Determination of High-Risk Zones for Local Wall Thinning

101 ISSN-1883-9894/10 © 2010 – JSM and the authors. All rights reserved.

E-Journal of Advanced Maintenance Vol.5-2 (2013) 101-112Japan Society of Maintenology

Determination of High-Risk Zones for Local Wall Thinning due to

Flow-Accelerated Corrosion Shunsuke UCHIDA1,*, Masanori NAITOH1, Hidetoshi OKADA1, Hiroaki SUZUKI1, Yoshiyuki TSUJI2, Seiichi KOSHIDUKA3 and Derek H. LISTER4

1 Institute of Applied Energy, 1-14-2 Nishi-Shimbashi, Minato-ku, Tokyo 105-0003, Japan 2 Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan 3 University of Tokyo7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan 4 University of New Brunswick, PO Box 4400, Fredericton, NB, Canada, E3B 5A3 ABSTRACT

Thousands of flow-accelerated corrosion (FAC)-possible zones cause long and costly inspection procedures for nuclear power plants as well as fossil power plants. In order to narrow down the number of inspection zones, a speedy and easy-to-handle tool for determination of FAC risk zones, a 1D FAC code, was developed. FAC risk was defined as the mathematical product of the possibility of wall thinning occurrence and its hazard scale. The local maximum thinning rate could be predicted with accuracy within a factor of 2 with the 1D FAC code. High FAC risk zones and high priority locations for thinning monitoring along entire cooling systems and the effects of countermeasures on mitigating the risks could be evaluated within a small amount of computer time. The fusion of prediction and monitoring might go well to improve plant performance.

*Corresponding author, E-mail: [email protected]

KEYWORDS

FAC, wall thinning, monitoring, mass transfer coefficient, temperature, pH, O2, risk analysis

ARTICLE INFORMATION

Article history: Received 5 November 2012 Accepted 3 June 2013

1. Introduction (Arial, 12pt)

Flow-accelerated corrosion (FAC) of condensate and feed water piping is still one of the key issues in determining the reliability of aged nuclear power plants (NPPs) as well as fossil fuelled power plants (FPPs) [1-3]. Early detection and prediction of FAC along major piping systems and application of suitable countermeasures such as water chemistry improvements are essential for preventing pipe rupture in aged plants [4]. There are generally two approaches to evaluating further wall thinning in FAC-affected zones; one is based on inspection and the other on prediction or estimation. Thousands of FAC-possible zones cause long and costly inspection procedures for plants, even if the number of zones is minimized on the basis of temperature and flow velocity. In order to narrow down the number of inspection zones, suitable prediction or estimation procedures for FAC occurrence should be applied and the resulting computer programs tuned with as many inspection data as possible. Such coupling of the estimation and inspection procedures should lead to effective and reliable preparation against FAC occurrence and propagation. Indeed, following the pipe rupture in the condensate water line at Mihama-3, the Nuclear and Industrial Safety Agency (NISA) of Japan has promoted parallel inspection and estimation approaches to FAC and has just started a project on advanced management of pipe wall thinning based on a “combined technology” of estimation and inspection [5].

Many basic equations related to FAC have already been reported [6-8]. A computer simulation code for FAC has been prepared for both evaluations of FAC occurrence zones and FAC rates based on the previously reported numerical equations [9-12]. Computer program packages for FAC evaluation, e.g., CHECWORKS, WATHEC and BRT-CICERO, have been available for many years and their accuracy and applicability have been confirmed [13-17]. Unfortunately, details of their theoretical basis and of the data bases of the program packages are classified due to intellectual property rights. Both traceability of the computer program package and its validation are required for making policy on plant reliability when applying the package calculations. The authors are developing computer simulation codes based on 6-step evaluation procedures for FAC

S. Uchida, et al./ Determination of High Risk Zones for Local Wall Thinning due to FAC

102

occurrence and wall thinning rate of NPPs and trying to publish their basic equations, major constants for calculation and the results for their validation [9-12].

A six-step procedure to estimate local wall thinning due to FAC is proposed as one part of the coupled

approach [11-12]. As a result of a V&V (verification and validation method) evaluation based on a comparison of calculated and measured wall thinning, it was confirmed that zones of high FAC risk could be identified with satisfactory safety margins via first three steps, and then, wall thinning rates could be predicted with an accuracy within a factor of 2. It was also confirmed that residual wall thicknesses after one year operation could be estimated with an error less than 20% [18-20]. Based on previous estimates of residual pipe wall thicknesses and evaluations of the effects of water chemistry on major materials in PWR secondary systems, O2 injection into the condensate water was planned and successfully carried out at a PWR plant in Japan [4, 21],

One of the disadvantages of the 3D FAC code was in 3D computational fluid dynamics (CFD) analysis, which required a lot of computational time and memory. In order to avoid individual variation for determination of FAC occurrence, the computer program packages for FAC occurrence evaluation from Step 1 through 3 were reassembled. The other purpose for developing the code reassembling step 1 through Step 3 was to prepare a speedy and easy-to-handle FAC code based on 1D CFD analysis and to apply it to the whole plant system in a restricted computer time in order to point out the locations where future problems might occur, pipe inspections should be required and early implementation countermeasures should be done [22]. For this purpose, not only the probability of serious wall thinning occurrence in the future but also a hazard scale of pipe rupture due to the serious wall thinning should be analyzed. FAC risk was defined as the mathematical product of the possibility of serious wall thinning occurrence and its hazard scale.

In this paper, computer code packages of determination procedures for high FAC risk zones based on the 1D FAC code are introduced and determination processes for high FAC risk zones are demonstrated. 2. WALL THINNING DUE TO FAC 2.1. Major Problems Related to Structural Materials

Major subjects related to materials in cooling systems and material atlases of pressurized water reactors (PWRs) and boiling water reactors (BWRs) are shown in Fig. 1 [23]. Stress corrosion cracking (SCC), e.g., intergranular SCC (IGSCC) for BWRs and primary water SCC (PWSCC) for PWRs, is one of the most frequently reported material problems for nuclear power plants but no serious accidents related to it have ever been reported. Defects of steam generator tubing often interrupt PWR plant operation but they have never caused a serious accident. Copper alloy heater tubes are applied for some PWRs, while stainless tubes are applied for most BWRs to mitigate corrosion product input into the reactors. In each system, uniformly controlled cooling water is in contact with different materials, which complicates corrosion problems. Corrosion behaviors are much affected by water qualities and differ according to the values of water qualities and the materials themselves.

