Results for “predictive state estimation” — Page 35

NASA NTRS2010

Using Facility Condition Assessments to Identify Actions Related to Infrastructure

To support cost effective, quality research it is essential that laboratory and testing facilities are maintained in a continuous and reliable state of availability at all times. NASA Langley Research Center (LaRC) and its maintenance contractor, Jacobs Technology, Inc. Research Operations, Maintenance, and Engineering (ROME) group, are in the process of implementing a combined Facility Condition Assessment (FCA) and Reliability Centered Maintenance (RCM) program to improve asset management and overall reliability of testing equipment in facilities such as wind tunnels. Specific areas are being identified for improvement, the deferred maintenance cost is being estimated, and priority is being assigned against facilities where conditions have been allowed to deteriorate. This assessment serves to assist in determining where to commit available funds on the Center. RCM methodologies are being reviewed and enhanced to assure that appropriate preventive, predictive, and facilities/equipment acceptance techniques are incorporated to prolong lifecycle availability and assure reliability at minimum cost. The results from the program have been favorable, better enabling LaRC to manage assets prudently.

Rubert, Kennedy F.↗

NASA NTRS2012

Preliminary Exploration of Adaptive State Predictor Based Human Operator Modeling

Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to quantify the effects of changing aircraft dynamics on an operator s ability to track a signal in order to eventually model a pilot adapting to changing aircraft dynamics. A gradient descent estimator and a least squares estimator with exponential forgetting used these data to predict pilot stick input. The results indicate that individual pilot characteristics and vehicle dynamics did not affect the accuracy of either estimator method to estimate pilot stick input. These methods also were able to predict pilot stick input during changing aircraft dynamics and they may have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot.

Trujillo, Anna C.↗

NASA NTRS2008

Automated Method for Estimating Nutation Time Constant Model Parameters for Spacecraft Spinning on Axis

Calculating an accurate nutation time constant (NTC), or nutation rate of growth, for a spinning upper stage is important for ensuring mission success. Spacecraft nutation, or wobble, is caused by energy dissipation anywhere in the system. Propellant slosh in the spacecraft fuel tanks is the primary source for this dissipation and, if it is in a state of resonance, the NTC can become short enough to violate mission constraints. The Spinning Slosh Test Rig (SSTR) is a forced-motion spin table where fluid dynamic effects in full-scale fuel tanks can be tested in order to obtain key parameters used to calculate the NTC. We accomplish this by independently varying nutation frequency versus the spin rate and measuring force and torque responses on the tank. This method was used to predict parameters for the Genesis, Contour, and Stereo missions, whose tanks were mounted outboard from the spin axis. These parameters are incorporated into a mathematical model that uses mechanical analogs, such as pendulums and rotors, to simulate the force and torque resonances associated with fluid slosh.

Source record↗

NASA NTRS2002

Estimating Single-Trial Responses in EEG

Accurate characterization of single-trial field potential responses is critical from a number of perspectives. For example, it allows differentiation of an evoked response from ongoing EEG. We previously developed the multiple component Event Related Potential (mcERP) algorithm to improve resolution of the single-trial evoked response. The mcERP model states that multiple components, each specified by a stereotypic waveform varying in latency and amplitude from trial to trial, comprise the evoked response. Application of the mcERP algorithm to simulated data with three independent, synthetic components has shown that the model is capable of separating these components and estimating their variability. Application of the model to single trial, visual evoked potentials recorded simultaneously from all V1 laminae in an awake, fixating macaque yielded local and far-field components. Certain local components estimated by the model were distributed in both granular and supragranular laminae. This suggests a linear coupling between the responses of thalamo-recipient neuronal ensembles and subsequent responses of supragranular neuronal ensembles, as predicted by the feedforward anatomy of V1. Our results indicate that the mcERP algorithm provides a valid estimation of single-trial responses. This will enable analyses that depend on trial-to-trial variations and those that require separation of the evoked response from background EEG rhythms

