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Full-Text Articles in Mechanical Engineering
Sustainable Aviation Fuels Approval Streamlining: Auxiliary Power Unit Lean Blowout Testing, Erin E. Peiffer, Joshua S. Heyne, Meredith Colket
Sustainable Aviation Fuels Approval Streamlining: Auxiliary Power Unit Lean Blowout Testing, Erin E. Peiffer, Joshua S. Heyne, Meredith Colket
Mechanical and Aerospace Engineering Faculty Publications
An underpinning hindrance in the market penetration of sustainable aviation fuel is the approval process for alternative jet fuels. One solution to this is to develop low-cost screening tools that can be implemented earlier in the approval process. Auxiliary power unit combustors historically show the most sensitivity to physical and volatile fuel properties, making it a useful tool in assessing potential alternative jet fuel effects at test conditions representative of operability stability limits. It is hypothesized that these observations can be explained via timescale analysis considering fuel droplet breakup and evaporation, combustor mixing, and chemical reactivity timescales on the progression …
Energy Analysis And Orbit Simulation Of Actuating Cubesat Solar Arrays, Justin T. Ehren
Energy Analysis And Orbit Simulation Of Actuating Cubesat Solar Arrays, Justin T. Ehren
Honors Theses
CubeSats are used in space research to explore new technologies and detect data to gain a better understanding of various areas of research and subjects affecting human life. CubeSats rely on a solar array to generate energy from the sun and perform their various functions in space. This research studies the energy capturing potential of various solar array configurations and positioning devices for CubeSats. The location and orientation of a CubeSat is simulated with MATLAB for both geo-synchronous and sunsynchronous orbits. Two degree-of-freedom (DoF) positioning devices are sufficient to continuously adjust the photovoltaic array to face towards the sun. Lower …
Towards A Pre-Processing Algorithm For Automated Arrhythmia Detection, Sarah Miller
Towards A Pre-Processing Algorithm For Automated Arrhythmia Detection, Sarah Miller
Honors Theses
There are a variety of different wearable fitness/cardiac monitoring devices that are currently used in many people’s day to day life. The primary cardiac function of these devices is to monitor heart rate, however we believe that they could be utilized to detect different forms of arrhythmia. In order to categorize and identify different forms of arrhythmia, we are utilizing published EKG data sets from existing databases as a basis for machine learning. The challenge that comes from the existing data sets is that the format they present the data in does not lend itself to machine learning, which requires …