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DATA ANALYTICS FOR ALLOY QUALIFICATION 20 March 2018

Office of Fossil Energy NETL-PUB-21550

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Cover Illustration: Materials Data Analytics Research

Suggested Citation: Krishnamurthy, N.; Maddali, S.; Verma, A.; Bruckman, L.; Carter, J.; French, R.; Romanov, V.; Hawk, J. Data Analytics for Alloy Qualification; NETLPUB-21550; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Pittsburgh, PA, 2017; 46 p.

An electronic version of this report can be found at: http://netl.doe.gov/research/on-site-research/publications/featured-technical-reports https://edx.netl.doe.gov/organization/data-science-initiative

Data Analytics for Alloy Qualification

Narayanan Krishnamurthy1, Siddharth Maddali1, Amit Verma2, Laura Bruckman2, Jennifer Carter2, Roger French2, Vyacheslav Romanov1, Jeffrey Hawk3 1 U.S.

Department of Energy, National Energy Technology Laboratory, 626 Cochrans Mill Road, Pittsburgh, PA 15236

2 Case

Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106

3 U.S.

Department of Energy, National Energy Technology Laboratory, 1450 SW Queen Avenue, Albany, OR 97321

NETL-PUB-21550 20 March 2017

NETL Contacts: Vyacheslav Romanov, Principal Investigator Jeffrey Hawk, Technical Portfolio Lead Bryan Morreale, Executive Director, Research and Innovation Center

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DATA ANALYTICS FOR ALLOY QUALIFICATION

Table of Contents EXECUTIVE SUMMARY ...........................................................................................................1 1. REVIEW OF RELEVANT WORK .....................................................................................3 1.1 ALLOY DESIGN CONSIDERATIONS FOR TURBINES AND CASINGS ......................................5 1.2 9% CR MARTENSITIC STEEL DATABASE ...........................................................................9 2. ALLOY ANALYTICS .........................................................................................................10 2.1 DATA SET & WORKFLOW ...............................................................................................10 2.2 EXPLORATORY ANALYSIS: SCATTER PLOTS AND PAIRWISE CORRELATION ....................12 2.3 CLUSTERING OF TENSILE SAMPLES: MEDOID CLUSTERING, GROUP ANALYSIS USING ANOVA .....................................................................................................................................12 2.3.1 2D & 3D Visualization with PAM and t-SNE ............................................................13 2.4 CLUSTERING OF CREEP SAMPLES ....................................................................................14 2.5 MODELING: STEP-WISE REGRESSION .............................................................................15 2.6 MODELING: DECISION TREES AND RANDOM FORESTS ....................................................15 2.6.1 Rank Contributors in Predicting Strength (YS) Using DT ........................................16 2.6.2 Rank contributors’ comparison for RT & MCR, using DT or step-wise regression .17 2.6.3 Suggesting new alloy compositions: Simulated alloy compositions and properties using RF regressor .................................................................................................................18 2.7 CREEP MODELING: NEURAL NETWORKS ........................................................................21 2.8 FEATURE ENGINEERING: VARIABLE IMPORTANCE ..........................................................21 3. RANK ORDER ANALYSIS OF PARTITIONED TENSILE AND CREEP TEST SAMPLES .....................................................................................................................................23 3.1 ANALYSIS OF SEGMENTED TENSILE TEST SAMPLES .......................................................23 3.2 ANALYSIS OF SEGMENTED CREEP TEST SAMPLES ..........................................................24 3.2.1 Larson Miller Parameter Models ..............................................................................24 3.2.2 Temperature Specific Models for RT and MCR .........................................................25 4. THERMO-CALC EQUILIBRIUM PHASES OF CREEP RESISTANT 9% CR STEELS ........................................................................................................................................26 4.1 INSIGHTS FROM CORRELATING COMPOSITION WITH PHASES USING THERMO-CALC ......28 5. CAUSAL NETWORK MODELING .................................................................................30 6. CONCLUSIONS ..................................................................................................................31 7. REFERENCES .....................................................................................................................32

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DATA ANALYTICS FOR ALLOY QUALIFICATION

List of Figures Figure 1. Systematic approach: ingesting data, exploratory analysis & visualization, modeling, and cross-validation .............................................................................................................. 10 Figure 2. Univariate Pairwise Correlations identify interaction terms and elements that can be substituted, e.g., Mo & W. Scatter plots of tensile properties (UTS & elongation, etc.) with test temperature show examples of piecewise linear relationships ....................................... 12 Figure 3. Clustering of tensile samples & ANOVA group analysis ............................................. 13 Figure 4. Partitioning Around Medoids: cluster visualization in 2D (left) & 3D (right).............. 13 Figure 5. t-SNE Visualization of Yield Strength (YS) shown as function of a number of optimal neighbors (5, 10, 30 – a.k.a. perplexity factor) ..................................................................... 14 Figure 6. Clustering of creep samples by Creep Stress (CS) for >104 h RT at 650 ºC – visualization in W-Mo-Cr space ........................................................................................... 14 Figure 7. Model selection using AIC in step-wise regression identifies the rank order of predictor variables of the best model. Note: Due to randomization procedure used in the algorithm, the exact rank order of significant contributors may differ on successive runs. ................... 15 Figure 8. Decision Trees to predict UTS: partitioning of tensile test data based on the features used to grow the decision tree. Left: composition wt% + test temperature. Middle: composition wt%. Right: top 8 variance compositional elements excluding Fe & C. ......... 16 Figure 9. DT prediction of YS at test temperature (TT.Temp) above the change-point of ~450 ºC & box plot (bottom) of sample strength of each branch of tree (top) ................................... 16 Figure 10. Predicted versus actual YS, using DT technique......................................................... 17 Figure 11. Simulated alloys’ Cr content extended to 10-28%, and the methodology for predicting the alloys properties with supervised machine learning using RFR models trained on the available tensile test data....................................................................................................... 19 Figure 12. Trends in strength and elongation of the simulated alloys with expanded wt% of the elements of interest (Cr & Co) as predicted from training data sets ..................................... 20 Figure 13. Left: ANN method illustration for creep modeling using LMP. Right: Relative importance of predictor variables for LMP – by Olden method ........................................... 21 Figure 14. Top: Variable importance in predicting YS, UTS, Elongation, and RA using RF. Bottom: Variable importance using Neural networks – Olden method for YS .................... 22 Figure 15. Analysis of the segmented tensile test data, partitioning individual alloys into two subgroups around observed change point temperature (~450 ºC) .............................................. 23 Figure 16. Creep test (Temperature–Stress–Time) plots, with the rupture tests running up to 100,000 h............................................................................................................................... 25 Figure 17. Thermo-Calc equilibrium phases of 9Cr-1Mo-V-Nb steel ......................................... 26 Figure 18. Thermal processing (annealing and tempering) temperatures for martensitic steels. Ms is the temperature at equilibrium, whereas free energy of the phase transformations between austenite and ferrite are equal in both directions. Lower Ms implies higher fraction of retained austenite after tempering. The tempering temperature should be below A1. ......... 27 Figure 19. Directed graph of composition, processing, and creep features of 9Cr steel .............. 30

