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INL/CON-13-28782PREPRINT

Development of Yield and Tensile Strength Design Curves for Alloy 617

International Workshop on Structural Materials for Innovative Nuclear Systems

Nancy Lybeck T.-L. Sham

October 2013

Development of Yield and Tensile Strength Design Curves for Alloy 617

Nancy Lybeck,*a T.-L. Shamb Idaho National Laboratorya

2525 Fremont Ave. P.O. Box 1625, MS 3605

Idaho Falls, ID 83415-3605 nancy.lybeck@inl.gov

Oak Ridge National Laboratoryb

One Bethel Valley Road P.O. Box 2008, MS-6155 Oak Ridge, TN 37831-6155

shamt@ornl.gov

Abstract

The U.S. Department of Energy Very High Temperature Reactor Program is acquiring data in preparation for developing an Alloy 617 Code Case for inclusion in the nuclear section of the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel (B&PV) Code. A draft code case was previously developed, but effort was suspended before acceptance by ASME. As part of the draft code case effort, a database was compiled of yield and tensile strength data from tests performed in air. Yield strength and tensile strength at temperature are used to set time-independent allowable stress for construction materials in B&PV Code, Section III, Subsection NH. The yield and tensile strength data used for the draft code case has been augmented with additional data generated by Idaho National Laboratory and Oak Ridge National Laboratory in the U.S. and CEA in France. The standard ASME Section II procedure for generating yield and tensile strength at temperature is presented, along with alternate methods that accommodate the change in temperature trends seen at high temperatures, resulting in a more consistent design margin over the temperature range of interest.

Introduction

The U.S. Department of Energy (DOE) has selected the high-temperature gas-cooled reactor (HTGR) design for the Next Generation Nuclear Plant (NGNP) Project. The NGNP will demonstrate the use of nuclear power for process heat, hydrogen, and electricity production. The reactor will be graphite moderated with helium as the primary coolant. Due to the high design temperature, the requirements of materials for the intermediate heat exchanger are among the most demanding. Based on the technical maturity, availability in required product forms, experience base, and high-temperature mechanical properties, Alloy 617 is the leading candidate construction material for the NGNP intermediate heat exchanger.

Alloy 617 is not currently qualified for use in American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel (B&PV) Code Section III, although it is allowed in Section I and Section VIII, Division 1 (non-nuclear service). A draft ASME Code case for incorporating Alloy 617 in Section III was developed in the early 1980s [1], but efforts to gain the approval from the ASME Code committees were discontinued due to a loss in interest by DOE and its contractor.

Minimum yield strength and average tensile strength at temperature are used to set time-independent allowable stress for structural materials in B&PV Code, Section III, Subsection NH. As part of the draft code case effort, a database was compiled of yield and tensile strength data from tests performed in air by Huntington Alloys, Inc. Section II, Part D, Appendix 5, recommends the submittal of tensile strength, yield strength, reduction of area, and elongation at 50°C intervals, from room temperature to 50°C above the maximum intended use temperature for three heats of appropriate product forms and sizes.

A large portion of the research in the U.S. DOE Very High Temperature Reactor (VHTR) Materials Research and Development Program is code qualification of Alloy 617 for successful and long-life application at the high-temperature conditions planned for the NGNP. Idaho National Laboratory (INL) has generated tensile test data for two different product forms at multiple temperatures. Additional data have been acquired from Oak Ridge National Laboratory (ORNL) and the French Alternative Energies and Atomic Energy Commission (CEA). These data sets have been combined with the original Huntington Alloys data to create a more complete data set for analysis, with 198 data points representing 14 heats and five product forms of Alloy 617, as seen in Table 1. Statistical equivalence of the older and newer data is considered for both yield and tensile strength to assess if the older, and more numerous, data can be leveraged in combination with data from newer heats produced with current mill practices, to set time-independent allowable stresses. The standard ASME Section II procedures for calculating yield strength and tensile strength at temperature are presented, along with alternate methods that accommodate the change in temperature trends seen at high temperatures, resulting in a more consistent design margin over the temperature range of interest.

