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    Space

    Code Beyond Earth

    Staff ReporterBy Staff ReporterOctober 20, 2026No Comments5 Mins Read
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    Top Highlights

    NASA Glenn develops software predicting aircraft engine and jet noise.

    Tools predict jet, fan, propeller, tonal, and broadband noise for quieter designs.

    Models range from fast simplified estimates to computationally intensive realistic analyses.

    Many validated tools are available to engineers through NASA’s Software Catalog.

    Software From NASA Glenn: Tools for Aircraft Noise and Propulsion Research

    NASA Glenn Research Center teams develop theory and software to help predict noise from aircraft engines. Engineers use these tools to assess propulsion-system designs, compare options and investigate ways to reduce noise. The software covers different questions and levels of detail, from fast early-stage estimates to analyses that represent more complex flows and geometries.

    No single code suits every task. Simpler models can deliver useful results quickly, but they rely on assumptions about the flow or hardware. More detailed methods can represent more of the physics, at the cost of greater computing effort and more demanding input data. NASA Glenn’s tools support different points along that trade-off.

    Predicting noise from jets

    Jet noise prediction depends on more than an acoustic model. The aerodynamic flow must also be represented accurately. NASA Glenn researchers therefore study acoustic-source models, turbulence models and advanced measurement systems alongside prediction methods.

    The JeNo code predicts far-field sound spectral density for axisymmetric jets at a user-selected observer location and across a chosen frequency range. It uses Lilley’s Acoustic Analogy with mean-flow solutions from a Reynolds-Averaged Navier-Stokes (RANS) code. RANS-based methods can produce results more quickly than approaches such as Large-Eddy Simulation (LES) or Direct Numerical Simulation (DNS), though the quality of JeNo’s acoustic predictions depends on the accuracy of the aerodynamic input. JeNo has been validated for unheated, axisymmetric jets in subsonic and low-supersonic conditions.

    MGBK preceded JeNo. NASA Glenn researchers have used it to predict jet-noise trends for complex aircraft-engine exhaust nozzles. They also used the code to assess the noise-reduction benefits of chevrons, the serrated edges found on some turbofan nozzles.

    Tools for fans, propellers and engine flows

    NASA Glenn’s other codes address distinct sources of propulsion noise. For fan design, V072 offers a quick way to estimate rotor-stator interaction tone noise using simplified models of fan flow and geometry. Its predictions can help engineers compare blade and vane counts, rotor-stator spacing, and stator-vane sweep and lean during early design.

    TFaNS extends that analysis. It predicts rotor-stator interaction tones and accounts for acoustic coupling between blade rows, including sound transmission and reflection. It can also propagate the tones to the far field. Like V072, it runs quickly and can help teams study design trends. Linflux is intended for cases where simplified models are not enough. It represents realistic three-dimensional flows and blade geometries, and predicts rotor-stator tone noise. That added detail requires significantly more computing resources. NASA Glenn’s Acoustics Branch has validated Linflux against experimental data.

    Other tools focus on broadband fan noise. BFANS estimates noise created when turbulence strikes rotor-blade and stator-vane leading edges. It takes turbulence intensity and characteristic length scale as inputs. Engineers can combine its results with measurements, including data from tests in NASA Glenn’s 9-by-15-foot Low Speed Wind Tunnel, to identify especially noisy fan-stage components.

    RSI calculates broadband noise produced when turbulence in the fan rotor wake interacts with the fan exit guide vanes. It reports upstream and downstream acoustic-power spectra by mode and frequency, based on the characteristics of the incoming turbulence. The frequencies can be selected as needed; they do not have to match harmonics of the fan’s blade-passing frequency.

    For propellers, WOBBLE predicts tone noise associated with varying inflow angle and estimates axial and circumferential directivity. Users specify inflow through the aircraft’s yaw, pitch and roll angles. Its acoustic model accounts for unsteady blade sources, including thickness and loading effects.

    The MPT code estimates multiple pure tone noise, also called “buzz saw” noise. This noise can arise from rotor-locked shocks when fan-blade tip speed is supersonic. MPT uses flow predictions from a computational fluid dynamics (CFD) code and can help engineers study effects of blade-to-blade differences, whether caused by manufacturing variation or by changes during hot operation.

    ADPAC is a flexible, validated CFD tool developed by the Allison Engine Company under contracts with NASA Glenn. It solves tightly coupled internal and external flows through short-duct turbofan engines, including future-concept designs. It can model two-dimensional, axisymmetric, three-dimensional or mixed configurations, as well as steady or unsteady flow. The code supports inviscid or viscous calculations, rotating and nonrotating components, and features such as bleeds, leakages and injection.

    Copies of JeNo, MGBK, V072, TFaNS, Linflux, BFANS, RSI, WOBBLE, MPT and ADPAC can be requested through NASA’s Software Catalog. The right choice depends on the question, available inputs and required level of detail. For more information about NASA Glenn research, software or data, use the center’s contact link.

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    John Marcelli is a staff writer for IO Tribune, with a passion for exploring and writing about the ever-evolving world of technology. From emerging trends to in-depth reviews of the latest gadgets, John stays at the forefront of innovation, delivering engaging content that informs and inspires readers. When he's not writing, he enjoys experimenting with new tech tools and diving into the digital landscape.

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