Ultra-deep well hoisting container reliability robust design
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Solution Overview
Problem
Current methods for designing ultra-deep well hoisting containers fail to accurately estimate reliability due to structural uncertainty and complex dynamic loads, leading to large deviations in design and maintenance, as they typically consider only single failure modes without accounting for probability correlations among multiple failure modes.
Innovation Solution
A reliability robust design method is developed, which involves establishing a parameterized model, using finite element analysis, Kriging method, saddlepoint approximation, and Clayton copula function to calculate failure probabilities and system reliability, considering multiple failure modes and their correlations, and optimizing structural parameters for improved reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If reliability is defined in a single failure mode, then the design process is simplified, but the estimation accuracy of hoisting container reliability deteriorates
Solution Approach 1:
The patent segments the reliability analysis into multiple independent failure modes (strength failure, stiffness failure, buckling failure) and analyzes each mode separately using finite element method. This segmentation allows the complex multi-mode reliability problem to be broken down into manageable sub-problems while maintaining overall accuracy through the copula function integration.
Solution Approach 2:
The patent uses a composite probabilistic model that combines multiple failure mode analyses with copula functions to create a comprehensive reliability assessment system. This composite approach integrates the probability distributions of different failure modes while accounting for their correlations, achieving high estimation accuracy without excessive complexity.
2Measurement precision
If multiple failure modes with probability correlation are considered, then reliability estimation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent transforms the complex multi-dimensional reliability problem into a more manageable form by changing parameters through the use of copula functions. The copula function converts correlated random variables into independent standard normal variables, simplifying the computational process while maintaining the accuracy of reliability estimation for multiple failure modes.
Solution Approach 2:
The copula function serves as an intermediary that bridges the gap between multiple correlated failure modes. It mediates the complex probability correlations among different failure modes by providing a standardized framework for integrating their joint probability distributions, thereby reducing computational complexity while preserving accuracy.
3Reliability
If structural uncertainty and dynamic load uncertainty are accounted for, then the reliability analysis becomes more comprehensive, but the design and maintenance deviations increase without proper methods
Solution Approach 1:
The patent implements a feedback mechanism by using the reliability analysis results to update and optimize the structural design parameters. The comprehensive reliability assessment considering structural and load uncertainties feeds back into the design process, allowing for iterative optimization that reduces design and maintenance deviations while maintaining comprehensive reliability coverage.
Data Source
AI summary
A reliability robust design method for multiple failure modes of an ultra-deep well hoisting container, including: defining randomness of a structural parameter, a material property, and a dynamic load of a hoisting container, and solving a random response of a structural failure for a random parameter using a design of experiment method; establishing reliability performance functions of each failure modes in accordance with failure criterion of the hoisting container; establishing a joint probability model of correlated failures using a copula theory in consideration of probability correlation between the failure modes; establishing, a system reliability model with failure correlation of the hoister container; establishing a sensitivity model concerning each random parameter for system reliability of the hoisting container; and establishing, in conjunction with an optimization design model, a reliability robust optimization design model for the hoisting container using a joint failure probability and parameter sensitivity as constraints.