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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRS1992

Vibrational behavior of adaptive aircraft wing structures modelled as composite thin-walled beams

The vibrational behavior of cantilevered aircraft wings modeled as thin-walled beams and incorporating piezoelectric effects is studied. Based on the converse piezoelectric effect, the system of piezoelectric actuators conveniently located on the wing yield the control of its associated vertical and lateral bending eigenfrequencies. The possibility revealed by this study enabling one to increase adaptively the eigenfrequencies of thin-walled cantilevered beams could play a significant role in the control of the dynamic response and flutter of wing and rotor blade structures.

Song, O.↗
NASA NTRS1984

Elastic model of the traction behavior of two traction lubricants

In the analysis of rolling-sliding concentrated contacts, such as gears, bearings and traction drives, the traction characteristics of the lubricant are of prime importance. The elastic shear modulus and limiting shear stress properties of the lubricant dictate the traction/slip characteristics and power loss associated with an EHD contact undergoing slip and/or spin. These properties can be deducted directly from the initial slope m and maximum traction coefficient micron of an experimental traction curve. In this investigation, correlation equations are presented to predict m and micron for two modern traction fluids based on the regression analysis of 334 separate traction disk machine experiments. The effects of contact pressure, temperature, surface velocity, ellipticity ratio are examined. Problems in deducing lubricant shear moduli from disk machine tests are discussed. Previously announced in STAR as N83-20116

Loewenthal, S. H.↗
NASA NTRS1992

Experimental, physical and numerical modeling of lunar regolith and lunar regolith structures

An extensive series of laboratory strength and deformation experiments have been performed on a lunar regolith simulant. Results of these experiments are compared to results from experiments on real lunar regolith from the Apollo and Luna missions to illustrate the suitability of this material in capturing the engineering properties lunar regolith. In addition, these results are used to calibrate a constitutive model used to describe its stress-strain behavior. This model, in conjunction with numerical analysis techniques, is used to predict the response i.e. material parameters) of lunar simulant under 1/6-g and low confining stress conditions. These tools are also used to predict the displacement response of a lunar soil embankment structure used to cover a first generation human habitat module, which might be used to accommodate the first astronauts revisiting the moon. These predictions are compared to physical models of this structure, which are tested in a geotechnical centrifuge in order to satisfy scaling relationships between prototype and model.

Perkins, Steven W.↗
NASA NTRS2014

Attendee Response to Guiding Questions of the September 2014 DOE Workshop on North Slope of Alaska Priorities

We advocate for several modeling approaches suitable to prove understanding of and capability to model aerosol behavior in nucleating droplets and ice and to model regional cloud systems at the mesoscale. The full complement of data that is ideal for particular study types is increasingly well understood; the ASR ISDAC Science Plan provides an excellent example of listing the data required for each study type targeted by that campaign. Whereas discussion here focused on the microscale and mesoscale, other studies should obviously focus on the regional, seasonal and global scale. When considering ice microphysics alone, laser focus on unconstrained model components could be usefully intensified (e.g., collision-coalescence kernels). Similarly, a focus on boundary layer structure would be deeply valuable to a wide range of model types. The expense of observations motivates efficient planning for observational data sets to support specific study approaches; the concept of closure is often useful (e.g., the surface energy budget).

nucleation↗
NASA NTRS1991

Relaxation processes in a low-order three-dimensional magnetohydrodynamics model

The time asymptotic behavior of a Galerkin model of 3D magnetohydrodynamics (MHD) has been interpreted using the selective decay and dynamic alignment relaxation theories. A large number of simulations has been performed that scan a parameter space defined by the rugged ideal invariants, including energy, cross helicity, and magnetic helicity. It is concluded that time asymptotic state can be interpreted as a relaxation to minimum energy. A simple decay model, based on absolute equilibrium theory, is found to predict a mapping of initial onto time asymptotic states, and to accurately describe the long time behavior of the runs when magnetic helicity is present. Attention is also given to two processes, operating on time scales shorter than selective decay and dynamic alignment, in which the ratio of kinetic to magnetic energy relaxes to values 0(1). The faster of the two processes takes states initially dominant in magnetic energy to a state of near-equipartition between kinetic and magnetic energy through power law growth of kinetic energy. The other process takes states initially dominant in kinetic energy to the near-equipartitioned state through exponential growth of magnetic energy.

