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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Micromechanics-based Modeling of Laminated SiC/SiC Ceramic Matrix Composites

The behavior and response of ceramic matrix composites (CMCs), in particular silicon carbide fiber reinforced silicon carbide matrix (SiC/SiC), is affected by many factors such as variation of fiber volume fraction, residual stresses resulting from processing of the composites at high temperature, random microstructures, and the presence of matrix flaws (e.g., voids, pores, cracks etc.) as well as general material nonlinearity and heterogeneity that occurs randomly in a composite. Residual stresses arising from the phase change of constituents are evaluated in this paper and it is shown that they do influence composite strength and need to be properly accounted for. Additionally, the microstructures (location of fiber centers, coating thickness etc.) of advanced CMCs are usually disordered (or random) and fiber diameter and strength typically have a distribution. They rarely resemble the ordered fiber packing (square, rectangular, or hexagonal) that is generally assumed in micromechanics-based models with periodic boundary conditions for computational expediency. These issues raise the question of how should one model such systems effectively? Can an ordered hexagonal packed repeating unit cell (RUC) accurately represent the random microstructure behavior? How many fibers need to be included to enable accurate representation? Clearly, the number of fibers within an RUC must be limited to insure a balance between accuracy and efficiency. NASA’s in-house micromechanics-based code MAC/GMC provides a framework to analyze such RUCs for the overall composite behavior and the FEAMAC computer code provides linkage of MAC/GMC to the commercial FEA code, ABAQUS. The appropriate level of discretization of the RUC as well as the analysis method employed, i.e., Generalized Method of Cells (GMC) or High Fidelity Generalized Method of Cells (HFGMC), is investigated in this paper in the context of a unidirectional as well as a cross-ply laminated CMC. Results including effective composite properties, proportional limit stress (an important design parameter) and fatigue are shown utilizing both GMC as well as HFGMC. Finally, a few multiscale analyses are performed on smooth bar test coupons as well as test coupons with features such as open-hole and double notches using FEAMAC. Best practices and guidance are provided to take these phenomena into account and keep a proper balance between fidelity (accuracy) and efficiency. Following these guidelines can account for important physics of the problem and provide significant advantages when performing large multiscale composite structural analyses.

Ceramic Matrix Composites↗
DOE OSTI2023

A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching

To design increasingly tough, resilient, and fatigue-resistant elastomers and hydrogels, the relationship between controllable network parameters at the molecular level (bond type, non-uniform chain length, entanglement density, etc.) to macroscopic quantities that govern damage and failure must be established. Many of the most successful constitutive models for elastomers have been rooted in statistical mechanical treatments of polymer chains. Typically, such constitutive models have used variants of the freely jointed chain model with rigid links. However, since the free energy state of a polymer chain is dominated by enthalpic bond distortion effects as the chain approaches its rupture point, bond extensibility ought to be accounted for if the model is intended to capture chain rupture. To that end, a new bond potential is supplemented to the freely jointed chain model (as derived in the u FJC framework of Buche and Silberstein (2021) and Buche et al. (2022)), which we have extended to yield a tractable, closed-form model of single chain behavior that should be amenable to continuum-level constitutive model development. Inspired by the asymptotically matched u FJC model response in both the low/intermediate chain force and high chain force regimes, a simple, quasi-polynomial bond potential energy function is derived. This bond potential exhibits harmonic behavior near the equilibrium state and anharmonic behavior for large bond stretches tending to a characteristic energy plateau (akin to the Lennard-Jones and Morse bond potentials). Using this bond potential, approximate yet highly-accurate analytical functions for bond stretch and chain force dependent upon chain stretch are established. Then, using this polymer chain model, a stochastic thermal fluctuation-driven chain rupture framework is developed. This framework is based upon a force-modified tilted bond potential that accounts for distortional bond potential energy, allowing for the derivation and subsequent calculation of the dissipated chain scission energy. Here, the cases of rate-dependent and rate-independent scission are accounted for throughout the rupture framework. The impact of Kuhn segment number on chain rupture behavior is also investigated. The model is fit to single chain mechanical response data collected from atomic force microscopy tensile tests for validation and to glean deeper insight into the molecular physics taking place. Due to their analytical nature, this polymer chain model and the associated rupture framework can in the future be implemented in finite element models accounting for fracture and fatigue in polydisperse elastomer networks.

