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NASA NTRS2017

Unsteady Aerodynamic Force Sensing from Strain Data

A simple approach for computing unsteady aerodynamic forces from simulated measured strain data is proposed in this study. First, the deflection and slope of the structure are computed from the unsteady strain using the two-step approach. Velocities and accelerations of the structure are computed using the autoregressive moving average model, on-line parameter estimator, low-pass filter, and a least-squares curve fitting method together with analytical derivatives with respect to time. Finally, aerodynamic forces over the wing are computed using modal aerodynamic influence coefficient matrices, a rational function approximation, and a time-marching algorithm.

aerodynamic load sensing↗

NASA NTRS2023

Orion Cabin Lighting System: Filter Workaround for Maintaining Crew Circadian Entrainment

Introduction: Suboptimal crew circadian entrainment (CCE) is common and results in cumulative fatigue and reduced cognitive function. This may promote use of psychostimulants and hypnotics. The Orion Cabin Lighting System (OCLS) consists of 15 dimmable lamps circuited to several zones. The OCLS monochromatic white lamp design is insufficient for maintaining CCE. While blue light (480-nm) elicits peak wake-cycle response, it is a powerful disruptor of CCE. We propose OCLS lamp filters (OLFs) as a workaround for maintaining CCE. Methods: Preliminary OLFs testing utilized a tunable-white LED to serve as a baseline lamp in the NASA Lighting Lab’s controlled environment (prototype OCLS lamps were unavailable). Illuminance and spectral irradiances were measured with the NASA Lighting Lab’s spectroradiometer. ConOps is proposed for OLFs. Results: OLFs markedly attenuated 480-nm light and illumination with minimal mass/volume requirements. ConOps includes in-flight deployment of OLFs and reducing OCLS lamp intensity prior to bedtime. Pros: minimal mass/volume, no added power, and a simplistic design. Cons: manual twice-daily articulation of OLFs, undetermined frangibility/flammability/off-gassing and effects on thermal regulation, and untested task illumination or color fidelity in the Orion cabin. Discussion: While OLFs aptly attenuate 480-nm light from our tunable LED lamp in the Lighting Lab’s controlled environment, they remain untested within the Orion cabin on OCLS lamps. Future tests include Orion cabin illumination optimization for appropriate Lux delivery and ensuring task/color fidelity with OLFs deployed. Adequate OCLS cooling should be similarly assessed. Other safety testing includes OLF frangibility/flammability/off-gassing in an enriched-O 2 /hypobaric environment. Together, these data will demonstrate OLFs provide a viable workaround for maintaining CCE further ensuring mission safety and success.

Carlos Rene Dostal↗

DOE OSTI2020

Calibration and Uncertainty Estimation Using the Ensemble Kalman Filter with a Large Subsurface Flow and Transport Model - 20321

At routinely monitored groundwater contamination sites, periodically measured environmental conditions such as groundwater levels and contaminant concentrations are used to inform and confirm a conceptual site model (CSM) and guide the development and calibration of a numerical groundwater flow and transport model. The calibration of groundwater flow and transport models after each measurement (sampling) event can illuminate deficiencies in a CSM, identify areas where additional monitoring is warranted, and predict the behavior of the system to guide decision making. However, manual and automated (e.g. PEST) model calibration tools can be time-consuming and computationally expensive to implement after each sampling event. Perhaps as a result, such calibration tools generally utilize all available monitoring data simultaneously rather than sequentially assimilating monitoring data one sampling event at a time as the results from sampling become available. A more real-time data assimilation approach may reduce parameter uncertainty, quantify the value of additional monitoring data, and produce a usable model more quickly and with less effort. To mitigate the potential time-consuming aspects of manual and widely applied automated calibration techniques, a data assimilation algorithm called the ensemble Kalman filter (EnKF) was evaluated as a relatively efficient method of model calibration and uncertainty assessment via the sequential integration of monitoring data into a model. The EnKF was able to successfully and efficiently assimilate monitoring and modeling data to calibrate a complex flow and transport model at a real-world site with significant subsurface heterogeneity, uncertainty, and 12 years of monitoring data (over 4,000 individual measurements of groundwater levels and over 2,500 measurements of contaminant concentrations). Starting with an uncalibrated model data from annual sampling events were sequentially assimilated, and the resultant predication errors and estimated parameter uncertainties were tracked. After all monitoring data were assimilated, both flow and transport residuals at the end of the EnKF process were comparable to those produced via a concurrent PEST calibration effort but required fewer model simulations. Both uncertainty and prediction errors decreased over time. In a real-time application, the adequacy of the model could be assessed after each sampling event. The benefits of such a real-time approach to utilizing monitoring data include reduced costs (in the form of model updates or site characterization efforts), early flagging of possible errors in the CSM, and a reduced risk of overfitting and corresponding increased confidence in model predictions. This tool may be particularly useful compared to other calibration techniques (e.g. manual, PEST) when model runtimes are long, calibration parameters are many, or parameter uncertainty is large. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

