NSE Nuclear Science & Engineering at MIT science : systems : society Progress and Challenges in Predictive Thermal Hydraulic Simulations Massachusetts Emilio Baglietto Institute of Technology
A new approach to Nuclear Reactor Design “…computational methods drive design” PWR Advanced PWR Vessel Reactor Vessel http://www.neimagazine.com/ 2 Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
CFD for Nuclear Applications “…computational methods drive design” Lumped parameter approaches are “still” the base for reactor design and licensing. 3- Dimensional “virtual reactor” models are necessary to reduce operating costs. 3-D TH phenomena can cause fatigue cracking, pipe deformations, and additionally lead to anticipated equipment failure. Developing a mitigation strategy requires understanding the mechanisms that lead to the failure: unsteady, 3- dimensional turbulent effects. Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Extended range Twin Operations (ETOPS) aka Engines Turning Or Passengers Swimming Extensive use of Predictive Simulation have allowed granting of this ETOPS capability prior to the A350 entrance in service www.youtube.com /user/WorlTop10 Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
DOE Sponsored Programs Aims to address key challenges of nuclear NEAMS provides support relevant to both energy industry, through new M&S reactor and fuel cycle R&D programs by technology insights. CASL will deploy a creating analytic tools, codes and methods technology step change (VERA) that for use by scientists and engineers who need supports today’s nuclear energy industry to simulate nuclear energy systems. NEAMS and accelerates future advances in the is developing a computational ToolKit which development of this cleaner energy source. is comprised of both reactor and fuel systems analysis capabilities that can be exercised either coupled or independently, larger reliance on legacy physics codes depending on the needs of the end user. early on the program, with selective development of new codes and models includes the entire fuel cycle, as well as advanced reactors. Timeline is therefore a longer one, to support a larger, challenging and continuously evolving scope. Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
A snapshot of the DOE Tools Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
2012 2014 2009 Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
REACTOR FUEL DESIGN APPLICATIONS Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Fuel Applications 1: Press. Drops Extensive validation/application Mature Application Tools have greatly CASE B = Baglietto, E., 2006, improved Anisotropic Turbulence Modeling for Accurate Rod Bundle Simulations, ICONE14 Models provide confidence (2006- K. Ikeda et al. “Study Of Spacer Grid Span Pressure Loss Under High Reynolds Number Flow Condition” - Proceedings of ICONE17 2014) 12 DP6 (psi) Trying to collect 10 CFD QKE CFD Ke guidelines to stop 8 CFD SST re-inventing the 6 wheel (at last) 4 QKE = Quadratic k-e Baglietto and Ninokata 2 SST = Menter SST Model 0 20000 30000 40000 50000 60000 70000 80000 R. Sugrue, M. Conner, J. Yan, E. Baglietto, 2013 - Pressure Drop Measurements and CFD Predictions for PWR Structural Grids, LWR Fuel Performance Meeting TopFuel, Sept. 15-19, 2013 - Charlotte, NC. Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Fuel Applications 1: Press. Drops Application of ASME V&V20 to Predict Uncertainties in CFD Calc. • First-of-kind calculation of uncertainties related to a CFD calculation for nuclear fuel application in the open literature with the ASME V&V20 method • CFD modeling to predict pressure losses in rod bundle is optimal • E < U val : E is lower than the upper limit of the possible error due to the CFD modeling assumptions and approximations • Modeling error within the "noise level" imposed by • Objective: evaluate the uncertainty due to the modeling, δ s the numerical, input, and experimental • δ D is known from AREVA’s large amount of uncertainties PLC experiments • Improving the CFD modeling is not possible • E = validation comparison error is known without an improvement on the numerical, from AREVA PLC validation between CFD geometric and experimental errors & experiment ASME V&V20 provides a method to evaluate components of δ S C. Lascar, E. Jan, K. Goodheart, T. Keheley, M. Martin, A. Hatman, A. Chatelain, E. Baglietto, 2013 - Example of Application of the ASME V&V20 to Predict Uncertainties in CFD Calculations, Proc. 15 th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-15), May 12-17, 2013 – Pisa, Italy. Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Fuel Applications 2: Velocity predictions Extensive (proprietary) validation/application Position: +2 D h ; Gap #4; Re = Re4 LDA PIV CFD 0.5 σ² PIV/CFD =σ² CFD +σ² PIV 0.4 Mature Application σ CFD,mean = 1% Normalized crossflow velocity [-] 0.3 PIV 0.2 Large validation 0.1 experience 0.0 -0.1 Consistent Industrial -0.2 -0.3 Application -0.4 -0.5 -0.06 0.00 0.06 0.12 0.18 0.24 0.30 0.36 0.42 0.48 0.54 Accuracy of experimental Normalized X [-] Position: 2 D h ; Gap #4; Re = Re1 LDA PIV CFD measurements is critical σ² PIV/CFD =σ² CFD +σ² PIV 1.46 σ CFD,mean = 1.8% Normalized crossflow velocity [-] 1.10 0.74 0.38 0.02 VALIDATION OF A CFD -0.34 METHODOLOGY TO PREDICT FLOW -0.70 FIELDS WITHIN ROD BUNDLES WITH -1.06 SPACER GRIDS - C. Lascar et al. -1.42 -1.78 -0.06 0.00 0.06 0.12 0.18 0.24 0.30 0.36 0.42 0.48 0.54 Normalized X [-] Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Fuel Applications 3: consensus Importance of mesh quality and turbulence modeling [nothing really new] Physica cally Ba Based Cl Closure Co Coeff ffici cient Grid quality and consistency is “essential” for robust application [experience!, no tets!!] Importance of Anisotropic approach, based on physical representation Demonstrates improved prediction at all locations, including Turbulence Levels EPRI Industrial Benchmark Quadratic RSM [EdF] RMS MS error ors s of the e axial al fluc uctua uation n veloc ocities es. Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Flow structures Turbulent Jets Can you explain the GENX Chevrons ?? A jet nozzle has a sharp edge at which the flow separates. The fixed, circular separation line tends to impose axisymmetry on the initial large- scale eddies. Axisymmetry can be broken by corrugating the lip of the nozzle, which breaks up axisymmetric vortices into smaller, irregular eddies. http://www.sussex.ac.uk/wcm/assets/media /313/content/9161.250x193.jpg 13 Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Mass flow measurement by means of orifice plates q m = p 1 2 2( p 1 - p 2 ) r C 4 d 1 - b 4 Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Mass flow measurement by means of orifice plates: LES Results 100% power level 80% power level Extruded 3D d 3D Base size 2D d 3D 50% power level Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
CFD Activities in Support of Thermal- hydraulic Modeling of SFR Fuel Bundles Emilio Baglietto, Joseph William Fricano, Eugeny Sosnovsky
Model Geometry Modeling inlet region of the test section shown to be important Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
In-Bundle Comparison (2014) 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 Analyzed the complete data set CDF of all the error of the measurement and nearest probe for all data points for all 7 cases 100% 2 1.5 80% exp 1 a 60% 0.5 b 0 40% c 2.5% 7.5% 12.5% 17.5% 22.5% 27.5% -0.5 0 5 10 15 20 25 30 35 18 Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Distorted fuel analysis 19 Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
Mass flow rate (kg/s) -0.05 0 0.05 0.1 0.15 0.2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Crossflow Plane Section Index 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Nominal Fully Deformed (Left: Nominal geometry; Right: Deformed geometry) 20
Irradiation-caused Deformation Consequences (coolant) Parameter Value Pressure drop -2.04% Hot channel outlet temperature +6.99K Average mass crossflux -11.4% Sodium temperature penalty factor 1.058 The sodium temperature penalty factor is: “The ratio of the hottest subchannel’s outlet temperature increase to the nominal difference between this subchannel’s inlet and outlet temperatures.” 21 Emilio Baglietto - NSE Nuclear Science & Engineering at MIT
NSE Nuclear Science & Engineering at MIT science : systems : society boiling heat transfer void fraction DNB Multiphase CFD … the grand challenge Massachusetts Institute of Emilio Baglietto - NSE Nuclear Science & Engineering at MIT Technology
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