GoldSim 2006 User Conference

Slide 1

Vancouver, B.C.

The Submodel Element

GoldSim 2006 User Conference

Slide 2

Vancouver, B.C.

The Concept of a Submodel Element

What does a GoldSim element actually do?– Takes inputs– Produces outputs– It may have a random component to its behavior (e.g.

Stochastic elements). What could you do if you were able to take an entire

GoldSim model and embed it as one element within a parent model?– The inner model could either:

• Do a deterministic simulation,• Do a Monte Carlo simulation, or• Do an optimization

– The inner model could be either dynamic or static The Glacier release of GoldSim will provide this

capability.

GoldSim 2006 User Conference

Slide 3

Vancouver, B.C.

Uncertainty Analysis Imagine a model of a reliability system…

– The system undergoes random failures and variable repair times, etc.

– You can use GoldSim to estimate its reliability, availability, throughput, etc.

But what if you are uncertain about some of its key input parameters?– Can you estimate its performance?– Can you develop confidence bounds on your

estimate?– Can you decide whether to go into production

vs doing more testing?

GoldSim 2006 User Conference

Slide 4

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Uncertainty Analysis (cont)

You can address these issues by using a ‘double Monte Carlo’ analysis:– An outer Monte Carlo loop samples the

uncertain system parameters.– An inner Monte Carlo loop simulates the

system’s random performance given specific values for the uncertain variables.

– The result: an understanding of the uncertainty in the system’s performance, e.g. “The probability that the mean warranty return rate will exceed 5%”.

GoldSim 2006 User Conference

Slide 5

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Uncertainty Analysis Example

Define two uncertain parameters: the Mean life (3 yrs) and Slope Factor (2) for a Weibull failure mode.

Add a Submodel element that contains a Reliability Function element, with the defined failure mode.

A warranty cost of $100 occurs for every failure in the first two years.

What is the 95% confidence limit for the mean warranty cost?– See Submodel1.gsm and Submodel1a.gsm

GoldSim 2006 User Conference

Slide 6

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Optimizing a Random (or Uncertain) System

What is the optimum design for a system that is subject to random effects?– The optimum for one possible set of random effects

may be quite different from the optimum for another set (e.g. if the earthquake occurs or not…).

– So you need to evaluate your objective function over the full range of possible performance (i.e., to get a PDF for the objective function).

– The true optimum design has to be based on STATISTICS for the objective function, eg:• “Minimize the expected total lifecycle cost while

allowing no more than a 2% likelihood of a catastrophic failure”

An optimizing parent can use a Submodel element to calculate the desired statistic(s) to optimize.

GoldSim 2006 User Conference

Slide 7

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Dynamic Optimization

What if your model needs to make an optimal decision at one or more points in time during a simulation?– Set up a static submodel that will find the

optimal solution and return it to the main model.

This could be used to simulate resource allocation decisions or other ‘local’ optimization problems.

GoldSim 2006 User Conference

Slide 8

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Example: Optimize the Life-Cycle Performance of a Manufactured Component (SubModel2.gsm)

This example minimizes the life-cycle costs for a manufactured product, subject to a constraint that the likelihood of a catastrophic failure is less than 2%.

The Submodel element does a stochastic simulation of the products operational performance and costs.

3.1416

MeanLife

3.1416

SlopeFactor

G S M

TheSystem

XXProd_Cost

XXAllCosts