CFD Methods for Improved Nuclear Economics and...

NSENuclear Science & Engineering at MITscience : systems : society

Massachusetts

Institute of

Technology

CFD Methods for Improved Nuclear Economics and Efficiency

Emilio Baglietto

Emilio Baglietto - NSE Nuclear Science & Engineering at MIT

2

Challenges in Reactor Design and Operation

Computational Fluid Dynamics has a key role in supporting today’s nuclear energy industry and accelerating future advances in the development of this cleaner energy source.

Industry, Academia and National Labs are working together in advancing the state of the art and the reliability of CFD for nuclear design and safety related applications

Sub-channel analysis support: support online/offline coupling with MCFD

Grid-to-Rod Fretting: fluid-structure interaction

turbulence excitations

Downcomer flow analysis: unsteady flow mixing in

complex geometry

Fuel Thermal Performance: accurate 3D flow and

thermal simulations

CRUD - CILC: Crud Induced Localized

Corrosion

Multiphase CFD: DNB methods PWRs

Void Predictions BWRs


3

The key focuses

Challenge 1 The accuracy and efficiency of the tools

Challenge 2 The integration of CFD

Challenge 3 The physical modeling and validation


4

Challenge 1

The tools: Discretization of simulation domain has long been the

bottleneck of the process Pain has often lead to simplifications/modification

which required time consuming evaluation, kills Predictive M&S potential

2006-2010CFD Simulation Group, PBMR

2005


20122009 2015


Fidelity + Efficiency

CFD + Neutronics full depletion cycle simulation: 14 state points, total time required for a complete depletion cycle: 44 hours on 1028 cores.

ANC power

Full Power 150MW*DAYS

1000MW*DAYS 2000MW*DAYS

44 hours /depletion-cycle proves that high fidelity CFD & Neutronics coupling is practical for engineering design for finalizing core design. The results will provide hot spot, boiling areas for CILC and crud simulation, fuel center line temperature, peak cladding temperature, and cross flow for GTRF.


7

Challenge 2

The integration: CFD is no longer a stand-alone tool, it is being

integrated in all design, licensing and operation processes.

Some examples: Fuel Reloads [CRUD evaluation] Plant O&M [Thermal Stratification, Cycling,

Striping] Plant Aging [Vessel and internals] Design Exploration [Fuel, internals, ECCS …] Uncertainty in plant performance indicators

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15


Mass flow measurement by means of orifice plates [2015]

qm =p

4C

1

1- b 4d2 2(p1 - p2 )r

CFD can be adopted successfully to reduce the mass flow rate uncertainty.

Reduction in measurement uncertainty can be leveraged to increase plant efficiency and economics


Uncertainty Characterization and Assessment for Performance Indicators of Nuclear Power Plants

Objective: Deliver a consistent approach to identify and quantify “representativeness*” uncertainty in nuclear power plant measurements.

Challenge: Complex spatial and temporal effects must be resolved to provide optimal performance.

Approach: Integrate experimental and simulation data to generate accurate uncertainty estimation with the potential to increase plant performance.

* uncertainty that arises from the inherent spatial or temporal variations of the quantity to be measured


10

Challenge 3

Physical modeling and validation: This is largely the role of Academia (but also the fun part) This is bread and butter of TH community…

1. The next step for Single Phase applications

2. The Multiphase-CFD grand challenge - DNB

… what are we trying to deliver


A sample application: Grid to rod fretting [GTRF]Pre-2010: Industry approach based on Forcing = f (K)

Finding: Unforeseen Coherent turbulence caused anticipated failure

Approach: Wall modeled LES captures failure accurately (but not industrially)

A. M. Elmahdi, R. Lu, M. E. Conner, Z. Karoutas, E. Baglietto, 2011: Flow Induced Vibration Forces on a Fuel Rod by LES CFD Analysis. Proceedings of the 14th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH14) Conference, Toronto, Ontario, Canada.



