Engine Calibration Models for Real-Time Virtual Optimization

The patent applies preliminary action by performing extensive measurements and creating comprehensive empirical models during the initial model creation phase. These pre-established models capture complex relationships between parameters, allowing rapid virtual optimization without needing extensive measurements during each optimization iteration. The model is prepared in advance to handle transient processes and non-steady-state operations.

The patent uses copying by creating virtual copies of the technical device through detailed empirical models that replicate real device behavior. These digital twins allow optimization to be performed in the virtual domain rather than requiring physical prototypes and extensive real-world measurements. The empirical models copy the complex relationships and behaviors of the actual device, enabling accurate virtual testing and optimization.

The patent applies segmentation by dividing the complex optimization problem into separate empirical models for different parameters and operating conditions. Each empirical model handles specific aspects of device behavior, allowing the computational task to be distributed and managed in smaller, more efficient units. This segmentation enables real-time performance by avoiding the need to compute all parameters simultaneously from first principles.

The patent uses parameter changes by transforming the computational approach from solving complex differential equations in real-time to using pre-computed empirical relationships. The empirical models are created offline with extensive computational resources, then used online with simple parameter substitutions. This changes the parameters of the computational method from high-intensity real-time calculation to low-intensity parameter evaluation, enabling real-time performance.

The patent applies preliminary action by performing comprehensive measurements and creating detailed empirical models during the initial setup phase. These pre-established models capture complex relationships between parameters through extensive offline measurements, allowing rapid virtual optimization without needing extensive measurements during each optimization iteration. The measurement work is done in advance when time is available, enabling fast subsequent optimization cycles.

The patent uses copying by creating virtual copies of the technical device through detailed empirical models that replicate real device behavior. These digital twins allow optimization to be performed in the virtual domain rather than requiring physical prototypes and extensive real-world measurements. The empirical models copy the complex relationships and behaviors of the actual device, enabling accurate virtual testing and optimization with minimal physical measurements.

The patent applies segmentation by dividing the complex optimization problem into separate empirical models for different parameters and operating conditions. Each empirical model handles specific aspects of device behavior, allowing the system to manage complexity through modular organization. This segmentation enables the system to handle various operating conditions by selecting and combining appropriate model segments rather than requiring a single monolithic complex model.

The patent uses universality by creating empirical models that can handle multiple operating conditions and transient processes through a unified framework. The empirical models are designed to be versatile, accommodating different operating scenarios (steady-state and non-steady-state) without requiring separate specialized models for each condition. This multi-functional approach manages complexity by providing a single adaptable modeling framework rather than multiple condition-specific models.