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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Modal Filters for Infrared Interferometry

Modal filters in the approximately equal to 10-micrometer spectral range have been implemented as planar dielectric waveguides in infrared interferometric applications such as searching for Earth-like planets. When looking for a small, dim object ("Earth") in close proximity to a large, bright object ("Sun"), the interferometric technique uses beams from two telescopes combined with a 180 phase shift in order to cancel the light from a brighter object. The interferometer baseline can be adjusted so that, at the same time, the light from the dimmer object arrives at the combiner in phase. This light can be detected and its infrared (IR) optical spectra can be studied. The cancellation of light from the "Sun" to approximately equal to 10(exp 6) is required; this is not possible without special devices-modal filters- that equalize the wavefronts arriving from the two telescopes. Currently, modal filters in the approximately equal to 10-micrometer spectral range are implemented as single- mode fibers. Using semiconductor technology, single-mode waveguides for use as modal filters were fabricated. Two designs were implemented: one using an InGaAs waveguide layer matched to an InP substrate, and one using InAlAs matched to an InP substrate. Photon Design software was used to design the waveguides, with the main feature all designs being single-mode operation in the 10.5- to 17-micrometer spectral range. Preliminary results show that the filter's rejection ratio is 26 dB.

Ksendzov, Alexander↗
NASA NTRS1991

Implementation of a modal filter on a five meter truss structure

Modal filtering is a spatial filtering technique which uses a weighted sum of a number of response measurements to extract the modal coordinates of the system from the physical response coordinates. No moving average or autoregressive calculations are required to implement the modal filter thus the modal coordinates may be calculated in real time. For practical implementation of the modal filter, the number and location of response locations must be chosen carefully. A modal filter is implemented on a five meter model space truss as a case study. The modal coordinates are extracted in real time using Hewlett Packard 3565 data acquisition and processig hardware. The effect of the number and location of response measurements on the performance of the modal filter is investigated. Applications of the modal filter to modal control and fast parameter identification are also discussed.

Shelly, S.↗
NASA NTRS2007

Characterization of Mid-Infrared Single Mode Fibers as Modal Filters

We present a technique for measuring the modal filtering ability of single mode fibers. The ideal modal filter rejects all input field components that have no overlap with the fundamental mode of the filter and does not attenuate the fundamental mode. We define the quality of a nonideal modal filter Q(sub f) as the ratio of transmittance for the fundamental mode to the transmittance for an input field that has no overlap with the fundamental mode. We demonstrate the technique on a 20 cm long mid-infrared fiber that was produced by the U.S. Naval Research Laboratory. The filter quality Q(sub f) for this fiber at 10.5 micron wavelength is 1000 +/- 300. The absorption and scattering losses in the fundamental mode are approximately 8 dB/m. The total transmittance for the fundamental mode, including Fresnel reflections, is 0.428 +/- 0.002. The application of interest is the search for extrasolar Earthlike planets using nulling interferometry. It requires high rejection ratios to suppress the light of a bright star, so that the faint planet becomes visible. The use of modal filters increases the rejection ratio (or, equivalently, relaxes requirements on the wavefront quality) by reducing the sensitivity to small wavefront errors. We show theoretically that, exclusive of coupling losses, the use of a modal filter leads to the improvement of the rejection ratio in a two-beam interferometer by a factor of Q(sub f).

single mode fibers↗
NASA NTRS1984

On the implementation of modal filters for control of structures

The most common technique for the control of structures is modal control. In modal control, the differential equations in terms of actual coordinates are replaced by a set of ordinary differential equations in terms of the modal coordinates known as modal equations. In designing feedback controls in conjunction with the modal equations, one must know the modal states for the modes targeted for control. The sensors measure actual states, however. The modal states can be estimated by means of a Luenberger observer or modal filters. The modal filters produce estimates of the modal states from distributed measurements of the states. If distributed measurements are not available, then they can be reconstructed from measurements at discrete points via interpolation. This paper examines various questions associated with the implementation of modal filters, such as the effect of choice of interpolation functions and sensors locations, as well as of measurement errors, on the state estimation process. The method is demonstrated by means of two numerical examples.

Meirovitch, L.↗
NASA NTRS1991

Analysis of structural response data using discrete modal filters

The application of reciprocal modal vectors to the analysis of structural response data is described. Reciprocal modal vectors are constructed using an existing experimental modal model and an existing frequency response matrix of a structure, and can be assembled into a matrix that effectively transforms the data from the physical space to a modal space within a particular frequency range. In other words, the weighting matrix necessary for modal vector orthogonality (typically the mass matrix) is contained within the reciprocal model matrix. The underlying goal of this work is mostly directed toward observing the modal state responses in the presence of unknown, possibly closed loop forcing functions, thus having an impact on both operating data analysis techniques and independent modal space control techniques. This study investigates the behavior of reciprocol modal vectors as modal filters with respect to certain calculation parameters and their performance with perturbed system frequency response data.

Freudinger, Lawrence C.↗
NASA NTRS1992

Development of an on-line parameter estimation system using the discrete modal filter

Ongoing development of an automated system for monitoring the frequency and damping of time varying structural systems is discussed. A spatial filtering technique called the discrete modal filter is used to uncouple multiple response measurements into modal coordinate responses. Since the modal coordinate responses are the responses of single-degree-of-freedom systems, the frequency and damping may be accurately estimated with short data records. An on-line monitoring system is discussed which may be useful in a variety of applications. The focus in this paper is on the application to aircraft flight flutter testing.

