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A unique perspective of system integration is presented in terms of statistical design and analysis. Advanced statistical concepts are employed to quantify the variance of the statistical models as well as to specify model truncation error. Three models are developed for this study: 1) a supersonic wing section; 2) a supersonic turbojet system and; 3) an integrated supersonic wing section and supersonic turbojet. The three models are analyzed and separately and surrogate models are developed for each model independently using Design of Experiments and advanced statistical analyses. The individual surrogate models are statistically validated compared to their respective models. The individual wing and turbojet surrogate models are then used to estimate the performance of the combined wing and turbojet system surrogate model performance.

Advancement in modern aircraft with the development of more dynamic and efficient technologies has led to these technologies increasingly operated near or at their operation limits. More comprehensive analysis methods based on high-fidelity models co-simulated in an integrated environment are needed to support the full utilization of these advanced technologies. Furthermore, the additional information provided by these new analyses needs to be correlated with updates to traditional metrics and specifications. One such case is the thermal limit requirement that sets the upper bound on a thermal system temperature. Traditionally, this bound is defined based on steady-state conditions. However, advanced thermal management systems experience dynamic events where the temperature is not static and may violate steady-state requirements for brief periods of time.

In this paper, first an analytical model of a synchronous generator with a pulsed constant power load (CPL) is developed and numerically compared with a detailed simulation model. The analytical model is shown to possess good predictive abilities, thus enabling its use for control purposes. Second, the generator has a proportionalintegral (PI) control inner-loop, whose task is to regulate the generator’s output voltage to a desired reference. A novel outer-loop predictive reference governor (PRG) is designed and tested via simulation. The PRG uses the analytical model to predict the output behavior of the generator over a short time window, and continuously modifies the reference given to the inner-loop in order to maintain stringent steady-state requirements, in spite of demanding power requirements at the CPL. Simulation results illustrate the significant performance advantages of using the PRG versus using the inner-loop PI controller alone.