Problems related to corrosion: zirconium corrosion: SG tube defects: IGSCC/PWSCC: FAC: LDI (erosion): LDI (corrosion)

4

1

5

32

6

condenser

a) PWR b) BWR

Major materialsstainless steelcarbon steelnickel base alloyzirconium alloycopper alloy/ titanium

reactor pressure

vessel

fuel assembly

steam generator

HP heater

LP turbine

moisture separator

LPheater

HP turbine

heater drain

pressurizer

secondary cooling systemprimary cooling system

6

52

31 4

44

6 pre-filterdemineralizer

reactor pressure vessel

HP heater

LP turbine

condenser

moisture separator

LP heater

HP turbine

heater drain

fuel assemblies

3

631

5

44

4 4


4

1

5

32

6


444

111

555

333222

666

condenser

a) PWR b) BWR



reactor pressure

vessel

fuel assembly

steam generator

HP heater

LP turbine

moisture separator

LPheater

HP turbine

heater drain

pressurizer


6

52

31 4

44

6

reactor pressure

vessel

fuel assembly

steam generator

HP heater

LP turbine

moisture separator

LPheater

HP turbine

heater drain

pressurizer


666

555222

333111 444

444444

666 pre-filterdemineralizer


HP heater

LP turbine

condenser

moisture separator

LP heater

HP turbine

heater drain

fuel assemblies

3

631

5

44

4 4

pre-filterdemineralizer


HP heater

LP turbine

condenser

moisture separator

LP heater

HP turbine

heater drain

fuel assemblies

333

666333111

555

444444

444 444

Fig. 1 Major subjects related to materials in cooling systems of nuclear power plants

FAC in single-phase flow has caused two serious accidents with PWRs [2, 3]. It has not led to serious

damage in BWR feed water piping due to continuous oxygen addition [24]. FAC in two-phase flow used to be a serious problem in heater drain lines in BWRs but it was mitigated by replacing carbon steel with chromium containing low alloy steel, and it was not so serious a problem in PWR heater drain lines due to the higher pH in them. But FAC in two-phase flow in heater drain lines under increasing steam quality is much

103


affected by droplet impingement on the pipe inner surface. The phenomenon is designated as liquid droplet impingement (LDI). LDI is divided into two types; one is determined by a mechanical process (designated as LDI (erosion) and the other is determined by a corrosion process (designated as LDI (corrosion)). LDI on the turbine blades is a typical pattern of LDI (erosion), which is often mitigated by improving surface hardness, e.g., application of stellite alloy coating. The final stage of heater drain lines with rather low steam velocity is subject to LDI (corrosion), while the stage with rather high steam velocity is subject to LDI (erosion). In the case of LDI, even if there are small holes, lower pressure in the piping than the ambient one results in in-leakage but not out-leakage, which does not cause serious environmental damage. From the viewpoint of plant risk, FAC is much more important than LDI, though any LDI problem should be reported to the government with detailing its roof cause and countermeasures for avoiding the same kind of problems in the future [25]. 2.2. Major Parameters to Determine FAC

FAC is determined by six parameters [12-13]. The thickness of the boundary layer is much affected by flow dynamics. Chromium content in the steel is another important parameter to determine FAC. Solubility of ferrous ion, which is also an important parameter to determine FAC rate, is expressed as a function of temperature and pH. Oxygen concentration ([O2]) in the boundary layer also plays an important role for oxidizing magnetite to hematite, which contributes to achieving much higher corrosion resistance. Ferrous ion concentration in the water, [Fe2+], is calculated with a chemical reaction model based on the obtained flow pattern, and then calculated [Fe2+] is fed back to the environmental parameters for the wall thinning calculation [26]. Generally speaking, FAC rate decreases inversely with two parameters, [O2] and [Fe2+] [26]. Power plants are usually operated under low [O2] and low [Fe2+]. For evaluation of the high FAC risk zone ((1) in Fig. 2), those parameters were taken off and considered as safety margins, and the others determined by 1D analysis were applied for the high FAC risk zone evaluation, where 1D mass transfer coefficient was multiplied by geometrical factors [22]. Wall thinning should be evaluated at least for the high FAC risk zones pointed out by the first 3 steps of the evaluation. For determination of the precise wall thinning rate ((2) in Fig. 2), 3D mass transfer coefficient based on the 3D CFD code calculation as well as the effects of the other parameters, temperature, pH, [Cr], [O2] and [Fe]. 3. DETERMINATION OF WALL THINNING RATE 3.1. Wall Thinning Rate due to FAC 3.1.1. Evaluation process.

Six calculation steps were prepared for predicting FAC occurrence and evaluating wall thinning rate (Fig.

2). Flow pattern and temperature in each elemental volume along the flow path are obtained with a 1D CFD code and then corrosive conditions, e.g., [O2] and electrochemical corrosion potential (ECP), along the flow path are calculated with the O2-N2H4 reaction code [27-28]. Precise flow patterns around the structure surface are calculated with a 3D CFD code and then distributions of mass transfer coefficients at the surface are obtained [29]. The high FAC risk zone is evaluated by coupling major FAC parameters obtained by Steps 1 through 3. At the indicated high FAC risk zone, wall thinning rates are calculated with the coupled model of static electrochemical analysis and dynamic double oxide layer analysis. As a final evaluation, residual lifetime of the pipes and applicability of countermeasures against FAC are evaluated in Step 6. The details of calculation procedures for Steps 3 and 5 were shown previously [11,22].

Step 1

Step 2

Step 3

Step 4

Step 5

Step 6

1D CFD code

1D O2-hydrazine reaction code

3D CFD code

Wall thinning calculation code

1D wall thinning calculation

Total evaluation [planning for preventive maintenance, analysis of plant system safety]

Periodic wall thinning measurement

Continuous wall thinning measurement

Evaluation of residual wall thickness

Selection of measuring point based on JASE code

Selection of measurementlocation for wall thinning

Improvement of 3D FAC code

Improvement of FAC codes

Corrosion (chemical) analysis Measurement and inspectionFlow dynamics analysis System analysis

Step 1

Step 2

Step 3

Step 4

Step 5

Step 6

1D CFD code


3D CFD code











Step 1

Step 2

Step 3

Step 4

Step 5

Step 6

1D CFD code


3D CFD code











Corrosion (chemical) analysis Measurement and inspectionFlow dynamics analysis System analysisCorrosion (chemical) analysis Measurement and inspectionFlow dynamics analysis System analysis


104

Fig. 2 Evaluation inspection steps for wall thinning due to FAC

In order to narrow down the number of inspection zones, evaluated prediction results are applied. Precise measurements at the restricted area are expected to obtain reliable data sets. And evaluated measured wall thickness data can be feedback for improvement of the prediction code. Such coupling of the estimation and inspection procedures should lead to effective and reliable preparation against FAC occurrence and propagation. 3.1.2. Wall thinning evaluation based on 3D FAC code.

From comparison of the calculated wall thinning rates due to FAC with hundreds of measured results for secondary piping of an actual PWR plant, it was confirmed that the calculated wall thinning rates agreed with the measured ones within a factor of 2 [20]. Most of the calculated/measured ratios were in the factor of two region, while those for the very low thinning rate were still outside the region and had values larger than a factor of two. One of the causes of the discrepancies between the measured and calculated results was the very low thinning rate with large errors by UT measurements. Certainly, calculation procedures should be improved, and measurement accuracies should also be improved. Mitigating the discrepancy between the calculated and measured values at low measured regions is a future subject for consideration, and the root reasons for underestimation should be carefully investigated to establish the proposed six-step process as a standard code for FAC evaluation. The reliability of piping is determined by residual thickness. So it is important to evaluate residual thickness with high accuracy. The accuracy of the evaluation model for pipe wall thickness both for bend piping and T junctions was confirmed to be less than 20 %, as shown in Fig. 3 [20].

