The creep behaviour of ASTM A437 grade B4B steel for steam turbine applications

This study is a continuation of a project to characterise ASTM A437 Grade B4B martensitic stainless steel for use In Hitachi Canadian Industries Ltd’s (HCI) steam turbine casing bolts. ASTM A437 Grade B4B steel is commercially available and was chosen for the study due to its chemical similarity to a proprietary steel currently used by HCI.High creep resistance is essential for any candidate so creep-rupture and creep-strain tests were performed at and above the intended service temperature of 538°C. Hardness measurements and transmission electron microscopy were performed on the steel in the as-received condition as well as on crept samples to determine the effect of elevated temperature on the development of the steel’s microstructure.During testing, it was found that ASTM A437 Grade B4B steel has a well defined second stage leading to an abrupt transition into the third stage. The second stage begins in the first 10% of its creep life, while the third stage begins at 90% of its creep life. This equates to 5% and 30% of the final strain, respectively, with an average final strain of 20%.Time-to-Rupture data show good similarity to the creep life as predicted using the Larson-Miller method. When plotted, the steady-state creep rate shows a definite correlation between the creep stress and temperature. From this an empirical relationship was developed to predict the steady-state creep rate. Transmission electron microscopy (TEM) results showed a significant change in the icrostructure between crept and as-received steel. Coarsening of carbides along grain boundaries most likely led to a recovery of the microstructure in the crept samples. Literature suggests that the composition of the carbides is most likely tungsten and molybdenum intermetalics and carbides that coarsened from the depletion of chromium from solution. This was supported by energy dispersive spectroscopy (EDS) analysis.The coarsening of carbides correlates with the decrease in creep resistance of the material and it is likely that the growth of precipitates and recovery of the microstructure causes the entry of the steel into third stage creep

A Data Analytic Methodology for Materials Informatics

A data analytic materials informatics methodology is proposed after applying different data mining techniques on some datasets of particular domain in order to discover and model certain patterns, trends and behavior related to that domain. In essence, it is proposed to develop an information mining tool for vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites as a case study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs or responses. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. A clustering technique, i.e., fuzzy C-means (FCM) algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis (PCA) as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens in separate clusters. In addition, an artificial neural network (ANN) model was used to explore the VGCNF/VE dataset. The ANN was able to predict/model the VGCNF/VE responses with minimal mean square error (MSE) using the resubstitution and 3olds cross validation (CV) techniques. Furthermore, the proposed methodology was employed to acquire new information and mechanical and physical patterns and trends about not only viscoelastic VGCNF/VE nanocomposites, but also about flexural and impact strengths properties for VGCNF/ VE nanocomposites. Formulation and processing factors (curing environment, use or absence of dispersing agent, mixing method, VGCNF fiber loading, VGCNF type, high shear mixing time, sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics

A Data Analytic Methodology for Materials Informatics

A data analytic materials informatics methodology is proposed after applying different data mining techniques on some datasets of particular domain in order to discover and model certain patterns, trends and behavior related to that domain. In essence, it is proposed to develop an information mining tool for vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites as a case study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs or responses. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. A clustering technique, i.e., fuzzy C-means (FCM) algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis (PCA) as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens in separate clusters. In addition, an artificial neural network (ANN) model was used to explore the VGCNF/VE dataset. The ANN was able to predict/model the VGCNF/VE responses with minimal mean square error (MSE) using the resubstitution and 3olds cross validation (CV) techniques. Furthermore, the proposed methodology was employed to acquire new information and mechanical and physical patterns and trends about not only viscoelastic VGCNF/VE nanocomposites, but also about flexural and impact strengths properties for VGCNF/ VE nanocomposites. Formulation and processing factors (curing environment, use or absence of dispersing agent, mixing method, VGCNF fiber loading, VGCNF type, high shear mixing time, sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics

Mechanical properties of materials for fusion power plants

Fusion power is the production of electricity from a hot plasma of deuterium and tritium, reacting to produce particles and 14 MeV neutrons, which are collected by a cooling system. Their kinetic energy is transformed into heat and electricity via steam turbines. The constant ux of neutrons on the rst wall of the reactor produces atomic displacement damage through collisions with nuclei, and gas bubbles as a result of transmutation reactions. This leads eventually to hardening and embrittlement. Designing a material able to withstand such intensity of damage is one of the main aim of research in the eld of controlled fusion. In the past decades, many experiments have been carried out to under- stand the formation of radiation{induced damage and quantify the changes in mechanical properties of irradiated steels, but the lack of facilities prevents us from testing candidate materials in a fusion{like environment. Modelling techniques are utilised here to extract information and principles which can help estimate changes in steels due to damage. The elongation and yield strength of various low{activation ferritic/ martensitic steels were modelled by neural networks and Gaussian processes. These models were used to make predictions which were compared to exper- imental values. Combined with other techniques and thermodynamic tools, it was possible to understand the evolution of the mechanical properties of irradiated steel, with a particular focus on the role of chromium and the roles of irradiation temperature and irradiation dose. They were also used to extrapolate data related to ssion and attempt to make predictions in fusion conditions. A set of general recommendations concerning the database used to train the neural networks were made and the usage of such a modelling technique in materials science is discussed. An attempt to optimise the performance of neural networks by suppress- ing some random aspects of the training is presented. Models of the elon- gation, yield strength and ductile-to-brittle transition temperature trained following this procedure were created and compared to classical models

