As part of the Department of Energy's Light Water Reactor Sustainability (DOE/LWRS) program, we are developing time-independent material models based on tensile tests and time-dependent material models based on cyclic tests for different reactor materials, such as 316 stainless steel (SS), 508 lowalloy steel (LAS) base metal, 316 SS-316 SS similar metal welds, and 316 SS-508 LAS dissimilar metal welds. Also, materials models are being developed under different environmental conditions, such as in air (at room temperature and 300 o C) and PWR primary loop water (at 300 o C). In our previous work, we presented time-dependent material models for 316 SS base metals [15][16][17]. In this report, we present tensile and fatigue test results and associated material models under different test and environmental conditions for 508 LAS base metal and 316 SS-316 SS similar metal welds.

At present, the fatigue life of nuclear reactor components is estimated based on empirical approaches, such as stress/strain versus life (S∼N) curves and Coffin-Manson type empirical relations. In most cases, the S∼N curves are generated from uniaxial fatigue test data, which may not truly represent the multi-axial stress state at the component level. Also, the S∼N curves are based on the final life of the specimen, which may not accurately represent the mechanistic time-dependent evolution of material behavior. These discrepancies lead to large uncertainties in fatigue life estimations. We propose a modeling approach based on evolutionary cyclic plasticity that can be used for developing finite element models of nuclear reactor components subjected to multi-axial stress states. These models can be used for more accurately predicting the stress-strain evolution over time in reactor components and, in turn, fatigue life. The model parameters were estimated for 316 stainless steel material, which are widely used in U.S. nuclear reactors. The model parameters were estimated for different test conditions to understand their evolution over time and their sensitivity to particular test conditions, such as the pressurized water reactor coolant condition.

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Offline and online fatigue crack growth prediction of Aluminum 2024 compact-tension (CT) specimens under variable loading has been modeled, using multivariate Gaussian Process (GP) technique. The GP model is a Bayesian statistic stochastic model that projects the input space to an output space by probabilistically inferring the underlying nonlinear function. For the offline prediction, the input space of the model is trained with parameters that affect fatigue crack growth, such as the number of fatigue cycles, minimum load, maximum load, and load ratio. For the online prediction, the model input space is trained using piezoelectric sensor signal features rather than training the input space with loading parameters, which are difficult to measure in a real time scenario. Principal Component Analysis (PCA) is used to extract the principal features from sensor signals. In both the offline and online case, the output space is trained with known associated crack lengths or crack growth rates. Once the GP model is trained, a new output space for which the corresponding crack length or crack growth rate is not known, is predicted using the trained GP model. The models are validated through several numerical examples.

Al 2024-T351 fatigue specimens have been modeled using a kernel-based multi-variate Gaussian Process approach. The Gaussian Process model projects fatigue affecting input variables to output crack growth by probabilistically inferring the underlying nonlinear relationship between input and output. The Gaussian Process approach not only explicitly models the uncertainty due to scatter in material microstructure parameter but it also implicitly models the loading sequence effect due to variable loading. The loading sequence effect is modeled through the Gaussian Process optimal hyperparameters by using the crack length data observed over the entire domain of spectrum loading. The performance in the crack growth prediction is evaluated for two covariance functions, a radial basis-based, anisotropic, covariance function and a neural network-based isotropic covariance function. Furthermore, the performance of different types of scaling, used to scale the input—output data space, is tested. It is found that for the radial basis-based anisotropic covariance function with normalized scaling, the prediction error is consistently lower compared to other combinations. In addition, the Gaussian Process model allows determination of the collapse load condition, which is a desirable feature for the online health monitoring and prognosis.

This paper discusses a material hardening models for welds made from 316 stainless steel (SS) to 316 SS. The model parameters were estimated from the strain-versus-stress curves obtained from tensile and fatigue tests conducted under different conditions (air at room temperature, air at 300 o C, and primary loop water conditions for a pressurized water reactor). These data were used to check the fatigue cycle dependency of the material hardening parameters (yield stress, parameters related to Chaboche-based linear and nonlinear kinematic hardening models, etc.). The details of the experimental results, material hardening models, and associated calculated results are published in an Argonne report (ANL/LWRS-15/2). This paper summarizes the reported material parameters for 316 SS-316 SS welds and their dependency on fatigue cycles and other test conditions. 1 Introduction At present, the fatigue life evaluation of nuclear power plant components has large uncertainties [1]. The relevant design codes [2, 3] allow elastic-analysis-based fatigue analysis of nuclear reactor components. Ideally, if stress and strain stay below the elastic limit, no fatigue would occur in the reactor components. However, safety-critical reactor components often fail due to fatigue damage associated with the reactor loading cycles and environmental conditions. In addition to fatigue damage, ratcheting of reactor components could happen due to the presence of stress concentration and/or plastic zones. The stress concentration and the plastic zone formation in the reactor metal could be due to weld residual stress formation, stress corrosion cracking, etc. Hence, for better accuracy, it is essential to estimate the fatigue and ratcheting damage of reactor components based on the results of elastic-plastic stress analysis rather than pure elastic stress analysis alone. Since ratcheting is a phenomenon closely related to the transient plastic deformation behavior, its nonlinear description requires the calculation of material hardening

