1 option
Development of Machine Learning Models for Predicting Wind Fields Around a Military Ground Vehicle Mechanical and Materials Engineering, University of Alabama
- Format:
- Book
- Conference/Event
- Author/Creator:
- Koomullil, Roy, author.
- Conference Name:
- 2024 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium (2024-08-13 : Novi, Michigan, United States)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2024
- Summary:
- During multi-day missions, military vehicles face different environmental conditions. Calculating high-fidelity flow fields for these varying conditions in real-time is an impossible task due to the significant computational time required. This paper discusses a machine learning (ML) based approach to predict the flow fields faster than real-time. The testcase for this ML model is taken as the FED-Alpha vehicle geometry, and the training data for the ML model is taken to be the high-fidelity simulation data from computational fluid dynamics studies involving various wind directions using Ansys/Fluent. The surface temperature of the vehicle is calculated based on the operating conditions of the vehicle using the software TAITherm from ThermoAnalytics, Incorporated Three different ML models were tested to estimate the accuracy of the predictions and time requirements
- Notes:
- Vendor supplied data
- Publisher Number:
- 2024-01-4110
- Access Restriction:
- Restricted for use by site license
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.