5 min read<\/p>\n
Preparations for Next Moonwalk Simulations Underway (and Underwater)<\/h1>\n<\/div>\n<\/div>\n<\/div>\n
Research teams at NASA\u2019s Glenn Research Center are involved in developing theory and software to predict the noise from aircraft engines. This software helps engineers to design and analyze new and improved aircraft propulsion systems. Copies of our software can be requested from NASA\u2019s Software Catalog.<\/a><\/p>\n Development of jet noise prediction codes is multifaceted. Since JeNo predicts jet noise using Lilley\u2019s Acoustic Analogy and the mean flow solutions from a Reynolds-Averaged Navier-Stokes (RANS) code, accurate acoustic predictions hinge on accurate aerodynamic predictions. This is why acoustic source modeling and turbulence modeling, together with the development of advanced measurement systems, are the leading areas of research in jet noise prediction at NASA Glenn. RANS-based jet noise prediction schemes based on Lilley\u2019s Acoustic Analogy offer relatively quick answers compared to those from alternative methods like Large-Eddy Simulations (LES) or Direct Numerical Simulation (DNS).<\/p>\n The JeNo jet noise prediction code calculates the far-field sound spectral density produced by axisymmetric jets at a user-specified observer location and frequency range. Currently, JeNo has been validated for subsonic and low supersonic, unheated axisymmetric jets. Copies of the JeNo code can be requested from NASA\u2019s Software Catalog.<\/a><\/p>\n Development of the MGBK code preceeded JeNo. Researchers at NASA GRC have used MGBK to predict trends in jet noise for complicated aircraft engine exhaust nozzles. MGBK was used to analyze the noise reduction benefits of chevrons\u2013serrations on teh edge of turbofan nozzles\u2013which are a familiar site on many commercial aircraft engines today. Copies of the MGBK codes can be requested from NASA\u2019s Software Catalog<\/a>.<\/p>\n Predicated on simplistic models of the fan flow and geometry, V072 has been proven useful in studying rotor\/stator interaction tone noise. When you need to design a low-noise fan stage, tone noise level predictions from V072 can help you make smart choices for such important design parameters as blade and vane counts, rotor-stator spacing, and stator vane sweep and lean. When you\u2019re after quick noise estimates early in the design cycle, V072 may be your tool of choice. Copies of V072 can be requested from NASA\u2019s Software Catalog.<\/a><\/p>\n Taking rotor\/stator interaction noise prediction to the next level, not only can TFaNS calculate the tone noise levels generated by rotor\/stator interaction it can also account for the blade row acoustic coupling effects such transmission and reflection and can propagate those tones to the far field. Like V072, TFaNS runs quickly and can be used in early design stages for studying trends. Copies of TFaNS can be requested from NASA\u2019s Software Catalog<\/a><\/p>\n When simple models are too simple, an analysis with Linflux gives you the power to study realistic, 3-D flows and blade geometries. Compared with V072 and TFaNS, Linflux requires significantly more computational resources since it models much more of the physics involved. Linflux predicts rotor\/stator tone noise and, like all the software developed by the NASA Glenn Acoustics Branch, has been rigorously validated against experimental data. Copies of Linflux can be requested from NASA\u2019s Software Catalog.<\/a><\/p>\n Using estimates of turbulence intensity and characteristic length scale as input, BFANS can be used to calculate the broadband noise generated by turbulence impinging on the leading edges of the rotor blades and stator vanes. Calculations with BFANS, combined with experimental data from tests at facilities like NASA Glenn\u2019s 9\u2019x 15\u2019 Low Speed Wind Tunnel, can be used to hunt down the noisiest components in a fan stage. Copies of BFANS can be requested from NASA\u2019s Software Catalog<\/a>.<\/p>\n RSI is a computer code for calculating the spectrum of broadband noise produced as a result of the interaction of the fan rotor wake turbulence with the fan exit guide vanes. The software computes the spectra of acoustic power upstream and downstream of the stator on a mode-by-mode basis at each frequency of interest given information about the incident turbulence characteristics. The target frequencies are arbitary and need not be harmonics of the blade passing frequency of the fan. Copies of RSI can be requested from NASA\u2019s Software Catalog.