0

10

20

30

40

50

0 10 20 30 40 50calculated (mm)

mea

sure

d (m

m) -20%

+20%

condensate water(146ºC)

feed water line(222ºC)

T-junction (drum: 190 ºC)

T-junction (pipe: 190 ºC)

0

10

20

30

40

50

0

10

20

30

40

50

0 10 20 30 40 500 10 20 30 40 50calculated (mm)

mea

sure

d (m

m) -20%

+20%

condensate water(146ºC)

feed water line(222ºC)

T-junction (drum: 190 ºC)

T-junction (pipe: 190 ºC)

Fig. 3 Calculated results based on 3D FAC code: residual thickness

3.2. Wall Thinning Rate Obtained from 1D FAC Code

Calculation procedures for Steps1 through 3 in Fig. 2 are shown in Fig. 4. Five of 6 parameters for FAC calculation are determined with sub-codes, while one of the parameters, ferrous ion concentration, is omitted for determination because of its conservative effects on FAC. The details for determination of the major parameters were shown previously [22].

1D CFD code


Select the systems for FAC calculation

Materials of piping and components ([Cr])

Temperature and flow velocity distributions

Plant operational history Hydrazine concentration

Oxygen concentration

pH

Regional maximum wall thinning rate

Geometry factors

Input data for wall thinning calculation


Piping and component configurations

1D CFD code


Select the systems for FAC calculation

Materials of piping and components ([Cr])

Temperature and flow velocity distributions

Plant operational history Hydrazine concentration

Oxygen concentration

pH

Regional maximum wall thinning rate

Geometry factors

Input data for wall thinning calculation


Piping and component configurations

105


Fig. 4 Calculation procedures and major input data of 1D FAC code (DREAM-FAC) 3.2.1 Mass transfer coefficient

In order to determine the approximate mass transfer coefficient, the mass transfer coefficient for 1D straight pipe geometry calculated by applying 1D flow velocity data was multiplied by the geometrical factor for a bend, orifice and other complex geometries [14-15]. The mass transfer coefficient is a function of temperature as well as flow velocity. So, first dimensionless quantities, e.g. Reynolds number and Schmidt number, for a given flow velocity were obtained and then mass transfer coefficient and thickness of the surface boundary layer for mass transfer were calculated based on the dimensionless quantities, which were expressed as empirical formulae for easy calculation [22].

Properties of cooling water: =28000/T (1) =1050exp(-0.001T) (2) x10-6 =2.67 x10-5/T/(exp(-0.001T) (3) D=6.33 x10-8 x (exp(-12000/(RT)) (4) Sc=/D (5)

Flow dynamics data: Re=du/ (6) 1/=0.023/d Re0.8Sc0.33 (7)

Mass transfer coefficient: hm=D/ (8) hm

*=hmKc (9)

Major geometrical factors, Kc, are listed in Table 1 [14-15]. From the latest experimental data, the geometrical factors for an orifice was expressed as a function of the orifice factor, designated as the ratio, do/dI.

Table 1 Geometrical factors for FAC

*1 dI: pipe inner diameter (m), r: radius of curvature (m)*2 Geometrical factor for orifice was redefined as a function of orifice geometry

dI: pipe inner diameter (m), dO: orifice diameter (m)*3 Geometrical factor for straight pipe: 0.04*4 Latest data for orifice measured at Nagoya University

reference velocity

0.6

r/dI*1=0.5 0.3

r/dI=1.5

r/dI=2.5

type of exposure

stagnation points of primary flow

at distributor and behind orifices

impact velocity

stagnation points of secondary flow

in bends and outlet diffusers

behind junctions

flow velocity

KC*3

stagnation points after separation vortices

without stagnant point in straight pipes

at and behind obstructions

flow velocity

flow velocitybehind orifices

dO/dI*2=0.41

dO/dI=0.5

dO/dI=0.61

0.09*4

0.08*4

0.06*4

0.75

0.23

0.15

0.15

0.16

0.04*3

*1 dI: pipe inner diameter (m), r: radius of curvature (m)*2 Geometrical factor for orifice was redefined as a function of orifice geometry

dI: pipe inner diameter (m), dO: orifice diameter (m)*3 Geometrical factor for straight pipe: 0.04*4 Latest data for orifice measured at Nagoya University

reference velocity

0.6

r/dI*1=0.5 0.3

r/dI=1.5

r/dI=2.5

type of exposure



impact velocity



behind junctions

flow velocity

KC*3




flow velocity


dO/dI*2=0.41

dO/dI=0.5

dO/dI=0.61

0.09*4

0.08*4

0.06*4

0.75

0.23

0.15

0.15

0.16

0.04*3

reference velocity

0.6

r/dI*1=0.5 0.3

r/dI=1.5

r/dI=2.5

type of exposure



impact velocity



behind junctions

flow velocity

KC*3




flow velocity


dO/dI*2=0.41

dO/dI=0.5

dO/dI=0.61

0.09*4

0.08*4

0.06*4

0.75

0.23

0.15

0.15

0.16

0.04*3

3.2.2. Complex geometrical factor.

Complicated geometry consisting of connections of bends and orifices and so on requires a complex geometrical factor, which is defined as follows [15].

Point A KcA

Point B just downstream of point A KcB


106

Distance between A and B: x (m) Complex geometrical factor: Kc

AB=KcB+ΔKc

A (10) ΔKc

A=KcA exp(-CG Δx/dI) (11)

3.2.3. Wall thinning evaluation due to 1D FAC code

Calculated results obtained with the 1D FAC code were compared with those obtained with the 3D FAC code, for which accuracy had been confirmed as within a factor of 2 [30]. The accuracy of the evaluation from the 1D FAC code was confirmed to be less than a factor of 2 as shown in Fig. 5.

0

0.1

0.2

0.3

0.4

0.5

0 0.1 0.2 0.3 0.4 0.5

Thi

nnin

g ra

te (

3D

cod

e)(a

rbitr

ary

unit

)

Thinning rate (1D code)(arbitrary unit)

Thi

nnin

g ra

te c

alcu

late

d w

ith

3D c

ode

(arb

itrar

y un

it)

0

1

2

3

0 1 2 3Thinning rate calculated with 1D code

(arbitrary unit)

-50%

+100%

for PWR secondary system （pH>9.2）for orifice experiment （pH: 7.3）

+100%

-50%

0

0.1

0.2

0.3

0.4

0.5

0 0.1 0.2 0.3 0.4 0.5

Thi

nnin

g ra

te (

3D

cod

e)(a

rbitr

ary

unit

)


0

0.1

0.2

0.3

0.4

0.5

0

0.1

0.2

0.3

0.4

0.5

0 0.1 0.2 0.3 0.4 0.50 0.1 0.2 0.3 0.4 0.5

Thi

nnin

g ra

te (

3D

cod

e)(a

rbitr

ary

unit

)


Thi

nnin

g ra

te c

alcu

late

d w

ith

3D c

ode

(arb

itrar

y un

it)

0

1

2

3

0 1 2 3Thinning rate calculated with 1D code

(arbitrary unit)

-50%

+100%

Thi

nnin

g ra

te c

alcu

late

d w

ith

3D c

ode

(arb

itrar

y un

it)

0

1

2

3

0

1

2

3

0 1 2 30 1 2 3Thinning rate calculated with 1D code

(arbitrary unit)

-50%

+100%

for PWR secondary system （pH>9.2）for orifice experiment （pH: 7.3）for PWR secondary system （pH>9.2）for orifice experiment （pH: 7.3）

+100%

-50%

Fig. 5 Comparison of wall thinning rates calculated with 1D and 3D FAC codes

3.2 4. Application of 1D FAC code for evaluation of FAC risks at plants.

