Acoustic Characterization of Flame Blowout Phenomenon

Combustor blowout is a very serious concern in modern land-based and aircraft engine combustors. The ability to sense blowout precursors can provide significant payoffs in engine reliability and life. The objective of this work is to characterize the blowout phenomenon and develop a sensing methodology which can detect and assess the proximity of a combustor to blowout by monitoring its acoustic signature, thus providing early warning before the actual blowout of the combustor. The first part of the work examines the blowout phenomenon in a piloted jet burner. As blowout was approached, the flame detached from one side of the burner and showed increased flame tip fluctuations, resulting in an increase in low frequency acoustics. Work was then focused on swirling combustion systems. Close to blowout, localized extinction/re-ignition events were observed, which manifested as bursts in the acoustic signal. These events increased in number and duration as the combustor approached blowout, resulting an increase in low frequency acoustics. A variety of spectral, wavelet and thresholding based approaches were developed to detect precursors to blowout. The third part of the study focused on a bluff body burner. It characterized the underlying flame dynamics near blowout in greater detail and related it to the observed acoustic emissions. Vorticity was found to play a significant role in the flame dynamics. The flame passed through two distinct stages prior to blowout. The first was associated with momentary strain levels that exceed the flames extinction strain rate, leading to flame holes. The second was due to large scale alteration of the fluid dynamics in the bluff body wake, leading to violent flapping of the flame front and even larger straining of the flame. This led to low frequency acoustic oscillations, of the order of von Karman vortex shedding. This manifested as an abrupt increase in combustion noise spectra at 40-100 Hz very close to blowout. Finally, work was also done to improve the robustness of lean blowout detection by developing integration techniques that combined data from acoustic and optical sensors.Ph.D.Committee Chair: Dr. Tim Lieuwen; Committee Member: Dr. B. T. Zinn; Committee Member: Dr. Jeff Jagoda; Committee Member: Dr. Jerry Seitzman; Committee Member: Dr. Marios Soterio

Cross-language Information Retrieval

Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for CLIR and outlines some open research questions.Comment: 49 pages, 0 figure

Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets

Enabling robots to learn novel visuomotor skills in a data-efficient manner remains an unsolved problem with myriad challenges. A popular paradigm for tackling this problem is through leveraging large unlabeled datasets that have many behaviors in them and then adapting a policy to a specific task using a small amount of task-specific human supervision (i.e. interventions or demonstrations). However, how best to leverage the narrow task-specific supervision and balance it with offline data remains an open question. Our key insight in this work is that task-specific data not only provides new data for an agent to train on but can also inform the type of prior data the agent should use for learning. Concretely, we propose a simple approach that uses a small amount of downstream expert data to selectively query relevant behaviors from an offline, unlabeled dataset (including many sub-optimal behaviors). The agent is then jointly trained on the expert and queried data. We observe that our method learns to query only the relevant transitions to the task, filtering out sub-optimal or task-irrelevant data. By doing so, it is able to learn more effectively from the mix of task-specific and offline data compared to naively mixing the data or only using the task-specific data. Furthermore, we find that our simple querying approach outperforms more complex goal-conditioned methods by 20% across simulated and real robotic manipulation tasks from images. See https://sites.google.com/view/behaviorretrieval for videos and code

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well to- wards unseen tasks with increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201

From Propeller Damage Estimation and Adaptation to Fault Tolerant Control: Enhancing Quadrotor Resilience

Aerial robots are required to remain operational even in the event of system disturbances, damages, or failures to ensure resilient and robust task completion and safety. One common failure case is propeller damage, which presents a significant challenge in both quantification and compensation. We propose a novel adaptive control scheme capable of detecting and compensating for multi-rotor propeller damages, ensuring safe and robust flight performances. Our control scheme includes an L1 adaptive controller for damage inference and compensation of single or dual propellers, with the capability to seamlessly transition to a fault-tolerant solution in case the damage becomes severe. We experimentally identify the conditions under which the L1 adaptive solution remains preferable over a fault-tolerant alternative. Experimental results validate the proposed approach, demonstrating its effectiveness in running the adaptive strategy in real time on a quadrotor even in case of damage to multiple propellers.Comment: 8 Pages, 8 Figure