Visualizing Whisper computation of the calculational margin [Slides]
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Abstract not provided.
A structured singular value (mu) analysis method of computing flutter margins has robust stability of a linear aeroelastic model with uncertainty operators (Delta). Flight data is used to update the uncertainty operators to accurately account for errors in the computed model and the observed range of aircraft dynamics of the aircraft under test caused by time-varying aircraft parameters, nonlinearities, and flight anomalies, such as test nonrepeatability. This mu-based approach computes predict flutter margins that are worst case with respect to the modeling uncertainty for use in determining when the aircraft is approaching a flutter condition and defining an expanded safe flight envelope for the aircraft that is accepted with more confidence than traditional methods that do not update the analysis algorithm with flight data by introducing mu as a flutter margin parameter that presents several advantages over tracking damping trends as a measure of a tendency to instability from available flight data.
The Highly Integrated Digital Electronic Control (HIDEC) program will demonstrate and evaluate the improvements in performance and mission effectiveness that result from integrated engine/airframe control systems. This system is being used on the F-15 airplane. An integrated flightpath management mode and an integrated adaptive engine stall margin mode are implemented into the system. The adaptive stall margin mode is a highly integrated mode in which the airplane flight conditions, the resulting inlet distortion, and the engine stall margin are continuously computed; the excess stall margin is used to uptrim the engine for more thrust. The integrated flightpath management mode optimizes the flightpath and throttle setting to reach a desired flight condition. The increase in thrust and the improvement in airplane performance is discussed.
The highly integrated digital electronic control (HIDEC) program will demonstrate and evaluate the improvements in performance and mission effectiveness that result from integrated engine-airframe control systems. This system is being used on the F-15 airplane at the Dryden Flight Research Facility of NASA Ames Research Center. An integrated flightpath management mode and an integrated adaptive engine stall margin mode are being implemented into the system. The adaptive stall margin mode is a highly integrated mode in which the airplane flight conditions, the resulting inlet distortion, and the engine stall margin are continuously computed; the excess stall margin is used to uptrim the engine for more thrust. The integrated flightpath management mode optimizes the flightpath and throttle setting to reach a desired flight condition. The increase in thrust and the improvement in airplane performance is discussed in this paper.
The highly integrated digital electronic control (HIDEC) program will demonstrate and evaluate the improvements in performance and mission effectiveness that result from integrating engine-airframe control systems. Currently this is accomplished on the NASA Ames Research Center's F-15 airplane. The two control modes used to implement the systems are an integrated flightpath management mode and in integrated adaptive engine control system (ADECS) mode. The ADECS mode is a highly integrated mode in which the airplane flight conditions, the resulting inlet distortion, and the available engine stall margin are continually computed. The excess stall margin is traded for thrust. The predicted increase in engine performance due to the ADECS mode is presented in this report.
The highly integrated digital electronic control (HIDEC) program will demonstrate and evaluate the improvements in performance and mission effectiveness that result from integrating engine-airframe control systems. Currently this is accomplished on the NASA Ames Research Center's F-15 airplane. The two control modes used to implement the systems are an integrated flightpath management mode and an integrated adaptive engine control system (ADECS) mode. The ADECS mode is a highly integrated mode in which the airplane flight conditions, the resulting inlet distortion, and the available engine stall margin are continually computed. The excess stall margin is traded for thrust. The predicted increase in engine performance due to the ADECS mode is presented in this report.
An efficient computational method is presented for stability robustness analysis with structured real-parameter perturbations. A generic model of a class of uncertain dynamical systems is used as an example. The parameter uncertainty is characterized by a real scalar, epsilon. Multilinearity of the closed-loop characteristic polynomial is exploited to permit application of the mapping theorem to calculate the stability robustness margin. It is found that sensitive geometry of the stability boundary in the epsilon, omega-plane renders problematic the calculation of the minimum epsilon as a function of omega. This difficulty is avoided by calculating the minimum distance to the image of the uncertainty domain over omega as a function of epsilon. It is also shown that a certain class of uncertain dynamical systems has the required multilinearity property and are thus amenable to the proposed technique.
This paper documents an invited presentation given to The Boeing Company, Seattle, Washington, on September 9, 1997. The audience consisted of structural dynamic and flight test engineers from the Boeing Commercial Airplane Group who were interested in discussing research which may be applied to future flight flutter test programs. A method to compute robust flutter margins is described which is a significant departure from traditional methods. This method uses the structured singular value, mu, to compute a flutter margin which directly accounts for modeling errors such that a worst-case flutter margin is computed with respect to those errors. This method may be applied in several ways. A post-flight application uses data sets from multiple test points to compute worst-case flutter margins and a worst-case flight envelope. An on-line implementation computes flutter margins at each test point to track the flutter margins during a flight test. This on-line implementation is the basis for a flutterometer flight test tool that displays the distance to flutter at a given test point. Such a tool was not previously possible using traditional flutter flight test analysis methods. The F/A-18 System Research Aircraft was used to demonstrate these applications using flight data recorded from test points throughout the flight envelope.
