Numerical Simulation for Industrial Applications on GARUDA

Numerical Simulation for
Industrial Applications on GARUDA
G. R. Shevare
Emeritus Fellow, Dept. of Aerospace Engg., IIT Bombay
Director, Zeus Numerix Pvt. Ltd.
Contents
 Introduction
 Industrial vs. Scientific Simulations
 Ready for Solving Industrial Problems?
 Solvers - Requirements
 Meshing and post-processing
 Case Studies
Closure
Simulation presented here are the efforts of my
colleagues in Zeus Numerix
Introduction
 Context : Experience in developing and
deploying simulation tools for industrial
products and processes on GARUDA
 Why Industrial Simulations?
 For immediate societal needs as compared to quest
for knowledge (scientific simulation)
 “What if scenarios” are easier in numerical
simulations
 Concept to market can be an order of magnitude
faster
 Numerical Simulations must be cheaper (and
faster) than experiments
 Some design must be first time right – space
exploration, nuclear reactor, stores separation, etc.
Industrial vs. Scientific Simulations
Industrial Simulations
Scientific Simulations
Purpose : to have useful,
safe and cheaper products
/ processes
Purpose: understand the
nature, gain knowledge
Overnight turn-around time Simulation time is less
is preferred
critical
No of simulations can be in
very large
No of simulations can be
handful
Multi-disciplinary in nature
These are rarely multidisciplinary
Computational domain is
complex and has to be
extracted from CAD
Scientist choose simple
computational domain
Simulations must be
affordable
Confirmation of “an idea”
is the issue
Ready for Solving Industrial Problems ?
Develop
enabling
technologies
Demonstrate
that
technologies
work
Put all
technologies
together
Learning the
use of
integrated
technologies
Maturity of
knowing
when not to
use
Scientific
papers, knowhow
Limited
success in
pioneering
applications
Generic code
useful for
industry
Extensive
validation and
convincing
industry
Industry
willing to pay
for numerical
simulations
We are here
 Two totally separate kinds of activities:
 Developing and demonstrated enabling
technologies
 Convincing DRDO Labs, PSUs, DAE, CSIR labs and
Pvt. Industry to use indigenous integrated HPC
technologies
Simulations is Interactive with designer in loop
(1)
CAD and
analysis / design
Requirements
Simulation ready
CAD, surface
meshes Volume
/ field meshes
(3)
Use the data in design (fluid dynamics /
heat transfer /structural integrity/ energy
consumption / environmental effects)
Solution of
Equations
describing the
physics
Extracting data
for engineering
analysis
1) Designer creates and owns CAD (virtual prototype of
industrial product)
2a, 2b,2c ) Conduct virtual experiments in 3D space
bounded by surface of the CAD model to simulate
behavior of the product on HPC
3) Designer modifies his product
Solvers
 Macroscopic simulation tools based on
 Newton's laws of motion
 Ampere’s laws, Faraday’s laws and Gauss Law –
(Maxwell equations)
 Use Physics cast in Partial Differential Equations
 Numerical methods are typically FEM or FVM
 Often used virtual simulations are:
 Rigid body dynamics
 Stress analysis and structural integrity
 Fluid flow and heat transfer
 Chemical reactions and Combustion
 Electromagnetic fields
Minimum Features required in a Solver
 Capable of using multiple spatial discretising
schemes
 Capable of using various mathematical
models; PDEs
 Should have multiple numerical schemes /
elements
 Must permit realistic boundary conditions
and initial conditions
 It should be possible to control data flow in
simulation; each simulation is different
 Simulations should be scalable on ever
changing computing hardware / software
 Should use standard file formats
Developing tools is a continuous process…
1st Generation 2nd Generation 3rd Generation 4th Generation 5th Generation
2008 - 2010
2006 - 2008
2011
2010 - 2011
Pre-2006
0.2 million
mesh points
were the
maximum
mesh points
we could
handle
1 million
mesh point on
serial
machine took
4 days
35 million
mesh point on
42 CPU
cluster took
20 days
2 million
mesh point on
serial
machine took
1 day
35 million
mesh point on
84 cores
takes less
than 14 hours
5th generation solver handles order of magnitude superior
Life Cycle of a typical solver
Accuracy  Problem Size  Duration & cost
Experimental results vs. Simulation Results
NASA Common
Research Model
Spatial
resolution
Tiny
Coarse
Medium
Fine
ExtraFIne
No of cells in
millions
Comments
Approx. 0.5 million
Capture trends
Approx. 2 million
Approx.
