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research.html
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<html>
<title>FerguNet</title>
<head>
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<header>Research</header>
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<!-- Main section header -->
<div class='body-content'>
<!-- RESEARCH INTERESTS -->
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<div class='block-section-title'>Research Interests</div>
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My research interests can be most broadly summarized as a general interest in the fields of fluid mechanics and turbulence. I have existing experience in both experimental and computational techniques for studying turbulent flows and turbulent mixing, and I am interested in expanding upon this experience to branch into additional fields of interest. The following sections provide a more in-depth view of what these fields of interest are and what I would like to study within them.<p>
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Turbulence & Model Development
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I am broadly interested in many aspects of turbulence as it appears in numerous fluid flows. This includes the study of turbulent flows using experimental or numerical methods in order to study the physics of these flows and the factors that influence them. I am also interested in utilizing these techniques to develop novel models to describe turbulent flow, ranging from more traditional RANS and LES models, to statistical descriptions of turbulent flow, and machine learning based model development (see below).
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Machine Learning in Fluid Mechanics
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The rise of artificial intelligence, machine learning, and neural networks in the last several years has been hard to miss. While generative networks such as ChatGPT have been most evident to the general public, there also appears to be substantial interest and promise in the application of this class of tools to topics of scientific relevance. My interests here are largely related to the application of physics-informed machine learning to experimental data with the hope of exploring the development of predictive turbulence models.
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Geophysical Fluid Mechanics & Atmospheric Turbulence
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My interests here are fairly broad, but can be generally summarized as an interest in the role of turbulence in atmospheric phenomena. In particular, I am interested in the role of turbulence in meteorological processes such as cloud formation and storm genesis, as well as how local conditions and their history influence observations. I am interested in both laboratory-scale experiments and full-scale field measurements to perform these studies, as well as in the comparisons between these experiments and numerical simulation results.
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Experimental Diagnostic Development
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Acquisition of high quality, high fidelity experimental data is useful in numerous contexts, from measurement of real-world flow processes to data suitable for simulation validation. Unfortunately, this data is often subject to limited resolution, high noise levels, and measurement uncertainty, complicating the usefulness of this information. In addition, the sensors used to take these measurements are often prohibitively expensive, further complicating data acquisition. I am interested in the design and development of new experimental analysis methods, sensors, and techniques with the hope of improving the types, quantities, and quality of experimental data that can be acquired. I am particularly interested in non-invasive in-situ measurements, as well as the development of low-cost sensing tools.
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Open Source Collaboration & Development
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I am interested in the development and use of standardized scientific data formats as this facilitates open data exchange, results verification, and simplifies the process of building upon existing data. While there are a great many standardized formats that are suitable for scientific data, ranging from simple comma-separated variable files to more complicated <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5</a> files, the schemas used to store (and interpret) this data is far from standardized in the scientific community. This often results in difficulty interpreting existing data when shared between groups or even individual researchers. It is my hope to develop libraries to facilitate standard methods for data storage and interpretation. In particular, I am interested in defining standardized data structures for use in the HDF5 format, and developing libraries in multiple languages to make this schema easy to interpret for other scientists.
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<!-- SKILLS AND EXPERIENCE -->
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<div class='block-section-title'>Technical Skills and Experience</div>
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This is a general overview of the skills and experience I possess, ranging from research subject fields I have been involved with to technical skills I have developed. This list should not be considered exhaustive, but it should give a sense of my existing skillsets and abilities. I have included links to relevant resources for more specific/niche skills and experiences in this list for further context.<p>
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Subject Fields
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Fields of research that I have experience with:
<ul class='block-list'>
<li>Fluid Mechanics (Incompressible)</li>
<li>Fluid Mechanics (Compressible)</li>
<li>Turbulence</li>
<li>Turbulent Mixing</li>
<li>Shock Tubes</li>
<li><a href="https://en.wikipedia.org/wiki/Richtmyer%E2%80%93Meshkov_instability" target="_blank">Richtmyer-Meshkov Instability</a></li>
<li><a href="https://en.wikipedia.org/wiki/Rayleigh%E2%80%93Taylor_instability" target="_blank">Rayleigh-Taylor Instability</a></li>
</ul>
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Experimental Research
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Skills that I have developed related to designing and conducting experiments as well as processing and analyzing experimental data:
<ul class='block-list'>
<li>Experimental Apparatus Design</li>
<li>Mechanical Design & Tolerancing</li>
<li>Laser-based diagnostics</li>
<li>Particle Image Velocimetry</li>
<li>Mie Scattering</li>
<li>Noise reduction</li>
<li>Basic machining & CNC</li>
<li>3D printing</li>
<li>Basic circuit design</li>
<li>Microcontroller-based timing & control</li>
<li><a href="https://en.wikipedia.org/wiki/Field-programmable_gate_array">FPGA</a></li>
<li>Uncertainty Quantification</li>
<li>Uncertainty Propagation</li>
</ul>
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Programming
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Various programming languages and libarires that I am familiar with:
<ul class='block-list'>
<li>Julia</li>
<li>DataFrames.jl</li>
<li>Python</li>
<li>Pandas</li>
<li>Numpy/Scipy</li>
<li>HDF5</li>
<li>NetCDF</li>
<li>LaTeX</li>
<li>C</li>
<li>C++</li>
<li>Cocoa</li>
<li>Swift</li>
<li>MATLAB/Octave</li>
<li>HTML</li>
<li>Javascript</li>
<li>Bash</li>
<li>Arduino</li>
<li>FPGA (Verilog)</li>
</ul>
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<!-- HPC & Simulation -->
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HPC & Simulation
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Codes, libraries, and techniques related to research and analysis conducted using high performance computing resources that I have worked with:
<ul class='block-list'>
<li>OpenMP</li>
<li>OpenMPI</li>
<li>OpenCL</li>
<li>CMA-ES</li>
<li>VisIt</li>
<li>Large scale simulation</li>
<li>GPU Acceleration</li>
</ul><p>
Additionally, I hold a Ph.D minor in Applied Mathematics with focus on methods for analysis and validation of numerical methods for PDE integration, matrix inversion, conditioning, and filtering.
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