Jacob Adams, Patrick Adrian, Ali Ayoub, Richard Ibekwe, W. Reed Kendrick, Haeseong Kim, Justin Kunimune, Peninah Levine, Yang Liu, Andrew Maris, Thanh Nguyen, Eli Sanchez, Paul Seurin, Lucas Shoji, Mybles Stapelberg, Amelia Trainer, Kai Van Brunt, Thomas Varnish, Yu-Jou Wang, and Weiyue Zhou.
Advanced high-resolution instruments are ubiquitously used by the science and engineering communities owing to their capabilities in capturing complex physical phenomena. We rely on such diagnostic tools to run high-throughput lab-scale experiments to study boiling heat transfer. Being a highly efficient mode of heat transfer, boiling is used extensively in most fields that require efficient energy conversion such as Light Water Reactors. The operation limits of energy systems that rely on boiling heat transfer are limited by phenomena that occur during boiling. One such occurrence is the boiling crisis, which can be devastating to a reactor’s operation and safety. With the development of high-resolution diagnostics, we can better visualize the boiling process up to the micro-scale. Some insights we gained from years of in-house experimentation have helped us identify bottlenecks in the practical implementation of these tools. High-resolution diagnostics require significant human intervention to complete a boiling experiment. The gigantic data acquired from such experiments require detailed processing and analysis before deciding on the next experiment. Such human-driven frameworks are both time-consuming and inefficient to experiment sustainably. Since high-throughput experiments are requisites to decipher the governing physics in fields we lack understanding in, our latest efforts have been to curb human intervention or automate experiments. Through this talk, I will highlight the recent research efforts in Prof Bucci’s Red Lab in reshaping the boiling heat transfer experimentation, leveraging Automated Science and Active Machine Learning. I will present the machine learning-based autonomous experimentation framework we have developed to analyze the experimental data generated through high-resolution infrared diagnostics in real-time. I will also shed light on our attempts to elucidate the physics governing the catastrophic boiling crisis, using an automated approach with a unique combination of deep neural networks, machine learning explainers, and high-resolution infrared diagnostics. I will end my talk by describing the ongoing effort in the Nuclear Science and Engineering department to develop automated experiments to increase the efficiency and scalability of boiling heat transfer research. The developed approach could forge a path for accelerated discoveries in other fields of scientific research apart from boiling heat transfer. WATCH PRESENTATION
Diamond is commonly known as the hardest material on earth. Now, it is also set to be one of the most powerful quantum devices. It outperforms classical sensors on many levels, including sensing magnetic fields with high sensitivity and high resolution. A contributing factor is the nitrogen-vacancy (NV) center in diamond, which is an atom-like spin defect that maintains useful quantum properties even at room temperature. Its optical initialization and readout, microwave control, and long coherence times make it a versatile platform for applications ranging from quantum communication to sensing and simulation. To usher such applications beyond their current limitations, we are designing a system of advanced periodic control. First, we develop a sensing protocol capable of extracting not only the magnitude of a magnetic field but also its direction, while retaining nanoscale resolution. Then, by introducing a control AC field, we invent an integrated quantum mixer and sensor. This down-converts the unmeasurable high frequency signal to a low frequency one, which allows us to probe any arbitrary-frequency signal field. Finally, we show that beyond quantum sensing, the periodic control provides a pathway to study common crystal symmetries (such as parity, rotation, particle-hole) by simulating them in a synthetic lattice in the time domain. Our results advance the development of high-performance quantum sensors and simulators based on quantum diamond and other quantum platforms, paving the way to characterize materials and simulate novel physics.
MIT’s ARC fusion reactor has the potential to demonstrate continuous power generation over extended periods of time. The proposed first plasma for the reactor is in 2035, giving a relatively short time frame to finalize material design choices. This is important because the extreme radiation exposure and temperature ranges expected for the reactor materials are conditions that have not been extensively researched. Developing and characterizing materials for specific irradiation and temperature conditions has historically been an extremely slow process. So, the bottleneck for developing systems like the ARC fusion reactor is determining how materials will fail, and the time to failure. Using Transient Grating Spectroscopy (TGS), which is a non-contact, and non-destructive method for characterizing materials, we can gather important information relevant for materials in fusion systems and understand more deeply the ways in which materials may fail. We have observed the effects of helium implantation, volumetric void swelling and irradiation induced precipitation on the properties of fusion relevant materials in experiments that spanned hours rather than days or weeks. This demonstrates the ability of TGS to serve as a new inference model for various radiation degradation modes in materials and speed up the time needed to characterize materials for use in fusion reactors. WATCH PRESENTATION
Superconducting REBCO magnets allow energy break-even conditions in smaller, thus less expensive, tokamaks. On the flip side, REBCO will suffer from a larger flux of neutrons and, without proper shielding, it will lose its ability to carry current without losses, Jc. To study the radiation tolerance of REBCO, our team built a cryogenic proton irradiation facility and measured Jc in-situ. Our work showed that heat partially reverses the damage caused by knock-on atoms. The next step is to design a cryogenic heat treatment, which requires a deeper understanding of Jc degradation and recovery. The consequences of radiation damage on Jc are a function of many factors, including operating temperature, magnetic field magnitude and angle, as well as temperature history. But ultimately, the superconducting properties are controlled by the microstructure which we can probe with positron spectroscopy. This technique provides an exciting opportunity to peer into the defect landscape, as we gradually raise the temperature of the irradiated specimen. Simultaneous measurements of Jc will draw a connection between microstructure and superconducting properties.Explaining the Jc recovery mechanism will let us maximize magnet healing within the maintenance schedule of a power plant. By extending the lifetime of the magnets, without increasing the size of the machine, our research enhances the cost competitiveness of fusion electricity.