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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SSL and LED life prediction and assessment of CCT shift

Solid-state lighting (SSL) products can have a predicted life of 70% lumen output (L70) from 26,000 to 40,000 hours using the LM-80-08 testing standards. Chromaticity shift, correlated color temperature (CCT) and lumen maintenance (LM) will dramatically reduce the nominal life of SSL luminaires. In this work, an off-the-shelf luminaire from Philips (AmbientLED) has been aged in a standard wet hot temperature operating life (WHTOL) of 85% relative humidity and 85°C (85/85) in order to assess reliability of prolonged exposer in a harsh environment. Failure criterion has been derived using the Arrhenius equation from the LM-80-08 standard, as well as the 60W LED Lamp test report from an isothermal environment of 45°C. This is a similar luminaire to the test vehicle used in this research. Data characterization between the two data sets has been carried out to determine the luminaires reliability and life under the 85/85 test conditions. This characterization allows for the determination of poor quality luminaire products in the market place. The distribution properties of the shifting mean values of CCT and LM were incorporated into the Bayesian Linear Regression (BLR) to determine the degradation pattern, in order to predict the remaining useful life (RUL) of the system due to aging before the end-of-life (EoL).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2001

Effect of Roller Profile on Cylindrical Roller Bearing Life Prediction: Comparison of Bearing Life Theories - Part 1

Four rolling-element bearing life theories were chosen for analysis and compared for a simple roller-race geometry model. The life theories were those of Weibull; Lundberg and Palmgren; Ioannides and Harris; and Zaretsky. The analysis without a fatigue limit of Ioannides and Harris is identical to the Lundberg and Palmgren analysis, and the Weibull analysis is similar to that of Zaretsky if the exponents are chosen to be identical. The resultant predicted life a each stress condition not only depends on the life equation used but also on the Weibull slope assumed. The least variation in predicted life with Weibull slope comes with the Zaretsky equation. Except for a Weibull slope of 1.11, at which the Weibull equation predicts the highest lives, the highest lives are predicted for the Zaretsky equation. For Weibull slopes of 1.5 and 2, both the Lundherg-Palmgren and Ioannides-Harris (where tau(sub u) = 0) equations predict lower lives than the ANSI/ABMA/ISO standard. Based upon the Hertz stresses for line contact, the accepted load-life exponent of 10/3 results in a maximum Hertz stress-life exponent equal to 6.6. This value is inconsistent with that experienced in the field. The assumption of as shear stress fatigue limit tau(sub u) results in Hertz stress-life exponents greater than are experimentally verifiable.

Poplawski, Joseph V.↗
NASA NTRS1977

Methodology for designing accelerated aging tests for predicting life of photovoltaic arrays

A methodology for designing aging tests in which life prediction was paramount was developed. The methodology builds upon experience with regard to aging behavior in those material classes which are expected to be utilized as encapsulant elements, viz., glasses and polymers, and upon experience with the design of aging tests. The experiences were reviewed, and results are discussed in detail.

Gaines, G. B.↗
NASA NTRS2005

Life Predicted in a Probabilistic Design Space for Brittle Materials With Transient Loads

Analytical techniques have progressively become more sophisticated, and now we can consider the probabilistic nature of the entire space of random input variables on the lifetime reliability of brittle structures. This was demonstrated with NASA s CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code combined with the commercially available ANSYS/Probabilistic Design System (ANSYS/PDS), a probabilistic analysis tool that is an integral part of the ANSYS finite-element analysis program. ANSYS/PDS allows probabilistic loads, component geometry, and material properties to be considered in the finite-element analysis. CARES/Life predicts the time dependent probability of failure of brittle material structures under generalized thermomechanical loading--such as that found in a turbine engine hot-section. Glenn researchers coupled ANSYS/PDS with CARES/Life to assess the effects of the stochastic variables of component geometry, loading, and material properties on the predicted life of the component for fully transient thermomechanical loading and cyclic loading.

Nemeth, Noel N.↗
DOE OSTI2024

Characterization of Precipitation-Strengthening Heat-Resistant Austenitic Stainless Steels for Life-Prediction Modeling

In this study, the role of minor alloying additions in 347H stainless steels (UNS34709, ASTM A240/240M) on creep-rupture properties at 650-750°C and microstructure evolution during isothermal exposure at 750°C has been investigated, aiming to provide the experimental dataset as boundary conditions of physics-based modeling for material/component life prediction. Four different 347H heats containing various amounts of boron and nitrogen additions were prepared and evaluated. The combined additions of B and N are found to stabilize the strengthening secondary M23C6 carbides and retarding the transition from M23C6 to sigma phase precipitates during thermal exposure. The observed kinetics of microstructure evolution reasonably explains the improvement of creep-rupture properties of 347H stainless steels with the B and N additions.

Yamamoto, Yukinori↗
DOE OSTI2017

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System

Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity and resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4% RMS error and resistance growth with 15% RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.