Shah, A. S.↗

NASA NTRSDate not supplied

Adaptive Learning for Reliability Analysis using Support Vector Machines

A novel algorithm is presented for adaptive learning of an unknown function that separates two regions of a domain.In the context of reliability analysis these two regions represent the failure domain, where a set of constraints or requirements are violated, and a safe domain where they are satisfied. The Limit State Function (LSF) separates these two regions. Evaluating the constraints for a given parameter point requires the evaluation of a computational model that may well be expensive. For this reason we wish to construct a meta-model that can estimate the LSFas accurately as possible, using only a limited amount of training data. This work presents an adaptive strategy employing a Support Vector Machine (SVM) as a meta-model to provide a semi-algebraic approximation of the LSF.We describe an optimization process that is used to select informative parameter points to add to training data at each iteration to improve the accuracy of this approximation. A formulation is introduced for bounding the predictions of the meta-model; in this way we seek to incorporate this aspect of Gaussian Process Models (GPMs) within anSVM meta-model. Finally, we apply our algorithm to two benchmark test cases, demonstrating performance that is comparable with, if not superior, to a standard technique for reliability analysis that employs GPMs

Adaptive learning↗

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 NTRS1984

Relationship between Allan variances and Kalman Filter parameters

A relationship was constructed between the Allan variance parameters (H sub z, H sub 1, H sub 0, H sub -1 and H sub -2) and a Kalman Filter model that would be used to estimate and predict clock phase, frequency and frequency drift. To start with the meaning of those Allan Variance parameters and how they are arrived at for a given frequency source is reviewed. Although a subset of these parameters is arrived at by measuring phase as a function of time rather than as a spectral density, they all represent phase noise spectral density coefficients, though not necessarily that of a rational spectral density. The phase noise spectral density is then transformed into a time domain covariance model which can then be used to derive the Kalman Filter model parameters. Simulation results of that covariance model are presented and compared to clock uncertainties predicted by Allan variance parameters. A two state Kalman Filter model is then derived and the significance of each state is explained.

Vandierendonck, A. J.↗

NASA NTRS1985

An experiment to detect gut monopoles

Recent advances in the development of Grand Unification Theories have led to several interesting predictions. One of these states that Grand Unification Monopoles (GUMs) exist as solutions in may nonabelian gauge theories. Another consequence of Unification is the possibility of baryon decay. The efficiency of the water tank detector in registering a Rubakov type decay will vary with both the interaction length and the GUM's velocity, expressed in terms of beta ( 0.01). The efficiency decreases at large values of because of the limited resolving time of the detector (approx. 50 ns). At lower values of Beta the time between interactions is such that the criterion of 4 events in 2 mu s can no longer be satisfied. The Rubakov experiment has now been in operation for almost 2 years with an estimated live time of 80%. During this time no candidate events have been observed leading to an estimated upper limit on the flux of 7.82 x 0.00001 m(-2) d(-1) Sr(-1). The ionization loss detection system has only recently come on line and as yet no results are available from this experiment.

Macneill, G.↗

NASA NTRS2007

NASA's Potential Contributions for Remediation of Retention Ponds Using Solar Ultraviolet Radiation and Photocatalysis

This Candidate Solution uses NASA Earth science research on atmospheric ozone and aerosols data (1) to help improve the prediction capabilities of water runoff models that are used to estimate runoff pollution from retention ponds, and (2) to understand the pollutant removal contribution and potential of photocatalytically coated materials that could be used in these ponds. Models (the EPA's SWMM and the USGS SLAMM) exist that estimate the release of pollutants into the environment from storm-water-related retention pond runoff. UV irradiance data acquired from the satellite mission Aura and from the OMI Surface UV algorithm will be incorporated into these models to enhance their capabilities, not only by increasing the general understanding of retention pond function (both the efficacy and efficiency) but additionally by adding photocatalytic materials to these retention ponds, augmenting their performance. State and local officials who run pollution protection programs could then develop and implement photocatalytic technologies for water pollution control in retention ponds and use them in conjunction with existing runoff models. More effective decisions about water pollution protection programs could be made, the persistence and toxicity of waste generated could be minimized, and subsequently our natural water resources would be improved. This Candidate Solution is in alignment with the Water Management and Public Health National Applications.

Underwood, Lauren W.↗

NASA NTRS2008

Automatic Hazard Detection for Landers

Unmanned planetary landers to date have landed 'blind'; that is, without the benefit of onboard landing hazard detection and avoidance systems. This constrains landing site selection to very benign terrain,which in turn constrains the scientific agenda of missions. The state of the art Entry, Descent, and Landing (EDL) technology can land a spacecraft on Mars somewhere within a 20-100km landing ellipse.Landing ellipses are very likely to contain hazards such as craters, discontinuities, steep slopes, and large rocks, than can cause mission-fatal damage. We briefly review sensor options for landing hazard detection and identify a perception approach based on stereo vision and shadow analysis that addresses the broadest set of missions. Our approach fuses stereo vision and monocular shadow-based rock detection to maximize spacecraft safety. We summarize performance models for slope estimation and rock detection within this approach and validate those models experimentally. Instantiating our model of rock detection reliability for Mars predicts that this approach can reduce the probability of failed landing by at least a factor of 4 in any given terrain. We also describe a rock detector/mapper applied to large-high-resolution images from the Mars Reconnaissance Orbiter (MRO) for landing site characterization and selection for Mars missions.

