Parallel computing for interactions between fluids and flexible structures with application to suspended longline aquaculture farms
The structural dynamics of the aquaculture farms in unsteady flow are essential to assess the performance and resilience of aquaculture farms in environmental change. Moreover, the feedback of the aquaculture farms to the flow is significant for the environment, ecology, and coastal management, such as hydrodynamics impacts, habitat resilience, nutrient transportation, wave attenuation, coastal erosion control, etc. The computational fluid dynamics (CFD) method is used to analyze the interaction between aquaculture farms and the flow. The longline aquaculture farms such as kelp farms and mussel farms are consisting of multiple flexible structures such as mussel droppers and kelp blades. Considering hundreds or thousands of large deformed structures in the fluid-structure interaction (FSI) computing is time-consuming. Therefore, computer science research and parallel computing implementation are essential to make progress on this project. The computer science aspects we initially envision are converting the FSI code to c++ from MATLAB, as well as parallelizing the code. If you have any ideas beyond that, we would love to hear them.
Understanding Covid-19 Pandemic through Social Media Discussion
Dr. Li has been collecting covid-19 tweets since March 2020 and currently has about 1.2 billion tweets. She is still collecting the tweets and expects to have more in the future. This project focuses on the understanding of the impact of covid-19 pandemic through social media discussion on Twitter. The following topics will be explored: 1). What are the top topics discussed regarding covid-19? How has the discussion of the topics changed over time? 2). What is sentiment/emotion of the topic by time, location, and gender? and 3). How to identify misinformation/fake news about covid-19.
The student will work on this project from start to finish using various data analytic methodology including data exploration, topic modelling, natural language processing and machine learning.
Model Mie scattering and light propagation through a high scattering medium using Monte Carlo simulation
In this project, we will first use numerical approaches to model light scattering off single particles using Monte Carlo simulation. We will obtain results that follow Rayleigh scattering and Mie scattering. The program will then be extended to simulate light propagation in a highly scattering turbid medium like biological tissue which consists of various arrangements of particles and bulk geometry and calculate the light distribution in the medium and on the boundary. The program will eventually be used for imaging tumors in biological tissue, which will be achieved through an inverse problem.
Bearing Condition Monitoring using Machine Learning
Machine failure and downtime was considerably low for less sophisticated machines developed during the first two industrial revolutions. Modern manufacturing facilities use highly complex and advance machines that require continuous health monitoring systems. Bearings are widely used in rotating equipment and machines to support load and to reduce friction. The presence of micron sized defects on the mating surfaces of the bearing components can lead to failure through a passage of time. Bearing health can be monitored by analyzing vibration signals acquired using an accelerometer and developing a machine learning framework for feature extraction and classification of the bearing conditions. The large size defects on bearing elements can be detected/identified by time domain and frequency domain analysis of its vibration signals. However, it becomes difficult to detect local bearing defects at their initial stage either due to their smaller size or presence of noise. In the proposed project, detection of local defects like crack and pits on bearing races will be carried out using machine learning. As a pilot project, simulated data of bearing conditions will be generated from MATLAB Simulink models and used for developing machine learning based predictive maintenance and condition monitoring algorithms. The trained model will be evaluated against the real bearing data and ground truth results. The project will be first implemented on a local machine and once successfully developed, will be ported to a cluster.
The machine learning frame work will include functions for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis. The health of bearings will be monitored by extracting features from vibration data using frequency and time-frequency methods. A student will learn how to organize and analyze sensor data imported from local files, cloud storage, and distributed file systems. The student will learn the complete machine learning project pipeline from data importing, filtering, feature extraction, data distribution, training, validation and testing of multiple machine learning algorithms and working with the clusters. The developed machine learning pipeline will be shared with the research community and the work will be published in a conference proceeding. The project requires MATLAB toolboxes for signal processing, machine learning, predictive maintenance, statistical analysis and deep learning. The future work of the project includes a large datasets of real bearing data and simulated data for predictive maintenance of the bearing using cluster-based machine learning framework. The estimated defect sizes will be predicted, compared and validated through measured actual crack width or pit diameter.