Numerical Investigation of Conjugated Heat Transfer of the Plate-Type Fuel Assembly in the Research Reactor

In nuclear reactors, the research of conjugated heat transfer between the fuel and coolant in the fuel assembly is fundamental for improving the safety, reliability and economy. The numerical approach based on Computational Fluid Dynamics (CFD) can be used to realize the rapid analysis of the conjugated heat transfer. Besides, the numerical simulation can provide detailed physical fields that are useful for the designing and optimizing of the fuel assembly. The plate-type fuels are generally used to enhance heat transfer in research reactors with high power density. In this study, a standard plate-type fuel assembly in the research reactor was taken into consideration. The solid-fluid conjugated heat transfer of the fuel assembly and coolant was numerically investigated. In the fluid region, the subcooled flow boiling simulation model was established by implementing the Rensselaer Polytechnic Institute model into the Euler multi-phase flow method. The results show that the conjugated heat transfer of the fuel assembly and coolant can be simulated using the model established in this work. The influence of fluid velocity, power density and the width of the flow channel on the temperature distribution and the conjugated heat transfer was investigated and discussed.

Patchy nuclear chain reactions

Stochastic fluctuations of the neutron population within a nuclear reactor are typically prevented by operating the core at a sufficient power, since a deterministic (i.e., exactly predictable) behavior of the neutron population is required by automatic safety systems to detect unwanted power excursions. In order to characterize the reactor operating conditions at which the fluctuations vanish, an experiment was designed and took place in 2017 at the Rensselaer Polytechnic Institute Reactor Critical Facility. This experiment however revealed persisting fluctuations and striking patchy spatial patterns in neutron spatial distributions. Here we report these experimental findings, interpret them by a stochastic modeling based on branching random walks, and extend them using a “numerical twin” of the reactor core. Consequences on nuclear safety will be discussed.

Innovating the Medical Imaging Course

Artificial intelligence (AI) and machine learning (ML), especially deep learning, have generated tremendous impacts throughout our society, including the tomographic medical imaging field. In contrast to computer vision and image analysis, which have been major application examples of deep learning and deal with existing images, tomographic medical imaging mainly produces cross-sectional or volumetric images of internal structures from sensor measurements. Recently, deep learning has started being actively developed worldwide for medical imaging, including both tomographic reconstruction and image analysis. While medical imaging is a well-established field, in which extensive teaching experience has been accumulated over the past few decades, updating the medical imaging course to reflect AI/ML influence is a new challenge given the rapidly changing landscape of AI-based medical imaging, particularly deep tomographic imaging. In the 2019 fall semester, the medical imaging course at Rensselaer Polytechnic Institute was modified to include an AI framework with positive feedback from students. Encouragingly, many students showed a strong motivation to learn AI in classes and hands-on projects, as confirmed in their survey reports. In the 2020 fall semester, we improved this course further, incorporating new advances. This article describes our teaching philosophy, practice, and considerations with respect to integrating deep learning, tomographic imaging, and hands-on programming to redefine the medical imaging course. Furthermore, given the persistent pandemic, online teaching and examination have become an integral part of higher education. These needs will be addressed as well, with the hope of developing an open course in the future.

CFD Simulation of Two-Phase Immersion Cooling Using FC-72 Dielectric Fluid

With more development in electronics system capable of having larger functional densities, power density is increasing. Immersion cooling demonstrates the highest power usage efficiency (PUE) among all cooling techniques for data centers and there is still interest in optimizing immersion cooling to use it to its full potential. The aim of this paper is to present the effect of inclination and thermal shadowing on two-phase immersion cooling using FC-72. For simulation of boiling, the RPI (Rensselaer Polytechnic Institute) wall boiling model has been used. Also, two empirical models were used for calculation of bubble departure diameter and nucleate site density. The boundary condition was assumed to be constant heat flux and the bath temperature was kept at boiling temperature of FC-72 and the container pressure is assumed to be atmospheric. this study showed that due to the thermal shadowing, boiling boundary layer can lay over the top chipset and increases vapor volume fraction over top chipsets. This ultimately causes increase in maximum temperature of second chip. The other main observation is with higher inclination angle of chip, maximum temperature on the chip decreases up to 3°C.

Patents targeting the Warburg effect for cancer therapy: an interview with William P Katt

Dr William Katt is a multidisciplinary scientist with particular focus in computational, synthetic and biological chemistry. He obtained his undergraduate degree at Rensselaer Polytechnic Institute and performed his graduate studies at Yale University, where he focused on designing small-molecule inhibitors of the Rho/Rho GEF interaction. Following those studies, Dr Katt accepted a fellowship from the American Cancer Society which funded his work at Cornell University, where he investigated small-molecule inhibitors of the enzyme glutaminase, a key player in cancer metabolism. Today, Dr Katt is a research associate at Cornell and maintains a number of collaborations with researchers across the nation examining glutaminase, cancer stem cells, nano-therapeutics and more, with the goal of developing therapeutic approaches that will eventually help patients in the clinic.