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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Constitutive properties of hypertrophied myocardium: cellular contribution to changes in myocardial stiffness

Recent studies have suggested that pressure overload hypertrophy (POH) alters the viscoelastic properties of individual cardiocytes when studied in isolation. However, whether these changes in cardiocyte properties contribute causally to changes in the material properties of the cardiac muscle as a whole is unknown. Accordingly, a selective, isolated, acute change in cardiocyte constitutive properties was imposed in an in vitro system capable of measuring the resultant effect on the material properties of the composite cardiac muscle. POH caused an increase in both myocardial elastic stiffness, from 20.5 +/- 1.3 to 28.4 +/- 1.8, and viscous damping, from 15.2 +/- 1.1 to 19.8 +/- 1.5 s (normal vs. POH, P < 0.05), respectively. Recent studies have shown that cardiocyte constitutive properties could be acutely altered by depolymerizing the microtubules with colchicine. Colchicine caused a significant decrease in the viscous damping in POH muscles (19.8 +/- 1.5 s at baseline vs. 14.7 +/- 1.3 s after colchicine, P < 0.05). Therefore, myocardial material properties can be altered by selectively changing the constitutive properties of one element within this muscle tissue, the cardiocyte. Changes in the constitutive properties of the cardiocytes themselves contribute to the abnormalities in myocardial stiffness and viscosity that develop during POH.

Non-NASA Center↗
NASA NTRS1999

Lamb wave characterization of the effects of long-term thermal-mechanical aging on composite stiffness

Lamb waves offer a promising method of evaluating damage in composite materials. The Lamb wave velocity is directly related to the material parameters, so an effective tool exists to monitor damage in composites by measuring the velocity of these waves. The Lamb Wave Imager (LWI) uses a pulse/receive technique that excites an antisymmetric Lamb mode and measures the time-of-flight over a wide frequency range. Given the material density and plate thickness, the bending and out-of-plane shear stiffnesses are calculated from a reconstruction of the dispersion curve. In this study, the time-of-flight as well as the elastic stiffnesses D11, D22, A44, and A55 for composite samples which have undergone combined thermal and mechanical aging are obtained. The samples examined include a baseline specimen with 0 cycles, specimens which have been aged 2350 and 3530 cycles at high strain levels, and one specimen aged 3530 cycles at low strain levels.

Aircraft↗
NASA NTRS2004

Synthesis, Characterization, and Modeling of Nanotube Materials with Variable Stiffness Tethers

Synthesis, mechanical testing, and modeling have been performed for carbon nanotube based materials. Tests using nanoindentation indicated a six-fold enhancement in the storage modulus when comparing the base material (no nanotubes) to the composite that contained 5.3 wt% of nanotubes. To understand how crosslinking the nanotubes may further alter the stiffness, a model of the system was constructed using nanotubes crosslinked with a variable stiffness tether (VST). The model predicted that for a composite with 5 wt% nanotubes at random orientations, crosslinked with the VST, the bulk Young's modulus was reduced by 30% compared to the noncrosslinked equivalent.

Frankland, S. J. V.↗
NASA NTRS1993

Influence of recreational activity and muscle strength on ulnar bending stiffness in men

Bone bending stiffness (modulus of elasticity [E] x moment of inertia [I]), a measure of bone strength, is related to its mineral content (BMC) and geometry and may be influenced by exercise. We evaluated the relationship of habitual recreational exercise and muscle strength to ulnar EI, width, and BMC in 51 healthy men, 28-61 yr of age. BMC and width were measured by single photon absorptiometry and EI by mechanical resistance tissue analysis. Maximum biceps strength was determined dynamically (1-RM) and grip strength isometrically. Subjects were classified as sedentary (S) (N = 13), moderately (M) (N = 18), or highly active (H) (N = 20) and exercised 0.2 +/- 0.2; 2.2 +/- 1.3; and 6.8 +/- 2.3 h.wk-1 (P < 0.001). H had greater biceps (P < 0.0005) and grip strength (P < 0.05), ulnar BMC (P < 0.05), and ulnar EI (P = 0.01) than M or S, who were similar. Amount of activity correlated with grip and biceps strength (r = 0.47 and 0.49; P < 0.001), but not with bone measurements, whereas muscle strength correlated with both EI and BMC (r = 0.40-0.52, P < 0.005). EI also correlated significantly with both BMC and ulnar width (P < 0.0001). Ulnar width and biceps strength were the only independent predictors of EI (r2 = 0.67, P < 0.0001). We conclude that levels of physical activity sufficient to increase arm strength influence ulnar bending stiffness.

