research presentation senthil

Senthil Kumar V – Research Overview B Tech

Chem Engg AC Tech - Anna U

1992-1996

M Tech Chem Engg IIT - Madras 1996-1998

Chemical Reaction Network Theory

Graph theoretic method to

detect multiple steady states and oscillations

Process calculations National Org Chem Ind Ltd

1998-1999 Engineer – Technical Services

Trouble shooting of HDPE plant – ZN catalyst based

slurry reactors

Operation Research Cell – Optimizing product portfolio

Process development BARC

1999-2000 Scientific Officer - C

Separation of Uranium from phosphatic ores through

multi-stage liquid extraction

Spreadsheet tool development

1

PhD Chem Engg

IISc 2000-2005

Statistical geometric analysis of hard-disk and hard-sphere

microstructures

Molecular simulations for rapid granular flows

Voronoi characterization of

micro-structures

Energy Storage & Thermal Modeling General Motors R&D

2005-2012, Senior Researcher

Fuel cell vehicle - Hydrogen storage - HSE CoE of US DoE 2008 - 2011

Lithium battery

- Nonlinear equivalent circuit model

Lithium battery – Physics based Reduced Order Model

Photo-thermal Nano-composites

Desalination & Battery Modeling Samsung Adv Inst Tech - India

2012-Current, Principal Engineer

Battery Management System – Physics based

Reduced Order Models

Nano-filtration membranes for sea water desalination

Patents: 5 & Publications: 12 (+3) Conference Proceedings: 9

This presentation does not contain any confidential or proprietary information

Thermodynamics Statistical Geometry: Voronoi Tessellation

Molecular Dynamics & MC Simulations

Statistical Mechanics

Basic Sciences

Fluid Mechanics Heat Transfer

Mass Transfer Reaction

Engineering

Basic Engineering

Hydrogen Storage, Li Battery, Desal

Process Design & Technology Evaluation

ROMs for Process control

Applied Math, Finite Elements

Applied Engineering

PhD

BTech, MTech Industrial R&D 2

Multi-scale & Multi-physics Research Experience

3

Voronoi tessellation of hard disk configurations

• As packing fraction increases Voronoi tessellation becomes regular. • Voronoi tessellation is a geometric framework which can describe disorder to order seamlessly. • It offers exact definitions of local volume and geometric neighbors. • Can we derive statistical measures using Voronoi tessellation which can describe structure and property of materials at different states of aggregation or disorder?

4

Cell volume distribution & Configurational entropy

J Chemical Physics 2005; 123: 114501

5

Computation of hard disk / hard sphere excess entropy

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Voronoi Neighbor Statistics

• A topological instability has a thermodynamic connection!


• Vertex A – formed by intersection of 3 planes – stable • Vertex B – formed by intersection of 4 planes – unstable • Small perturbations – Vertex B breaks down to form a tiny

quadrilateral face – Topological instability. • Perturbed FCC lattice has 14 neighbors instead of 12 (for

rhombic dodecahedron)

7

Sheared Voronoi Neighbor Statistics

• Higher inelasticity (lower coefficient of restitution) disorder to order transition occurs at a higher volume fraction.

• Ordered layers sliding past each other offer lesser resistance to flow than a disordered structure.

• Velocity dependent coefficient of restitution and shear amorphization lead to a non-hydrodynamic pathway to shear thickening.

Physical Review E 2006; 73: 051305

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Bond-orientational analysis of sheared structures


• Sheared granular flows showed high degree of crystallinity, but has only about 40% FCC structures.

• Further investigation showed that sheared structures are a mixture of FCC, HCP and BCT structures.

• Flow analogues of martensitic transformations, occurring in rapidly quenched metals.

Li Battery: Detailed Model & Its Reduction

e- e-

Separator Lithium Metal

Oxide Graphite

- +

LiC6 x Li++Li1-xC6+x e- x Li++ LiMO2 +x e- Li1+xMO2

Li+

Discharge of a Lithium Metal Oxide cell

“Current”

Loss of electrons Oxidation Anode Gain of electrons Reduction Cathode

Load

9

Solid

Electrolyte Electrolyte

Solid

Electrolyte

5 phases in 3 regions (n, s & p)

Isothermal 1D model: Mass and Charge balances for each phase.

5 phases x 2 balances =10 PDEs

The detailed electrochemical model is difficult to use for packs, on-board control , calendar / cycle life predictions, detailed parameters estimation, inclusion of complex degradation mechanisms etc.

Nonlinear PDEs

Linear ODEs

Nonlinear algebraic expressions

Approximations

Nonlinearity in a system is not bothersome if it can be relegated to

algebraic evaluations.

Ideal Reduction

Model reduction achieved : 10 coupled PDEs to 5 linear ODEs.

