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Results for “Stochastic Processes”

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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Atmospheric Infrared Sounder Version 7 Near-Real-Time Product and Imagery Released by NASA GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been the home of data processing, archive, and distribution services for data from the Atmospheric Infrared Sounder (AIRS) mission since its launch in 2002. The GES DISC provides service to both AIRS routine and Near Real-Time (NRT) products. The AIRS NRT products are an important element in the Land, Atmosphere Near real-time Capability for EOS (LANCE). In collaboration with AIRS Project, the GES DISC has just released products from the Version 7 algorithm. The new version algorithm provides significant improvements over the previous version. The most substantial advances are: improved consistency between day and night water vapor; improved total column ozone and temperature; improved infrared-only (IR-only) retrievals, especially in high latitude regions; an improved Stochastic Cloud Clearing Neural Network used as a first guess at the initial value in the iterative retrieval process; and removal of ambiguity in surface classification in the IR-only retrieval algorithm. In addition, the GES DISC produces AIRS NRT imagery. The AIRS NRT imagery are generated by mosaicking and mapping the available AIRS 6-minute retrieval granules to a global projection. The images are constantly refreshed when new granules are produced. The AIRS NRT Viewer and LANCE Worldview provide visualization services to online users for AIRS NRT imagery. The data products used to generate this imagery include atmospheric temperature, humidity, precipitation, cloud, Dust Score, CO, and SO2. In this presentation, we will demonstrate visualization of the AIRS NRT imagery from the new Version 7, and demonstrate some improvements over the previous version. Progress on improving the AIRS NRT imagery, a collaboration project with the AIRS Applications Development Team at NASA Jet Propulsion Laboratory (JPL), will also be presented.

Feng Ding↗
NASA NTRSDate not supplied

Flutter and Stochastic Gust Constraint Studies of the Undeflected CRM using MPhys

Aircraft design must ultimately address gust and flutter characteristics to achieve airworthiness. As modern aircraft continue to become more lightweight and flexible, these aeroelastic phenomena are more likely to drive the design process and should thus be considered early. Designing for ride quality due to typical gust spectra is also of interest to ensure a smooth passenger experience. The present work demonstrates the new implementation of stochastic gust constraints in MPhys/OpenMDAO, in addition to flutter and static stress constraints, among others. Design studies are shown for the NASA Common Research Model, including both fuel burn minimization as well as multiobjective optimization of fuel burn and landing gross weight. Two sets of optimization problems are covered, differing mainly by the gust quantity of interest, which include tip displacement and root bending moment. Fuel burn penalties imposed by the flutter and gust constraints are also discussed.

aeroelasticity↗
DOE OSTI2025

Compact in-vacuum gamma-ray spectrometer for high-repetition rate PW-class laser–matter interaction

With the advent of high repetition rate laser facilities, novel diagnostic tools compatible with these advanced specifications are required. This paper presents the design of an active gamma-ray spectrometer intended for these high repetition rate experiments, with particular emphasis on functionality within a PW level laser-plasma interaction chamber’s extreme conditions. The spectrometer uses stacked scintillators to accommodate a broad range of gamma-ray energies, demonstrating its adaptability for various experimental setups. In addition, it has been engineered to maintain compactness, electromagnetic pulse resistance, and ISO-5 cleanliness requirements while ensuring high sensitivity. The spectrometer has been tested in real conditions inside the PW-class level interaction chamber at the BELLA center, LBNL. The paper further details the calibration process, which utilizes a 60 Co radioactive source, and describes the unfolding technique implemented through a stochastic minimization method.

47 OTHER INSTRUMENTATION↗
NASA NTRS1983

Measurements and predictions for nonevaporating sprays in a quiescent environment

Yule et al. (1982) have conducted a study of vaporizing sprays with the aid of laser techniques. The present investigation has the objective to supplement the measurements performed by Yule et al., by considering the limiting case of a spray in a stagnant environment. Mean and fluctuating velocities of the continuous phase are measured by means of laser Doppler anemometry (LDA) techniques, while Fraunhofer diffraction and slide impaction methods are employed to determine drop sizes. Liquid fluxes in the spray are found by making use of an isokinetic sampling probe. The obtained data are used as a basis for the evaluation of three models of the process, including a locally homogeneous flow (LHF) model, a deterministic separated flow (DSF) model, and a stochastic separated flow (SSF) model. It is found that the LHF and DSF models do not provide very satisfactory predictions for the test sprays, while the SSF model does provide reasonably good predictions of the observed structure.

