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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of Utah |
| Country | United States |
| Start Date | Jul 01, 2022 |
| End Date | Jun 30, 2026 |
| Duration | 1,460 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2225879 |
The scientific understanding of reacting flows, such as those encountered in fires and car engines, often relies on simulations to gain insight into how the flow behaves and interacts with chemical reactions. Such simulations enable predictive capabilities that enhance the design of safe infrastructure such as tunnels and fire-safe buildings, cleaner burning reactors such as coal boilers, and are of national importance.
Unfortunately, conventional methods for reacting flow simulations are often expensive, both computationally and economically. For example, a single high-fidelity indoor fire calculation could take up two weeks of calculation even on the fastest supercomputers. The proposed research aims at challenging conventional simulation methods by eliminating the costly elements in a simulation and replacing them with suitable approximations, without sacrificing accuracy and fidelity.
The proposed methods are expected to halve the cost simulations and are easy to implement in existing software. The work will enable faster turnaround times for simulations at a lower cost thus enhancing design and analysis workflows of reacting flow systems.
This proposal aims to develop fast high-accuracy solvers for low-Mach turbulent reacting flows by replacing expensive calculations of the pressure field with suitable approximations that do not affect accuracy or stability. These methods will halve the cost of simulations of complex flows such as fires leading to faster turnaround times and enabling higher-resolution calculations.
Preliminary results for incompressible flows showed speedups of up to 60% compared to conventional methods. The proposed methodologies will be prototyped in Python and verified using the method of manufactured solutions. They will then be implemented in a large-scale multiphysics code to study and demonstrate performance and scalability of the new algorithms.
The proposed algorithms are agnostic to reaction models and combustion chemistry, radiation, and other multiphysics phenomena making them extremely flexible and applicable to a very wide range of variable density and reacting flow problems.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
University of Utah
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