Reduced order network models for predicting capillary phenomena
This technology utilizes reduced order network models to simulate porous transport phenomena, providing a cost-effective and rapid alternative to traditional CFD methods. Capable of handling multi-physics scenarios, it predicts fluid dynamics with high statistical accuracy.
Overview
This innovative solution leverages reduced order network models to simulate capillary phenomena in porous media. By approximating the pore space as a network with 1D connections, the technology offers a significant speed-up in computational time and cost compared to traditional Computational Fluid Dynamics (CFD) approaches. The models can simulate multi-physics scenarios, including thin films and heat transfer, making them highly versatile for predicting fluid dynamics and associated parameters such as leakage rate and flow distribution.
Technical specifications
- Reduced Order Model: Uses 1D network connections to simplify the modeling of pore spaces, akin to neural networks.
- Multi-Physics Capability: Coupled with sub-models for thin film dynamics, heat transfer, and phase change.
- Fluid Compatibility: Adaptable for both Newtonian and non-Newtonian fluids, with capabilities for transient two-phase flow simulations.
- Statistical Validity: Offers statistically significant outcomes by running ensembles of stochastic models in parallel.
- Validation: Models validated against published data and experimental results, with ongoing in-house experiments for further validation.
Technology readiness level
Currently at TRL 4, this technology has been validated in laboratory settings and is undergoing further development for broader application. The modular nature of the platform allows for ongoing enhancements and adaptability to new fluid types and conditions.
About University of Cincinnati
The University of Cincinnati is a comprehensive public research university with an applied, urban-serving character and a significant clinical enterprise. Industry engages through one of the nation's largest cooperative education programs, placing students year-round with corporate R&D and operations teams and creating an on-ramp to sponsored research. An innovation district near campus hosts co-located corporate labs, startup space, and shared prototyping facilities, while the university's integration with a major hospital system enables clinical studies and translation. Research is supported by competitive federal funding from agencies such as NIH and NSF, along with state and industry partnerships. A dedicated technology transfer office manages IP, licensing, corporate agreements, and startup formation, providing flexible models for collaboration.
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