Solomon, A. S. P.↗
NASA NTRS1992

Observations on the Proper Orthogonal Decomposition

The Proper Orthogonal Decomposition (P.O.D.), also known as the Karhunen-Loeve expansion, is a procedure for decomposing a stochastic field in an L(2) optimal sense. It is used in diverse disciplines from image processing to turbulence. Recently the P.O.D. is receiving much attention as a tool for studying dynamics of systems in infinite dimensional space. This paper reviews the mathematical fundamentals of this theory. Also included are results on the span of the eigenfunction basis, a geometric corollary due to Chebyshev's inequality and a relation between the P.O.D. symmetry and ergodicity.

Berkooz, Gal↗
NASA NTRS1994

Theory review: Line-driven instability and other causes of structure and variability in hot-star winds

The winds of the hot, luminous, OB stars are driven by the line-scattering of the star's continuum radiation flux. Several kinds of observational evidence indicate that such winds are highly structured and variable. This paper will review possible theoretical causes of such wind structure. For relatively small-scale, stochastic variability, I review the role of the strong intrinsic instability of the line-driving process itself. for larger scale structure, I describe recent efforts to examine how disturbances from the underlying, rotating star can be translated outward into propagating features in the wind.

Owocki, Stanley P.↗
NASA NTRS1980

Multispectral data acquisition and classification - Computer modeling for smart sensor design

In this paper a model of the processes involved in multispectral remote sensing and data classification is developed as a tool for designing and evaluating smart sensors. The model has both stochastic and deterministic elements and accounts for solar radiation, atmospheric radiative transfer, surface reflectance, sensor spectral reponses, and classification algorithms. Preliminary results are presented which indicate the validity and usefulness of this approach. Future capabilities of smart sensors will ultimately be limited by the accuracy with which multispectral remote sensing processes and their error sources can be computationally modeled.

Park, S. K.↗
NASA NTRSDate not supplied

Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.

quantum↗
NASA NTRS1982

Fokker-Planck response of stochastic satellites

The present investigation is concerned with the effects of stochastic geometry and random environmental torques on the pointing accuracy of spinning and three-axis stabilized satellites. The study of pointing accuracies requires a knowledge of the rates of error growth over and above any criteria for the asymptotic stability of the satellites. For this reason the investigation is oriented toward the determination of the statistical properties of the responses of the satellites. The geometries of the satellites are considered stochastic so as to have a phenomenological model of the motions of the flexible structural elements of the satellites. A widely used method of solving stochastic equations is the Fokker-Planck approach where the equations are assumed to define a Markoff process and the transition probability densities of the responses are computed directly as a function of time. The Fokker-Planck formulation is used to analyze the response vector of a rigid satellite.

Huang, T. C.↗
NASA NTRS1989

Probabilistic methods for robot motion determination

The outline of a research effort in probabilistic approaches to robot motion determination is presented. A heuristic path planner that has met with considerable success in the JPL telerobot testbed is presented together with an interpretation of its performance. The relevance of techniques from stochastic geometry and stochastic diffusion is addressed, as is the possibility of using sensor data directly in the motion determination process.

Balaram, J.↗
NASA NTRS2021

High Angular Resolution Gravitational Wave Astronomy

Since the very beginning of astronomy the location of objects on the sky has been a fundamental observational quantity that has been taken for granted. While precise two dimensional positional information is easy to obtain for observations in the electromagnetic spectrum, the positional accuracy of current and near future gravitational wave detectors is limited to between tens and hundreds of square degrees, which makes it extremely challenging to identify the host galaxies of gravitational wave events or to detect any electromagnetic counterparts. Gravitational wave observations provide information on source properties that is complementary to the information in any associated electromagnetic emission. Observing systems with multiple messengers thus has scientific potential much greater than the sum of its parts. A gravitational wave detector with higher angular resolution would significantly increase the prospects for finding the hosts of gravitational wave sources and triggering a multi-messenger follow-up campaign. An observatory with arcminute precision or better could be realised within the Voyage 2050 programme by creating a large baseline interferometer array in space and would have transformative scientific potential. Precise positional information of standard sirens would enable precision measurements of cosmological parameters and offer new insights on structure formation; a high angular resolution gravitational wave observatory would allow the detection of a stochastic background and resolution of the anisotropies within it; it would also allow the study of accretion processes around black holes; and it would have tremendous potential for tests of modified gravity and the discovery of physics beyond the Standard Model.