36 MATERIALS SCIENCE↗
NASA NTRS2019

The Relationship between Workload, Performance and Fatigue in a Short-Haul Airline

Short-haul flights are associated with irregular work schedules and increased workload, due to frequent takeoffs and landings. We examined the relationship between pilot workload, performance and subjective fatigue during normal short-haul operations. Ninety airline pilots (8 female), mean age 33 (±8 years) completed a NASA-Task Load Index (NASA-TLX), a Psychomotor Vigilance Task (PVT; a reaction time test sensitive to sleep loss) and a Samn-Perelli (SP) fatigue scale, over a period of 20 duty days at top-of-descent on 2762 short-haul flights. The duty days included either 2 or 4 flights per day starting at different times as scheduled during normal operations. Workload was measured using the six NASA-TLX scales: mental demand, physical demand, temporal demand, effort, performance and frustration. Lapses (reaction times [RT] > 500ms) were calculated for the PVT. Spearman correlations were calculated to identify relationships between the NASA-TLX, PVT lapses, and SP. The six scales of NASA-TLX were positively correlated with the PVT lapses (p < 0.01) showing an increase in workload when lapses increased. There was a positive correlation between subjective fatigue as measured by the SP fatigue scale and each of the six scales of NASA-TLX (p < 0.001) suggesting that pilots reported higher workload when perceived levels of fatigue were higher. Of the six workload scales, mental demand and performance were rated the highest (mental: M = 40.99, SD = 20.32; performance: M = 41.61, SD = 20.71) and effort was rated the lowest (M = 15.59, SD = 8.98). Preliminary analyses suggest that high workload is associated with poorer PVT performance and increased self-reported fatigue in this population of short-haul pilots. Future studies should explore how other workload factors (i.e. flight hours, time of day) influence self-reported and objective fatigue measures.

TLX↗
NASA NTRSDate not supplied

Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.

multiscale analysis↗
NASA NTRS2023

Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.

multiscale analysis↗
DOE OSTI2023

Development of a health monitoring framework: Application to a supercritical pulverized coal-fired boiler

In this study, this work details the development of a physics-based equipment health monitoring framework for a supercritical boiler, using first-principles models to estimate the remaining useful life (RUL) of its components. The framework accounts for fatigue and creep life consumption, generating spatio-temporal variations in mechanical and thermal stress. Analysis of the stress profile throughout the boiler highlights the finishing superheater inlet steam header as a vulnerable location susceptible to damage from cycling operation. The framework also yields quantified uncertainty in the RUL projection for specific locations, accounting for uncertainties in material properties and boiler operation. Results indicate that operational uncertainties (e.g., seasonal variation and operational strategy) and material properties (e.g., rupture time coefficients, Young’s modulus, yield strength, and coefficient of thermal expansion) significantly impact the RUL of the finishing superheater inlet steam header. Additionally, case studies demonstrate the use of the health monitoring framework as a predictive tool for operational planning under uncertainty, including scenarios with and without updates on the operation of the boiler.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Wake-up and fatigue mechanisms in ferroelectric Hf 0.5 Zr 0.5 O 2 films with symmetric RuO 2 electrodes

In this work, the mechanisms leading to wake-up and fatigue in ferroelectric hafnium zirconium oxide thin film devices with symmetric RuO 2 electrodes are investigated via polarization, relative permittivity, dielectric nonlinearity, pyroelectric coefficient, and microfocus X-ray diffraction (XRD) measurements. The devices are observed to wake-up for up to 10 3 bipolar pulsed field cycles, after which fatigue occurs with the polarization approaching zero following 10 8 cycles. Wake-up is accompanied by a decrease in both high-field permittivity and hysteresis loop pinching and an increase in pyroelectric coefficient, indicating that the wake-up process involves a combination of transformations from the tetragonal to the orthorhombic phase and domain depinning from defect redistribution. Fatigue is observed to coincide with an increase in irreversible domain wall motion and a decrease in pyroelectric coefficient. Finite pyroelectric coefficients are measured on fully fatigued devices, indicating that domain pinning is a strong contributor to fatigue and that fatigued devices contain domain structures that are unable to switch under the fields applied for measurement. Microfocus XRD patterns measured on each device reveal that the phase constitution is qualitatively unaffected by the field cycling and resultant polarization fatigue. This data indicates that the wake-up process has contributions from both phase transformations and domain depinning, whereas the fatigue process is driven primarily by domain pinning, and the near-zero measured switchable polarization is actually a poled device with immobile domains. These observations provide insight into the physical changes occurring during field cycling of HfO 2 -based ferroelectrics while examining a possible oxide electrode material for silicon CMOS device implementation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2017

