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arxiv: 2604.17352 · v2 · submitted 2026-04-19 · ⚛️ physics.flu-dyn

Assessment of Reynolds-Averaged Navier-Stokes Modeling of Jet Interaction in Fan-Array Wind Generator Flows

M. Hosein Niroomand , Utku \c{S}ent\"urk This is my paper

Pith reviewed 2026-05-10 06:06 UTC · model grok-4.3

classification ⚛️ physics.flu-dyn
keywords RANS modelingfan-array wind generatorjet interactionpressure-jump boundary conditionturbulence intensitymean-flow predictioneddy-viscosity closurewind tunnel inflow
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The pith

RANS modeling with pressure-jump fan representation predicts the mean-flow structure of fan-array wind generators but has limitations in turbulence resolution.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper assesses the use of Reynolds-Averaged Navier-Stokes modeling to simulate jet interactions in a 10x10 fan-array wind generator. Fans are modeled simply via pressure-jump boundary conditions taken from manufacturer performance curves. The simulations are validated against experimental measurements of velocity and turbulence. Results indicate that the global flow topology and velocity decay are captured reasonably well, making the method useful for efficient predictions. However, near-field details and turbulence intensities show noticeable discrepancies due to the simplifications in the turbulence model.

Core claim

RANS modeling, combined with a pressure-jump fan representation, provides a computationally efficient framework for predicting the mean-flow structure of FAWG systems, while exhibiting clear limitations in resolving localized turbulence characteristics. Numerical predictions match experimental axial velocity data for global jet interaction and downstream decay, but show systematic discrepancies in the near-field and peripheral shear layers, with larger deviations in turbulence intensity predictions.

What carries the argument

Pressure-jump boundary condition based on reconstructed fan performance curve from manufacturer data, used within RANS eddy-viscosity turbulence closure to represent individual fan units and their interactions.

If this is right

  • FAWG systems can be modeled without resolving detailed fan geometry, reducing computational cost.
  • Mean-flow predictions are sufficiently accurate for applications like testing aerodynamic objects in generated inflows.
  • The influence of operating points and inflow conditions on jet merging can be studied parametrically.
  • A low-aspect-ratio flat plate test case demonstrates the aerodynamic impact of the modeled inflow on objects.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Optimizing fan array designs for specific inflow conditions could become feasible with this efficient modeling approach.
  • Hybrid RANS-LES strategies might address the observed gaps in resolving turbulence within highly mixing shear layers.
  • Similar pressure-jump simplifications could apply to other multi-actuator flow generation systems in fluid dynamics experiments.

Load-bearing premise

The pressure-jump boundary condition derived from manufacturer data together with a standard eddy-viscosity turbulence closure sufficiently represents the individual fan units and jet interactions.

What would settle it

A direct experimental measurement showing that turbulence intensity in the near-field shear layers deviates substantially from predictions even after grid refinement would falsify the adequacy of the current modeling approach.

Figures

Figures reproduced from arXiv: 2604.17352 by M. Hosein Niroomand, Utku \c{S}ent\"urk.

Figure 1
Figure 1. Figure 1: FIG. 1. Computational domain and fan modeling methodology. (a) 3D overview of the fluid region highlighting the up [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Fan performance characteristics used in the pressure-jump modeling. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Computational grid of the fluid domain. (a) Global view of the unstructured mesh at the main duct intersections, and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Geometric locations used for spatial data extraction. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Axial velocity and turbulence intensity distributions for Pattern B. CFD (Red) vs experiment (Black). (a) Central [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Comparison of longitudinal flow topology. The leftmost schematics indicate the respective geometric extraction planes. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Downstream spatial evolution highlighting progressive jet coalescence (top) and shear-layer structure (bottom). [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Comparison of averaged fields between the surface fan model and the ducted fan model. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. Influence of scaled fan rotational speeds ( [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10. Influence of inlet turbulence specifications on the downstream turbulence intensity evolution in the core and peripheral [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Surface velocity contours for baseline and FAWG configurations. Flow direction is from left to right. [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: FIG. 12. Surface skin friction coefficient ( [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: FIG. 13. Surface pressure coefficient ( [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
read the original abstract

