Collaborative Research: Nexus of Simulation, Sensing and Actuation for Aerodynamic Vibration Reduction of Wind Turbine Blades


PI:  Qingli Dai

The objective of this collaborative research project is to advance the smart blade system through innovations in areas of advanced computational models of fluid-structure interactions, sensors and actuators. Wind energy, an important source of clean and renewable energy, is becoming a major component of the U.S. energy portfolio. The interest in large capacity wind turbines as an economical way to harvest wind energy has significantly increased in recent years. Wind turbine blades are over 100m in length and the trend of increasing the size of the blades continues. However, increases in the size of wind turbine blades means that aerodynamic vibrations need to be managed to prevent catastrophic failures. The collaborative project team takes an innovative perspective to advance the smart turbine blade technology. The hypothesis of this research is that aerodynamic vibrations in wind turbine blades can be effectively mitigated with bio-inspired strategies for flow sensing, surface morphological change and fluid-structure interactions. The specific goals of this research project are 1) to understand blade vibration dynamics with advanced modeling of fluid-structure interactions; 2) to study the mechanism of bio-sensing for flow turbulence determination and to implement a feasible sensor design strategy; and 3) to understand and emulate the functions of “smart fins” and “smart denticles” for aerodynamic vibration reductions. A systematic approach will be undertaken by combining modeling, sensing and actuation strategies. The smart blade system performance will also be validated via simulation-based virtual testing and reduced-scale model experiments. All of these aim to advance the state of art in the smart wind turbine blades.

This project presents a great opportunity to advance smart blade technologies, which include intelligent components for blade vibration reduction. A unique bio-inspired strategy will be pursued to prevent catastrophic failures of wind turbine blades by effectively mitigating the aerodynamic vibrations. The strategy will also improve the operational efficiency of the wind energy system. All of these advances will have important impacts on the safe and efficient production of wind energy.


Qingli Dai
Qingli Dai


Evaluating Export Container Pooling Options in Minnesota, Wisconsin and Michigan’s Upper Peninsula


PI:  Pasi Lautala

This research effort identified barriers for communication and collaboration which preclude ISO containers from markets in the mid-West where export shippers need them to participate in the new economy. The research focused on issues that limit export container availability in northern Minnesota and northern Wisconsin by conducting literature reviews and cataloging existing best practices in comparable regions. Additionally, the potential adoption and corresponding gain to exporters in the Twin Cities, Fox River Valley, the Warsaw metropolitan area and the Twin ports (Duluth-Superior) was assessed. Case studies of efficient equipment assignment and pooling strategies were used to investigate how competitive disadvantage can be reduced in areas unable to obtain containers at a reasonable cost for their export.

The final report can be found here.  Evaluating Export Container Pooling Options in MN, WI, and MI’s Upper Peninsula

Pasi Lautala
Pasi Lautala

Impact of High Speed Passenger Trains on Freight Train Efficiency in Shared Railway Corridors

Sponsor:  National University Rail Center

PI:  Kuilin Zhang

Task 1: Literature review. A comprehensive literature review will be conducted on existing research in railway capacity and train delay to assess the state of knowledge and to ensure that all relevant previous work is incorporated into the work to be conducted in this proposed project.

Task 2: Analytical corridor capacity model. The core task of this research project is to develop an analytical framework to estimate rail corridor capacity under mixed high-speed passenger traffic and regular freight traffic. The outcome will help quantify the following: how high-speed passenger trains affect the capacity of a shared railway corridor, what are the relationships among various operational and design factors (e.g. speed, headway, and siding spacing), and how do these design factors affect the railroad capacity.

Task 3: Simulation validation. A commercial software called Rail Traffic Controller (RTC) will be used to evaluate the effects of homogeneous and heterogeneous train operations. We will analyze train delays caused by introducing passenger trains on a single track freight network (the most common track configuration in North America). We will then validate the analytical model proposed in Task 2.

Task 4: Optimization model and design guidelines. Based on the validated capacity model, optimization models and design guidelines (e.g. speed, headway, and infrastructure design) will be developed to maximize corridor capacity for both freight and passenger traffic. The optimization model will cope with train delays due to the knock-on effects, i.e. meet, pass, overtake, and possible delay propagation in a mixed traffic system.

Task 5: Policy development and analysis. The proposed modeling framework will be used as the basis for policy analysis (regarding planning, management, and operations of the shared rail corridor). Several key issues such as infrastructure investment, service charge/pricing, and public subsidies (for accommodating the high-speed passenger trains) will be addressed to support decision making for both public agencies and the private sector.

Task 6: Final report. Each individual task will be documented in a progress report. The final report will include the literature review, model development, validation, and key technical and policy findings. We will publish journal papers and make conference presentations to disseminate findings from this project. Michigan Tech will lead the efforts in Tasks 1, 2, 4, 5, 6, and will participate in all the remaining tasks.