<\/a><\/p>\n Propeller noise can be strongly affected by any inflow that leads to unsteady blade loading. The WOBBLE code computes the tone noise associated with propellers with varying inflow angle and predicts the axial and circumferential directivity. Inflow angle is specified in terms of yaw, pitch, and roll angles of the aircraft. The acoustic model is exact and accounts for all unsteady sources such as thickness and loading effects from the blades. Copies of WOBBLE can be requested from NASA\u2019s Software Catalog<\/a>.<\/p>\n Multiple pure tone noise (aka \u201cbuzz saw\u201d noise), created by rotor locked shocks present when the fan blade tip speed is supersonic, can be estimated with the MPT code. The MPT codes also use flow predictions from a Computational Fluid Dynamics (CFD) code as input. This tool can be used by engineers to study the real-world effects of blade-to-blade difference caused during fabrication (manufacturing variations) or during hot-running operation (blade setting angle and untwist variations). Copies of the MPT code can be requested from NASA\u2019s Software Catalog.<\/a><\/p>\n Developed by the Allison Engine Company under contracts with Glenn Research Center, ADPAC solves tightly coupled internal\/external flows through future-concept short-duct turbofan engines. ADPAC is one of our flexible validated CFD tools. Characteristics of the problems that can be modeled with ADPAC include: 2D\/axisymmetric\/3D\/mixed, steady\/unsteady, inviscid\/viscous, rotating\/non-rotating\/mixed components, with bleeds, leakages, and injection. ADPAC was developed specifically for propulsion system aerodynamic analysis Copies of the ADPAC code can be requested from NASA\u2019s Software Catalog.<\/a><\/p>\n Need to reach us for more information about our research, software, or data? Get in touch via the link below.<\/p>\n \t<\/a><\/p><\/div>\n NASA\u2019s Glenn Research Center at Lewis Field in Cleveland and the Neil Armstrong Test Facility in Sandusky, Ohio, house ground\u2026<\/p>\n<\/p><\/div>\n<\/p><\/div>\n \t\t\t<\/a> \n\t\t\t\t\t\t\t\tScience in the Air<\/span><\/p>\n<\/p><\/div>\n<\/p><\/div>\n \t\t\t<\/a> \n\t\t\t\t\t\t\t\tDoing Business with Glenn<\/span><\/p>\n<\/p><\/div>\n<\/p><\/div>\n \t\t\t<\/a> \n\t\t\t\t\t\t\t\tAerosciences<\/span><\/p>\n<\/p><\/div>\n<\/p><\/div>\n \t\t\t<\/a>\n\t\t\t\t<\/div>\n<\/p><\/div>\n<\/p><\/div>\n Powered by WPeMatico<\/a><\/small><\/p>\n Get The Details…<\/a> Research teams at NASA\u2019s Glenn Research Center are involved in developing theory and software to predict the noise from aircraft engines. This software helps engineers to design and analyze new and improved aircraft propulsion systems. Copies of our software can be requested from NASA\u2019s Software Catalog. JeNo Development of jet noise prediction codes is multifaceted. [\u2026] Continue reading ![\"\"](\"https:\/\/www.nasa.gov\/wp-content\/uploads\/2026\/10\/new-dual-stream-nozzle-chevron-hot-exhaust-jet-jet-noise-prediction-code-e1642003352433-300x300-1.jpg?w=300\"){kind=icon}
<\/a><\/figure>JeNo<\/h2>\n
MGBK<\/h2>\n
V072<\/h2>\n
TFaNS<\/h2>\n
Linflux<\/h2>\n
BFANS<\/h2>\n
RSI<\/h2>\n
WOBBLE<\/h2>\n
MPT<\/h2>\n
ADPAC<\/h2>\n
Contact Us<\/h2>\n
\n\t\tSend an email<\/span><\/p>\nDiscover Related Topics<\/h2>\n<\/p><\/div>\n<\/p><\/div>\n
\n\t\t\t\t\t\t\t\tGlenn Labs and Test Facilities<\/span><\/p>\n<\/h3>\n
![\"\"](\"https:\/\/www.nasa.gov\/wp-content\/uploads\/2023\/01\/GRC-2019-C-03368-1.jpg?w=1536\")
<\/figure>\n<\/p><\/div>\n
\n\t\t\t\t\t<\/p>\n![\"\"](\"https:\/\/www.nasa.gov\/wp-content\/uploads\/2023\/06\/afrc2016-0073-72jb.jpg?w=1536\")
<\/figure>\n<\/p><\/div>\n
\n\t\t\t\t\t<\/p>\n![\"\"](\"https:\/\/images-assets.nasa.gov\/image\/GRC-2020-C-00102\/GRC-2020-C-00102~large.jpg?w=1920&h=1344&fit=clip&crop=faces%2Cfocalpoint\")
<\/figure>\n<\/p><\/div>\n
\n\t\t\t\t\t<\/p>\n![\"\"](\"https:\/\/www.nasa.gov\/wp-content\/uploads\/2023\/03\/sls-buffet-model_full.jpg?w=1024\")
<\/figure>\n<\/p><\/div>\n
\nL. Danielle Koch <\/p>\n","protected":false},"excerpt":{"rendered":"