A high fidelity video delivery system for real-time flight simulation research

This paper presents a mathematical model for the simulation of rotor blade flexibility in real-time helicopter flight dynamics applications that also employs sufficient modeling fidelity for prediction of structural blade loads. A matrix/vector-based formulation is developed for the treatment of elastic blade kinematics in the time domain. A novel, second-order-accurate, finite-difference scheme is employed for the approximation of the blade motion derivatives. The proposed method is coupled with a finite-state induced-flow model, a dynamic wake distortion model, and an unsteady blade element aerodynamics model. The integrated approach is deployed to investigate trim controls, stability and control derivatives, nonlinear control response characteristics, and structural blade loads for a hingeless rotor helicopter. It is shown that the developed methodology exhibits modeling accuracy comparable to that of non-real-time comprehensive rotorcraft codes. The proposed method is suitable for real-time flight simulation, with sufficient fidelity for simultaneous prediction of oscillatory blade loads.

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Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

Volume 2B: 44th Design Automation Conference ◽

10.1115/detc2018-86043 ◽

2018 ◽

Author(s):

Xiangxue Zhao ◽

Shapour Azarm ◽

Balakumar Balachandran

Keyword(s):

Dynamical System ◽

Real Time ◽

Measurement Data ◽

Data Driven ◽

Sensor Data ◽

Model Parameters ◽

High Fidelity ◽

System Behavior ◽

Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

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