eVTOL Noise Assessment via Flight Envelope Down-Sampling
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Solution Overview
Problem
Existing systems lack comprehensive methods to assess and optimize the noise generated by electric vertical take-off and landing (eVTOL) vehicles during operational conditions, which is crucial for the development and deployment of urban air mobility solutions.
Innovation Solution
A computer-implemented method that defines a vehicle model, determines aerodynamic and propulsion performance, performs flight-dynamics simulations, down-samples flight status data, and conducts high-fidelity flow simulations to calculate in-flight and on-ground noise characteristics, using look-up tables and computational fluid dynamics to optimize flight paths and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive noise assessment simulations are performed for all flight conditions, then noise prediction accuracy is improved, but computational time and resource requirements increase significantly
Solution Approach 1:
The flight envelope is segmented into discrete representative flight conditions using down-sampling techniques. Instead of simulating all possible flight conditions, the continuous flight envelope is divided into key representative points that capture the essential noise characteristics, reducing computational requirements while maintaining prediction accuracy.
Solution Approach 2:
A reduced-order model is created that copies the essential noise characteristics from high-fidelity simulations. The reduced-order model uses down-sampled flight condition data to generate noise predictions that replicate the accuracy of comprehensive simulations but with significantly reduced computational cost.
2Measurement precision
If high-fidelity flow simulations are conducted for all flight conditions, then aeroacoustic performance accuracy is improved, but device complexity and computational resources increase
Solution Approach 1:
Instead of performing high-fidelity simulations for all flight conditions (excessive action), the method performs high-fidelity simulations only for a selected set of representative flight conditions (partial action). The down-sampling process identifies the essential flight conditions that capture the majority of noise characteristics, avoiding unnecessary computational complexity.
Solution Approach 2:
The method changes the parameter set from all possible flight conditions to a reduced set of representative conditions. By transforming the continuous flight envelope into discrete representative points through down-sampling, the complexity of managing and processing all flight condition parameters is significantly reduced while maintaining acoustic performance accuracy.
3Reliability
If flight status data is sampled at high rate, then flight envelope completeness is improved, but data processing complexity and storage requirements increase
Solution Approach 1:
The essential flight condition parameters are extracted from the continuous high-rate sampled data through down-sampling techniques. Instead of processing all sampled data points, the method extracts and retains only the representative flight conditions that define the noise envelope, significantly reducing data volume while maintaining completeness.
Solution Approach 2:
Redundant flight status data is discarded through down-sampling, keeping only the essential representative conditions. The down-sampling process recovers the critical noise envelope information from the large dataset, separating the essential signal from redundant data to reduce storage and processing requirements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate prediction and optimization of eVTOL noise footprints, facilitating safer and quieter operations by identifying optimal flight paths and controlling vehicles based on noise and performance indicators.
Implementation Method 1
a high-fidelity flow simulation (which may be a Computational Fluid Dynamics (CFD) simulation) of the vehicle is performed using the reduced dataset
Implementation Method 2
automatically determining aerodynamic performance and propulsion performance of the vehicle based on the defined computer-based model
Data Source
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AI summary
Embodiments assess vehicle noise. One such embodiment defines a computer-based model of a vehicle and automatically determines aerodynamic performance and propulsion performance of the vehicle based on the defined computer-based model. Responsively, a flight-dynamics simulation of the vehicle is performed using the determined aerodynamic performance and propulsion performance. Performing the flight-dynamics simulation produces flight status data. The flight status data is automatically down-sampled to generate a reduced dataset. A high-fidelity flow simulation of the vehicle is performed using the reduced dataset. Performing the high-fidelity flow simulation determines in-flight aerodynamic and aeroacoustic performance of the vehicle. In turn, based on the determined in-flight aerodynamic and aeroacoustic performance, noise physical characteristics of the vehicle are determined and an indication of the determined noise physical characteristics is stored in computer memory.