Technical System Calibration Under Stochastic Load Cycles
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Solution Overview
Problem
Conventional calibration methods for technical systems are not robust against real-world operational influences, such as random environmental conditions and uncertainties, leading to undesirable deviations in output variables during actual operation.
Innovation Solution
A method that executes a load cycle multiple times under the influence of random variables, optimizing a stochastic cost function using a risk measure to account for uncertainties, thereby making the calibration more robust against random influences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If calibration is performed using a single deterministic load cycle, then the calibration process is simple and deterministic, but the result is not robust against random real-world operational influences
Solution Approach 1:
The patent applies preliminary action by performing multiple load cycle realizations and computing the probability distribution and risk measure before finalizing the calibration. This advance preparation allows the system to account for random influences beforehand, making the calibration robust without adding complexity during actual operation.
Solution Approach 2:
The patent creates multiple copies of the load cycle (realizations) to simulate different operational scenarios. By analyzing these copies and computing statistical measures, the method captures the variability of real-world conditions without requiring the actual physical system to be exposed to all possible random influences.
2Reliability
If multiple load cycle realizations are executed to account for random influences, then the calibration robustness improves, but the computational effort increases
Solution Approach 1:
The patent replaces physical experimentation with computational modeling. Instead of physically testing the system under multiple random conditions, the method uses simulated load cycle realizations and computational algorithms to evaluate the probability distribution and risk measure, significantly reducing time and resource requirements.
Solution Approach 2:
The patent changes the approach from optimizing single deterministic parameters to optimizing the risk measure derived from probability distributions. This parameter transformation allows the method to handle randomness systematically while maintaining computational efficiency through structured mathematical optimization.
3Adaptability or versatility
If the cost function is made stochastic to reflect real-world uncertainties, then the calibration better represents actual operating conditions, but the optimization becomes more complex
Solution Approach 1:
The patent introduces the risk measure as an intermediary between the stochastic cost function and the optimization process. This intermediary transforms the complex stochastic optimization problem into a deterministic optimization of a scalar value, making the problem tractable while still accounting for all the uncertainty in the underlying cost function.
Solution Approach 2:
The patent transforms the stochastic optimization problem by changing the objective parameter from the random cost function to its risk measure equivalent. This parameter change converts an intractable stochastic optimization into a standard deterministic optimization that can be solved with conventional methods.
Data Source
AI summary
Various aspects of the present disclosure are directed to methods for calibrating a technical system with respect to stochastic influences during real operation of the technical system. In one example embodiment of the present disclosure, the method includes the steps of: determining the values of a number of control variables, carrying out the calibration on the basis of a load cycle which results in a sequence of a number of operating points, executing the load cycle multiple times under the influence of at least one random influencing variable, with each realization of the load cycle resulting in a random sequence of the number i of operating points, defining a risk measure of the probability distribution, with which the probability distribution is mapped to a scalar variable, and optimizing the risk measure by varying the number of control variables in order to obtain optimal control variables for calibration.