Computational Aeroacoustics Prediction of Acoustic Transmission Through a Realistic 2D Stator

Hixon, Ray

doi:10.2514/6.2011-2706

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Bouley et al first demonstrated the feasibility for both noise propagation (impinging acoustic wave) and generation (impinging vorticity wave) in a rectilinear cascade of flat plates. The first results of noise propagation have recently been obtained in a bifurcated waveguide [14], and successfully compared with numerical high-order results by Hixon [22].…”

Section: Analytical Modelsmentioning

confidence: 99%

Turbomachinery Noise Predictions: Present and Future

Moreau

2019

Acoustics

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In future Ultra-High By-Pass Ratio turboengines, the turbomachinery noise (fan and turbine stages mainly) is expected to increase significantly. A review of analytical models and numerical methods to yield both tonal and broadband contributions of such noise sources is presented. The former rely on hybrid methods coupling gust response over very thin flat plates of finite chord length, either isolated or in cascade, and acoustic analogies in free-field and in a duct. The latter yields tonal noise with unsteady Reynolds-Averaged Navier–Stokes (u-RANS) simulations, and broadband noise with Large Eddy Simulations (LES). The analytical models are shown to provide good and fast first sound estimates at pre-design stages, and to easily separate the different noise sources. The u-RANS simulations are now able to give accurate estimates of tonal noise of the most complex asymmetric, heterogeneous fan-Outlet Guiding Vane (OGV) configurations. Wall-modeled LES on rescaled stage configurations have now been achieved on all components: a low-pressure compressor stage, a transonic high-pressure turbine stage and a fan-OGV configuration with good overall sound power level predictions for the latter. In this case, hybrid Lattice–Boltzmann/very large-eddy simulations also appear to be an excellent alternative to yield both contributions accurately at once.

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Section: Analytical Modelsmentioning

confidence: 99%

Turbomachinery Noise Predictions: Present and Future

Moreau

2019

Acoustics

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“…Interference patterns are seen upstream where the reflected mode combines with the incident mode. The same case as numerically simulated by Hixon 23 with the BASS code is reported in Figure 7(b). The same qualitative sound field is obtained, except that the additional interference patterns downstream of the cascade are attributed to spurious reflections on the right side boundary of the computational domain.…”

Section: Turboengine Fan Noisementioning

confidence: 95%

“…Instantaneous pressure maps for the transmission of an oblique wave through the NASA SDT test case cascade. (a) Mode-matching technique and (b) numerical simulation with the code BASS, from Hixon 23. …”

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confidence: 99%

Advanced noise modeling for future propulsion systems

Moreau

Roger

2018

International Journal of Aeroacoustics

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In order to meet noise specifications for future foreseen aircraft propulsion systems, such as for ultrahigh bypass ratio turbofans and contra-rotating open rotors, the dominant turbomachinery noise mechanisms need to be modeled accurately at an early design stage. Two novel methods are presented here, which could significantly improve the existing analytical noise models. For the high-solidity ultrahigh bypass ratio, a mode-matching technique based on a modal expansion of acoustic and vortical variables in each subdomain of a blade row is shown to accurately reproduce sound generation and propagation in two-dimensional bifurcated channels and in three-dimensional annular unstaggered flat-plate cascades. For the low solidity contra-rotating open rotors, several extensions to Amiet's compressible isolated airfoil theory are coupled with Curle's and Ffowcs Williams and Hawkings' acoustic analogy in the frequency domain within a strip theory framework, to yield both far-field tonal and broadband noise. Including sweep in both tonal and broadband noise models is shown to significantly improve the comparison with experiments on a stationary swept airfoil in a uniform turbulent stream and on a realistic contrarotating open rotor geometry at approach conditions.

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“…Although each game supports the practice of different fundamental literacy skills (rhyming and spelling), both skills are indicators of a meta-linguistic skillset known as phonological awareness. To generate the SIMSTUDENt's true mastery of each word in each game, we first generate a theoretical "phonological" mastery for each of the 39 ARPAbet phonemes (Hixon et al, 2011), uniformly at random (m p ∈ [−1, 1]). The phonological mastery that underlies the word-mastery of both games is an implicit modeling assumption, based on decades of research in early childhood literacy development, that there exists a link between a student's rhyming and spelling ability with respect to specific words and phonemes (Høien et al, 1995).…”

Section: Simulating True Masterymentioning

confidence: 99%

Lifelong Personalization via Gaussian Process Modeling for Long-Term HRI

et al. 2021

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Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. Because of the need for personalized interaction data to drive this process, long-term (or longitudinal) interactions between users and agents, which unfold over a series of distinct interaction sessions, have attracted substantial research interest. In recognition of the expanded scope and structure of a long-term interaction, researchers are also adjusting the personalization models and algorithms used, orienting toward “continual learning” methods, which do not assume a stationary modeling target and explicitly account for the temporal context of training data. In parallel, researchers have also studied the effect of “multitask personalization,” an approach in which an agent interacts with users over multiple different tasks contexts throughout the course of a long-term interaction and learns personalized models of a user that are transferrable across these tasks. In this paper, we unite these two paradigms under the framework of “Lifelong Personalization,” analyzing the effect of multitask personalization applied to dynamic, non-stationary targets. We extend the multi-task personalization approach to the more complex and realistic scenario of modeling dynamic learners over time, focusing in particular on interactive scenarios in which the modeling agent plays an active role in teaching the student whose knowledge the agent is simultaneously attempting to model. Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively and continually manage its own training data, allowing a modeling agent to remove or reduce the weight of observed data from its training set, based on interactive context cues. We evaluate this method in a series of simulation experiments comparing different approaches to continual and multitask learning on simulated student data. We expect this method to substantially improve learning in Gaussian Process models in dynamic domains, establishing Gaussian Processes as another flexible modeling tool for Long-term Human-Robot Interaction (HRI) Studies.

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Computational Aeroacoustics Prediction of Acoustic Transmission Through a Realistic 2D Stator

Cited by 4 publications

References 15 publications

Turbomachinery Noise Predictions: Present and Future

Turbomachinery Noise Predictions: Present and Future

Advanced noise modeling for future propulsion systems

Lifelong Personalization via Gaussian Process Modeling for Long-Term HRI

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