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List of Tables Table 1. Cleaned-up tensile & creep data set: 47 columns & 2800 rows; captures test outcomes, alloying elements, heat treatment, average grain size, and tensile or creep test outcome .... 11 Table 2. Top 5 contributor comparison of DT vs. step-wise regression (example)...................... 17 Table 3. Analysis of the segmented top contributors to strength: Cr, V, NI, Si, B & N; Group 1 (389), Group 2 (332) – see color map in Fig. 15 .................................................................. 24 Table 4. Analysis of the segmented modeling contributors to the properties of RT & MCR (both log-transformed) as well as LMP, for the test temperatures (500, 600, and 700 ºC)............ 25 Table 5. Pearson’s correlation of the phase mole fraction with wt% fractions in tensile dataset . 28 Table 6. Pearson’s correlation of the phase mole fraction with wt% fractions in creep dataset .. 29

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DATA ANALYTICS FOR ALLOY QUALIFICATION

Acronyms, Abbreviations, and Symbols Term

Description

NETL

National Energy Technology Laboratory

CWRU

Case Western Reserve University

PC 2-D, 3-D

pulverized coal two-dimensional, three-dimensional

AIC

Akaike information criterion

BIC

Bayesian information criterion

RT, MCR, LMP

Rupture Time, Minimum Creep Rate, Larson-Miller Parameter

YS, UTS, RA, EL

Yield Strength, Ultimate Tensile Strength, Reduction in Area, Elongation

CS or σ Thermo-Calc

(applied) creep stress software for thermodynamic equilibrium calculations to obtain stable and meta-stable phases in alloy systems

DICTRA

software for multi-component diffusion equations for obtaining precipitate/ phase distribution in alloy systems

equiv.

equivalent

BCC

body centered cubic

FCC

face centered cubic

AGS

average (prior austenite) grain size

GB

grain boundary

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DATA ANALYTICS FOR ALLOY QUALIFICATION

Acknowledgments This work was completed as part of National Energy Technology Laboratory (NETL) research for the U.S. Department of Energy’s (DOE) Advanced Alloy Development Program. This project was supported in part by an appointment to the Internship/Research Participation Program at the National Energy Technology Laboratory, U.S. Department of Energy, administered by the Oak Ridge Institute for Science and Education.

V

EXECUTIVE SUMMARY •

9-12% Cr-steels are used as structural material in boiler and turbine (rotor/casing) applications, with a power plant designed to operate for at least 30 years at either subor supercritical conditions (high pressure and temperature). In high-efficiency power plants, some components must withstand high stress and temperature for 100,000 h of operation without failure (Abe, 2011; Abe, et al., 2008; Coleman & Newell Jr., 2007; Viswanathan, 1989; Viswanathan & Bakker, 2001; Viswanathan & Bakker, 2001; Masuyama, 2001). Motivation for this project came from the need for rigorous and time-consuming alloy qualification (standardization) process, for crucial applications. Material scientists have traditionally manipulated alloying element composition and thermomechanical processing to achieve specific mechanical properties in alloys. The goals of this work were to develop a systematic and data-centric framework for analysis and characterization of materials information, to explore new alloy compositions and/or processing, and to better predict stress-rupture lifetime and other mechanical properties. A small subset of carbon steels with similar ferritic or martensitic lath microstructure (and average prior austenite grain size) was chosen for this analysis.

•

Data Analytics tools were used to extract variable associations in predicting tensile (yield strength, elongation) and creep (minimum creep rate and rupture time) behavior. Pairwise correlations were done as part of exploratory analysis. Various techniques were used to delineate different classes of alloys: principle components, stochastic neighbor embedding (t-SNE), and similarity-based clustering. Visualization of different classes of alloys was then presented in 2D/3D. Variables of non-zero variance & frequent occurrences were used to predict strength or ductility. Multi-variate and stepwise regression was used to determine rank order of variables in explaining mechanical property. Recursive partitioning techniques can also be used in predictive modeling and determining variables of interest. After the step-wise regression using AIC and the mean squared error had initially been attempted, we then re-focused on recursive partitioning using decision trees and random forests as well as the Olden method using neural networks; these techniques generated somewhat differing major contributors that affect the test output. Different techniques on the partitioned dataset also produced subtle differences in relationships between the property/performance variables and the ranked contributors.

•

To address non-linearity across the previously identified martensitic-ferritic 9-12% Cr-steel composition clusters, further analyses of tensile and creep data were carried out in clusters. Initially, similarity based clustering using hierarchical, k-NN & medoid methods was used; subsequently, recursive partitioning techniques were used. The emphasis was on partitioning the dataset using observed change-point behavior with temperature in tensile tests and temperature–stress–time plots of creep tests.

•

LMP transformation and the change-point in tensile strength, both showed dependence on temperature. While the tensile strength change-point occurred at about 450 °C, all the creep tests were done at above that temperature. The observations of strong dependence on temperature above the change-point led to generation of separate models for rupture time (RT) and minimum creep rate (MCR) at 500, 600 &

2 700 ℃, respectively, in order to shift the focus to generating better physics insights rather than solely improving the data fit. Thermo-Calc was used to obtain molefraction of different alloys in the dataset. Further correlation analysis was done to understand composition–phase/precipitate–property linkage using Pearson correlation. Using Thermo-Calc, alloy composition was linked with the probable equilibrium phases present in the alloy. Transition temperature range between BCC and FCC phases was correlated with the tempering temperature. Laves phase (an intermetallic [(Fe,Cr)2(Mo,W)] phase) showed strong negative dependence on Nb content. M23C6 mole fraction showed one-to-one dependencies with C wt%. Z-phase (a complex nitride, Cr(V,Nb)N) formation leads to complete dissolution of MX nitrides, which explains the complete absence of MX precipitates in the equilibrium phases. M3P, AlN, and MnS form as impurities, and they show one-to-one dependencies with P, Al, and S content, respectively. •

The research team comprised NETL federal work force, NETL postdoctoral appointments, and a team of researchers from Case Western Reserve University.

•

This research demonstrated that Materials Data Analytics methodology utilizing combined data-driven and physics-based approaches to predictive model development is very useful for non-linear model development, selection, and refinement. It is also showing importance of incorporating phase/precipitate volume and microstructure information to guide the data-driven analysis.