hazard detection↗

NASA NTRS1988

Saturation and the limit of jet mixing enhancement by single frequency plane wave excitation: Experiment and theory

The limitations of single frequency plane wave excitation in mixing enhancement are investigated for a circular jet. Measurements made in an 8.8 cm diameter jet are compared with a theoretical model. The measurements are made to quantify mixing at excitation amplitudes up to 2 percent of the jet exit velocity. The initial boundary layer state, the exit mean and fluctuating velocity profiles and spectra are documented for all cases considered. The amplitude of the fundamental wave is recorded along the jet axis for various levels of excitation. As the amplitude of excitation is increased the jet spreading rate is increased, but beyond a saturation amplitude further increases have no effect on the spreading. The experimental results are compared with theoretical estimates. In the theory the flow is split into the mean flow, large scale motions, and fine scale turbulence. Shape assumptions for the mean flow, and fine scale turbulence along with the shape for the large scale motions obtained from a linear stability theory provide the closure. The experimental results compare reasonably well with predictions.

Raman, Ganesh↗

NASA NTRS1988

Saturation and the limit of jet mixing enhancement by single frequency plane wave excitation - Experiment and theory

The limitations of single frequency plane wave excitation in mixing enhancement are investigated for a circular jet. Measurements made in an 8.8 cm diameter jet are compared with a theoretical model. The measurements are made to quantify mixing at excitation amplitudes up to 2 percent of the jet exit velocity. The initial boundary layer state, the exit mean and fluctuating velocity profiles and spectra are documented for all cases considered. The amplitude of the fundamental wave is recorded along the jet axis for various levels of excitation. As the amplitude of excitation is increased the jet spreading rate is increased, but beyond a saturation amplitude further increases have no effect on the spreading. The experimental results are compared with theoretical estimates. In the theory the flow is split into the mean flow, large scale motions, and fine scale turbulence. Shape assumptions for the mean flow, and fine scale turbulence along with the shape for the large scale motions obtained from a linear stability theory provide the closure. The experimental results compare reasonably well with predictions.

Raman, Ganesh↗

NASA NTRSDate not supplied

Coaxial Rotor CFD Validation and ML Surrogate Model Generation

A fixed-pitch speed-controlled coaxial rotor system was tested in the NASA Langley Transonic Dynamics Tunnel in September 2022. The rotors have a diameter of 1.35 meters and an inter-rotor spacing of 25% of the diameter. Though this test was focused on the NASA New Frontiers Dragonfly mission, the resulting dataset is relevant to a wide array of applications of multirotor vehicles, especially those using fixed-pitch variable-speed rotors. Most notably beyond Dragonfly, perhaps, is the application of coaxial rotor pair configurations for eVTOL and Urban Air Mobility (UAM) aircraft. This work provides a thorough CFD validation study quantifying coaxial rotor performance estimation with accuracy on order 5-10% using an efficient hybrid BEMT-URANS flow solver over a wide range of operating conditions. This accuracy was achieved using novel approaches for the construction of both the C81 airfoil performance lookup tables and the BEMT rotor model. These novel approaches are combined with advanced scripting to further accelerate the commercial off-the-shelf CFD solver on GPU accelerated machines. Finally, the development of machine learning surrogate models is presented to produce highly efficient and accurate rotor performance predictions for fixed-pitch variable-speed multirotor aircraft over the complete flight regime including scout, cruise, climb, descent, as well as limiting cases of vortex-ring and windmill-brake states.

Coaxial↗

NASA NTRS1992

Radar determination of Mars surface properties

Radar studies of Mars have provided measurements of surface texture on scales of centimeters to hundreds of meters and measurements of surface material properties. Texture (rms surface tilts and estimates of small-scale roughness) may be inferred from dispersion and/or polarization of the radar echo; material properties (reflectivity of dielectric constant) are derived from echo strength. Mars is a diverse target; depending on location, rms surface tilts have been found to vary over the range 0.25 to 10 deg while reflectivity covers at least 3 to 13 percent. Plains units are the most variable, having both the smoothest and roughest surfaces, cratered terrain can be considered predictable and 'average' by comparison. Recent data identify scattering by small structures (perhaps rocks on or near the surface) as playing a more important role than previously recognized. Scattering by the residual ice cap near Mars' south pole is particularly unusual. The present state of radar surface studies is summarized.

