Clouds strongly interact with solar radiation and as a result small changes in cloud cover have big impact on the Earth’s surface temperature. Currently, the effects of clouds are one of the largest sources of uncertainty in climate projections.
Georgios Matheou, associate professor of mechanical engineering, stands by his video projection at the National Academy of Sciences. The exhibit, “Chaosmosis: Assigning Rhythm to the Turbulent” is on display through Feb. 23.
Recent computer technology, however, is enabling scientists and engineers to create cloud simulations in controlled environments.
Georgios Matheou, associate professor of mechanical engineering in the School of Mechanical, Aerospace and Manufacturing Engineering, is using a mathematical model called large-eddy simulation to replicate cloud physics and create cloud models. These simulations help improve weather forecasts and climate projections while contributing to the field of fluid dynamics—a discipline that describes the flow of liquids and gases.
Instead of requiring surgery to insert a solid scaffold, the gel could be simply injected into the knee, a much less invasive procedure
Prof. Thanh Nguyen (right) and graduate student Tra Vinikoor (left).
A lifetime of activity can gradually erode the cartilage that cushions our joints. Someday, we might simply inject a gel to repair it, University of Connecticut researchers report in the Oct. 6 issue of Nature Communications.
By Claire Tremont, Manager of Communications and Digital Strategy
Operated through the University of Connecticut School of Engineering, the Digital Design Research, Analysis, and Manufacturing (D2REAM) Center – an academic-government-industry partnership that will develop groundbreaking modeling and simulation capabilities for the next generation of Army ground vehicle systems – aims to support advanced structural digital design and manufacturing, and discovery of novel metamaterials.
By using the strong research ecosystems at UConn, the center, which launched in July, looks to build a stronger partnership between academia, government, and industry. The center is supported by a $4 million round of funding in its first year, and by an additional $5 million in the second year.
“Our objective is to formulate and develop novel digital engineering models that will help the Army make better predictions, which in turn will further reduce the need to build physical prototypes,” says Mechanical Engineering Professor and Department Head Horea Ilies, who leads the center along with Castleman Professor of Engineering Innovation Associate Professor Julián Norato. “We have at UConn one of the strongest computational design and manufacturing groups in the nation.”
UConn’s team of Mechanical Engineering Graduate students achieved a remarkable victory, securing first place at the national hackathon event hosted by The Computer & Information in Engineering (CIE) Division of the American Society of Mechanical Engineers (ASME). This annual competition featured 34 participants from 18 institutions and took place from August 20 to 23, 2023, at the Boston Park Plaza in Boston, MA.
From left: PhD students Leidong Xu, Zihan Wang, and Prof. Hongyi Xu
The dynamic duo of Leidong Xu and Zihan Wang, both PhD students affiliated with Prof. Hongyi Xu’s Computation Design for Manufacturing Laboratory, earned the grand prize of $1,400 for their outstanding performance. The second-place team received $700, while the third-place team received $350.
The ASME-hosted hackathon presented an invaluable opportunity for participants to immerse themselves in the practical applications of data science and machine learning techniques to solve real-world engineering challenges. The primary objective of this competition was to develop realistic textures for solid objects created using computer-aided design (CAD) software. These textures were expected to mimic the behavior of real-world materials like metals and alloys across various scales.
UConn’s triumph at this national event is a testament to the exceptional talent and dedication of its Mechanical Engineering students, showcasing their ability to harness cutting-edge technology to address complex engineering problems. The students and Prof. Xu delved deeper into their journey leading up to and during the hackathon below.
What were the key challenges you and your team encountered during the hackathon, and how did you overcome them?
The hackathon event has a tight timeframe, and it is a huge challenge for us to develop a complete and polish project. To overcome it, we allocate time wisely and finally get all results done in one week.
Could you provide insights into the innovative solution you developed for the hackathon challenge?
Zihan and Leidong enhanced an existing system that utilized 2D microstructure images to recreate 3D microstructures that are statistically equivalent. Our advanced framework employs a Transfer Learning model to capture essential features from the granular microstructures of alloys. Notably, we’ve augmented computational efficiency through
parallel computing, which also allows our generated microstructures to be incorporated into intricate 3D volumes like tubes, helical gears, and turbo blades. Our methodology integrates transfer learning via VGG-19, style transfer techniques for texture synthesis, and a multi-GPU parallel approach. Beyond its technical prowess, our framework addresses a crucial design hurdle, bridging the gap between microstructures and designers’ vision seamlessly.
What lessons or takeaways do you think other aspiring participants can learn from your experience?
With the rapid evolution of machine learning methodologies in recent times, it’s imperative for researchers to first understand the inherent characteristics of their data before selecting an approach. From there, adapting and tweaking existing frameworks or strategies can be pivotal in optimizing results.
How did your preparation and training beforehand impact your performance during the hackathon?
We are very familiar with the programming and visualization tools we used during the hackathon. Additionally, we possess sufficient expertise in pre-trained deep learning models, image-processing methods, and style transfer techniques. This proficiency greatly expedited our problem-solving process throughout the hackathon.
Were there any unexpected twists or turns during the competition that forced you to adapt your approach?
