Session: ASME Undergraduate Student Design Expo

Paper Number: 166912

Payload Design and Control for Atmospheric Sensor Platform 

The payload system in this research project is designed to serve as an autonomous atmospheric sensing platform, integrating advanced descent control, environmental monitoring, and data transmission capabilities. This research project aims to develop and test a compact autonomous atmospheric sensing platform designed for deployment at high altitudes with robust descent control and real-time environmental monitoring capabilities. The system consists of a modular container-payload assembly, where the payload is aerodynamically structured with a nose cone, fitting securely within the container for optimal stability during ascent. At peak altitude, the container will deploy a parachute to initiate a controlled descent rate of 20 meters per second, mitigating descent acceleration forces. Subsequently, at 75% of the maximum altitude, the payload separates from the container, transitioning to an auto-gyro descent system engineered to stabilize at a precise descent rate of 5 meters per second, achieving dynamic equilibrium under rotational descent forces.

Two stabilized cameras are integrated within the payload to document critical phases of deployment and descent. One camera captures the separation event and parachute deployment, while the second camera, angled downward at 45 degrees from the nadir and oriented north, provides a spin-stabilized view of the Earth’s surface during descent. This visual data supports real-time validation of descent mechanics and assists in post-flight trajectory analysis.

A comprehensive suite of sensors, including temperature, battery voltage, altitude, auto-gyro rotation rate, acceleration, magnetic field, and GPS modules, enables in-situ environmental and positional data acquisition. This data is transmitted at 1 Hz to a ground station, facilitating continuous, real-time atmospheric profiling throughout both ascent and descent. The collected data provides a rich dataset for studying descent dynamics, environmental variations, and the behavior of payload systems under changing atmospheric conditions.

The project addresses critical engineering challenges, including selecting durable materials and structural designs optimized for high-altitude deployment stresses. Rigorous attention is given to secure electronic component mounting, vibration-resistant connections, and adequate battery restraints to ensure operational integrity under variable aerodynamic loads. By integrating robust descent control, advanced sensor technologies, and optimized structural elements, this platform is an autonomous system capable of collecting high-altitude data, contributing valuable insights into atmospheric dynamics, and informing future advancements in payload design and deployment methodologies.

Through this project, our team will gain hands-on experience designing and deploying an autonomous atmospheric sensing platform, advancing our understanding of high-altitude systems and real-time data acquisition. By successfully developing a payload with controlled descent mechanics, real-time telemetry, and integrated environmental sensors, we will achieve a functional platform to gather critical data on atmospheric conditions and descent dynamics.

The results will allow us to validate the effectiveness of descent mechanisms, including the auto-gyro system and parachute deployment, in achieving targeted descent rates and stabilization. Our team will analyze data from barometric pressure, IMU, and GPS sensors to assess altitude, orientation, and positional tracking accuracy. Additionally, by evaluating video footage from the onboard cameras, we will gain insights into the stability and effectiveness of our descent control, particularly in maintaining a consistent, spin-stabilized view of the ground.

Throughout this project, we will develop expertise in multiple technical domains, including sensor integration, microcontroller programming, structural engineering, and aerodynamics. Using Unreal Engine for simulation will further enhance our capabilities in digital prototyping, allowing us to optimize designs and troubleshoot virtually before physical tests. These skills are directly transferable to aerospace, robotics, and environmental research, equipping us as emerging researchers with a multidisciplinary foundation.

As a research endeavor, this project offers prospective outcomes, such as contributing to atmospheric sensing technology and descent system optimization, which can be applied to future aerial and space exploration missions. By compiling our findings into a detailed report, we aim to share insights with the academic community, supporting the growth of knowledge in high-altitude payload design and autonomous environmental monitoring systems. This project enriches our technical skills and prepares us for advanced research and professional roles in science and engineering.

Presenting Author: James Femi-Oyetoro' Tennessee Technological University

Presenting Author Biography: James D. Femi-Oyetoro is a graduate student in the Department of Mechanical Engineering at Tennessee Technological University. He has established a solid academic foundation and extensive research experience in force reconstruction, modal testing, structural health monitoring, machine learning, and additive manufacturing. His industry experience is diverse, including a notable internship at General Electric (Research) Aerospace as a Fellow Intern in the Material Mechanics and Durability group, a stint at Nigeria Railway Corporation where he contributed to enhancing the passenger capacity of the rail transit system, and a research internship at the National Center for Energy Efficiency and Conservation in Lagos, Nigeria. Here, his team identified significant energy savings opportunities. He holds an M.S. in Mechanical Engineering from Tennessee Technological University, where he played a crucial role in developing innovative data-driven models to optimize the manufacturing processes of 3D printed parts. He received his B.S. in Mechanical Engineering from the University of Ibadan, Nigeria 2016.

Authors:

Sam Loparo Tennessee Technological University
Md Mashiur Shoummo Tennessee Technological University
James Femi-Oyetoro' Tennessee Technological University
Bruce Jo Tennessee Technological University