Scholarly Works, Aerospace and Ocean Engineering

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https://hdl.handle.net/10919/23164
Research articles, presentations, and other scholarship

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Browsing Scholarly Works, Aerospace and Ocean Engineering by Subject "0913 Mechanical Engineering"

Now showing 1 - 4 of 4
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    Acoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier-Stokes-Based Jet Noise Prediction
    Zhang, Xin-Lei; Xiao, Heng; Wu, Ting; He, Guowei (American Institute of Aeronautics and Astronautics, 2021-12-13)
    The Reynolds-averaged Navier–Stokes (RANS)-based method is a practical tool to provide rapid assessment of jet noise-reduction concepts. However, the RANS-based method requires modeling assumptions to represent noise generation and propagation, which often reduces the predictive accuracy due to the model-form uncertainties. In this work, the ensemble Kalman filter-based acoustic inversion method is introduced to reduce uncertainties in the turbulent kinetic energy and dissipation rate based on the far-field noise and the axial centerline velocity data. The results show that jet noise data are more effective from which to infer turbulent kinetic energy and dissipation rate compared to velocity data. Moreover, the inferred noise source is able to improve the estimation of the turbulent flowfield and the far-field noise at unobserved locations. Further, the noise model parameters are also considered uncertain quantities, demonstrating the ability of the proposed framework to reduce uncertainties in both the RANS and noise models. Finally, one realistic case with experimental data is investigated to show the practicality of the proposed framework. The method opens up the possibility for the inverse modeling of jet noise sources by incorporating far-field noise data that are relatively straightforward to be measured compared to the velocity field.
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    Analytic Costate Initialization from Rough State-Trajectory Estimates
    Skamangas, Emmanuel E.; Lawton, John A.; Black, Jonathan T. (American Institute of Aeronautics and Astronautics, 2021-09-07)
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    An Approach for Computing Parameters for a Lagrangian Nonlinear Maneuvering and Seakeeping Model of Submerged Vessel Motion
    Jung, Seyong; Brizzolara, Stefano; Woolsey, Craig A. (IEEE, 2021-07-01)
    In this study, hydrodynamic forces on a submerged vessel maneuvering near a free surface are determined using a reformulated Lagrangian nonlinear maneuvering and seakeeping model derived using Lagrangian mechanics under ideal flow assumptions. A Lagrangian mechanics maneuvering model is first reformulated to simplify the computation of parameters; then, incident wave effects are incorporated into the reformulation; finally, the parameters are computed using a medium-fidelity time-domain potential-flow panel code. Predictions from the reformulated Lagrangian nonlinear maneuvering and seakeeping model, whose parameters are computed using the methods described here, are compared with direct numerical computations in two steps for a prolate spheroid maneuvering in the longitudinal plane near the free surface. First, the hydrodynamic force and moment predicted by the model are compared with solutions from the panel code for sinusoidal motion in surge, heave, and pitch in calm water. Second, the hydrodynamic force and moment are investigated for cases where the spheroid maneuvers to approach the surface in calm water and in plane progressive waves. To conclude, a physically intuitive formulation of the Lagrangian nonlinear maneuvering and seakeeping model is presented for control applications and simulations.
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    Neural network based pore flow field prediction in porous media using super resolution
    Zhou, Xu-Hui X.; McClure, James; Chen, Cheng; Xiao, Heng (2021)
    Previous works have demonstrated using the geometry of the microstructure of porous media to predict the ow velocity fields therein based on neural networks. However, such schemes are purely based on geometric information without accounting for the physical constraints on the velocity fields such as that due to mass conservation. In this work, we propose using a super-resolution technique to enhance the velocity field prediction by utilizing coarse-mesh velocity fields, which are often available inexpensively but carry important physical constraints. We apply our method to predict velocity fields in complex porous media. The results demonstrate that incorporating the coarse-mesh flow field significantly improves the prediction accuracy of the fine-mesh flow field as compared to predictions that rely on geometric information alone. This study highlights the merits of including coarse-mesh flow field with physical constraints embedded in it.