As a result of the Steps 1 and 2 calculation, five of six parameters could be determined with 1D CFD and O2-N2H4 reaction calculations. Evaluation procedures for FAC occurrence are shown in Fig. 4. Temperature along the flow path was calculated with the 1D CFD code and pH was determined from plant experience with chemical injection. Mass transfer coefficient for straight piping could be calculated by applying a flow velocity distribution along the flow path obtained from the 1D CFD calculation into Eqs. (1) through (9) and then they could be multiplied by the appropriate geometrical factor to obtain the mass transfer coefficient for complex geometries, e.g., bend, orifice and others (11). 3.2.5. Target system for 1D FAC code evaluation

Distributions of flow velocity and temperature along the flow in the cooling system shown in Fig. 6 were calculated with the 1D system simulation code, RELAP5 [22]. Condensate water from the main condensers is polished by the demineralizers, heated by multi stages of condensate and feed water heaters and then fed into the steam generators (SGs).

condensate water heater

maincondenser

feed water heater

steam generator

demineralizer

deaerator

①*

②

⑫⑤⑥⑦

⑧

⑨

⑩ ⑪

⑬⑭

⑮

⑯

③④

condensate water heater

maincondenser

feed water heater

steam generator

demineralizer

deaerator

①*

②

⑫⑤⑥⑦

⑧

⑨

⑩ ⑪

⑬⑭

⑮

⑯

③④

Fig. 6 Schematic diagram of PWR secondary cooling system *The numbers correspond to the location numbers in Table 2

107


Flow patterns throughout the whole system were calculated with RELAP5 in Step 1. Calculation

conditions and results are shown in Table 2. And then, precise flow patterns were calculated with 2-3D CFD codes based on 1D calculation results in Step 4.

Table 2 Major input data for 1D FAC code

location

①

②

③

④

⑤

⑥

⑦

⑧

⑨

⑩

⑪

⑫

⑬

⑭

⑮

⑯

[Cr](%)

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

innerdiameterdI (m)

0.620

0.381

0.381

0.425

0.425

0.425

0.425

0.687

0.437

0.381

0.519

0.483

0.483

0.483

0.701

0.354

wallthickness

Tw (mm)

20.0

12.7

12.7

16.0

16.0

16.0

16.0

12.0

10.0

12.7

20.0

38.0

38.0

38.0

56.0

26.2

flowvelocity

v （ m/s)

3.58

3.16

3.23

2.59

2.63

2.70

2.75

3.15

3.68

4.17

4.49

5.19

5.19

5.44

5.16

5.06

pressureP (MPa)

2.64

2.62

2.35

2.38

2.04

1.71

1.41

1.39

0.97

3.22

3.19

7.21

7.12

7.01

7.01

7.04

tempe-rature

T (℃ )

33.0

33.0

74.0

74.0

98.0

129.0

146.0

146.0

178.9

186.0

186.0

188.0

188.0

222.0

222.0

222.0

enthalpyH (kJ/kg)

140

140

312

312

412

543

616

616

754

793

793

801

801

954

954

954

hazard scale

enthalpy x dI2

(a.u)

0.19

0.07

0.16

0.19

0.26

0.34

0.38

1.00

0.50

0.40

0.73

0.64

0.64

0.76

1.61

0.41

geometry

T-junction

bend

bend

bend

bend

bend

bend

bend+orifice

T-junction

bend

bend

bend

bend

bend

T-junction

bend

geometricalfactor

Kc

0.16

0.23

0.23

0.23

0.23

0.15

0.15

0.5

0.16

0.15

0.3

0.3

0.3

0.3

0.6

0.3

location

①

②

③

④

⑤

⑥

⑦

⑧

⑨

⑩

⑪

⑫

⑬

⑭

⑮

⑯

location

①

②

③

④

⑤

⑥

⑦

⑧

⑨

⑩

⑪

⑫

⑬

⑭

⑮

⑯

[Cr](%)

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

[Cr](%)

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

0.001

innerdiameterdI (m)

0.620

0.381

0.381

0.425

0.425

0.425

0.425

0.687

0.437

0.381

0.519

0.483

0.483

0.483

0.701

0.354

innerdiameterdI (m)

0.620

0.381

0.381

0.425

0.425

0.425

0.425

0.687

0.437

0.381

0.519

0.483

0.483

0.483

0.701

0.354

wallthickness

Tw (mm)

20.0

12.7

12.7

16.0

16.0

16.0

16.0

12.0

10.0

12.7

20.0

38.0

38.0

38.0

56.0

26.2

wallthickness

Tw (mm)

20.0

12.7

12.7

16.0

16.0

16.0

16.0

12.0

10.0

12.7

20.0

38.0

38.0

38.0

56.0

26.2

flowvelocity

v （ m/s)

3.58

3.16

3.23

2.59

2.63

2.70

2.75

3.15

3.68

4.17

4.49

5.19

5.19

5.44

5.16

5.06

flowvelocity

v （ m/s)

3.58

3.16

3.23

2.59

2.63

2.70

2.75

3.15

3.68

4.17

4.49

5.19

5.19

5.44

5.16

5.06

pressureP (MPa)

2.64

2.62

2.35

2.38

2.04

1.71

1.41

1.39

0.97

3.22

3.19

7.21

7.12

7.01

7.01

7.04

pressureP (MPa)

2.64

2.62

2.35

2.38

2.04

1.71

1.41

1.39

0.97

3.22

3.19

7.21

7.12

7.01

7.01

7.04

tempe-rature

T (℃ )

33.0

33.0

74.0

74.0

98.0

129.0

146.0

146.0

178.9

186.0

186.0

188.0

188.0

222.0

222.0

222.0

tempe-rature

T (℃ )

33.0

33.0

74.0

74.0

98.0

129.0

146.0

146.0

178.9

186.0

186.0

188.0

188.0

222.0

222.0

222.0

enthalpyH (kJ/kg)

140

140

312

312

412

543

616

616

754

793

793

801

801

954

954

954

enthalpyH (kJ/kg)

140

140

312

312

412

543

616

616

754

793

793

801

801

954

954

954

hazard scale

enthalpy x dI2

(a.u)

0.19

0.07

0.16

0.19

0.26

0.34

0.38

1.00

0.50

0.40

0.73

0.64

0.64

0.76

1.61

0.41

hazard scale

enthalpy x dI2

(a.u)

0.19

0.07

0.16

0.19

0.26

0.34

0.38

1.00

0.50

0.40

0.73

0.64

0.64

0.76

1.61

0.41

geometry

T-junction

bend

bend

bend

bend

bend

bend

bend+orifice

T-junction

bend

bend

bend

bend

bend

T-junction

bend

geometry

T-junction

bend

bend

bend

bend

bend

bend

bend+orifice

T-junction

bend

bend

bend

bend

bend

T-junction

bend

geometricalfactor

Kc

0.16

0.23

0.23

0.23

0.23

0.15

0.15

0.5

0.16

0.15

0.3

0.3

0.3

0.3

0.6

0.3

geometricalfactor

Kc

0.16

0.23

0.23

0.23

0.23

0.15

0.15

0.5

0.16

0.15

0.3

0.3

0.3

0.3

0.6

0.3

3.2.6. Plant chemistry parameters

Chemistry parameters can be controlled daily. Major chemical parameters at Plant A are shown in Fig. 7. Even when hardware parameters and flow parameters are fixed for long term operation, chemical parameters are controlled due to plant needs. The data of pH and hydrazine concentration were from the plant operational recorder, while oxygen concentration was calculated with the O2-hydrazine reaction code.

