The tool will simulate full-scale catalytic converters with realistic chemistry in less than overnight turnaround time. The simulator will support on the order of 1000 channels with implicit coupling between fluid flow, heat transfer (including conjugate heat transfer), mass transfer, and heterogeneous chemical reactions. This will allow catalytic converter developers to explore several design modifications including the size of the converter, the number of channels per square inch of the monolith, the flow rate, and the catalyst loading, among others. This will make catalytic converter modeling more useful and affordable to the industry, especially for small/mid-sized manufacturing and engineering firms.
What’s New:
August 2023
RNET has recently been awarded a Department of Energy Phase II SBIR to develop a Machine Learning Based Data Compression (MLDC) algorithm for numeric simulation data.
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July 2023
RNET has recently been awarded a NASA Phase I SBIR to develop a Rapid Data Analytics Platform using machine learning uses a novel dimensional reduction algorithm.
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June 2023
RNET has recently been awarded an NIH Phase II SBIR to design and develop Machine Learning tools to help Pathologists overcome the limitations of current computing hardware to design more accurate deep learning models for use in clinical diagnostics. These models will be able to analyze very large digitized images of glass slides (i.e., Whole Slide Images) to aid pathologists in tasks like cancer detection. The ability to analyze these images in their entirety instead of in small parts will improve the diagnostic accuracy of models and will accelerate algorithm development efforts.