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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

System identification of the Large-Angle Magnetic Suspension Test Facility (LAMSTF)

The Large-Angle Magnetic Suspension Test Facility (LAMSTF), a laboratory-scale research project to demonstrate the magnetic suspension of objects over wide ranges of attitudes, has been developed. This system represents a scaled model of a planned Large-Gap Magnetic Suspension System (LGMSS). The LAMSTF system consists of a planar array of five copper electromagnets which actively suspend a small cylindrical permanent magnet. The cylinder is a rigid body and can be controlled to move in five independent degrees of freedom. Five position variables are sensed indirectly by using infra-red light-emitting diodes and light-receiving phototransistors. The motion of the suspended cylinder is in general nonlinear and hence only the linear, time-invariant perturbed motion about an equilibrium state is considered. One of the main challenges in this project is the control of the suspended element over a wide range of orientations. An accurate dynamic model plans an essential role in controller design. The analytical model of the LAMSTF system includes highly unstable real poles (about 10 Hz) and low-frequency flexible modes (about 0.16 Hz). Projection filters are proposed to identify the state space model from closed-loop test data in time domain. A canonical transformation matrix is also derived to transform the identified state space model into the physical coordinate. The LAMSTF system is stabilized by using a linear quadratic regulator (LQR) feedback controller. The rate information is obtained by calculating the back difference of the sensed position signals. The reference inputs contain five uncorrelated random signals. This control input and the system reponse are recorded as input/output data to identify the system directly from the projection filters. The sampling time is 4 ms and the model is fairly accurate in predicting the step responses for different controllers while the analytical model has a deficiency in the pitch axis.

Huang, Jen-Kuang↗
DOE OSTI2024

Residual Stress in Cold Spray SS304L Measured Via Neutron Diffraction and Comparison of Analytical Models to Predict the Residual Stress

Here, this study employs neutron diffraction to investigate the relationship between residual stress and coating thickness in cold sprayed 304L austenitic stainless steel. Results show that shot peening predominantly impacts the residual stress profile, leading to substantial in-plane compressive force. The impact of laser heating, a widely used method to alter cold spray's microstructural properties, on the coating's residual stress is also analyzed. The findings indicate that the maximum compressive residual stress in the in-plane component is mainly independent of coating thickness, which suggests that the material properties determine the maximum residual stress. The cold sprayed deposits possessed compressive, nearly biaxial strain and stresses. After laser heating, these stresses were replaced by tensile residual stresses. Two analytical models, the Tsui and Clyne and the Boruah models, for predicting residual stresses are also evaluated, and both models provide reasonable fits to the experimental data. At this point, the deviations between the experimental results and the models are principally caused by the inability of the current models to address plastic deformation and relaxation, and the residual stresses generated by thermal gradients.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, in this work we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction (≈ O(-6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1977

Prelaunch testing of the laser geodynamic satellite (LAGEOS)

The LAGEOS was extensively tested optically prior to launch. The measurement techniques used are described and resulting data is presented. Principal emphasis was placed on pulse spreading characteristics, range correction for center of mass tracking, and pulse distortion due to coherent effects. A mode-locked freqeuncy doubled Nd:YAG laser with a pulse width of about 60 ps was used as the ranging transmitter and a crossfield photo-multiplier was used in the receiver. High speed sampling electronics were employed to increase receiver bandwidth. LAGEOS reflected pulses typically had a width of 250 ps with a variability in the range correction of less than 2 mm rms. Pulse distortion due to coherent effects was inferred from average waveforms and appears to introduce less than + or - 50 ps jitter in the location of the pulse peak. Analytic results on this effect based on computer simulations are also presented. Theoretical and experimental data on the lidar cross section were developed in order to predict the strength of lidar echoes from the satellite. Cross section was measured using a large aperture laser collimating system to illuminate the LAGEOS. Reflected radiation far-field patterns were measured using the collimator in an autocollimating mode. Data were collected with an optical data digitzer and displayed as a three-dimensional plot of intensity versus the two far-field coordinates. Measurements were made at several wavelengths, for several types of polarizations, and as a function of satellite orientation.

Fitzmaurice, M. W.↗
NASA NTRS1978

Characterization of the effects of metallic impurities on silicon solar cell performance

The effects of controlled concentrations of secondary impurities (Al, C, Ca, Cr, Cu, Fe, Mg, Mn, Mo, Ta, Ti, V, Zn, and Zr), incorporated alone or in combinations into Czochralski and float zone crystals, on the performance of silicon solar cells were investigated. Impurity concentrations ranged from 10 to the 11th to 10 to the 17th/cu cm. Solar cells were fabricated by a conventional diffusion process and characterized by computer reduction of current-voltage data. The data suggest that performance loss primarily results from reduction of the base diffusion length. On the basis of this assumption, a first order analytic model which predicts cell performance as a function of impurity concentrations is developed. Calculated performance parameters are in good agreement with observation, except for some impurities, such as Fe, Cu and Ni, which degrade cells via recombination and defects in the junction space-charge region. N-base devices are less affected by impurities, although degradation mechanisms appear to be the same as in p-devices. There seems to be very limited interaction between impurities in multiply-doped cells.

