UROP Research Mentor Project Submission Portal: Submission #796
Submission information
Submission Number: 796
Submission ID: 14606
Submission UUID: 619dd5d6-ad9f-4ebc-9caf-22846327e40c
Submission URI: /urop-research-mentor-project-submission-portal
Submission Update: /urop-research-mentor-project-submission-portal?token=OzMtg_I7-57AP7E2K4Wid7G2Gs94Ff77CvauuTkTC9c
Created: Thu, 08/15/2024 - 10:29 AM
Completed: Sun, 08/18/2024 - 09:55 PM
Changed: Fri, 09/13/2024 - 09:17 AM
Remote IP address: 73.42.15.2
Submitted by: Anonymous
Language: English
Is draft: No
Webform: UROP Project Proposal Portal
Submitted to: UROP Research Mentor Project Submission Portal
Research Mentor Information
Pedro Fernandez-Caban
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Dr.
Faculty
FAMU-FSU College of Engineering
Civil and Environmental Engineering
Additional Research Mentor(s)
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Overall Project Details
AI-Driven Automated Experimentation in a Wind Tunnel
wind tunnel, autonomous experimentation, buildings, turbulence, machine learning
No
1
Civil Engineering, Mechanical Engineering, Industrial Engineering, Computer Science
FAMU-FSU College of Engineering
FSU Bus (Seminole Express)
In-person
10
Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
This project aims to pioneer a self-driven experimental approach to study wind effects on the built environment. The goal is to integrate efficient and robust AI-driven tools into wind tunnel modeling to investigate complex wind flow behavior around buildings and other civil infrastructure (e.g., bridges, towers, etc.).
Numerical deep learning algorithms will be leveraged to assist wind tunnel modelers in the decision-making process to autonomously identify critical wind flow conditions that can cause damage to civil structures.
Components of the self-driven approach include air velocity and pressure sensors that collect information (i.e., data) on the flow conditions in the wind tunnel. This information is then processed and interpreted by deep learning models to generate new wind tunnel test configurations (e.g., adjust the position of the sensors, change the wind direction, etc.). Re-configuration of wind tunnel conditions is automated (using actuators) to rapidly investigate different turbulent regions in the wind tunnel.
Numerical deep learning algorithms will be leveraged to assist wind tunnel modelers in the decision-making process to autonomously identify critical wind flow conditions that can cause damage to civil structures.
Components of the self-driven approach include air velocity and pressure sensors that collect information (i.e., data) on the flow conditions in the wind tunnel. This information is then processed and interpreted by deep learning models to generate new wind tunnel test configurations (e.g., adjust the position of the sensors, change the wind direction, etc.). Re-configuration of wind tunnel conditions is automated (using actuators) to rapidly investigate different turbulent regions in the wind tunnel.
The undergraduate student will be tasked with the following responsibilities:
1) Assist in the fabrication and instrumentation (e.g., installation of pressure sensors) of scaled building models
2) Collect wind tunnel velocity and pressure measurements
3) Perform data quality checks of wind tunnel data using basic statistical analysis procedures
1) Assist in the fabrication and instrumentation (e.g., installation of pressure sensors) of scaled building models
2) Collect wind tunnel velocity and pressure measurements
3) Perform data quality checks of wind tunnel data using basic statistical analysis procedures
1) Knowledge on fundamental statistical analysis approaches and procedures (required)
2) Programming experience in Python or Matlab (recommended)
3) Experience working with Excel or Google Sheets (required)
2) Programming experience in Python or Matlab (recommended)
3) Experience working with Excel or Google Sheets (required)
Sharing your own experience - I often share my experience as an undergraduate research assistant and how it motivated me to pursue a career in academia.
https://scholar.google.com/citations?user=nzBOhdoAAAAJ&hl=en
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Yes
Tuesday, September 3
3:30-4:00pm (1st Meeting): https://fsu.zoom.us/j/98348258903
4:00-4:30pm (2nd Meeting): https://fsu.zoom.us/j/99296404437
4:30-5:00pm (3rd Meeting): https://fsu.zoom.us/j/98493123599
3:30-4:00pm (1st Meeting): https://fsu.zoom.us/j/98348258903
4:00-4:30pm (2nd Meeting): https://fsu.zoom.us/j/99296404437
4:30-5:00pm (3rd Meeting): https://fsu.zoom.us/j/98493123599
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UROP Program Elements
Yes
Yes
Yes
Yes
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2024
https://cre.fsu.edu/urop-research-mentor-project-submission-portal?element_parents=elements/research_mentor_information/headshot_optional_&ajax_form=1&_wrapper_format=drupal_ajax&token=OzMtg_I7-57AP7E2K4Wid7G2Gs94Ff77CvauuTkTC9c