CFD Methods for Improved Nuclear Economics and...
Transcript of 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
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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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
20122009 2015
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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.
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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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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Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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.
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
LES
PANSRP04
URANS
Continuous Resolution of Turbulence
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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.
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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)
Emilio Baglietto - NSE Nuclear Science & Engineering at 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.
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
GEN-II Heat Partitioning: validation and reduction
1. 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
4. Account surface
quenching after bubble
departure
5. Account for bubble
interaction on surface
• 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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
GEN-II Heat Partitioning: validation and reduction
1. 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
4. Account surface
quenching after bubble
departure
5. Account for bubble
interaction on surface
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)
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
GEN-II Heat Partitioning: validation and reduction
1. 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
4. Account surface
quenching after bubble
departure
5. Account for bubble
interaction on surface
• 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
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
GEN-II Heat Partitioning: validation and reduction
1. 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
4. Account surface
quenching after bubble
departure
5. Account for bubble
interaction on surface
• Wait time
Hsu’s criterion+
Analytic solutionForced convection transient boundary layer
Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
GEN-II Heat Partitioning: validation and reduction
1. 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
4. Account surface
quenching after bubble
departure
5. Account for bubble
interaction on surface
• 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.