Uncertainty Quantification for Aircraft Noise Emission Simulation: Methods and Limitations

Bertsch, Lothar; Mulani, Sameer B.; Schäffer, Beat

doi:10.2514/1.j061143

Cited by 10 publications

(3 citation statements)

References 65 publications

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“…The discrepancies in results shown in the earlier tool benchmark [6] are in good agreement with parallel research activities conducted by DLR and Empa on underlying simulation uncertainties [7,8] for the noise emission models as applied by ONERA and DLR in their codes CARMEN and PANAM, respectively. The simulation uncertainties for immission levels are furthermore strongly dependent on the current operating conditions along the flight path, the modeling of the ground attenuation, and on the receiver distance due to prevailing propagation effects.…”

Section: Comparison Of Methodssupporting

confidence: 78%

Flyover noise evaluation of low-noise technologies applied to a blended wing body aircraft

et al. 2023

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In the frame of the European research project ARTEM (Aircraft noise Reduction Technologies and related Environmental iMpact), new aircraft architectures and alternative propulsion systems, e.g. Blended Wing Body (BWB) and geared turbofan engine concept, are investigated, as well as innovative noise reduction technologies such as metamaterials and low noise high-lift device systems. A noise impact assessment has been performed on a long-haul BWB concept developed by Roma Tre University, using the System Noise Prediction Tools of ONERA (CARMEN) and DLR (PANAM). First, shielding effects on the main noise sources are discussed, through installation effects hemi-spheres at chosen frequencies around the aircraft. Based on the shielding assessment, detailed take-off and landing procedures are simulated for several aircraft configurations. Two motorisations of the BWB are evaluated (BPR 8 and BPR 12), provided by RWTH Aachen. Finally, the most promising low noise technologies developed in the frame of ARTEM are applied, and their impact on noise levels on the ground is discussed. It can be demonstrated how the specific aircraft configuration, the engine type and the additional low-noise technology result in a significant overall noise reduction. A joint conference paper presents flyover auralisations based on these noise emission calculations.

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Section: Comparison Of Methodssupporting

confidence: 78%

Flyover noise evaluation of low-noise technologies applied to a blended wing body aircraft

et al. 2023

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“…A single plume in a crossflow case is considered in the present analysis in order to demonstrate the use of UQ techniques for the specific problem and its potential for more complex cases, such as fume evolution on a ship deckhouse. The use of Uncertainty Quantification (UQ), to improve the accuracy and reliability of numerical simulations, is increasingly effective in many engineering fields for industrial applications thanks to recent improvements in both soft-computing algorithms and hardware performance [31][32][33][34]. With the use of the UQ techniques, it can be quantified how uncertainty propagates through the physical problems and how it can affect the simulation results.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification Analysis of Exhaust Gas Plume in a Crosswind

Domenico

Cravero

2023

Energies

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The design of naval exhaust funnels has to take into account the interaction between the hot gases and topside structures, which usually includes critical electronic devices. Being able to predict the propagation trajectory, shape and temperature distribution of an exhaust gas plume is highly strategic in different industrial sectors. The propagation of a stack plume can be affected by different uncertainty factors, such as those related to the wind flow and gas flow conditions at the funnel exit. The constant growth of computational resources has allowed simulations to gain a key role in the early design phase. However, it is still difficult to model all the aspects of real physical problems in actual applications and, therefore, to completely rely upon the quantitative results of numerical simulations. One of the most important aspects is related to input variable uncertainty, which can significantly affect the simulation result. With this aim, the discipline of Uncertainty Quantification provides several methods to evaluate uncertainty propagation in numerical simulations. In this paper, UQ procedures are applied to a CFD simulation of a single plume in a crossflow. The authors test the influence of the uncertainty propagation of the chimney exit velocity and the main flow angle on the plume flow development. Two different UQ methods are applied to the analysis: the surrogate-based approach and the polynomial chaos expansion method. A comparison of the two methods is performed in order to find their pros and cons, focusing on the different and detailed quantities of interest.

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“…This paper presents the development of an N1 estimation model based on a machine learning approach that addresses the limitations of previous models. Since airframe noise clearly dominates over engine noise during approaches [9], the N1 estimation is applied for departures only.…”

Section: Introductionmentioning

confidence: 99%

Thrust estimation for departing jet aircraft using position data based on a machine learning approach

Meister

2022

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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Aircraft noise simulations need to be highly accurate, for which they require either thrust or the closely related rotational speed of the engine's low-pressure compressor, N1, as input parameter. However, often only position data are available, and N1 or thrust has to be estimated. Current parameter estimation models are only available for few aircraft types and often need additional, non-readily available information. In this contribution, a machine learning approach using the random forest algorithm to estimate N1 of narrow and wide body aircraft during departure is presented. To train the models, flight data recorder (FDR) data were used. The datasets were divided in training and validation data to identify the best combination of features and avoid overfitting. The resulting N1 estimation models only require readily available position and ground temperature data. The performance of the models is evaluated by comparing the noise calculation levels obtained using estimated N1 values to those obtained using N1 values from FDR data.

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Uncertainty Quantification for Aircraft Noise Emission Simulation: Methods and Limitations

Cited by 10 publications

References 65 publications

Flyover noise evaluation of low-noise technologies applied to a blended wing body aircraft

Flyover noise evaluation of low-noise technologies applied to a blended wing body aircraft

Uncertainty Quantification Analysis of Exhaust Gas Plume in a Crosswind

Thrust estimation for departing jet aircraft using position data based on a machine learning approach

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