Simulering av granulära material

GRAM – Gruvprogram inom ProcessIT

Dr Martin ServinUMIT Research Lab / Department of Physics

2010-06-03

The challenge

simulation tool for - understanding- optimization- re-design

requires - fast large-scale

simulations- suitable models- analysis of large

data sets

The challenge

simulation time =Th

⎛

⎝⎜⎞

⎠⎟

number oftimesteps

{× Δt(NP)

computing timeper timestep

1 24 34 × NaNbL Nn⎡⎣ ⎤⎦variablecombinations

1 244 34 4×

1P×1A

⎛

⎝⎜⎞

⎠⎟

accelerationfactor

1 24 34

The challenge

T =10s

h=10−4s

Δt=10−3NP / 1000s=1s for NP =106

NaNbL Nn⎡⎣ ⎤⎦=100

P =A=1

⎡

⎣

⎢⎢⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥⎥⎥

⇒ 107s≈100 dagar

simulation time =Th

⎛

⎝⎜⎞

⎠⎟

number oftimesteps

{× Δt(NP)

computing timeper timestep

1 24 34 × NaNbL Nn⎡⎣ ⎤⎦variablecombinations

1 244 34 4×

1P×1A

⎛

⎝⎜⎞

⎠⎟

accelerationfactor

1 24 34

The challenge

T =10s

h=10−2s

Δt=10−3NP / 1000s=1s for NP =106

NaNbL Nn⎡⎣ ⎤⎦=100

1P×1A

⎛

⎝⎜⎞

⎠⎟=

1100

⎡

⎣

⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥

⇒ 1000s=15min

simulation time =Th

⎛

⎝⎜⎞

⎠⎟

number oftimesteps

{× Δt(NP)

computing timeper timestep

1 24 34 × NaNbL Nn⎡⎣ ⎤⎦variablecombinations

1 244 34 4×

1P×1A

⎛

⎝⎜⎞

⎠⎟

accelerationfactor

1 24 34

T =10s

h=10−4s

Δt=10−3NP / 1000s=1s for NP =106

NaNbL Nn⎡⎣ ⎤⎦=100

P =A=1

⎡

⎣

⎢⎢⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥⎥⎥

⇒ 107s≈100 dagar

Teaser

900.000 particles, water, ~3m, 10/1 sim ratio

Teaser

Teaser

Overview

• Background• The processes• Project status – results• Outlook• People

Background

• UmU: research on high-performance physics based visual simulation

• Oryx Simulations: making heavy machinery training simulators (1998)

• Algoryx Simulations: spin-off company -> AgX Multiphysics Toolkit (2006)

• Volvo CE: optimization/re-design of loading / digging

• LKAB: optimization/re-design of balling plant

• ProcessIT pre-study (PellIT - 2009)

• Research projects: Algoryx, Fraunhofer, Univ Kaiserslauten, LKAB, Oryx, ProcessIT, UmU, Volvo CE (2010 - 2013)

The processes

• Balling plant (LKAB)• Wheel loader (Volvo CE / Oryx)

Balling plant

• Mono-sized spherical pellets

• Understand flow patterns and forces

• Outlet design

• Control variables

• Agglomeration process

• 100K – 100M particles

• Time scales 1ms – 5min

In:

- fines (ore + binding)

- undersized pellets

Out:

- green pellets

Return:

- undersized pellets

- oversized pellets (crush)

100 ton/h

Wheel loading

• Optimal loading, digging, tillage

• Minimize time, fuel, wear on tool and wheels

• Maximize operator comfort and support

• Re-design of load bucket and motion control

• Coupling AgX simulations, operator VE with Volvo CEs Simulink drive train models

Shmulevich et al, Terramechanics (2007)

Project status – pellet part

Fast large-scale simulations

• stable DAE integrator at large timestep (100%)

• parallel solver for large sparse MLCPs (25%)

• merge/split acceleration (50%)

• adaptive resolution (10%)

• contact reduction (25%)

Suitable models

AnalysisOptimization

Project status – pellet part

Fast large-scale simulations

• stable DAE integrator at large timestep (100%)

• parallel solver for large sparse MLCPs (25%)

• merge/split acceleration (50%)

• adaptive resolution (10%)

• contact reduction (25%)

Suitable models

• rigid body discrete elements (75%)

