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Computational Modeling for Improved Materials and Structures

Odegard, GregProf. Odegard is the Richard and Elizabeth Henes Professor of Computational Mechanics in the Department of Mechanical Engineering – Engineering Mechanics at Michigan Technological University. His research is focused on computational modeling of advanced materials and structures for the aerospace, power transmission, and alternative fuels industries.

Thomas Edison once said, “I have not failed. I’ve just found 10,000 ways that won’t work”. As computers become increasingly fast, more opportunities exist to design new technologies in a purely computational environment. Computational modeling can cut development costs, speed up the design process, and provide insights where traditional Edisonian methods can’t. Prof. Odegard’s research group is involved in two main projects that utilize computational modeling for new technologies.

Prof. Odegard is the MTU site director of a National Science Foundation (NSF) Industry/University Collaborative Research Center (I/UCRC) titled “Center for Novel High Voltage/Temperature Materials and Structures”. The goal of this center is to leverage federal and industrial funding to develop new materials to withstand harsh environments. Specifically, the center is focused on materials for the power transmission and aerospace industries. Computational modeling has helped speed up the process of developing new highly electrically conducting aluminum alloys for power transmission lines and temperature-resistant composite materials for aerospace vehicles. For these projects, Prof. Odegard’s team is working closely with center members Boeing, General Cable, Bonneville Power Administration, and Western Area Power Administration, and CTC Global.

Figure 1 – Computational modeling of conformable CNG tank

Figure 1 – Computational modeling of conformable CNG tank

As part of a $2.1M grant from Southwestern Energy, Prof. Odegard’s team is using computational modeling to facilitate the development of a conformable compressed natural gas (CNG) fuel tank for light-duty trucks. Traditional CNG tanks have a cylindrical geometry, which make them awkward to use in smaller vehicles and trucks. In conjunction with REL Inc., a Calumet-based partner in the project, the computational modeling is being used to help design conformable CNG tanks (Figure 1) before they are fabricated and tested, which greatly reduces the overall development costs.


Computational Intelligence Aids in Explosive Hazard Detection

Havens, TimTo detect buried explosive hazards in places like Afghanistan and to save the lives 
of civilians and US soldiers, Michigan Tech researcher Tim Havens realizes it requires
 a team: a team of sensors.

A new $983,000 research project, “Heterogeneous Multisensor Buried Target Detection Using Spatiotemporal Feature Learning,” will look at how forward-looking ground- penetrating radar, LiDAR, and video sensors can be combined synergistically to see into the ground, capture high-quality images, and then automatically notify the operator of threats. With funding from the US Army Research Laboratory’s Army Research Office (ARO), Havens and Tim Schulz, professor of electrical and computer engineering, will work with three Michigan Tech PhD students to create a high probability-of-detection/low false-alarm rate solution.

“It’s a very difficult problem
 to solve because most of the radar energy bounces right off the surface of the earth.” says Havens, the William and Gloria Jackson Assistant Professor of Computer Systems
 and ICC Center for Data Sciences Director. “ It’s hard enough finding the targets, but coupled with that is the amount of data that these sensors produce is massive. It is the perfect project to combine Tim’s (Schulz) statistical signal processing background and my machine learning in big data expertise. This technology has the potential to not only save lives, but also to advance the basic science of how to combine large amounts of sensor data and information together to get a whole that is better than the sum of its parts,” Havens explains.

This new project builds upon a previous sensor-related work Havens and collaborators completed between 2013-2015 and a current project on which Havens and Joe Burns, a Senior Research Scientist at Michigan Tech Research Institute, collaborate. These projects, also funded by the US Army, study signal processing and computer-aided detection and classification using both vehicle-mounted forward-looking and handheld downward-looking ground-penetrating radars. In total, Havens and his collaborators have secured over $2.5 million in funding to develop solutions for this problem.

Figure 1. High-level overview of multi-sensor feature learning and fusion for forward-looking explosive hazard detection. iECO: Improved evolution-constructed (features). DBN: Deep belief network. CNN: Convolutional neural network. GAMKLp: ℓp norm-based genetic algorithm for multiple kernel learning (feature-level fusion method). DeFIMKL: Decision level fuzzy integral multiple kernel learning.

 

The Army currently fields ground-penetrating radars in its fleet. The problem is they cannot detect hazards until they’re right above them, putting a multi-million dollar radar—and Soldiers—directly in the path of danger.

“The big ideas here are to process big data to obtain better images, see into the ground in a high-fidelity manner, and to develop algorithms that automatically find buried threats—even notifying operators of w
hat those threats might be—all while keeping the Soldiers and equipment as far away as possible,” Havens adds.

Havens has partnered with the Army since he was a PhD student in 2008.

