Research Funding Updates

Eunshin Byon

Eunshin Byon
Title: BIGDATA: IA: Collaborative Research: From Bytes to Watts – A Data Science Solution to Improve Wind Energy Reliability and Operation
Funding Source: National Science Foundation
The critical barrier to cost effective wind power is partly rooted in wind stochasticity, severely complicating wind power production optimization and cost reduction. Therefore, the long-term viability of wind energy hinges upon a good understanding of its production reliability, which is affected in turn by the predictability of wind and power productivity of wind turbines. Furthermore, the productivity of a wind turbine comprises two aspects: its ability of converting wind into power during its operation and the availability of wind turbines. To enhance wind energy reliability and productivity, modern wind farms are equipped with a large number and variety of sensors.  However, all these data are currently analyzed only in their respective domains.  This project will address the big data challenges, including how to best use spatio-temporal data for wind forecast and how to use data of different nature (wind, power, load etc.) and data of different sources (physical data versus computer simulation data) for power production assessment in a computationally efficient manner. The proposed research activities will demonstrate how dramatically data science innovations can benefit the wind industry.

Eunshin Byon
Title: Collaborative Proposal: A Framework for Assessing the Impact of Extreme Heat and Drought Climate Scenarios on Urban Energy Production and Consumption
Funding Source: National Science Foundation
The modern electric grid, which includes both electricity consumers and producers, faces significant challenges and uncertainties, particularly as a result of potential climate change and extreme weather events. Different from regional- or national-level management, electricity management in densely populated cities poses unique challenges such as elevated electricity demand due to localized characteristics such as urban heat islands. The objective of this research is to provide rigorous and integrative methods for effectively managing city-scale electricity systems during periods of extreme heat and drought. Findings from this research have the potential to significantly impact the way cities and electric utility operators manage electric grids under extreme heat and drought conditions.

Amy Cohn

Amy Cohn
Title: Scheduling Tool with St. Joes
Funding Source: Saint Joseph Mercy Health System
This work will be conducted in collaboration with Research Area Specialist Billy Pozehl, undergraduate CHEPS researchers Anna Learis (IOE), Kevin Li (Engineering Physics), Dale Mallette (CS), Steven MacPherson (CS), and Bassel Salka (IOE), and the St. Joseph surgical chief residents.


Clive D’Souza and Sheryl Ulin

Clive D’Souza

Sheryl Ulin

 

 

 

 

 


Title:
Interactive Training and Direct Assistance to Reduce Worker Risk of Musculoskeletal Disorders
Funding Source: State of Michigan
Work-related musculoskeletal disorders such as low back injuries and upper limb musculoskeletal disorders are a major cause of disability and workers’ compensation throughout the United States. The objective of this project is to provide employers and workers with information and procedures necessary to identify and control the conspicuous ergonomics workplace risk factors of musculoskeletal disorders.

Ruiwei Jiang

Ruiwei Jiang
Title: Collaborative Research: Enhancing Power System Resilience via Data-Driven Optimization
Funding Source: National Science Foundation
As a backbone of the U.S. infrastructure, the electricity grid transmits around 400 billion dollars of electricity across the country every year. This grid is increasingly vulnerable because of more frequent and severe natural disasters, in addition to the long-standing challenges from random equipment failures and operation errors. The evaluation and mitigation of disruption-related risks and impacts are often computationally prohibitive due to random weather conditions, high-dimensional data and decisions, and the combinatorics nature of component failures. This project will derive analytical models and scalable solution methods to assist system operators to better evaluate and mitigate disruptions. The analytical models employ publically available data on meteorology and transmission availability, and the solution methods will be evaluated on test instances with industrially relevant sizes.

Judy Jin

Judy Jin
Title: Optimizing Reliability Tests for Display by Integrating Reliability Analysis of Field Failure Data
Funding Source: Samsung
This one year research project aims to develop a systematic method to improve LCD display reliability tests through integrating field failure data analysis. It will also enhance the understanding of failure modes of LCD display and provide better reliability modeling and prediction to reduce unexpected failures during field operations.

Viswanath Nagarajan

Viswanath Nagarajan
Title: CAREER: New Mathematical Programming Techniques in Approximation and Online Algorithms
Funding Source: National Science Foundation

 

 

 

Siqian Shen

Siqian Shen
Title: Extreme‐Scale Stochastic Optimization and Simulation via Learning‐Enhanced Decomposition and Parallelization
Funding Source: Department of Energy Early Career Research Program, Office of Advanced Scientific Computing Research
The purpose of this research is to incorporate machine learning techniques into decomposition algorithms for solving stochastic optimization and simulation models using high performance computing. We consider a broad class of complex decision‐making problems, where discrete or continuous decisions are made before and/or after knowing multiple and potentially correlated sources of uncertainties. Examples include Cloud Computing service scheduling, sensor deployment for monitoring critical infrastructures, and other resource allocation and operational problems in energy and national security.

Siqian Shen and Ruiwei Jiang
Title: Collaborative Research: Emerging Optimization Methods for Planning and Operating Shared Mobility Systems under Uncertain Budget and Market Demand
Funding Source: National Science Foundation
As the government and private companies start launching new mobility sharing programs with diverse objective criteria and practical restrictions, they are often constrained to unknown budget for launching new services versus expanding existing ones and also face uncertain customer demands responding to different mobility-sharing forms. The objective of this project is to develop mathematical models and efficient algorithms for shared mobility system design and optimization. The research will: (i) push both theoretical and computational frontiers of optimization methods in new transportation problems; (ii) impact applications of shared mobility that relate to critical civil infrastructures, supply chain & logistics, and other service industries. This is a collaborative research project co-investigated by Prof. Siqian Shen, Prof. Ruiwei Jiang, and Prof. Mengshi Lu (Purdue University).