Summer Research Institute | 2015 Research
Kurt Aikens, Assistant Professor of Physics will be focusing on the topic of wall modeling in large eddy simulations (LES). LES is used to predict solutions to a wide variety of turbulent airflow problems but its application has been limited by the expense of including solid surfaces in the simulations. The costs of such cases are high because small vortical features develop near solid surfaces that require extremely fine computational grids for accurate predictions. The main goal of a wall model, therefore, is to reduce simulation costs by making reasonable assumptions about the behavior of the near-wall flow. There is much room for improvement, however (see Slotnick et al., “CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences”, NASA CR/2014-218178). This summer, a methodology developed previously by Dr. Aikens will be validated further by directly comparing with results from an accurate but more expensive formulation and available experimental data. We will also pursue an idea to reduce simulation expenses by making an inviscid (i.e., zero viscosity) assumption. If viable, this would reduce simulation costs by about 50% and 33% for subsonic and supersonic flows, respectively. Lastly, and time permitting, we will incorporate surface roughness effects into the model.
Houghton students August Gula and Thomas Eckert will be working with physics professor Mark Yuly on research related to inertial confinement fusion (ICF) in collaboration with scientists from SUNY Geneseo and the University of Rochester Laboratory for Laser Energetics. In ICF, high-powered lasers deposit a large amount of energy into a small pellet of nuclear fuel, triggering a nuclear fusion reaction. In order to characterize the fusion reaction, a system has been developed using 12C activation. Samples of purified graphite are placed at several locations around the ICF target chamber, where they are exposed to the flux of neutrons produced in the fusion reaction. By far, the biggest remaining obstacle to implementing this diagnostic technique is that it depends on accurate knowledge of the 12C(n,2n) cross section, which has not been well-measured. Previously an experiment to measure this cross section was performed at Ohio University; this summer we will analyze the data that were collected, and perform a necessary side-experiment to measure the efficiency of the detectors.
John Rowley, assistant professor of chemistry, and students Grace Hollenbeck and Sarah Rexroad will continue developing methods for the synthesis of biodegradable materials. Glycopolymers are a particularly interesting class of materials as they contain chemical functionalities that mimic signaling receptors on the surface of cells. Biodegradable glycopolymers that can degrade or be absorbed by the body have potential applications in drug-delivery, tissue engineering, and biomedical research. Our goal is to complete the chemical synthesis and characterization of new biodegradable glycopolymers.
Brandon Hoffman, assistant professor of physics, and two Houghton College students (Margaret Kirkland and Laurel Vincett) will be collaborating with Shefford Baker at the Cornell Center for Materials Research (CCMR) at Cornell University to study thin silver films. Today’s technology requires the use of metal films with thicknesses of only a few hundred nanometers or less. Not much is known about the properties of materials this small and the present models do not accurately describe the films. Therefore, thin silver films will be produced in a high vacuum deposition chamber and studied with x-ray diffraction (XRD) and possibly electron backscatter diffraction (EBSD) in order to characterize the microstructures and transformations of the films. The goal of these experiments will be to improve the general model that describes similar thin metal films.
Deep learning emerged out of research on neural networks has gained popularity in recent years by its state-of-the-art performances in machine learning. As a new way of using multiple layer neural networks, deep learning like our brain can engage learning at multiple levels or scales, which makes it closer to one of its original goals: artificial intelligence. By its very nature, deep learning is particularly well suited for learning high-dimensional perceptual data such as speech, images, text, and natural language where multiple level learning is a must. The big text data generated by users on social web sites such as Google, Facebook, Twitter, Amazon all need to be analyzed to extract useful knowledge for different goals. In this summer research, professor of math and computer science Dr. Wei Hu and his two students Brian Dickinson and Michael Ganger are going to develop methods to characterize the positive and negative movie reviews using deep learning.
Associate Professor of Chemistry, Karen Torraca, will work with two students this summer toward the development of a “green” synthetic method for the conversion of alcohols to ketones or aldehydes. The current standard synthetic processes require large amounts of heavy metals and generate a lot of hazardous environmental waste. Although there is a strong research emphasis across academia to develop better oxidation processes, very few new processes have actually been implemented in large-scale manufacturing due to the lack of robustness. Our ultimate goal will be to develop not only a “green” process, but one that is amenable to large-scale use where it will have the greatest environmental impact. Our research will focus on the use of palladium and other transition metal catalysts to complete the oxidation of various alcohols to ketones or aldehydes under mild conditions.