The researchers are going to establish a start-up to deploy their AI Software called 'AISoft' to develop solutions to engineering problems in varied fields such as in thermal management, semiconductors, automobile, aerospace and electronic cooling applications.
"We tested AIsoft and used it to solve such thermal management problems. We found it to be nearly million-fold faster compared to existing solutions currently used in the field," said Vishal Nandigana, Assistant Professor, Fluid Systems Laboratory, Department of Mechanical Engineering.
"Our AI works on any generalised rectilinear and curvilinear input geometry. Our research saves the computational time, which is the bottleneck to solve most engineering problems, Nandigana added.
The researchers utilised a data-driven AI and a deep learning model to arrive at solutions for engineering problems after training the AI with data sets.
These prior data sets can be from existing big data in the relevant engineering industry where there are lots of experimental data available.
Also, if data is not available for training the AI, it can be generated using commercially-available CFD (Computational Fluid Dynamics) software on small independent pieces of the full-blown problem.
This idea is new and is only now being looked at by a few research groups across the world. Most of these research groups use Convolutional Neural Networks (CNN) or C-GAN (conditional generative adversarial network) to solve engineering problems.
They have also developed hardware products using graphics processing unit (GPU) and multi-threading processing to solve thermal management problems in thermal and electronic cooling industries.
Both the software and hardware products are several times faster than commercial numerical method software and open source software in the market.
These algorithms will solve a lot of pressing problems for industries and can also be used for educational purposes.
( With inputs from IANS )