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© 2015, All rights reserved.

Visit our event page at: www.Darsim.CiTG.TUDelft.nl/events

1st DARSim Seminar on Porous Media Flow Modeling & Simulation within the Faculty of Civil Engineering and Geosciences of TU Delft

14:40 – 14:45: Welcome and introduction Hadi Hajibeygi*, Mark Bakker, Timo Heimovara

14:45 – 15:05: Modeling of biogeochemical reactions in large-scale systems André van Turnhout* , Timo Heimovaara

15:10 – 15:30: Optimal use of subsurface space with Aquifer thermal energy storage Smart Grid Martin Bloemendal*, Vahab Rostampour, Marc Jaxa-Rosen

15:35 – 15:55: Modeling multi-path leakage of geosequestered CO2 Mehdi Musivand Arzanfudi*, Rafid Al Khouri

16:00 – 16:20: Dynamics of polymer rheology through different pore-shapes in microfluidic channels

Durgesh Kawale*, P. Boukany, Michiel Kreutzer, William Rossen, Pacelli Zitha

16:25 – 16:30: Conclusion remarks

Various high-quality research activities on modeling and simulation of porous media flow for different applications are being conducted in our Faculty. Many of them are multi-disciplinary in nature, and thus benefit from a more communicative and supportive environment among researchers with different expertise and interests. These series of seminars are developed to further facilitate such an environment by enhancing communication and broadening the knowledge of individual researchers of our Faculty, in the exciting field of porous media flow modeling and simulation. As to start, taking the chance of Darcy Lecture by Prof. Rainer Helmig on the same day (12:00, Room E), this event is planned. All researchers and students in our Faculty are highly motivated to attend (and volunteer to give a talk in future events) so that it only becomes a good start, but definitely not an end.

25 September 2015 – Lecture Room E – 14:40-16:30 (Darcy Lecture by Prof. Rainer Helmig is on the same day, 12:00-13:45, Room E)

PLEASE ARRIVE ON TIME AND INFORM YOUR FRIENDS & COLLEAGUES

1 Gray box modeling of MSW

Modeling of biogeochemical reactions in large scale systems How far can we get from black box to white box?

A.G. van Turnhout, R. Kleerebezem, T.J. Heimovaara

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What are controlling processes?

Leachate recirculation

/9


Gray modeling toolbox

Gray box modeling: defining a mechanistic reaction network that optimally describes the measured data (…somewhere between black box and white box…)

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Gray modeling toolbox

Gray box modeling: defining a mechanistic reaction network that optimally describes the measured data (…somewhere between black box and white box…)

Module 1:

• Define forward model in spreadsheet

• Concentrations in liquid, gas and solid phase

• Kinetic reactions & inhibitions

• Equilibrium reactions

• Parameter values

• Parameter estimation based on

thermodynamics

• Perfectly mixed batch

• Assembling reactions into generic structure • Fully coupled numerical integration of kinetic &

equilibrium reactions in time

Module 2:

• Analysis of network with Bayesian statistics

• Advanced MCMC algorithm (DREAM(ZS))

• Prior information to posterior information

• Joint (& marginal) posterior probability

distributions

• Judge performance of network based on

quantitative criteria • Overall model performance (error)

• Bayesian information criterion (BIC)

• Practical identifiability of parameters • Kullback-Leibler divergence (DKL)

• Agreement of posterior quantiles with

parameter ranges from published ‘ideal case’

experiments

/9


Results

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Application

• Upscaling: optimal reaction network for biochemistry (module 1) coupled with water retention model for landfill scale

• Finding optimal mechanistic reaction network for aerated lysimeter experiments & mass transport limitation during anaerobic digestion

• Generic & flexible approach allows wide application: • Parameter identification from measured data • Pre-modeling of experiment for optimal start • Conceptual model building for heterogeneous environmental systems

• (De)nitrification in biogrouting • pH control and CO2 sequestration with minerals by environmental biotechnological

processes

• Finding optimal forward model for source term in reactive transport or Darcy flow models

• Or implementing Darcy flow, reactive transport into module 1 and finding an optimal coupled reaction network

• Coupled diffusion transport with biogeochemistry for investigation of biocorrosion

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Questions

Contact details: [email protected] van Turnhout, A. G.; Kleerebezem, R.; Heimovaara, T. J. ‘How to find the optimal mechanistic reaction network describing your data?’ Environmental Modelling and Software (under review)

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1st DARSim Seminar on Porous Media Flow

Modeling & Simulation

Computational Mechanics

Civil Engineering and Geosciences

Delft University of Technology

Mehdi Musivand Arzanfudi

1. Wellbore leakage

2. Cap layer leakage

3. Coupled leakage

Models:

Partition of UnityΩ+

Ω-Γd

Γ

n

Adaptive FEM XFEM

State variables exhibiting different nature are treated

using different discretization technique

State variables exhibiting different nature are treated

using different discretization technique

Solid deformation with crack propagation

Advection

Diffusion

Heterogeneous

layered system

Diffusion: Standard Galerkin

Deformation: Standard Galerkin

Advection: Level-set method: tracing the front

Moving Partition of Unity: modelling the front

Heterogeneous geometry: Stationary Partition of Unity

Crack propagation: Propagating Partition of Unity

Integrated in a single numerical model

Diffusion: Standard Galerkin

Deformation: Standard Galerkin

Advection: Level-set method: tracing the front

Moving Partition of Unity: modelling the front

Heterogeneous geometry: Stationary Partition of Unity

Crack propagation: Propagating Partition of Unity

Conceptual model

• Initially filled with air

• A supercritical CO2 at the

bottom of wellbore

• Two fluid: CO2 & air

• Initially filled with air

• A supercritical CO2 at the

bottom of wellbore

• Two fluid: CO2 & air

Conceptual model

CO2 Phase diagram

Drift Flux Model (1D)