• particle fluid / SPH (25%)

• hybrid (5%)

• simplified dry friction model (10%)

AnalysisOptimization

Project status – pellet part

Fast large-scale simulations

• stable DAE integrator at large timestep (100%)

• parallel solver for large sparse MLCPs (25%)

• merge/split acceleration (50%)

• adaptive resolution (10%)

• contact reduction (25%)

Suitable models

• rigid body discrete elements (75%)

• particle fluid / SPH (25%)

• hybrid (5%)

• simplified dry friction model (10%)

Analysis

• Model identification (25%)

• Simulation validation (5%)

• Flow patterns and forces (5%)

Optimization

Project status – pellet part

Fast large-scale simulations

• stable DAE integrator at large timestep (100%)

• parallel solver for large sparse MLCPs (25%)

• merge/split acceleration (50%)

• adaptive resolution (10%)

• contact reduction (25%)

Suitable models

• rigid body discrete elements (75%)

• particle fluid / SPH (25%)

• hybrid (5%)

• simplified dry friction model (10%)

Analysis

• Model identification (25%)

• Simulation validation (5%)

• Flow patterns and forces (5%)

Optimization

• Outlet design (0%)

• Control variables (0%)

Project status – pellet part

v⊥ =2.2 m/s

vP =0.2 m/s

v⊥ v

P

θ =60Ο

r =1.8 m

rasvinkel α ≈40Ο

Project status – pellet part

Project status – pellet part

Project status – pellet part

Project status – pellet part

Project status – pellet part

Project status – pellet part

Project status – pellet part

Project status – pellet part

Outlook

• Market for dynamical simulation tools for granular matter and machines?

• New projects?• optimal design of off-road vehicle boggies and tracks - SLU• dynamic load forces and mechanical wear on tools for excavation,

mining, forestry – LTU

Staff

Researchers• Dr Martin Servin, UMIT / Department of Physics• Dr Claude Lacoursière, UMIT / HPC2N• PhLic Kenneth Bodin, UMIT / HPC2N

• Prof Mats G Larson, UMIT / Department of Mathematics• Prof Bo Kågström, UMIT / Department of Computing Science / HPC2N

PhD, project assistants and master thesis• Pengfei Tian, Mona Forsman, Stefan Hedman, Olof Sabelström, Adam

Sernheim, John Nordberg

Algoryx, Oryx, Volvo CE

LKAB: Kent Tano, Kjell-Ove Michelsson

Thank you!

UMIT Research Lab

En strategisk satsning inom beräkningsteknik, visuell simulering och optimering

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Samtidigt rekrytering avforskningsledare inom datorgrafik och visualisering

Utrustning: display,  interaktion,  simulatorer,  programvaror,  datorer,  3d-skrivare,  3d-scanners mm

UMIT Research Lab- delområden idag

Computational design optimization – M Berggren

Computational mathematics – M Larson

Control system – A Shiriaev/L Freidovich

Flexible and scalable IT infrastructures (Grids & clouds) – E Elmroth

Interactive multiphysics and complex mechanical systems – M Servin/C Lacoursière

IT management – J Holmström

Parallel and scientific computing – B Kågström

UMIT Research Lab- Tillämpningsprojekt - ett urval

Modeling and simulation of granular matter and machinesVolvo CE, LKAB, Algoryx, Oryx

Inverse Problems for ElectromagneticsValutec AB, SP Trätek

Optimal Control and DesignDAS Audio SA

Numerical Algorithms for Stabilization of Linear Systems with Periodic Coefficients - German Aerospace Center (DLR)

Simulator Based DesignKomatsu Forest

Effektivt nyttjande av muticore och parallellismIBM, Intel, Microsoft, Nvidia, Sony Ericson

Migration of large-scale virtual machinesSAP Research

Simulation of fluids in bearingsSKF

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Industrial and applied mathematicsParallel and multicore computing

Industriell ekonomi – logistik, kombinatorisk optimering, riskanalys. Energiteknik – energieffektivisering, drivmedel.Teknisk fysik - tillämpad spektroskopi och detektion. Interaktion och designTeknisk datavetenskap - industriell programvaruteknik, flexibel och

skalbar IT-infrastrukturBioteknikMiljösystemteknik – miljökemi/systembiologi, livscykelanalysAutomation/Reglerteknik