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Development of Unsteady Aerodynamic Reduced-Order Models for Aeroelasticity
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Reduced-order modeling for rapid mission planning of the Mars 2020 Helicopter
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High Fidelity Adaptively Refined CFD and Reduced Order Models of a High Aspect Ratio Aeroelastic Wing Wind Tunnel Model
Robust adaptive mesh refinement for transonic conditions is one of the most difficult challenges to advancing CFD usage for certification by analysis. Transonic fluid-structure interaction cases represent an even more difficult challenge for adaptive mesh refinement. Production aircraft furthermore have structural models that have gaps and discontinuities that make the interdomain transfer of loads and deflections difficult. This adds to the challenge of adaptive mesh refinement. An initial attempt to create an adaptive mesh refinement process that refines regions of high flow gradient based around the HeldenMesh software is presented. The method is applied to the Integrated Adaptive Wing Technology Maturation (IAWTM) configuration. That has developed an aspect ratio 13.5 wing aeroelastic wind-tunnel model for testing in the Transonic Dynamics Tunnel (TDT). The high levels of deformation of this wing make the adaptation of meshes for the static shape difficult and has necessitated some creativity in how to efficiently adapt meshes. A time-domain identification of the unsteady system based on flexible modes using the adapted meshes is performed.
High Fidelity Adaptively Refined CFD and Reduced Order Models of a High Aspect Ratio Aeroelastic Wing Wind-Tunnel Model
The NASA Advanced Air Transport Technology (AATT) goal of reduced fuel burn for transport aircraft has led to the NASA N+3 High Aspect Ratio Wing (HARW) subproject. This project requires identifying, developing, and demonstrating key technologies and integrated multidisciplinary solutions to enable a safe, high performance, aeroelastic wing. Since this aircraft will have a high aspect ratio wing, aeroelasticity is expected to be a major issue in the design. In this paper high fidelity computational fluid dynamics (CFD) is performed with flow adapted meshes. A system identification of the aerodynamics is developed using both a multi-modal multi-sine time-marching and a multi-mode linear frequency domain method. GLA, MLA and flutter suppression simulations will be performed.
State consistence of data-driven reduced order models for parametric aeroelastic analysis
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A Temporal Order Modeling Approach to Human Action Recognition from Multimodal Sensor Data
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The determination of third order linear models from a seventh order nonlinear jet engine model
Results are presented that demonstrate how good reduced-order models can be obtained directly by recursive parameter identification using input/output (I/O) data of high-order nonlinear systems. Three different methods of obtaining a third-order linear model from a seventh-order nonlinear turbojet engine model are compared. The first method is to obtain a linear model from the original model and then reduce the linear model by standard reduction techniques such as residualization and balancing. The second method is to identify directly a third-order linear model by recursive least-squares parameter estimation using I/O data of the original model. The third method is to obtain a reduced-order model from the original model and then linearize the reduced model. Frequency responses are used as the performance measure to evaluate the reduced models. The reduced-order models along with their Bode plots are presented for comparison purposes.
Spacecraft dynamics characterization and control system failure detection. Volume 3: Control system failure monitoring
We discuss the application of Generalized Parity Relations to two experimental flexible space structures, the NASA Langley Mini-Mast and Marshall Space Flight Center ACES mast. We concentrate on the generation of residuals and make no attempt to implement the Decision Function. It should be clear from the examples that are presented whether it would be possible to detect the failure of a specific component. We derive the equations from Generalized Parity Relations. Two special cases are treated: namely, Single Sensor Parity Relations (SSPR) and Double Sensor Parity Relations (DSPR). Generalized Parity Relations for actuators are also derived. The NASA Langley Mini-Mast and the application of SSPR and DSPR to a set of displacement sensors located at the tip of the Mini-Mast are discussed. The performance of a reduced order model that includes the first five models of the mast is compared to a set of parity relations that was identified on a set of input-output data. Both time domain and frequency domain comparisons are made. The effect of the sampling period and model order on the performance of the Residual Generators are also discussed. Failure detection experiments where the sensor set consisted of two gyros and an accelerometer are presented. The effects of model order and sampling frequency are again illustrated. The detection of actuator failures is discussed. We use Generalized Parity Relations to monitor control system component failures on the ACES mast. An overview is given of the Failure Detection Filter and experimental results are discussed. Conclusions and directions for future research are given.
Assessment and development of second order turbulence models
Second order turbulence models describe the effect of mean flow and external agencies (such as buoyancy) on the evolution of turbulence. Therefore, in principle, these models give a more accurate description of complicated flow fields (e.g. flows with large anisotropy in turbulence, such as near the leading edge of a turbine blade) than the two equation models. The objectives of this document are to assess the performance of the various second order turbulence models in benchmark flows and to seek improvements where necessary in models for the pressure correlation term in the scalar flux equation and for the scalar dissipation equation.
Updating High-Order Aeroservoelastic Models from Low-Order System Identification Results
Estimating aircraft models from test data requires several simplifying assumptions that introduce biases into the parameter estimates. In this paper, these biases are defined and a method for estimating the biases is discussed. Having an estimate of the bias allows the parameters estimated from test to be integrated into a high-order model. The Integrated Adaptive Wing Technology Maturation (IAWTM) wind tunnel model is discussed and the bias is demonstrated for one of the testing configurations. The methodology was able to estimate these biases and apply corrections to high-order aeroelastic models to improve the fit to test data. The consideration of the biases allows more meaningful comparisons and avoids the erroneous differences between pretest predictions and the fitted model.
