1. GoldSim 2006 User Conference Slide 1 Vancouver, B.C. The Submodel Element

2. 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.

3. 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?

4. GoldSim 2006 User Conference Slide 4 Vancouver, B.C. 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%”.

5. GoldSim 2006 User Conference Slide 5 Vancouver, B.C. 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

6. GoldSim 2006 User Conference Slide 6 Vancouver, B.C. 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.

7. GoldSim 2006 User Conference Slide 7 Vancouver, B.C. 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.

8. GoldSim 2006 User Conference Slide 8 Vancouver, B.C. Example: Optimize the Life-Cycle Performance of a Manufactured Component (SubModel2.gsm)