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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 109 records · Page 6

Solving inverse problems in stochastic models using deep neural networks and adversarial training

Inverse problems associated with stochastic models constitute a significant portion of scientific and engineering applications. In such cases the unknown quantities are distributions. The applicability of traditional methods is limited because of their demanding assumptions or prohibitive computational consumption; for example, maximum likelihood methods require closed-form density functions, and Markov Chain Monte Carlo needs a large number of simulations. We propose a new method that estimates the unknown distribution by matching the statistical properties between observed and simulated random processes. We leverage the expressive power of neural networks to approximate the unknown distribution and use a discriminative neural network for computing the statistical discrepancies between the observed and simulated random processes. Here we demonstrated numerically that the proposed methods can estimate both the model parameters and learn complicated unknown distributions.

42 ENGINEERING↗

A chaotic attractor in timing noise from the Vela pulsar?

Fourteen years of timing residual data from the Vela pulsar have been analyzed in order to determine if a chaotic dynamical process is the origin of timing noise. Using the correlation sum technique, a dimension of about 1.5 is obtained. This low dimension indicates underlying structure in the phase residuals which may be evidence for a chaotic attractor. It is therefore possible that nonlinear dynamics intrinsic to the spin-down may be the cause of the timing noise in the Vela pulsar. However, it has been found that the stimulated random walks in frequency and frequency derivative often used to model pulsar timing noise also have low fractal dimension, using the same analysis technique. Recent work suggesting that random processes with steep power spectra can mimic strange attractors seems to be confirmed in the case of these random walks. It appears that the correlation sum estimator for dimension is unable to distinguish between chaotic and random processes.

Harding, Alice K.↗

Power spectra of quasi-periodic oscillations in luminous X-ray stars

A variety of partial random processes are considered in order to study the effects on the power-density spectrum of several physical phenomena that may occur in the beat-frequency modulated accretion (BFMA) model of QPOs. The relevant physical properties of BFMA model are summarized, and the class of random processes used to represent the X-ray intensity time series predicted by the model are introduced. The dependence of the power-density spectrum on pulse shape is discussed. Pulse-lifetime and orbital-frequency distributions are introduced, illustrating how these affect the power-density spectrum. The effect of nonuniform initial-pulse phase distributions and of nonuniform pulse-time distributions are considered. The effects of a particular type of pulse-to-pulse correlation that may be natural in the context of the BFMA model and that greatly increases the power of the oscillations relative to that in the red noise is analyzed.

Shibazaki, N.↗

Predicting initial trans-membrane pressure across cycles in the ultrafiltration process using random forest

With growing freshwater scarcity, direct potable reuse (DPR) systems that reclaim wastewater for drinking are becoming increasingly important for sustainable water supply. Reliable operation requires minimizing downtime in ultrafiltration (UF) units, where membrane fouling leads to elevated trans-membrane pressure (TMP). This study develops data-driven regression models based on random forest (RF) and autoregressive (AR) approaches to forecast the initial TMP at the start of each UF filtration cycle in a pilot-scale DPR system. The RF model consistently outperforms baseline methods, including historical mean, last observation carried forward, and AR models, across multiple forecast horizons, achieving the lowest root mean square error. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent input variables across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is assessed for both direct and recursive RF modelling approaches. The proposed RF framework establishes a robust foundation for predictive monitoring and real-time optimization of UF operations, supporting sustainable and reliable water reuse.

direct potable reuse↗

Nonstationary atmospheric boundary layer turbulence simulation

Report on a new and general technique for simulating atmospheric turbulence-like random processes which are statistically homogeneous along the horizontal and nonhomogeneous along the vertical. This technique is general in the sense that it can be used for a broad class of similar problems. Like the other presently available schemes, the techniques presented are based on the Dryden hypothesis and Taylor's frozen eddy hypothesis; however, they go a step further by utilizing certain self-similarity properties of the Dryden spectral density function which permits the development of height invariant filters. These filters are in turn used to generate vertically homogeneous (statistically) random processes from which turbulence at any specified level in the boundary layer can be simulated, thus facilitating the simulation of a nonstationary turbulence process along the flight path of an aircraft during take-off or landing.

Fichtl, G. H.↗

Estimation of correlation functions by stochastic approximation.

