scholarly journals Flight Stability Analysis of a Flexible Rocket Using Finite Elements and Reduced-Order Modeling

2021 ◽  
Author(s):  
Kyle Davidson
Keyword(s):  
Solution Process ◽  
Reduced Order Modeling ◽  
Flight Simulation ◽  
Aerospace Vehicles ◽  
Reduced Order ◽  
Aerodynamic Loading ◽  
The Stability ◽  
Fin Thickness ◽  
Programming Package ◽  
Primary Instability

The coupling of advanced structural and aerodynamic methods is a complex and computationally demanding task. In many cases, simplifications must be made. For the flight simulation of flexible aerospace vehicles, it is common to reduce the overall structure down to a series of linked degenerate structures such as Euler-Bernoulli beams in order to expedite the structural portion of the solution process. The current study employs the sophistication and generality of finite-element based modeling with the concepts of reduced-order modeling to create a general flexible-body flight simulation program. The program was created for use with the MATLAB-Simulink programming package. A parametric analysis on the stability of flexible rockets is performed and results are presented for a variety of rocket configurations based on the SPHADS-1 vehicle under development at Ryerson University. The primary instability mode under study is that associated with the flapping and twisting motions of the tailfins under aerodynamic loading. By varying the average fin thickness, both stable and unstable behaviour is recorded for a variety of flight conditions.

scholarly journals Flight Stability Analysis of a Flexible Rocket Using Finite Elements and Reduced-Order Modeling

2021 ◽  
Author(s):  
Kyle Davidson
Keyword(s):  
Solution Process ◽  
Reduced Order Modeling ◽  
Flight Simulation ◽  
Aerospace Vehicles ◽  
Reduced Order ◽  
Aerodynamic Loading ◽  
The Stability ◽  
Fin Thickness ◽  
Programming Package ◽  
Primary Instability

The coupling of advanced structural and aerodynamic methods is a complex and computationally demanding task. In many cases, simplifications must be made. For the flight simulation of flexible aerospace vehicles, it is common to reduce the overall structure down to a series of linked degenerate structures such as Euler-Bernoulli beams in order to expedite the structural portion of the solution process. The current study employs the sophistication and generality of finite-element based modeling with the concepts of reduced-order modeling to create a general flexible-body flight simulation program. The program was created for use with the MATLAB-Simulink programming package. A parametric analysis on the stability of flexible rockets is performed and results are presented for a variety of rocket configurations based on the SPHADS-1 vehicle under development at Ryerson University. The primary instability mode under study is that associated with the flapping and twisting motions of the tailfins under aerodynamic loading. By varying the average fin thickness, both stable and unstable behaviour is recorded for a variety of flight conditions.

Lattice Boltzmann Flow Simulations With Applications of Reduced Order Modeling Techniques.

2014 ◽  
Author(s):  
Donald L. Brown ◽  
Jun Li ◽  
Victor M. Calo ◽  
Mehdi Ghommem ◽  
Yalchin Efendiev
Keyword(s):  
Lattice Boltzmann ◽  
Reduced Order Modeling ◽  
Reduced Order ◽  
Flow Simulations ◽  
Modeling Techniques

Machine learning–based reduced-order modeling of hydrodynamic forces using pressure mode decomposition

2021 ◽  
pp. 095441002199986
Author(s):  
Hassan F Ahmed ◽  
Hamayun Farooq ◽  
Imran Akhtar ◽  
Zafar Bangash
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Stokes Equations ◽  
Decomposition Analysis ◽  
Hydrodynamic Forces ◽  
Reduced Order Modeling ◽  
Navier Stokes ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Lift And Drag

In this article, we introduce a machine learning–based reduced-order modeling (ML-ROM) framework through the integration of proper orthogonal decomposition (POD) and deep neural networks (DNNs), in addition to long short-term memory (LSTM) networks. The DNN is utilized to upscale POD temporal coefficients and their respective spatial modes to account for the dynamics represented by the truncated modes. In the second part of the algorithm, temporal evolution of the POD coefficients is obtained by recursively predicting their future states using an LSTM network. The proposed model (ML-ROM) is tested for flow past a circular cylinder characterized by the Navier–Stokes equations. We perform pressure mode decomposition analysis on the flow data using both POD and ML-ROM to predict hydrodynamic forces and demonstrate the accuracy of the proposed strategy for modeling lift and drag coefficients.

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