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The research that I am currently conducting is in the area of Passively Actuated Deformable Airfoils. The largest presence of airfoils today is contained within the aerospace and transportation industries. Like those on commercial and military aircraft, the basic teardrop airfoil shape is augmented with a series external structures which aid in take-off, landing, and cruising flight. While they perform specific and important functions, they add additional weight to a system which is highly immersed in weight management. What my research is looking into, is try find a way to develop an internal structure for an airfoil that would provide similar shape change, without the added external mechanisms. To do this, I am using two different computational software packages. COMSOL Multiphysics allows for the examination of the fluid-structure interaction of the airfoil and moving air. Using different internal rib structures, a goal of finding an appropriate structure is hoped to be achieved. In addition, I am using the computational fluid dynamics package Fluent to help visualize velocity and pressure fields over deformed and undeformed airfoil shapes. If this software was not available through the academic research cluster, this research would extremely slow process. The governing physics behind these simulations is complex enough that without the computing power of the cluster, I do not believe that we would be able to perform it. In the last twenty or so years, a focus has shifted from passive actuation to active actuation. Hopefully, this research will help to launch a renewed interested in this field.

Ke Betty Li

I am a researcher in the Department of Civil and Environmental Engineering. Our research focuses on the investigation of how various contaminants affect the ground water quality and how we could design remediation systems. An important approach we are using for this type of investigation is modeling contaminant fate and transport in the subsurface on computers. The resources provided by Tufts Cluster Center are very important to us. Our simulations usually take days or even weeks on a single CPU. The clusters can either expedite each simulation if we use simulators that enable parallel computing, or allow us to simulate multiple serial processes simultaneously. The significant improvement in computing efficiency is critical for us to commit work quality to funding sponsors. We expect that our work will improve the cuurent understanding of contamination in the subsurface, provide cutting-edge assessment tools, and stimulate innovative treatment technologies.

Eric Miller

Our work concerns the development of tomographic processing methods for environmental remediation problems. Specifically, we are interested in using electrical resistance tomography (ERT) to estimate the geometry of regions of the subsurface contaminated by chemicals such as TCE or PCE. Though the concept of ERT is not unlike the more familiar computed axial tomography (CAT) used for medical imaging, the physics of ERT are a bit more complicated thereby leading to computationally intensive methods for turning data into pictures. Luckily these computational issues are, at a high level, easily parallelizable. Thus, we have turned to Star-P as the tool of choice for the rapid synthesis of our algorithms.

Michael A. Simon

Nonlinear dynamic modeling of Lepidopteron mechanosensors

The Trimmer Lab is interested in the control of locomotion and other movements in soft bodied animals. I have been analyzing the activity of a specific mechanosensor trying to understand how it influences abdominal movement, a critical question for animals with no rigid components. One particularly powerful analytical tool for analyzing such sensors is nonlinear analysis using Gaussian white noise as a stimulus. One challenge of this technique, however, is that it is computationally complex. Even storing the matrices involved in these computations is beyond the capabilities of the typical personal computer. The Tufts Linux Research Cluster offers me the resources necessary to run these computations and analyze the results without needing to invest in new, complicated, or expensive analytical hardware or software. It also allows me to use software that would have been difficult to acquire for our lab, alone. Without this resource, following this line of inquiry would have proved a costly endeavor, possibly prohibitively so. We hope to apply our results to the development of computer and robotic models, with the eventual goal of designing a soft robot, a groundbreaking engineering application with substantial implications for design in the biomedical engineering arena, as well as in other areas of engineering.

Katherine L. Tucker

Use of the Bioinformatics cluster has been invaluable to our research. We use a genetic analysis software named SOLAR which is Linux/Unix based. This software and the methods used in it are cutting edge. We are able to perform varous genetic computations with ease. In the past some student have had to do these calculations by hand because of a lack of access to such software. However, hand calculations are only possible for small sample sizes and simple genetic analysis. Our current work with Solar includes over 5,000 individuals and we are using some of the most advanced methods available. The cluster allows us to do large computational runs that would not be otherwise possible. Thus, our current work would not have been able without access to SOLAR on the bioinformatics cluster. In addition, this type of analysis is being more common and will be a greater part of our efforts in future years. Use of the bioinformatics cluster helps our research to remain competitive and important in our grant application process. Our lab is the first to use SOLAR on the bioinformatics cluster, however, since we have been using it, many labs have inquired about how to gain access. I sincerely thank you for your work in helping us gain access to the software and the service you have provided through the Bioinformatics cluster.

Jeffery S. Jackson

I am a grad student in Mechanical Engineering and I am conducting research on microfluidic mixers. I use the Cluster01 to create and run fluid flow models on COMSOL Multiphysics. The COMSOL program solves the Navier Stokes equations for transient fluid flow and the convection diffusion equation. For the models that I create to be accurate, though, they require more elements and time steps than my computer, or the computers in the EPDC, can handle. This is where the cluster comes in very handy. I usually have the Cluster run any model that is more complicated than a 2D model with 30,000 elements. The most complicated model I have had the cluster solve consisted of 90,000 elements. This model took 30 hours for the Cluster to solve, which is something that no other computer resource I have access to could do. Another nice benefit of the Cluster is being able to use it from home. I live in Providence, RI and it takes me two hours to get to Tufts by train. So, I only come in when I have to. Having remote access to the Cluster makes this possible. Without the Cluster, or the very helpful people who provide excellent technical support, I would never have been able to do the research I needed to to finish my Master's Thesis.