Consideration of the autocorrelation function of a zero-mean stationary random process. The techniques are applicable to processes with nonzero mean provided the mean is estimated first and subtracted. Two recursive techniques are proposed, both of which are based on the method of stochastic approximation and assume a functional form for the correlation function that depends on a number of parameters that are recursively estimated from successive records. One technique uses a standard point estimator of the correlation function to provide estimates of the parameters that minimize the mean-square error between the point estimates and the parametric function. The other technique provides estimates of the parameters that maximize a likelihood function relating the parameters of the function to the random process. Examples are presented.

Habibi, A.↗

Evolution of a meteorite crater as a process of random displacements

An examination of the ages and sizes of 114 terrestrial impact craters shows that their aging kinetics can be described by the diffusion laws. The macrodiffusion coefficient which determines random displacements of mineral masses on the Earth has a mean value of 0.02 sq m/year. The amount of matter in a crater that contains information about the impact event decreases with time according to the 1/T law. The basic characteristic parameter of a crater is its initial area, inasmuch as sufficiently large craters are nearly surficial formations. The relaxation time of a crater is proportional to its initial area.

Zotkin, I. T.↗

Approximate effect of parameter pseudonoise intensity on rate of convergence for EKF parameter estimators

When using parameter estimation methods based on extended Kalman filter (EKF) theory, it is common practice to assume that the unknown parameter values behave like a random process, such as a random walk, in order to guarantee their identifiability by the filter. The present work is the result of an ongoing effort to quantitatively describe the effect that the assumption of a fictitious noise (called pseudonoise) driving the unknown parameter values has on the parameter estimate convergence rate in filter-based parameter estimators. The initial approach is to examine a first-order system described by one state variable with one parameter to be estimated. The intent is to derive analytical results for this simple system that might offer insight into the effect of the pseudonoise assumption for more complex systems. Such results would make it possible to predict the estimator error convergence behavior as a function of the assumed pseudonoise intensity, and this leads to the natural application of the results to the design of filter-based parameter estimators. The results obtained show that the analytical description of the convergence behavior is very difficult.

Hill, Bryon K.↗

Statistical error model for a solar electric propulsion thrust subsystem

The solar electric propulsion thrust subsystem statistical error model was developed as a tool for investigating the effects of thrust subsystem parameter uncertainties on navigation accuracy. The model is currently being used to evaluate the impact of electric engine parameter uncertainties on navigation system performance for a baseline mission to Encke's Comet in the 1980s. The data given represent the next generation in statistical error modeling for low-thrust applications. Principal improvements include the representation of thrust uncertainties and random process modeling in terms of random parametric variations in the thrust vector process for a multi-engine configuration.

Bantell, M. H.↗

A New Process for Fabricating Random Silicon Nanotips

An improved process for the fabrication of random arrays of silicon nanotips has been demonstrated to be feasible. Relative to other such processes, this process offers advantages of low cost and simplicity. Moreover, this process can readily be combined with other processes used to fabricate integrated circuits and other monolithic silicon structures. Arrays of silicon nanotips are subjects of research and development efforts directed toward utilizing them as field emitters in flat-panel displays, vacuum microelectronics, and microwave devices. Other silicon-nanotip-fabrication processes developed thus far predominantly include lithography, etching, and/or elaborate deposition steps followed by oxide sharpening steps and are both process intensive as well as expensive. In addition to being cheaper and simpler, the present process can efficiently produce silicon nanotips that range in height from a few microns to several tens of microns and are distributed over large areas. The process mentioned here can be summarized as consisting of (1) the growth of micro-etch masks on a silicon substrate, followed by (2) etching away of the masks, along with some of the substrate, to make an array of sharp tips. In the first step of the process, a cleaned silicon substrate is subjected to reactive ion etching (RIE) in a certain mixture of oxygen and carbon tetrafluoride under radio-frequency excitation. This process step results in the growth of fluorine based compounds in the form of stumps randomly distributed on the substrate. These stumps are known in the art as polymer RIE grass. The dimensions of these stumps are of the order of hundreds of nanometers, the exact values depending on process time and gas composition. The areal density of the stumps decreases with increasing process time as they grow and merge with neighboring stumps. These stumps constitute the micro-etch masks for the next step of the process. In the second step of the process, the substrate covered with the microetch masks is subjected to deep reactive ion etching (DRIE) process, which consists of cycles of reactive ion etching alternating with passivation (the Bosch process). The gas used in the etching substeps is sulfur hexafluoride (SF6); the gas used in the passivation substeps is octafluorocyclobutane (C4F8). The portions of the substrate directly under the RIE grass stubs are etched more slowly than are the portions between the stubs. Hence, what remains at the end of the process, after the stubs and parts of the substrate have been etched away, are silicon spikes where the stubs were (see figure). In a variation of the process, one starts with a silicon or silicon-on-insulator substrate with the intent to etch through the full thickness of the substrate. That is to say, one chooses the thickness so that the DRIE step releases individual nanotips. Such individual silicon nanotips may have utility as microscopic probes in biomedical applications.