NETL Technical Report Series

3 1. Review of Relevant Work Martensitic and/or ferritic steels containing Cr form the backbone of the United States’ fleet of coal fired power plants. In general, these alloy classes are inexpensive to produce and can be recycled, which makes them favorable to be used as boiler and steam turbine components such as tubing, piping, headers, rotors, and buckets. Since many power plant components are very large and/or very thick (up to 100 mm for some components), the thermal stresses that arise at start-up and when swing-loading electricity production at peak times must be minimized. The enhanced thermal conductivity and lower thermal expansion coefficient of martensitic and ferritic alloys therefore makes them a more favorable option than austenitic stainless steels, especially when coupled with the cost difference between the alloy classes. (Masuyama, 2001; Mayer & Masuyama, 2008; Igarashi, 2008) For coal-fired power plants in the United States that operate at or below 570 ℃, CrMoV, NiCrMoV, and steels with less than 5% Cr (henceforth all compositions are given in weight percent unless otherwise noted) make up the majority in tonnage in steam turbine and boiler components. For hotter sections of the boiler and for the majority of the steam turbine components, 9–12% Cr steels are used due to their superior strength and creep performance. Currently, 620 ℃ is the approximate maximum use temperature for 9–12% Cr steels because long-term microstructural instabilities preclude maintaining creep strength at 650 ℃ for times greater than or equal to 100,000 hours. (Viswanathan, 1989) Increasing the attractiveness of coal fired power plants is accomplished by reducing the cost of construction, by increasing efficiency, or both. If a tempered martensitic ferritic steel alloy could be developed for use at 650 ℃, significant improvement in efficiency with simultaneous reduction in greenhouse gas emissions would be obtained at a fraction of the cost required to build a 700 ℃ power plant; a 700 ℃ power plant will require substantial quantities of austenitic stainless steels and/or nickel-based superalloys as the main high temperature materials of construction. A power plant constructed from tempered martensitic ferritic steel capable of operation at 650 ℃ would obviate the need for a 700 ℃ power plant and would enable focusing research efforts on developing a 760 ℃ advanced ultra-supercritical plant, which will require precipitation strengthened nickel-based superalloys, and is the next step up in efficiency improvement and pollution reduction. (Viswanathan & Bakker, 2001; Viswanathan & Bakker, 2001; Kern, et al., 2002; Viswanathan, et al., 2005; Viswanathan, et al., 2006; Knezevic, et al., 2008; Viswanathan, et al., 2009a; Viswanathan, et al., 2009b; Rojas, et al., 2011) Research in improving the temperature and pressure capability of power plant steels has been active since the 1950s (Masuyama, 2001). Metallurgical advancements in the steels of construction have been a direct driving force in boosting steam conditions in power plants from subcritical to ultra-supercritical for net efficiency gains. In 1968 Fujita developed the famous TAF steel (Takahash, et al., 1975) which nearly met today’s creep lifetime requirement at 650 ℃. A major feature of the TAF steel’s creep resistance was accounted for by the high carbon (C) and boron (B) levels in the base steel (Azuma, et al., 2002). Even though it exhibited exemplary creep performance, the TAF steel proved extremely difficult to fabricate and to weld, and these aspects point to a global issue with these alloys. If the creep strength requirement is met in addition to oxidation and corrosion requirements, which in itself is by no means a trivial accomplishment, it is still not enough to have a useful product for power plant applications. These materials must be fabricable on a multiple tonnage scale to produce components that can be welded and ultrasonically inspected. Weldability is required both for the installation of NETL Technical Report Series

4 headers, piping, and tubing and for weld-repairing cracks that may develop in the boiler and turbine components during service. A successful alloy must therefore meet a range of requirements in order to be accepted for use. The goal of NETL Advanced Alloy Development program is to produce a tempered martensitic ferritic steel capable of use at 650 ℃ for 100,000 hours or greater. As such it is necessary to understand high temperature strengthening mechanisms, and how to preserve these mechanisms for the required timeframes through appropriate microstructure design and control. The competing microstructural effects in advanced 9–12% Cr tempered martensitic ferritic steels include the following (Bhadeshia, 2001; Abe, 2008; Helis, et al., 2009; Lu, 2015; Hawk, et al., 2015; Hawk, et al., 2017): 1. The necessary amount of C, N, V, Nb, Ti, and/or Ta to generate MX precipitates to impede dislocation motion and stabilize subgrain structure; 2. A balanced amount of Mo and/or W for solid solution strengthening, precipitation hardening, and subgrain stabilization through generation of M23C6 and small Laves phase precipitates; 3. Addition of Co and/or C as austenite stabilizing elements and to suppress deltaferrite formation during normalizing heat treatment; 4. Addition of Cu to act as nucleation sites for the Cu-base precipitates for alternative strengthening; 5. Addition of B to reduce the coarsening kinetics of M23C6 precipitates, and therefore, to aid in stabilization of subgrain structures: B segregating along packet and block boundaries and free B homogeneously distributing in the matrix can be even more effective on strengthening than B contained in M23C6 carbides; B in steels of a suitable content of N is contributable to the enhancement of creep strength through increasing the amount and the stability of fine precipitates such as VN – However, high content of N is expected to reduce such effects of B probably through the precipitation of BN, although no experimental evidence has been obtained: prediction of creep life using Larson-Miller parameter (LMP) suggests that high N–high B steel shows lower creep strength compared with high N–low B, low N–low B, and low N–high B steels (Abe, et al., 2008; El-Kashif, et al., 2002); 6. The optimum level of Si and Mn and/or Rare Earth treatments to enhance oxidation/corrosion resistance; 7. The correct amount of Cr to provide oxidation resistance while maintaining creep strength, because Cr additions larger than 9% adversely affect the creep performance. Novel high temperature steel design concepts seek to eliminate the sources of microstructural instability found in the tempered martensitic ferritic steels currently used in coal-fired power plants. Amelioration of the sources of microstructural instability such as Z-phase formation, Laves phase formation and subsequent growth, and coarsening of the MX and M23C6 precipitates is the key to long-term microstructural stability. (Sawada, et al., 2007; Prat, et al., 2010a; Prat, et al., 2010b; Prat, et al., 2013; Prat, et al., 2014; Lu, 2015) Improving the efficiency of pulverized coal (PC) power plants means increasing the temperature (primarily) and pressure (to a lesser degree) of the working fluid (steam). As an example,

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5 consider a PC steam plant operating at 538 ℃/18.5 MPa. If the steam temperature is raised to 593 ℃ while the pressure increased to 30 MPa, the efficiency of the plant can be increased by approximately 6%. If the steam temperature is further increased to 650 ℃, then an additional overall 8% increase in efficiency can be obtained for the hypothetical plant. At the present time, the most modern PC power plants operate at roughly 600-610 ℃ and at pressures ranging from 25 to 30 MPa. PC power plants operating in this range have an efficiency approaching 42% (dependent on a number of other factors like source water to cool the steam, airfoils design, sealing integrity between airfoil and casing, etc.). In each instance, the improvement in efficiency means less greenhouse gas emitted to the environment per equivalent unit of energy produced. 1.1