Stribling, Troy↗
NASA NTRS1976

A heuristic finite-state model of the human driver in a car-following situation

An approach to modeling human driver behavior in single-lane car following which is based on a finite-state decision structure is considered. The specific strategy at each point in the decision tree was obtained from observations of typical driver behavior. The synthesis of the decision logic is based on position and velocity thresholds and four states defined by regions in the phase plane. The performance of the resulting assumed intuitively logical model was compared with actual freeway data. The match of the model to the data was optimized by adapting the model parameters using a modified PARTAN algorithm. The results indicate that the heuristic model behavior matches actual car-following performance better during deceleration and constant velocity phases than during acceleration periods.

Burnham, G. O.↗
NASA NTRS1992

Reducing software mass through behavior control

Attention is given to the tradeoff between communication and computation as regards a planetary rover (both these subsystems are very power-intensive, and both can be the major driver of the rover's power subsystem, and therefore the minimum mass and size of the rover). Software techniques that can be used to reduce the requirements on both communciation and computation, allowing the overall robot mass to be greatly reduced, are discussed. Novel approaches to autonomous control, called behavior control, employ an entirely different approach, and for many tasks will yield a similar or superior level of autonomy to traditional control techniques, while greatly reducing the computational demand. Traditional systems have several expensive processes that operate serially, while behavior techniques employ robot capabilities that run in parallel. Traditional systems make extensive world models, while behavior control systems use minimal world models or none at all.

Miller, David P.↗
NASA NTRS1998

Long Term Degradation of Polyimide Composites

The durability of polymer matrix composites exposed to harsh environments is a major concern. Surface degradation and damage are observed in polyimide composites used in air at 125-300 C. It is believed that diffusion of oxygen into the material and oxidative chemical reactions are responsible. Previous work has characterized and modeled diffusion behavior, and thermogravimetric analyses (TGAS) have been carried out in nitrogen, air, and oxygen to provide quantitative information on thermal and oxidative reactions. However, the model developed using these data did not successfully extrapolate TGA data down to conditions seen in service. A test program that focuses on lower temperatures and makes use of isothermal tests was undertaken to achieve a better understanding of the degradation reactions under use conditions. A new, low-cost technique was developed to collect chemical degradation data for isothermal tests lasting over 200 hours in the temperature range 125-300 C. Results indicate complex behavior not captured by the previous model, including the presence of weight-adding reactions. Weight gain reactions dominated in the 125-225 C temperature range, while weight loss reactions dominated beyond 225 C. The data obtained from isothermal tests and earlier TGAs is used to develop an advanced model of the material behavior.

Patekar, Kaustubh A.↗
NASA NTRS1992

Magnetic flux relaxation in YBa2Cu3O(7-x) thin film: Thermal or athermal

The magnetic flux relaxation behavior of YBa2Cu3O(7-x) thin film on LaAlO3 for H parallel c was studied in the range of 4.2-40 k and 0.2-1.0 T. Both the normalized flux relaxation rate (S) and the net flux pinning energy (U) increase continuously from 1.3 x 10 exp -2 to 3.0 x 10 exp -2 and from 70-240 meV respectively, as the temperature (T) increases from 10 to 40 K. This behavior is consistent with the thermally activated flux motion model. At low temperatures, however, S is found to decrease much more slowly as compared with kT, in contradiction to the thermal activation model. This behavior is discussed in terms of the athermal quantum tunneling of flux lines. The magnetic field dependence of U, however, is not completely understood.