Manohara, Harish↗

Exponential Bound for the Heating Rate of Periodically Driven Spin Systems

For the nuclear spin system of a solid in the presence of an inhomogeneous magnetic field, we have found a rigorous bound for the heating rate of the system under the action of a high-frequency magnetic field, which is applied, for example, to create effective Hamiltonians. We consider the autocorrelation function (ACF) of a spin rotating in a local field whose fluctuations are specified by a Gaussian random process. The correlation function of a random field is taken as the sum of a static inhomogeneous contribution and a time-dependent contribution expressed self-consistently via the spin ACF. The ACF singularities on the imaginary time axis whose coordinates determine the exponents of exponential asymptotics in the high-frequency domain are investigated. The dependences of the coordinates on field inhomogeneity for various approximations have been derived. The wing of the ACF spectrum in the cumulant approximation is shown to serve as a rigorous upper bound for the wing of the ACF spectrum and, consequently, for the heating rate of the system when subjected to variable magnetic fields. We have established that randomly distributed inhomogeneous magnetic fields increase the wings of the ACF spectra and, thus, speed up the system’s heating.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Principles of communication engineering.

Textbook on communication engineering emphasizing random processes, information and detection theory, statistical communication theory, applications, etc

COMMUNICATION THEORY↗

An Overdetermined System for Improved Autocorrelation Based Spectral Moment Estimator Performance

Autocorrelation based spectral moment estimators are typically derived using the Fourier transform relationship between the power spectrum and the autocorrelation function along with using either an assumed form of the autocorrelation function, e.g., Gaussian, or a generic complex form and applying properties of the characteristic function. Passarelli has used a series expansion of the general complex autocorrelation function and has expressed the coefficients in terms of central moments of the power spectrum. A truncation of this series will produce a closed system of equations which can be solved for the central moments of interest. The autocorrelation function at various lags is estimated from samples of the random process under observation. These estimates themselves are random variables and exhibit a bias and variance that is a function of the number of samples used in the estimates and the operational signal-to-noise ratio. This contributes to a degradation in performance of the moment estimators. This dissertation investigates the use autocorrelation function estimates at higher order lags to reduce the bias and standard deviation in spectral moment estimates. In particular, Passarelli's series expansion is cast in terms of an overdetermined system to form a framework under which the application of additional autocorrelation function estimates at higher order lags can be defined and assessed. The solution of the overdetermined system is the least squares solution. Furthermore, an overdetermined system can be solved for any moment or moments of interest and is not tied to a particular form of the power spectrum or corresponding autocorrelation function. As an application of this approach, autocorrelation based variance estimators are defined by a truncation of Passarelli's series expansion and applied to simulated Doppler weather radar returns which are characterized by a Gaussian shaped power spectrum. The performance of the variance estimators determined from a closed system is shown to improve through the application of additional autocorrelation lags in an overdetermined system. This improvement is greater in the narrowband spectrum region where the information is spread over more lags of the autocorrelation function. The number of lags needed in the overdetermined system is a function of the spectral width, the number of terms in the series expansion, the number of samples used in estimating the autocorrelation function, and the signal-to-noise ratio. The overdetermined system provides a robustness to the chosen variance estimator by expanding the region of spectral widths and signal-to-noise ratios over which the estimator can perform as compared to the closed system.

Keel, Byron M.↗

Spectral Ambiguity of Allan Variance

We study the extent to which knowledge of Allan variance and other finite-difference variances determines the spectrum of a random process. The variance of first differences is known to determine the spectrum. We show that, in general, the Allan variance does not. A complete description of the ambiguity is given.

Allan Variance variances finite-difference varianc↗

Probability Functions for Random Responses: Prediction of Peaks, Fatigue Damage, and Catastrophic Failures

This report reviews a number of theoretical matters in random process theory which can be applied to physical problems such as predicting peaks, structural fatigue damage, and catastrophic structural failures. The presentation emphasizes the basic assumptions which are involved, and discusses how to properly interpret the theoretical results. Various engineering examples are given as illustrations.

FATIGUE↗