5 million
Approx. 15 million
Approx.
40 million
Reasonable results
Usable data in design
Results match with Expt. Data
Scatter  exptl. scatter
Mesh Generation – divide the 3D space in simple
shapes such as hexahedrons/ tetrahedrons
Unstructured meshes
for high fidelity
simulations
Futuristic octree
meshes for fidelity
simulations
Post-processing – Extract useful information
from large data sets
 Home grown post-processor a must
 This is the “other” interface with simulation
engineer / designer
 Home grown post-processors are a must for
extracting the engineering data from field data
It is impossible to
extract heat
transfer data in
hundreds of subchannels using
commercial postprocessors
Case # 1 : Simulation of Fire in Buildings
 Statement of the Problem: Predict temperature
and movement of smoke in building under fire
Fire Characteristics
Fire Intensity
Fire Location
Fire Coordinates (x,y,z)**
Fire Area
Duration of Fire
10 MW
Cafeteria
Corridor
(0,0,78.8)
(-16.5,0,101.7)
5m x 5m
3m x 3m
5 MINUTES
Ambient Conditions
Ambient Wind Velocity
Ambient Temperature
Westerly (1.5 m/s at 10 m)
35 deg C
Building Status
Refuge Openings
Sky Light Openings
Atrium - Apartment
Interface
HVAC Operation
13
All full open
Fully open
Segregated; smoke proof
wall/door
NIL
Simulation of Fire in Buildings
3.68 m/s
3.58 m/s
3.44 m/s
3.28 m/s
3.09 m/s
2.84 m/s
2.44 m/s
1.50 m/s
The tallest building in Mumbai
14
Results with 10 MW fire
 High temperature dilutes the smoke in
terms of CO, soot. Draft is produced due to
buoyancy
 Lower refuge floors are safe. Upper refuge
floors will attract smoke. Accumulation can
be reduced by removing obstacles from
skylight
 Moderate wind does not affect fire and
smoke scenario much
 The removal of residual smoke from
corridors needs jet fans
 On 24 Cores simulation requires one week
 Many buildings are simulated in Mumbai and
Ratnagiri
Case # 2: Airframe-engine intake integration
 Statement of Problem : Simulate pressure
distribution at face of intake of an engine
 The air entering intake must satisfy the
following conditions otherwise the engine
stops in flight
 Distortions for the intakes must be less than 0.1
 Pressure recovery should be more than 95 %
 Turbulence level should be less than 0.01
 Maximum Planar wave peak-to-peak should be less
than 0.01
 Intake should not produce more drag
 The above conditions must be ensured for
various altitude, speed and attitude of aircraft
Results
 Possibly the first-ever “engine fouling” study
for military aircraft
 Approx. 43 million cells
 Flow at the intake sampled every 1
millisecond.