Davis, J. R.↗
NASA NTRS1980

Analytical investigation of aerodynamic characteristics of highly swept wings with separated flow

Many modern aircraft designed for supersonic speeds employ highly swept-back and low-aspect-ratio wings with sharp or thin edges. Flow separation occurs near the leading and tip edges of such wings at moderate to high angles of attack. Attempts have been made over the years to develop analytical methods for predicting the aerodynamic characteristics of such aircraft. Before any method can really be useful, it must be tested against a standard set of data to determine its capabilities and limitations. The present work undertakes such an investigation. Three methods are considered: the free-vortex-sheet method (Weber et al., 1975), the vortex-lattice method with suction analogy (Lamar and Gloss, 1975), and the quasi-vortex lattice method of Mehrotra (1977). Both flat and cambered wings of different configurations, for which experimental data are available, are studied and comparisons made.

Reddy, C. S.↗
NASA NTRS1984

Rapid discrimination of granitic rock compositions by low-resolution near-infrared reflectance

The slopes of near-infrared spectra between approximately 1 and 2 microns from quartz-bearing plutonic rocks are strongly correlated with rock chemistry determined by X-ray spectrometry. The empirically derived predictive equations provide compositional data of adequate precision and resolution to discern patterns of regional geochemical variation in granitic batholithic rocks of southern California. As an analytical method, infrared spectrometry is rapid and inexpensive, and the method has potential in applications to direct field measurements and to data from aircraft and spacecraft scanner systems of relatively low spectral and spatial resolution, provided vegetative cover and surface alteration are not prohibitively masking.

Baird, A. K.↗
NASA NTRS1987

Evaluation of capillary reinforced composites for anti-icing

This paper discusses the evaluation of glass capillary reinforced advanced composite structures for anti-icing purposes. The concept involves embedding glass capillary tubes on the surface of a composite structure and ducting heated air through the tubes. A computer program was developed to predict the anti-icing performance of such tubes and a test program was conducted to demonstrate the actual performance of this system. Test data and analytical code results were in excellent agreement. Both indicate the feasibility of using capillary tubes for surface heating in order to combat ice accumulation on advanced composite structures.

Ciardullo, Samuel W.↗
NASA NTRS1985

Combustor-diffuser interaction program

A collection of empirical and analytical design methodology on combustor-diffuser systems was completed. A fully elliptic code was selected for analyzing the combustor-diffuser interaction. Analytical model predictions were obtained for all flow conditions at which Phase 2 tests will be conducted. These results will be compared with laser doppler velocimeter data. Furthermore, a generalized nonorthogonal grid system is being developed to accurately simulate complex wall shapes.

Srinivasan, R.↗
NASA NTRS1987

Blade-vortex interaction noise predictions using measured blade surface pressures

The generation of helicopter noise by blade-vortex interactions during descent under impulsive conditions is investigated analytically. A noise-prediction technique is developed on the basis of the dipole source term of the Ffowcs-Williams/Hawkings equation and applied to data from simultaneous blade-pressure and acoustic measurements obtained by Cowan et al. (1986) on a 10-ft-diameter 4-blade rotor model in a wind tunnel. Preliminary results show that input-blade-airload azimuth resolution of 1 deg or better and computational azimuth step size of 2 deg or less are required to achieve good agreement between predicted and recorded acoustic time histories. The need for more sophisticated methods to model chordwise input data and for a more extensive experimental data base is indicated.

Ziegenbein, Perry R.↗
DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗
NASA NTRS1977

Analytical prediction of moisture absorption/desorption in resin matrix composites exposed to aircraft environments

The moisture absorption/desorption behavior of resin matrix composites was mathematically modeled by classical diffusion theory using an effective diffusion coefficient. Good agreement was found between calculated moisture content and published data for T300/5208 graphite fiber reinforced epoxy matrix composite. Weather Bureau data for Langley Air Force Base and Norfolk, Va., were used to calculate the amount of moisture a T300/5208 composite panel would contain if exposed outdoors. Results obtained by using average monthly weather data for several high aircraft usage locations around the world suggest that, except for desert areas, geographical locations should have only minimal effect on the moisture absorption level reached in composites. Solar radiation data together with cloud and wind information were included in the analysis to estimate an effective temperature of the composite panel during ground exposure.

Unnam, J.↗
DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗
NASA NTRS1982

The prediction of long term viscoelastic properties of fiber reinforced plastics

A method for the experimental and analytical accelerated characterization of long term viscoelastic properties is presented. The time-temperature-stress superposition principle (TTSSP) is shown to serve as the basis for determining long term compliance data from short term creep test results. Nonlinear viscoelastic theories are discussed to provide the framework for the mathematically modeling of such a process. A time dependent Tsai-Hill-Zhurkov failure theory is used to determine long term failure properties from short term data. Compliance and failure data are incorporated in an incremental lamination theory to make long term laminate predictions. Comparisons are made between theory and experiment.

Brinson, H. F.↗
NASA NTRS1974

A substructure energy method for prediction of Space Shuttle modal damping

The results of this program demonstrate the validity of a dissipative energy approach for predicting the damping of a four-component Space Shuttle model by means of modal parameters obtained from tests of the individual components. A relationship between modal damping energy per cycle and peak strain (or kinetic) energy is first determined empirically from test data for each component. Undamped analytical models of each component are also developed, and combined into a system model from which are obtained modal kinetic (or strain) energies for its respective modes. These data are then used with the empirical damping curves to apportion the proper amount of damping energy to each component in a combined system mode, and thereby allow a prediction of damping ratio.

Kana, D. D.↗