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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

An Artificial-Intelligence and Machine-Learning-Based Methodology to Conduct Seemingly Strain-Controlled Fatigue Test in a Pressurized-Water-Reactor-Test-Loop-Autoclave, While Not Controlling the Strain

In general, low cycle fatigue analysis of pressurized water reactor (PWR) components, requires strain-controlled fatigue test data such as using strain versus life (ε–N) curves. Conducting strain-controlled fatigue tests under in-air conditions is not an issue. However, controlling strain in a PWR-test-loop-autoclave is a challenge, since an extensometer cannot be placed in a narrow autoclave (typically used in a high-temperature-pressure PWR-test-loop). This is due to lack of space inside an autoclave that houses the test specimen. In addition, installing a contact-type extensometer in the path of a high-pressure flow can be a challenge. These difficulties of using an extensometer inside an autoclave led us to use an outside-autoclave displacement sensor which measures the displacement of pull-rod-specimen assembly. However, in our study (based on in-air fatigue test data), we found that a pull-rod-controlled based fatigue test can lead to substantial cyclic hardening/softening resulting in substantially different cyclic strain amplitudes and their rates compared to the desired cyclic strain amplitudes and its rates. In this paper, we propose an Artificial-Intelligence and Machine-Learning based technique such as using k-means clustering technique to improve the pull-rod-control based fatigue test method, such that the gage-area strain amplitude and rates can reasonably be achieved. In support of this, we present the fatigue test results for both 316 SS base and 81/182 dissimilar-metal-weld specimens.

42 ENGINEERING↗
DOE OSTI2025

In situ Visible Light and Thermal Imaging Data from a Laser Powder Bed Fusion Additive Manufacturing Process Co-Registered to X-ray Computed Tomography and Fatigue Data

This dataset is comprised of in situ sensing data collected during a laser-based powder bed fusion additive manufacturing process, as well as rasterized scan path information, post-build X-ray computed tomography (XCT), and fatigue test results. A total of 64 cylinders, approximately 15 mm in diameter and 102 mm tall, were printed out of stainless steel 316H on a Colibrium Additive Concept Laser M2 Series 5 machine. Parameters known to produce dense material were used to construct 56 of these cylinders, while the remaining 8 cylinders were printed with relatively high energy density parameters prone to producing keyhole pores. In addition, two spatter generation blocks were constructed upstream of the 64 cylinders such that ejecta produced during the melting of the spatter generators were stochastically seeded onto the 64 cylinders. Based on previous experiments, these spatter particles were theorized to produce stochastic lack-of-fusion pores. During the construction of the build, high-resolution images of reflected light in the visible spectrum were captured both before and after recoating for each print layer. Additionally, temporally integrated thermal imaging in the near infrared spectrum produced integrated sum and max images on a layerwise basis. The multimodal in situ data has been co-registered to the build plate coordinate system, allowing for identification of process anomalies (e.g., spatter particles) apparent in the two sensors. Following construction of the build, the cylinders were subjected to XCT to identify internal flaws, and the resulting data have also been registered to the build plate coordinate system. Finally, 60 of the 64 cylinders were machined into fatigue coupons conforming to ASTM E466 and subsequently subjected to either high- or -low-cycle fatigue testing. The results of the fatigue tests have also been included in the dataset, and the XCT data corresponded to the approximate location of the gauge sections of the machine fatigue specimen geometry.

42 ENGINEERING↗
DOE OSTI2021

Quantitative Non-Destructive Evaluation of Fatigue Damage Based on Multi-Sensor Fusion

Based on sensor fusion and machine learning, this project developed a novel non-destructive evaluation (NDE) methodology, which consists of a remaining useful life (RUL) prediction framework and regression models for predicting residual stress and full width at half maximum (FWHM). A series of fatigue testing experiments were conducted using 5052-H32 aluminum alloy specimens. All specimens were measured using linear ultrasonic (LU) and nonlinear ultrasonic (NLU) testing methods non-destructively. Machine learning models were developed to use LU and NLU measurements to predict loading condition, fatigue level, residual stress, and FWHM. It was demonstrated that the developed methodology could distinguish new and fatigue specimens with an accuracy of 97.53%. Also, the prediction errors for residual stress and FWHM were as low as 4.73% and 1.62%, respectively. An interactive database was created to publicly share the data generated from the project. It is envisioned that the developed NDE technology will equip manufacturers with a responsive screening system for incoming used metallic components, and potentially lead to a significant increase in using used metallic components for remanufacturing.

