Irradiation Shift – Questions and Answers

Q1 – The need to estimate irradiation shift Reactor Pressure Vessel (RPV)

Start of life toughness Asdg afdgasdge afgteasrd artea srdg dfvcxbgsjs dzffg srthy Why do we need to estimate irradiation shift? Quite simply it is because irradiation makes reactor pressure vessel materials less tough. All nuclear reactor components (Slide 2) are made from materials that are very resistant to service conditions. However, they may degrade through life due to the influence of the operating temperature, the pressure and temperature cycles, and the effect of the water in the circuits. In the case of the RPV (Slide 3a) one of the the most important materials properties for safety is toughness. RPVs are made from well-established materials that have high toughness (Slide 3b) before the RPV is put into service. RPV materials may be subject to the same, or similar, degradation mechanisms as other reactor components. However, (Slide 4) large areas of the RPV are also exposed to high levels of neutron irradiation. This causes very fine scale microstructural changes in the material (Slide 5), which can considerably affect its mechanical properties. The good start of life properties gradually change with time and the nice start of life RPV may be much less tough at end of life (Slide 6). The damage caused by irradiation is progressive (Slide 7); it does not saturate. The mechanisms of damage are complex and can depend on a large number of environmental and materials factors. There can be substantial differences in the rates at which toughness degrades between types of RPV and even between RPVs of the same design. In some cases Before they enter service, RPV materials have high toughness (resistance to fracture)

Neutron irradiation Core RPV Shielding Irradiation fluence (dose) is a measure of total exposure of the material to neutron irradiation Irradiation flux (dose rate) is a measure of the rate of exposure of the material to neutron irradiation

Effect of neutron irradiation (simulation) Dislocation loop Solute atom cluster Vacancy cluster (vacancy-solute atom complex) Box size 29x29x29nm Vacancies white; Interstitials black Cu red; Mn green; Ni blue With thanks to Stéphanie Jumel, EDF, France

Effect of irradiation and ageing Irradiation gradually degrades the toughness of RPV steels (operation at temperature may have a similar but smaller effect, even without irradiation)

Irradiation damage Progressive; does not saturate Complex, not fully understood Irradiation damage may be affected by: Environmental factors Irradiation fluence (dose); flux (dose rate); time Irradiation temperature Material factors composition: Cu, Ni, Mn, Si, P, C, N … strength; product form (plate, forging, weld); microstructure Can vary substantially between types of RPV, between RPVs of the same type and within individual RPVs

Variability of irradiation damage Data are from surveillance and test reactor irradiations for a wide range of materials and irradiation environments Charpy shift provides a measure of irradiation damage

Fracture assessment ΔT = Irradiation Shift Fracture toughness (MPa Ö m) vs. temperature ( o C) 250 ΔT = Irradiation Shift End of life KIC Ductile 200 Start of life KIC KI (SIF) transient due to cooling 150 Toughness . ΔT 100 Margin 50 Brittle -200 -100 100 200 300 Temperature

High toughness shift reduces the margin Fracture toughness (MPa Ö m) vs. temperature ( o C) 250 ΔT ΔT = Irradiation Shift Toughness 200 Start of life End of life 150 KI transient ΔT 100 Margin 50 Margin -200 -100 100 200 300 Temperature

Q2 - Experimental approaches for irradiation shift determination Ideally material would be cut from prolongations of the most important components of the RPV and machined into fracture toughness specimens which are irradiated in the RPV surveillance capsules during its service Core RPV Surveillance capsule Shielding

In practice use Charpy testing Historical reasons Fracture toughness testing was not developed when surveillance testing started Surveillance specimens are loaded at start of life Practical reasons Charpy tests are simpler and require a smaller volume of material (surveillance capsule space is limited) There are reasonably good correlations between fracture toughness and Charpy shift

Charpy test method Rough fracture surfaces show that high levels of energy has been absorbed; the specimen was tough Fractured specimen

Charpy test results Energy, J ΔT41J oC Upper shelf (ductile)  – unirradiated  - irradiated Upper shelf (ductile) ΔT41J