Shelley, S. J.↗
NASA NTRS2013

Modal Filtering for Control of Flexible Aircraft

Modal regulators and deformation trackers are designed for an open-loop fluttering wing model. The regulators are designed with modal coordinate and accelerometer inputs respectively. The modal coordinates are estimated with simulated fiber optics. The robust stability of the closed-loop systems is compared in a structured singular-value vector analysis. Performance is evaluated and compared in a gust alleviation and flutter suppression simulation. For the same wing and flight condition two wing-shape-tracking control architectures are presented, which achieve deformation control at any point on the wing.

Shape Control↗
NASA NTRS2014

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

Modal Filtering↗
NASA NTRS2016

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

Modal Filtering↗
NASA NTRSDate not supplied

Using Distributed Fiber-Optic Strain Sensing to Estimate Modal Generalized Modal Coordinates from Flight-Test Data

Background and Motivation - Problem - Contemporary aircraft carry around structural mass so that the flutter instabilities lie well outside of the operational envelope. - Better methods of measuring the structural state could allow reduction of the extra structural weight. - Modal filtering - Standard method in structural analysis - Deformations are a combination of mode shapes - Modal filtering is estimating these modal coordinates from data - Often ordinary least squares methods - Often applied to simpler test articles - Factor Analysis - Classic (in psychology) analysis method - Measurements are a combination of small number of unmeasurable variables - Lessons from this factor can be adapted to improve the modal filtering methods

Jeffrey Ouellette↗
NASA NTRSDate not supplied

Using Distributed Fiber-optic Strain Sensing to Estimate Generalized Modal Coordinates from Flight-test Data

Background and Motivation - Problem - Contemporary aircraft carry around structural mass so that the flutter instabilities lie well outside of the operational envelope. - Better methods of measuring the structural state could allow reduction of the extra structural weight - Modal filtering - Standard method in structural analysis - Deformations are a combination of mode shapes - Modal filtering is estimating these modal coordinates from data - Often ordinary least squares methods - Often applied to simpler test articles - Factor Analysis - Analysis method from psychology - Measurements are a combination of small number of unmeasurable variables. - Lessons from factor analysis can be adapted to improve the modal filtering methods

Jeffrey Ouellette↗
NASA NTRS1989

Single-mode projection filters for modal parameter identification for flexible structures

Single-mode projection filters are developed for eigensystem parameter identification from both analytical results and test data. Explicit formulations of these projection filters are derived using the orthogonal matrices of the controllability and observability matrices in the general sense. A global minimum optimization algorithm is applied to update the filter parameters by using the interval analysis method. The updated modal parameters represent the characteristics of the test data. For illustration of this new approach, a numerical simulation for the MAST beam structure is shown by using a one-dimensional global optimization algorithm to identify modal frequencies and damping. The projection filters are practical for parallel processing implementation.

Huang, Jen-Kuang↗
NASA NTRS1987

Projection filters for modal parameter estimate for flexible structures

Single-mode projection filters are developed for eigensystem parameter estimates from both analytical results and test data. Explicit formulations of these projection filters are derived using the pseudoinverse matrices of the controllability and observability matrices in general use. A global minimum optimization algorithm is developed to update the filter parameters by using interval analysis method. Modal parameters can be attracted and updated in the global sense within a specific region by passing the experimental data through the projection filters. For illustration of this method, a numerical example is shown by using a one-dimensional global optimization algorithm to estimate model frequencies and dampings.

Huang, Jen-Kuang↗
NASA NTRS1988

Single-Mode Projection Filters for Modal Parameter Identification for Flexible Structures

Single-mode projection filters are developed for eigensystem parameter identification from both analytical results and test data. Explicit formulations of these projection filters are derived using the orthogonal matrices of the controllability and observability matrices in the general sense. A global minimum optimization algorithm is applied to update the filter parameters by using the interval analysis method. The updated modal parameters represent the characteristics of the test data. For illustration of this new approach, a numerical simulation for the MAST beam structure is shown by using a one-dimensional global optimization algorithm to identify modal frequencies and damping. Another numerical simulation of a ten-mode structure is also presented by using a two-dimensional global optimization algorithm to illustrate the feasibility of the new method. The projection filters are practical for parallel processing implementation.

Huang, Jen-Kuang↗
DOE OSTI2020

Experimental Modal Analysis Using Phase Quantities from Phase-Based Motion Processing and Motion Magnification

Phase-based motion processing and the associated Motion Magnification that it enables has become popular not only for the striking videos that it can produce of traditionally stiff structures visualized with very large deflections, but also for its ability to pull information out of the noise floor of images so that they can be processed with more traditional optical techniques such as digital image correlation or feature tracking. While the majority of papers in the literature have utilized the Phase-based Image Processing approach as a pre-processor for more quantitative analyses, the technique itself can be used directly to extract modal parameters from an image, noting that the extracted phases are proportional to displacements in the image. Therefore, once phases are extracted, they can be fit using traditional experimental modal analysis techniques. This produces a mode “shape” where the degrees of freedom are phases instead of physical motions. These phases can be scaled to produce on-image visualizations of the mode shapes, rather than operational shapes produced by bandpass filtering. Modal filtering techniques can also be used to visualize motions from an environment on an image using the modal phases as a basis for the expansion.

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