Monte Carlo Simulation Acceleration via Component-Specific Distribution Scaling
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Solution Overview
Problem
Conventional Monte Carlo simulations require a large number of runs to accurately evaluate system reliability, often resulting in insufficient simulation conditions and low confidence in predicting performance, especially for complex systems with varying component instances, leading to either overly optimistic or pessimistic results.
Innovation Solution
The method involves applying component-specific acceleration factors to scale input parameter distributions, allowing a limited number of simulation runs to effectively evaluate system margins by representing a wider range of conditions, such as transforming a 3 sigma range to a 5 sigma range using acceleration factors like 5/3, ensuring that failure-inducing conditions are adequately tested.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of simulation runs are performed to accurately evaluate system reliability, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies parameter changes by modifying the input parameter distributions through acceleration factors. Specifically, it scales the standard deviation of input parameters (e.g., transforming a 3 sigma range to a 5 sigma range) to expand the coverage of simulation conditions. This allows a limited number of simulation runs to effectively evaluate a wider range of system margins and reliability conditions without requiring a proportionally large increase in simulation time.
2Reliability
If the number of simulation runs is increased to capture rare failure conditions, then reliability assessment accuracy is improved, but productivity decreases
Solution Approach 1:
The patent changes the distribution parameters of input variables by applying acceleration factors that scale the standard deviation. This transformation allows the simulation to cover a broader range of conditions (e.g., extending from 3 sigma to 5 sigma coverage) with the same number of runs, thereby improving the accuracy of rare failure condition assessment without sacrificing simulation throughput.
Solution Approach 2:
The patent performs preliminary calculation of acceleration factors based on the desired coverage range and component characteristics before executing the simulation. This preliminary action optimizes the input parameter distributions in advance, ensuring that the subsequent simulation runs are efficiently directed toward capturing relevant failure conditions without unnecessary computational overhead.
3Measurement precision
If component-specific acceleration factors are applied to scale input parameter distributions, then measurement precision for system margins is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by implementing component-specific acceleration factors tailored to each component's characteristics and its contribution to system-level failures. Instead of using a uniform scaling approach, the method selectively adjusts the standard deviation of input parameters for individual components based on their sensitivity and impact, thereby improving system margin assessment accuracy while managing model complexity through targeted rather than universal modifications.
Data Source
AI summary
A method of accelerating a Monte Carlo (MC) simulation for a system including a first component having a first input parameter and a second component having a second input parameter. The simulation model provided includes a first component model including a first model parameter corresponding to the first input parameter and a second component model having a second model parameter corresponding to the second input parameter. A first acceleration factor for the first component and a second acceleration factor for the second component are calculated based on at least the respective number of instances. A first scaled distribution is computed from the first distribution and a second scaled distribution is computed from the second distribution based on the respective acceleration factors. The MC simulation for the system is run, wherein values for the first model parameter value and second model parameter value are obtained based on the respective scaled distributions.