DPM: A deep learning PDE augmentation method with application to large-eddy simulation

A framework is introduced that leverages known physics to reduce overfitting in machine learning for scientific applications. The partial differential equation (PDE) that expresses the physics is augmented with a neural network that uses available data to learn a description of the corresponding unknown or unrepresented physics. Training within this combined system corrects for missing, unknown, or erroneously represented physics, including discretization errors associated with the PDE's numerical solution. For optimization of the network within the PDE, an adjoint PDE is solved to provide high-dimensional gradients, and a stochastic adjoint method (SAM) further accelerates training. Additionally, the approach is demonstrated for large-eddy simulation (LES) of turbulence. High-fidelity direct numerical simulations (DNS) of decaying isotropic turbulence provide the training data used to learn sub-filter-scale closures for the filtered Navier–Stokes equations. Out-of-sample comparisons show that the deep learning PDE method outperforms widely-used models, even for filter sizes so large that they become qualitatively incorrect. It also significantly outperforms the same neural network when a priori trained based on simple data mismatch, not accounting for the full PDE. Measures of discretization errors, which are well-known to be consequential in LES, point to the importance of the unified training formulation's design, which without modification corrects for them. For comparable accuracy, simulation runtime is significantly reduced. A relaxation of the typical discrete enforcement of the divergence-free constraint in the solver is also successful, instead allowing the DPM to approximately enforce incompressibility physics. Since the training loss function is not restricted to correspond directly to the closure to be learned, training can incorporate diverse data, including experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1978

An analysis of the multiple model adaptive control algorithm

Qualitative and quantitative aspects of the multiple model adaptive control method are detailed. The method represents a cascade of something which resembles a maximum a posteriori probability identifier (basically a bank of Kalman filters) and a bank of linear quadratic regulators. Major qualitative properties of the MMAC method are examined and principle reasons for unacceptable behavior are explored.

Greene, C. S.↗

NASA NTRS1999

An Application of Rotation- and Translation-Invariant Overcomplete Wavelets to the Registration of Remotely Sensed Imagery

A wavelet-based image registration approach has previously been proposed by the authors. In this work, wavelet coefficient maxima obtained from an orthogonal wavelet decomposition using Daubechies filters were utilized to register images in a multi-resolution fashion. Tested on several remote sensing datasets, this method gave very encouraging results. Despite the lack of translation-invariance of these filters, we showed that when using cross-correlation as a feature matching technique, features of size larger than twice the size of the filters are correctly registered by using the low-frequency subbands of the Daubechies wavelet decomposition. Nevertheless, high-frequency subbands are still sensitive to translation effects. In this work, we are considering a rotation- and translation-invariant representation developed by E. Simoncelli and integrate it in our image registration scheme. The two types of filters, Daubechies and Simoncelli filters, are then being compared from a registration point of view, utilizing synthetic data as well as data from the Landsat/ Thematic Mapper (TM) and from the NOAA Advanced Very High Resolution Radiometer (AVHRR).

