computational fluid dynamics for reactor design &...

Massachusetts Institute of Technology

NSENuclear Science & Engineering at MIT

science : systems : society

Computational Fluid Dynamics for Reactor Design & Safety-Related Applications

Emilio [email protected]

web.mit.edu/newsoffice/2012/baglietto-better-reactors.html

STAR Korean Conference 2013Better reactors grow from better simulations

An Industrial/Research/Academic viewWearing multiple hats:

Massachusetts Institute of Technology

Assistant Professor of Nuclear Science and Engineering, Massachusetts Institute of Technology.

Deputy Lead TH Methods Focus Area, CASL – a US Department of Energy HUB.

Nuclear Industry Sector SpecialistCD-adapco

Member of NQA-1 Software Subcommittee.

Disclaimer: the following slides are intended for general discussion. They represent the personal view of the author and not that of MIT, CASL or the ASME NQA-1 Software Subcommittee.


Nuclear Industry Competitiveness CFD for Nuclear Reactor Design Leveraging the research/academia efforts

Computational Microscopes Multi-scale Applications CFD as Multi-physics platform

CFD for Advanced Reactor Concepts Fast Reactors Fuel VHTRs – virtual experiments

CFD for Safety Related Applications The US-NRC example

Contents

Emilio Baglietto - Nuclear Science & Engineering at MIT

Background 2011- present Assistant Professor of Nuclear Science and Engineering, MIT

2006-2011 Director Nuclear Application, CD-adapco

2004-2006 Research Associate, Tokyo Institute of Technology

20122009

PBM

R

2005


CASL: The Consortium for Advanced Simulation of Light Water ReactorsA DOE Energy Innovation Hub forModeling & Simulation of Nuclear Reactors

Task 1: Develop computer models that simulate nuclear powerplant operations, forming a “virtual reactor” for the predictivesimulation of light water reactors.Task 2: Use computer models to reduce capitaland operating costs per unit of energy, …… 5


• Local T&H conditions such as pressure, velocity, cross flow magnitude can be used to address challenge problems: oGTRF oFADoDebris flow and blockage• The design TH questions under normal operating and accident conditions such as:oLower plenum flow anomalyoCore inlet flow mal-distributionoPressure dropoTurbulence mixing coefficients

input to channel codeoLift forceoCross flow between fuel

assembliesoBypass flow

• The local low information can be used as boundary conditions for micro scale models.

Model 1 Model 2

A “Typical” Multi-Scale ProblemFull-core performance is affected by localized phenomena


STAR-CCM+ Platform for MultiphysicsHigh Fidelity T-H / Neutronics / CRUD / Chemistry Modeling

Petrov, V., Kendrick, B., Walter, D., Manera, A., Impact of fluid-dynamic 3D spatial effects on the prediction of crud deposition in a 4x4 PWR sub-assembly - NURETH15, 2013


STAR-CCM+ Platform for MultiphysicsHigh Fidelity T-H / Neutronics / CRUD / Chemistry Modeling

Petrov, V., Kendrick, B., Walter, D., Manera- NURETH15, 2013


Not only Fuel Related Applications 10

Mature Applications Fuel

Pressure Drops Crud (CIPS/CILC) Vibrations (GTRF)

System and BOP Transient Mixing Hot Leg Streaming Thermal Striping SG performance Cooling Towers Interference

Fuel Cycle and Beyond Design Basis Applications Spent fuel transportation and

Storage


Multiphase CFD… better physical understanding

boiling heat transfer DNBvoid fraction


Improved Spacers DesignCFD Predictions of DNB

13J. Yan, et al - Evaluating Spacer Grid CHF Performance

by High Fidelity 2-Phase Flow Modeling – TOPFUEL2013

CFD–based CHF modeling development being performed by Westinghouse Nuclear Fuel.

5x5 test bundle PWR experiment from the ODEN CHF test facility were modeled in CFD using the latest 2-phase boiling model.

Excellent trend agreement in CHF predictions.

Novel understanding of fundamental physics allows improving the CHF performance.