NASA Discipline Musculoskeletal↗
NASA NTRS2003

Effect of High Impact or Non-impact Loading Activity on Bone Bending Stiffness and Mineral Density

Material properties of conical bone, including mineral density (BMD) and its geometry is closely related to its load-carrying capacity. These two primary components determine the strength of conical bone. High impact loading involving acceleration and deceleration movements used in gymnastics induce higher BMD of the affected bone compared to the non-impact acceleration and deceleration movements used in swimming. Study of these two groups of athletes on bone bending stiffness has not been reported. The purpose of this study was to compare differences in bone bending stiffness and BMD between competitive female synchronized swimmers and female gymnasts. Thirteen world class female synchronized swimmers (SYN) and 8 female gymnasts (GYM), mean age 21 +/- 2.9 yr. were recruited for this study. We used a mechanical response tissue analyzer (Gaitscan, NJ) to calculate EI, where E is Young's modulus of elasticity and I is the cross-sectional moment of inertia. EI was obtained from tissue response to a vibration probe placed directly on the skin of the mid-region of tibia and ulna. BMD of the heel and wrist were measured with a probe densitometer (PIXI, Lunor, WI). The SYN were taller than (p < 0.05) the GYM but weighed the same as the GYM. EI obtained from tibia and ulna of the SYN (291 +/- 159 and 41 +/- 19.4, respectively) were not significantly different from thc GYM (285 +/- 140 and 44 +/- 18.3, respectively). BMD of the heel and wrist in GYM were higher than in SYN (p < 0.001). High impact weight-bearing activities promote similar bone strength but greater BMD response than non-impact activities performed in a buoyant environment.

Liang, Michael T. C.↗
NASA NTRS2006

Gas Seal Pad With Herringbone-Grooved Rotor-Stiffness and Load Capacity

The principle of herringbone-grooved journal bearings has been applied to the case of a seal disc running under a finger seal pad. The inward pumping action of herringbone grooves on the disc generates load capacity and stiffness to maintain a fluid film and prevent contact of the pad and disc. This mechanism does not depend on a converging film under the pad, such as analyzed in previous works. Analysis shows that significant stiffness and load capacity can be supplied by herringbone grooves. In order for the grooves to be effective, the seal pressure drop must be taken outside of the grooved portion of the rotor, but this may be acceptable in order to gain freedom from maintaining a precise film convergence.

Flemming, David P.↗
NASA NTRS2010

Experimental Characterization and Analytical Comparison of Rolling Element Bearing Radial Stiffness

Rocket engine turbopumps often employ rolling element bearings for rotor support. These bearings frequently provide the primary mechanical interface between the stationary and rotating parts, and are relied upon to provide an adequate stiffness characteristic to ensure small rotor deflection due to a variety of sources of radial load. The rotordynamic design of a turbopump, however, requires a more detailed understanding of the bearing's load-deflection characteristic, to accurately predict critical speed location and response. Accordingly, engineers have developed tools to predict the load-deflection relationship for rolling element bearings. These tools allow the engineer to account for rolling element bearing geometric parameters as well as operational parameters, many of which have been shown to significantly influence a bearing's force-deflection characteristics. To that end, a static test rig has been designed, built, and tested to experimentally measure the radial stiffness of a rolling element bearing in a realistic mounting configuration. The objective of this paper is to report these experimental results and provide a comparison to analytical predictions.

