Data-driven Pressure Recovery in Diffusers
| dc.contributor.author | Salazar, Juan Augusto Paredes | |
| dc.contributor.author | Goel, Ankit | |
| dc.contributor.author | Costich, Rowen | |
| dc.contributor.author | Koca, Meliksah | |
| dc.contributor.author | Tumuklu, Ozgur | |
| dc.contributor.author | Amitay, Michael | |
| dc.date.accessioned | 2026-01-22T16:18:27Z | |
| dc.date.issued | 2025-12-11 | |
| dc.description | 2026 Scitech Forum, 12–16 January, Orlando, FL | |
| dc.description.abstract | This paper investigates the application of a data-driven technique based on retrospective cost optimization to optimize the frequency of mass injection into an S-shaped diffuser, with the objective of maximizing the pressure recovery. Experimental data indicated that there is an optimal injection frequency between 100 Hz and 300 Hz with a mass flow rate of 1 percent of the free stream. High-fidelity numerical simulations using compressible unsteady Reynolds-Averaged Navier-Stokes (URANS) are conducted to investigate the mean and temporal features resulting from mass injection into an S-shaped diffuser with differing injection speeds and pulse frequencies. The results are compared with experiments to confirm the accuracy of the numerical solution. Overall, 2-D simulations are relatively in good agreement with the experiment, with 3-D simulations currently under investigation to benchmark the effect of spanwise instabilities. Simulation results with the proposed data-driven technique show improvements upon a baseline case by increasing pressure recovery and reducing the region of flow recirculation within the diffuser. | |
| dc.description.sponsorship | OT is thankful for computational resources from the Center for Computational Innovations at Rensselaer Polytechnic Institute and for computational resources granted by NSF-ACCESS for the project PHY240018. Financial support for OT for this research was provided by RPI. AG acknowledges computational resources granted by NSF-ACCESS for the project MCH250107. | |
| dc.description.uri | http://arxiv.org/abs/2512.10801 | |
| dc.format.extent | 22 pages | |
| dc.genre | conference papers and proceedings | |
| dc.genre | preprints | |
| dc.identifier | doi:10.13016/m2iowt-fxzj | |
| dc.identifier.uri | https://doi.org/10.48550/arXiv.2512.10801 | |
| dc.identifier.uri | http://hdl.handle.net/11603/41451 | |
| dc.language.iso | en | |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Mechanical Engineering Department | |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | UMBC Estimation, Control, and Learning Laboratory (ECLL). | |
| dc.subject | Physics - Fluid Dynamics | |
| dc.subject | Electrical Engineering and Systems Science - Systems and Control | |
| dc.title | Data-driven Pressure Recovery in Diffusers | |
| dc.type | Text | |
| dcterms.creator | https://orcid.org/0000-0001-7486-1231 | |
| dcterms.creator | https://orcid.org/0000-0002-4146-6275 |
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