Engineering topics

Listwan, Joseph

Publications and source records attributed to Listwan, Joseph.

5 records

DOE OSTI2023

Progress Report of Alloy 709 Performance in Liquid Sodium

With improved high-temperature properties, Alloy 709 shows great potential for advanced reactor applications. To understand the effects of sodium on A709, small tensile specimens were machined from two commercial heats and subject to sodium exposure in two forced convection loops operated by Argonne. Thermal aging experiments were also conducted in parallel to isolate any sodium-specific effects. An increasing trend in yield strength and a decrease trend in ultimate tensile strength can be seen with the increasing exposure time, suggesting microstructural changes at these temperatures. Nonetheless, the thermally aged and sodium exposed samples behaved similarly, implying no sodium-specific effect at these temperatures. In contrast, 316H SS exposed to sodium at 650°C showed a strong sodium effect. The different responses of A709 and 316H SS to sodium exposure is of interest and requires additional effort to understand. The cold traps of the sodium loop were replaced early in FY23. During a maintenance operation, an unexpected sodium leak incident occurred at one of the sodium loops, triggering an emergency response. An investigation into the incident revealed that the leak happened at a VCR fitting on the recently replaced cold trap. The metal gasket of the leaked VCR fitting was not compressed sufficiently. A dimensional mismatch between the new component and old piping configuration was responsible for a significant misalignment. A new cold trap with improved design is in production. The new design provides additional flexibility and an access point for post-installation helium leak check and for venting of trapped gas. These design features will make the system more error-tolerant and the installation quality verifiable. A thermodynamic analysis was performed to understand the carburization-decarburization behavior of A709 in sodium environment. The equilibrium carbon activity in the alloy was evaluated with M 23 C 6 , TiC, and NbC. The carburization-decarburization boundary was calculated with different methods developed previously. The results suggests that A709 will be carburized in SMT-1 and SMT-2, and can be decarburized in low-carbon-activity environments such as EBR-II at temperatures above 620-650°C. Nonetheless, A709 should be less likely to be decarburized than 316H SS when exposed to the same low-carbon-activity environment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Effects of Sodium Exposure on the Tensile Properties of Grade 91 steel

Two heats of G91 steel have been investigated in liquid sodium at 550- 650°C to understand their corrosion behaviour, microstructural evolution, and tensile properties. Sodium exposure experiments of subsized, sheet-type tensile specimens of G91 were performed in Argonne’s forced convection sodium materials testing loops, SMT-1 and SMT-2. Maximum exposure times of ~49,000 h at ~38,000 h, and ~20,000 h have been achieved at 550, 600, and 650°C, respectively. Thermal aging experiments of G91 steel were also conducted in parallel, and the results were compared with those of the sodium-exposed specimens to isolate the effect of thermal aging from the effect of sodium exposure. It was found that the yield stress and ultimate tensile strength of G91 were reduced by more than 50% after sodium exposures at 650°C due to decarburization. The reduction in tensile strength was accompanied by the dissolution of M 23 C 6 carbides and excessive grain growth. The effect of sodium at 550 and 600°C was insignificant for exposure times up to ~49,000 h and ~38,000 h, respectively. The effect of sodium on the microstructural stability and tensile properties of G91 is largely dependent on the carbon mass transfer in the alloy-sodium system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Progress Report on Performance of A709 and G91 Steels in Sodium

Specimens in six different processing and heat treatment conditions of A709 H58776 were exposed to sodium at 550, 600, and 650°C, respectively for various exposure times. G91 base metal was tested in sodium at 550, 600, and 650°C, and G91 weldment at 550 and 600°C, respectively. Parallel thermal aging experiments were conducted on G91 and A709 to obtain thermal aging data for comparison with sodium exposure results to separate the thermal and sodium effects. The corrosion data obtained on A709 steel continue to show low corrosion rates, which indicate good compatibility of A709 with sodium when oxygen content is controlled. G91 has also shown acceptable corrosion rates over the temperature range of 550-650°C in sodium. Thermal aging or sodium exposures reduced the tensile strength, uniform elongation, and total elongation for A709 ESR, AOD specimens. These specimens consistently showed higher yield stress and tensile strength and lower uniform and total elongations after sodium exposure than after thermal aging under comparable testing conditions. It implies that there was an additional effect of sodium exposure that gave rise to an increase in tensile strength of A709. Dynamic strain aginginduced flow serrations were completely removed by either thermal aging or sodium exposure at 650°C, which imply that carbon or nitrogen in solution was removed from the solution and formed precipitates during aging or sodium exposures. The tensile data ruled out the possibility of decarburization under the sodium exposure conditions. The HOMO specimens behaved somewhat differently from the ESR and AOD specimens in sodium. While thermal aging and sodium exposures at 550 and 600°C decreased the yield stress and the ultimate tensile strength of G91, there was virtually no additional effect resulting from sodium exposures at these two temperatures. In contrast, sodium exposures at 650°C had a drastic effect on the yield stress and the ultimate tensile strength of G91. It is suggested that G91 experienced decarburization in the 650°C sodium environments. Carbon concentrations in sodium in the SMT-1 and SMT-2 loops were determined by a foil equilibration method. The estimated carbon concentration was in the range of 0.8-1.2 ppm in the SMT-1 loop and 0.3-0.7 ppm in the SMT-2 loop. The carbon activity in sodium in the SMT-2 loop was estimated to be 0.03-0.08 at 600°C, and 0.08-0.2 at 550°C. The carbon activity in sodium at 650°C in the SMT-1 loop was 0.04-0.07. Equilibrium simulation of the carburization – decarburization processes was conducted for A709 and G91 steels exposed in sodium environments at temperatures of 550-700°C. The carbon activity-concentration relationship for G91 was re-evaluated by considering four phases in G91, i.e. bcc ferrite, M 23 C 6 , NbC and VC carbides. It was found that the carburization-decarburization process in G91 steel was dictated by M 23 C 6 carbides at high carbon activities, while NbC carbides dominated the process at low carbon activities. Formation of NbC increases the decarburization resistance of G91 steel. It remains to be understood whether the decarburization resistance provided by MC carbides in G91 can be maintained during long-term operations of SFRs. The carbon concentration-activity relationship for A709 was calculated based on the equilibrium of fcc-austenite and M 23 C 6 carbide phase. The calculations showed that A709 decarburizes at 650°C in the SMT-1 loop environments, which is different from the experimental findings. The carbon concentration-activity relationship in A709 was further evaluated by considering four phases, including fcc-austenite, M 23 C 6 , NbC, and TiC carbide phases. TiC is the most stable carbides among the three carbide phases. The carburization-decarburization process in A709 is dictated by M 23 C 6 carbides at high carbon activities and by TiC at low carbon activities. Formation of TiC increases the decarburization resistance of A709 steel. Because of the complexity of the precipitation process in A709 during sodium exposures, detailed characterization of precipitates in sodium-exposed specimens and improved thermodynamic models are needed to understand the carburization-decarburization behavior of A709. Kinetic analysis of carbon transfer will be investigated for G91 and A709 in future work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