Alloy Design Considerations for Turbines and Casings

At each stage in steam turbine development, the goal has been to maintain, as much as possible, the mechanical property levels of the preceding generation of steam turbine components. Also, of concern is maintaining the same start-up capability of the new machine relative to the preceding generation steam turbine to demonstrate reliability and flexibility of the turbine design. Consequently, alloy development has primarily focused on improving the creep strength (while not compromising oxidation resistance) of the next generation construction material relative to the older preceding generation, while at the same time trying to maintain the other physical and mechanical property levels. In the case of sub-critical steam turbines, forged and heat treated 1%CrMoV (heat treated to produce bainitic microstructure) has been the main material of construction for HP and IP rotors, while forged 12% Cr steel (e.g., 422, 403, 403Cb and 403Cb+) has been used for airfoils. The rotor casing and valve chest have been manufactured from the cast version of 1%CrMoV. Alloys used for bolts and valve internals are determined based on steam turbine operating conditions and manufacturer preference based on specific design constraints. (Viswanathan, 1989; Masuyama, 2001; Bhadeshia, 2001; Igarashi, 2008; Tanaka, 2008; Kern, 2008; Simms, 2011) Complicating alloy development activities for the rotors and casings are the size requirements of those components, which mean that the final material must age hardenable at thicknesses up to 1200 mm while maintaining a balance between strength (tensile and creep) on the one hand and toughness on the other. Another complicating factor is that any alloy developed for use as a rotor or casing must be weldable. The rotor must be weldable because a wear resistant overlay is needed, where it is seated, and for the instances when weld repairs are required when cracks form during use. (Some steam turbine manufacturers employ a welded rotor design, which obviously requires alloys used in the design to be weldable to each other.) For casings and valve chests, pipes from the boiler must be attached to inlets in these locations. Also, these components are prone to cracking and must be weldable so repairs can be made when such cracks occur during use. From a purely manufacturing perspective the material used for the rotor and airfoils must be easy (and rapidly too) to machine, and all materials of construction must be inexpensive with respect to austenitic steels and nickel alloys. Given these constraints, ferritic/martensitic steels have been, and remain, the construction materials of choice for large steam turbines. It is critical to understand the details of microstructural evolution in high Cr-base steels, especially strengthening and degradation during creep exposure. Precipitation strengthening in

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6 the newer 9%Cr-based steels revolves around the M23C6 and MX phases (although M2X also can form in these materials during heat treatment at selected temperatures). Of primary importance is the MX phase because creep resistance can be increased dramatically by precipitation of this phase, most notably as nanometer size VN particles. However, MX particles with a stoichiometry of Nb(C,N) can also form. To a first approximation, the VN precipitate volume fraction is controlled by the N content in the alloy. Conversely, VC does not precipitate in these advanced Cr steels because the solubility of VC in them is high. (Kozeschnik & Holzer, 2008; Abe, 2008; Lu, 2015) One theme emerged from the COST 501 alloy development program, i.e., three different steel types were identified with the potential to meet the requirements for >600 ℃ long-term application. The main difference with respect to 12% Cr (actually a base 12%Cr, 1%Mo, 0.3%V steel) was in the reduced levels of C, Cr and V, and optimized levels of Nb and N with controlled Al content. These three alloy classes are broadly defined as follows: 9Cr-1.5Mo-100 ppm B type; 10Cr-1Mo-1W type; and 10Cr-1.5Mo type. (Kern, et al., 2002; Knezevic, et al., 2002; Mayer & Masuyama, 2008; Merckling, 2008) Alloy development in Japan progressed along similar lines, except W is preferred to Mo (generally, Fe2Mo-type Laves precipitates at temperatures 0.70 (Ni-Co, Ni-Fe, Cr-Fe etc.) that are perhaps related to the empirical rules used in design of new alloys by substitution

b.

Strong dependence (>0.55) of material strength on operant/test conditions, and the tempering treatment

c.

Some correlations whose reasoning cannot be readily explained by domain knowledge, e.g., S-P (0.51)

Figure 2. Univariate Pairwise Correlations identify interaction terms and elements that can be substituted, e.g., Mo & W. Scatter plots of tensile properties (UTS & elongation, etc.) with test temperature show examples of piecewise linear relationships 2.3

Clustering of Tensile Samples: Medoid Clustering, Group Analysis Using ANOVA

Tensile samples were segmented into 9 groups using Partitioning Around Medoids (PAM) clustering (Berkhin, 2006). The significant 7 principal-component scores of alloying elements were used in clustering. PAM uses a similarity metric like hierarchical clustering and not a distance based metric like k-means. ANOVA & Tukey HSD tests were done to test for

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13 differences in mean strength across the groups, to aggregate the initial 9 to the final 3 groups of tensile samples.

Figure 3. Clustering of tensile samples & ANOVA group analysis Notably, the PAM’s implementation distinctly partitions clustering around the major known high-Cr martensitic steel classification groups, such as E911, P91/92, CPJ alloys, etc. The reported heat treatment conditions appear to be quite uniform within each one of the composition clusters, thus providing little variance for the data-driven model development. 2.3.1 2D & 3D Visualization with PAM and t-SNE The 9 clusters obtained from PAM clustering can be visualized in two or three dimensions (Pison, et al., 1999) as shown in Fig. 4.

Figure 4. Partitioning Around Medoids: cluster visualization in 2D (left) & 3D (right)

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14 t-SNE stochastic neighbor embedding as proposed by van der Maaten and Hinton (2008) preserves local distance across scales, i.e., it reveals structure at many different scales and is nonparametric.

Figure 5. t-SNE Visualization of Yield Strength (YS) shown as function of a number of optimal neighbors (5, 10, 30 – a.k.a. perplexity factor) 2.4

Clustering of Creep Samples

The creep samples were grouped by the Creep Stress (35, 50, 70, 90 & 110 MPa) that different alloy chemistry can endure 104 hours before rupture at 650 ℃. The alloying elements that promote M(V,Nb)X(C,N) and M(Cr,Fe,Mo,W)23C6 stabilization and Laves phase affect creep life. As mentioned earlier, the MX dissolution and formation of Z-phase (Cr-rich V/Nb nitrides) deteriorates the creep life (Maruyama, et al., 2001; Bhadeshia, 2001).

Figure 6. Clustering of creep samples by Creep Stress (CS) for >104 h RT at 650 ºC – visualization in W-Mo-Cr space

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15 2.5

Modeling: Step-Wise Regression

Linear regression is a convenient optimization technique for prediction/estimation of dependent variables, that can be used as a practical alternative to more advanced statistical methods, if the underlying assumptions are valid, i.e., the residuals are normally distributed and independent (additionally, several other due-diligence checks are usually recommended, such as residuals’ linearity, homoscedasticity, no autocorrelation, no correlation with predictors, zero means, small Cook's distance). However, when the relationship between predictor variables is not linear and if we are not sure of the distribution of the residuals, one has to transform the variables in question and test if the normality and independence assumptions are met before using regression models. Variable transformations and explicit modeling of interaction terms are necessary when there are correlated variables: log(Elongation) ~ Test.Temp + V + Cr + W + Cu + Si + Mn + Cr:Mn + Mn:Si –since the elongation did not have a constant variance across the samples, the log transformation linearized the variable and made it suitable for linear regression; the normality of the variable was verified using the quantile-quantile (Q-Q) plot. Note: Inclusion of explicit interaction terms Cr:Mn + Mn:Si improved the model prediction by 5%. Model selection and rank order contributions can be obtained using step-wise regression and choosing the sum squared error or AIC as the model improvement criteria (Kutner, et al., 2005). See Fig. 7 for the step-wise model selection technique.