With the limited time at hand, we realized that we needed to capitalize on the advantage of using pre-trained deep learning models to tackle the challenge effectively. Initially, we had planned to build our solution from the ground up, training our own models and optimizing them for the specific problem we were addressing. However, given the tight timeframe, this approach would have consumed a substantial portion of our available time. Upon evaluating our situation, we recognized that leveraging pre-trained models could provide us with a significant head start. These models were already trained on vast amounts of data and had learned complex patterns, making them well-suited for our problem as well. This shift in strategy allowed us to save precious time on training and focus more on adapting the model to our specific needs.
Looking ahead, what are your aspirations or goals in the field of technology and innovation after your victory at the ASME 2023 CIE Hackathon?
Our victory at the ASME 2023 CIE Hackathon has reinforced our drive to further refine and innovate our current framework. We see a multitude of avenues for enhancement. Specifically, we’re eager to develop a fully automated system for image analysis and labeling, which would drastically streamline the process. Another focus is to fine-tune our parallel algorithm to produce microstructure images with even greater resolution. Moreover, in a bid to consolidate our findings and methods, we’re excited about our upcoming collaboration with Sandia National Laboratories. Our joint effort aims to encapsulate our hackathon project into a comprehensive journal paper, sharing our innovations with the broader scientific community.
How do you envision leveraging the skills and experiences gained from the hackathon in your future projects and endeavors?
IDETC/CIE hackathon is an opportunity to engage with real-world engineering problems, moving beyond academic theory. This setting will allow me to apply my technical knowledge in a practical context, enhancing my understanding of the mission and challenges of national labs and leading industry companies. Participating in the hackathon in a team will serve as an excellent opportunity for honing my teamwork and cooperative abilities. The exchange of ideas, innovation, and sense of camaraderie within such events play a critical role in my future career.
Can you provide insights into your background in coding? How long have you been coding, and what initially sparked your interest in this field?
We started on our computational research journey during our undergraduate years. For us, coding transcends mere functionality; it is an art form. We firmly believe that the elegance and precision of the code play a pivotal role in determining the quality of the final research output. Consequently, we always strive to craft our code with extra care and refinement, ensuring that it not only fulfills its intended purpose but also stands as a testament to our dedication and passion.
Were there any specific coding languages or technologies that played a crucial role in your solution for the hackathon challenge?
We heavily relied on the PyTorch package, which is based on the Python programming language, to implement our innovative idea. Beyond that, deep learning methods and image analysis techniques are both very important to contribute to our success.
How do you plan to continue developing your coding skills and staying updated with the latest advancements in technology?
To ensure sustained progress in our coding capabilities and awareness of cutting-edge developments, we’ve mapped out a multi-faceted approach. This includes actively participating in tech competitions, which challenges our problem-solving abilities and exposes us to diverse perspectives. Furthermore, attending conferences allows us to gain firsthand insights from industry leaders and pioneers. Finally, keeping abreast of the latest
Approximately 537 million people worldwide are affected by diabetes mellitus, a condition characterized by high blood sugar levels. By 2030, it is projected that this number will increase to 643 million. Among individuals with diabetes, almost half are older adults aged 65 or above who have type 2 diabetes. As the global population ages and the number of people with diabetes continues to rise rapidly, this age-related disease poses a significant challenge in the medical and socioeconomic realms.
In people with diabetes and cardiovascular disease, the leading cause of death and disability is a condition called atherosclerotic vascular disease. This disease involves the progressive narrowing and hardening of blood vessels due to complex processes such as calcification, glycation, and crosslinking. However, identifying the specific molecules responsible for this degradation process remains a persistent challenge in the field.
On the other hand, gaining a deeper understanding of the causes of this disease can help us develop strategies to prevent, diagnose, or even reverse the loss of elasticity in arterial tissues.
The research funded by this new NIH R56 grant and carried out in Prof. Anna Tarakanova’s group, aims to develop such a deeper understanding by investigating the mechanical deterioration of arterial elastic tissue at various levels, ranging from the sub-molecular to tissue scales. The research will develop a computational framework that simulates and unravels the molecular mechanisms behind the mechanical deterioration of arteries during aging and disease.
A new, biodegradable piezoelectric device far more powerful than previous devices could make brain cancers more treatable, a team of Mechanical Engineering researchers report in the June 14 issue of Science Advances.
The research team. From left to right: Kazem Kazerounian, Thanh Nguyen, Feng Lin, Thinh Le, Meysam Chorsi, and Horea Ilies.
The group, developed a novel sensor from electrospun crystals of glycine, an amino acid that is a common protein in the body, and has been recently found to be strongly piezo-electric.
NSF Early Career Development (CAREER) Program awards are highly prestigious, offered to early-career faculty members who demonstrate the potential to serve as academic role models in research and education.
Three ME faculty members have received this prestigious award in 2022. Congratulations to all three recipients!
Hongyi Xu
Anna Tarakanova
George Matheou
Prof. Xu’s award will support his group’s research on design of mixed stochasticity structural systems. The award received by Prof. Tarakanova will support fundamental research to understand complex changes to elastin that occur in aging and disease. Prof. Matheou’s grant will focus on large scale computational models of low could transitions in the atmosphere to support a better understanding of their impact on climate change.
With these three awards, the total number of NSF CAREER or DoD Young Investigator Awards won by ME faculty since 1996 increases to 25 with seven of these awards having been received in the last three years!
Prof. Anna Tarakanova, an assistant professor of mechanical engineering, and two of her graduate students, Genny Kunkel and Mohammad Madani, develop computational models describing which parts of the spike protein flex, how mutations affect that flexibility, and how it all affects antibodies’ grip.
