Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Data-driven Pressure Recovery in Diffusers

Juan Augusto Paredes Salazar, Ankit Goel, Rowen Costich, Meliksah Koca, Ozgur Tumuklu, Michael Amitay

TL;DR

This work addresses improving pressure recovery in an S-shaped diffuser by applying retrospective cost adaptive control (RCAC) to optimally modulate jet frequency in real time. It couples high-fidelity 2-D unsteady RANS simulations (OpenFOAM, SST k–ω) with a data-driven, model-free RCAC framework that uses velocity-pressure measurements to adapt actuation without detailed flow models. Results show RCAC increases average pressure recovery and reduces recirculation relative to a baseline, with the controller converging to distinct effective jet frequencies; 3-D effects and spanwise instabilities are acknowledged for future work. Overall, the study demonstrates the viability of data-driven adaptive control to enhance internal-flow performance in diffusers, offering a path toward robust, model-agnostic flow-control strategies.

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.

Data-driven Pressure Recovery in Diffusers

TL;DR

This work addresses improving pressure recovery in an S-shaped diffuser by applying retrospective cost adaptive control (RCAC) to optimally modulate jet frequency in real time. It couples high-fidelity 2-D unsteady RANS simulations (OpenFOAM, SST k–ω) with a data-driven, model-free RCAC framework that uses velocity-pressure measurements to adapt actuation without detailed flow models. Results show RCAC increases average pressure recovery and reduces recirculation relative to a baseline, with the controller converging to distinct effective jet frequencies; 3-D effects and spanwise instabilities are acknowledged for future work. Overall, the study demonstrates the viability of data-driven adaptive control to enhance internal-flow performance in diffusers, offering a path toward robust, model-agnostic flow-control strategies.

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.

Paper Structure

This paper contains 17 sections, 1 theorem, 34 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Numerical Model and Diffuser Configuration
  3. Governing Equations
  4. Continuity and Momentum
  5. Energy Equation
  6. Equation of State
  7. Turbulence Model: $k$--$\omega$ SST
  8. Wall Modelling
  9. Numerical Solver
  10. Diffuser Geometry and Control Parameters
  11. Results and Discussion
  12. Data-driven Control for Pressure Recovery
  13. Review of Retrospective Cost Adaptive Control
  14. Sampled-data Controller Implementation
  15. Implemetation Details for Interface with Diffuser Model Simulation
  16. ...and 2 more sections

Key Result

Proposition III.1

For all $k \ge 0$, let $\theta_{k+1}$ denote the global minimzer of $J_k(\hat{\theta})$ given by eq:Jg. Then, $\theta_{k+1}$ is given by where and $P_k$ satisfies

Figures (9)

  • Figure 1: Computational domain for the diffuser along with the location of the control jet and grid points.
  • Figure 2: Comparison of the pressure values at the inlet using two grid resolutions with the experiment gartner2019mitigation.
  • Figure 3: Spatial distribution of the velocity (m/s) (left), along with black streamlines, and the pressure field (Pa) (right) near the Aerodynamic Interface Plane (AIP) using the 2-D model at 1 s.
  • Figure 4: Time history of the axial velocity inside the separation bubble (left) at $(x,y)$ = (0.1761, 0.1084) m and the corresponding frequency spectrum (right).
  • Figure 5: Comparison of the calculated (blue) pressure recoveries with the experiment (red) gartner2019mitigation along the AIP plane for steady (left) and low-RMS jet (right) control case.
  • ...and 4 more figures

Theorems & Definitions (1)

  • Proposition III.1