Vitta, Satish↗
NASA NTRS1991

Magnetic flux relaxation in YBa2Cu3)(7-x) thin film: Thermal or athermal

The magnetic flux relaxation behavior of YBa2Cu3O(7-x) thin film on LaAlO3 for H is parallel to c was studied in the range 4.2 - 40 K and 0.2 - 1.0 T. Both the normalized flux relaxation rate S and the net flux pinning energy U increase continuously from 1.3 x 10(exp -2) to 3.0 x 10(exp -2) and from 70 to 240 meV respectively, as the temperature T increases from 10 to 40 K. This behavior is consistent with the thermally activated flux motion model. At low temperatures, however, S is found to decrease much more slowly as compared with kT, in contradiction to the thermal activation model. This behavior is discussed in terms of the athermal quantum tunneling of flux lines. The magnetic field dependence of U, however, is not completely understood.

Vitta, Satish↗
NASA NTRS1992

Driven and unloading aspects of magnetospheric substorms

Issues concerning the 'driven' versus 'unloading' nature of substorms are presented. The original concepts attendant to this debate are presented and substorms are concluded to inextricably combine aspects that are driven with aspects that represent a loading-unloading system. For isolated substorms, the magnetosphere-ionosphere system is shown to exhibit a bimodal response to solar wind changes. A 20 min response characteristic is associated with the driven aspect of substorms, while a 1 hr response time is associated with unloading. It is found that for strong solar wind input conditions, the magnetospheric response becomes more nearly unimodal. This is interpreted in terms of a nonlinear dynamical evolution of the system. Simple analog models are described which capture the essence of the nonlinear magnetospheric behavior. These models exhibit chaotic transitions for strong driving conditions: this may explain the observed behavior of the magnetosphere during strong geomagnetic activity.

Baker, D. N.↗
NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗
NASA NTRS1995

Comparisons of the Maxwell and CLL gas/surface interaction models using DSMC

The behavior of two different models of gas-surface interactions is studied using the Direct Simulation Monte Carlo (DSMC) method. The DSMC calculations examine differences in predictions of aerodynamic forces and heat transfer between the Maxwell and the Cercignani-Lampis-Lord (CLL) models for flat plate configurations at freestream conditions corresponding to a 140 km orbit around Venus. The size of the flat plate represents one of the solar panels on the Magellan spacecraft, and the freestream conditions correspond to those experienced during aerobraking maneuvers. Results are presented for both a single flat plate and a two-plate configuration as a function of angle of attack and gas-surface accommodation coefficients. The two-plate system is not representative of the Magellan geometry but is studied to explore possible experiments that might be used to differentiate between the two gas-surface interaction models. The Maxwell and CLL models produce qualitatively similar results for the aerodynamic forces and heat transfer on a single flat plate. However, the flow fields produced with the two models are qualitatively different for both the single-plate and two-plate calculations. These differences in the flowfield lead to predictions of the angle of attack for maximum heat transfer in a two plate configuration that are distinctly different for the two gas-surface interactions models.

Hedahl, Marc O.↗
NASA NTRS1988

Unified constitutive model for single crystal deformation behavior with applications

Single crystal materials are being used in gas turbine airfoils and are candidates for other hot section components because of their increased temperature capabilities and resistance to thermal fatigue. Development of a constitutive model which assesses the inelastic behavior of these materials has been studied in 2 NASA programs: Life Prediction and Constitutive Models for Engine Hot Section Anisotropic Materials and Biaxial Constitutive Equation Development for Single Crystals. The model has been fit to a large body of constitutive data for single crystal PWA 1480 material. The model uses a unified approach for computing total inelastic strains (creep plus plasticity) on crystallographic slip systems reproducing observed directional and strain rate effects as a natural consequence of the summed slip system quantities. The model includes several of the effects that have been reported to influence deformation in single crystal materials, such as shear stress, latent hardening, and cross slip. The model is operational in a commercial Finite Element code and is being installed in a Boundary Element Method code.

Walker, K. P.↗