25 ENERGY STORAGE↗
DOE OSTI2024

Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion

In system prognostics and health management, multivariable degradation models have been widely developed to predict the life of complex systems using degradation data of multiple Performance Characteristics (PCs). Recent studies have detected a Long-Term Memory (LTM) effect among the degradation process of various PCs, implying a strong coupling phenomenon between the future degradation behavior and historical degradation trajectory. Although the LTM has been widely integrated into single-PC-based degradation modeling, it has not been considered in multi-PC-based scenarios. To capture LTM among multiple PCs, this article proposes a novel LTM-integrated Multivariate Degradation Model (MDM) for system life prediction based on multivariate fractional Brownian motion, which simultaneously incorporates the cross-correlation among different PCs. To estimate parameters of the LTM-integrated MDM, a maximum likelihood method is developed. Here, two likelihood-ratio hypothesis tests are developed to test the existence of the overall and individual LTM effect among multiple PCs. Both simulation studies and physical experiments on the performance degradation of solar energy conversion and storage devices are conducted to validate the proposed model. Results reveal that the proposed LTM-integrated MDM significantly outperforms existing MDMs in life prediction, while the lifetime uncertainty is heavily underestimated by those traditional approaches that neglect the LTM.

42 ENGINEERING↗
NASA NTRS2003

Life-Prediction Parameters of Sapphire Determined for the Design of a Space Station Combustion Facility Window

To characterize the stress corrosion parameters and predict the life of a sapphire window being considered for use in the International Space Station's Fluids and Combustion Facility, researchers at the NASA Glenn Research Center conducted stress corrosion tests, fracture toughness tests, and reliability analyses, as shown in the figures. Standardized test methods, developed and updated by the author under the auspices of American Society for Testing and Materials, were employed. One interesting finding is that sapphire exhibits a susceptibility to stress corrosion in water similar to that of glass. In addition to generating the stress corrosion parameters and fracture toughness data, closed-form expressions for the variances of the crack growth parameters were derived. The expressions allow confidence bands to be easily placed on life predictions of ceramic components. Brittle materials such as sapphire and quartz are required for windows in a variety of applications such as the Fluids and Combustion Facility. To minimize the launch weight of such facilities, researchers must design the windows to be as lightweight as possible. The safe use of lightweight, brittle windows in structural applications is limited by two factors: low fracture toughness and slow crack growth, or stress corrosion. Stress corrosion of these and other optical materials can occur in relatively common environments, such as humid air. Access to the data has been requested by designers for use in the life prediction of a Northrop Grumman F16 instrument window and a Jet Propulsion Laboratory instrument window. One Space Act Agreement has been formed. Future work includes the measurement of the life of subscale windows.

Salem, Jonathan A.↗
NASA NTRS2000

Ceramic Matrix Composites (CMC) Life Prediction Method Development

Advanced launch systems (e.g., Reusable Launch Vehicle and other Shuttle Class concepts, Rocket-Based Combine Cycle, etc.), and interplanetary vehicles will very likely incorporate fiber reinforced ceramic matrix composites (CMC) in critical propulsion components. The use of CMC is highly desirable to save weight, to improve reuse capability, and to increase performance. CMC candidate applications are mission and cycle dependent and may include turbopump rotors, housings, combustors, nozzle injectors, exit cones or ramps, and throats. For reusable and single mission uses, accurate prediction of life is critical to mission success. The tools to accomplish life prediction are very immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for a variety of space propulsion applications. This paper describes an approach to satisfy the need to develop an integrated life prediction system for CMC that addresses mechanical durability due to cyclic and steady thermomechanical loads, and takes into account the impact of environmental degradation.

Levine, Stanley R.↗
NASA NTRS1985

Thrust chamber life prediction

The reusable life of the Space Shuttle main engine (SSME) is influenced by the cyclic life of the regeneratively liquid cooled main combustion chamber (MCC). During an operational duty cycle the MCC liner is subjected to a large transient thermal gradient that imparts a high thermal cyclic strain to the liner hot gas wall. Life predictions of such chambers have usually been based on low cycle fatigue (LCF) evaluations. Hot-fire testing, however, has shown significant mid-channel wall deformation and thinning during accrued cyclic testing. This phenomenon is termed cyclic creep and appears to be significantly accelerated at elevated temperatures. An analytical method that models the cyclic creep phenomenon and its application to thrust chamber life prediction is presented. The chamber finite element geometry is updated periodically to account for accrued wall thinning and distortion. Failure is based on the tensile instability failure criterion. Cyclic life results for several chamber life enhancing coolant channel designs are compared to the typically used LCF analysis that neglects cyclic creep. The results show that the usable cyclic creep life is approximately 30 to 50% of the commonly used LCF life.

