Test & Evaluation Methods for Emerging Technology and Domains
Development of new technology always incorporates model testing. This is certainly true for Read More hypersonics, where flight tests are expensive and testing of component- and system-level models has significantly advanced the field. Unfortunately, model tests are often limited in scope, being only approximations of reality and typically only partially covering the range of potential realistic conditions. In this talk, we focus on the problem of real-time detection of the shock train leading edge in high-speed air-breathing engines, such as dual-mode scramjets. Detecting and controlling the shock train leading edge is important to the performance and stability of such engines, and a problem that has seen significant model testing on the ground and some flight testing. Often, methods developed for shock train detection are specific to the model used. Thus, they may not generalize well when tested in another facility or in flight as they typically require a significant amount of prior characterization of the model and flow regime. A successful method for shock train detection needs to be robust to changes in features like isolator geometry, inlet and combustor states, flow regimes, and available sensors. Such data can be difficult or impossible to obtain if the isolator operating regime is large. To this end, we propose the an approach for real-time detection of the isolator shock train. Our approach uses real-time pressure measurements to adaptively estimate the shock train position in a data-driven manner. We show that the method works well across different isolator models, placement of pressure transducers, and flow regimes. We believe that a data-driven approach is the way forward for bridging the gap between testing and reality, saving development time and money.
Estimating Pure-Error from Near Replicates in Design of Experiments
Modeling & Simulation
In design of experiments, setting exact replicates of factor settings enables estimation of Read More pure-error; a model-independent estimate of experimental error useful in communicating inherent system noise and testing of model lack-of-fit. Often in practice, the factor levels for replicates are precisely measured rather than precisely set, resulting in near-replicates. This can result in inflated estimates of pure-error due to uncompensated set-point variation. In this article, we review previous strategies for estimating pure-error from near-replicates and propose a simple alternative. We derive key analytical properties and investigate them via simulation. Finally, we illustrate the new approach with an application.