Table 1. Sources of tensile and yield strength data for analysis

Heat Product Form Source Number of Data Points

Material Vendor

VDM Plate CEA 17 VDM

XX00A1USL BAR Huntington 7 Huntington

XX00A4USL BAR Huntington 12 Huntington

XX00A5USL BAR Huntington 10 Huntington

XX05A4UK BAR Huntington 22 Huntington

XX07A7UK BAR Huntington 22 Huntington

XX00A1USL CR SHEET Huntington 8 Huntington

XX00A5USL CR SHEET Huntington 3 Huntington

XX20A5UK CR SHEET Huntington 12 Huntington

XX26A8UK CR SHEET Huntington 21 Huntington

XX00A3USL FORGING Huntington 4 Huntington

XX00A3USL PLATE Huntington 4 Huntington

188155 BAR INL 30 VDM

314626 Plate INL 9 VDM

XX01A3US Plate ORNL 10 Huntington

XX09A4UK Plate ORNL 7 Huntington

Yield Strength Analysis

The ASME Section II method for determining the yield strength at temperature can be summarized as follows [2]:

1. The yield strength data are normalized by dividing by the average room temperature yield strength for the respective heat.

2. A best fit trend curve is generated for the normalized yield strength data as a function of test temperature.

3. The minimum yield strength at temperature is defined as, where is the specification minimum yield strength at

room temperature.

Additionally, the temperature trend of is required to be either constant or decreasing with increasing temperature.

Yield Strength at Temperature Design Curves

Generally, a fifth-order polynomial is used to obtain the trend curve of the normalized strength data per ASME Code practice. Following the approach taken in [3], a better fit to the normalized yield strength data can be achieved by using a piecewise continuous exponential decay function so that:

where T is the temperature in Celsius, and the model parameters , , , , , , and are estimated from the data. The exponential decaying

function enforces the requirement that the strength curve decreases with increasing temperature.

The ASME Section II methodology outlined above was used to generate the yield strength at temperature. The specification minimum yield strength at room temperature for Alloy 617 is 240 MPa. Two curves were generated: one was based on Huntington and ORNL data, which were both generated from older heats manufactured by Huntington, and the other on the combined data set. As seen in Figure 1, the combined data set gives lower values, with a maximum deviation of 3 MPa below 800ºC, and 22 MPa above 800ºC.

Figure 1: Estimated yield strength at temperature using the ASME method with a piecewise exponential

The margin between the yield strength at temperature and the yield strength data decreases noticeably as temperature increases, providing a conservative lower bound at lower temperatures, but running through the data at the highest temperatures.

Based on these results, alternate methods were explored for modeling yield strength that take into account the change in temperature trends seen at high temperatures. The piecewise exponential decay model used above was fit to the raw yield strength data, rather than the normalized data, resulting in a model for yield strength at temperature in the form

where T is the temperature in Celsius, and the model parameters , , , , , , and are estimated from the data.

Figure 2 shows the yield strength data along with the best fit exponential decay model (solid black curve). The horizontal line represents the specification minimum yield strength at room temperature. The ASME method was used with the best fit decay model for the normalized data to generate the minimum yield strength at temperature (solid orange line). Tabulated values in the ASME B&PV Section II for yield strength are shown by the red asterisks. The lower bound of an approximate 95% confidence interval for an individual prediction (i.e., a prediction

bound) was generated based on the decay fit to the raw data (dashed blue line). Finally, the best fit exponential decay model for the raw data was offset by the maximum difference between the best fit line and the 95% prediction bound (dashed green line). The identified parameters from the best least-squares fit to the normalized and raw yield strength data are given in Table 2. For the 95% prediction bound and constant offset curves, the corresponding yield strength at temperature would be the lower of 240MPa and the values from these two curves.

