Hardware-in-the-loop (HIL) modeling is a powerful way of modeling complicated systems. However, some hardware is expensive to use in terms of time or mechanical wear. In cases like these, optimizing using the hardware can be prohibitively expensive because of the number of calls to the hardware that are needed. Variable fidelity optimization can help overcome these problems. Variable fidelity optimization uses less expensive surrogates to optimize an expensive system while calling it fewer times. The surrogates are usually created from performing a design of experiments on the expensive model and fitting a surface to the results. However, some systems are too expensive to create a surrogate from. One such case is that of a flapping flight model. In this thesis, a technique for variable fidelity optimization of HIL has been created that optimizes a system while calling it as few times as possible. This technique is referred to as an intelligent DOE. This intelligent DOE was tested using simple models of various dimension. It was then used to find a flapping wing trajectory that maximizes lift. Through testing, the intelligent DOE was shown to be able to optimize expensive systems with fewer calls than traditional variable fidelity optimization would have needed. Savings as high as 97% were recorded. It was noted that as the number of design variables increased, the intelligent DOE became more effective by comparison because the number of calls needed by a traditional DOE based variable fidelity optimization increased faster than linearly, where the number of hardware calls for the intelligent increased linearly.

New helicopter rotor designs are desired that offer increased efficiency, reduced vibration, and reduced noise. This problem is multidisciplinary, requiring knowledge of structural dynamics, aerodynamics, and aeroacoustics. Rotor optimization requires achieving multiple, often conflicting objectives. There is no longer a single optimum but rather an optimal trade-off space, the Pareto Frontier. Rotor Designers in industry need methods that allow the most accurate simulation tools available to search for Pareto designs. Computer simulation and optimization of rotors have been advanced by the development of "comprehensive" rotorcraft analysis tools. These tools perform aeroelastic analysis using Computational Structural Dynamics (CSD). Though useful in optimization, these tools lack built-in high fidelity aerodynamic models. The most accurate rotor simulations utilize Computational Fluid Dynamics (CFD) coupled to the CSD of a comprehensive code, but are generally considered too time consuming where numerous simulations are required like rotor optimization. An approach is needed where high fidelity CFD/CSD simulation can be routinely used in design optimization. This thesis documents the development of physics based rotor simulation frameworks. A low fidelity model uses a comprehensive code with simplified aerodynamics. A high fidelity model uses a parallel processor capable CFD/CSD methodology. Both frameworks include an aeroacoustic simulation for prediction of noise. A synergistic process is developed that uses both frameworks together to build approximate models of important high fidelity metrics as functions of certain design variables. To test this process, a 4-bladed hingeless rotor model is used as a baseline. The design variables investigated include tip geometry and spanwise twist. Approximation models are built for high fidelity metrics related to rotor efficiency and vibration. Optimization using the approximation models found the designs having maximum rotor efficiency and minimum vibration. Various Pareto generation methods are used to find frontier designs between these two anchor designs. The Pareto anchors are tested in the high fidelity simulation and shown to be good designs, providing evidence that the process has merit. Ultimately, this process can be utilized by industry rotor designers with their existing tools to bring high fidelity analysis into the preliminary design stage of rotors.