LeMoigne, Jacqueline↗

NASA NTRS1999

An Application of Rotation- and Translation-Invariant Overcomplete Wavelets to the registration of Remotely Sensed Imagery

LeMoigne, Jacqueline↗

NASA NTRS2008

Making Complex Electrically Conductive Patterns on Cloth

A method for automated fabrication of flexible, electrically conductive patterns on cloth substrates has been demonstrated. Products developed using this method, or related prior methods, are instances of a technology known as 'e-textiles,' in which electrically conductive patterns ar formed in, and on, textiles. For many applications, including high-speed digital circuits, antennas, and radio frequency (RF) circuits, an e-textile method should be capable of providing high surface conductivity, tight tolerances for control of characteristic impedances, and geometrically complex conductive patterns. Unlike prior methods, the present method satisfies all three of these criteria. Typical patterns can include such circuit structures as RF transmission lines, antennas, filters, and other conductive patterns equivalent to those of conventional printed circuits. The present method overcomes the limitations of the prior methods for forming the equivalent of printed circuits on cloth. A typical fabrication process according to the present method involves selecting the appropriate conductive and non-conductive fabric layers to build the e-textile circuit. The present method uses commercially available woven conductive cloth with established surface conductivity specifications. Dielectric constant, loss tangent, and thickness are some of the parameters to be considered for the non-conductive fabric layers. The circuit design of the conductive woven fabric is secured onto a non-conductive fabric layer using sewing, embroidery, and/or adhesive means. The portion of the conductive fabric that is not part of the circuit is next cut from the desired circuit using an automated machine such as a printed-circuit-board milling machine or a laser cutting machine. Fiducials can be used to align the circuit and the cutting machine. Multilayer circuits can be built starting with the inner layer and using conductive thread to make electrical connections between layers.

Chu, Andrew↗

NASA NTRS2011

Fast Image Texture Classification Using Decision Trees

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Thompson, David R.↗

NASA NTRS2001

A Two-Dimensional Variational Analysis Method for NSCAT Ambiguity Removal: Methodology, Sensitivity, and Tuning

In this study, we apply a two-dimensional variational analysis method (2d-VAR) to select a wind solution from NASA Scatterometer (NSCAT) ambiguous winds. 2d-VAR determines a "best" gridded surface wind analysis by minimizing a cost function. The cost function measures the misfit to the observations, the background, and the filtering and dynamical constraints. The ambiguity closest in direction to the minimizing analysis is selected. 2d-VAR method, sensitivity and numerical behavior are described. 2d-VAR is compared to statistical interpolation (OI) by examining the response of both systems to a single ship observation and to a swath of unique scatterometer winds. 2d-VAR is used with both NSCAT ambiguities and NSCAT backscatter values. Results are roughly comparable. When the background field is poor, 2d-VAR ambiguity removal often selects low probability ambiguities. To avoid this behavior, an initial 2d-VAR analysis, using only the two most likely ambiguities, provides the first guess for an analysis using all the ambiguities or the backscatter data. 2d-VAR and median filter selected ambiguities usually agree. Both methods require horizontal consistency, so disagreements occur in clumps, or as linear features. In these cases, 2d-VAR ambiguities are often more meteorologically reasonable and more consistent with satellite imagery.

Hoffman, R. N.↗

DOE OSTI2025

Applying Machine Learning and Bayesian Inference to Identify and Locate Moving Anthropogenic Sources Using Distributed Acoustic Sensing Data

Distributed acoustic sensing (DAS) systems, which use existing telecommunication fibers, offer high‐resolution capabilities ideal for recording anthropogenic sources. However, the complexity of urban environments and the large amount of data recorded by DAS require automated methods to efficiently detect and categorize anthropogenic sources. Here, we evaluate how well three machine learning models (k‐nearest neighbor [k‐NN], convolutional neural networks, and recurrent‐convolutional neural networks) can identify various anthropogenic sources recorded by DAS. Our findings reveal that both k‐NN and neural network methods perform well in high signal‐to‐noise ratio (SNR) settings. However, their accuracy decreases at SNRs <4. We also use Kalman filtering, a form of Bayesian inference, on backprojected locations of these sources to recover locations that generally fall within standard smartphone Global Positioning System errors. By combining machine learning and Kalman filter results, we calculate a multidimensional model of moving anthropogenic sources. These results demonstrate the potential of DAS data in urban seismology for accurately identifying and locating such sources. Depending on the research objectives, these sources can be further studied or filtered out to improve the quality of seismic data for earthquake studies. Such methods provide a valuable tool for urban seismology and seismic hazard analysis.