Updating High-Order Aeroservoelastic Models from Low-Order System Identification Results
Estimating aircraft models from test data requires several simplifying assumptions that introduce biases into the parameter estimates. In this paper, these biases are defined and a method for estimating the biases is discussed. Having an estimate of the bias allows the parameters estimated from test to be integrated into a high-order model. The Integrated Adaptive Wing Technology Maturation (IAWTM) wind tunnel model is discussed and the bias is demonstrated for one of the testing configurations. The methodology was able to estimate these biases and apply corrections to high-order aeroelastic models to improve the fit to test data. The consideration of the biases allows more meaningful comparisons and avoids the erroneous differences between pretest predictions and the fitted model.
Singular perturbation analysis of AOTV-related trajectory optimization problems
The problem of real time guidance and optimal control of Aeroassisted Orbit Transfer Vehicles (AOTV's) was addressed using singular perturbation theory as an underlying method of analysis. Trajectories were optimized with the objective of minimum energy expenditure in the atmospheric phase of the maneuver. Two major problem areas were addressed: optimal reentry, and synergetic plane change with aeroglide. For the reentry problem, several reduced order models were analyzed with the objective of optimal changes in heading with minimum energy loss. It was demonstrated that a further model order reduction to a single state model is possible through the application of singular perturbation theory. The optimal solution for the reduced problem defines an optimal altitude profile dependent on the current energy level of the vehicle. A separate boundary layer analysis is used to account for altitude and flight path angle dynamics, and to obtain lift and bank angle control solutions. By considering alternative approximations to solve the boundary layer problem, three guidance laws were derived, each having an analytic feedback form. The guidance laws were evaluated using a Maneuvering Reentry Research Vehicle model and all three laws were found to be near optimal. For the problem of synergetic plane change with aeroglide, a difficult terminal boundary layer control problem arises which to date is found to be analytically intractable. Thus a predictive/corrective solution was developed to satisfy the terminal constraints on altitude and flight path angle. A composite guidance solution was obtained by combining the optimal reentry solution with the predictive/corrective guidance method. Numerical comparisons with the corresponding optimal trajectory solutions show that the resulting performance is very close to optimal. An attempt was made to obtain numerically optimized trajectories for the case where heating rate is constrained. A first order state variable inequality constraint was imposed on the full order AOTV point mass equations of motion, using a simple aerodynamic heating rate model.
Current status of system identification methodology
Large space structures, system identification, model formulation, experimental design, model order and structure determination, parameter estimation, reduced order modeling, and closed loops are considered.
Active rejection of persistent disturbances in flexible space structures
A dynamic compensator for active rejection of persistent disturbances in flexible space structures is designed on the principle of the H(infinity)-optimization of the sensitivity transfer function matrix. A general state space solution is formulated to the multiinput multioutput H(infinity)-optimal control problem, allowing the use of the H(infinity)-optimal synthesis algorithm for the state-space models of space structures that result from model order reduction. Disturbances encountered in flexible space structures, such as shuttle docking, are investigated using the high-mode and the reduced-order models of a cantilevered two-bay truss, demonstrating the applicability of the H(infinity)-optimal approach.
Physical lumping methods for developing linear reduced models for high speed propulsion systems
In gasdynamic systems, information travels in one direction for supersonic flow and in both directions for subsonic flow. A shock occurs at the transition from supersonic to subsonic flow. Thus, to simulate these systems, any simulation method implemented for the quasi-one-dimensional Euler equations must have the ability to capture the shock. In this paper, a technique combining both backward and central differencing is presented. The equations are subsequently linearized about an operating point and formulated into a linear state space model. After proper implementation of the boundary conditions, the model order is reduced from 123 to less than 10 using the Schur method of balancing. Simulations comparing frequency and step response of the reduced order model and the original system models are presented.
The 4th order GISS model of the global atmosphere
The new GISS 4th order model of the global atmosphere is described. It is based on 4th order quadratically conservative differences with the periodic application of a 16th order filter on the sea level pressure and potential temperature equations, a combination which is approximately enstrophy conserving. Several short range forecasts indicate a significant improvement over 2nd order forecasts with the same resolution (approximately 400 km). However the 4th order forecasts are somewhat inferior to 2nd order forecasts with double resolution. This is probably due to the presence of short waves in the range between 1000 km and 2000 km, which are computed more accurately by the 2nd order high resolution model. An operation count of the schemes indicates that with similar code optimization, the 4th order model will require approximately the same amount of computer time as the 2nd order model with the same resolution. It is estimated that the 4th order model with a grid size of 200 km provides enough accuracy to make horizontal truncation errors negligible over a period of a week for all synoptic scales (waves longer than 1000 km).
Exponential order statistic models of software reliability growth
Failure times of a software reliabilty growth process are modeled as order statistics of independent, nonidentically distributed exponential random variables. The Jelinsky-Moranda, Goel-Okumoto, Littlewood, Musa-Okumoto Logarithmic, and Power Law models are all special cases of Exponential Order Statistic Models, but there are many additional examples also. Various characterizations, properties and examples of this class of models are developed and presented.