This UberCloud use case #200 describes how India’s National Institute of Health was able for schizophrenia – and potentially Parkinson’s disease, depression, and other brain disorders – to replace the current highly risky procedure of brain-invasive operations with an innovative technique of non-invasive low-risk treatment based on CAE that is also significantly more affordable.

CFD is demanding both in terms of computing power and simulation time. Artificial Neural Networks (ANN) can learn complex dependencies between high-dimensional variables. In this UberCloud case study, the goal of the approach is to apply an ANN to solve fluid flow problems to significantly decrease time-to-solution while preserving much of the accuracy of a traditional CFD solver.

With the following four UberCloud case studies we are demonstrating the growing acceptance of computer-aided engineering (CAE), especially in new user communities and application areas. Major reasons for this trend are continuous improvements in user-friendly CAE software, the ongoing price/performance improvement in high-performance computing (HPC), and easy and affordable access to powerful computing in the cloud.

Program Advantages:

– Automates multiple sequential or parallel siting analyses, where each analysis may itself execute multiple steps.

– Provides the flexibility to define the workflow and the set of steps that form the analysis, as well as the dependencies among them.

– Serves as a corporate knowledge base while providing automated reporting & searching of previous analyses.

– Provides the same simplified user experience and global access to the entire wind farm engineering team and the sales organization