42 ENGINEERING↗
DOE OSTI2026

Draft ASME Code Case to qualify L-PBF 316H material for Section III, Division 5 applications

This report documents the AMMT program’s development and submission of a draft ASME Code Case to qualify Laser Powder Bed Fusion (L PBF) Type 316H stainless steel for Section III, Divi-sion 5 Class A and SM high temperature nuclear applications. It summarizes the technical basis, the comprehensive high temperature mechanical test database assembled between 2023–2026, and the proposed code language and qualification framework submitted to ASME. The work was co-ordinated across multiple national laboratories and leverages prior ASME efforts to integrate additive manufacturing into the Boiler & Pressure Vessel Code. The body of the report describes the experimental database and analysis supporting the Code Case: tensile, creep, fatigue, creep fatigue, and thermal aging tests collected from multiple additive manufacturing sites, machine types, and powder lots, with material processed by a solution anneal heat treatment. The dataset — including both full size and subsized specimens and tests oriented parallel and perpendicular to build direction — shows limited tensile anisotropy, tensile properties comparable to wrought 316H, creep strength within the scatter of wrought material, but markedly reduced creep ductility above about 650 °C associated with rapid σ phase formation in L PBF microstructures. The draft Code Case itself prescribes a staged qualification model (manufacturing process qualification, component qualification, and per build witness testing), treats L PBF components as equivalent to Type 316 weld metal for design and inspection, and requires mechanical, chemical, and metallographic controls tied to ASTM/ISO 52946. Key acceptance criteria include tensile tests within a 90% prediction interval of the AMMT dataset, a creep fatigue screening test adapted from ASME Section III, Division 5, Subsection HB, HBB 2800 but with the cycle acceptance reduced to 100 for L PBF material, and double volumetric inspection of production components. The report concludes that the present data support treating L PBF 316H as analogous to conventional fusion weld metal for Division 5 design and inspection, while highlighting important caveats: the σ phase driven loss of creep ductility above ~650 °C, preliminary indications of enhanced creep fatigue sensitivity in some lots, and remaining gaps in long term aging and additional cyclic testing. Recommended next actions include completing outstanding cyclic and long duration creep/aging tests on the solution annealed condition, supporting inclusion of the 316H chemistry and heat treatment in ASTM/ISO 52946, and continuing engagement with ASME and NRC during balloting and review to enable industry adoption.

Messner, Mark C. (ORCID:0000000200404385)↗
DOE OSTI2025

Additively manufacturing high-temperature aluminum alloy for automotive engine pistons

The purpose of the project was to evaluate DuAlumin-3D, an ORNL designed Al alloy, for additive manufacturing of high-performance automotive components. The project consisted of additively manufacturing a cylindrical block of representative material which was then heat treated before machining into test coupons. Testing included tensile tests as a function of temperature, high-cycle fatigue testing as a function of temperature, density and elastic modulus measurements, and thermophysical property measurements. Fractography was also performed on failed high-cycle fatigue specimens. The results were compared against benchmark AM Al alloys and current high-performance conventionally manufactured alloys. Overall, the DuAlumin-3D material outperformed the incumbent materials in mechanical properties, especially at elevated temperatures, while maintaining broadly similar thermophysical properties. The results of this study support further maturation of the material for additive manufacturing of high-performance automotive components.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Training material models using gradient descent algorithms

High temperature design requires accurate constitutive models to describe material inelastic deformation and failure behavior. Oftentimes, calibrating accurate models devolves into the problem of fitting the model parameters against experimental test data. Here, we present the pyopmat package, an open source framework for calibrating constitutive models against experiment data subjected to various loading conditions using machine learning techniques. The package calculates the exact gradient of the model response with respect to the parameters using a combination of automatic differentiation and the adjoint method. Given this exact gradient, we compare the performance of several gradient-based optimization techniques in fitting realistic constitutive models against data. Here, we demonstrate the efficiency and accuracy of our package through example problems using both synthetic data, generated using known parameter sets, under monotonic and cyclic loading conditions and also with an example applying the techniques developed here to actual high temperature creep-fatigue test data.