Kuilin Zhang
Kuilin Zhang

Improving Log Transportation with Data Based Monitoring and Analysis in Northern Wisconsin and Upper Peninsula of Michigan

Sponsor:  University of Wisconsin Superior

PI:  Pasi Lautala

Minimizing transportation costs is essential in the forest products industry, but understanding of system inefficiencies requires sufficient data. While most individual forest products companies collect data on origins and destinations of truck trips, little is known about the actual movements in between. One alternative to collect data on truck movements is with Global Positioning Systems (GPS) data receivers. The objective of this research is to use inexpensive GPS tracking devices to analyze truck movements and using data to identify potential improvements to the performance of log transportation system in northern Wisconsin and the Upper Peninsula of Michigan. The research included three major steps within its one year time frame: (1) selecting GPS technology and performing a pilot test, (2) first round of data collection and analysis for any improvement opportunity to be implemented in next round, (3) second round of data collection, analysis and conclusions. Three industrial companies participated with the research team to set up GPS devices inside their designated trucks

Pasi Lautala
Pasi Lautala

Hawaii County Roadsoft Data Integration Services

Sponsor:  Hawaii County Public Works

PI:  Gary Schlaff

Hawaii County is an active user of the Asset Management system Roadsoft.  Data collected by Hawaii County and its contractors will be integrated into their current version of Roadsoft for use by the County.  The Center for Technology & Training (CTT) staff will update the road centerline file, integrate the county streetlight data,  road rating data, and signs along new roads.

Gary Schlaff
Gary Schlaff


Implementation of Unmanned Aerial Vehicles (UAVs) for Assessment of Transportation Infrastructure – Phase II

Sponsor:  Michigan Department of Transportation

PI:  Colin Brooks

Through Phase I of MDOT’s “Evaluating the Use of Unmanned Aerial Vehicles for Transportation Purposes” project, the Michigan Tech combined project team was successfully able to plan, demonstrate, and document UAV capabilities in the assessment of transportation assets.

With the rapid development of UAV’s, MDOT has requested additional research concerning their use for transportation asset management.  The work plan of the MTU combined team includes:


  • Collect data from the UAV platform using sensing technology in near-time (as real-time as can be achieved) demonstrating, developing, and implementing storage capabilities of large amounts of data, usage of data, and application development that complements current data usage and application at MDOT.
  • Provide data collection from UAVs to the MDOT Data, Use, Analysis, and Process (DUAP) project that meets the quality, low latency delivery and data format requirements.
  • Provide a report that describes and recommends optional methods to store and distribute potentially large imaging, point cloud, and 3D surface datasets created through UAV-based data collection.


  • Demonstrate, develop, and implement high-accuracy simultaneous thermal/photo/video/Light Detection and Ranging (LIDAR) measurement using a high-fidelity sensor-fused UAV positioning approach.


  • Demonstrate the capabilities to complete aerial remote sensing data collections to meet MDOT mapping and construction monitoring needs.  Coordinate with MDOT Survey Support to identify pilot projects and meet data delivery needs satisfying MDOT requirements for spatial data collection as it pertains to data density, absolute and relative 3D positional accuracy.


  • Demonstrate, develop and implement uses of data collection from UAV(s) and sensors for operations, maintenance, design, and asset management.


  • Demonstrate, develop and implement enhanced testing of UAV-based thermal imaging for bridge deck structural integrity.
  • Compare data collected from UAV sensors to current data collected and systems used at MDOT for highway assets/operations.


  • Demonstrate, develop, and implement systems management and operations uses.


  • Provide a benefit/cost analysis and performance measures that define the return on investment as a result of deploying UAVs and related sensory technologies for transportation purposes.


  • Secure a Federal Aviation Administration (FAA) Certificate of Authorization (COA) to complete the tasks and deliverables.
Colin N. Brooks
Colin N. Brooks
Tess Ahlborn
Tess Ahlborn
Timothy Havens
Timothy Havens
Thomas Oommen
Thomas Oommen
Kuilin Zhang
Kuilin Zhang

Rail Embankment Stabilization Needs on the Hudson Bay Railway


PI:  Thomas Oommen

Hudson Bay Railway (HBR)is a key trade link that connects domestic origins and destinations in Canada and the United States with the export origins and destinations in Europe through the Port of Churchill. Over the last 80 years, the deterioration of the HBR embankment due to thawing discontinuous permafrost, combined with poor geotechnical properties of the muskeg soil has caused significant challenges in maintaining the HBR line. Warming climate has further added to this challenge, causing major concern for the owner of the line, Omnitrax. Developing sustainable methods to reliably identify, monitor, and mitigate issues along the transportation infrastructure in cold regions is critical for safe and efficient operations.