Simpson, Richard A.↗

NASA NTRS2002

Shedding light on proteins, nucleic acids, cells, humans and fish

I was trained as a physicist in graduate school. Hence, when I decided to go into the field of biophysics, it was natural that I concentrated on the effects of light on relatively simple biological systems, such as proteins. The wavelengths absorbed by the amino acid subunits of proteins are in the ultraviolet (UV). The wavelengths that affect the biological activities, the action spectra, also are in the UV, but are not necessarily parallel to the absorption spectra. Understanding these differences led me to investigate the action spectra for affecting nucleic acids, and the effects of UV on viruses and cells. The latter studies led me to the discovery of the important molecular nature of the damages affecting DNA (cyclobutane pyrimidine dimers) and to the discovery of nucleotide excision repair. Individuals with the genetic disease xeroderma pigmentosum (XP) are extraordinarily sensitive to sunlight-induced skin cancer. The finding, by James Cleaver, that their skin cells were defective in DNA repair strongly suggested that DNA damage was a key step in carcinogenesis. Such information was important for estimating the wavelengths in sunlight responsible for human skin cancer and for predicting the effects of ozone depletion on the incidence of non-melanoma skin cancer. It took experiments with backcross hybrid fish to call attention to the probable role of the longer UV wavelengths not absorbed by DNA in the induction of melanoma. These reflections trace the biophysicist's path from molecules to melanoma.

NASA Discipline Radiation Health↗

NASA NTRS2012

Discovery of Small-Scale Spiral Structures in the Disk of SAO 206462 (HD 135344B)(exp 1): Implications for the Physical State of the Disk from Spiral Density Wave Theory

We present high-resolution, H-band, imaging observations, collected with Subaru /HiCIAO, of the scattered light from the transitional disk around SAO 206462 (HD 1353448). Although previous sub-mm imagery suggested the existence of the dust-depleted cavity at r <= 46 AU, our observations reveal the presence of scattered light components as close as O".2 (approx 28 AU) from the star. Moreover , we have discovered two small-scale spiral structures lying within 0".5 (approx 70 AU). We present models for the spiral structures using the spiral density wave theory, and derive a disk aspect ratio of h approx. 0.1, which is consistent with previous sub-mm observations. This model can potentially give estimates of the temperature and rotation profiles of the disk based on dynamical processes. independently from sub-nun observations. It also predicts the evolution of the spiral structures, which can be observable on timescales of 10-20 years, providing conclusive tests of the model. While we cannot uniquely identify the origin of these spirals, planets embedded in the disk may be capable of exciting the observed morphology. Assuming that this is the case, we can make predictions on the locations and, possibly, the masses of the unseen planets. Such planets may be detected by future multi-wavelengths observations,

Muto, T.↗

NASA NTRS2012

Discovery of Small-Scale Spiral Structures in the Disk of SAO 206462 (HD 135344B): Implications for the Physical State of the Disk from Spiral Density Wave Theory

We present high-resolution, H-band, imaging observations, collected with Subaru/HiCIAO, of the scattered light from the transitional disk around SAO 206462 (HD 135344B). Although previous sub-mm imagery suggested the existence of the dust-depleted cavity at r approximates 46 AU, our observations reveal the presence of scattered light components as close as 0".2 (approx 28 AU) from the star. Moreover, we have discovered two small-scale spiral structures lying within 0".5 (approx 70 AU). We present models for the spiral structures using the spiral density wave theory, and derive a disk aspect ratio of h approx 0.1, which is consistent with previous sub-mm observations. This model can potentially give estimates of the temperature and rotation profiles of the disk based on dynamical processes, independently from sub-mm observations. It also predicts the evolution of the spiral structures, which can be observable on timescales of 10-20 years, providing conclusive tests of the model. While we cannot uniquely identify the origin of these spirals, planets embedded in the disk may be capable of exciting the observed morphology. Assuming that this is the case, we can make predictions on the locations and, possibly, the masses of the unseen planets. Such planets may be detected by future multi-wavelengths observations.

Grady, C. A.↗