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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Remaining Life Prediction of SNF Storage Canisters Exposed to CISCC Environments

• DOE Standardized SNF Storage Canisters o DOE designed standard spent nuclear fuel (SNF) storage canisters for storage of DOE SNF. o DOE canisters are significantly different from commercial MultiPurpose Canisters (MPC) in size. o MPC canisters are large, a height = 15.8 ft, OD = 68”, WT = 0.5”. o DOE canisters are small with 18” / 24” diameter, 10’ / 15’ length. • Integrity Evaluation of DOE versus MPC Canisters o Many investigations have been performed for MPC canisters. o Limit investigations were performed for DOE standard canisters. Most were done at Idaho National Lab (INL). o DOE has sponsored integrity studies to evaluate weld integrity using drop tests and FEA simulations. o No evaluation on CISCC/service life of DOE canister in literature. o MPC canister: 4 axial welds, 1 center girth weld, 2 closing welds.

ZHU, Xiankui↗
DOE OSTI2021

Wind Turbine Drivetrain Reliability Assessment and Remaining Useful Life Prediction

This presentation describes a methodology for predicting probability of failure of wind turbine gearbox bearings failed by axial cracking. This methodology was applied to a commercial MW size wind plant and generated results were correlated with actual failures. This work is partnered with WindESCo and funded through DOE's Technology Commercialization Fund.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2020

Shell Predictive Battery Life Models (SPBLM) [SWR-20-35]

Battery life models to predict the capacity fade and resistance growth of lithium-ion batteries from various manufacturers with different chemistries. The battery life model is trained on experimental test data, and accounts for calendar and cycling aging mechanisms as functions of cell environmental conditions and use. The battery life model is then used to predict capacity fade and resistance growth under any arbitrary aging scenario.

Smith, Kandler↗
DOE OSTI2023

Accelerated test protocols to predict service life and durability of solid oxide fuel cells

Reliable accelerated test protocols are needed for solid oxide fuel cell research to facilitate rapid learning on key durability issues, identify potential modes of failure expeditiously, and eventually predict the calendar lifetime of an electrochemical cell. In this work, solid oxide fuel cells operated at a constant current density were compared to cells undergoing accelerated measurements, which are composed of intermittent current injection to the cell. A general accelerated test profile was developed by cycling a solid oxide fuel cell from open circuit to a predetermined operating current density that is the same as the current density during a steady-state operation, to accelerate the local redox environment. The following parameters were studied: current density, operation temperature, moist level, sintering temperature, cycling current, cycling frequency, and operation time. Up to 1,320,000 cycles were generated in this work. The cell degradation was accelerated by nearly 10 times, suggesting the feasibility of using this protocol for acceleration test to predict life performance and durability of solid oxide fuel cells.

08 HYDROGEN↗
DOE OSTI2021

A method for predicting failure statistics for steady state elevated temperature structural components

This paper presents the initial development of a high temperature life prediction method that accounts for the variability in the material properties of Grade 91 steel. The method accounts for material variability by fitting a variable 3-parameter Weibull distribution to experimental rupture data and accounts for the variability of creep deformation on the steady-state stresses via a Monte Carlo approach. To ensure reasonable computational times, the model represents the material as an extremely viscous Stokes fluid with a non-Newtonian viscosity, therefore solving the stress relaxation problem with a steady, static, instead of transient, analysis. Furthermore, the complete statistical analysis combines this model for creep deformation with a probabilistic model for creep rupture to evaluate the probability of premature failure for a set of sample problems, comparing the predicted failure statistics to the design life predicted by the ASME Boiler and Pressure Vessel Code rules.

42 ENGINEERING↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2022

Battery calendar aging and machine learning

Batteries continue to attract immense attention due to the critical need for new energy technologies that can help humankind transition to a net-zero future. To meet global goals for implementation of new batteries, the research community needs to more coherently consider how to go about the process of innovation, development, and validation of new technologies. Current practices for bringing a new battery technology from the benchtop to deployment can be very time consuming and complex, given that batteries can have distinct aging pathways based on both how they are cycled and their storage conditions. In nearly all use cases, batteries experience periods of active cycling and also extended periods of rest. Thus, calendar time often occurs when a battery is close to fully charged. Currently, the combined cycle and calendar life aspects receive inconsistent attention during most stages of research and development. For batteries to fulfill the critical role envisioned to meet global energy demands, greater uniformity in practice is needed to alleviate potential delays caused by the inconsistent acquisition of aging data. In this study, we present our thoughts and recommendations on the need to further advance commonality and availability of calendar aging data for emerging high-energy and long-duration batteries. While there is a broad need to adopt best practices for both calendar and cycle life assessment, a push to greater uniformity in approach and reporting is sorely needed to allow calendar life predictions to harness many of the advances that have recently been seen for cycle life prediction using machine learning (ML) and other techniques.

25 ENERGY STORAGE↗
DOE OSTI2023

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. In conclusion, two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An extrapolation method for strain ranges and hold times in developing the EPP+SMT creep-fatigue design curves for Alloy 617

Experimental and numerical studies in developing the integrated Elastic–Perfectly Plastic (EPP) plus Simplified Model Test (SMT) design methodology, referred to as the EPP+SMT method, continued in FY2022. This report focuses on the methods for extrapolating the EPP+SMT creep-fatigue (CF) design curves at long hold times and low strain ranges. In this study, the available CF failure data on Alloy 617 at 950°C were analyzed to determine a set of CF failure criteria. At very low strain ranges and long hold times, CF failure data are not accessible by experiments because of the extraordinarily long test durations and the inability of the test machines to accurately control these small strain ranges. A CF experimental approach with the concept of block-strain range CF testing protocol was developed. Tests using this protocol were conducted to generate the needed information for calibrating material parameters of the numerical material models. The Time Fraction based method and Dissipated Energy method were used to extrapolate the CF life curves to low strain ranges and long hold times. Based on the new experimental approach and CF life prediction methods, the CF life curves with various hold times were developed for Alloy 617 at 950°C. In addition, an experiment was designed and is being performed to verify the predicted CF curves at 950°C. The extrapolation procedure will be applied at lower temperatures to complete the development of the EPP+SMT CF design curves for Alloy 617 in F2023.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accurate Effective Stress Measures: Predicting Creep Life for 3D Stresses Using 2D and 1D Creep Rupture Simulations and Data

Operating structural components experience complex loading conditions resulting in 3D stress states. Current design practice estimates multiaxial creep rupture life by mapping a general state of stress to a uniaxial creep rupture correlation using effective stress measures. The data supporting the development of effective stress measures are nearly always only uniaxial and biaxial, as 3D creep rupture tests are not widely available. This limitation means current effective stress measures must extrapolate from 2D to 3D stress states, potentially introducing extrapolation error. In this work, we use a physics-based, crystal plasticity finite element model to simulate uniaxial, biaxial, and triaxial creep rupture. Here, we use the virtual dataset to assess the accuracy of current and novel effective stress measures in extrapolating from 2D to 3D stresses and also explore how the predictive accuracy of the effective stress measures might change if experimental 3D rupture data was available. We confirm these conclusions, based on simulation data, against multiaxial creep rupture experimental data for several materials, drawn from the literature. The results of the virtual experiments show that calibrating effective stress measures using triaxial test data would significantly improve accuracy and that some effective stress measures are more accurate than others, particularly for highly triaxial stress states. Results obtained using experimental data confirm the numerical findings and suggest that a unified effective stress measure should include an explicit dependence on the first stress invariant, the maximum tensile principal stress, and the von Mises stress.

36 MATERIALS SCIENCE↗