10

Li Battery: Model reduction methodology & Voltage predictions

nnninin

n

nninnn

in

n

ninn

n

nn

inin

nn

n

inn

nnninn

nn

n

inn

nnnn

jlatqdt

dq

D

l

dt

dcl

tqD

ltctc

xlDl

tqtctxc

tjtxjl

q

x

cD

x

jlatqdt

cdl

l

q

x

cD

x

jtax

cD

xt

c

13

3

2,

,&

1

1

22

2

2

2

22

2

2

22

22

2

222

222

2

2

2

222

22

22

Nominal current pulse Max % error 0.2%

High current pulse Max % error 2%

ROM: J of Power Sources, 2013; 222: 426

• Volume averaging reduces PDEs to ODEs. • But profile or gradient information is lost. It is

recovered using Profile based approximations, in terms of relevant internal variables.

• All approximations accurate on volume averaging.

n s p

Hydrogen Storage: Hierarchical modeling approach

Refueling << Discharge << Dormancy << Venting

minutes << hours << days << weeks

Slower processes Fast process

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0-D bed model

System model

Transport resistances & Process design

Slow processes

Drive cycle response & System integration

1-D bed model Refueling

2-D bed model Flow maldistribution & Tank design

Engineering calculations

2-D pellet model Optimum pellet shape & size

MS Excel

Matlab

Simulink

Comsol

Comsol

Comsol

CGH2 supply

LN2 HX Ti = 80 K,

Pi = 153 bar

350 bar

Expander

JT valve

Tf = 68 K, Pf= 20 bar A

Engineering calculations – Process design

US Patent: 2009/0107155, Granted

0-D model results: Dormancy, Venting & Discharge

12 Int J of Hydrogen Energy 2009; 34: 5466

5 kg 300 miles 0.5 kg 30 miles

Tm,

hQ

Tm f ,

om

ff Tm ,

A

System simulation results: FTP-75 drive cycle, 0.23 kW heating rate

The adsorbed phase responds to the slowly

varying demand

The gas phase responds to the

fluctuating demand

13

hQ

Hot gas recirculation

Electrical heating

Int J of Hydrogen Energy 2012; 37: 2862

1D model results: Refueling & Optimal tank design

K 1380 T

K 80fT

14

Single axial flow bed Single radial flow bed 7-cartridge radial flow bed

Pa 9.455P Pa 6.99P Pa 7.4P

min. 24.4 Isobaric fillt min. 27.4 Isobaric fillt min. 46.4 Isobaric fillt


.

2-D model results: Flow maldistribution & Pellet design

sT

15


Under review in Int J of Hydrogen Energy, 2013

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Nano-composites for Windshield Deicing

Incident beam

L1

Composite

x 0

L2

Ice

Tamb Tamb

x = L2 x = -L1

To be submitted to Int J of Heat and Mass Transfer, 2013

• 3D simulation showed that, the heat source could be homogenized. • An analytical Fourier solution was derived for the bilayer system with heat source in the composite layer.

Graphene based nano-composites, with electrical heating

US Patent: 2011/0297661, Filed

17

Nano-filtration membrane design for Sea-water Desalination

Accepted in Desalination (Elsevier), April 2013

Na+

Cl-

-

-

Cl- Cl-

Cl- Cl- Na+

Na+

Na+

Na+

Na+

Na+

Na+

Na+

-

-

• Donnan Steric Pore Model (DSPM) has Nernst-Planck equation for transport of charged species, along with electro-neutrality, dielectric exclusion etc. • A Reduced Order Model (ROM) was derived using constant potential gradient approximation, leading to an algebraic algorithm amenable for spreadsheet implementation, for rapid process evaluation. • With experimental data on reduced water flux at higher salinity, the mass transfer coefficient in the concentration boundary layer can be back-calculated – Concentration Polarization. • Using these mass transfer coefficients along with DSPM – ROM, the experimental salt rejections were well described, with membrane charge density and dielectric constant of ordered water within pores as the two model parameters.

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Summary: Multi-scale Modeling – Methodology & Applications

Mass Balance

Charge Balance

Momentum Balance

Energy Balance

Ang. Momentum Balance

Constitutive relations

Thermo-physical properties

Reaction kinetics

Ab initio, DFT

Molecular & MC Simulations

Meso scale methods

Process Development, Technology Evaluation

Performance Modeling

Physics-based ROMs for Process Control

Macro scale or Continuum Model Equations

Model Applications Material Specifics Atomistic &

Meso-scale Models

Experimental data for properties / kinetics estimation & model validation. Applied math techniques for model solution methods.

Personal picks for potential future research

Meso scale: Voronoi based mesh-free methods, Continuum scale: Spectral methods, Model order reduction: Volume averaging & Profile based approximations.

Direct application of meso scale methods when length / time scales are not too large.

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Appendix 1: Voronoi Fluid Particle Dynamics – Springel, Heß

http://www.mpa-garching.mpg.de/~volker/

http://arxiv.org/abs/1109.2218v1

1D Reimann shock wave problem

Azimuthal velocity in Gresho vortex test

http://arxiv.org/abs/1208.0351v1





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Appendix 2: Voronoi tessellation in mesh free methods

Meshfree Particle Methods, Li and Liu, Springer, 2004.

http://www.astro.rug.nl/~weygaert/tim1publication/jigsaw/pepespanol_jigsaw.vers2.pdf

Voronoi Fluid Particles – Serrano, Espanol, Flekkoy, Coveney, et al.

J Stat Phys 121 (2005) 133 -147.



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