geometrical centerline of CJ I (left) and CJ II (right)

Fig. 5

Streamwise velocity (V/Vb) at different distances from the nozzles edge

Fig. 6

Contour plot of the velocity magnitude ((U2+V2)/Vb) in the geometrical center-plane (z = 0) measured by the PIV (left) and predicted by the low Re kϵ (right)

Fig. 11

Comparison of the numerically predicted (CFD) and statistically estimated (RSM) characteristic points (MP: top, CP1: middle, and CP2: bottom) versus the Reynolds number

Fig. 8

Residual plots of MP in the region II–SJ after using the backward elimination method, the normal probability plot of the residuals (top left), the residuals versus the fits (top right), the histogram of the residuals (bottom left), and the residuals for each test case (bottom right)

Fig. 9

Comparison of numerically predicted (CFD) and statistically estimated (RSM) characteristic points (MP: top, CP1: middle, and CP2: bottom) versus the airflow rate, Q (m3/s)

Fig. 10

Comparison of the numerically predicted (CFD) and statistically estimated (RSM) characteristic points (MP: top, CP1: middle, and CP2: bottom) versus S/d

Fig. 7

Residual plots of CP1 in the region II–SJ before stepwise regression, normal probability plot of residuals (top left), residuals versus fits (top right), histogram of residuals (bottom left), and residuals for each test case (bottom right)

Fig. 15

Contour plot of CP2 (m) in regions I–I (left), I–II (middle), and II–SJ (right) versus d – S (top, V  = 8 m/s), d – V (middle, S  = 0.012 m), and S – V (bottom, d  = 0.009 m)

Fig. 16

Surface plot of CP2 (m) in regions I–I (left), I–II (middle), and II–SJ (right) versus d – S (top, V = 8 m/s), d – V (middle, S = 0.012 m), and S – V (bottom, d = 0.009 m)

Fig. 12

Contour (top) and surface (bottom) plot of MP (m) in various regions (I–I and II–SJ) versus S – V (left, d  = 0.007 m), d – V (middle, S  = 0.014 m), and d – S (right, V  = 12 m/s)

Fig. 13

Contour plot of the CP1 (m) in regions I–I (left), I–II (middle), and II–SJ (right) versus d – S (top, V  = 10 m/s), d – V (middle, S  = 0.014 m), and S – V (bottom, d  = 0.009 m)

Fig. 14

Surface plot of CP1 (m) in regions I–I (left), I–II (middle), and II–SJ (right) versus d – S (top, V  = 10 m/s), d – V (middle, S  = 0.014 m), and S – V (bottom, d  = 0.009 m)

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