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Events for January 15, 2020
Wed, Jan 15, 2020 @ 12:00 PM - 02:00 PM
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
Wed, Jan 15, 2020 @ 01:00 PM - 02:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Take part in a live tutorial to help you navigate Viterbi Career Gateway, a powerful job and internship search tool available ONLY to Viterbi students.
Remember to bring your laptop!
For more information about Labs & Open Forums, please visit viterbicareers.usc.edu/workshops.
Audiences: All Viterbi
Contact: RTH 218 Viterbi Career Connections
Wed, Jan 15, 2020 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Jay P. Gore, Purdue
Talk Title: High-Performance Computing Model for Bio-Fuel Combustion with Artificial Intelligence
Abstract: Lean blowout (LBO) calculations and statistical analysis for a conventional (A-2) and an alternative bio-jet fuel (C-1) are performed in a realistic gas turbine combustor geometry. The high-performance computing methodology is developed based on large eddy simulation (LES) models for turbulence and detailed chemistry and flamelet based models for combustion. The bio-jet fuel (C-1) exhibits significantly larger CH2O concentrations in the fuel-rich regions compared to the conventional petroleum fuel (A-2) at an identical equivalence ratio. As expected, the temperature of the recirculating hot gases is an important parameter for maintaining a stable flame. If this temperature falls below a certain threshold value for a given fuel, the evaporation rates and heat release rates decrease significantly and cause lean blowout. This study established the minimum recirculating gas temperature needed to maintain a stable flame for the A-2 and C-1 fuels. Artificial Intelligence (AI) models, based on high fidelity LES data, aimed at early identification of the incipient LBO condition. Sensor-based monitoring using a Support Vector Machine (SVM) detected the onset of LBO approximately 20 ms ahead of the event. A convolutional autoencoder was trained for feature extraction from the mass fraction of the OH for all time-steps resulting in significant dimensionality reduction. The extracted features along with ground truth labels are used to train a support vector machine (SVM) model for binary classification. The binary classification indicated an LBO approximately 30 ms ahead of the actual blowout. This and other early results highlight the promise of AI in much needed engine health monitoring.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Audiences: Everyone Is Invited
Contact: Tessa Yao