Figure 2: Analysis of Alloy 617 yield strength at temperature

Table 2: Best-fit parameter coefficients for normalized and raw yield strength

Parameter Normalized data Raw data 6.84562963934E-01 2.38153391390E+02 3.60968416894E-01 1.13078337003E+02 -5.75159974584E-03 -3.89777152722E-03

8.27494333565E+02 8.24563909014E+02 -2.88565545061E-01 -1.33424255707E+02

9.98257402054E+00 2.87168957389E+03 -2.80957331723E-03 -2.46522971908E-03

Statistical Equivalence of the Yield Strength Data Sets

The combined data set consists of 198 sets of tensile data from 14 heats of material in five different product forms, as shown in Table 1. The Huntington data that was used in the suspended draft code case represents approximately two-thirds of the new data set. The Huntington data are very old; a natural question that arises is if the newer heats of Alloy 617 have similar, or even better, tensile properties as the older heats represented by the Huntington data. The ORNL data were also generated from an older heat produced by Huntington; the INL and CEA data were generated from newer heats produced by VDM.

An analysis was performed on the yield strength data to examine the statistical equivalence of the data sets. The data from INL and CEA were combined in one group (labelled VDM), and the Huntington and ORNL data formed the second group (labelled Huntington). The best piecewise exponential model was generated for each data group. The results are shown in Figure 3, where the solid lines are the best estimate curve and the dashed lines are the 95% confidence bounds for the expected value (mean). The 95% confidence bounds are overlapping for the entire temperature range; there is no evidence to suggest a difference in the two data sets.

Figure 3: Analysis of statistical equivalence of the yield strength data

Tensile Strength Analysis

The ASME method for determining the tensile strength at temperature can be summarized in the following three steps [2]:

1. The tensile strength data are normalized by dividing by the average room temperature tensile strength for the associated heat.

2. A best fit trend curve is fit to the normalized tensile strength data as a function of test temperature.

3. The average tensile strength at temperature is defined as, where is the specification minimum tensile strength

at room temperature.

Again, the temperature trend of is required to be either constant or decreasing with increasing temperature.

Tensile Strength at Temperature Design Curves

Following the approach taken in [3], a piecewise continuous exponential decay function was selected to fit the normalized yield strength data:

where T is the temperature in Celsius, and the model parameters , , , , , and are estimated from the data. Parameter estimation was

performed using least squares estimation, enforcing continuity at .

The ASME Section II methodology outlined above was used to generate the tensile strength at temperature. Two curves were generated: one was based on the original draft code case data and the ORNL data (labelled Huntington), and the other on the complete data set. As seen in Figure 4, the additional data cause changes in the tensile strength at temperature curve. For higher temperatures, the resulting curve is more conservative, with a maximum difference of 86 MPa.

Figure 4: Estimated tensile strength at temperature using the ASME method with a piecewise exponential decay model

As with the yield strength at temperature curve, the position of the curve relative to the average of the data changes with temperature. Near room temperature, the curve lies slightly below the average value of the data. At higher temperatures, the curve lies above the average value of the data.

Based on these results, the piecewise exponential decay model used above was fit to the raw tensile strength data, rather than the normalized data, resulting in a model for tensile strength at temperature in the form:

where T is the temperature in Celsius, and the model parameters , , , , , and are estimated from the data. Parameter estimation was

performed using least squares estimation, enforcing continuity at .

Figure 5 shows the tensile strength data along with the best fit exponential decay model (solid black curve). The horizontal line represents the specification minimum tensile strength at room temperature. The ASME Section II method was used with the best fit decay model for the normalized data to generate the tensile strength at temperature (solid

orange line). Tabulated values in the ASME B&PV Section II Part D for tensile strength at temperature are shown by the red asterisks. The lower bound of an approximate 95% confidence interval for the expected value (mean) was generated based on the model fit to the raw data (dashed pink line). Finally, the best fit exponential decay model for the raw data was offset by the maximum difference between the best fit curve and the 95% confidence bound (dashed green line). The identified parameters from the best least-squares fit to the normalized and raw tensile data are given in Table 3. For any of these curves, the corresponding tensile strength at temperature would be the smaller of the curve and 655 MPa.