36 MATERIALS SCIENCE↗
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 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↗
DOE OSTI2024

FY24 Integrated Results for High-Temperature Mechanical Testing of LPBF 316H Stainless Steel

This report provides the mechanical test data of laser powder bed fusion (LPBF) 316H stainless steel (SS) collected during Fiscal Year 2024 (FY 24) under the US Department of Energy, Office of Nuclear Energy’s Advanced Materials and Manufacturing Technologies program, along with the current state of microstructural-based understandings of the behaviors. Materials with variabilities in manufacturing site, machine, laser parameters, powder chemistry, porosity, specimen geometry and heat treatment were tested in high-temperature tension, creep, fatigue and creep-fatigue. Electron microscopy and optical microscopy were performed on selected materials before and after the tests to provide microstructural-based understandings to the mechanical behavior. It was discovered that the as-built (AB) and stress-relieved (SR) materials exhibit similar behaviors in tension and creep, as do the solution-annealed (SA) and hot-isostatic pressed (HIP) materials. The AB and SR materials are softer but more ductile than the SA and HIP materials in tension. In creep, the LPBF materials have comparable rupture times but lower rupture strains compared to the wrought materials. The AB and SR materials have low creep rupture strains (<10%) when tested at 720°C and 800°C, while the SA and HIP materials are much more ductile. The LPBF materials exhibit large scatter in fatigue and creep-fatigue lives. The cyclic lives of creep-fatigue tests are lower than those of fatigue tests. In fatigue and creep-fatigue, SR materials outperform SA materials. A common observation from the cyclic tests is that, given the same post-build heat treatment, a lower initial peak stress generally results in a longer cyclic life regardless of hold time. It was also discovered that the batch variation can be more impactful than heat treatment, as demonstrated by the overall better performance of one batch of material than another, regardless of the heat treatment. The results provided insights into how different factors impact the behaviors of LPBF 316H SS. An outlook to FY 25 work scope is provided.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development of a Machine-Learned Cruise Guide Indicator for Rotorcraft

This paper presents a machine-learned virtual cruise guide indicator (vCGI) for Chinook helicopters. Two temporal neural networks were trained and evaluated on measured data from 55 flight tests, one for the fore rotor and another for the aft rotor, to predict a vCGI value, which protects 23 components from fatigue damage during steady-state conditions. Three different classes of machine learning architectures were evaluated for prediction of the vCGI from time sequences: a temporal convolutional neural network with 1D dilated causal convolutions, a long short-term memory recurrent neural network, and an attention-based transformer architecture. The final average model accuracy on unseen flight data is currently greater than 93% for CGI values which could result in fatigue damage and 90% for normal operation CGI values. Model accuracy was improved through a series of advancements in:(1) selection of optimal training data using temporal collective variables and unsupervised learning, (2) dataset augmentation with maximum-entropy temporal collective variables, and (3) implementation of a mixture-of-experts classification- regression approach using an adversarial classification approach to assign maneuver labels. The results are presented for each advancement in model development along with lessons learned in training machine learning models on real- world, time-dependent rotorcraft data.

Boyer, Mathew↗
DOE OSTI2024

Microstructural effects on the rotating bending fatigue behavior of Ti–6Al–4V produced via laser powder bed fusion with novel heat treatments

The rotating bending fatigue (RBF) behavior (fully reversed, R = -1) of additively manufactured (AM) Ti–6Al–4V alloy produced via laser powder bed fusion (PBF-L) was investigated with respect to different microstructures achieved through novel heat treatments. The investigation herein seeks to elucidate the effect of microstructure by controlling variables that can affect fatigue behavior in Ti–6Al–4V, such as chemistry, porosity, and surface roughness. In order to control these variables, different hot isostatic pressing (HIP) treatments at 800 °C, 920 °C, and 1050 °C with a 920 °C temper were applied to three sets of Ti–6Al–4V cylinders that originated from the same PBF-L build, such that there were 30 tests per condition. After HIP treatment, the specimens were machined and tested. The highest runout stress was achieved after sub-β transus HIP at 800 °C for 2 h at 200 MPa of pressure. A significant drop in fatigue strength was attributed to large prior-β grains and grain boundary α resulting from super-β transus HIP treated specimens. In conclusion, for the sub-β transus HIP specimens, differences in fatigue strength were attributed to α lath thickness, relative dislocation density, and dislocation boundary strengthening.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Additive Manufacturing Flaw Assessment Methodology