Environmental Stability and Oxidation Behavior of HfO2-Si and YbGd(O) Based Environmental Barrier Coating Systems for SiCSiC Ceramic Matrix Composites

Ceramic environmental barrier coatings (EBC) and SiCSiC ceramic matrix composites (CMCs) will play a crucial role in future aircraft propulsion systems because of their ability to significantly increase engine operating temperatures, improve component durability, reduce engine weight and cooling requirements. Advanced EBC systems for SiCSiC CMC turbine and combustor hot section components are currently being developed to meet future turbine engine emission and performance goals. One of the significant material development challenges for the high temperature CMC components is to develop prime-reliant, environmental durable environmental barrier coating systems. In this paper, the durability and performance of advanced Electron Beam-Physical Vapor Deposition (EB-PVD) NASA HfO2-Si and YbGdSi(O) EBC bond coat top coat systems for SiCSiC CMC have been summarized. The high temperature thermomechanical creep, fatigue and oxidation resistance have been investigated in the laboratory simulated high-heat-flux environmental test conditions. The advanced NASA EBC systems showed promise to achieve 1500C temperature capability, helping enable next generation turbine engines with significantly improved engine component temperature capability and durability.

Ceramic Matrix Composites↗
NASA NTRS2020

Evaluation of Performance, Fatigue and Workload during Real-time, Reactive Telerobotic Mission Control Operations

Telerobotic operations involve a user operating a robot from a different location from where the activity is taking place. It can be difficult for humans to maintain performance when faced with physiological stressors, such as fatigue or illness. Operations require vigilance for extended periods, raising safety concerns surrounding operator’s fatigue and workload. Every hour of wakefulness increases the drive to sleep, causing decreased reaction time (RT), increased lapses in attention (RT > 500 ms), and decreased memory capability. The NASA Task Load Index (NASA-TLX) was created to evaluate the multiple attributes surrounding workload (e.g., mental and physical demands). The upcoming Volatiles Investigating Polar Exploration Rover (VIPER) mission involves remotely controlling a lunar vehicle from an Earth-based mission control station. The goal of VIPER is to excavate the moon for volatiles (water) deep in the surface near the South Pole for a 100-day, continuous operation.

Fatigue↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2015

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

Leser, Patrick E.↗
DOE OSTI2020

Comparative investigations of multi-fidelity modeling on performance of electrostatically-actuated cracked micro-beams

Silicon is a commonly used material for the fabrication of beams for use in micro-electrical-mechanical systems (MEMS). Although silicon is a brittle material, it has been shown to accumulate fatigue damage at the micro-scale. Understanding the effect this has on the overall device performance is critical to the design of reliable devices. Analytical methods for modeling damage provide expedient results but are limited by broad modeling assumptions. Numerical models account for more detailed physical phenomena but can be computationally intensive. In this work, two different crack scenarios are modeled using both analytical techniques and 3D computational simulations. First, the effects of a single surface crack on the static deflection and natural frequency of an electrostatically actuated micro-beam are formulated and compared. Then, a new method for approximating damage associated with realistic distributed crack networks is formulated for use in an analytical model and numerical simulations. A method for utilizing experimentally derived crack statistics to inform the analytical and numerical distributed crack models is developed. Good agreement between the analytical and numerical models is obtained for both crack scenarios. Altogether, these models can be used to effectively simulate a variety of damage and fatigue behaviors in silicon-based MEMS devices.

42 ENGINEERING↗
NASA NTRS1983

Spacecraft materials research: A NASA perspective

This paper reviews NASA's spacecraft materials research program. This is a multicenter program and includes research in the following areas: space environmental effects on materials, low expansion composites, fatigue and fracture of composites, thermal control coatings, and contamination. Research to date has concentrated on current graphite-reinforced composites and polymer systems, and developing analytical models to explain observed changes in mechanical, physical, and optical properties. As a result of these research efforts, new experimental facilities have been developed to simulate the space environment and measure the observed property changes. Chemical and microstructural analyses have also been performed to establish damage mechanisms and the limits for accelerated testing. The implications of these results on material selection and system performance are discussed, and additional research needs and opportunities in the area of tougher resin/matrix and metal/matrix composites are identified.

