The Acme of the Catholic Left: Catholic Activists in the US Sanctuary Movement, 1982-1992

Honors (Bachelor's)HistoryUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/91814/1/aarbekem.pd

Rapid gust response simulation of large civil aircraft using computational fluid dynamics

ABSTRACTSeveral critical load cases during the aircraft design process result from atmospheric turbulence. Thus, rapidly performable and highly accurate dynamic response simulations are required to analyse a wide range of parameters. A method is proposed to predict dynamic loads on an elastically trimmed, large civil aircraft using computational fluid dynamics in conjunction with model reduction. A small-sized modal basis is computed by sampling the aerodynamic response at discrete frequencies and applying proper orthogonal decomposition. The linear operator of the Reynolds-averaged Navier-Stokes equations plus turbulence model is then projected onto the subspace spanned by this basis. The resulting reduced system is solved at an arbitrary number of frequencies to analyse responses to 1-cos gusts very efficiently. Lift coefficient and surface pressure distribution are compared with full-order, non-linear, unsteady time-marching simulations to verify the method. Overall, the reduced-order model predicts highly accurate global coefficients and surface loads at a fraction of the computational cost, which is an important step towards the aircraft loads process relying on computational fluid dynamics.</jats:p

Physics-Informed Neural Networks for Parametric Compressible Euler Equations

The numerical approximation of solutions to the compressible Euler and Navier-Stokes equations is a crucial but challenging task with relevance in various fields of science and engineering. Recently, methods from deep learning have been successfully employed for solving partial differential equations by incorporating the equations into a loss function that is minimized during the training of a neural network. This approach yields a so-called physics-informed neural network. It is not based upon classical discretizations, such as finite-volume or finite-element schemes, and can even address parametric problems in a straightforward manner. This has raised the question, whether physics-informed neural networks may be a viable alternative to conventional methods for computational fluid dynamics. In this article we introduce an adaptive artificial viscosity reduction procedure for physics-informed neural networks enabling approximate parametric solutions for forward problems governed by the stationary two-dimensional Euler equations in sub- and supersonic conditions. To the best of our knowledge, this is the first time that the concept of artificial viscosity in physics-informed neural networks is successfully applied to a complex system of conservation laws in more than one dimension. Moreover, we highlight the unique ability of this method to solve forward problems in a continuous parameter space. The presented methodology takes the next step of bringing physics-informed neural networks closer towards realistic compressible flow applications

Model reduction for gust load analysis of free-flying aircraft

The coupling of computational fluid dynamics and rigid body dynamics promises enhanced multidisciplinary simulation capability for aircraft design and certification. Industrial application of such coupled simulations is limited however by computational cost. In this context, model reduction can retain the fidelity of the underlying model while decreasing the computational effort. A model reduction technique is presented herein based on modal decomposition and projection of the non-linear residual function. Flight dynamics eigenmodes are obtained with an operator-based identification procedure which is capable of calculating these global modes of the coupled Jacobian matrix also for an industrial use case with nearly 50 million degrees-of-freedom. Additional modes based on proper orthogonal decomposition to describe the aerodynamic response due to gust encounter are combined with the eigenmode basis. Results are presented for initial disturbance analysis using flight dynamics modes only and for gust encounter simulations using the combined modal basis. Overall, the reduced model is capable of predicting the full order results accurately

Rapid Computational Aerodynamics for Aircraft Gust Response Analysis

Computational engineering methods play a more and more important role in building aircraft that move people and goods. Particular in high-speed civil air transport increased usage of higher fidelity simulation tools are expected to enable greener designs with a reduced environmental footprint. The challenge of including computational fluid dynamics in aircraft loads and aeroelasticity is addressed herein. During the aircraft design and certification process a tremendous number of dynamic responses to atmospheric turbulence need to be analysed. Current industrial loads computations are based on corrected linear potential flow methods which offer fast predictions but suffer several drawbacks once aerodynamic non-linearities occur. Instead, aerodynamic loads offered by computational fluid dynamics are highly accurate also at these non-linear conditions. However, computational cost necessary for performing time-marching simulations makes these methods prohibitive for unsteady loads in an industrial context. This work addresses how to efficiently introduce computational fluid dynamics based aerodynamics during gust loads analysis. It is shown that using frequency domain methods in conjunction with reduced order modelling techniques based on modal decomposition and projection offer accurate models which can be analysed at low cost. The three requirements of such an industrial gust loads process are, first, the need for high accuracy, secondly, a significant reduction of runtime compared to unsteady full order time-marching simulations, and thirdly, the ability to automatise the generation and solution process of the reduced model as well as the design and certification process. Therefore, the linearised frequency domain method is extended towards gust responses by altering the right-hand side forcing term. An aerodynamic reduced order model is constructed by computing a modal basis using proper orthogonal decomposition and projecting the linearised equations afterwards. Finally, a coupled aeroelastic model is obtained by combining the aerodynamics model with eigenmodes of the coupled Jacobian matrix for the structural vibration and projecting the coupled linearised equations. The final small sized aeroelastic model enables the inclusion of highly accurate loads during time-critical gust loads analysis and provides the opportunity to introduce these loads in a wider multidisciplinary context. Thus it is a substantial step towards establishing computational fluid dynamics for unsteady aeroelastic analysis