Mass balance

Momentum balance

Energy balance

Mathematical model: Navier–Stokes Equations

( ) ( ) 0mm m m m dv v n z z

t z

ρρ ρ δ

∂ ∂+ + ⋅ − =

∂ ∂

( )

22

2

2

1

2 2

sin2

mm m m m m m

mm m m d m m

i

vh v p v h

t z

v Qv h n z z v g

r

ρ ρ

ρ δ ρ θπ

∂ ∂ + − + + ∂ ∂

+ + ⋅ − = −

( ) ( ) ( )2 2 sin4

m m mm m m m m m d m

i

f v vpv v v n z z g

t z z r

ρρ ρ γ ρ δ ρ θ

∂ ∂ ∂+ + + ⋅ − =− − −

∂ ∂ ∂Inertia Advection Jump Pressure

Drop

Friction Gravity

Level-set method: Trace CO2 – Air interface

Partition of Unity method: Modelling CO2 – Air interface

Standard Galerkin: Diffusion

Integrated in a prototype code

Numerical model: Mixed Discretization Scheme

+Constitutive relationships

Reservoir Pressure = 7.5 MPa

CO2 State in Reservoir: Supercritical

Temperature = 70ºC

Density = 250 kg/m3134 10k −= ×

( )2co

b

p

m R z z

kv p p

µ == −

Well Data

Deviation angle [degree] 90

Well inner radius [m] 0.1

Well casing thickness [m] 0.02

Casing thermal conductivity [W m-1 K-1] 0.6

Roughness of the wellbore [-] 5.0×10-6

Formation Data

Surface temperature [K] 275.15

Geothermal Gradient [K/m] 0.058

Density

Pressure

Velocity

Temperature

Mesh Dependency

4 elements 100 elements20 elements

Well Data

Deviation angle [degree] 90

Well inner radius [m] 0.1

Well casing thickness [m] 0.02

Casing thermal conductivity [W m-1 K-1] 0.6

Roughness of the wellbore [-] 5.0×10-6

Formation Data

Surface temperature [K] 275.15

Geothermal Gradient [K/m] 0.058

ˆ 1 m/smv =

5 3

6

1.114575 10 4.44427125 10 2000 sˆ Pa

9 10 2000 s

t tp

t

× + × <= × ≥

310 0.27 2000 sˆ kg/m

550 2000 sm

t t

tρ

+ <= ≥

Liquid

Supercritical

Gas

Mixture

CO2 phase diagram

Standard FEM Mixed Discretization

Scheme

Standard FEM

Standard FEM

Mixed Discretization Scheme

Temporal domain: Multiple time stepping

Spatial domain: Staggered technique

Multi-leakageOnly cap-layer Only wellbore

80 elements 204 elements

999 elements792 elements

Mesh Dependency

Structured mesh

Fixed mesh

Coarse mesh

Geometry independent mesh

Reduced CPU time and capacity

ReferencesMusivand Arzanfudi M., Al-Khoury R., Sluys L. J. : A Computational Model for Fluid Leakage in

Heterogeneous Layered Porous Media. Advances in Water Resources. 73, 214-226 (2014).

Musivand Arzanfudi M., Al-Khoury R. : A computational model for CO2 leakage through a

wellbore. International Journal for Numerical Methods in Fluids. 77, 477-507 (2015).

Musivand Arzanfudi M., Saeid S., Al-Khoury R., Sluys L. J. : Modeling Geosequestered CO2

Leakage Mechanisms. Under Review.

1 Challenge the future

Optimal use of subsurface space

with ATES smart grids

Martin Bloemendal,

Vahab Rostampour,

Marc Jaxa-Rosen

DARsim seminar 9-25-2015 TUDeft


Aquifer Thermal Energy Storage

• Sustainable space heating and cooling

• Climate and aquifers conditions

• Accumulate in urban area’s

[Bloemendal et al. 2015]

Introduction


Aquifer Thermal Energy Storage

Introduction


Content

• Details of the problem

• Proposed solution

• Challenges

Introduction


Energy use Subsurface space use

• Planning is based on estimated max Rth

• Problems:

• Unpredictable and strongly varying

• System use 60% of permit

• Max Rth never occurs at same time

• Why fix use in too big static permits?

[Bloemendal et al. 2014, Sommer 2015, Koenders et al. 2013]

Details of the problem / Proposed solution / Challenges

x*Rth


ATES Smart Grids project

From autonomous and individual controlled..

… to collaborating systems, sharing information to optimize

own and overall efficiency


[Ostrom 2009, Bloemendal et al. 2014, Caljé 2011]


Conceptual

• Agent based model

TPM

• Building model & control

DCSC

• Groundwater modelling

CEG



Modelling framework



Example of results

academic test case


[Jaxa-Rosen et al. 2015, Li 2014; Sommer 2015]


Challenges

• Assessment framework

- Individual users

- Governments

• Approach

- Design parameters

- Performance indicators

- Survey with authorities and users



Challenges

• Analytical model for well temperature

- Keep track of temperature in well

- How to deal with overlap?

• Approach

• Single ATES system

• Incorporate overlapping



1

1

1

1 1 12

0

( )0

k k in

k k amb

k k in in k kk k

k k k

V V s V

V T T

T V T V T Tamb AV T s s rc

V V V

Temperature in well



Optimal use of subsurface space

with ATES smart grids

Martin Bloemendal,

Vahab Rostampour,

Marc Jaxa-Rosen

[email protected]

mailto:[email protected]

© 2015, all rights reserved. darsim thanks all ... faculteit/afdelingen... · the chance of darcy...

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