Figure 5: Analysis of Alloy 617 tensile strength at temperature

Table 3: Best-fit parameter coefficients for normalized and raw tensile strength

Parameter Normalized data Raw data 1.00809519519E+00 7.71748461184E+02 -3.98933842187E-04 -2.93524772281E-01 7.24094783303E+02 7.22747336965E+02 -3.91607682652E-02 -2.84714696511E+01 2.18506626389E+01 1.74564181628E+04 -4.64136570776E-03 -4.69127568771E-03

Statistical Equivalence of the Tensile Strength Data Sets

To analyze statistical equivalence of the data sets, the normalized tensile strength data from INL and CEA were combined in one group (labelled VDM), and the Huntington and ORNL data formed the second group (labelled Huntington). The best least-squares fit exponential decay model was generated for each data group, as seen by the solid lines in

Based on these results, linear models were fit to the Huntington and VDM data sets for lower ( 700ºC) and higher (>700ºC) temperatures. The results shown in Figure 7 are consistent with the previous analysis, with overlapping confidence bounds for lower temperatures, and non-overlapping

confidence bounds above 700°C. This indicates there is a difference between the data sets for higher temperatures. Because the additional data sets have lower tensile strength values, the resulting design curves will be more conservative for the joint data set.

Figure 6. The results show that the 95% confidence bounds for the mean (dashed lines) are overlapping for lower temperatures (approximately 0-700°C), but that they are not overlapping for higher temperatures.

Based on these results, linear models were fit to the Huntington and VDM data sets for lower ( 700ºC) and higher (>700ºC) temperatures. The results shown in Figure 7 are consistent with the previous analysis, with overlapping confidence bounds for lower temperatures, and non-overlapping confidence bounds above 700°C. This indicates there is a difference between the data sets for higher temperatures. Because the additional data sets have lower tensile strength values, the resulting design curves will be more conservative for the joint data set.

Figure 6: Statistical equivalence of the tensile strength data

Figure 7: Model fit to tensile strength grouped by temperature and data source

Conclusions

The main objective of this study is to determine whether tensile data determined from recent material heats with current mill practice are statistically equivalent to the historical data generated from heats with older mill practice. It was found that the yield strength data from the newer Alloy 617 heats are consistent with those from the older Huntington heats. However, the tensile strength data from the newer Alloy 617 heats are slightly lower than those from the Huntington heats.

Sham, Eno, and Jensen [3] first noted an inconsistent margin resulting from using the ASME Section II methodologies for yield strength and tensile strength at temperature for the large temperature ranges of interest for Alloy 617. Sham et al. [3] suggested an alternate method for generating the design curves using the Huntington data set. Kim et al. repeated the analysis using an expanded data set including data collected through world-wide literature surveys, data from manufacturing companies, and KAERI data [4]. In this paper, the alternate methodology was presented with minor modifications for a data set including data from Huntington, CEA, ORNL, and INL.

The SAS procedure NLIN was used to generate all curve fits, as well as the prediction bounds. The 95% prediction bound for yield strength at temperature provides a consistent, meaningful statistical lower bound to summarize the yield strength data, and would be a good candidate for use as yield strength at temperature. The 95% confidence bound for mean tensile strength at temperature provides a consistent, meaningful statistical lower bound for average tensile strength, and would be a good candidate for tensile strength at temperature.

Acknowledgements

This manuscript has been co-authored by Battelle Energy Alliance, LLC under Contract No. DE-AC07-05ID14517, and by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725, with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government

retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

The authors would like to acknowledge Richard Wright of Idaho National Laboratory for supporting this work.

References

[1] J.M. Corum and J.J. Blass (1991), “Rules for Design of Alloy 617 Nuclear Components to Very High Temperatures,” Proceedings of 1991 ASME Pressure Vessels and Piping Division Conference, ASME PVP Vol. 215, pp. 147-153.

[2] “An International Code 2010 ASME Boiler & Pressure Vessel Code, 2011a Addenda: Section II Part D Properties (Metric) Materials,” ASME, 2011.

[3] T.—L. Sham, D.R. Eno, K.P. Jensen (2008), “Treatment of High Temperature Tensile Data for Alloy 617 and Alloy 230,” Proceedings of 2008 ASME Pressure Vessels and Piping Division Conference, PVP2008-61128, Chicago, Illinois.

[4] W.-G. Kim, S.-N. Yin, J.-Y. Park, S.-D. Hong, Y.-W. Kim (2012), “An improved methodology for determining tensile design strengths of Alloy 617,” Journal of Mechanical Science and Technology 26(2), PP. 379-387; DOI 10.1007/s12206-011-1024-5.