Last developments in the modeling of supersonic ejectors ?· Last developments in the modeling of supersonic…

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  • Last developments in the modeling of

    supersonic ejectors

    1

    Sergio CROQUERa*, Sbastien PONCETa, Zine AIDOUNbaFacult de gnie, Dpartement de gnie mcanique, Universit de Sherbrooke, Sherbrooke, Canada

    bCanmetENERGY-Natural Ressources Canada, Varennes, Canada

    Orford, 11-12 janvier 2018

    3me assemble annuelle du CREEPIUS

  • Introduction Supersonic Ejectors

    Key benefits:

    No moving parts

    Simple operation

    Low grade energy as input

    Handle single- and two-phase flows

    An apparatus which harvests the high energy of a jet (primary flow), to entrain and

    compress a secondary flow.

    Image: http://www.ertc.od.ua/en/about_ert_en.html 2S. Croquer

  • Introduction Applications

    3S. Croquer

    Ejector Expansion Ref. Cycle (EERC)

    Condenser

    Evaporator

    Compressor

    Valve

    SeparatorEjector

    Condenser

    Evaporator

    Ejector

    Valve

    Generator

    Pump

    Heat Driven Ref. Cycle (HDRC)

    Desalination

    AC system in electric vehicles

    Emergency systems in nuclear facilities

    Gas wells

    Rebreathers Scuba diving

    Refrigeration

    http://www.mazdalimited.com/images/steam-jet-bosster-ejector-header-img.jpg http://articles.sae.org/6741/

  • Introduction Why a numerical study of ejectors?

    4S. Croquer

    Ejector test benches are limited by:

    Small dimensions

    Thermal insulation

    Hazardous working fluids

    Extreme operating conditions

    Numerical modeling:

    Thermodynamic (0D) models

    1D models

    CFD

    Images: R134a ejector test bench at CanmetEnergy

    Assembly

    Components

  • Developments in Numerical Modelling of Ejectors

    5S. Croquer

    While RANS modelling approaches are already well established for

    supersonic ejectors, two new roads are possible:

    - Injection of Droplets in the Constant Area Section of a Supersonic

    Ejector for Shock Attenuation

    - Large-Eddy Simulation of an Air Supersonic Ejector

    (In course)

    Explore new modelling techniques

    Exploit current RANS models

    2

    1

  • R134a ejector with droplet injection

    6S. Croquer

    Primary Inlet

    PP0, TP0

    Secondary Inlet

    PS0, TS0Outlet

    ShockD

    nd

    Droplet Injection

    de

    Dinj

    0,0

    0,2

    0,4

    0,6

    0,8

    26 28 32 33

    Po

    rtio

    n o

    f to

    tal lo

    sse

    s

    [%]

    Section 1 Section 2

    Tsatout [oC]

    Rationale:

    Shocks and mixing are responsible for about

    60% of the exergy lost in the ejector (Croquer

    et al. 2016).

    Droplets might attenuate the shock train

    intensity and attack this source of losses.

    The effects on a supersonic ejector are

    assessed using a combined approach (RANS

    + thermodynamic modelling).

    Mixing Shocks

    Injection of Droplets in the Constant Area Section of a Supersonic Ejector for

    Shock Attenuation

  • R134a ejector with droplet injection

    7S. Croquer

    Operating

    Point

    Primary Inlet Secondary Inlet Outlet

    T [C] P [kPa] T [C] P [kPa] T [C] P [kPa]

    1 89.4 2598 20.0 415 29.4 757

    2 94.4 2889 20.0 415 32.5 827

    3 99.2 3188 20.0 415 35.4 897

    1

    2

    3

    Pre

    ssu

    re

    Enthalpy

    1

    2

    3

    Ejector geometry and operating conditions

    R134a

    Reference: Garcia et al. An experimental investigation of a R-134a ejector refrigeration system.

    International Journal of Refrigeration, 46 (2014) 105-113

  • R134a ejector with droplet injection

    8S. Croquer

    RANS ModelSecondary inlet:Total P and Total T

    Primary inlet:

    Total P and Total T

    Outlet: Static

    pressure

    Smooth, adiabatic walls

    2D axi-symmetric domain

    Mesh: structured (650000 elements) with 21

    wall-adjacent prismatic layers

    Gas properties: REFPROP Eq. database

    Turbulence model: k- SST low-Reynolds

    formulation

    Steady state

    Schemes: 2nd order upwind (advection) and

    2nd order centered (diffusion)

    Numerical Parameters Droplet injection

    Discrete phase (Lagrangian frame)

    Coupling with main flow: two-way

    (momentum and thermal energy exchanges)

    Constant properties

    Breakup model: WAVE (We > 100)

    RANS model accuracy (w/o droplet injection):

    - 5% in terms of entrainment ratio

    - 2% in terms of compression ratio

    Croquer et al., Turbulence modeling of a single-phase R134a supersonic ejector. Part 1:

    Numerical benchmark, Int. J. of Refrigeration, 61, p.140-152, 2016.