An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, micron, computes a stability margin which directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The micron margins are robust margins which indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 SRA using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.
An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, mu, computes a stability margin that directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The mu margins are robust margins that indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 Systems Research Aircraft using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.
RINA is a general post-processor system which performs data reduction, post processing and post analysis of NASTRAN results. RINA can be run in either batch or time-sharing mode. The system, which is an expansion of the NASDAT program, performs the following tasks: (1) scan of extreme values for displacements, forces, stresses and margins of safety, (2) computations of envelopes for displacements, forces, stresses and margins of safety, (3) computations of allowables and margins of safety; and (4) generation of NASTRAN tables needed for reanalysis computations. Usage of all these options provides the analyst with an efficient tool for the study and interpretation of NASTRAN analysis results and their presentation for project documentation.
Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.
We present an engineering-oriented summary of statistical methods for estimation of the reliability (or equivalently, failure probability) of one-shot devices such as explosive detonators and other weapon components. Estimates may be given as single points or intervals, based on pass/fail tests, margin analysis, computational models, expert judgment, or a combination of these. We focus on highly reliable devices for which few or no failures are expected to occur in testing.
Stability analysis of high performance aircraft must account for errors in the system model. A method for computing flutter margins that incorporates flight data has been developed using robust stability theory. This paper considers applying this method to update flutter margins during a post-flight or on-line analysis. Areas of modeling uncertainty that arise when using flight data with this method are investigated. The amount of conservatism in the resulting flutter margins depends on the flight data sets used to update the model. Post-flight updates of flutter margins for an F/A-18 are presented along with a simulation of on-line updates during a flight test.
Flight flutter testing is an integral part of flight envelope clearance. This paper discusses advancements in several areas that are being investigated to improve efficiency and safety of flight test programs. Results are presented from recent flight testing of the F/A-18 Systems Research Aircraft. A wingtip excitation system was used to generate aeroelastic response data. This system worked well for many flight conditions but still displayed some anomalies. Wavelet processing is used to analyze the flight data. Filtered transfer functions are generated that greatly improve system identification. A flutter margin is formulated that accounts for errors between a model and flight data. Worst-case flutter margins are computed to demonstrate the flutter boundary may lie closer to the flight envelope than previously estimated. This paper concludes with developments for a distributed flight analysis environment and on-line health monitoring.
Simple and elegant derivations of recent results concerning the computation of infinity-norm real-parameter margins for stabilized, mass-spring dynamical systems with the masses and the spring constraints as uncertain parameters are presented. The authors introduce the concept of critical frequency and gain for stabilized conservative systems whose uncertain parameters do not necessarily appear multilinearly in the numerator and denominator of the plant transfer function. An approach to parameter margin computation is presented.
This paper describes a flight test demonstration of a system for identification of the stability and handling qualities parameters of a helicopter-slung load configuration simultaneously with flight testing, and the results obtained.Tests were conducted with a UH-60A Black Hawk at speeds from hover to 80 kts. The principal test load was an instrumented 8 x 6 x 6 ft cargo container. The identification used frequency domain analysis in the frequency range to 2 Hz, and focussed on the longitudinal and lateral control axes since these are the axes most affected by the load pendulum modes in the frequency range of interest for handling qualities. Results were computed for stability margins, handling qualities parameters and load pendulum stability. The computations took an average of 4 minutes before clearing the aircraft to the next test point. Important reductions in handling qualities were computed in some cases, depending, on control axis and load-slung combination. A database, including load dynamics measurements, was accumulated for subsequent simulation development and validation.
This paper describes a flight test demonstration of a system for identification of the stability and handling qualities parameters of a helicopter-slung load configuration simultaneously with flight testing, and the results obtained. Tests were conducted with a UH-60A Black Hawk at speeds from hover to 80 kts. The principal test load was an instrumented 8 x 6 x 6 ft cargo container. The identification used frequency domain analysis in the frequency range to 2 Hz, and focussed on the longitudinal and lateral control axes since these are the axes most affected by the load pendulum modes in the frequency range of interest for handling qualities. Results were computed for stability margins, handling qualities parameters and load pendulum stability. The computations took an average of 4 minutes before clearing the aircraft to the next test point. Important reductions in handling qualities were computed in some cases, depending on control axis and load-sling combinations. A database, including load dynamics measurements, was accumulated for subsequent simulation development and validation.