Luckie, Thomas William [Sandia National Laboratori↗

NASA NTRS2009

Chemical Source Inversion using Assimilated Constituent Observations in an Idealized Two-dimensional System

We present a source inversion technique for chemical constituents that uses assimilated constituent observations rather than directly using the observations. The method is tested with a simple model problem, which is a two-dimensional Fourier-Galerkin transport model combined with a Kalman filter for data assimilation. Inversion is carried out using a Green's function method and observations are simulated from a true state with added Gaussian noise. The forecast state uses the same spectral spectral model, but differs by an unbiased Gaussian model error, and emissions models with constant errors. The numerical experiments employ both simulated in situ and satellite observation networks. Source inversion was carried out by either direct use of synthetically generated observations with added noise, or by first assimilating the observations and using the analyses to extract observations. We have conducted 20 identical twin experiments for each set of source and observation configurations, and find that in the limiting cases of a very few localized observations, or an extremely large observation network there is little advantage to carrying out assimilation first. However, in intermediate observation densities, there decreases in source inversion error standard deviation using the Kalman filter algorithm followed by Green's function inversion by 50% to 95%.

Tangborn, Andrew↗

NASA NTRS2001

On-Orbit Calibration of Redundant Spacecraft Gyros by Optimal Reduction to Three Axes

The Aqua spacecraft will carry four single-axis gyros configured with three orthogonal axes and one skew axis. This redundancy presents a challenge for batch methods of on-orbit gyro calibration that use a spacecraft rotation model deterministically related to gyro data, in that sensor data can respond to at most three angular velocity components. When the number of gyros, N, is greater than 3, the 3xN matrix, G, that reduces the N gyro measurements to three body-frame angular-velocity components cannot be fully determined by such methods; there are many such matrices that produce essentially the same angular velocity history. In such a case, spacecraft operators require information about the Nx3 gyro linear response matrix, R, that relates gyro outputs to the body-frame angular velocities causing them. This matrix provides sufficient information to determine multiple reduced-dimension G-matrices for use in case of failure or degradation of one or more gyros, as well as to determine an optimal 3xN G for the fully-functional configuration. A viable proposal is to apply a 3xN pre-filter matrix, F, to the N gyro outputs before carrying out a conventional gyro calibration procedure. The angular-velocity history emerging from conventional calibration may then be used as input data, together with the same gyro data that generated it, to fit the alignment, scale-factor, and bias parameters of each gyro axis in turn. A difficulty of such a proposal is the arbitrariness in the choice of F. Due to gyro noise, different pre-filter matrices produce different calibrations. This paper presents a method of choosing F that is based on optimizing gyro consistency in the limit of infinite weight on gyro data, as compared to sensor data. The choice of F is independent of a priori alignment and is based on the gyro data alone. The method is applicable to any N of three or more, but reduces to conventional batch-estimation methodologies when N = 3. Results of computational comparison among calibration simulations using various choices of F will be presented for the Aqua gyro configuration with N = 4.

Radomski, M. S.↗

DOE OSTI2021

Simultaneous imaging of two-phase velocities in particle-laden flows by two-color optical phase discrimination

In this Letter, we present a particle image/tracking velocimetry (PIV/PTV) technique for simultaneous velocity measurement of both fluid and particle phases, adopting newly developed optical phase discrimination methods and novel optical particles. Spherical acrylic (PMMA) particles of diameter ∼ <#comment/> O ( 100 µ <#comment/> m ) were used as the particle phase, while fine B A M : E u 2 + phosphors of diameter ∼ <#comment/> O ( 1 µ <#comment/> m ) were used as the fluid tracer. Under Nd:YAG 355 nm laser excitation, both the laser-induced fluorescence (LIF) from PMMA and laser-induced phosphorescence (LIP) from B A M : E u 2 + provided sufficiently strong signals for PIV imaging with two non-intensified cameras and were clearly separable for phase discrimination using spectral filters and temporal profiles. The advantages of the PIV/PTV method include the relatively low cost of B A M : E u 2 + phosphors, high sphericity and narrow size distribution of PMMA particles with LIF emission, and direct optical discrimination eliminating artifacts, while requiring much less computational capacity for PIV/PTV processing of complex particle-laden flows.

Bi, Xiaopeng (ORCID:0000000190847325)↗

NASA NTRS1981

A new method of Curie depth evaluation from magnetic data: Theory

An approach to estimating the Curie point isotherm uses the classical Gauss method inverting a system of nonlinear equations. The method, slightly modified by a differential correction technique, directly inverts filtered Magsat data to calculate the crustal structure above the Curie depth, which is modeled as a magnetized layer of varying thickness and susceptibility. Since the depth below the layer is assumed to be nonmagnetic, the bottom of the layer is interpreted as the Curie depth. The method, once fully developed, tested, and compared with previous work by others, is to be applied to a portion of the eastern U.S. when sufficient Magsat data are accumulated for the region.