Figure 7. Model selection using AIC in step-wise regression identifies the rank order of predictor variables of the best model. Note: Due to randomization procedure used in the algorithm, the exact rank order of significant contributors may differ on successive runs. 2.6

Modeling: Decision Trees and Random Forests

Recursive partitioning techniques, viz., decision trees (DT) and random forests, are non-linear methods to partition data based on information-theoretic criteria (mutual information, entropy, Gini impurity, etc.). The advantage of this technique over regression is that it does not make assumptions on residual’s distribution and can handle interaction between predictor variables.

NETL Technical Report Series

16 Fig. 8 shows the use of decision trees (DTs) to predict ultimate tensile strength (UTS). While DT is great for prediction, it’s clear from the figure, that it does not generalize (i.e. a tree is specific to its training data). A random forest overcomes overfitting by basing its estimate on an ensemble of trees (Liaw & Wiener, 2002; Breiman, 2001).

Figure 8. Decision Trees to predict UTS: partitioning of tensile test data based on the features used to grow the decision tree. Left: composition wt% + test temperature. Middle: composition wt%. Right: top 8 variance compositional elements excluding Fe & C. 2.6.1 Rank Contributors in Predicting Strength (YS) Using DT Decision Tree (DT) output for tensile data. YS, compositional wt% and test temperature were used to partition the tensile data using Gini impurity (Olden & Jackson, 2002).

Figure 9. DT prediction of YS at test temperature (TT.Temp) above the change-point of ~450 ºC & box plot (bottom) of sample strength of each branch of tree (top)

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17 The distribution of YS of samples in each partitioned branch is shown beneath the tree. Rank contributors from the DT for tensile data suggest that V, Mo, Nb & C are the major elements that affect YS. Decision trees ability to predict YS is shown in Fig. 10.

Figure 10. Predicted versus actual YS, using DT technique The mean value of each branch of a tree leaf node is the predicted value (if predictions were close to actual, the scatter plot would be like Y = X); the spread around the mean value on each leaf node shows the extent to which the mean captures the properties of the leaf-node samples. 2.6.2 Rank contributors’ comparison for RT & MCR, using DT or step-wise regression Regardless of the method, log(CS) was shown to be most strongly related to both RT and MCR. The difference in role of other contributors to prediction of creep properties, using either regression or DT, is shown in Table 2. For comparison, see the differences between Random Forests (a recursive partitioning technique with bootstrap sampling with replacement) and the Olden method using neural networks (NN) as further shown in Section 2.8 (see Fig. 14). Table 2. Top 5 contributor comparison of DT vs. step-wise regression (example) Creep Prop

550

Number of Samples 349

600

327

650

261

550

160

600

142

650

119

Temperature

Regression DT Regression log(RT) DT Regression DT Regression DT Regression log(MCR) DT Regression DT

Goodness adj-R2

Top 5 Contributors log(CS) log(CS) log(CS) log(CS) log(CS) log(CS) log(CS) log(CS) log(CS) log(CS) log(CS) log(CS)

C V Ni Si Co Si Co Nb Co Cr Co Mo

Mo N Mo V Mo N B C Mo W S Cu

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Cr Si W Nb W V W Co W Nb W B

V Nb Nb N Nb Mn V N V C V W

0.7 0.77 0.85 0.73 0.81 0.91

18 The Random Forest (RF) technique being an ensemble average is more robust than DT & NN algorithms. Ensemble learning methods operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees, to correct for decision trees' habit of overfitting to their training set. The use of alternative machine learning algorithms reveals subtle differences in the relationships between a target variable and its predictors, which must be investigated (Lantz, 2013). 2.6.3 Suggesting new alloy compositions: Simulated alloy compositions and properties using RF regressor One of the key objectives of this project was use the extracted information to guide the design of new experiments aimed at closing the key data and knowledge gaps. Particularly, it is important to investigate if the number and length of additional tests can be reduced. This can be accomplished in part by using the data-driven models to explore the alloy composition space outside the available data clusters. The methodology for using RF regressor (RFR) models to explore predicted properties of the projected alloy compositions as well as examples of the alloys with extended wt% fractions for Cr are illustrated in Fig. 11 (it was similarly done for Co). The results for varying Cr and Co weight fractions showed smooth predictions through interpolation/extrapolation for strength. Discontinuous estimates for elongation could be a result of either noisier elongation measurements or a lack of training samples for the regressor to interpolate/extrapolate with higher confidence. One can clearly see 2-3 classes of the simulated alloys, based on aggregation around the mean value of strength, for both Cr and Co. As a sanity check, one may ascertain that the alloy strength drops with the test temperature increasing from 600 to 677 ℃. – In a simple solid solution, the strength must drop with increasing temperature due to decreasing Peierls stress, increasing dislocation mobility, and/or possible activation of additional slip systems. As a side note, one should be cautioned that, due to potential activation of new deformation mechanisms in a more ductile phase (e.g., dynamic precipitation or strain aging caused by either grain boundary sliding, incipient melting, or stress concentration) the strength may actually increase at higher temperatures. Superalloys can gain their strength from a range of mechanisms at different temperatures. At low temperatures, Hall-Petch strengthening occurs via increasing (up to its saturation) dislocation density and reducing grain/sub-grain size. Homogeneous precipitation of the fine Ni3(Al,Ti,Nb) intermetallic phase, coherently embedded in a FCC Nibase solid solution matrix (Liu, et al., 2011), provides dislocation barriers at moderate temperatures. Coarse grains (or directionally-solidified single-crystal alloys) and very highvolume fractions of the intermetallic can provide higher temperature strength by reducing grain boundary (GB) sliding and forcing significant climb. The Kear-Wilsdorf mechanism is a very well-known example for Ni-base superalloys, where super-dislocations can cross-slip at high temperature and dissociate, giving rise to phase boundaries and stacking faults. However, of practical Ni-base superalloys, the strength does decrease with temperature. Some notable exceptions are low-volume alloys, e.g., based on the Ni3Al intermetallic. This structure is unusual in that its mechanical strength steadily increases from room temperature up to ~700 ℃ (due to the anti-phase boundary mechanisms described above).