A hybrid prognosis model is being developed for real-time residual useful life estimation of metallic aircraft structural components. The prognosis framework combines information from off-line physics-based, off-line data driven and on-line system identification based predictive models. The present paper focuses on the later two components of an integrated, hybrid prognosis model. These components are explicitly based on Gaussian process based data driven approach within a Bayesian framework. Fatigue crack behavior of Aluminum 2024 compacttension (CT) specimens under variable loading has been modeled using this multivariate Gaussian process technique. The Gaussian process model projects the input space to an output space by probabilistically inferring the underlying non-linear function relating input and output. For the off-line prediction the input space of the model is trained with parameters that affect fatigue crack growth, such as number of fatigue cycles, minimum load, maximum load, and load ratio. For the case of online prediction, the model input space is trained using features found from piezoelectric sensor signals rather than training the input space with loading parameters, which are difficult to measure in a real flight-worthy structure. In both the off-line and on-line case the output space is trained with known associated crack lengths. Once the Gaussian process model is trained, a new output space for which the corresponding crack length or damage state is not known is predicted using the trained Gaussian process model. Concepts are validated through several numerical examples. Nomenclature GP = Gaussian Process PCA = Principal Component Analysis KPCA = Kernel Principal Component Analysis M = Number of sensor observation used for PCA or KPCA at any fatigue cycle instancesPublic reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

This report presents an update on the environmental fatigue research that is being conducted at Argonne National Laboratory in support of the Department of Energy's Light Water Reactor Sustainability (LWRS) program. Argonne is developing a fully mechanistic fatigue evaluation approach without using empirical fatigue (S~N) curves. This approach is based on the fundamental concept of the time evolution of progressive fatigue damage rather than on the conventional S~N curve approaches using end-of-life data. In FY 2017, we performed extensive validation of this approach with respect to fatigue test data for 316 stainless steel [1]. This validation was performed for different loading cases, including constant, variable, and random amplitude. In the present FY 2018 semi-annual report, we present the further advances of Argonne's environmental fatigue research work in the context for more practical applications. In this report, we discuss a methodology for fully mechanistic (i.e., not using S~N curves) fatigue life evaluation of reactor components subjected to realistic loading cycles, namely, design-basis loading cycles. The loading cycles include plant heat-up, full-power, and cool-down operations. As a test case, we considered a typical pressurized water reactor surge line, which is made of 316 SS. To perform the fatigue simulation for thousands of fatigue cycles in a computationally cost effective way, we modified our previous desktop-based finite element (FE) modeling approach to work in a high-performance computing (HPC) framework. For the HPC implementation, we developed a hybrid framework based on commercial FE software (ABAQUS), open-source FE software (WARP3D), and Argonne-developed evolutionary cyclic-plasticity modeling methods. We validated this HPC-based cycle-by-cycle damage model for the entire fatigue life of a Pressurized Water Reactor (PWR) surge line (SL) pipe with respect to assumed loading cycles. The simulated fatigue life was found to be 5855 cycles, which is 85% accurate as compared to the corresponding small-specimen-based experimental fatigue life (6914 cycles). Also, the simulated stress history captures the cyclic hardening and softening behavior of the material for entire fatigue cycles. The FE simulation of the PWR SL pipe was conducted in a reasonable time of 12.5 days. These results show the promise that a fully mechanistic (not using S~N curves) fatigue life evaluation of a safety-critical nuclear reactor component (or even other safety critical components like those in aircraft, aero-engines, etc.) is possible. We anticipate that this type of methodology will drastically reduce the uncertainly associated with conventional fatigue life estimates based on empirical S~N curves. We also proposed an FE model that is based on a hybrid full-component and single-element approach and that can readily be used by industry if HPC resources are not available. In this approach, a single-cycle FE simulation has to be performed first for the required loading cycle. Then, the resulting strain/stress p...