Q3 - Differences between shift measurement methods for WWER and other PWRs The differences in methods are small For WWER reactors the Russian PNAE G-7-002-86 code requires that the Charpy transition reference energy is varied according to material strength In US-based codes the reference energy level is fixed at 41J However the reference energy level has little effect on shift in most cases There are also small differences in Charpy testing standards (There are also differences in methods of estimating start of life fracture toughness)

Q4 – Correlation between Charpy and fracture toughness shift Charpy and toughness shifts are closely correlated, for all RPV steels: ΔTFracture toughness ≈ ΔTCharpy However the correlations are scattered and there may be some deviations from the 1:1 relationship

Correlations for US RPV steels US weld metals (left) show a 1:1 Charpy/toughness shift correlation, but in base materials (right), toughness shift is 16% higher than Charpy shift. Note that there is significant scatter (from Sokolov and Nanstad – Ref 42 in IAEA report)

Correlation for Russian steels ΔT0 and ΔTF are well-correlated, however …

… other evidence (VVER-440 materials) Some data indicate that toughness shift in VVER materials may be larger than Charpy shift (From Brumovský et al)

Interpretation of correlations Charpy and fracture toughness tests are very different from each other, so a 1:1 shift correlation is not obvious A difference in the tests is that Charpy tests measure the energy absorbed to fracture, including both brittle and ductile components, the toughness test measures the brittle component only Irradiation can affect both components to different extents depending on the steel. It may be that the Russian indexing procedure (higher referencing energy) is better for compensating for this factor

Q5 - Reliable shift estimation Regulations Regulations Require ΔPredicted – ΔActual > x + Margin for uncertainties Shift + margin for fracture assessment Shift + margin for fracture assessment 13.5m Require ΔPredicted – ΔActual > x (Unknown) error (bias) in prediction is ΔPredicted – ΔActual (Unknown) error (bias) in prediction is ΔPredicted – ΔActual + Margin for uncertainties Actual shift (ΔActual) (we do not know this) Actual shift (ΔActual) (we do not know this) ΔPredicted for important locations ΔPredicted for important locations Reliable shift and correlation models Reliable shift and correlation models Model justification Model justification Accurate values and uncertainties of materials and environmental variables at important locations Accurate values and uncertainties of materials and environmental variables at important locations NPC

Q6 – Test reactor data Are widely used Very versatile, can choose Irradiation temperature, flux, fluence, spectrum Test specimen type (including large KIC) Controlled experiments, accurate data Very useful in developing understanding and data to supplement surveillance data

Example of test reactor – HFR Petten

Lyra facility in HFR

Time/flux differences in test reactors Flux and fluence are for neutron energy greater than 1MeV 75y 15y 3y 0.5y 1m 1w Possible RPV fluence after 75 years Times to achieve fluence values at a given flux y(ears) m(onths) w(eeks) Mark Kirk P034 IGRDM-15

Important considerations for test reactor irradiations Acceleration of irradiation damage rate may affect the irradiation shift at a given fluence High flux can delay precipitation damage in Cu-containing steels Increasing flux may change irradiation damage mechanisms, for example producing unstable matrix defects (UMDs) (Nanstad et al 2008) Late blooming phases (LBPs) may be produced in very high fluence, very long time, low flux irradiations As a result irradiation shifts estimated using test reactor irradiations may be different from those in power reactors Test reactor data should be used with great care, particularly in cases when they might give lower shifts than those in power reactors

Q7 – Available irradiation shift models for predictions beyond current data Predictions for extended lifetimes are needed for economic as well as safety reasons If adequate surveillance capsules remain in the RPV, there is no problem from the point of view of safety Accurate irradiation shift models are required if it is not possible to do adequate surveillance testing and for economic reasons

Q8 - Model development methods Earliest models were essentially empirical – statistical fits to data As understanding developed, particularly in the 1980s and later, several mechanistically-based correlation (MBC) models were developed More recently, thanks to further developments in understanding and increased computer power, several groups have worked to develop multi-scale multi-physics (MSMP or MSP) models