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19 Figure 11. Simulated alloys’ Cr content extended to 10-28%, and the methodology for predicting the alloys properties with supervised machine learning using RFR models trained on the available tensile test data

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20

Figure 12. Trends in strength and elongation of the simulated alloys with expanded wt% of the elements of interest (Cr & Co) as predicted from training data sets

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21 2.7

Creep Modeling: Neural Networks

Creep is a tendency to permanent deformation in alloys over time, under the influence of mechanical stress levels below the yield strength. Norton’s law states that minimum creep rate is proportional to σn where n is the stress exponent. Monkman–Grant analysis suggests that Rupture Time (RT) is inversely proportional to the m-th (usually, 0.8–0.95) power of the minimum (steady state) Creep Rate, for simple metals and alloys. The Creep Stress (CS or σ) test results plotted versus time on a log-log scale are inherently piecewise linear as shown by Callister and Rethwisch (2011, p.283, Fig. 8.31). Regression modeling for creep, in terms of Rupture Time (RT) and Minimum Creep Rate (MCR) was only able to capture 99% of variance. The goal for parametric modeling using LMP is to obtain it for each class of alloy to improve on the RT and MCR predictions of each of the alloys. Linear model prediction of RT and MCR of the alloys without LMP only captures 100 kbar (therefore we ignored it here). Tensile Properties: BCC phase concentration increases with temperature, while YS and UTS decreases at higher temperature; which is reflected in the negative sign of Pearson’s correlation (-0.67, -0.77). FCC effect on YS and UTS is low (-0.14, -0.13) because γ-Fe is absent at the temperatures below AC1. Laves shows a strong negative dependence on Nb (-0.58) and a positive effect on strength (0.60). M23C6 showed strong dependence (0.99,0.84) on C and B (known to stabilize M23C6). Z-phase (ZPhase & ZPhase2) shows strong dependences on N, B, Mo, W, & Nb, V (0.95, -0.9, -0.76, 0.56 & 0.84, 0.66). Borides (M3B2 & M2B) of Mo & W showed a strong positive effect on strength, YS (0.76, 0.67). Table 5. Pearson’s correlation of the phase mole fraction with wt% fractions in tensile dataset

Creep Properties: BCC phase shows strong dependence on Ni, Co, and Cr ( -0.73, 0.69, -0.57) known to be austenite stabilizers. Laves is strongly related to Nb (-0.71). σ-phase is strongly dependent on Fe and Mn (-0.83, 0.86). G-phase was absent at 450–750 ºC. Though Laves, M23C6, and σ phases are known to affect the creep life of an alloy, the pairwise linear correlations of these phases were weak for MCR and RT – this is understandable as these NETL Technical Report Series

29 properties have multiple mechanisms that cannot be easily captured by pair-wise correlations alone. FCC2 showed a weak correlation with MCR and RT (0.49, -0.27). Laves, σ, and M23C6 phases show negligible effect on MCR. Table 6. Pearson’s correlation of the phase mole fraction with wt% fractions in creep dataset

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30 5. Causal Network Modeling We used the causal network modeling tools developed at CMU and at the Center for Causal Discovery (www.ccd.pitt.edu) led by the University of Pittsburgh, to see if we could find relationships in the alloying elements using the tensile data. The Tetrad software implements different algorithms (FGS, PC, etc.) to find relationships and their conditional probability. We ran the FGS on a subset of tensile samples to obtain a directed factor graph (FG). Having a very complex network of the conditional probabilities (Fig. 19) computed between the alloying elements, average grain size (AGS), processing and test temperatures, and tensile properties, more detailed analysis is required to verify significance of these findings.

Figure 19. Directed graph of composition, processing, and creep features of 9Cr steel

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31 6. CONCLUSIONS One important takeaway from this project so far is that complexity of the phase transformations and microstructure evolution in advanced multi-component alloys, with major influence on their mechanical properties, inevitably leads to inefficiency of direct application of unbiased (i.e., without assistance of the physics-based modeling) data analytics methods like linear regression across the entire available data space. The lack of experimental data on the alloys microstructure further compounds the difficulties in extracting information about the convoluted non-linear relationships directly from the data. To address these problems, the project team has developed several promising approaches aimed at discovery and refinement of the physics-based constitutive equations/relationships hidden in the data as being supported by and/or consistent with the domain knowledge. This research has demonstrated a systematic framework of analyzing data for 9Cr steel samples: • • • • • •

• • •

Data ingestion, data imputation (deciding what to do with missing data), exploratory analysis, and clustering were performed before detailed modeling and cross validation; Several clustering & visualization techniques (e.g., k-NN, PAM, and t-SNE) were alternatively used to partition the compositional data space for further analysis; Step-wise regression and random forest algorithms were used in the supervised learning framework, by splitting data into training and test sets to arrive at generalized models; Compositional space of the alloys was expanded, and properties of the simulated alloys were predicted through interpolation and extrapolation; Analysis of the tensile data suggests that decreasing Si, increasing Cr, V & Ni, while carefully managing the convoluted B and N effects, tends to improve alloy strength; Analysis of the creep data suggests that increasing relative amounts of carbide formers and austenite stabilizers (Ni, Co & Cr) may be beneficial; Mo & W may improve creep life without affecting strength – Laves precipitate ((Mo, W)-rich intermetallic) is known to affect creep life after short creep exposure; Co was found to be a strong contributor for MCR. Co and Ni are known to have opposing effects on diffusion coefficient (while Ni increases D, Co decreases it) – This could explain how Co stabilizes microstructure and increases creep life of alloy; Thermo-Calc – was used to obtain equilibrium volume fraction of different phases that have been shown to evolve and affect creep rupture life; Causal network modeling was attempted (the results must be validated); Different visualization and optimization / predictive algorithms, give slightly different relationships between predictor variables. To understand and interpret validity of the results, one must verify the assumptions in the input parameters for the algorithm and look for underlying physical phenomena that could explain the revealed relationships.