14J. Yan, et al - Evaluating Spacer Grid CHF Performance by High Fidelity 2-Phase Flow Modeling – TOPFUEL2013

Improved Spacers Design

RCIC SYSTEM 19

MO MO

HO

HO Control valve

Turbine

stop valve

#2

TIME

70 HOURS

20 HOURS#3

TIME

RCIC

RCIC

M. Pellegrini, M. Naitoh, E. Baglietto

UNITS 2 & 3: PCV PRESSURE 20

0

0.2

0.4

0.6

3/1112:00

3/120:00

3/1212:00

3/130:00

3/1312:00

Prim

ary

cont

ainm

ent v

esse

l pr

essu

re (M

Pa[a

bs])

Date/time

U N I T 2

U N I T 3EARTHQUAKE

3/11 14:46


SPARGER MAIN DIFFERENCES 21

0.283 m

1.275 m2577 mm

0.680 m

D = 0.025 m

D=0.010 m0.033 m

0.036 m

0.065 m

U N I T 3U N I T 2VERTICAL JET HORIZONTAL

JETS


1F3 GEOMETRY 22

sparger

Detail of holes mesh size

Elements size in the pool = 0.1~0.2 m

Region A size = 1 mm

Region B size = 2 mm

Regio

n B

~ 8 m

Pool pressure boundary


1F3 TEMPERATURE IN THE SPARGER 23

steam flow

Tpool = 30°C

~ 3.0 m

Large water head creates differences between mass flow rate between holes in the

vertical direction

2 seconds real time

Region A

Region B


POOLEX STB-28-4 EXPERIMENT 24

Experimental results

• Large visible chugging

phenomenon

• Bubble collapse time = 80 ms

• Bubble diameter = 380 mm

• Collapse speed = 3 m/s

pool detailfacility sketch

T pool = 62 °CSteam Mass Flux = 8 kg/m2s

steam inlet

380 mm

219.1 mm


PRELIMINARY RESULTS: CHUGGING 25

1.00

0.75

0.50

0.25

0.00

volume fraction

PIPE

MOUTH

0.3 kg/s

0.3 kg/s

Flow enters the pool.

Large turbulence is created, increased condensation

CONDENSATION MASS TRANSFER



FIRST BUBBLE ANALYSIS GROWTH 26

STB-28-4 MEASUREMENTS

STAR-CCM+ RESULTS

Animation

of the first

bubble

• Chugging phenomenon can be recreated only for the first bubble

• Bubble collapse velocity and phenomenon stability is highly dependent on

the modeling assumptions

• More physical investigation and sensitivity analysis is required


And what about advanced concepts?

27

NuScale

Power

ASTRID

ORNL Geometry and Instrumentation

28 Images from Fontana et al. [6]

Model Geometry

Modeling inlet region of the test

section shown to be important

29

In-Bundle Comparison

-0.5

0

0.5

1

1.5

2

0 5 10 15 20 25 30 35

exp

a

b

c

30

Compare to 36 different thermocouples for each case

Plot below shows the experimental measurement for each

thermocouple matches the at least one of the CFD probes

Analyze the whole data set

CDF of all the error of the measurement and nearest probe for

all data points for all 7 cases

40%

50%

60%

70%

80%

90%

100%


DNS-grade Pebble Bed Flow Modelling

Impact:

• A DNS database for pebble bed simulations to support industrial applications

• Optimization of flow and temperature distribution allowing improved fuel performance and reliability

Solution: Quasi-DNS simulations have been used to collect a virtual database and develop improved simulation guidelines based on RANS modeling.

Challenge: Accurately predict the flow and heat transfer in random beds of pebble fuel cooled by helium.

The tight geometrical configuration does not allow accurate experimental measurements

Shams et al. Nuclear Engineering and Design, Vol. 242-261-263 - 2012-2013


Better Reactors Grow from Better Simulations I strongly believe this! 3D CFD results allow better

understanding, more generality and fast prototyping.

Mature Single Phase Applications A large number of validated applications for LWRs. Fundamental Design tool for Advanced and Innovative

Concepts [LMFBR, VHTR, MoltenSalt …]

Multiphase CFD is stepping up Already applied for design, successfully. Drastically enhanced robustness will derive from more

physically based closures.

Some Conclusions

computational fluid dynamics for reactor design &...

Documents