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The challenge: efficient resolution of flow structures

Objective: develop a x50 faster approach for GTRF assessment

Finding: URANS cannot resolved coherent structures leading to GTRF

Approach: Introduce a novel approach to turbulence resolution


LES

PANSRP04

URANS

Continuous Resolution of Turbulence


16

URANS LES DNS

New approach – STRUCTured based resolution

New model

Computational cost Control hybrid formulation

inside coherent structures, i.e. regions with rapid mean deformation and poor scale separation

Eliminate grid / length scale dependency

Achieve stability using a single-point dynamic averaging

Hybrid URANS

STRUCT-T Transport average formulation

𝑘𝑚𝑘𝑡𝑜𝑡

= min 1.75𝑡𝑟𝜏, 1

D𝜏

D𝑡=

𝜕

𝜕𝑥𝑗𝜈 + 𝜈𝑡

𝜕𝜏

𝜕𝑥𝑗+

𝑡𝑚𝜏− 1


STRUCT Model DevelopmentSquare cylinderApplication: external flows, bluff body flows

Easy case

Hybrid models:Good resultsNo grid convergence

STRUCT model:Good resultsGrid convergence

T-junction mixingApplication: turbulent mixing, thermal fatigue

Challenging case

Hybrid models:Poor resultsNo grid convergence

STRUCT model:Good resultsGrid consistency

Mild separationApplication: internal flows, e.g. in nuclear systems

Challenging case

Hybrid models:Wrong predictionsNo grid convergence

STRUCT model:Good resultsGrid consistency

Thermal StripingApplication: High Temperature reactors

Challenging case

Hybrid models:Wrong predictionsNo grid convergence

STRUCT model:Good resultsGrid convergence

In all test cases, the STRUCT approach demonstrates LES-like capabilities on meshes much coarser than those required for LES.

The STRUCT model has shown to consistently improve the prediction of the baseline URANS model

Provide a significant reduction in computational cost, between 20 and 80 times with respect to LES.


The challenge: efficient resolution of flow structures

Objective: develop a x50 faster approach for GTRF assessment

Finding: STRUCT approach shows capability to capture the forcing with similar results to LES

Approach: Continue testing a complete STRUCT formulation for general application


19

The DNB “Moonshot”

Despite decades of research and modelling, predicting DNB is still one of the major engineering challenges when designing systems that rely on multiphase heat transfer.

NUCLEAR the complexity of the physics at play has prevented the emergence of accurate predictive models and has led to the use of substantial margins on the power rating of PWR.

Yadigaroglu, 2015

Accurate and robust DNB prediction is akin to a “Moonshot” for the thermal-hydraulic community.

©

The objective


New physical understanding

NOTE: these animations present transient boiling In this context they are used to exemplify the physical insights that can be gained from their review Credits: Matteo Bucci (MIT)


Toward the DNB Moonshot

Pool boiling (Case A) and film boiling (Case B) lead to identical CHF! No influence of the macro-hydrodynamics.

CASL Report: L3:THM.CLS.P9.06 Y. Liu, M. Srivastava, N. Dinh. Micro-hydrodynamics in High Heat Flux Boiling

I do not need to “depend” on a CHF modelI can implement a CHF mechanism

I can track the wet and dry surface in a “cell”

allows me to split the heat transfer into 2 components

𝒒"𝒕𝒐𝒕 =𝑨𝒅𝒓𝒚 𝒒"𝒗𝒂𝒑𝒐𝒓_𝒇𝒊𝒍𝒎 +

(𝟏 − 𝑨𝒅𝒓𝒚)𝒒"𝑵𝒖𝒄𝒍𝒆𝒂𝒕𝒆

“.. as the heat flux increases, heat removed by the wetted area can’t keep up, leading to larger coalescence between bubbles, and further decreases in wetted area, resulting in surface dryout“

Dry (black) and wetted (grey)areas on a boiling surface.The amount of dry spots onthe surface drives the DNBinception.

