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NSE Nuclear Science & Engineering at MIT science : 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

Emilio Baglietto - NSE Nuclear Science & Engineering at MIT

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

Emilio Baglietto - NSE Nuclear Science & Engineering at MIT

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

2006-2010 CFD 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

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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Emilio Baglietto - NSE Nuclear Science & Engineering at MIT

Mass flow measurement by means of orifice plates [2015]

qm = p

4 C

1

1- b 4 d2 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

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

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

13

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

PANS RP04

URANS

Continuous Resolution of Turbulence

Emilio Baglietto - NSE Nuclear Science & Engineering at MIT

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

𝑘𝑚 𝑘𝑡𝑜𝑡

= min 1.75 𝑡𝑟 𝜏 , 1

D𝜏

D𝑡 =

𝜕

𝜕𝑥𝑗 𝜈 + 𝜈𝑡

𝜕𝜏

𝜕𝑥𝑗 +

𝑡𝑚 𝜏 − 1

Emilio Baglietto - NSE Nuclear Science & Engineering at MIT

STRUCT Model Development Square cylinder Application: external flows, bluff body flows

Easy case

Hybrid models: Good results No grid convergence

STRUCT model: Good results Grid convergence

T-junction mixing Application: turbulent mixing, thermal fatigue

Challenging case

Hybrid models: Poor results No grid convergence

STRUCT model: Good results Grid consistency

Mild separation Application: internal flows, e.g. in nuclear systems

Challenging case

Hybrid models: Wrong predictions No grid convergence

STRUCT model: Good results Grid consistency

Thermal Striping Application: High Temperature reactors

Challenging case

Hybrid models: Wrong predictions No grid convergence

STRUCT model: Good results Grid 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

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-hydraul