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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Selective attention and the auditory vertex potential. 2: Effects of signal intensity and masking noise

A randomized sequence of tone bursts was delivered to subjects at short inter-stimulus intervals with the tones originating from one of three spatially and frequency specific channels. The subject's task was to count the tones in one of the three channels at a time, ignoring the other two, and press a button after each tenth tone. In different conditions, tones were given at high and low intensities and with or without a background white noise to mask the tones. The N sub 1 component of the auditory vertex potential was found to be larger in response to attended channel tones in relation to unattended tones. This selective enhancement of N sub 1 was minimal for loud tones presented without noise and increased markedly for the lower tone intensity and in noise added conditions.

Schwent, V. L.↗
NASA NTRS1982

The noise of BL Lacertae

Results are presented from an intensive optical and IR monitoring program of the flux and polarization characteristics of BL Lac. It is found that the polarization variations increase in amplitude with increasing time interval, and that the path traced out by the polarization vector in the Q-U plane is a random walk. In view of earlier measurements of BL Lac, the polarization fluctuations can be represented at low frequencies by the flat power spectrum of white noise, up to a frequency of 0.05 cycles/day. Above this frequency, the spectrum steepens to that of a random walk. A model for BL Lac suggested by the polarimetric noise can be constructed from independent sources of light with randomly oriented, strong polarization. Small random differences in spectral index from source to source could also explain the variable wavelength dependence of polarization.

Moore, R. L.↗
NASA NTRS1997

Response Classification Images in Vernier Acuity

Orientation selective and local sign mechanisms have been proposed as the basis for vernier acuity judgments. Linear image features contributing to discrimination can be determined for a two choice task by adding external noise to the images and then averaging the noises separately for the four types of stimulus/response trials. This method is applied to a vernier acuity task with different spatial separations to compare the predictions of the two theories. Three well-practiced observers were presented around 5000 trials of a vernier stimulus consisting of two dark horizontal lines (5 min by 0.3 min) within additive low-contrast white noise. Two spatial separations were tested, abutting and a 10 min horizontal separation. The task was to determine whether the target lines were aligned or vertically offset. The noises were averaged separately for the four stimulus/response trial types (e.g., stimulus = offset, response = aligned). The sum of the two 'not aligned' images was then subtracted from the sum of the 'aligned' images to obtain an overall image. Spatially smoothed images were quantized according to expected variability in the smoothed images to allow estimation of the statistical significance of image features. The response images from the 10 min separation condition are consistent with the local sign theory, having the appearance of two linear operators measuring vertical position with opposite sign. The images from the abutting stimulus have the same appearance with the two operators closer together. The image predicted by an oriented filter model is similar, but has its greatest weight in the abutting region, while the response images fall to nonsignificance there. The response correlation image method, previously demonstrated for letter discrimination, clarifies the features used in vernier acuity.

Ahumada, Albert J., Jr.↗
NASA NTRS2020

The Atacama Cosmology Telescope: arcminute-resolution maps of 18,000 square degrees of the microwave sky from ACT 2008-2018 data combined with Planck

This paper presents a maximum-likelihood algorithm for combining sky maps with disparate sky coverage, angular resolution and spatially varying anisotropic noise into a single map of the sky. We use this to merge hundreds of individual maps covering the 2008–2018 ACT observing seasons, resulting in by far the deepest ACT maps released so far. We also combine the maps with the full Planck maps, resulting in maps that have the best features of both Planck and ACT: Planck’s nearly white noise on intermediate and large angular scales and ACT’s high-resolution and sensitivity on small angular scales. The maps cover over 18 000 square degrees, nearly half the full sky, at 100, 150 and 220 GHz. They reveal 4 000 optically-confirmed clusters through the Sunyaev Zel’dovich effect (SZ) and 18 500 point source candidates at > 5σ, the largest single collection of SZ clusters and millimeter wave sources to date. The multi-frequency maps provide millimeter images of nearby galaxies and individual Milky Way nebulae, and even clear detections of several nearby stars. Other anticipated uses of these maps include, for example, thermal SZ and kinematic SZ cluster stacking, CMB cluster lensing and galactic dust science. The method itself has negligible bias. However, due to the preliminary nature of some of the component data sets, we caution that these maps should not be used for precision cosmological analysis. The maps are part of ACT DR5, and will be made available on LAMBDA no later than three months after the journal publication of this article, along with an interactive sky atlas.

