Poster Session II
Project Type
Poster
Project Funding and Affiliations
None
Faculty Mentor’s Full Name
Anh Nguyen
Faculty Mentor’s Department
Computer Science
Abstract / Artist's Statement
A Distributed IoT Cyber-Physical System for Non-Invasive Acoustic Mineral Extraction via Z-Scanning Phased Arrays
Abstract: Traditional placer mineral extraction relies on energy demanding mechanical mobilization and high-water usage. This research introduces a novel Cyber-Physical System (CPS) that achieves non-invasive vertical migration of high-density within non-Newtonian slurries using low power usage for targeted depths. Unlike conventional vibration which induces segregation (sinking), this system utilizes a floating, waterproof phased acoustic array to generate a Z-Scanning Helical Wavefront carrying Orbital Angular Momentum. This acoustic vortex, in concert with Time-Reversed Acoustics (TRA), creates a "telescoping funnel" logic that entraps particles via centripetal force while simultaneously reducing the medium’s yield stress through 30-48kHz ultrasonic cavitation.
Importantly, this physical process is governed by robust Industrial IoT architecture. Because the rheological properties of field mud are variable and unknown, the system employs an Edge-to-Cloud control loop. A Raspberry Pi 4 Edge Gateway performs sensor integration—correlating mechanical shear wave propagation (via accelerometers) with acoustic cavitation signatures (via piezoelectric microphones)—to execute a local reinforcement learning algorithm. This agent autonomously tunes the phased array’s frequency chirp (40Hz–120Hz) to match the localized resonant frequency of the medium in real-time.
Telemetry data, vibration, and cavitation efficiency, is streamed to a Google Firebase cloud backend, enabling remote "Digital Twin" visualization and command injection. This research demonstrates that by coupling Time-Reversed Acoustics with a cloud-native control software environment, heavy mineral extraction can be transformed from a brute-force mechanical process into a precision, data-driven operation suitable for extensive lab and field-based study.
Category
Physical Sciences
Distributed IoT Cyber-Physical System for Non-Invasive Acoustic Mineral Extraction via Z-Scanning Phased Arrays
UC South Ballroom
A Distributed IoT Cyber-Physical System for Non-Invasive Acoustic Mineral Extraction via Z-Scanning Phased Arrays
Abstract: Traditional placer mineral extraction relies on energy demanding mechanical mobilization and high-water usage. This research introduces a novel Cyber-Physical System (CPS) that achieves non-invasive vertical migration of high-density within non-Newtonian slurries using low power usage for targeted depths. Unlike conventional vibration which induces segregation (sinking), this system utilizes a floating, waterproof phased acoustic array to generate a Z-Scanning Helical Wavefront carrying Orbital Angular Momentum. This acoustic vortex, in concert with Time-Reversed Acoustics (TRA), creates a "telescoping funnel" logic that entraps particles via centripetal force while simultaneously reducing the medium’s yield stress through 30-48kHz ultrasonic cavitation.
Importantly, this physical process is governed by robust Industrial IoT architecture. Because the rheological properties of field mud are variable and unknown, the system employs an Edge-to-Cloud control loop. A Raspberry Pi 4 Edge Gateway performs sensor integration—correlating mechanical shear wave propagation (via accelerometers) with acoustic cavitation signatures (via piezoelectric microphones)—to execute a local reinforcement learning algorithm. This agent autonomously tunes the phased array’s frequency chirp (40Hz–120Hz) to match the localized resonant frequency of the medium in real-time.
Telemetry data, vibration, and cavitation efficiency, is streamed to a Google Firebase cloud backend, enabling remote "Digital Twin" visualization and command injection. This research demonstrates that by coupling Time-Reversed Acoustics with a cloud-native control software environment, heavy mineral extraction can be transformed from a brute-force mechanical process into a precision, data-driven operation suitable for extensive lab and field-based study.