ME-43600 4ST: Aero-Thermal Fluids Laboratory
This webpage acts as a supplement to ME-43600: Aero-Thermal Fluids Lab. Concepts of fluid dynamics, heat transfer, and thermodynamics are explored using a wind tunnel, a refrigeration unit, a centrifugal pump-turbine unit, a pipe flow unit, a fin heat transfer device and a heat exchanger. All investigations and design projects are performed in class groups and presented in oral and/or written form. The projects shown on this page can be completed on an individual basis to further enrich the student's education relating to engineering.
In this continuation of the audio processing in Python series, I will be discussing the live frequency spectrum and its application to tuning a guitar. I will introduce the idea of nodes and antinodes of a stringed instrument and the physical phenomena known as harmonics. This will give us a better idea of how to tune the guitar string-by-string and also discern the notes of a given chord - all calculated using the FFT function in Python.
Raspberry Pi 3B+ acoustic analysis using Python. Audio recording and signal processing with Python, beginning with a discussion of windowing and sampling, which will outline the limitations of the Fourier space representation of a signal. Discussion of the frequency spectrum, and weighting phenomenon in relation to the human auditory system will also be explored. Lastly, the significance of microphone pressure units and conversion to the decibel will be briefly introduced and explained.
Fourier Series has been widespread in applications of engineering ranging from heat transfer, vibration analysis, fluid mechanics, noise control, and much more. After evolutions in computation and algorithm development, the use of the Fast Fourier Transform (FFT) has also become ubiquitous in applications in acoustic analysis and even turbulence research. In this tutorial, I describe the basic process for emulating a sampled signal and then processing that signal using the FFT algorithm in Python. This will allow the user to get started with analysis of acoustic-like signals and understand the fundamentals of the Fast Fourier Transform.