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32 7. REFERENCES Abe, F., 2008. Precipitate design for creep strengthening of 9% Cr tempered martensitic steel for ultra-supercritical power plants. Science and Technology of Advanced Materials, 9(1), pp. 013002.1-15. Abe, F., 2008. Strengthening mechanisms in steel for creep and creep rupture. In: F. Abe, T. Kern & R. Viswanathan, eds. Creep resistant steels. Boca Raton: CRC Press LLC, pp. 279-304. Abe, F., 2011. Strengthening mechanisms in creep of advanced ferritic power plant steels based on creep deformation analysis. In: Y. Weng, H. Dong & Y. Gan, eds. Advanced steels: The recent scenario in steel science and technology. Berlin: Springer, pp. 409-422. Abe, F., 2015. Creep behavior, deformation mechanisms, and creep life of Mod.9Cr-1Mo steel. Metallurgical and Materials Transactions A, 46(12), pp. 5610-5625. Abe, F., Horiuchi, T., Taneike, M. & Sawada, K., 2004. Stabilization of martensitic microstructure in advanced 9Cr steel during creep at high temperature. Materials Science and Engineering: A, 378(1-2), pp. 299-303. Abe, F., Kern, T.-U. & Viswanathan, R. eds., 2008. Creep-resistant steels. Boca Raton: Woodhead Publishing. Azuma, T., Miki, K., Tanaka, Y. & Ishiguro, T., 2002. Effect of B on microstructural change during creep deformation in high Cr ferritic heat resistant steel. Journal of the Iron and Steel Industry of Japan, 88(10), pp. 678-685. Barbadikar, D. et al., 2015. Effect of normalizing and tempering temperatures on microstructure and mechanical properties of P92 steel. International Journal of Pressure Vessels, Volume 132133, pp. 97-105. Berkhin, P., 2006. A survey of clustering data mining techniques. In: J. Kogan, C. Nicholas & M. Teboulle, eds. Grouping multidimensional data: Recent advances in clustering. Berlin: Springer, pp. 25-71. Bhadeshia, H., 2001. Design of ferritic creep-resistant steels. ISIJ International, 41(6), pp. 626640. Breiman, L., 2001. Random forests. Machine Learning, 45(1), pp. 5-32. Callister, W. & Rethwisch., D., 2011. Materials science and engineering: An introduction. 9 ed. New York: John Wiley & Sons. Chi, C.-y.et al., 2012. The precipitation strengthening behavior of Cu-rich phase in Nb contained advanced Fe–Cr–Ni type austenitic heat resistant steel for USC power plant application. Progress in Natural Science: Materials International, 22(3), pp. 75-85. Coleman, K. & Newell Jr., W., 2007. P91 and beyond: Welding the new-generation Cr-Mo alloys for high-temperature service. Welding Journal, 86(8), pp. 29-33. Dimmler, G., Weinert, P., Kozeschnik, E. & Cerjak, H., 2003. Quantification of the Laves phase in advanced 9–12% Cr steels using a standard SEM. Materials Characterization, 51(5), pp. 341352. El-Kashif, E., Asakura, K. & Shibata, K., 2002. Effects of nitrogen in 9Cr–3W–3Co ferritic heat resistant steels containing boron. ISIJ International, 42(12), p. 1468–1476. NETL Technical Report Series

33 Goodfellow, I., Bengio, Y. & Courville, A., 2016. Deep learning. Boston: The MIT Press. Hald, J., 2008. Microstructure and long-term creep properties of 9–12% Cr steels. International Journal of Pressure Vessels and Piping, 85(1-2), pp. 30-37. Hald, J., 2016. Prospects for martensitic 12% Cr steels for advanced steam power plants. Transactions of the Indian Institute of Metals, 69(2), pp. 183-188. Hastie, T., Tibshirani, R. & Friedman, J., 2001. The elements of statistical learning: Data mining, inference, and prediction. 2 ed. New York: Springer. Hawk, J., Jablonski, P. & Cowen, C., 2015. Creep resistant high temperature martensitic steel. USA, Patent No. USPO 9,181,597 B1. Hawk, J., Jablonski, P. & Cowen, C., 2017. Creep resistant high temperature martensitic steel. USA, Patent No. USPO 9,556,503. Heaton, J., 2015. Artificial intelligence for humans, Volume 3: Deep learning and neural networks. 1 ed. Chesterfield: Heaton Research, Inc.. Helis, L. et al., 2009. Effect of cobalt on the microstructure of tempered martensitic 9Cr steel for ultra-supercritical power plants. Materials Science and Engineering A, Volume 510-511, pp. 8894. Hsieh, C.-C. & Wu, W., 2012. Overview of intermetallic sigma (𝜎) phase precipitation in stainless steels. ISRN Metallurgy, Volume 2012, pp. 732471.1-16. Igarashi, M., 2008. Alloy design philosophy of creep-resistant steels. In: F. Abe, T. Kern & R. Viswanathan, eds. Creep resistant steels. Boca Raton: CRC Press LLC, pp. 539-572. Kern, T.-U., 2008. Using creep-resistant steels in turbines. In: F. Abe, T. Kern & R. Viswanathan, eds. Creep resistant steels. Boca Raton: CRC Press LLC, pp. 573-596. Kern, T.-U., Stauble, M. & Scarlin, B., 2002. The European efforts in materials development for 650 °C USC power plants - COST522. ISIJ International, 42(12), pp. 1515-1519. Knezevic, V. et al., 2008. Design of martensitic/ferritic heat-resistant steels for application at 650 °C with supporting thermodynamic modelling. Materials Science and Engineering A, 477(1-2), pp. 334-343. Knezevic, V. et al., 2002. Martensitic/ferritic super heat-resistant 650 °C steels—design and testing of model alloys. ISIJ International, 42(12), pp. 1505-1514. Kozeschnik, E. & Holzer, I., 2008. Precipitation during heat treatment and service: Simulation and strength contribution. In: F. Abe, T. Kern & R. Viswanathan, eds. Creep resistant steels. Boca Raton: CRC Press LLC, pp. 305-328. Kutner, M., Nachtsheim, C., Neter, J. & Li, W., 2005. Applied linear statistical models. 5 ed. New York: McGraw-Hill/Irwin. Lantz, B., 2013. Machine learning with R. Birmingham: Packt Publishing. Liaw, A. & Wiener, M., 2002. Classification and regression by randomForest. R News, 2(3), pp. 18-22.

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34 Liu, J.-l.et al., 2011. Influence of temperature on tensile behavior and deformation mechanism of Re-containing single crystal superalloy. Transactions of Nonferrous Metals Society of China, Volume 21, pp. 1518-1523. Lu, Q., 2015. Computational design of heat resistant steels with evolving and time-independent strengthening factors, Ph.D. Dissertation. Delft, The Netherlands: Delft University of Technology. Maddi, L. et al., 2016. Effect of Laves phase on the creep rupture properties of P92 steel. Materials Science and Engineering: A, Volume 668, pp. 215-223. Maruyama, K., Sawada, K. & Koike, J.-i., 2001. Strengthening mechanisms of creep resistant tempered martensitic steel. ISIJ International, 41(6), pp. 641-653. Masuyama, F., 2001. History of power plants and progress in heat resistant steels. ISIJ International, 41(6), pp. 612-625. Masuyama, F., 2008. Specifications for creep-resistant steels: Japan. In: F. Abe, T. Kern & R. Viswanathan, eds. Creep resistant steels. Boca Raton: CRC Press LLC, pp. 155-173. Mayer, K.-H. & Masuyama, F., 2008. The development of creep-resistant steels. In: F. Abe, T. Kern & R. Viswanathan, eds. Creep resistant steels. Boca Raton: CRC Press LLC, pp. 15-77. Merckling, G., 2008. Specifications for creep-resistant steels: Europe. In: F. Abe, T. Kern & R. Viswanathan, eds. Creep resistant steels. Boca Raton: CRC Press LLC, pp. 78-154. NIMS, 1994. Creep Data Sheet, No. 13B, Online: National Institute for Materials Science, Japan. NIMS, 1997a. Creep Data Sheet, No. 19B, Online: National Institute for Materials Science, Japan. NIMS, 1997b. Creep Data Sheet, No. 44, Online: National Institute for Materials Science, Japan. NIMS, 1998. Creep Data Sheet, No. 10B, Online: National Institute for Materials Science, Japan. NIMS, 2005. Creep Data Sheet, No. 46A, Online: National Institute for Materials Science, Japan. NIMS, 2012. Creep Data Sheet, No. 48A, Onine: National Institute for Materials Science, Japan. NIMS, 2013a. Creep Data Sheet, No. 51A, Online: National Institute for Materials Science, Japan. NIMS, 2013b. Creep Data Sheet, No. 52A, Online: National Institute for Materials Science, Japan. NIMS, 2014. Creep Data Sheet, No. 43A, Online: National Institute for Materials Science, Japan. Olden, J. & Jackson, D., 2002. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1), pp. 135-150. Phaniraj, M. et al., 2017. Understanding dual precipitation strengthening in ultra-high strength low carbon steel containing nano-sized copper precipitates and carbides. Nano Convergence, 4(1), pp. 16.1-8. Pison, G., Struyf, A. & Rousseeuw, P., 1999. Displaying a clustering with CLUSPLOT. Computational Statistics & Data Analysis, 30(4), pp. 381-392.