An evolution fatigue data and flaw tolerance of components produced using the Powder Bed Fusion (PBF) Additive Manufacturing (AM) process is documented in this report. Initial differences in fatigue data for AM components compared to smooth bar fatigue data indicated a need for a more detailed analysis of AM data available in technical literature. The investigation was initiated to support the development of a fatigue analysis methodology for AM components to support the of codification of AM technology for pressure equipment. The project was initiated to collect and analyze stainless steel 304L and 316L AM fatigue test data and corresponding process and quality information to develop S-N and E-N based fatigue data representation. Additional AM fatigue test data including Inconel Ti-6-4 and aluminum alloys were also considered for comparison purposes Metallic AM parts tend to contain various forms of defects distributed throughout the part. If an AM part is subjected to fatigue loading in service, a fatigue analysis needs to be performed during the design process to ensure an acceptable service life for the part. Post-process machining and polishing do not to improve fatigue resistance in any significant degree. The low cycle fatigue regime is of particular interest to this project in support of flaw acceptance criteria currently under development by ASME’s BPTCS/BNCS. Internal defects become exposed as external surface defects during machining for the machining of the AM part to final dimensions. This implies that as long as inherent AM defects are within a controlled limit in terms of both size and distribution characteristics, the corresponding fatigue test data in terms of either S-N (stress life) or E-N (strain life) can be investigated and characterized to establish fatigue properties of AM parts for design and fatigue evaluation purposes. The resulting S-N or E-N curves and their scatter bands can be used to derive fatigue design allowable stress values by capturing the effects of distributed discontinuities within an acceptable limit.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Data-Driven Method for Modeling Creep-Fatigue Stress- Strain Behavior Using Neural ODEs

In this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress–strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The study employs uniaxial creep–fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. The first model, known as the black-box model, comprehensively describes the strain–stress relationship using a Neural ODE equation. To interpret this black-box model, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) technique, transforming the black-box model into an equation-based model using symbolic regression. The second model, the Neural flow rule model, incorporates Hooke’s Law for the linear elastic component, with the nonlinear part characterized by a Neural ODE. Both models are trained with experimental data to accurately reflect the observed stress–strain behavior. We conduct a detailed comparison with the standard Chaboche model, which includes three back stresses. Our results demonstrate that the neural network-based ODE models precisely capture the experimental creep–fatigue mechanical behavior, exceeding the standard Chaboche model’s accuracy. Furthermore, an interpretable model derived from the black-box neural ODE model through symbolic regression achieves accuracy comparable to the Chaboche model, enhancing its interpretability. The results highlight the potential of neural network-based ODE models to depict complex creep–fatigue behavior, eliminating the necessity for experts to define a specific, material-focused model form.

creep-fatigue↗
DOE OSTI2024

Very high cycle fatigue characteristics of laser beam powder bed fused AlSi10Mg: A systematic evaluation of part geometry

This study explores the influence of geometry and part size on defect distribution, melt pool size, and mechanical characteristics in laser beam powder bed fused (LB-PBF) AlSi10Mg. Five distinct geometries—hourglass, small rod, small block, large block, and large rod—were fabricated under identical process parameters. Fully reversed ultrasonic fatigue testing, operating at a frequency of 20 kHz, was conducted to assess the very high cycle fatigue properties. The findings indicated that part geometry had a major impact on the fatigue properties of the material in the very high cycle fatigue regime. Specimens machined from large rods and large blocks had the lowest porosity and highest fatigue resistance. Microstructural analysis indicated that hourglass, small rod, and small block specimens had shallower melt pools and overlap depths compared to other geometries. This observation suggests a higher cooling rate in specimens with smaller cross-sectional areas, leading to the increased presence of entrapped gas pores and a lack of fusion defects. Understanding the relationship between part geometry and fatigue properties in LB-PBF components offers insights for optimizing design and manufacturing processes in additive manufacturing applications.

AlSi10Mg↗
DOE OSTI2023

Machining of Alloy 709 Creep-fatigue Specimens from G. O. Carlson Heat

Alloy 709 has been selected as the next candidate material for Section III, Division 5 qualification in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC) for elevated-temperature nuclear construction. The qualification data package requires an assortment of information and material data including tensile, creep, and creep-fatigue performance at elevated temperatures from different material heats. The goal of the project is to generate a dataset to support qualification of Alloy 709 material for ASME BPVC design code. Working towards the project goal, Idaho National Laboratory needs to conduct a series of tests on three different heats to support the A709 code case development. At present, there is a gap in the data package for one of the heats: Heat number 58776 manufactured by G.O. Carlson. To address this data gap, Argonne National Laboratory transmitted five plates of A709 heat 58776 manufactured by G.O. Carlson to Idaho National Laboratory. These plates were solution annealed at 1150°C and heat treated at 775°C for 10 hours. The objective of this specification is to procure a series of creep-fatigue specimens to support the qualification data package. The creep-fatigue specimen design captures the cyclic material performance at elevated temperature. The material performance data generated from specimens machined herein will support the ASME BPVC code case development and establish design limits, and design life curves for Alloy 709 material.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Direct Multimodal Nanoscale Visualization of Early Phosphorus-Based Antiwear Tribofilm Formation

