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The development of electromechanical actuators (EMAs) is the key technology to build an all-electric aircraft. One of the greatest hurdles to replacing all hydraulic actuators on an aircraft with EMAs is the acquisition, transport and rejection of waste heat generated within the EMAs. The absence of hydraulic fluids removes an attractive and effective means of acquiring and transporting the heat. To address thermal management under limited cooling options, accurate spatial and temporal information on heat generation must be obtained and carefully monitored. In military aircraft, the heat loads of EMAs are highly transient and localized. Consequently, a FEA-based thermal model should have high spatial and temporal resolution. This requires tremendous calculation resources if a whole flight mission simulation is needed. A lumped node thermal network is therefore needed which can correctly identify the hot spot locations and can perform the calculations in a much shorter time.

In the aviation community, there is a high priority to develop all-electric aircraft. Electro-mechanical actuation systems would replace traditional, large, heavy and difficult-to-maintain hydraulic actuation systems. This movement from hydraulic actuation to electrical actuation enhances the flexibility to integrate redundancy and emergency system in future military aircraft. Elimination of the hydraulic fluid removes the possibility of leakage of corrosive hydraulic fluid and the associated fire hazard, as well as environmental concerns. The switch from hydraulic to electrical actuation provides additional benefits in reduced aircraft weight, improved survivability and improved maintainability. The heat load in an electro-mechanical actuation (EMA) is highly transient and localized in nature; therefore a phase change material could be embedded in the heat generating components to store peak heat load.

A study on the FEA simulation of the springback prediction is summarized. The discussion is focused on the accuracy of the simulation and the factors that might contribute to the inaccurate results of the simulation. First, springback simulation results based on both 3 parameter Barlat plasticity model and transversely anisotropic plasticity model are studied. The numisheet'93 2D draw bending problem and a rail type automobile panel are selected for the material model study using LS-DYNA and LS-NIKE. The simulations based on different friction coefficients along the rolling direction and the transverse direction are studied using the DaimlerChrysler in-house FEA code Cform. A study is also performed to investigate the different springback behavior due to friction coefficient changes between the flat tooling area and the curved tooling area. Some other issues such as the dynamic effect and the penalty factor effect are also briefly discussed.