Sigurd Naess↗
NASA NTRS1992

Temporal averaging in a turbulent environment - Compensation for phase drifts in phase shifting interferometry

In an effort to improve the signal to noise in an interference experiment, we have developed a method to remove systematic phase drift between data sets acquired over long time intervals. Using this technique, it is possible to average repeatedly acquired phase measurements and improve the phase estimate without sacrificing spatial resolution. Results from tests using real-time phase stepping holographic interferometry applied to cantilever bending of a piezoelectric bimorph indicate that white noise has been reduced from 3 to less than 1 deg (lambda/360) by averaging 36 phase compensated data sets before object bending and 36 data sets after bending.

Ovryn, B.↗
NASA NTRS1976

Selective attention and the auditory vertex potential. I - Effects of stimulus delivery rate. II - Effects of signal intensity and masking noise

The effects of varying the rate of delivery of dichotic tone pip stimuli on selective attention measured by evoked-potential amplitudes and signal detectability scores were studied. The subjects attended to one channel (ear) of tones, ignored the other, and pressed a button whenever occasional targets - tones of a slightly higher pitch were detected in the attended ear. Under separate conditions, randomized interstimulus intervals were short, medium, and long. Another study compared the effects of attention on the N1 component of the auditory evoked potential for tone pips presented alone and when white noise was added to make the tones barely above detectability threshold in a three-channel listening task. Major conclusions are that (1) N1 is enlarged to stimuli in an attended channel only in the short interstimulus interval condition (averaging 350 msec), (2) N1 and P3 are related to different modes of selective attention, and (3) attention selectivity in multichannel listening task is greater when tones are faint and/or difficult to detect.

Schwent, V. L.↗
NASA NTRS2000

The Impact of ENSO on Extratropical Low Frequency Noise in Seasonal Forecasts

This study examines the uncertainty in forecasts of the January-February-March (JFM) mean extratropical circulation, and how that uncertainty is modulated by the El Nino/Southern Oscillation (ENSO). The analysis is based on ensembles of hindcasts made with an Atmospheric General Circulation Model (AGCM) forced with sea surface temperatures observed during; the 1983 El Nino and 1989 La Nina events. The AGCM produces pronounced interannual differences in the magnitude of the extratropical seasonal mean noise (intra-ensemble variability). The North Pacific, in particular, shows extensive regions where the 1989 seasonal mean noise kinetic energy (SKE), which is dominated by a "PNA-like" spatial structure, is more than twice that of the 1983 forecasts. The larger SKE in 1989 is associated with a larger than normal barotropic conversion of kinetic energy from the mean Pacific jet to the seasonal mean noise. The generation of SKE due to sub-monthly transients also shows substantial interannual differences, though these are much smaller than the differences in the mean flow conversions. An analysis of the Generation of monthly mean noise kinetic energy (NIKE) and its variability suggests that the seasonal mean noise is predominantly a statistical residue of variability resulting from dynamical processes operating on monthly and shorter times scales. A stochastically-forced barotropic model (linearized about the AGCM's 1983 and 1989 base states) is used to further assess the role of the basic state, submonthly transients, and tropical forcing, in modulating the uncertainties in the seasonal AGCM forecasts. When forced globally with spatially-white noise, the linear model generates much larger variance for the 1989 base state, consistent with the AGCM results. The extratropical variability for the 1989 base state is dominanted by a single eigenmode, and is strongly coupled with forcing over tropical western Pacific and the Indian Ocean, again consistent with the AGCM results. Linear calculations that include forcing from the AGCM variance of the tropical forcing and submonthly transients show a small impact on the variability over the Pacific/North American region compared with that of the base state differences.