operating hours (k EFPH)

10.0

9.8

9.6

9.4

9.2

9.00 50 100 150 200

pH (

-)

101

100

10-1

10-2

10-3

10-4

[O2]

(pp

b)

0

0.2

0.4

0.6

0.8

1

[N2H

4] (

ppm

)

pH[N2H4]

[O2]*

1 2 3 4 5

6 7 8 910

1112

13

1415

16cycle number

* [O2]：calculated with O2-hydrazine reaction analysis code


10.0

9.8

9.6

9.4

9.2

9.00 50 100 150 200

pH (

-)

101

100

10-1

10-2

10-3

10-4

[O2]

(pp

b)

0

0.2

0.4

0.6

0.8

1

[N2H

4] (

ppm

)

pH[N2H4]

[O2]*

1 2 3 4 5

6 7 8 910

1112

13

1415

16cycle number


10.0

9.8

9.6

9.4

9.2

9.0

10.0

9.8

9.6

9.4

9.2

9.00 50 100 150 2000 50 100 150 200

pH (

-)

101

100

10-1

10-2

10-3

10-4

101

100

10-1

10-2

10-3

10-4

[O2]

(pp

b)

0

0.2

0.4

0.6

0.8

1

[N2H

4] (

ppm

)

pH[N2H4]

[O2]*

1 2 3 4 5

6 7 8 910

1112

13

1415

16cycle number

pH[N2H4]

[O2]*

1 2 3 4 5

6 7 8 910

1112

13

1415

16cycle number

* [O2]：calculated with O2-hydrazine reaction analysis code

Fig. 7 History of water chemistry control at Plant A 4. RESULTS AND DISCUSSIONS 4.1. Evaluation of FAC Occurrence

As a result of the Step 1 and 2 calculations, five of six parameters can be determined with the 1D system simulation code and O2-N2H4 reaction calculations. Evaluation procedures for FAC occurrence are shown in Table 3. Temperature along the flow path is calculated with the 1D system simulation code and pH is determined from plant experience with chemical injection. The mass transfer coefficient for straight piping can be calculated by applying the flow velocity distribution along the flow path obtained from the 1D system simulation to Eqs. (1) through (9) and then it can be multiplied by the geometrical factor to obtain mass transfer coefficient for complex geometries, e.g., bend and orifice.


108

Temperature and pH factors are integrated into the ferrous ion solubility with the function. Temperature also affects the mass transfer coefficient. The effect of chromium and oxygen on FAC was shown previously [21]. The effect of ferrous ion concentration is omitted in the evaluation procedures, at least for the present stage. Five sets of factors to determine wall thinning rate are multiplied to determine the thinning rate.

Table 3 Wall thinning rate calculated with the 1D FAC code

location

①

②

③

④

⑤

⑥

⑦

⑧

⑨

⑩

⑪

⑫

⑬

⑭

⑮

⑯

innerdiameter

dI (m)

0.6200.3810.3810.4250.4250.4250.4250.6870.4370.3810.5190.4830.4830.4830.7010.354

flowvelocity(m/s)

3.583.163.232.592.632.702.753.153.684.174.495.195.195.445.165.06

temperature

T (℃ )

33.033.074.074.098.0129.0146.0146.0178.9186.0186.0188.0188.0222.0222.0222.0

mass transfercoefficient

hm (m/s)

1.50E-041.50E-043.18E-042.61E-043.57E-045.00E-045.87E-045.96E-049.45E-041.13E-031.12E-031.30E-031.30E-031.66E-031.47E-031.66E-03

geometry

T-junctionbendbendbendbendbendbend

bend+orificeT-junction

bendbendbendbendbend

T-junctionbend

GFKc

0.160.230.230.230.230.150.150.50.160.150.30.30.30.30.60.3

maximumhm

(m/s)


H-

0.070.100.220.180.250.220.260.890.450.511.011.171.171.492.651.50

T&pH-

factors

0.650.651.161.161.311.221.051.050.620.530.530.510.510.200.200.20

Cr-

0.990.990.990.990.990.990.990.990.990.990.990.990.990.990.990.99

O-

1111111111111111

Fe-

1111111111111111

da/dt(a.u)

0.050.070.250.210.320.270.270.930.280.270.530.590.590.290.520.30

pH (-): 9.2, [O2] (ppb): 0, [Fe2+] (ppb): 0

location

①

②

③

④

⑤

⑥

⑦

⑧

⑨

⑩

⑪

⑫

⑬

⑭

⑮

⑯

location

①

②

③

④

⑤

⑥

⑦

⑧

⑨

⑩

⑪

⑫

⑬

⑭

⑮

⑯

innerdiameter

dI (m)

0.6200.3810.3810.4250.4250.4250.4250.6870.4370.3810.5190.4830.4830.4830.7010.354

innerdiameter

dI (m)

0.6200.3810.3810.4250.4250.4250.4250.6870.4370.3810.5190.4830.4830.4830.7010.354

flowvelocity(m/s)

3.583.163.232.592.632.702.753.153.684.174.495.195.195.445.165.06

flowvelocity(m/s)

3.583.163.232.592.632.702.753.153.684.174.495.195.195.445.165.06

temperature

T (℃ )

33.033.074.074.098.0129.0146.0146.0178.9186.0186.0188.0188.0222.0222.0222.0

temperature

T (℃ )

33.033.074.074.098.0129.0146.0146.0178.9186.0186.0188.0188.0222.0222.0222.0


hm (m/s)



hm (m/s)


geometry




T-junctionbend

geometry




T-junctionbend

GFKc

0.160.230.230.230.230.150.150.50.160.150.30.30.30.30.60.3

GFKc

0.160.230.230.230.230.150.150.50.160.150.30.30.30.30.60.3

maximumhm

(m/s)


maximumhm

(m/s)


H-

0.070.100.220.180.250.220.260.890.450.511.011.171.171.492.651.50

0.070.100.220.180.250.220.260.890.450.511.011.171.171.492.651.50

T&pH-

factors

0.650.651.161.161.311.221.051.050.620.530.530.510.510.200.200.20

0.650.651.161.161.311.221.051.050.620.530.530.510.510.200.200.20

Cr-

0.990.990.990.990.990.990.990.990.990.990.990.990.990.990.990.99

0.990.990.990.990.990.990.990.990.990.990.990.990.990.990.990.99

O-

1111111111111111

1111111111111111

Fe-

1111111111111111

1111111111111111

da/dt(a.u)

0.050.070.250.210.320.270.270.930.280.270.530.590.590.290.520.30

da/dt(a.u)

0.050.070.250.210.320.270.270.930.280.270.530.590.590.290.520.30

pH (-): 9.2, [O2] (ppb): 0, [Fe2+] (ppb): 0

The detectable change in wall thickness (detectable wall thickness) is around 0.1 mm in the laboratory and

around 0.3 mm in plants. Even if pipe wall thickness is measured every year, detectable wall thinning rate will be around 0.3 mm/y. The FAC occurrence threshold, 0.5 mm/y, might be acceptable for the present stage of discussion, while further discussion should be carried out by changing the value. 4.2. Discussions 4.2.1 Time margin evaluation

