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The Air Force Research Laboratory (AFRL), in cooperation with the University of Dayton Research Institute (UDRI) and Fairchild Controls Corporation, is building a test facility to study the use of advanced vapor cycle systems (VCS) in an expanded role in aircraft thermal management systems (TMS). It is dedicated to the study and development of VCS control and operation in support of the Integrated Vehicle ENergy Technology (INVENT) initiative. The Two Phase Thermal Energy Management System (ToTEMS1) architecture has been shown through studies to offer potential weight, cost, volume and performance advantages over traditional thermal management approaches based on Air Cycle Systems (ACS). The ToTEMS rig will be used to develop and demonstrate a control system that manages the system capacity over both large amplitude and fast transient changes in the system loads.

The Air Force Research Laboratory (AFRL), in cooperation with the University of Dayton Research Institute (UDRI) and Fairchild Controls Corporation, is operating an in-house advanced vapor compression refrigeration cycle system (VCS) test rig known as ToTEMS (Two-Phase Thermal Energy Management System). This test rig is dedicated to the study and development of VCS control and operation in support of the Energy Optimized Aircraft (EOA) initiative and the Integrated Vehicle ENergy Technology (INVENT) program. Previous papers on ToTEMS have discussed the hardware setup and some of the preliminary data collected from the system, as well as the first steps towards developing an optimum-seeking control scheme. A key goal of the ToTEMS program is to reduce the risk associated with operating VCS in the dynamic aircraft environment.

Detailed machine models are, and will continue to be, a critical component of both the design and validation processes for engineering future aircraft, which will undoubtedly continue to push the boundaries for the demand of electric power. This paper presents a survey of experimental testing procedures for typical synchronous machines that are applied to brushless synchronous machines with rotating rectifiers to characterize their operational impedances. The relevance and limitations of these procedures are discussed, which include steady-state drive stand tests, sudden short-circuit transient (SSC) tests, and standstill frequency response (SSFR) tests. Then, results captured in laboratory of the aforementioned tests are presented.

In this work we present our recent effort in developing a novel heat exchanger based on endothermic chemical reaction (HEX reactor). The proposed HEX reactor is designed to provide additional heat sink capability for aircraft thermal management systems. Ammonium carbamate (AC) which has a decomposition enthalpy of 1.8 MJ/kg is suspended in propylene glycol and used as the heat exchanger working fluid. The decomposition temperature of AC is pressure dependent (60°C at 1 atmosphere; lower temperatures at lower pressures) and as the heat load on the HEX increases and the glycol temperature reaches AC decomposition temperature, AC decomposes and isothermally absorbs energy from the glycol. The reaction, and therefore the heat transfer rate, is controlled by regulating the pressure within the reactor side of the heat exchanger. The experiment is designed to demonstrate continuous replenishment of AC.

Model based design is a standard practice within the aerospace industry. However, the accuracies of these models are only as good as the parameters used to define them and as a result a great deal of effort is spent on model tuning and parameter identification. This process can be very challenging and with the growing complexity and size of these models, manual tuning is often ineffective. Many methods for automated parameter tuning exist. However, for aircraft systems this often leads to large parameter search problems since frequency based identification and direct gradient search schemes are generally not suitable. Furthermore, the cost of experimentation often limits one to sparse data sets which adds an additional layer of difficulty. As a result, these search problems can be highly sensitive to the definition of the model fitness function, the choice of algorithm, and the criteria for convergence.