Browsing by Author "Kauffman, Justin"

Now showing 1 - 4 of 4
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    Improving Deep Learning for Maritime Remote Sensing through Data Augmentation and Latent Space
    Sobien, Daniel; Higgins, Erik; Krometis, Justin; Kauffman, Justin; Freeman, Laura J. (MDPI, 2022-07-07)
    Training deep learning models requires having the right data for the problem and understanding both your data and the models’ performance on that data. Training deep learning models is difficult when data are limited, so in this paper, we seek to answer the following question: how can we train a deep learning model to increase its performance on a targeted area with limited data? We do this by applying rotation data augmentations to a simulated synthetic aperture radar (SAR) image dataset. We use the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique to understand the effects of augmentations on the data in latent space. Using this latent space representation, we can understand the data and choose specific training samples aimed at boosting model performance in targeted under-performing regions without the need to increase training set sizes. Results show that using latent space to choose training data significantly improves model performance in some cases; however, there are other cases where no improvements are made. We show that linking patterns in latent space is a possible predictor of model performance, but results require some experimentation and domain knowledge to determine the best options.
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    Multi-Physics Modeling of Electrochemical Deposition
    Kauffman, Justin; Gilbert, John; Paterson, Eric G. (MDPI, 2020-12-11)
    Electrochemical deposition (ECD) is a common method used in the field of microelectronics to grow metallic coatings on an electrode. The deposition process occurs in an electrolyte bath where dissolved ions of the depositing material are suspended in an acid while an electric current is applied to the electrodes. The proposed computational model uses the finite volume method and the finite area method to predict copper growth on the plating surface without the use of a level set method or deforming mesh because the amount of copper layer growth is not expected to impact the fluid motion. The finite area method enables the solver to track the growth of the copper layer and uses the current density as a forcing function for an electric potential field on the plating surface. The current density at the electrolyte-plating surface interface is converged within each PISO (Pressure Implicit with Splitting Operator) loop iteration and incorporates the variance of the electrical resistance that occurs via the growth of the copper layer. This paper demonstrates the application of the finite area method for an ECD problem and additionally incorporates coupling between fluid mechanics, ionic diffusion, and electrochemistry.
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    The Quantized Velocity Finite Element Method
    Cook, Charles (Virginia Tech, 2024-04-23)
    The Euler and Navier-Stokes-Fourier equations will be directly expressed as distribution evolution equations, where a new and proper continuum prescription will be derived. These equations of motion will be numerically solved with the development of a new and unique finite element formulation. Out of this framework, the 7D phasetime element has been born. To provide optimal stability, a new quantization procedure is established based on the principles of quantum theory. The entirety of this framework has been coined the "quantized velocity finite element method" (QVFEM). The work performed herein lays the foundational development of what is hoped to become a new paradigm shift in computational fluid dynamics.
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    Shape Matching for Reduced Order Models of High-Speed Fluid Flows
    Dennis, Ethan James (Virginia Tech, 2024-08-30)
    While computational fluid dynamics (CFD) simulations are an indispensable tool in modern aerospace engineering design, they bear a severe computational burden in applications where simulation results must be found quickly or repeatedly. Therefore, creating computationally inexpensive models that can capture complex fluid behaviors is a long-sought-after goal. As a result, methods to construct these reduced order models (ROMs) have seen increasing research interest. Still, parameter dependent high-speed flows that contain shock waves are a particularly challenging class of problems that introduces many complications in ROM frameworks. To make approximations in a linear space, ROM techniques for these problems require that basis functions are transformed such that discontinuities are aligned into a consistent reference frame. Techniques to construct these transformations, however, fail when the topology of shocks is not consistent between data snapshots. In this work, we first identify key features of these topology changes, and how that constrains transformations of this kind. We then construct a new modeling framework that can effectively deal with shockwave interactions that are known to cause failures. The capabilities of the resulting model were evaluated by analyzing supersonic flows over a wedge and a forward-facing step. In the case of the forward-facing step, when shock topology changes with Mach number, our method exhibits significant accuracy improvements. Suggestions for further developments and improvements to our methodology are also identified and discussed