Recent developments in computer capabilities and software enabled the application of deterministic optimization and Robust Design methods in real world aero engine development programs. This paper describes the methods used and shows several applications of this technology. The first example is the application of a Monte-Carlo simulation to support design decisions in the HP turbine casing air system. Here the main goal was to achieve a robust design addressing the variation of build tolerances on flow areas. The variation of parameters as mass flows, pressures and temperatures based on 5000 permutations of the base model give a high confidence level for achieving reliable system behavior for a large population of engines. In addition, dependencies of result parameters on input variations indicate the main levers for system improvement. A second example is the optimization of compressor discs. Here the main emphasis was on the influence of manufacturing tolerances and on the best method to evaluate these tolerances for longer running analysis tasks. Therefore, results of a full Monte-Carlo simulation are compared with results based on two surrogate models, a response surface and a Taylor series expansion. As a final example the optimization of a HP turbine disc for which a Design of Experiment has been performed to generate a response surface model is discussed. Using the response surface data the life variability due to assumptions in the thermal modeling have been quantified and used to adjust the constraints for the subsequent deterministic optimization for weight of the HP turbine. Using deterministic optimization and especially Robust Design methods a considerable decrease in development time and cost as well as an increased product quality and reliability have been achieved. However, deterministic optimization methods alone normally drive designs on to the constraint boundaries, leading to “cliff-edge” designs. Therefore, the application of Robust Design methods is required to increase the product reliability. These methods still require a considerable computing effort, so the widespread application is just starting.
Uncertainty Quantification of an Asset Evaluation for an Oilfield Property Incorporating Response-Surface Monte-Carlo Simulation with Stochastic Oil Price Models
