Indirect Tire Wear Modeling from Scaled Tire Specifications
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Solution Overview
Problem
Existing tire wear models, particularly those based on finite element analysis (FEA), are computationally expensive and time-consuming, making it impractical to simulate wear rates at different tread depths, and direct measurements of tire wear state are difficult and imprecise for real-time predictions.
Innovation Solution
Developing indirect tire wear models by scaling tire parameters from accessible FEA models to create models for tires lacking such models, using publicly available specifications and empirical relationships, allowing for quick and accurate tire wear predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If finite element analysis (FEA) models are used for tire wear prediction, then prediction accuracy is improved, but computational time and cost increase significantly
Solution Approach 1:
The patent creates simplified copy models of FEA tire wear models by scaling parameters from a control tire FEA model to match target tire specifications. These copy models replicate the predictive functionality of full FEA models but execute much faster, resolving the contradiction between accuracy and computational time.
Solution Approach 2:
The patent transforms complex FEA models into simplified models by changing parameters from full physical simulations to scaled parameter relationships. The scaling factors and empirical relationships convert detailed FEA outputs into quick prediction formulas, maintaining accuracy while reducing computational burden.
2Reliability
If multiple tire wear simulations are performed at different tread depths, then comprehensive wear rate data is obtained, but computational cost increases to months
Solution Approach 1:
The patent performs preliminary scaling of tire parameters from the control tire FEA model to create pre-configured simplified models for multiple tread depths. This preliminary action allows subsequent wear simulations to be executed quickly without requiring months of computational energy, while still providing comprehensive wear rate data across different conditions.
3Loss of information
If direct tire wear measurements are taken in real-time, then current wear state data is obtained, but measurement precision and ease of operation deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where simplified tire wear models continuously predict wear states based on scaled parameters and operating conditions. This feedback loop provides real-time wear state information without requiring imprecise direct measurements, maintaining information availability while avoiding measurement precision problems.
Data Source
AI summary
A system and method are disclosed for indirect tire wear modeling and implementation. Data storage network has stored thereon accessible finite element (FEA) models and corresponding direct tire wear models for each of various types of tires. A computing network is functionally linked to the data storage network and configured to iteratively develop a control model scaling values for various tire parameters for a selected control tire, from the types of tires having a corresponding accessible FEA model, to respective values for the tire parameters for an arbitrary type of tire lacking a corresponding accessible FEA model. For a provided first type of tire lacking a corresponding accessible FEA model, corresponding values are obtained for the tire parameters, and an indirect tire wear model is generated for the first type of tire based on the first control model, the corresponding direct tire wear model, and the obtained tire parameter values.