 2 secs. of flight requires 8 days of simulation
 Flight is cleared
Total press pressure
variation in the leeward
side intake
Total press pressure
variation in the windward
side intake
Case # 3 : Heat Transfer in Nuclear Reactor
 Fast Breeder Reactors use liquid sodium to
extract heat generated by nuclear fuel
 Lower heat extraction leads to meltdown
 Higher heat extraction leads to inefficiency
 Due to limitations on the tools and
computing power full scale simulations are
rare
 Extracting the useful design data from the
simulations requires developing special
processing tools
 Simulation (carried out for IGCAR ) was
possible due to GARUDA- possibly a world
record
Flow of Liquid sodium in Fuel sub-assembly
Pressure variation
Results from literature
Simulations on GARUDA
Max of 17 pins only
Complete assembly of 217 pins
Estimated 60 GB for 100 millions
cells
Used 8 GB for 60 million cells
Used Tet meshes (inferior results)
Developed and deployed
automated Hex meshes (better
results)
Case # 4 : Simulation of Noise
 Noise is unsteady pressure field of extremely
small amplitude
 Flow noise is due to turbulence and
interaction of shock with turbulence
 It has serious implications especially in high
speed jets
 Direct simulation of noise suffer from the
scalability and requires peta-scale computing
resources
 Hybrid methods first obtain aerodynamic
noise sources. The noise transmission is
simulated separately
 Simulation pertains to calculation of noise
around ARIANE launch vehicle
Results for ARAINE vehicle
 12 M cells
 One week of simulation for each flight Mach number
Case # 5 : Filling of moulds
 Casting of metals and plastic parts should not
produce blow holes or pores
 Location of risers and rate of filling dictates
 Quality of mould
 Productivity
 Loss of material
 Numerical Simulations involving PDEs
satisfying conservation of mass, momentum
and energy for solid, liquid and air can be
carried out
 Heat transfer rate at interface between mould
and the solid requires BC based on empirical
information
 Each simulation can take days
Case # 5 : Solidification of moulds
Blue colour – air
Red colour - liquid
Initial Simulations - Filling of a jar with water
(No solidification attempted yet)
Case # 6 : Why model physics through PDEs?
 Some problems defy usage of PDEs as the
mathematical modes
 Discrete particles (sph) methods is an
alternative
t = 4 microseconds
t =100 microseconds
Case # 7 : Stores Separation Studies
 Aircrafts and stores are designed &
manufactured (or acquired) separately
 When used in a sortie, stores such as drop
tanks and missiles are required to be
dropped, ejected or launched from aircraft
 In dense atmosphere there is a possibility of
stores colliding with the mother aircraft
 Wind tunnel tests can not be carried out.
Controlled flight tests of separation is very
risky and costly
 Numerical simulation is a cheaper, safer and
better option
 Hundreds of simulations are required for safe
separation of stores in the flight envelope
Tools required for Stores Separation Studies
Interpolate & transfer solution
Start
Initial volume
data to new volume mesh
mesh
Generate new volume
mesh
Run Steady State CFD
Simulation to Obtain
Forces and Moments
No
Store
collides with
aircraft
objects?
Yes
Add external forces like,
ejector modelling
and gravity.
Change orientation of
store surface mesh.
Compute new position
and orientation of store
from 6-DOF module
Yes
More
dynamics
iterations
?
No
Stop
Results
 Each t takes close to 2.5 hours. This includes:
meshing,
interpolation of old solution on newly generated mesh,
finding out new solution,
calculation of aerodynamic loads,
finding out new position & orientation and
calculation of missed distance
 1 m of Stores Separation study requires – approx.
5 days on a HPC
 Two test flights may get cleared. Two in pipeline
Case # 7 : Visibility of aircraft to RADAR
 Survivability of aircraft is as important as its
lethality
 Survival of aircraft depends on its visibility to
enemy RADAR
 Visibility is measured in terms of radar cross
section (RCS)
 RCS depends on shape of aircraft, its
orientation with respect to transmitting /
receiving antennas
 RCS also depends on material of aircraft
 To reduce RCS, radar absorbing materials are
required
 Estimation of RCS and characterization of
materials requires solution Maxwell equations
Results
Metallic Object
Incident Field : 7GHz, Excitation: Along the
axis, Vertical polarisation
CFDExpert-Lite for Education & SMEs
 Off-the-shelf CFD software are very costly
for small and medium firms of pumps, heat
exchangers, etc.
 CFDExpert-Lite to be launched as a GARUDA
application next month
30
Acknowledgements
 GARUDA Computing Resources are being
used engineering simulations
 IIT Bombay for encouraging “Numerical
Simulation for Industrial Applications”
 DRDO, CSIR& DAE Labs, PSUs, Armed
forces for Patronizing Indigenous code
development and using the simulation
results in their design and operations
 Numerical Simulations have arrived in
Industrial Applications
Thank You