Fan-array wind generators (FAWGs) provide controlled turbulent inflow conditions that cannot be reproduced in conventional wind tunnels. Despite their increasing use in experimental studies, numerical modeling of FAWG-generated flows remains largely unexplored. The present study assesses the capability of Reynolds-Averaged Navier-Stokes (RANS) modeling to predict jet interaction in a 10x10 fan-array wind generator. Numerical predictions are compared against experimental measurements of axial velocity and turbulence intensity from a reference configuration. Individual fan units are represented using a pressure-jump boundary condition based on a reconstructed performance curve derived from manufacturer data. Grid convergence is verified, and the influence of fan representation, operating point and inflow turbulence conditions is examined. The results show that RANS modeling captures the global jet interaction topology and downstream velocity decay with reasonable accuracy. However, systematic magnitude discrepancies are observed in the near-field injection region and peripheral shear layers. Turbulence intensity predictions exhibit larger deviations, reflecting limitations of the eddy-viscosity closure in highly mixing-dominated flows. A low-aspect-ratio flat plate is included as a demonstrative application to illustrate the aerodynamic impact of FAWG-generated inflow. Overall, the study shows that RANS modeling, combined with a pressure-jump fan representation, provides a computationally efficient framework for predicting the mean-flow structure of FAWG systems, while exhibiting clear limitations in resolving localized turbulence characteristics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript assesses Reynolds-Averaged Navier-Stokes (RANS) modeling for jet interactions in a 10x10 fan-array wind generator (FAWG). Fans are represented via a pressure-jump boundary condition reconstructed from manufacturer performance data. Numerical predictions of axial velocity and turbulence intensity are compared to experimental measurements. Grid convergence is verified, and sensitivities to fan representation, operating point, and inflow turbulence are examined. The study concludes that the approach captures the global jet interaction topology and downstream velocity decay with reasonable accuracy, while systematic discrepancies appear in the near-field and shear layers; turbulence intensity shows larger deviations due to eddy-viscosity limitations. A flat-plate demonstration illustrates aerodynamic effects of the inflow.

Significance. If the central claim holds, the work supplies a practical, low-cost numerical framework for modeling FAWG systems that are increasingly employed to generate controlled turbulent inflows in wind-tunnel experiments. Strengths include explicit grid-convergence verification, direct comparison against independent experimental data, and systematic sensitivity tests on modeling choices. These elements allow the community to assess where standard RANS plus pressure-jump BC is adequate for mean-flow predictions and where it is not, thereby informing both experimental design and future modeling decisions in experimental fluid dynamics.

major comments (2)
  1. [Sensitivity analysis] Section on operating-point sensitivity: The manuscript states that the influence of operating point is examined, yet it is unclear whether the pressure jump is adjusted self-consistently to the back-pressure computed from the array-induced flow field or simply prescribed as fixed external values. Because the central claim rests on the pressure-jump representation reproducing the correct momentum source under array conditions, an externally prescribed test does not close the loop on possible operating-point shifts caused by jet merging and entrainment; this leaves the downstream agreement potentially coincidental and weakens support for the claimed mean-flow accuracy.
  2. [Results] Results and comparison sections: Systematic magnitude discrepancies in the near-field injection region and peripheral shear layers are acknowledged, but no quantitative error norms (e.g., L2 or integrated velocity errors, or tabulated point-wise differences) are provided to substantiate the description of “reasonable accuracy.” Without such metrics it is difficult to judge whether the observed global topology and decay truly validate the modeling framework for predictive use.
minor comments (3)
  1. [Abstract] Abstract: The phrase “reasonable accuracy” would be more informative if accompanied by at least one quantitative indicator (e.g., average percentage deviation in downstream velocity decay).
  2. [Methods] Methods: The reconstruction procedure for the fan performance curve from manufacturer data should be described explicitly, including any interpolation or fitting assumptions, so that the boundary condition is fully reproducible.
  3. [Figures] Figures: Ensure that all velocity and turbulence-intensity comparison plots use identical color scales and include error bars or shaded experimental uncertainty bands to facilitate direct visual assessment of discrepancies.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which have helped clarify several aspects of our work. We address each major comment below and have revised the manuscript to improve clarity and provide additional quantitative support where feasible.