Schubert, Siegfried D.↗
NASA NTRS1987

Validation of an interior noise prediction model for a composite cylinder

An acoustic modal analysis has been performed in the cavity of a composite cylinder model of an aircraft fuselage. The filament wound, composite shell is 12 feet long and 5.5 feet in diameter. A one-half-in. thick plywood floor is attached to the shell 69 deg from the vertical centerline through the bottom of the shell. The acoustic modal frequencies were obtained from a sound pressure level and phase survey conducted throughout the interior volume bounded by the floor, endcaps and stiffened shell, while being excited by white noise from a loudspeaker source. The measured acoustic resonance frequencies and mode shapes compare well with analytical predictions from the Propeller Aircraft Interior Noise (PAIN) model. Details of the theory and derivation of the acoustic characteristics have been included. Reverberation time measurements, using the integrated impulse technique, have been performed to determine acoustic loss factors. These measured loss factors have been input to the PAIN program in order to more accurately predict the space-averaged interior noise of the composite cylinder.

Beyer, Todd B.↗
NASA NTRS1982

Noise transmission into semicylindrical enclosures through discretely stiffened curved panels

An analytical study of sound transmission into semicylindrical enclosures through discretely stiffened curved elastic panels is presented. The transmitted sound is estimated by solving the acoustic wave equation for the interior acoustic field, a Galerkin-like method being used. This solution is then coupled to the vibration of the stiffened panels. The response characteristics of these panels are determined by using a modal analysis where the modes are obtained by the finite element-strip method. Numerical results include spectra of the interior sound pressure due to white noise, turbulent boundary layer and propeller noise inputs.

Chang, M. T.↗
NASA NTRS1997

Accurate Magnetometer/Gyroscope Attitudes Using a Filter with Correlated Sensor Noise

Magnetometers and gyroscopes have been shown to provide very accurate attitudes for a variety of spacecraft. These results have been obtained, however, using a batch-least-squares algorithm and long periods of data. For use in onboard applications, attitudes are best determined using sequential estimators such as the Kalman filter. When a filter is used to determine attitudes using magnetometer and gyroscope data for input, the resulting accuracy is limited by both the sensor accuracies and errors inherent in the Earth magnetic field model. The Kalman filter accounts for the random component by modeling the magnetometer and gyroscope errors as white noise processes. However, even when these tuning parameters are physically realistic, the rate biases (included in the state vector) have been found to show systematic oscillations. These are attributed to the field model errors. If the gyroscope noise is sufficiently small, the tuned filter 'memory' will be long compared to the orbital period. In this case, the variations in the rate bias induced by field model errors are substantially reduced. Mistuning the filter to have a short memory time leads to strongly oscillating rate biases and increased attitude errors. To reduce the effect of the magnetic field model errors, these errors are estimated within the filter and used to correct the reference model. An exponentially-correlated noise model is used to represent the filter estimate of the systematic error. Results from several test cases using in-flight data from the Compton Gamma Ray Observatory are presented. These tests emphasize magnetometer errors, but the method is generally applicable to any sensor subject to a combination of random and systematic noise.

Sedlak, J.↗
NASA NTRS2017

Time and Measurement Days

Questions in data analysis involving the concepts of time and measurement are often pushed into the background or reserved for a philosophical discussion. Some examples are: a) Is causality a consequence of the laws of physics, or can the arrow of time be reversed? b) Can we determine the arrow of time of an event? c) Do we need the continuum hypothesis for the underlying function in any measurement process? d) Can we say anything about the analyticity of the underlying process of an event? e) Would it be valid to model a non-analytical process as function of time? f) What are the implications of all these questions for classical Fourier techniques? However, in the age of big data gathered either from space missions supplying ultra-precise long time series, or e.g. LIGO data from the ground, the moment to bring these questions to the foreground seems arrived. The limitations of our understanding of some fundamental processes is emphasized by the lack of solution for problems open for more than 2 decades, such as the non-detection of solar g-modes, or the modal identification of main sequence stellar pulsators like delta Scuti stars. Flicker noise or 1/f noise, for example, attributed in solar-like stars to granulation, is analyzed mostly only to apply noise reduction techniques, neither considering the classical problem of 1/f noise that was introduced a 100 years ago, nor taking into account ergodic or non-ergodic solutions that make inapplicable spectral analysis techniques in practice. This topic was discussed by Nicholas W. Watkins during the ITISE meeting held in Granada in 2016. There he presented preliminary results of his research on Mandelbrot's related work. We reproduce here his quotation of Mandelbrot (1999) "There is a sharp contrast between a highly anomalous ("non-white") noise that proceeds in ordinary clock time and a noise whose principal anomaly is that it is restricted to fractal time", suggesting a connection with the above proposed topics that could be phrased as the following additional questions:a) Is self-organized criticality (SOC) frequent in astrophysical phenomena? b) Could all fractals in nature be considered stochastic? c) Could we establish mathematical/physical relationships between chaotic and fractal behaviors in time series? d) Could the differences between fractals and chaos in terms of analyticity be used to understand the residuals of the fitting of stellar light curves? In this meeting we would like to approximate these problems from a holistic and multidisciplinary perspective, taking into account not only technical issues but also the deeper implications. In particular the concept of connectivity (introduced in Pascual-Granado et al. A&A, 2015) could be used to implement, within the framework of ARMA processes, an "arrow of time" (see attached document), and so studying the possible implications in the concept of time as envisaged by Watkins.

