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Month: October 2023
Measurement of non-equilibrium in high-speed hydrogen jet flames using spontaneous Raman scattering
Abstract: Mixing-induced vibrational non-equilibrium was studied in the turbulent shear layer between a high-speed jet and a surrounding hot-air co-flow. The vibrational and rotational temperatures of N2 and O2 were determined by fitting measured spontaneous Raman scattering spectra to a model that allows for different vibrational and rotational temperatures. The mixing of the jet fluid with the co-flow gases occurs over microsecond time scales, which is sufficiently fast to induce vibrational non-equilibrium in the mixture of hot and cold gases. The effect of fluctuating temperatures on the time-averaged Raman measurement was quantified using single-shot Rayleigh thermometry. The Raman scattering results were found to be insensitive to fluctuations except where the flame is present intermittently. Vibrational non-equilibrium was detected in nitrogen but not in oxygen. This difference between species temperatures violates the two-temperature assumption often used in the modeling of high-temperature non-equilibrium flow. A multiple-pass cell was constructed to obtain single-shot Raman scattering measurements in the turbulent shear layer using a pulsed stretched laser. These measurements agreed with the previous time-average results and allowed us to make measurements near the fluctuating base of a lifted flame – a region where time-averaged measurements do not give meaningful results.
Biographical Sketch: Prof. Philip L. Varghese holds the Ernest H. Cockrell Centennial Chair in Engineering at The University of Texas at Austin and has an international reputation in the areas of rarefied and non-equilibrium flows and optical diagnostics for combustion and plasmas. He received his Bachelor of Technology degree from the Indian Institute of Technology in Madras in 1976, an MS from Syracuse University in 1977, and a PhD from Stanford University in 1983 all in Mechanical Engineering. He was a post-doctoral Scholar in the Molecular Physics Laboratory at SRI International and joined UT Austin in 1983 in the department of Mechanical Engineering. He was promoted to Associate Professor in 1988 and transferred to Aerospace Engineering in 1989. He was promoted to full Professor in 1995 and has been the Director of the Center for Aeromechanics Research since 1999. He served as Chair of the Department from 2009-2012.
Among numerous awards he was Fulbright Senior Scholar in France in 1993 and was awarded the Boeing-A.D. Welliver Faculty Fellowship by the Boeing Company in 1998. He received the Lockheed Martin Aeronautics Company Award for Excellence in Engineering Teaching in Spring 2003, and was elected to the Academy of Distinguished Teachers at the University of Texas in 2005. In February 2012 he was selected Professor of the Year by the Senate of College Councils at UT Austin and was awarded The University of Texas System Regents’ Outstanding Teaching Award in August 2016.
Dr. Varghese’s research focuses on understanding the basic molecular processes occurring in high speed, high temperature, and non-equilibrium flows. This is an inter-disciplinary field, requiring a synthesis of physics and chemistry with the more traditional engineering disciplines of fluid mechanics, heat transfer, and thermodynamics. He applies his work to the study of hypersonic and rarefied flows, plasmas, and combustion. He has established a laser diagnostics laboratory for experimental studies in combustion and plasma discharges. He also has an active program in planetary scale simulations of rarefied flows and has developed a novel technique for accurate solutions of the Boltzmann equation using quasi-particle simulation. His research publications have been extensively referenced and a recent search showed over 3800 citations of his work on Google Scholar. He is co-inventor on six US patents related to applications of Raman spectroscopy.
New Digital Design Center Aids U.S. Army Vehicle Production
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.”
Read more on the UConn Today
and visit https://dream.engineering.uconn.edu/.
UConn Graduate Students Win First Prize at Annual ASME Hackathon
by Joanna Giano, Written Communications Assistant
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.
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