Erin Munro

I'm studying Computational Neuroscience in the Math department. My research consists of doing MANY simulations. That being said, I would not be able to do this research without the cluster! I simulate networks of thousands of neurons interacting. While there are some simulations that take a few minutes, the majority of them take 45 minutes to an 1.5 hours on one node. The last time I calculated, I'd like to run over a month's worth of these simulations. On top of this, I've run several very important simulations that take 1.5 days on 16 nodes. I had to run these simulations in order to try to reproduce results from Roger Traub's research. My current project is to try to explain these results. We tried to find a simpler way to explain them without reproducing the full model, but we found that we couldn't do it. With the cluster, I have been able to reproduce the results to the best of my ability. Furthermore, I've been able to dissect the model, and run many more simulations to get a much better understanding of what is going on in his results. I feel like I'm coming close to fully explaining the results, and have just presented a talk at BU explaining my ideas. None of this would have been possible without the cluster.

Casey Foote

My research for my MS in Mechanical Engineering is based on using the software available on the cluster to model a cold forging process. This model, paired with experimental data, will then be used to develop a tool to predict forging work piece cracking. The tool will provide a manufacturer of airfoils for use in the aircraft engine industry a method to rapidly develop new processing while avoiding costly physical trials.

Aurelie Edwards

My graduate student Christopher Mooney performs simulations of unsteady, turbulent fluid flow in a bioreactor with a stir-bar, using Femlab engineering software. Prior to having access to the Tufts cluster, he was experiencing extensive memory usage problems. On a PC with 2GB of RAM using Windows XP, he was only able to
access about 40% of the memory, due to fragmentation issues, and his simulations did not converge. We were both relieved to learn that we could have access to the
Tufts cluster and its Linux platform that offers 4GB+ of memory space. The latter has thankfully allowed us to solve increasingly complex models. For example, using his PC, Chris could solve finite element Navier-Stokes fluid flow problems with an element mesh density that limited the problem to about 100,000 degrees of freedom, beyond which he ran out of memory. He often received "low mesh quality" error messages that hindered the mathematical convergence of the solution. On the cluster, he now has enough memory to refine the mesh and run models with 300,000 degrees of freedom. Chris still runs into "out of memory" problems on the cluster, but much less frequently. The technical staff at Femlab, when told of the kinds of problems we envision solving in the coming years, suggested using a server with 10 to 16GB of memory space to run these models with adequate mesh resolution. In other words, if you were to increase the capacity of the Tufts cluster, we would be takers!

Gabriel Wachman

I use the cluster to conduct experiments relating to my work in machine learning. I am in the computer science department. The experiments I have been running have generally been to aid in the comparison of different learning algorithms. By running many experiments over a range of parameters, I can collect data that helps me to draw conclusions on the behavior of the algorithms. Without the cluster, much of the work I have done would have been impossible or at best severely limited.

Alexandre B. Sousa

I am a grauate student with the High Energy Physics Group and as part of the MINOS experiment collaboration, I have been one of the main people responsible for mass event reconstruction using the Fermilab fixed-target farm. Earlier this year, a Mock Data Challenge was issued to the experiment in order to shake down reconstruction and analysis shortcomings before real data collection starts in January. This effort requested the generation of a rather large MonteCarlo sample, which was subsequently reconstructed at Fermilab. However, the generation of the MC sample was quite hard to setup at Fermilab, where space constraints, e-bureaucracy and competition with other experiments meant we would not be able to do it in a timely manner. That was when I decided to test the Tufts Linux Cluster to perform this task. I was setup with an area on the /cluster/shared space within a day of my original request, and after a few tests, I was able to generate 80% of the total necessary MC sample in less than a week. I was of course lucky to be almost the exclusive user of the cluster for that period, but I really had no problems setting things up and using it in what is seen as a nice success of the Tufts High energy Physics Group. Giving this success we have volunteered to become one of the spearheading institutions taking part on the upcoming MC generation effort which should start later this month, and the gained experience was transformed in a document and relayed to other institutions that are starting to run their own clusters and hope to join this effort. I have used the cluster a second time to do a customized reprocessing data for the CC nue analysis group, which I integrate, which required compilation in the cluster of the MINOS Offline Software, installation of a mysql database and assembling some shell scripts to handle the job output. That went quite well, and the full data sample was processed in 2 hours, with about 1 day of setup. Having worked for 2 years with the Fermilab batch farm, I was mainly impressed by the speed of the network connection of the CPU nodes to the I/O node, almost 20 times the Fermilab data transfer speeds and also by the great flexibility of use given to the users, which implied minimal
back and forth contact with the admins and dramatically improved work efficiency.