J. Jung, S. J. Kim and J. Kim. Observations of the Critical Heat Flux Process During Pool Boiling of FC-72. April 2014.


GEN-II Heat Partitioning: Quick Overview1. Mechanistic

Representation of Bubble

Lift off and Departure

Diameters

2. Accurate evaluation of

evaporation heat flux by

modeling effective

microlayer

3. Account for sliding bubble

effect on heat transfer and

nucleation sites

Flow

4. Account surface quenching

after bubble departure

5. Account for bubble

interaction on surface


Pressure = 1.0 bar and 10°C Subcooling

Pressure = 2.0 bar and 15°C Subcooling

GEN-II Heat Partitioning: assessment Validation performed against MIT boiling curves Allows validating separate model components Calibration-free – demonstrated generality

deriving from improved physical representation

Evaporation term is not dominant contribution

Effect of bubble sliding dominates Flow Boiling Heat Transfer (previously postulated by Basu)

The new model demonstrates improved predictions at all conditions

Enhanced robustness at higher heat fluxes

SLIDING: Dominant effect on heat

transfer and nucleation sites

Bucci, Su, 2015


GEN-II Heat Partitioning: validation and reduction

1. Mechanistic



Diameters



modeling effective

microlayer

3. Account for sliding

bubble effect on heat

transfer and nucleation

sites

4. Account surface

quenching after bubble

departure



• Klausner v1 – Vapour bubble departure in Forced convection boiling (1992)

• One of the first ideas. Only describes Bubble departure.• Klausner v2 – Unified model for prediction of bubble detachment

diameters (1992-93)• Most common Klausner model. Estimates departure and lift-off using

force balance• No. of assumptions / estimated parameters reduced

• Klausner v3 – Bubble Force and Detachment models (2001)• Recent and very comprehensive. Pool, Flow in horizontal, vertical

and inclined in one model.• Estimates departure, liftoff diameters and sliding trajectory length

• Klausner v4 – Stochastic model (1997)• Not often tested, still to be evaluated

• Sugrue, Yun, Situ, Colombo• Modifications of Klausner v2 in most cases. Contain assumed

parameters and data-fitted constants

In collaboration with W. Ambrosini and T. Mazzocco – Universita’ di Pisa



1. Mechanistic



Diameters



modeling effective

microlayer




sites

4. Account surface


departure



In collaboration with W. Ambrosini and T. Mazzocco – Universita’ di Pisa

• Klausner, Zeng, 1993 – R113 horizontal flow, Departure and Liftoff (different sets of data)

• Thorncroft, 1998 – vertical upward and downward flow boiling of FC-87

• Thorncroft, 1998 – horizontal and vertical pool boiling data

• Sugrue, 2012 – Water inclined flow

• Situ, 2005 – Water vertical flow

• Prodanovic, 2002 – Water vertical flow

• Kandlikar, Stumm, 1995 – horizontal flow. Very slow bubble growth. Shows limits of assumptions in Klausner v2 (surface tension cant be neglected for smaller bubbles)



1. Mechanistic



Diameters



modeling effective

microlayer




sites

4. Account surface


departure



• Departure Frequency

𝒇 =𝟏

𝑻=

𝟏

𝒕𝒘 + 𝒕𝒈

Mechanistic idea: force balance for growth time + transient conduction [Hsu criterion] for wait time

Models that employ this method include

Han and Griffith

Basu

Yeoh and Tu

Not usable in this framework



1. Mechanistic



Diameters



modeling effective

microlayer




sites

4. Account surface


departure



• Wait time

Hsu’s criterion+

Analytic solutionForced convection transient boundary layer



1. Mechanistic



Diameters



modeling effective

microlayer




sites

4. Account surface


departure



• Re-evaluate flow boiling data to accurately quantify instantaneous vs total NSD

• IR camera data postprocessed via Matlab• Provides heat-flux and temperature fields

over time.• Novel algorithm to detect all active nucleation

sites.• Uses maps of high Temp variation over time +

detection of high density “spots” = Nucleation Sites.

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