Brazil 2014ugm Response Surface Model Predict Flammable Gas Cloud

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    A Response Surface Model to PredictFlammable Gas Cloud Volume in Offshore

    Modules

    Tatiele Dalfior FerreiraSvio Souza Venncio Vianna

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    Research Group Overview

    University of Campinas (Unicamp)

    Founded on October 5, 1966.

    Has three campuses and comprises 22 units of

    teaching and research

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    Research Group Overview

    The Faculty of Chemical Engineering (FEQ) was

    created in April of 1990 as a Department of

    UNICAMP.

    Today, it is divided in four departments: DDPP,

    DEMBio, DESQ and DEPro.

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    Problem Description

    Gas explosion safety is a concern in industrial

    plants, particulary in oil and gas industry.

    The offshore oil installations involve risk of

    accidents with a major loss-potential.

    Explosion of theoffshore platform

    Piper-Alpha located inthe North Sea in 1988.

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    Problem Description

    Explosion Risk analysishelp risk decisionsto prevent and control accidents:

    - Quantify the blast overpressure generate over

    distance and time.

    - Gas dispersion study.

    Three-dimensional analysis using CFD

    Mathematical models (in conceptual design)

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    Problem Description

    The flammable gas cloud size in na offshoremodule:

    - Ventilation: wind speed and direction and geometry

    of the module.

    - Release: leak rate, direction and location and gas

    density.

    Cleaver et al (1999):

    = / 2

    /2

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    Problem Description

    The use Response Surface Methodology

    (RSM) to estimate flammable gas cloud size

    in offshore modules (Huser and Kevernvold,

    2000):

    =

    1 + 1 + 2

    = +

    =

    =

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    Methodology

    Geometry and Mesh:

    ANSYS

    ICEM - 11.0

    Unstructured mesh

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    Methodology

    Release conditions:

    - Natural gas composition:

    Component Molar Fraction

    C1 0.861

    C2 0.071

    C3 0.030

    C4 0.013

    C5+ 0.010

    CO2 0.005

    N2 0.010

    Pressurized pipeline

    with average pressure

    200kgf/cm2

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    Methodology

    Variables of Interest:

    Ventilation and Leak rate: R (Cleaver et al.

    (1999) e Huser e Kvernvold (2000))

    0.03 < R< 0.3

    Wind and Leak directions: (phi)

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    Methodology

    Wind Directions:

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    Methodology

    Leak Directions:

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    Methodology

    Leak and Wind directions: (phi)

    = 0

    leak and wind inthe same direction

    = 180 leak and windin opposite direction

    0

    45

    90

    135

    180

    225

    270

    315

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    Methodology

    Leak and Wind direction: (phi)

    Quadrant 01= 0, 45e 90

    Quadrant 02= 90, 135e 180

    Quadrant 03= 180, 225e 270

    Quadrant 04= 270, 315e 360

    0

    45

    90

    135

    180

    225

    270

    315

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    Methodology

    Design of the Simulations

    Design ofExperiments 32

    for each quadrant

    Scenario Quadrant R

    01 01 0 0.03

    02 01 45 0.03

    03 01 90 0.03

    04 01 0 0.1505 01 45 0.15

    06 01 90 0.15

    07 01 0 0.3

    08 01 45 0.3

    09 01 90 0.3

    10 02 90 0.03

    11 02 135 0.03

    12 02 180 0.03

    13 02 90 0.15

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    Methodology

    Boundary Conditions and Solver Parameters

    ANSYS

    CFX -15.0

    Region Bondary Condition

    Surfaces No slip

    Leakage Prescribed mass flow

    Ventilation Prescribed velocity

    Contour of the Computational Domain Relative Pressure = 0

    Parameres Value or type

    Reference Pressure 1 atm

    Flow Regime Stationary and Incompressible

    Turbulence Model k -

    Advection Scheme Upwind

    Time Scale Automatic (1.0)

    Convergence Criterion RSM

    Maximum Residual 1x10-5

    Mn. and Mx. Number of Iteractions 100 - 1000

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    Results

    A gas leak

    Simulation of an axisymmetric jet:

    - Air

    - Orifice Diameter = 2,7mm

    - Mach Number at the exit = 1

    - Air density at the exit = 2,25kg/m3

    Birch (1987)

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    Results

    Simulation of an axisymmetric jet

    Comparison between the experimental data and the result of the simulation of an

    axisymmetric jet.

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    Results

    - Air

    - Average Pressure =

    137,9kPa

    - Mass flow rate = 0,15kg/s

    Wakes (2002)

    Simulation of a high aspect ratio jet

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    Results

    Simulation of a high aspect ratio jet

    Comparison between the experimental data and the result of the simulation of a high

    aspect ratio jet.

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    Results

    Wind Analysis

    Superficial atmospheric boundary

    layer: =

    Hanna (1982)

    (01)

    = (02)

    = 10 (03)

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    Results

    Values of L , z0e pfor neutral atmospheric and sea

    surface

    AICHE (2000)

    Sea level: 0 m

    Platform level: 55 m

    Monin-Obukhov lenght (L) (m) > 100

    Surface roughness (z0) (m) 1x10-4

    Power coefficient (p) 0.15

    Wind speed profile tests

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    Results

    Simulation of the wind speedprofile using equations 01 (a); 02

    (b) and 03 (c).

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    Results

    Example of gas dispersion simulation

    R = 0.15

    = 135

    LFL: 5%

    UFL: 15%

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    Results

    Development of the Response Surfaces

    Quadrant 01

    Quadrant 03

    Quadrant 02

    Quadrant 04

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    Results

    Quadrant 01 Quadrant 02

    Quadrant 03 Quadrant 04

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    Results

    Validation of the Model

    Comparison between the CFD results with the values predicted by the

    model considering random simulated cases.

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    Conclusion and next steps

    A Response Surface Model was developed forprediction of flammable gas cloud size in anoffshore module using two main variables: R e ;

    The results provided by the model were well fitted

    with CFD data within a tolerance of 50%;

    The model is a simplified prediction form and can

    be applied in early stages of the design when little

    is known about the geometry or when thegeometrical model is not available;

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    Conclusion and next steps

    Through the model every possible release scenariocan be assessed in a risk analysis, which is

    infeasible in CFD simulations;

    The proposed model can be very useful when

    combining with Monte Carlo techniques tocalculate probabilistic cloud sizes.

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    [email protected]

    [email protected]

    Thank you!