Abacus 2.0 is ideal for those companies that do not want to wait or have the budget to procure their own hardware. A solution at your finger tip.
Abacus 2.0, hosted at DeIC National HPC Centre, SDU is remotely accessed through a secure SSH connection from anywhere in the world. With our job queue system, you can prepare and run jobs from your workstation, but with the added security and low-latency interconnections of a single location cluster.
With DeIC National HPC Centre, SDU all clients receive
- a free trial project to test the facility
- free of charge basic technical support, such as user account issues, accessing the system, and loading your software. ‘Advanced’ technical support is charged.
There are variety of methods for commercial organisations to gain access to the HPC facility. Whether as part of a research collaboration or working within a larger established consortia or even asking for assistance on a consultancy basis. If you wish to make use of Abacus 2.0 then please send an email to email@example.com and we can then direct your interest appropriately.
The price for non-academic users does not include the co-funding from DeIC and are aligned with current market prices.
Please also see SDU Erhverv for the range of options your company has to easily gain access to University of Southern Denmark students and graduates.
Examples of current industrial collaborations
Understanding the elastic properties of car tires
Carsten Svaneborg collaborates with Continental to understand the elastic properties of their materials better. Continental wants to learn how to apply molecular dynamics simulation methods to understand rubber materials at the molecular level and make even better tires.
On Abacus 2.0 coarse-grain models of rubber materials are made that enable simulations of large systems with thousands of very long polymer molecules as well as simulations of pulling these model materials, such as one would pull an elastic band. In the course of a simulation the elastic forces are measured as well as the polymer molecules conformational response to deformation of the network.
Project leader: Carsten Svaneborg, Ph.D. Associate Professor Department of Physics, Chemistry and Pharmacy, SDU
Read more here.
Fault Detection and Prediction in Offshore Wind Turbines
This project is collaboration with Siemens Wind Power (SWP) and Lindøe Offshore Research Centre (LORC). The aim of this project is to develop a computationally fast and inexpensive fault detection and prediction method for offshore wind turbines. The research group has access to the database of all the turbines (onshore and offshore) commissioned by SWP for the last 20 years around the world. Each turbine is equipped with approx. 600 sensors measuring at 2.5 Hz sampling rate. The number of potential faults in a turbine spans more than 60k cases.
The group uses MATLAB (signal processing and statistics toolboxes) and having access to HPC facilities has accelerated the calculations of the statistical properties of their big data.
Project leader: Esmaeil S. Nadimi, Associate Professor, Ph.D., ECE., Applied Statistical Signal Processing Group (πSeG), The Maersk Mc-Kinney Moller Institute, Faculty of Engineering , SDU