Solving problems of optimization under uncertainty as statistical decision problems

Current state-of-art practice for airfoil has revealed an unexpected difficulty: experience has indicated that a deterministic for discrete operating conditions may result in dramatically inferior performance when the actual conditions are different from these, somewhat arbitrarily chosen, design values. Extensions to multipoint have proven unable to adequately remedy the problem of localized optimization. This paper presents an intrinsically statistical approach and demonstrates how the shortcomings of multi-point with respect to localized optimization can be overcome. The practical examples also reveal how the relative likelihood of each of the operating conditions is automatically taken into consideration during the process. This is a key advantage over the use of multipoint methods.