data analysis↗
DOE OSTI2024

Weak-Form Latent Space Dynamics Identification

This software showcases the enhanced capabilities of the Latent Space Dynamics Identification (LaSDI) algorithm through the application of the weak form, resulting in WLaSDI. WLaSDI first compresses the data, then projects it onto test functions, and subsequently learns the local latent space models. Notably, WLaSDI demonstrates significantly improved robustness to noise. Using weak-form equation learning techniques, WLaSDI achieves local latent space modeling. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form ensures robust and precise latent space recovery, enabling fast, robust, and accurate simulations. We demonstrate the efficacy of WLaSDI against LaSDI using several common benchmark examples, including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in 1D inviscid Burgers' simulations with up to 100% Gaussian white noise, WLaSDI maintains relative errors consistently below 6%, whereas LaSDI errors can exceed 10,000%. Similarly, in radial advection simulations, WLaSDI keeps relative errors below 16%, compared to potential errors of up to 10,000% with LaSDI. Additionally, WLaSDI achieves significant speedups, such as a 140X speedup in 1D Burgers' simulations compared to the corresponding full order model.

Choi, Youngsoo↗
NASA NTRS1991

Comparison of different fractal dimension measuring algorithms for RE-TM M-O films

Noise in magneto-optical recording devices is discussed. In general, it appears that either the divider technique or amplitude spectrum technique may be used interchangeably to measure the fractal dimension (D) in the domain wall structure of ideal images. However, some caveats must be observed for best results. The divider technique is attractive for its simplicity and relatively modest computation requirements. However, it is sensitive to noise, in that noise pixels that touch the domain boundary are interpreted as being part of the boundary, skewing the measurement. Also, it is not useful in measuring nucleation-dominated films or domains that have significant amounts of structure within the interior of the domain wall. The amplitude spectrum method is more complex, and less intuitive than the divider method, and somewhat more expensive to implement computationally. However, since the camera noise tends to be white, the noise can be avoided in the measurement of D by avoiding that portion of the curve that is flat (due to the white noise) when the least squares line is fit to the plot. Also, many image processing software packages include a Fast Fourier Transformation (FFT) facility, while the user will most likely have to write his own edge extraction routine for the divider method. The amplitude spectrum method is a true two dimensional technique that probes the interior of the domain wall, and in fact, can measure arbitrary clusters of domains. It can also be used to measure grey-level images, further reducing processing steps needed to threshold the image.

Bernacki, Bruce E.↗
NASA NTRS1975

Estimation and Analysis of Nonlinear Stochastic Systems

The algebraic and geometric structures of certain classes of nonlinear stochastic systems were exploited in order to obtain useful stability and estimation results. The class of bilinear stochastic systems (or linear systems with multiplicative noise) was discussed. The stochastic stability of bilinear systems driven by colored noise was considered. Approximate methods for obtaining sufficient conditions for the stochastic stability of bilinear systems evolving on general Lie groups were discussed. Two classes of estimation problems involving bilinear systems were considered. It was proved that, for systems described by certain types of Volterra series expansions or by certain bilinear equations evolving on nilpotent or solvable Lie groups, the optimal conditional mean estimator consists of a finite dimensional nonlinear set of equations. The theory of harmonic analysis was used to derive suboptimal estimators for bilinear systems driven by white noise which evolve on compact Lie groups or homogeneous spaces.

