Browsing by Subject "Python"

Now showing 1 - 5 of 5
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    AI Implementation in Digital Microfluidics
    (2021) Hein, Henry R
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    The Expansion of Digital Microfluidic Systems
    (2021) Anderson, Chase
    Digital microfluidics (DMF) is a technology that allows for movement and manipulation of liquid using electricity. By charging and discharging conductive pads upon a grid, one is able translate, split, and mix droplets to perform conventional wet lab operations. By minimizing handling time, chemical dangers, and laboratory wastes (e.g. pipette tips); DMF stands at the forefront of modern bio-chemical implementations. Most DMF prototypes/platforms utilize a single grid space, and this should change. In hopes to connect 4 ,16 , 64, or n-many grids for that matter; current DMF designs do not have required infrastructure. Each pad must be charged to upwards of 300 volts, so hardware, separate from the brains of the system, is required to facilitate normal operation. Like the heart, DMF platforms operate on beats and must be synchronous. Often, a microcontroller (Arduino, PIC, STM32) is used; however, microcontrollers operate sequentially, meaning each instruction given to the machine must execute one after the other. If one wished to parallelize the process of DMF, a separate computing paradigm must be used. Enter the FPGA (Field Programmable Gate Array). Being able to process multiple lightweight channels of data at a time, the FPGA enables the end user to have thousands if not millions of droplets moving synchronously across hundreds of grid systems. The research task was to find an elegant solution to the problem of parallelizing DMF systems using FPGAs.
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    Python Attitude Heading and Reference System
    (2014-07-23) Taylor, Brian
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    A Python Implementation of a Drift-Diffusion Model to Capture Ion Migration in Perovskite Solar Cells
    (2021-06) Anderson, Nathan
    Charge carrier dynamics and ion migration are attributed to the current-voltage hysteresis in perovskite solar cells (PSCs). This study implements a drift-diffusion model in Python to simulate the characteristic current-voltage scans for realistic device architectures. The novel work in this research involve the integration of a transfer-matrix optical model to the drift-diffusion model with ion migration, the implementation of a Radau 5th order solver to the method of lines, and a demonstration that standard Python libraries can handle stiff systems of differential algebraic equations. A comparative analysis with published works was conducted to validate the algorithm. It was found that the simulation was able to capture fast carrier dynamics under a variety of experimental conditions. Lastly, it is shown that the model captures physically relevant trends in PSCs.
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    Streamlined structural design optimization with interactive visualization for multi-objective design spaces
    (2024) Suliin, Jacob
    A common task for design engineers is the balancing of conflicting design objectives through the application of design of experiment (DOE) based methods to explore a design space. Traditional approaches to this problem require modeling the geometry with graphical user interface (GUI) based modeling software to generate the DOE runs. Visualizing these design tradeoffs, particularly when there are more than two objectives, is also a challenging task that often needs to be done manually. This thesis provides a streamlined approach to structural design optimization with interactive plotting for multi-objective design spaces, enabling engineers to easily visualize the objective space and identify optimal solutions. Utilizing Python and its available libraries, in conjunction with the innovative Jupyter Notebook environment, parameterized geometry is generated using Python code, and a static structural analysis is performed through a DOE. The resulting dataset from the DOE features five output objectives and is used in an optimization process generating Pareto optimal solutions, and thus a Pareto front for given pairs of constraints is generated. An interactive 2D scatter plot is generated with the Pareto front data, allowing users to quickly investigate the relationships between the inputs and output objectives. Additionally, two approaches for visualizing the tradeoff between more than two objectives are presented, enabling an informed decision to be made when selecting a candidate solution when balancing more than two objectives. These approaches are demonstrated on two example structural design problems. Finally, the potential application of large language models (LLMs) to the code based approach used in this thesis is discussed.