A Critical Analysis of the Conventionally Employed Creep Lifing Methods

The deformation of structural alloys presents problems for power plants and aerospace applications due to the demand for elevated temperatures for higher efficiencies and reductions in greenhouse gas emissions. The materials used in such applications experience harsh environments which may lead to deformation and failure of critical components. To avoid such catastrophic failures and also increase efficiency, future designs must utilise novel/improved alloy systems with enhanced temperature capability. In recognising this issue, a detailed understanding of creep is essential for the success of these designs by ensuring components do not experience excessive deformation which may ultimately lead to failure. To achieve this, a variety of parametric methods have been developed to quantify creep and creep fracture in high temperature applications. This study reviews a number of well-known traditionally employed creep lifing methods with some more recent approaches also included. The first section of this paper focuses on predicting the long-term creep rupture properties which is an area of interest for the power generation sector. The second section looks at pre-defined strains and the re-production of full creep curves based on available data which is pertinent to the aerospace industry where components are replaced before failure

Microstructural-Based Modeling Framework for High Temperature Behavior of Ferritic-Martensitic Steels Using Crystal Plasticity and Grain Boundary Finite Element Approaches

Ferritic/martensitic 9-12Cr steel alloys, have had widespread use as structural materials in power plants. Among this family of alloys, Grade 91 (Gr91) steel was a landmark in the development of 9-12Cr alloys. However, the unique microstructure complexity of the alloy has raised doubt regarding the techniques of data extrapolation in estimating its service-life for operation in next-generation power plants at higher temperatures and presssures. Conservatism becomes essential when the alloy is to be used in components lasting the life-cycle of power plants without replacement.This dissertation develops a physically-based microstructural model for creep rupture at 600 degrees Celsius for Gr91 steel as well as fundamental modeling tools that apply more broadly to microstructural modeling in metals. Key features of the Gr91 modeling framework capture the mechanical behavior of its prior austenite grains (PAG) and grain boundaries. Ultimately, a constitutive expression was adopted that captured the response from experiments conducted in the creep strain rate regime.An initial model intended to simulate low-cycle fatigue was first developed using the idea of geometrically necessary dislocations (GNDs) in crystal plasticity (CP) framework. That necessitated evaluating strain gradients and a patch-recovery method was implemented to recover a linear elastic deformation gradient field across the domain in linear elements. A Lie-group to Lie-algebra mapping was used to preserve orthogonality when projecting the rotation tensor from the elements’ Gauss points to the nodes.A statistically-stored dislocation density model was investigated to span the regimes of moderate strain rates (tension tests) to low strain rates (creep tests). Calibration of this model was possible against tension tests, but its application to creept tests suggested that other dislocation mechanisms were present during the primary creep regime of Gr91. Therefore, the CP model in the PAGs was changed to represent dislocation climb-glide motion and recovery along with linear viscous diffusional creep for point defect diffusion. This revised model more closely captured the measurements of creep response.Lastly, a robust Discontinuous Galerkin method is proposed to model the grain boundary interface elements to address traction oscillations observed for cohesive models. Stability and convergence are assessed along with non-conforming meshes

Effect of Microstructure on High-Temperature Mechanical Behavior of Nickel-Base Superalloys for Turbine Disc Applications

Engineers constantly seek advancements in the performance of aircraft and power generation engines, including, lower costs and emissions, and improved fuel efficiency. Nickel-base superalloys are the material of choice for turbine discs, which experience some of the highest temperatures and stresses in the engine. Engine performance is proportional to operating temperatures. Consequently, the high-temperature capabilities of disc materials limit the performance of gas-turbine engines. Therefore, any improvements to engine performance necessitate improved alloy performance. In order to take advantage of improvements in high-temperature capabilities through tailoring of alloy microstructure, the overall objectives of this work were to establish relationships between alloy processing and microstructure, and between microstructure and mechanical properties. In addition, the project aimed to demonstrate the applicability of neural network modeling to the field of Ni-base disc alloy development and behavior. A full program of heat-treatment, microstructural quantification, mechanical testing, and neural network modeling was successfully applied to next generation Ni-base disc alloys. Mechanical testing included hot tensile, hot hardness, creep deformation, creep crack growth, and fatigue crack growth. From this work the mechanisms of processing-structure and structure-property relationships were studied. Further, testing results were used to demonstrate the applicability of machine-learning techniques to the development and optimization of this family of superalloys.Ph.D.Committee Chair: Saxena, Ashok; Committee Member: Gokhale, Arun; Committee Member: Helmink, Randolph; Committee Member: Neu, Richard; Committee Member: Thadhani, Nares