  • R134a ejector with droplet injection

    9S. Croquer http://www.enmodes.de/references/validation/validation-turbulence/

    Thermodynamic Model

    Outlet

    L1

    D

    nd

    L2 L3 L4 L5 L7L6

    de

    Dinj

    Input:

    - Geometry: primary throat, constant area section and diffuser diameters

    - Operating conditions: inlet P and T, outlet P

    - Efficiency coefficients for inlet accelerations, mixing and diffusion

    Output:

    Double-choke entrainment ratio

    Limiting compression ratio

    Assumptions:

    - Real gas equations (CoolProp)

    - Uniform values at each cross section

    - Entrainment ratio depends on effective area

    - Mixing occurs at Constant Area Section

    - Normal shock in the Constant Area Section after complete mixing

    - Losses represented via isentropic and mixing efficiencies

    The model has been extensively validated in single- and two-phase flow operations in Croquer et al.,

    Thermodynamic modeling of supersonic gas ejector with droplets, Entropy, 19(579), p.1-21, 2017.

  • R134a ejector with droplet injection

    10S. Croquer

    Comparison between the RANS and Thermodynamic model

    Ma number Temperature

    Pressure

    Inlet conditions: OP2

    Injection:

    Diameter: 500 microns

    Temperature: 260 K

    Fraction: 10% of primary mass flow rate

  • R134a ejector with droplet injection

    11S. Croquer

    Internal shock structure

    Injection location

    No injection

    1%

    5%

    10%

  • R134a ejector with droplet injection

    12S. Croquer

    Changes in flow properties

    -100

    102030405060

    L4 L5 L6 L7

    Tem

    per

    atu

    re [

    oC

    ]

    Location

    0% 1% 2% 5% 10%

    Injection fraction

    100

    300

    500

    700

    900

    L4 L5 L6 L7

    Pre

    ssu

    re [

    kP

    a]

    Location

    0% 1% 2% 5% 10%

    Injection fraction

  • R134a ejector with droplet injection

    13S. Croquer

    Shock attenuation

    Pressure jump Ma jump

    With increasing injection fraction, the shock pressure and Ma jumps reduce

  • R134a ejector with droplet injection

    14S. Croquer

    Effects on performance Limiting pressure

    Injection locationNo injection

    10%

    u

    Injection locationNo injection

    10%

  • R134a ejector with droplet injection

    15S. Croquer

    Effects on performance Ejector efficiency

    With increasing injection fraction, the ejector efficiency reduces

  • R134a ejector with droplet injection

    16S. Croquer

    Effects on performance Exergy accounting

    Conclusion:

    Droplet injection attenuates shock, reducing the importance of associated

    exergy losses

    Exergy losses associated with injection overcome any potential benefit

    The additional entropy generated with the injection, reduces the maximum

    double-choke compression ratio.

  • Motive throat 6mm x 50 mm

    Constant area section 27 mm x 50mm

    Full length 1.52 m

    Primary flow 5 bar 300 K

    Secondary flow 0.97 bar 300 K

    Outlet 1.2 bar

    Large-Eddy Simulation of an Air Supersonic Ejector

    17S. Croquer

    10mm

    Secondary:

    Ptotal, Ttotal

    Pstatic

    Primary:

    Ptotal, Ttotal

    Adiabatic walls

    Problem description

    Re = 6.6E5, Ma = 1.72

    Experimental facility at the Universit Catholique de Louvain

  • Large-Eddy Simulation of an Air Supersonic Ejector

    18S. Croquer

    Numerical setup:

    - Solver: AVBP (HPC LES code developed by CERFACS)

    - Air as perfect gas

    - Imposed wall log-laws (Average Y+ = 20)

    - Schemes: TTG4A (4th order finite-element scheme)

    - Numerical stability: Jameson sensor based on pressure fluctuations

    Problem

    definition

    Meshing and

    Numerical setup

    Calculations 1:

    Flow stabilization

    Calculations 2:

    Capturing StatisticsPost-processing

    Challenges:

    - Flow initialization

    - Wall treatment

    - Shock Handling

    - Data storage and post-processing

    Roadmap

    1.5x Artificial viscosity 2x Artificial viscosity

  • Large-Eddy Simulation of an Air Supersonic Ejector

    19S. Croquer

    Nodes1632 nodes, AMD

    Opteron 6172

    For each node:

    Cores 12

    RAM 32GB

    Theoretical performance

    201.6 Gflops

    Computations running in the Mammouth Parallle 2 cluster (Universit de Sherbrooke)

    https://wiki.calculquebec.ca/

    1 Gflop = 1e9 operations per second

    Cluster characteristics

    MeshUnstructured

    237 Millions cells (55 Millions nodes)

    Simulated time 0.030803 seconds 10 tc

    Computing wall-clock time 36 days / tc

    Nodes 100 (12 cores per node)

    https://www.usherbrooke.ca/recherche/en/infrastructure/centre-for-scientific-computing/

    Flow stabilization: 8tc

    Collecting flow statistics: 2tc (so far)

  • Large-Eddy Simulation of an Air Supersonic Ejector

    20S. Croquer

    Schlieren images (Experimental)

    LES

    Preliminary Results

    Schlieren images (Experimental)

    LES

  • Large-Eddy Simulation of an Air Supersonic Ejector

    21S. Croquer

    Q-criterion surfaces colored

    by the vorticity sense

    Preliminary Results

  • Large-Eddy Simulation of an Air Supersonic Ejector

    22S. Croquer

    Preliminary Results

  • Large-Eddy Simulation of an Air Supersonic Ejector

    23S. Croquer

    Preliminary Results

  • 24S. Croquer

    Thank you!