Earhart, Eric↗
NASA NTRS2010

Stiffness and Damping Coefficient Estimation of Compliant Surface Gas Bearings for Oil-Free Turbomachinery

Foil gas bearings are a key technology in many commercial and emerging Oil-Free turbomachinery systems. These bearings are non-linear and have been difficult to analytically model in terms of performance characteristics such as load capacity, power loss, stiffness and damping. Previous investigations led to an empirically derived method, a rule-of-thumb, to estimate load capacity. This method has been a valuable tool in system development. The current paper extends this tool concept to include rules for stiffness and damping coefficient estimation. It is expected that these rules will further accelerate the development and deployment of advanced Oil-Free machines operating on foil gas bearings

DellaCorte, Christopher↗
NASA NTRS2012

Stiffness and Damping Coefficient Estimation of Compliant Surface Gas Bearings for Oil-Free Turbomachinery

Foil gas bearings are a key technology in many commercial and emerging oilfree turbomachinery systems. These bearings are nonlinear and have been difficult to analytically model in terms of performance characteristics such as load capacity, power loss, stiffness, and damping. Previous investigations led to an empirically derived method to estimate load capacity. This method has been a valuable tool in system development. The current work extends this tool concept to include rules for stiffness and damping coefficient estimation. It is expected that these rules will further accelerate the development and deployment of advanced oil-free machines operating on foil gas bearings.

Della-Corte, Christopher↗
NASA NTRS2011

Numerical Dissipation and Wrong Propagation Speed of Discontinuities for Stiff Source Terms

In compressible turbulent combustion/nonequilibrium flows, the constructions of numerical schemes for (a) stable and accurate simulation of turbulence with strong shocks, and (b) obtaining correct propagation speed of discontinuities for stiff reacting terms on coarse grids share one important ingredient - minimization of numerical dissipation while maintaining numerical stability. Here coarse grids means standard mesh density requirement for accurate simulation of typical non-reacting flows. This dual requirement to achieve both numerical stability and accuracy with zero or minimal use of numerical dissipation is most often conflicting for existing schemes that were designed for non-reacting flows. The goal of this paper is to relate numerical dissipations that are inherited in a selected set of high order shock-capturing schemes with the onset of wrong propagation speed of discontinuities for two representative stiff detonation wave problems.

Yee, H. C.↗
NASA NTRS2012

Numerical Dissipation and Wrong Propagation Speed of Discontinuities for Stiff Source Terms

In compressible turbulent combustion/nonequilibrium flows, the constructions of numerical schemes for (a) stable and accurate simulation of turbulence with strong shocks, and (b) obtaining correct propagation speed of discontinuities for stiff reacting terms on coarse grids share one important ingredient - minimization of numerical dissipation while maintaining numerical stability. Here coarse grids means standard mesh density requirement for accurate simulation of typical non-reacting flows. This dual requirement to achieve both numerical stability and accuracy with zero or minimal use of numerical dissipation is most often conflicting for existing schemes that were designed for non-reacting flows. The goal of this paper is to relate numerical dissipations that are inherited in a selected set of high order shock-capturing schemes with the onset of wrong propagation speed of discontinuities as a function of stiffness of the source term and the grid spacing.

Yee, H. C.↗
NASA NTRS2017

Influence of Shear Stiffness Degradation on Crack Paths in Uni-Directional Composite Laminates

Influence of shear stiffness degradation in an element, due to damage, on crack paths in uni-directional laminates has been demonstrated. A new shear stiffness degradation approach to improve crack path prediction has been developed and implemented in an ABAQUS/Explicit frame work using VUMAT. Three progressive failure analysis models, built-in ABAQUS (TradeMark), original COmplete STress Reduction (COSTR) and the modified COSTR damage models have been utilized in this study to simulate crack paths in five unidirectional notched laminates, 15deg, 30deg, 45deg, 60deg and 75deg under uniaxial tension load. Results such as crack paths and load vs. edge displacement curves are documented in this report. Modified COSTR damage model shows better accuracy in predicting crack paths in all the uni-directional laminates compared to the ABAQUS (TradeMark) and the original COSTR damage models.

Satyanarayana, Arunkumar↗
NASA NTRS2023

Prediction of Stiffness and Fatigue Lives of Polymer Matrix Composite Laminates Using Artificial Neural Networks

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, both Python and MATLAB-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 developed for both platforms. Results show that the both neural net types can provide an excellent estimate of initial stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminate. RNNs are better able to capture the shape of the fatigue curve of a laminate. This tool can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. The associated surrogate models could 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 multiscale analyses a viable industrial tool for large scale structural problems.

Composite↗
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 OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗
NASA NTRS1965

Stiffness matrix structural analysis

Computer program for stiffness of structure with lumped masses, weightless members, and many degrees of freedom - structural dynamics

COMPUTER PROGRAM↗