Kasper, H. J.↗
DOE OSTI2022

Bat554: Machine Learning for Accelerated Life Prediction and Cell Design

This presentation describes work associated with the early prediction of life and failure modes using machine learning. Key areas which will be highlighted are the generation of synthetic data, the use of deep learning and other machine learning models and the use of machine learning to train life prediction models.

25 ENERGY STORAGE↗
DOE OSTI2017

Mechanical Reliability and Life Prediction of Coated Metallic Interconnects within Solid Oxide Fuel Cells

Metallic cell interconnects (IC) made of ferritic stainless steels, i.e., iron-based alloys, have been increasingly favored in the recent development of planar solid oxide fuel cells (SOFCs) because of their advantages in excellent imperviousness, low electrical resistance, ease in fabrication, and cost effectiveness [1]. Typical SOFC operating conditions inevitably lead to the formation of oxide scales on the surface of ferritic stainless steel, which could cause delamination, buckling, and spallation resulting from the mismatch of the coefficient of thermal expansion and eventually reduce the lifetime of the interconnect components. Various protective coating techniques have been applied to alleviate these drawbacks [2]. In the present work, a fracture-mechanics-based quantitative modeling framework has been established to predict the mechanical reliability and lifetime of the spinel-coated, surface-modified specimens under an isothermal cooling cycle. Analytical solutions have been formulated to evaluate the scale/substrate interfacial strength and determine the critical oxide thickness in terms of a variety of design factors, such as coating thickness, material properties, and uncertainties. The findings then are correlated with the experimentally measured oxide scale growth kinetics to quantify the predicted lifetime of the metallic interconnects.

Solid oxide fuel cell (SOFC), Ferritic stainless s↗
NASA NTRS1984

Thermal barrier coating life prediction model development

In order to fully exploit thermal barrier coatings (TBCs) on turbine components and achieve the maximum performance benefit, the knowledge and understanding of TBC failure mechanisms must be increased and the means to predict coating life developed. The proposed program will determine the predominant modes of TBC system degradation and then develop and verify life prediction models accounting for those degradation modes. The successful completion of the program will have dual benefits: the ability to take advantage of the performance benefits offered by TBCs, and a sounder basis for making future improvements in coating behavior.

Hillery, R. V.↗
NASA NTRS2001

Effect of Load Rate on Tensile Strength of Various CFCCs at Elevated Temperatures: An Approach to Life Prediction Testing

Strength of three continuous fiber-reinforced ceramic composites, including SiC/CAS-11, SiC/MAS-5 and SiC/SiC, was determined as a function of test rate in air at 1100 - 1200 C. All three composite materials exhibited a strong dependency of strength on test rate, similar to the behavior observed in many advanced monolithic ceramics at elevated temperatures. The application of the preloading technique as well as the prediction of life from one loading configuration (constant stress-rate) to another (constant stress loading) suggested that the overall macroscopic failure mechanism of the composites would be the one governed by a power-law tyw of damage evolution/accumulation, analogous to slow crack growth commonly observed in advanced monolithic ceramics. It was further found that constant stress-rate testing could be used as an alternative to life prediction test methodology even for the composite materials at least for the short range of lifetime.

Choi, Sung R.↗
NASA NTRS1985

Thermal barrier coating life-prediction model development

Life predictions are made for two types of strain-tolerant and oxidation-resistant Thermal Barrier Coating (TBC) systems produced by commercial coating suppliers to the gas turbine industry. The plasma-sprayed TBC system, composed of a low-pressure plasma spray (LPPS) applied oxidation-resistant NiCrAlY bond coating and an air-plasma-sprayed yttria (8 percent) partially stabilized zirconia insulative layer, is applied by both Chromalloy and Klock. The second type of TBC is applied by the electron-beam/physical vapor deposition process by Temescal. Thermomechanical and thermochemical testing of the program TBCs is in progress. A number of the former tests has been completed. Fracture mechanics data for the Chromalloy plasma-sprayed TBC system indicate that the cohesive toughness of the zirconia layer is increased by thermal cycling and reduced by high temperature exposure at 1150 C. Eddy current technology feasibility has been established with respect to nondestructively measuring zirconia layer thickness of a TBC system. High pressure turbine blades have been coated with program TBC systems for a piggyback test in a TFE731-5 turbofan factory engine test. Data from this test will be used to validate the TBC life models.

Strangman, T. E.↗
NASA NTRS2003

Ceramic Matrix Composites (CMC) Life Prediction Development - 2003

Accurate life prediction is critical to successful use of ceramic matrix composites (CMCs). The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many reusable and single mission launch vehicle propulsion and airframe applications. This paper describes an approach and progress made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation of C/SiC.

Levine, Stanley R.↗
DOE OSTI2025

Physical Interpretation of Early Battery Life Prediction Models

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), d Q· d V −1 , and ΔV(SOC) curves. A thorough study on the dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and d Q· d V −1 models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.

25 ENERGY STORAGE↗
DOE OSTI2023

A Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle Tires

In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. Here, the proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life.

Data balancing↗