FAC risk was defined as the mathematical product of the possibility of serious wall thinning occurrence and its hazard scale. For this, not only probability of serious wall thinning occurrence in the future but also the hazard scale of pipe rupture due to the serious wall thinning should be analyzed (Table 2). It was defined that the pipe suddenly ruptured when its thickness reached the minimum permissible thickness. For determining the probability of pipe rupture due to FAC, the time margin for pipe rupture was evaluated. Basic approaches toward probability evaluation of pipe rupture due to FAC are shown in Fig. 8.

operating time (arbitrary unit)

pipe

thic

knes

s (a

rbitr

ary

unit）

time for evaluation

measured thickness

original thickness (measured)

original thickness (designed)

*1*2

*4*3

minimum permissible thickness(in a severe earthquake)

*1 calculation based on design data*2 calculation based on measured original thickness*3 calculation based on measured thickness*4 prediction of the effects of water chemistry improvement

time margin for maintenance

minimum permissible thickness(normal operation)

operating time (arbitrary unit)

pipe

thic

knes

s (a

rbitr

ary

unit）

time for evaluation

measured thickness

original thickness (measured)

original thickness (designed)

*1*2

*4*3

minimum permissible thickness(in a severe earthquake)

*1 calculation based on design data*2 calculation based on measured original thickness*3 calculation based on measured thickness*4 prediction of the effects of water chemistry improvement

time margin for maintenance

minimum permissible thickness(normal operation)

Fig. 8 Conceptual drawing for predicting pipe wall thickness and their uncertainty

4.2.2. Probability analysis

Prediction accuracy for wall thinning rate was within a factor of 2, which resulted in wall thinning rate as a Gaussian probability profile (Fig. 9 a)). At the same time evaluation errors for original pipe wall thickness and

109


minimum possible thickness required for the pipe also should be considered as Gaussian probability (Fig. 9 b)). Combining both uncertainties for evaluation of thinning rate and the margin for thinning resulted in probability of the time for pipe rupture (Fig. 9 c)). The time margin for pipe rupture was designated as the time reaching 5 % of the peak value.

thinning rate (mm/y)

0

1

2

3

prob

abili

ty d

ensi

ty (

-)

10-1 100 101

a) Predicted thinning rate

predicted thinning rate (within a factor of 2)

0

0.5

1.0

0 5 10 15 20thickness (mm)

prob

abili

ty d

ensi

ty (

-) deviation s: 0.5 mm(for original and permissible thickness)

b) Evaluated margin thickness

0

0.05

0.10

0.15

0.20

0 10 20 30 40 50operating time (y)

prob

abili

ty d

ensi

ty (

-)

0.5 mm/y

thinning rate：1 mm/y

time margin

margin in minimumpermissible thickness：10mm

c) Rupture time


0

1

2

3

prob

abili

ty d

ensi

ty (

-)

10-1 100 101




0

1

2

3

prob

abili

ty d

ensi

ty (

-)

10-1 100 101


0

1

2

3

prob

abili

ty d

ensi

ty (

-)

0

1

2

3

prob

abili

ty d

ensi

ty (

-)

10-1 100 10110-1 100 101



0

0.5

1.0


prob

abili

ty d

ensi

ty (



0

0.5

1.0


prob

abili

ty d

ensi

ty (

-)0

0.5

1.0

0

0.5

1.0

0 5 10 15 200 5 10 15 20thickness (mm)

prob

abili

ty d

ensi

ty (



0

0.05

0.10

0.15

0.20


prob

abili

ty d

ensi

ty (

-)

0.5 mm/y


time margin


c) Rupture time

0

0.05

0.10

0.15

0.20


prob

abili

ty d

ensi

ty (

-)

0.5 mm/y


time margin

0

0.05

0.10

0.15

0.20

0

0.05

0.10

0.15

0.20


prob

abili

ty d

ensi

ty (

-)

0.5 mm/y


time margin


c) Rupture time Fig. 9 Probability densities of thinning rate, margin in thickness and rupture time

There have been many discussions on the definition of hazard scale of pipe rupture. Simply put, the

hazard was defined as the volume of effluent steam and water from the ruptured mouth, which was enthalpy of water in the pipe multiplied by the square of the pipe inner diameter. Both are shown in Table 2. 4.2.3. Relationship of time margin for rupture and relative effects of rupture

Calculated time margin for time rupture and hazard scale are shown in Fig. 10 a). In order to understand the risk, the relationship of time margin and hazard scale is shown in Fig. 10b).

time margin

relative effects

0

20

40

60

80

100

120

140

location

tim

e m

argi

n fo

r ru

ptur

e (y

)

0

0.2

0.4

0.6

0.8

1.0

rela

tive

haz

ard

scal

e (-

)

①②③④⑤⑥⑦⑧⑨⑩⑪⑫⑬⑭⑮⑯0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40

time margin for rupture (y)

rela

tive

haza

rd s

cale

(-） primary location for

inspection and maintenance

a) Time margin and hazard scale according to location b) Relationship of time margin and hazard scale

time margin

relative effects

0

20

40

60

80

100

120

140

location

tim

e m

argi

n fo

r ru

ptur

e (y

)

0

0.2

0.4

0.6

0.8

1.0

rela

tive

haz

ard

scal

e (-

)

①②③④⑤⑥⑦⑧⑨⑩⑪⑫⑬⑭⑮⑯

time margin

relative effects

time margin

relative effects

0

20

40

60

80

100

120

140

location

tim

e m

argi

n fo

r ru

ptur

e (y

)

0

0.2

0.4

0.6

0.8

1.0

rela

tive

haz

ard

scal

e (-

)

①②③④⑤⑥⑦⑧⑨⑩⑪⑫⑬⑭⑮⑯

0

20

40

60

80

100

120

140

location

tim

e m

argi

n fo

r ru

ptur

e (y

)

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

rela

tive

haz

ard

scal

e (-

)

①②③④⑤⑥⑦⑧⑨⑩⑪⑫⑬⑭⑮⑯①②③④⑤⑥⑦⑧⑨⑩⑪⑫⑬⑭⑮⑯0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40


rela

tive

haza

rd s

cale



0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40


rela

tive

haza

rd s

cale

(-）

0

0.2

0.4

0.6

0.8

1.0

0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 400 10 20 30 40


rela

tive

haza

rd s

cale



a) Time margin and hazard scale according to location b) Relationship of time margin and hazard scale

Fig. 10 Probability distributions of time margin and hazard scale of pipe rupture

By considering the time margin and hazard scale, the number of inspection zones can be narrowed down and continuous wall thick monitoring can be applied at high priority locations. At the same time precise data from continuous measurement of wall thickness can be applied for tuning the prediction tool and improving prediction accuracy. 4.2.5. Major countermeasures for FAC

FAC can be mitigated by controlling one or two parameters shown in Fig. 2 out of the FAC occurrence region. Major countermeasures to mitigate FAC occurrence and wall thinning rate are listed in Table 4. By applying the 1D FAC code, the effects of countermeasures on FAC mitigation can be evaluated. One of the most effective countermeasures to avoid the high FAC risk zone is application of chromium containing materials. Chromium application is among the best countermeasures when applied at the plant design stage, but in already constructed plants, replacement of structural materials is a costly procedure with high material, fabrication and labor costs. The easiest why to mitigate FAC is water chemistry control, e.g., increasing pH of oxygen injection. Not only the target effects but also adverse effects should be evaluated for final decision, what countermeasure to apply.