Tenny, D. R.↗
NASA NTRS1988

Composite structural materials

A decade long program to develop critical advanced composite technology in the areas of physical properties, structural concept and analysis, manufacturing, reliability, and life predictions is reviewed. Specific goals are discussed. The status of the chemical vapor deposition effects on carbon fiber properties; inelastic deformation of metal matrix laminates; fatigue damage in fibrous MMC laminates; delamination fracture toughness in thermoplastic matrix composites; and numerical analysis of composite micromechanical behavior are presented.

Loewy, Robert G.↗
DOE OSTI2020

Enhancement of Unsteady and 3D Aerodynamics Models using Machine Learning

Unsteady aerodynamics will be an important part of the floating wind turbines of the future operating under high shear across the rotor disk coupled with platform motion and atmospheric turbulence. We develop an unsteady aerodynamics and dynamic stall model using a long short-term memory variant of recurrent neural networks. The neural network model is trained using the oscillating airfoil data set from Ohio State University. The predictions from our machine learning (ML)-based model show good agreement with the experimental data and other state-of-the-art dynamic stall models for a wide range of airfoils, Reynolds numbers and reduced frequencies. In some cases the predictions are better than the Beddoes-Leishman model implementation in OpenFAST, when using the default coefficients. The ML-based model is also able to capture the key physics associated with dynamic stall, such as the precedence of moment stall before lift stall and cycle-to-cycle variations in the aerodynamic response. The new unsteady aerodynamics model is expected to improve prediction of fatigue loads for yaw-based wake-steering control scenarios in actuator-line and actuator-disc simulations of wind farms. Our methodology for training the ML-model provides a pathway for improving design level tools using high-fidelity computational fluid dynamics (CFD) simulations in the future.

17 WIND ENERGY↗
NASA NTRS1992

Acoustic fatigue characterization of carbon/carbon panels

Data from a sonic fatigue test of a blade-stiffened carbon/carbon panel is analyzed to determine the progression of damage to failure. The reduction in stiffness, as observed from acceleration measurements taken during the test, is correlated with the physical damage. Damage was measured through visual inspection, thermographic measurements, and through the novel use of vibration data collected using a scanning laser vibrometer.

Rizzi, S. A.↗
NASA NTRS2002

The effects of chronic alcohol consumption and exercise on the skeleton of adult male rats

BACKGROUND: Lifestyle factors are known to affect skeletal development and integrity. Specifically, running has been reported to increase risk of fatigue fractures, whereas chronic alcohol consumption has been shown to reduce bone formation and bone mass. The combined effect of exercise and alcohol on the skeleton has yet to be explored, although alcohol consumption is common among certain physically active populations (e.g., military recruits, college athletes). It was hypothesized that chronic alcohol consumption would accentuate the inherent risk associated with endurance running exercise. METHODS: Six-month-old male Sprague Dawley rats were assigned to one of five groups: baseline, exercise-alcohol diet, exercise-normal diet, sham-alcohol diet, and sham-normal diet. Alcohol-fed rats (35% caloric intake) received a liquid diet ad libitum. Normal animals were pair-fed the identical diet with a maltose dextrin caloric substitute. Exercise was conducted on a motorized treadmill 5 days/wk for 16 weeks. Sham rats were placed on a stationary treadmill for matching time periods. Fluorochrome labels were administered 3 days before baseline and at 10 and 2 days before animals were killed. Heart, soleus, and rectus femoris muscles were wet weighed to assess the effects of training. Tibiae were collected for static and dynamic histomorphometric measurements on cancellous and cortical bone. RESULTS: Muscle weights were larger in the exercised rats versus the sham rats. Alcohol had no significant effect on skeletal muscle weight but did result in larger heart weights in both alcohol-treated groups. Cancellous and periosteal bone formation rates were significantly decreased in the alcohol-fed rats versus rats on the normal diet and were associated with a significant reduction in trabecular thickness in the tibial metaphysis. Cortical and cross-sectional areas were also significantly lower in the alcohol-fed groups compared with the non-alcohol-fed groups. Exercise had no significant effect on cancellous or cortical bone measurements. CONCLUSIONS: Chronic alcohol consumption significantly reduced bone formation. Exercise had no effect on the bone and did not attenuate any of the negative effects of alcohol. The results suggest that alcohol consumption weakens the skeleton and increases the incidence of endurance-exercise-related bone injuries. Thus, individuals who are participating in endurance exercise and consuming alcohol may be at greater risk for exercise-related skeletal injuries. Further investigation of the potential for alcohol to induce detrimental effects on the hearts of individuals participating in endurance exercise is indicated.

NASA Discipline Cell Biology↗