Uncertainty Quantification of CFD Model Assumptions Against Sonic Boom Noise Prediction of a Commercial Supersonic Transport

2022 ◽  
Author(s):  
Makoto Endo ◽  
Ben D. Phillips
Keyword(s):  
Uncertainty Quantification ◽  
Sonic Boom ◽  
Noise Prediction ◽  
Cfd Model ◽  
Supersonic Transport

Meteorological Parameters Affecting Supersonic Transport Operations

1967 ◽  
Vol 20 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Richard Scherhag ◽  
Gunter Warnecke ◽  
Werner Wehry
Keyword(s):  
Working Group ◽  
Meteorological Parameters ◽  
Sonic Boom ◽  
Atmospheric Ozone ◽  
Atmospheric Conditions ◽  
Atmospheric Parameters ◽  
Free Atmosphere ◽  
Supersonic Transport ◽  
Supersonic Aircraft ◽  
Initial Task

In 1965, following the Eastbourne Conference, the British, French and German Institutes of Navigation formed a Working Group to make a study of the environment in which the supersonic transport will operate and of its implications for the navigation of such aircraft. The Group's initial task has been one of education, largely through discussion of a series of papers submitted to it. Some of the papers considered have already been published in the Journal (Vol. 19) and a further selection is published below. Table I was contributed by Mr. G. E. Beck. The illustrations to these papers have not all been reproduced.1. Atmospheric Conditions. It will be useful to distinguish between different kinds of atmospheric influences on supersonic aircraft operations. They may be classed as follows:(a) Sporadic effects near the ground(b) Sporadic effects in the free atmosphere(c) Effects on sonic boom(d) Effects of atmospheric ozone(e) Permanently effective atmospheric parameters, such as temperature, density and wind.

scholarly journals Multifidelity Uncertainty Quantification of a Commercial Supersonic Transport

2020 ◽  
Vol 57 (3) ◽  
pp. 491-500 ◽  
Author(s):  
Thomas K. West ◽  
Ben D. Phillips
Keyword(s):  
Uncertainty Quantification ◽  
Supersonic Transport

A Collaboration-based Approach to CFD Model Validation and Uncertainty Quantification (VUQ) Using Data from a Laminar Helium Plume

2016 ◽  
Vol 97 (2) ◽  
pp. 427-449
Author(s):  
Weston M. Eldredge ◽  
Pál Tóth ◽  
Laurie Centauri ◽  
Eric G. Eddings ◽  
Kerry E. Kelly ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Model Validation ◽  
Cfd Model ◽  
Using Data ◽  
Helium Plume

scholarly journals System Noise Prediction Uncertainty Quantification for a Hybrid Wing–Body Transport Concept

AIAA Journal ◽  
2020 ◽  
Vol 58 (3) ◽  
pp. 1157-1170 ◽  
Author(s):  
Jason C. June ◽  
Russell H. Thomas ◽  
Yueping Guo
Keyword(s):  
Uncertainty Quantification ◽  
Noise Prediction ◽  
Prediction Uncertainty ◽  
System Noise

scholarly journals A Nested Ensemble Filtering Approach for Parameter Estimation and Uncertainty Quantification of Traffic Noise Models

2019 ◽  
Vol 10 (1) ◽  
pp. 204
Author(s):  
Kai Huang ◽  
Yurui Fan ◽  
Liming Dai
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Uncertainty Quantification ◽  
Ensemble Kalman Filter ◽  
Traffic Noise ◽  
Noise Model ◽  
Accurate Estimation ◽  
Noise Prediction ◽  
Ensemble Filtering ◽  
Update Process

In this study, a nested ensemble filtering (NEF) approach is advanced for uncertainty parameter estimation and uncertainty quantification of a traffic noise model. As an extension of the ensemble Kalman filter (EnKF) and particle filter methods, the proposed NEF method improves upon the ensemble Kalman filter (EnKF) method by incorporating the sample importance resampling (SIR) procedures into the EnKF update process. The NEF method can avoid the overshooting problem (abnormal value (e.g., outside the predefined ranges, complex values) in parameter or state samples) existing in the EnKF update process. The proposed NEF method is applied to the traffic noise prediction on the Trans-Canada Highway in the City of Regina to demonstrate its applicability. The results indicate that: (a) when determining parameters in the traffic noise prediction model, the NEF method provides accurate estimation; (b) the model parameters can be recursively corrected with the NEF method whenever a new measurement becomes available; (c) the uncertainty in the traffic noise model (should be the noise itself) can be well reduced and quantified through the proposed NEF approach.

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