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35 Prat, O. et al., 2010a. Investigations on coarsening of MX and M23C6 precipitates in 12% Cr creep resistant steels assisted by computational thermodynamics. Materials Science and Engineering A, 527(21-22), pp. 5976-5983. Prat, O. et al., 2010b. Investigations on the growth kinetics of Laves phase precipitates in 12% Cr creep-resistant steels: Experimental and DICTRA calculations. Acta Materialia, 58(19), pp. 6142-6153. Prat, O. et al., 2014. Study of nucleation, growth and coarsening of precipitates in a novel 9%Cr heat resistant steel: Experimental and modeling. Materials Chemistry and Physics, 143(2), pp. 754-764. Prat, O. et al., 2013. The role of Laves phase on microstructure evolution and creep strength of novel 9%Cr heat resistant steel. Intermetallics, Volume 32, pp. 362-372. Robson, J. & Bhadeshia, H., 1997. Modelling precipitation sequences in powerplant steels: Part 2 – Application of kinetic theory. Materials Science and Technology, 13(8), pp. 640-644. Rojas, D. et al., 2011. 9%Cr heat resistant steels: Alloy design, microstructure evolution and creep response at 650 °C. Materials Science and Engineering A, 528(15), pp. 5164-5176. Sawada, K., Kushima, H., Kimura, K. & Tabuchi, M., 2007. TTP diagrams of Z phase in 9–12% Cr heat-resistant steels. ISIJ International, 47(5), pp. 733-739. Shen, Y., Liu, H., Shang, Z. & Xu, Z., 2015. Precipitate phases in normalized and tempered ferritic/martensitic steel P92. Journal of Nuclear Materials, Volume 465, pp. 373-382. Simms, N., 2011. Environmental degradation of boiler components. In: J. Oakey, ed. Power plant life management and performance improvement. Cambridge, UK: Woodhead Publishing Limited, pp. 145-179. Takahash, N., Fujita, T. & Yamada, T., 1975. Effect of boron on long period creep rupture strength of 12% Cr heat resisting steel. Journal of the Iron and Steel Industry of Japan, 61(9), pp. 2263-2273. Tamura, I., Sekine, H. & Tanaka, T., 1988. Thermomechanical processing of high-strength lowalloy steels. Oxford: Butterworth-Heinemann. Tamura, M. et al., 2013. Larson–Miller constant of heat-resistant steel. Metallurgical and Materials Transactions A, 44(6), pp. 2645-2661. Tanaka, Y., 2008. Production of creep-resistant steels for turbines. In: F. Abe, T. Kern & R. Viswanathan, eds. Creep resistant steels. Boca Raton: CRC Press LLC, pp. 174-214. Thermo-Calc Software, 2017. TCFE9: TCS steels/Fe-alloys database, v9. [Online] Available at: http://www.thermocalc.com/products-services/databases/thermodynamic/ van der Maaten, L. & Hinton, G., 2008. Visualizing data using t-SNE. Journal of Machine Learning Research, Volume 9, pp. 2579-2605. Viswanathan, R., 1989. Damage mechanisms and life assessment of high-temperature components. Metals Park, OH: ASM International. Viswanathan, R. & Bakker, W., 2001. Materials for ultrasupercritical coal power plants – Turbine materials: Part II. Journal of Materials Engineering and Performance, 10(1), pp. 96101. NETL Technical Report Series

36 Viswanathan, R. & Bakker, W., 2001. Materials for ultrasupercritical coal power plants—Boiler materials: Part 1. Journal of Materials Engineering and Performance, 10(1), pp. 81-95. Viswanathan, R. et al., 2009b. Steam turbine materials for ultrasupercritical coal power plants, Final Technical Report, DOE DE-FC26-05NT42442/OAQDA-OCDO 05-02(B), Washington, DC: U.S. Department of Energy. Viswanathan, R. et al., 2005. U.S. program on materials technology for ultra-supercritical coal power plants. Journal of Materials Engineering and Performance, 14(3), pp. 281-292. Viswanathan, R., Sarver, J. & Tanzosh, J., 2006. Boiler materials for ultra-supercritical coal power plants—Steamside oxidation. Journal of Materials Engineering and Performance, 15(3), pp. 255-274. Viswanathan, R., Shingledecker, J. & Hawk, J., 2009a. Effect of creep in advanced materials for use in ultrasupercritical coal power plants. In: Proceedings: Creep & Fracture in High Temperature Components: Design & Life Assessments Issues : 2nd ECCC Creep Conference, April 21-23, 2009, Zurich, Switzerland. Lancaster(PA): DEStech Publications, Inc., pp. 31-43.

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Randall W. Gentry Deputy Director Science & Technology Strategic Plans & Programs National Energy Technology Laboratory U.S. Department of Energy

Bryan D. Morreale Executive Director Research & Innovation Center National Energy Technology Laboratory U.S. Department of Energy

Madhava Syamlal Senior Fellow Computational Engineering Science & Technology Strategic Plans & Programs National Energy Technology Laboratory U.S. Department of Energy John Wimer Associate Director Strategic Planning Science & Technology Strategic Plans & Programs National Energy Technology Laboratory U.S. Department of Energy Briggs White Technology Manager Crosscutting Research Strategic Planning Science & Technology Strategic Plans & Programs National Energy Technology Laboratory U.S. Department of Energy

Regis Conrad Director Division of Crosscutting Research Office of Fossil Energy U.S. Department of Energy

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