CFD Methods for Improved Nuclear Economics and ¢â‚¬¢ Thorncroft, 1998 ¢â‚¬â€œvertical upward and downward

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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

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