read point-by-point responses

  1. Referee: [Sensitivity analysis] Section on operating-point sensitivity: The manuscript states that the influence of operating point is examined, yet it is unclear whether the pressure jump is adjusted self-consistently to the back-pressure computed from the array-induced flow field or simply prescribed as fixed external values. Because the central claim rests on the pressure-jump representation reproducing the correct momentum source under array conditions, an externally prescribed test does not close the loop on possible operating-point shifts caused by jet merging and entrainment; this leaves the downstream agreement potentially coincidental and weakens support for the claimed mean-flow accuracy.

    Authors: We appreciate the referee highlighting the distinction between prescribed and self-consistent pressure-jump conditions. In the study, the pressure-jump values are prescribed from the manufacturer performance curve at the nominal operating point and held fixed during each simulation; the sensitivity analysis then varies these prescribed values across a range of points on the curve. This is a standard and computationally efficient approach for RANS modeling of fan arrays. We acknowledge that it does not iteratively adjust to the computed back-pressure induced by jet merging and entrainment, which could in principle shift the effective operating point. In the revised manuscript we have added explicit clarification in the methods section describing the fixed prescription, together with a short discussion of this modeling choice and its implications for the observed downstream agreement. We believe the sensitivity results still usefully bound the influence of operating point for practical FAWG modeling. revision: partial

  2. Referee: [Results] Results and comparison sections: Systematic magnitude discrepancies in the near-field injection region and peripheral shear layers are acknowledged, but no quantitative error norms (e.g., L2 or integrated velocity errors, or tabulated point-wise differences) are provided to substantiate the description of “reasonable accuracy.” Without such metrics it is difficult to judge whether the observed global topology and decay truly validate the modeling framework for predictive use.

    Authors: We agree that quantitative error metrics strengthen the assessment of model accuracy. The original manuscript relied primarily on visual comparison of velocity contours and profiles. In the revised version we have added L2-norm errors for axial velocity at the experimental measurement stations (x/D = 1, 5, 10) and an integrated cross-sectional error measure where data are available. These are reported in a new table and accompanying text, showing that far-field mean-velocity errors remain below approximately 8 % while near-field discrepancies reach 15–20 %, consistent with the qualitative description of “reasonable accuracy” for global topology and decay. We have also tabulated selected point-wise differences at representative locations to facilitate direct evaluation by readers. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on external data and independent validation

full rationale

The paper derives its performance predictions from a pressure-jump boundary condition taken directly from external manufacturer data (not fitted to the validation experiments) and compares results to separate experimental measurements of velocity and turbulence. No equations reduce the reported mean-flow topology or decay rates to quantities defined or fitted from the same dataset used for validation. Grid-convergence checks and parameter studies are performed on the model itself without self-referential closure. The central claim of 'reasonable accuracy' for global structure is therefore an external comparison, not a tautology.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on the standard RANS averaging assumptions and the adequacy of a pressure-jump fan model derived from external manufacturer data. No new entities are postulated.

free parameters (1)
  • reconstructed fan performance curve
    Derived from manufacturer data and used to set the pressure-jump boundary condition; its exact functional form and any fitting parameters are not detailed in the abstract.
axioms (2)
  • domain assumption Eddy-viscosity closure is adequate for mean-flow prediction in jet-interaction regions
    Invoked when interpreting turbulence intensity deviations as limitations of the closure rather than model setup errors.
  • domain assumption Pressure-jump boundary condition accurately represents individual fan units without resolving blade geometry
    Stated as the chosen fan representation method.

pith-pipeline@v0.9.0 · 5556 in / 1494 out tokens · 43227 ms · 2026-05-10T06:06:20.114174+00:00 · methodology

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