Accelerating Nanofluidics Research with IoT: A Case Study at the University of Kansas
Technology Category
- Analytics & Modeling - Digital Twin / Simulation
- Robots - Parallel Robots
Applicable Industries
- Equipment & Machinery
- Life Sciences
Applicable Functions
- Procurement
- Product Research & Development
Use Cases
- Digital Twin
- Virtual Reality
About The Customer
The customer in this case study is Eric Lee, a graduate student at the University of Kansas. His research focuses on nanofluidics, specifically nanowetting problems. Nanofluidics is the study of nanoflows in and around nanosized objects, and is the basis for miniaturization of microfluidic devices down to the nanoscale. Eric uses molecular dynamics codes, such as LAMMPS, for his computational analyses. His work requires him to run multiple analyses quickly and efficiently, necessitating access to a large computing cluster that can work seamlessly with LAMMPS.
The Challenge
Eric Lee, a graduate student at the University of Kansas, was conducting research on nanofluidics, specifically nanowetting problems. His work involved the use of molecular dynamics codes, such as LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), for computational analyses. However, to fully leverage the scalability of LAMMPS and expedite his analyses, he required access to a large computing cluster that could work seamlessly with LAMMPS. He also needed a platform that could support his specific version of LAMMPS and any custom scripts he wanted to upload. The challenge was to find a solution that could provide the necessary computational power and software compatibility, while also being user-friendly and responsive to his specific needs.
The Solution
The solution came in the form of Rescale, a platform that supports LAMMPS and many other software codes across domains. Rescale's hardware was state-of-the-art, promising faster run times for LAMMPS jobs. Initially, Rescale did not include some custom LAMMPS packages necessary for Eric's work, but their engineers quickly resolved this issue. The platform also provided quick and helpful customer support, responding to Eric's queries within a few hours and providing useful screenshots to guide him in setting up his LAMMPS job. The process to run a job on Rescale was simple and straightforward, involving setting up a simulation name, choosing the number of cores needed, uploading the input file and associated data files, choosing the analysis type, and submitting the job.
Operational Impact
Quantitative Benefit
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