Systems and methods to ensure robustness for engineering autonomy

Abstract

Methods and systems are provided for evaluating robustness of engineering components. An example method includes receiving an engineering problem definition for a multidimensional engineering domain of an engineering component, in which the engineering problem definition includes a description of a plurality of engineering features of the engineering component. Executing an optimal stratified sampling algorithm that obtains a plurality of probabilistic samples from the multidimensional engineering domain. Selecting optimal combinations of higher fidelity models, reduced order models and response surfaces for execution of the probabilistic samples. Executing the probabilistic samples using the selected combinations to determine a respective engineering response for each engineering featureof the plurality of engineering features. Generating a robustness scorecard for the engineering component based on the determined engineering responses to quantify a robustness score for each engineering feature of the plurality of engineering features. Presenting the robustness scorecard to a user.

Description

technical field [0001] The present invention relates generally to sampling systems, and more particularly to systems for evaluating the robustness of engineering products via optimal stratified sampling of multidimensional design spaces. Background technique [0002] Factors of safety are often used to minimize the possibility of engineering product failures. The safety factor assists against multiple variations to ensure a robust engineered product. The use of security factors is often limited by several technical issues. For example, safety factors are not known a priori and will often be highly dependent on the complexity of the analyzed engineering system. As such, using a factor of safety can often lead to overly conservative engineering calculations. [0003] The derivative method is also used to analyze the robustness of engineering products (robustness, robustness). These derivative methods sometimes involve evaluating the derivative or slope around a critical de...

AI Technical Summary

Problems solved by technology

MC-based sampling for robustness assessment is limited by several technical issues

For example, MC-based sampling methods do not scale well with increasing dimensionality

Moreover, MC-based sampling methods stretch massive computational resources and lack the ability to handle non-normal response distributions for modeling and displaying variability

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