Alumni Impacts: Sandia National Laboratories' Jacqueline Chen excels in the world of engineering
When Jacqueline Chen walked into Ohio State’s placement office of the early 1980s as one of the few women in the mechanical engineering program, she had a chance encounter with a booth that resulted in an offer that would forever change her life.
She had already been in the program for a few years and completed an internship with Ohio State Professor Lit Su Han in his wind turbine lab on campus and nearing graduation needed to set herself up for success when she completed her studies.
Sandia National Laboratories gave Chen the opportunity to apply for its graduate One-Year-on-Campus Program for women and minorities, which included a summer internship prior to starting the master’s program.
Chen did just that.
She completed a summer internship at Sandia’s Albuquerque, New Mexico lab, working on radioisotope thermoelectric generators for power generation on spacecrafts and then went on to participate in its on-campus program to receive her master’s from the University of California at Berkeley in 1982.
Nearly 40 years later, Jacqueline Chen is a Senior Scientist at Sandia National Laboratories, leading a computational combustion research group. Also, she is the PI of the DOE Exascale Computing Project on turbulent combustion, a multi-laboratory project to develop high-fidelity combustion multi-physics simulation software for exascale supercomputers on the horizon.
“The last 39 years have been great and went by quickly,” Chen said. “I never intended to stay that long at Sandia but it has been such a fun, fun ride. I have mentored over two dozen postdocs in my research group who have gone on to have successful careers, and working at the Sandia’s Combustion Research Facility (a DOE collaborative user facility) has enabled visitors from around the world to collaborate with us. It has been a really wonderful experience for me to perform research on turbulent combustion with computing resources on the world's largest supercomputers that the Department of Energy operates at the Leadership Computing Facility at Oak Ridge National Laboratories.”
When Chen started at Sandia, she was working in its applied mechanics group at the labs’ San Francisco Bay Area location in Livermore on heat transfer and numerical analysis related to national security issues.
After a few years, Chen decided she wanted a change and also had a desire to receive her PhD. Through Sandia’s Doctoral Study Program, she attended Stanford and studied fluid mechanics and turbulence research.
At Stanford, Chen worked with Professor Brian Cantwell and Dr. Naji Mansour from NASA Ames Research Center performing first-principles simulations called direct numerical simulations on some of the largest Cray supercomputers that were housed at NASA Ames back in the mid-80s.
Returning to Sandia with more of her interests lying in combustion and computational fluid dynamics simulation, Chen decided to move to the Combustion Research Facility, where she still works today.
Her research involves peering inside of engine cylinders virtually through high-fidelity direct numerical simulations with excruciating detail, focusing on the intricate coupling between turbulence and chemistry that takes place to try and identify ways to maximize efficiency.
“It's very hard to measure the details of turbulence-chemistry interactions at the small-length scales and fast-time scales that are relevant,” Chen said. “So computation is used to complement experiments and allow us to get a glimpse of these interactions that control ignition timing or combustion rates that determine an engine’s performance and emissions.”
Chen credits Sandia and the Department of Energy’s Office of Basic Energy Sciences and the Advanced Scientific Computing Research Office with providing sustained support and a vibrant environment to work with a top notch group of peers, and allowing her to use some of the best computing technology available to perform her research.
“I had so much fun working with these people, many of whom started out as postdocs and became life-long collaborators,” she said. “The work has been exceptionally interesting coupling fundamental chemical science and turbulent transport with high performance computing on computing resources at the cutting edge, because it is just like, ‘why would you want to go somewhere else?’.”
All of this work has not gone unnoticed. Throughout her career, she has received multiple distinctions and recognitions. In 2018 Chen was elected a member of the National Academy of Engineering. She received the 2018 Achievement Award by the Society of Women Engineers, and most recently in July, Chen was named a Department of Energy Office of Science Distinguished Scientists Fellow.
The DOE Office of Science Distinguished Scientists Fellowship comes with a three-year sponsorship for research of the fellows choosing.
For Chen, she plans to use this sponsorship to create an open-source software framework to investigate how to integrate machine learning concurrently with massively parallel simulations of turbulent reacting flows. Her goal is to extract a reduced order surrogate model representation of the high-dimensional composition space with lots of species and chemical reactions with far fewer dimensions while retaining the accuracy of the original simulation. Machine learning will also enable the detection of anomalous behavior that can be used to steer further analysis.
According to Chen, this will allow for machine learning to identify any anomalies occurring during the simulation at any given time. With the sheer volume of data generated from these simulations it cannot all be saved with sufficient frequency to capture the anomalies and analyzed at a later date.
“For example, it might be localized ignition that could lead to knock in an engine or localized extinction which could lead to misfire. You may miss those events if you save the data on storage too infrequently,” she said. “It’s best to perform the machine learning and analytics while the simulation is running. So we want to build the computational framework to allow other machine learning experts and computer scientists to be able to test their numerical algorithms for machine-learning and also to develop reduced order models that are predictive like the high-fidelity simulations but with a fraction of the computational cost.”
Chen has mentored countless post-docs and aspiring researchers during her nearly 40 years at Sandia. She has come across all types of people and has garnered some great advice for MAE undergrads at Ohio State.
“Study hard while you're at OSU. Take advantage of all the opportunities you come across there. Like, for me, it was that internship that once you delve into the work, it opens up your eyes as to what other possible pathways exist for either further education or career development,” she said. “Don’t be afraid to be opportunistic and accept a challenge in a new area. Don't overplay it.”
For anyone who works their way through school and has a change of heart, Chen assures that there is plenty of time and there are always opportunities to change just like she did.
Nearly 40 years ago, Jacqueline Chen walked into Ohio State’s placement office in the early 80s as one of the few women in the mechanical engineering program, but today, she walks into Sandia National Laboratories working on some of the most cutting edge research in her field.