Temporal Filtering for Vehicle Fluid Dynamic Data Analysis
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Solution Overview
Problem
Current methods for simulating vehicle-related fluid dynamic and aerodynamic conditions, such as crosswind stability, struggle to accurately identify slow transient and local spatial variations, which are difficult to replicate in wind tunnels and traditional computer simulations, leading to incomplete understanding of vehicle geometry's sensitivity to external disturbances.
Innovation Solution
A method involving the calculation of temporal filtered values from data points representing fluid dynamic properties like pressure, friction, or temperature, using an effective filtering time period that distinguishes between rapid and slow variations, allowing for the analysis of these variations over time and space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional spatial filtering methods are applied to smooth data points, then spatially smoothed data can be obtained for visual analysis, but local spatial variations and rapid transient changes are smoothed out and lost
Solution Approach 1:
The patent divides the filtering process into two independent segments: temporal filtering (smoothing rapid changes over time) and spatial filtering (smoothing local variations across sensor positions). By applying these filters separately and selectively, the system preserves important local spatial variations while still enabling visual analysis of slow transient variations, thus resolving the contradiction between ease of visual analysis and preservation of local information.
Solution Approach 2:
The patent introduces dynamic, adaptive filtering where the filtering time period and spatial weighting are adjusted based on the specific characteristics of the data and analysis needs. This dynamic approach allows the system to preserve local spatial variations when they are significant while still providing smoothed visual analysis when appropriate, resolving the static contradiction between information preservation and visualizability.
2Quantity of substance
If wind tunnel tests or traditional CFD simulations are used to study fluid dynamic conditions, then comprehensive data can be obtained, but slow transient variations and local spatial variations difficult to imitate in controlled environments cannot be accurately captured
Solution Approach 1:
The patent applies temporal filtering as a preliminary processing step to raw sensor data before analysis. This preliminary action of smoothing rapid fluctuations preserves the underlying slow transient variations while removing high-frequency noise, enabling accurate detection of these variations even in real-world driving conditions that cannot be replicated in controlled wind tunnel or traditional CFD environments.
Solution Approach 2:
The patent introduces temporal filtering as an intermediary processing layer between raw sensor data and analysis. This intermediary step transforms the raw data into a form that reveals slow transient variations while maintaining the connection to original measurement conditions, allowing accurate capture of these variations without requiring controlled environment tests.
3Loss of information
If all raw data points are analyzed without filtering, then complete information is retained, but rapid variations make it difficult to visually discern meaningful patterns
Solution Approach 1:
The patent segments the data processing into distinct temporal and spatial filtering stages, allowing selective application of smoothing operations. This segmentation enables the system to retain complete information through the original data while providing filtered versions for visual pattern recognition, thus resolving the contradiction between information completeness and ease of pattern detection.
Solution Approach 2:
The patent applies partial filtering - not all data is filtered to the same degree. The temporal filtering is applied with specific time constants that preserve slow transient variations while smoothing rapid fluctuations, providing just enough smoothing to enable visual pattern recognition without excessive loss of meaningful information.
Data Source
AI summary
Method for identifying slow transient variations and/or local spatial variations in vehicle related fluid dynamic conditions of a physical property in a set of data points. The method includes obtaining a first set of data points, calculating a temporal filtered value of the representation of the physical property for at least a portion of the first set of data points distributed over the total time period, combining at least a portion of the calculated temporal filtered values to obtain a second set of data points, and analysing in time sequence the second set of data points over at least a portion of the total time period.