A Process Evaluation and Visualization Framework for Unmanned Aerial System (UAS) Noise Certification Testing
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Abstract
Expanding upon recent research into the effectiveness of noise testing processes for Unmanned Aerial Systems (UAS), this study is seeking to develop methods for process analysis and efficiency assessment for a model-based framework to further optimize noise certification procedures. The original framework is capable of detecting certification testing procedure inefficiencies and analyzing the time and cost of flight testing. In this study, the framework’s capabilities are extended through advanced modeling techniques using Markov Chains and graph theory, extensive Monte Carlo simulations, and a broader application of Design of Experiments to capture a wider range of variables and their impact on the noise testing outcomes and overall efficiency. Moreover, the enhanced framework quantifies and assesses the complexity level of testing procedures to provide insights on the associated uncertainty and risks. Additionally, it enables the prediction of process bottlenecks and establishes trade-offs more accurately. This work introduces enhanced decision support relying on different elements including sensitivity analyses, histogram overlays, and operational viewpoints. These capabilities allow stakeholders to interactively explore several noise testing strategies, compare their effectiveness and potential performance gains, and acquire insights on the resulting certification burden from different aspects.
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FAA (GR00005596)
Date
2025-01
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