Examples of early empirical models Cottrell (1957) ΔT = A(Φ)0.5 US NRC Regulatory Guide 1.99 Revision 1 (1977) ΔT = [40 + 1000(Cu-0.08) + 5000(P – 0.008)] x (Φ/1019)0.5 (Cu-0.08)=0 when Cu <= 0.08; similarly for the P term ΔT = shift; Φ = fluence Varsik and Byrne (ASTM STP 683 - 1979) ΔT = [377.9 log(CR) + 331.9] x (Φ/3x1019)0.43 (for welds) CR = chemistry ratio = {[1.5Ni + Si + 0.5C + 0.5(Mn – 0.5)]/(0.5 + 0.5Mo)} x Cu

MBC model example (Debarberis et al) The left-hand plot shows how the model shows the contributions from three components of irradiation damage; the right-hand plot shows how the model may be fitted to WWER steel data

MBC model example (Nikolaev, Yu) Improved residuals for the Nikolaev models: VVER-440 (left, ○) VVER-1000 (right,●), compared with the Russian Regulatory Guide (+, ○)

MBC model example (EONY) Matrix feature (e.g. loops) Copper-rich precipitates Effect of dose rate Effect of product form Effective copper content Eason, Odette, Nanstad and Yamamoto (EONY) model

MBC Model example, new WWER-1000 model Margin Total shift For base: m = 0,8; AF = 1,45 оС For weld: m = 0,8; AF = 1exp(2Ceq), оС Thermal ageing shift F is fluence, n/m2 (E>0.5MeV) F0 = 1x1022; t is time Irradiation shift Effect of composition for weld

Development of more accurate models Empirical and MBC models are fitted to data; extrapolation outside the database (or interpolation into sparse data regions) may be unreliable Accuracy (and confidence) can be increased by developing greater understanding of irradiation damage mechanisms and refining the models Confidence can also be increased by validation testing (eg of material from decommissioned RPVs) But MBC models cannot be fully validated because unexpected effects may occur for combinations of conditions for which there are no data In principle, MSP models can be founded on known physics, can be fully validated and can make accurate predictions for materials and irradiation environments for which no data exist However, it may take many more years before a sufficiently well validated MSP model is available

Current benefits of MSP models Provide valuable insights into damage mechanisms Provide a framework for collaborative research between different organizations and technical disciplines Can help identify critical issues and guide design of future experiments

Q9 Consolidated conclusions from Capture Irradiation shift for WWER-440 steels Damage has three or four components, potentially including P, Cu, P-Cu interaction and non-solute dependent defects Fluence exponents may be different from ⅓, and differ between the damage components The Russian reference model [Δ = 800(P + 0.07Cu)F1/3] may not be conservative At fluences beyond RPV end of life, total damage may start increasing at a high rate; can be explained by a P segregation model Shift at a given dose increases as flux reduces; research reactor data may be non conservative Analytical modelling can describe the observed dependencies and relationship between irradiation and re-irradiation response

Consolidated conclusions WWER-1000 steels Damage increases with Ni and Mn, and reduces with Si Fluence dependency is unclear, the exponent may be as high as 1 The Russian reference model for Ni >1.5% (Δ = AF1/3, where A = 20 for weld, = 23 for base) may not be conservative Shift increases as flux reduces Thermal ageing can have a significant influence on shift

Consolidated conclusions Fracture toughness behaviour Toughness shift may be 20% greater than Charpy shift for both base and weld At high levels of irradiation damage shape of toughness vs temperature transition curve may change (flatten) Shift modelling Improved mechanistically-based correlation models and physical models are being developed There are limits to the data available for modelling (quantity, limited variability of materials, appropriate flux levels). Accuracy of model inputs (fluence, composition) have been assessed

Q10 Open issues Functional form for shift model Amaev et al Nikolaev Yu. Russian regulatory models Debarberis et al analytical model Eason et al (EONY) (part of) US Reg Guide 1.99 Rev 2

Open issues Accuracy of models (outside surveillance data range) Mechanisms of Ni effects and Ni-Mn interaction Mechanism of Si effect Possibility of UMDs at high flux Possibility LBPs at low flux, long time Unknown unknowns Convergence of models for different types of steel Relationship between CV and KIC shifts Effect of irradiation on KIC transition slope Integration of SOL and Δ models and uncertainties Future direction for shift model development More data to develop and validate new models Late Blooming Phase? Miller et al (2005)