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A Tool for the Parametric Modelling of Aircraft Landing Gear

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This paper outlines the development of a script which utilizes a series of user-defined input parameters to construct base-level CAD models of aircraft landing gear. With an increased focus on computation development of aircraft models to allow for a rapidprototyping

This paper outlines the development of a script which utilizes a series of user-defined input parameters to construct base-level CAD models of aircraft landing gear. With an increased focus on computation development of aircraft models to allow for a rapidprototyping design process, this program seeks to allow designers to check for the validity of design integration before moving forward on systems testing. With this script, users are able to visually analyze the landing gear configurations on an aircraft in both the gear up and gear down configuration. The primary purpose this serves is to determine the validity of the gear's potential to fit within the limited real estate on an aircraft body. This, theoretically, can save time by weeding out infeasible designs before moving forward with subsystem performance testing. The script, developed in Python, constructs CAD models of dual and dual-tandem main landing gear configurations in the CAD program Rhino5. With an included design template consisting of 33 parameters, the script allows for a reasonable trade off between conciseness and flexibility of design.

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2018-05

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Determining Emotive Correlates in Music through Music Information Retrieval and Artificial Intelligence

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Advances in computational processing have made big data analysis in fields like music information retrieval (MIR) possible. Through MIR techniques researchers have been able to study information on a song, its musical parameters, the metadata generated by the song's listeners,

Advances in computational processing have made big data analysis in fields like music information retrieval (MIR) possible. Through MIR techniques researchers have been able to study information on a song, its musical parameters, the metadata generated by the song's listeners, and contextual data regarding the artists and listeners (Schedl, 2014). MIR research techniques have been applied within the field of music and emotions research to help analyze the correlative properties between the music information and the emotional output. By pairing methods within music and emotions research with the analysis of the musical features extracted through MIR, researchers have developed predictive models for emotions within a musical piece. This research has increased our understanding of the correlative properties of certain musical features like pitch, timbre, rhythm, dynamics, mel frequency cepstral coefficients (MFCC's), and others, to the emotions evoked by music (Lartillot 2008; Schedl 2014) This understanding of the correlative properties has enabled researchers to generate predictive models of emotion within music based on listeners' emotional response to it. However, robust models that account for a user's individualized emotional experience and the semantic nuances of emotional categorization have eluded the research community (London, 2001). To address these two main issues, more advanced analytical methods have been employed. In this article we will look at two of these more advanced analytical methods, machine learning algorithms and deep learning techniques, and discuss the effect that they have had on music and emotions research (Murthy, 2018). Current trends within MIR research, the application of support vector machines and neural networks, will also be assessed to explain how these methods help to address the two main issues within music and emotion research. Finally, future research within the field of machine and deep learning will be postulated to show how individuate models may be developed from a user or a pool of user's listening libraries. Also how developments of semi-supervised classification models that assess categorization by cluster instead of by nominal data, may be helpful in addressing the nuances of emotional categorization.

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2018-12

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Design of Monocoque Chassis for Formula SAE

Description

The project consists of steps that a Formula SAE team could take into developing their first carbon fiber monocoque chassis. The project is based on an interview with a successful team that has build carbon monocoques for the last several

The project consists of steps that a Formula SAE team could take into developing their first carbon fiber monocoque chassis. The project is based on an interview with a successful team that has build carbon monocoques for the last several years. The project covers the steps into designing a carbon monocoque, including aspects that need to be highlighted in the design process as well as an outline of the overall rules and regulations regarding carbon fiber monocoques. The project also encompasses simple finite element analysis procedure that would introduce teams into carbon fiber composite sandwich analysis and its applications in racecar monocoques. The project also includes steps in manufacturing a carbon fiber monocoque beginning from methods to acquire necessary materials to the final process of de-molding the monocoque. The method has been used before from several FSAE teams, proving its viability. The goal is that through this report, teams could have an idea of where to start in developing their carbon monocoques and have a clear path to take on going from initial designs up until a final finished product.

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2018-05

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Leading Edge Geometry Effects on Pressure Drag and Pressure Thrust for Various Wing Geometries

Description

The purpose of this paper is to discover what geometric characteristics of a wing and airfoil help to maximize leading edge suction through experimental testing. Three different stages of testing were conducted: a Proof of Concept, a Primary Experiment, and

The purpose of this paper is to discover what geometric characteristics of a wing and airfoil help to maximize leading edge suction through experimental testing. Three different stages of testing were conducted: a Proof of Concept, a Primary Experiment, and a Secondary Experiment. The Proof of Concept shows the effects of leading edge suction and the benefits it can posses. The Primary Experiment provided inconclusive data due to inaccuracies in the equipment. As a result, the Secondary Experiment was conducted in order to reduce the error effect as much as possible on the data. Unfortunately the Secondary Experiment provided inaccurate data as well. However, this paper does provide enough evidence to begin to question some of the long held beliefs regarding theoretical induced drag and whether it is true under all circumstances, or if it is only a good approximation for airfoils with full leading-edge suction effects.

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2017-05

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Study of Exhaust Throttling Effects on SI Engine Performance

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To determine the effects of exhaust heat recovery systems on small engines, an experiment was performed to measure the power losses of an engine with restricted exhaust flow. In cooperation with ASU's SAE Formula race team, a water brake dynamometer

To determine the effects of exhaust heat recovery systems on small engines, an experiment was performed to measure the power losses of an engine with restricted exhaust flow. In cooperation with ASU's SAE Formula race team, a water brake dynamometer was refurbished and connected to the 2017 racecar engine. The engine was mounted with a diffuser disc exhaust to restrict flow, and a pressure sensor was installed in the O2 port to measure pressure under different restrictions. During testing, problems with the equipment prevented suitable from being generated. Using failure root cause analysis, the failure modes were identified and plans were made to resolve those issues. While no useful data was generated, the project successfully rebuilt a dynamometer for students to use for future engine research.

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2017-05