Marcus, S. I.↗
NASA NTRS1986

An Investigation of Energy Transmission Due to Flexural Wave Propagation in Lightweight, Built-Up Structures

A technique to measure flexural structure-borne noise intensity is investigated. Two accelerometers serve as transducers in this cross-spectral technique. The structure-borne sound power is obtained by two different techniques and compared. In the first method, a contour integral of intensity is performed from the values provided by the two-accelerometer intensity technique. In the second method, input power is calculated directly from the output of force and acceleration transducers. A plate and two beams were the subjects of the sound power comparisons. Excitation for the structures was either band-limited white noise or a deterministic signal similar to a swept sine. The two-accelerometer method was found to be sharply limited by near field and transducer spacing limitations. In addition, for the lightweight structures investigated, it was found that the probe inertia can have a significant influence on the power input to the structure. In addition to the experimental investigation of structure-borne sound energy, an extensive study of the point harmonically forced, point-damped beam boundary value problem was performed to gain insight into measurements of this nature. The intensity formulations were also incorporated into the finite element method. Intensity mappings were obtained analytically via finite element modeling of simple structures.

Mickol, John Douglas↗
DOE OSTI2024

Weak-form latent space dynamics identification

Recent work in data-driven modeling has demonstrated that a weak formulation of model equations enhances the noise robustness of a wide range of computational methods. In this paper, we demonstrate the power of the weak form to enhance the LaSDI (Latent Space Dynamics Identification) algorithm, a recently developed data-driven reduced order modeling technique. We introduce a weak form-based version WLaSDI (Weak-form Latent Space Dynamics Identification). WLaSDI first compresses data, then projects onto the test functions and learns the local latent space models. Notably, WLaSDI demonstrates significantly enhanced robustness to noise. With WLaSDI, the local latent space is obtained using weak-form equation learning techniques. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form guarantees a robust and precise latent space recovery, hence allowing for a fast, robust, and accurate simulation. We demonstrate the efficacy of WLaSDI vs. LaSDI on several common benchmark examples including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in the case of 1D inviscid Burgers' simulations with the addition of up to 100% Gaussian white noise, the relative error remains consistently below 6% for WLaSDI, while it can exceed 10,000% for LaSDI. Similarly, for radial advection simulations, the relative errors stay below 15% for WLaSDI, in stark contrast to the potential errors of up to 10,000% with LaSDI. Moreover, speedups of several orders of magnitude can be obtained with WLaSDI. For example applying WLaSDI to 1D Burgers' yields a 140X speedup compared to the corresponding full order model.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

Linear-Quadratic-Gaussian Regulator Developed for a Magnetic Bearing

Linear-Quadratic-Gaussian (LQG) control is a modern state-space technique for designing optimal dynamic regulators. It enables us to trade off regulation performance and control effort, and to take into account process and measurement noise. The Structural Mechanics and Dynamics Branch at the NASA Glenn Research Center has developed an LQG control for a fault-tolerant magnetic bearing suspension rig to optimize system performance and to reduce the sensor and processing noise. The LQG regulator consists of an optimal state-feedback gain and a Kalman state estimator. The first design step is to seek a state-feedback law that minimizes the cost function of regulation performance, which is measured by a quadratic performance criterion with user-specified weighting matrices, and to define the tradeoff between regulation performance and control effort. The next design step is to derive a state estimator using a Kalman filter because the optimal state feedback cannot be implemented without full state measurement. Since the Kalman filter is an optimal estimator when dealing with Gaussian white noise, it minimizes the asymptotic covariance of the estimation error.

Choi, Benjamin B.↗
DOE OSTI2024

Physics-Informed Active Learning With Simultaneous Weak-Form Latent Space Dynamics Identification

The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. Here, to enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared with the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5%–10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1%–7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1779x speed-up.

97 MATHEMATICS AND COMPUTING↗