 


White Carbon Materials for Advanced Heat Management

Yoke Khin YapDr. Yoke Khin Yap, professor in the Michigan Tech Department of Physics at Michigan Technological University (Michigan Tech), has invented a novel class of boron nitride (BN) nanomaterials for advanced heat management. BN phases are structurally similar to those of carbon solids. We have hexagonal phase-BN (h-BN), cubic phase-BN (c-BN), BN nanotubes (BNNTs), BN nanosheets (BNNSs, mono- and few- layered h-BN sheets). These BN structures are analogous to graphite, diamonds, carbon nanotubes (CNTs), and graphene, respectively [1]. Therefore, BN materials can be referred as “white carbon” as they are white in appearance due to their large band gap (~6eV).

Despite the structural similarity, the properties of BN materials are different from those of carbon solids. For example, graphite is electrically conducting while h- BN is insulating due to their large band gap. A common property among the BN and carbon materials is their high heat conductivity that hold potential applications for advanced heat management. BN nanostructures are predicted to have a thermal conductivity, as high as 2000 W/m-K, about 10-times higher than that of metals [2]. Therefore, BN materials can be in contact with active electrical components to dissipate heat without the risk of an electrical short circuit.

Dr. Yap is a leading expert in BN nanomaterials, specializing in the technology of direct synthesis of BNNTs and wavy BNNSs on substrates. BNNTs developed by Dr. Yap are of high purity and high quality, two desirable attributes for applications in electronic devices. The wavy BNNSs are unique in that they have full surface contact with the substrates. They also have wavy edges that stick out from the substrate surface to enhance the contact area with the surrounding cool air/environment. Michigan Tech demonstrates that the coatings of BNNTs and wavy BNNSs can both enhance the heat dissipation rate of hot Silicon chips by as much as 250% in static ambient air.

Figure 1 shows the appearance of BNNTs (top row) and the wavy BNNSs (bottom row) under a scanning electron microscope. As shown, BNNTs are long in length (~40 microns), offering a large contact surface area with air, an important feature to accelerate heat dissipation. However their small diameter (20-50nm), results in a very small contact area with the hot substrate surface.

YKYap boron nitride nanomaterials

In contrast, the wavy BNNSs offer a much larger surface area to contact with the hot substrate surface. Their wavy edges also provide an enhanced contact area with the surrounding cool air but smaller than that offered by BNNTs. The Yap research group have combined the benefits of both materials by growing BNNTs on top of the wavy BNNSs. Results indicate that such uniquely combined BNNT/BNNS structures in the presences of gas flows promote cooling better than BNNTs and BNNSs alone.

Finally, the Michigan Tech team has also demonstrated that these BNNSs and BNNTs can be transferred to desired surfaces. They found that BNNTs and BNNSs grown on Si substrates can be peeled and transferred on to fresh Si substrates. This suggests that these novel BN nanomaterials can be transferred on to hot surfaces of electrical and electronic devices to promote cooling. Michigan Tech has filed a utility patent application and is seeking industry partners to help commercialize the technology. Please contact Michael Morley (mcmorley@mtu.edu) for further information.

 

References

[1]. Y. K. Yap, “B-C-N Nanotubes, Nanosheets, Nanoribbons, and Related

Nanostructures,” http://www.azonano.com/article.aspx?ArticleID=2847

[2]. T. Ouyang, Y. P. Chen, Y. Xie, K. K. Yang, Z. G. Bao, J. X. Zhong, “Thermal

Transport in Hexagonal Boron Nitride Nanoribbons,” Nanotechnology 21, 245701

(2010).


Aerial Unpaved Road Assessment (AURA) System Attracting International Interest

A team of Michigan Tech researchers led by Colin Brooks, has been evaluating the use of unmanned aerial vehicles (UAV’s) for unpaved road analysis and characterization.   A research grant from both the both the U.S. and Michigan Department of Transportation has helped transform the research team’s efforts into a commercial product that has recently gained both national and international interest.

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Colin Brooks and his team at the Michigan Tech Research Institute, outfitted a UAV with a high-resolution, 36-megapixel digital camera to gather data by taking pictures while flying over unpaved roads.  Using 3D processing software, proprietary distress detection analysis algorithms, and GIS tags, the data is sent to an asset management system used in geospatial decision support tools.  The tools help locate, characterize, and prioritize problems such as wash-boarding, ruts,  potholes and erosion.  Additionally, the software provides an up-to-date inventory of unpaved roads, something that many of the agencies managing these roads do not currently have.

Several U.S. based and one Brazilian transportation agency have partnered with Michigan Tech to make improvements to the distress detection software. There are over 1.4 million miles of unpaved roads in the United States, accounting for over 1/3 of the U.S. total.   In Brazil, the ratio is just the opposite, with well over 2/3 of roadways unpaved.   Road maintenance assessments in Brazil and several places in the U.S. have begun using the tools to help manage the problems. 

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Unpaved roads provide a vital part of a nation’s transportation system with road management under the responsibility of local governments and transportation agencies, which are in need of rapid, repeatable methods that are cost-efficient and easily deployable in a budget-limited environment.   A particular focus is on making the system rapidly deployable for cost-effectively detecting deficiencies in the unpaved road components of the transportation system, including preventing of further damage to the transportation network through timely management of unpaved road assets.