Applicable water chemistry improvements are different in PWRs, BWRs and FPPs. Increasing pH is


110

widely applied in FPPs but its use is restricted for some PWRs with copper heater tubes due to serious copper corrosion when pH is higher than 9.3. Oxygen injection is another effective candidate to mitigate FAC and it is commonly applied for BWRs. In FPPs, all-volatile treatment with oxidizing conditions (AVT(O)) and oxygenated treatment (OT) are applied to mitigate FAC, but in PWRs oxygen injected into the feed water should be removed at the SG inlet to avoid SG tube corrosion. There is a tradeoff of oxygen in the feed water and being oxygen-free at the SG inlet. Optimal water chemistry control to satisfy both the corrosive conditions at the feed water piping and those at SG tubing can be evaluated and established by quantitative evaluation of wall thinning rate under water chemistry improvement [21].

Table 4 Major countermeasures for FAC in PWR, BWR and fossil power plants

Plants equipment material water chemistry improvement remarkswhere the feed improvement pH [O2]water flows in

PWR steam generator increasing [Cr] increasing pH AVT(H&O) minimizing [O2] at SG inlet

BWR reactor core increasing [Cr] - (neutral) oxygen [O2] generatedinjection due to radiolysis

Fossil boiler increasing [Cr] increasing pH AVT(O)OT

AVT: all volatile treatment OT: oxygenated treatmentAVT(H&O): oxygen addition to AVT(R)AVT(O): AVT (oxidizing conditions (residual oxygen present))AVT(R): AVT (reducing conditions (added reducing agent))

Plants equipment material water chemistry improvement remarkswhere the feed improvement pH [O2]water flows in

PWR steam generator increasing [Cr] increasing pH AVT(H&O) minimizing [O2] at SG inlet

BWR reactor core increasing [Cr] - (neutral) oxygen [O2] generatedinjection due to radiolysis

Fossil boiler increasing [Cr] increasing pH AVT(O)OT

AVT: all volatile treatment OT: oxygenated treatmentAVT(H&O): oxygen addition to AVT(R)AVT(O): AVT (oxidizing conditions (residual oxygen present))AVT(R): AVT (reducing conditions (added reducing agent))

5. CONCLUSIONS

The conclusions are summarized as follows. 1. The1D FAC code has been developed based on the 3D FAC code. It was confirmed that estimation accuracy

of wall thinning rate obtained with the 1D FAC code was within a factor of 2. This speedy and easy-to-handle tool for FAC occurrence can be applied for evaluation of FAC occurrence probability for entire plant systems.

2. FAC risk designated as the mathematical product of the probability serious wall thinning determined from the time margin for pipe rupture and the hazard scale of pipe rupture could be applied for selection of thinning detection location and suitable FAC mitigation application.

3. Computer code packages of determination procedures for high FAC risk zones were introduced and determination processes were demonstrated.

4. The 3D FAC code could be improved by applying precise wall thinning data obtained by continuous monitoring at the prior locations suggested by prediction. The fusion of prediction and monitoring might go well to improve plant performance.

ACKNOWLEDGMENTS

Development of the analysis models has been supported by the Innovative and Viable Nuclear Energy Technology Development Project of the Ministry of Economy, Trade and Industry, as “Development of Evaluation Method on Flow-induced Vibration and Corrosion of Components in Two-phase Flow by Coupled Analysis” (2005-2007). The authors express their sincere thanks to the sponsor who gave them the chance to develop the model and to draw attention to many related research achievements. The evaluation of the model by comparing the calculated results with the measured has been carried out as a part of the project studies sponsored by the Nuclear and Industrial Safety Agency (NISA). The authors express their sincere thanks to NISA for its sponsorship and acceptance of publication of the results. NOMENCLATURE CG: constant（CG = 0.231）[30] Cs solubility (mol/m3) [Cr]: chromium content (%)

d: equivalent diameter (m) dI: pipe inner diameter (m) D: diffusion constant (m2/s)


111

FCr: chromium factor (-) Fox: oxygen factor (-) hm: mass transfer coefficient (m/s) hm

*: geometry calibrated mass transfer coefficient (m/s)

Kc: geometrical factor (-) [superscripts: A, at point A; B, at point B; AB, synthesizing A and B]

R: gas constant, 8.31 (J/mol/K) Re: Reynolds number (-) Sc: Schmidt number (-) t: exposure time (s)

T: temperature (K) Tw: wall thickness (m) u: average velocity (m/s) x: distance (m) : thickness of boundary layer (m) : pH (-) : viscosity (Pa s) : kinematic viscosity (m2/s) : density (kg/m3)

ABBREVIATIONS 1-3D: one- to three-dimensional BWR: boiling water reactor CFD: computational flow dynamics ECP: electrochemical corrosion potential FAC: flow accelerated corrosion

FPP: fossil fuel power plant NPP: nuclear power plant PWR: pressurized water reactor SG : steam generators V&V: verification and validation

REFERENCES [1]. R. B. Dooley, “Flow Accelerated Corrosion in Fissile and Combined Cycle/HRSG Plants”, Power Plant

Chemistry”, Power Plant Chemistry, 10 (2), 68 (2008). [2] Secondary Piping Rupture Accident at Mihama Power Station, Unit 3, of the Kansai Electric Power Co., Inc.

(Final Report), Rev. 1, Nuclear and Industrial Safety Agency, Tokyo, Japan (Translated by the Japan Nuclear Energy Safety Organization) (2005).

[3] C. Z. Czajkowski, “Metallurgical Evaluation of an 18 Inch Feedwater Line Failure at the Surry Unit 2 Power Station”, NUREG/CR-4868 BNL-NUREG-52057 (1987).

[4] S. Uchida, M. Naitoh, H. Okada, T. Ohira, S. Koshizuka and D. H. Lister, “Application of Coupled Electrochemistry and Oxide Layer Growth Models to Water Chemistry Improvement against Flow Accelerated Corrosion in the PWR Secondary System”, Corrosion 2011, Paper No. 112398, Mar. 14-17, Houston, TX, USA, National Association of Corrosion Engineers (2011)

[5] N. Sekimura, “Securing the Stability of Aging NPP Considering Fukushima Daichi Lessons Learning-Improving Inspection System and Aging Management and Planning”, Proc. 3rd Int. Conf. on Nuclear Power Plant Life Management, Saly Lake City, UT, USA, May 14-18, 2012, International Atomic Energy Agency (2012).

[6] L. E. Sanchez-Caldera, “The Mechanism of Corrosion-Erosion in Steam Extraction Lines of Power Stations”, Ph. D. Thesis, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts (1984).

[7] G. J. Bignold, K. Garbett, R. Garnsey and I. S. Woolsey, “Erosion-corrosion in nuclear steam generators”, Proc. Second Meeting on Water Chemistry of Nuclear Reactors, British Nuclear Engineering Society, London, 5 (1980).

[8] D. H. Lister and L. C. Lang, “A Mechanistic Model for Predicting Flow-assisted and General Corrosion of Carbon Steel in Reactor Primary Coolants”, Proceedings of the International Conference on Water Chemistry of Nuclear Reactor Systems, 142, Avignon, France, April 22-26, 2002, French Nuclear Energy Society (2002).

[9] M. Naitoh, S. Uchida, K. Koshizuka, S. Ninokata, N. Hiranuma, K. Dozaki, K. Nishida, M. Akiyama and H. Saitoh, “Evaluation Methods for Corrosion Damage of Components in Cooling Systems of Nuclear Power Plants by Coupling Analysis of Corrosion and Flow Dynamics (I), Major targets and development strategies of the evaluation methods”, J. Nucl. Sci. Technol., 45 [11], 1116 (2008).

