Conceptual Design and Technical Risk Analysis of Quiet Commercial Aircraft Using Physics-Based Noise Analysis Methods

Uncertainty;Probabilistic analysis;Approximation methods;Concurrent design;Turbomachinery;First principles;Engine

Olson, Erik Davin ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Uncertainty;    Probabilistic analysis;    Approximation methods;    Concurrent design;    Turbomachinery;    First principles;    Engine;   

Others  :  https://smartech.gatech.edu/bitstream/1853/11486/1/olson_erik_d_200608_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

An approach was developed which allows for design studies of commercial aircraft using physics-based noise analysis methods while retaining the ability to perform the rapid tradeoff and risk analysis studies needed at the conceptual design stage.A prototype integrated analysis process was created for computing the total aircraft EPNL at the Federal Aviation Regulations Part 36 certification measurement locations using physics-based methods for fan rotor-stator interaction tones and jet mixing noise.The analysis process was then used in combination with design of experiments to create response surface equations (RSEs) for the engine and aircraft performance metrics, geometric constraints and takeoff and landing noise levels.In addition, Monte Carlo analysis was used to assess the expected variability of the metrics under the influence of uncertainty, and to determine how the variability is affected by the choice of engine cycle.Finally, the RSEs were used to conduct a series of proof-of-concept conceptual-level design studies demonstrating the utility of the approach.The study found that a key advantage to using physics-based analysis during conceptual design lies in the ability to assess the benefits of new technologies as a function of the design to which they are applied.The greatest difficulty in implementing the physics-based analysis proved to be the generation of design geometry at a sufficient level of detail for high-fidelity analysis.