Understanding the mechanism of antiwear (AW) tribofilm formation and how to tune surface chemistry to control functionality is essential for the development of the next generation of oil lubricants. In particular, understanding and optimizing early AW tribofilm formation can increase the energy efficiency of mechanical systems. However, the mechanism for how these films form is not well understood. The majority of prior work has focused on analyzing only end-of-test surfaces long after the film has formed. In this work, we develop an in situ multimodal chemical imaging methodology to directly visualize the early formation of AW films on steel surfaces. We investigate an oil formulation containing a phosphorus-based additive commonly used to protect surfaces from wear and fatigue processes in machine elements, such as gears, bearings, and sliding contacts. Using nanoscale multimodal chemical imaging on combined platforms of atomic force microscopy (AFM) coupled directly with in situ nano-infrared (nano-IR) spectroscopy, and further combined ex situ with time-of-flight secondary ion mass spectrometry (ToF-SIMS), we demonstrate a direct correlation between changes in friction and local surface chemistry. In these experiments, the AFM probe acts as a single asperity contact to generate the tribofilm as well as a tool to analyze it in situ as it is forming. To verify our in situ measurements, we compare these results to the ex situ ToF-SIMS of macroscale block-on-ring tribometer-formed samples. We report the understanding gained here on how AW films form and how film properties can be modified by tuning the chemistry of the additives will facilitate developing transmission fluids to meet increasing demands for vehicle performance and efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Environmentally Assisted Fatigue in Light Water Reactor Environment

This report summarizes the Environmentally Assisted Fatigue (EAF) research conducted at ANL under the US DOE Light Water Reactor Sustainability (LWRS) program. Starting from a rich background in theoretical and experimental EAF, ANL previously developed an approach to evaluate fatigue performance of reactor materials in light water reactor environments with the correction factor F en . The approach was based on a large body of experimental work performed at ANL and elsewhere, and was consistent with American Society of Mechanical Engineers (ASME)’s methodology governing the design and construction of reactor components. In recent years, the program was focused on component fatigue prediction and made several major and fundamental contributions in this area. These accomplishments help meet the needs identified by the industry concerning component level fatigue predictions in complex, transient conditions. The main contribution of the ANL program involved the development of a system-level model for estimating residual strain and life of nuclear reactor coolant system components under connected-system-thermal-mechanical boundary conditions. The goal was to predict the stress hotspots, strain residuals, strain amplitudes and the resulting fatigue lives. Thermal-mechanical stress analysis was performed considering thermal stratification and a design-basis reactor loading cycle. Based on the finite element (FE) model results, the strain residuals, strain amplitudes and resulting fatigue lives of reactor coolant system (RCS) components were predicted. The results show that some of the RCS components can have significantly different strain amplitudes, residual strain, and fatigue lives, despite having similar geometry and material. In addition, the simulated component-level strain profile can guide the selection of appropriate test inputs for conducting laboratory-scale EAF tests. Building upon the system-level model, ANL developed a digital twin (DT) framework to predict the structural states and associated fatigue life of components in real-time. This framework is a comprehensive system designed to predict the structural states and fatigue lives of reactor components. It includes multiple models and integrates artificial intelligence (AI), machine learning (ML), and FE based modeling tools to evaluate the structural states and fatigue lives.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Mechanical response of HFIR-irradiated M5FRAMATOME cladding under simple and complex loading conditions

This study investigates the mechanical behavior of High Flux Isotope Reactor (HFIR) irradiated M5FRAMATOME cladding under simple and complex loading conditions through axial tensile and reversible cyclic bending. Axial tension specimens were pre-machined prior to HFIR irradiation while cyclic bend specimens were inserted as intact tubes. Tests articles were neutron-irradiated to 4 and 16 dpa, and specimens were tested at ORNL's hotcell facilities. The axial tension tests were conducted under constant displacement control, and the reversible cyclic bend tests were performed using ORNL's Cyclic Integrated Reversible-Bending Fatigue Tester (CIRFT) apparatus. Results showed that mechanical response of Cr-coated and uncoated M5FRAMATOME cladding were similar and independent of irradiation dose for axial tension tests, while Cr-coated specimens’ reversible cyclic bend behavior differed from uncoated counterparts. For all tests, irradiation temperature showed a significant impact on the mechanical behavior. Below 280°C, all axial tensile specimens whether coated or not behaved similarly. Above 280°C, YS and UTS showed decrease with increasing irradiation temperature. A similar behavior was also observed in cyclic bend tests as well. The mechanical damage during cyclic bend tests was linked to damage accumulation in unirradiated Cr-coated zircaloy-4 specimens, and the effect of irradiation temperature was related to changing characteristics of defect mobility during high temperature irradiation.

Cinbiz, Nedim [ORNL] (ORCID:0000000346268515)↗