Won, I. J.↗

NASA NTRS2013

Orion MPCV Touchdown Detection Threshold Development and Testing

A robust method of detecting Orion Multi ]Purpose Crew Vehicle (MPCV) splashdown is necessary to ensure crew and hardware safety during descent and after touchdown. The proposed method uses a triple redundant system to inhibit Reaction Control System (RCS) thruster firings, detach parachute risers from the vehicle, and transition to the post ]landing segment of the Flight Software (FSW). The vehicle crew is the prime input for touchdown detection, followed by an autonomous FSW algorithm, and finally a strictly time based backup timer. RCS thrusters must be inhibited before submersion in water to protect against possible damage due to firing these jets under water. In addition, neglecting to declare touchdown will not allow the vehicle to transition to post ]landing activities such as activating the Crew Module Up ]righting System (CMUS), resulting in possible loss of communication and difficult recovery. A previous AIAA paper gAssessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module h concluded that a strictly Inertial Measurement Unit (IMU) based detection method using an acceleration spike algorithm had the highest safety margins and shortest detection times of other methods considered. That study utilized finite element simulations of vehicle splashdown, generated by LS ]DYNA, which were expanded to a larger set of results using a Kriging surface fit. The study also used the Decelerator Systems Simulation (DSS) to generate flight dynamics during vehicle descent under parachutes. Proto ]type IMU and FSW MATLAB models provided the basis for initial algorithm development and testing. This paper documents an in ]depth trade study, using the same dynamics data and MATLAB simulations as the earlier work, to further develop the acceleration detection method. By studying the combined effects of data rate, filtering on the rotational acceleration correction, data persistence limits and values of acceleration thresholds, an optimal configuration was determined. The lever arm calculation, which removes the centripetal acceleration caused by vehicle rotation, requires that the vehicle angular acceleration be derived from vehicle body rates, necessitating the addition of a 2nd order filter to smooth the data. It was determined that using 200 Hz data directly from the vehicle IMU outperforms the 40 Hz FSW data rate. Data persistence counter values and acceleration thresholds were balanced in order to meet desired safety and performance. The algorithm proved to exhibit ample safety margin against early detection while under parachutes, and adequate performance upon vehicle splashdown. Fall times from algorithm initiation were also studied, and a backup timer length was chosen to provide a large safety margin, yet still trigger detection before CMUS inflation. This timer serves as a backup to the primary acceleration detection method. Additionally, these parameters were tested for safety on actual flight test data, demonstrating expected safety margins.

Daum, Jared↗

NASA NTRS1988

Laboratory calibration of field reflectance panels

A method used for calibrating field reflectance panels in the visible and shortwave infrared wavelength range is described. The directional reflectance factor of painted barium sulfate (BaSO4) panels is determined. The reference for this method is the hemispherical reflectance of pressed polytetrafluoroethylene (halon) powder prepared according to National Bureau of Standards (NBS) directions. The panels and a radiometer are mounted on rotation stages to measure the reflectance factor at different incidence and view angles. The sensor can be any laboratory or field filter radiometer small enough to mount on the apparatus. The method is used to measure the reflectance factors of halon and BaSO4 panels between 0.45 and 0.85 micrometers. These reflectance factors are compared to those measured by a field apparatus. The results agree to within 0.013 in reflectance at incidence angles between 15 and 75 degrees.

Biggar, S. F.↗

NASA NTRS1973

Analytical study to define a helicopter stability derivative extraction method, volume 1

A method is developed for extracting six degree-of-freedom stability and control derivatives from helicopter flight data. Different combinations of filtering and derivative estimate are investigated and used with a Bayesian approach for derivative identification. The combination of filtering and estimate found to yield the most accurate time response match to flight test data is determined and applied to CH-53A and CH-54B flight data. The method found to be most accurate consists of (1) filtering flight test data with a digital filter, followed by an extended Kalman filter (2) identifying a derivative estimate with a least square estimator, and (3) obtaining derivatives with the Bayesian derivative extraction method.

Molusis, J. A.↗