Conferences, Lectures, & Seminars
Events for January
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AME Seminar
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/
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AME Seminar
Wed, Jan 22, 2020 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Roger Ghanem, USC
Talk Title: Probabilistic Learning on Manifolds: The Small Data Challenge
Abstract: As the pace of technological innovation and scientific discovery continues to grow, so does the interest in accelerating their integration. We are thus, increasingly, faced with the task of product development without the benefit of hindsight or historical failures. Examples of this evolving paradigm include new materials and novel configurations of complicated systems with complex behavior. This challenge is exacerbated by the growing interactions between technological and socio-economic systems where failure of a technological component can have implications on social trends and public policy, thus highlighting the need to characterize extreme events both for each component and at the system level. The standard paradigm of mapping knowledge into engineered systems where new systems are essentially construed as perturbations of older systems is not equipped for these emerging requirements. Recent approaches under the general heading of Machine Learning (ML) are motivated by the explosion in sensing technologies. Fundamental advances in these ML methods are being realized at the interface of data science and physics constraints.
In this talk I will describe a recent effort within my group along these ML lines. I will focus on one particular approach, the Probabilistic Learning on Manifolds (PMoL), which is relevant under conditions of small data. This approach aims to augment a (small) training dataset with realizations that share with it some key features making these realizations credible surrogates of the original data. These features consist of 1) co-location on a manifold, and 2) statistical consistency. Thus as a first step, we associated a manifold with the training set, that we believe represents all the fundamental constraints (such as physics). We rely on diffusion maps constructs to delineate the manifold. Construed as fluctuating within this manifold, the training dataset is statistically more significant. As a second step, we generate samples on the manifold that have the same probability distribution as the training set. To this end, we construct a projected Ito equation whose invariant measure is that of the training set, and whose samples are constrained to the manifold.
I will show how the above ideas are used as building blocks in a scramjet optimization problem and the design of a digital twin for a structural composite.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
AME Seminar
Wed, Jan 29, 2020 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Chris Roh, Caltech
Talk Title: Hydrofoiling Honeybee
Abstract: Honeybees display a unique bio-locomotion strategy at the air-water interface. When waters adhesive force traps them on the surface, their wetted wings lose ability to generate aerodynamic thrust. However, they adequately locomote, reaching a speed up to three body lengths-1. Honeybees use their wetted wings as hydrofoils for their water surface propulsion. Their locomotion imparts hydrodynamic momentum to the surrounding water in the form of asymmetric waves and a deeper water jet stream, generating approximately 20 μN average thrust. The wing kinematics show that the wings stroke plane is skewed, and the wing supinates and pronates during its power and recovery strokes, respectively. The flow under a mechanical model wing mimicking the motion of a bees wing further shows that non-zero net horizontal momentum is imparted to the water, demonstrating net thrust. Moreover, a periodic acceleration and deceleration of water is observed, which provides additional forward movement by recoil locomotion. Scaling analysis of the hydrodynamic forces associated with the wing motion indicates that the wings utilize added mass force (unsteady inertial force associated with the pulling of the water attached to the wing). Hydrofoiling highlights the versatility of their flapping-wing systems that are capable of generating propulsion with fluids whose densities span three orders of magnitude. This discovery inspires a novel aerial-aquatic hybrid vehicle.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