Gravitational waves↗
NASA NTRS1974

Parameter estimation for terrain modeling from gradient data

A method is developed for modeling terrain surfaces for use on an unmanned Martian roving vehicle. The modeling procedure employs a two-step process which uses gradient as well as height data in order to improve the accuracy of the model's gradient. Least square approximation is used in order to stochastically determine the parameters which describe the modeled surface. A complete error analysis of the modeling procedure is included which determines the effect of instrumental measurement errors on the model's accuracy. Computer simulation is used as a means of testing the entire modeling process which includes the acquisition of data points, the two-step modeling process and the error analysis. Finally, to illustrate the procedure, a numerical example is included.

Dangelo, K. R.↗
NASA NTRS1986

A stochastic model for particle impingements on orbiting spacecraft

A general methodology for simulating particle impingements on orbiting spacecraft is developed. Major steps in the modeling process are presented as (1) modeling objective, (2) construction of the spacecraft geometrical model, (3) simulation of the particles in the space environment, (4) particle impact and subsequent events of interest, and (5) results of the simulation. A simulation of the expected meteoroid impingements on the Hubble Space Telescope and the resulting angular momentum transfers which can cause telescope pointing disturbances is given to illustrate these methods.

Howell, L. W., Jr.↗
NASA NTRS1986

Utilizing remote sensing of Thematic Mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

LANDSAT thematic mapper (TM) data are being used to refine and validate a stochastic spatial computer model to be applied to coastal resource management problems in Louisiana. Two major aspects of the research are: (1) the measurement of area of land (or emergent vegetation) and water and the length of the interface between land and water in TM imagery of selected coastal wetlands (sample marshes); and (2) the comparison of spatial patterns of land and water in the sample marshes of the imagery to that in marshes simulated by a computer model. In addition to activities in these two areas, the potential use of a published autocorrelation statistic is analyzed.

Browder, J. A.↗
NASA NTRS1986

A stochastic model for particle impingements on orbiting spacecraft

A general methodology for simulating particle impingements on orbiting spacecraft is developed. Major steps in the modeling process are presented as (1) modeling objective, (2) construction of the spacecraft geometrical model, (3) simulation of the particles in the space environment, (4) particle impact and subsequent events of interest, and (5) results of the simulation. A simulation of the expected meteoroid impingements on the Hubble Space Telescope and the resulting angular momentum transfers which can cause telescope pointing disturbances is given to illustrate these methods.

Howell, Leonard W., Jr.↗
DOE OSTI2026

Optimisation of the Kaplan hydropower system via PID 2 and digital twin

Here, this paper proposes a proportional–integral-double–derivative (PID 2 ) optimisation method for the Kaplan hydropower system by building a digital twin. The study first uses one multilayer perceptron (MLP) to model the hydroturbine dynamic and then adopts three connected MLPs to model the generator dynamic, both in an open-loop fashion. Inspired by stochastic distribution control (SDC) theory, we regard the training of the turbine's neural network model as a process control problem, and we propose minimising entropy loss to update the network parameters. The next step is to build the digital twin by connecting the neural network models with a PID 2 controller and a lead-lag exciter and run the whole model in a closed-loop fashion. After that, a binary search approach is applied to optimise the PID 2 parameters based on the obtained digital twin model. The simulation results show that the proposed method can reduce the mean square tracking error by more than 90%. Furthermore, the method is extended to jointly optimise the PID 2 controller and excitation system gains through multiobjective optimisation, leveraging Pareto frontier analysis to balance active power and voltage tracking performance. Simulation results confirm the effectiveness of the proposed method, achieving a 83.46% reduction in relative mean square error of active power, a 47.13% reduction in terminal voltage tracking error, and an 82.78% improvement in the overall scalarized objective.

Hydropower system↗