The objectives of this research are:

1. Defining a rating system for severity of railway conditions in permafrost affected areas.

2. Designing a “Best Practices Guide” to diagnose, document and perform corrective actions addressing each severity rating.

3. Creating a long term solution for embankment stability.

Thomas Oommen
Thomas Oommen


Building the ENGIN (Exploring Next Generation IN-Vehicle INterfaces) Consortium at MTTI

Sponsor:  Michigan Tech Transportation Institute (MTTI)

PI:  Myounghoon (Philart) Jeon

In the proposed initiative, the PI aims to provide a phased path for an innovative research and educational program at Michigan Tech focused on the driving domain. The proposed effort will eventually lead to a sustainable, officially recognized Driving Research Center under the Michigan Tech Transportation Insitutute MTTI). To this end, the PI plans to (1) initiate a collaborative driving research project with like-minded domain experts; (2) expand the scope across the Michigan Tech campus and build an ENGIN (Exploring Next Generation IN-vehicle INterfaces) consortium that can identify and develop additional driving-related research projects together; (3) make continuous efforts to secure external funding for driving-related projects; and (4) develop a more systematic driving education for undergraduates and graduates and K-12 outreach program by lining-up and integrating related courses across departments, hosting regular seminars with invited external speakers, expanding current and developing new outreach programs, and organizing international workshops and conferences.

Coordinating the initiative is Steven Landry, PhD student in Applied Cognitive Science and Human Factors Graduate Program.

For more information on the project, see

Myounghoon Jeon
Myounghoon Jeon


Novel Optimization Algorithms for Oversaturated Traffic Network Coordination

Sponsor:  Michigan Tech Transportation Institute (MTTI)

PI:  Ossama Abdelkhalik

The objective of this project is to solve the Oversaturated Traffic Network Signal Coordination Planning (OTNSCP) optimization problem, through a new problem formulation and using the recent HGGA optimization method. The long term goal is to developed software tools that model and optimize traffic networks both off-line and online, taking into consideration the network uncertainties and travelers’ behavior.

The proposed problem formulation handles the OTNSCP optimization problem as a grouping problem, rather than the standard optimization for the individual signals green times. In the off-line optimization of traffic networks using the standard formulation, the number of variables is the number of design green times. In the proposed formulation, it is assumed that some of the traffic signals in the network have the same green time value; in other words a subset of the traffic network signals has the same green time value for all signals in the subset. The network may have several subsets. The number of signals in each subset is a variable to be optimized.

The nature of the OTNSCP problem is that signals should be coordinated (using green times) to maximize the overall network throughput. So, instead of formulating the problem to optimize the individual signals’ green times, it would be more natural and efficient if it is formulated as a distribution of green times over the network of signals, collectively, as a group to maximize the objective function.

Ossama Abdelkhalik


Rail Crossing Behavior with Naturalistic Driving Study (NDS)

Sponsor:  Michigan Tech Transportation Institute (MTTI)

PI:  Dave Nelson

Michigan Tech researchers have been involved in developing a program to investigate driver behavior at highway-rail grade crossings over the last couple of years. After a long downward trend for grade crossing accidents and fatalities, the statistics for problems at crossings have plateaued over the last few years.

One of the ways to address the issue is by comparing actual driver behavior at crossing with simulated one. Starting in late 2013 results from the Naturalistic Driving Study (NDS), conducted under a major Strategic Highway Research Program (SHRP) 2 program have been available to researchers that will allow us to fill some of the gap, and begin the process of validating the ongoing simulator research.  This initiative grant is requested to secure NDS data for early analysis, so a stronger case can be made to funding agencies for ongoing research.   The Michigan Tech research team will acquire camera (forward and backward video) and vehicle performance data (braking, throttle, and other data) showing what drivers were doing during normal day-to-day driving situations at grade crossings. Our initial results seem to indicate that drivers are not looking for trains at crossings.  This is a big issue, especially at passive crossings, as drivers do not stop for trains if they do not look for them.  We will use the NDS data to confirm that finding, and then compare the NDS data with our simulation research findings to validate the simulator process.   Information about the NDS project can be found at, a document from the NDS, The SHRP 2 Naturalistic Driving Study, is included in this package.

This project will analyze driver behaviors at highway-rail grade crossings.  Reducing collisions between cars and trains at these crossings has long been a goal of the Federal Rail Administration, the Federal Highway Administration, and the Federal Transit Administration.  Improved understanding of driver behaviors can lead to improved traffic control devices for rail crossings and help meet this goal.  The results of this study will also be used to validate research Michigan Tech is working on with a driving simulator to investigate crossing behaviors in a less costly fashion.

Dave Nelson
Dave Nelson