[10] S. Uchida, M. Naitoh, Y. Uehara, H. Okada, N. Hiranuma, W. Sugino and S. Koshizuka, “Evaluation methods for corrosion damage of components in cooling systems of nuclear power plans by coupling analysis of corrosion and flow dynamics (II) Evaluation of corrosive conditions in PWR secondary cooling system,” J. Nucl. Sci. Technol., 45[12], 1275 (2008).

[11] S. Uchida, M. Naitoh, Y. Uehara, H. Okada, N. Hiranuma, W. Sugino, S. Koshizuka and D. H. Lister, “Evaluation methods for corrosion damage of components in cooling systems of nuclear power plans by coupling analysis of corrosion and flow dynamics (III) Analysis and dynamic double oxide layer analysis,” J. Nucl. Sci. Technol., 46[1], 31 (2009).


112

[12] S. Uchida, M. Naitoh, Y. Uehara, H. Okada, T. Ohira, H. Takiguchi, W. Sugino and S. Koshizuka, “Evaluation Methods for Corrosion Damage of Components in Cooling Systems of Nuclear Power Plants by Coupling Analysis of Corrosion and Flow Dynamics (IV), Comparison of Wall Thinning Rates Calculated with the Coupled Model of Static Electrochemical Analysis and Dynamic Double Oxide Layer Analysis and Their Values Measured at a PWR plant”, J. Nucl. Sci. Technol., 47 [2], 184 (2010).

[13] V. K. Chexal, R. B. Dooley, D. P. Munson and R. M. Tilley, ”Control of Flow-Accelerated Corrosion in Fossil, Co-Generation and Industrial Steam Plants”, 529, IWC-97-57 (1997).

[14] V. H. Keller, “Erosion Korrosion on Nassddamfturbine”, VGB Kraftwerkstechnik, 54, Heft 5, May (1974). [15] W. Kastner, M. Erve, N. Henzel and B. Stellwag, “Calculation Code for Erosion Corrosion. Induced Wall

Thinning in Piping System”, Nucl. Eng. Design, 119, 431 (1990). [16] H. G. Heitmann and P. Schub, “Initial experience gained with a high pH value in the secondary system of

PWRs”, Proc. Third Meeting on Water Chemistry of Nuclear Reactors, British Nuclear Engineering Society, London, 243 (1983).

[17] S. Trevin, M. Persoz and T. Knook, “Making FAC Calculations With BRT-CICEROTM and Updating to Version 3.0”, Proc. 17th International Conference on Nuclear Engineering, ICONE17-75341, Brussels, Belgium, July 12–16, 2009 , p. 81, American Society of Mechanical Engineers (2009).

[18] S. Uchida, M. Naitoh, H. Okada, T. Ohira, S. Koshizuka and D. H. Lister, “Evaluation of FAC Simulation Code Based on Verification and Validation”, Power Plant Chemistry, 12 (9), 550 (2010).

[19]. M. Naitoh, S. Uchida, H. Okada and S. Koshizuka, “Validation of Code System DRAWTHREE-FAC for Evaluation of Wall Thinning due to Flow Accelerated Corrosion by PWR Feed Water Piping Analysis”, Proceedings of the ASME 2011 Pressure Vessel & Piping Division Conference, PVP2011, PVP2011- 57120, July 17-21, 2011, Baltimore, Maryland, USA, American Society of Mechanical Engineers (2011)

[20] S. Uchida, M. Naitoh, H. Okada, T. Ohira, S. Koshizuka and D. H. Lister, “Verification and Validation of Evaluation Procedures for Local Wall Thinning due to Flow Accelerated Corrosion and Liquid Droplet Impingement”, Nucl. Technol., 178 (3) 280 (2012).

[21] W. Sugino, T. Ohira, N. Nagata, A. Abe and H. Takiguchi, “Effect of Water Chemistry Improvement on Flow Accelerated Corrosion in Light-Water Nuclear Reactor”, Proc. Symp. Water Chemistry and Corrosion in Nuclear Power Plants in Asia 2009, Nagoya, Japan, Oct. 28-30, 2009, Division of Water Chemistry, Atomic Energy of Japan, 2009, (in CD).

[22] S. Uchida, M. Naitoh, H. Okada, H. Suzuki, S. Koikari, S. Koshizuka and D. H. Lister, ” Determination Procedures of High Risk Zones for Local Wall Thinning due to Flow Accelerated Corrosion”, Nucl. Technol., 180 (1), 65-77 (2012).

[23] S. Uchida, M. Naitoh, H. Okada, S. Koshizuka and D. H. Lister, ” Evaluation Methods for Flow Accelerated Corrosion and Liquid Droplet Impingement in Nuclear Power Plants”, Proc. Int. Conf. on Flow Accelerated Corrosion, FAC2010, May 4-7, 2010, Lyon, France, EdF (2008) (in CD).

[24] M. Izumiya, A. Minato, F. Hataya, K. Ohsumi, Y. Ohshima, S. Ueda, ‘‘Corrosion and/or erosion in BWR plants and their countermeasures,’’ Proc. Int. Symp. on Water Chemistry and Corrosion Problems in Nuclear Reactor Systems and Components, IAEA-SM-264/4 (1983).

[25] H. Okada, S. Uchida, M. Naitoh, J. Xiong and S. Koshizuka, “Evaluation Methods for Corrosion Damage of Components in Cooling Systems of Nuclear Power Plants by Coupling Analysis of Corrosion and Flow Dynamics (V), Flow Accelerated Corrosion under Single- and Two- Phase Flow Conditions”, J. Nucl. Sci. Techol., 48 [1], 65 (2011)

[26] S. Uchida, M. Naitoh, H. Okada, S. Koshizuka and D. H. Lister, ” Effects of Ferrous Ion and Oxide Particle on Wall Thinning due to Flow Accelerated Corrosion ”, ECS Trans., 41 (25), 77 (2011).

[27] The RELAP5 Code Development Team, “RELAP5/MOD3 Code Manual – Volume 1: Code Structure, System Methods, and Solution Methods“, NUREG/CR-5535, INEL-95/0174 Vol. 1, Idaho National Laboratory (1995).

[28] S. Uchida, M. Naitoh, Y. Uehara, H. Okada, N. Hiranuma, W. Sugino and S. Koshizuka, “Evaluation methods for corrosion damage of components in cooling systems of nuclear power plans by coupling analysis of corrosion and flow dynamics (II) Evaluation of corrosive conditions in PWR secondary cooling system,” J. Nucl. Sci. Technol., 45 [12], 1275 (2008).

[29] R. Takahashi, K. Matsubara and H. Koike, “Implementation of a Consultative Expert System for Advanced Fluid Dynamics Analysis Code - α Flow”, Proc. 2nd International Forum on Expert System and Computer Simulation in Energy Engineering, ICHMT, Erlangen, Germany, Mar. 17-20, 1992, p.4-L-1, University of Erlangen (1992).

[30] H. Suzuki, S. Uchida, M. Naitoh, H. Okada, Y. Nagaya, Y. Tsuji, S. Koshizuka and D. H. Lister, “Verification and Validation of 1D FAC Code for Determination of High FAC Risk Zones”, Nucl. Technol., 183 (1), 62-74 (2013).

Documents

Determination of High-Risk Zones for Local Wall Thinning