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Research Papers: Flows in Complex Systems

Visualization and Validation of Ejector Flow Field With Computational and First-Principles Analysis

[+] Author and Article Information
Adrienne B. Little

George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332

Yann Bartosiewicz

Institute of Mechanics, Materials,
and Civil Engineering (iMMC),
Université catholique de Louvain (UCL),
Louvain-la-Neuve 1348, Belgium
e-mail: yann.bartosiewicz@uclouvain.be

Srinivas Garimella

George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: sgarimella@gatech.edu

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received July 4, 2014; final manuscript received October 22, 2014; published online February 9, 2015. Assoc. Editor: John Abraham.

J. Fluids Eng 137(5), 051107 (May 01, 2015) (12 pages) Paper No: FE-14-1353; doi: 10.1115/1.4029534 History: Received July 04, 2014; Revised October 22, 2014; Online February 09, 2015

Passive, heat actuated ejector pumps offer simple and energy-efficient options for a variety of end uses with no electrical input or moving parts. In an effort to obtain insights into ejector flow phenomena and to evaluate the effectiveness of commonly used computational and analytical tools in predicting these conditions, this study presents a set of shadowgraph images of flow inside a large-scale air ejector and compares them to both computational and first-principles-based analytical models of the same flow. The computational simulations used for comparison apply k-ε renormalization group (RNG) and k-ω shear stress transport (SST) turbulence models to two-dimensional (2D), locally refined rectangular meshes for ideal gas air flow. A complementary analytical model is constructed from first principles to approximate the ejector flow field. Results show that on-design ejector operation is predicted with reasonable accuracy, but accuracy with the same models is not adequate at off-design conditions. Exploration of local flow features shows that the k-ω SST model predicts the location of flow features, as well as global inlet mass flow rates, with greater accuracy. The first-principles model demonstrates a method for resolving the ejector flow field from relatively little visual data and shows the evolving importance of mixing, momentum, and heat exchange with the suction flow with distance from the motive nozzle exit. Such detailed global and local exploration of ejector flow helps guide the selection of appropriate turbulence models for future ejector design purposes, predicts locations of important flow phenomena, and allows for more efficient ejector design and operation.

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References

Figures

Grahic Jump Location
Fig. 1

Schematic of ejector with corresponding qualitative pressure and velocity profiles. Adapted from Ref. [1].

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Fig. 2

(a) Photograph of the visualization section and (b) dimensioned drawing of same section showing exact 3D geometry of motive nozzle, suction nozzle, mixing section, and diffuser. Dotted line in (b) indicates the area available for visualization. Shaded areas indicate the computational domain and region visualized in shadowgraph images.

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Fig. 3

Schematic of overall system indicating locations of pressure and temperature measurements, and orifice plates for flow rate measurement. Inset shows schematic of shadowgraph visualization setup.

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Fig. 4

Experimental characteristic curve of ejector operation for Pmotive = 3.5 bar and Psuction = 1 bar shown by dotted line. Diamond-shaped data points indicate MER values for CFD simulations for the one on-design and one off-design condition considered for comparison in this study.

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Fig. 5

Centerline static pressure versus axial position for k-ε turbulence model. An ∼18% increase in the number of elements shows negligible change in the solutions.

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Fig. 6

Centerline static pressure versus axial position for k-ω turbulence model. An ∼18% increase in the number of elements shows negligible change in the solutions.

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Fig. 7

(Top) Image from off-design CFD simulation (Pmotive = 3.5 bar, Psuction = 1 bar, and Poutlet = 1.5 bar) using k-ε RNG turbulence model indicating region locations. (Middle) Schematic used for analytical model, identifying important geometric values. (Bottom) Graph shows contours of static pressure along the motive jet center and jet boundary, indicating specific points where data were taken for states 3–6 in Table 2.

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Fig. 8

Experimental image and CFD data for on-design condition with Pmotive = 3.5 bar, Psuction = 1 bar, and Poutlet = 1.2 bar. Visual comparison using contours produced by k-ε RNG turbulence model. Bottom graph shows static pressure along ejector central axis, comparing values for k-ε RNG to k-ω SST data. Three major flow features are indicated at x = −22.6 mm (expansion), x = −13.0 mm (compression), and x = −5.8 mm (expansion).

Grahic Jump Location
Fig. 9

Experimental image and CFD data for off-design condition with Pmotive = 3.5 bar, Psuction = 1 bar, and Poutlet = 1.5 bar. Visual comparison using contours produced by k-ε RNG turbulence model. Bottom graph shows static pressure along ejector central axis, comparing values for k-ε RNG to k-ω SST data. Three major flow features are indicated at x = −22.6 mm (expansion), x ≈ −13.2 mm (compression), and x ≈ −7.1 mm (expansion).

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