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In an earlier paper, the authors described how Model-Based System Engineering could be utilized to provide a virtual Hardware-in-the-Loop simulation capability, which creates a framework for the development of virtual ECU software by providing a platform upon which embedded control algorithms may be developed, tested, updated, and validated. The development of virtual ECU software is increasingly valuable in automotive control system engineering because vehicle systems are becoming more complex and tightly integrated, which requires that interactions between subsystems be evaluated during the design process. Variational analysis and robustness studies are also important and become more difficult to perform with real hardware as system complexity increases. The methodology described in this paper permits algorithm development to be performed prior to the availability of vehicle and control system hardware by providing what is essentially a virtual integration vehicle.

For a series plug-in hybrid electric vehicle (PHEV), it is critical that batteries be sized to maximize vehicle performance variables, such as fuel efficiency, gasoline savings, and zero emission capability. The wide range of design choices and the cost of prototype vehicles calls for a development process to quickly and systematically determine the design characteristics of the battery pack, including its size, and vehicle-level control parameters that maximize the net present value (NPV) of a vehicle during the planning stage. Argonne National Laboratory has developed Autonomie, a modeling and simulation framework. With support from The MathWorks, Argonne has integrated an optimization algorithm and parallel computing tools to enable the aforementioned development process. This paper presents a study that utilized the development process, where the NPV is the present value of all the future expenses and savings associated with the vehicle.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

The addition of metal nanoparticles to standard coolant fluids dramatically increases the thermal conductivity of the liquid. The properties of the prepared nanofluids will allow for lighter, smaller, and higher efficiency spacecraft thermal control systems to be developed. Nanofluids with spherical or rod-shaped metal nanoparticles were investigated. At a volume concentration of 0.5%, the room temperature thermal conductivity of a 2 nm spherical gold nanoparticle-water solution was increased by more than 10% over water alone. Silver nanorods increased the thermal conductivity of ethylene glycol by 53% and water by 26%.

New dc to dc converter topologies are presented which are suitable for high density high power supplies. Topological variations of the basic inverse dual converter (IDC) circuit such as the transformer coupled, the multiphase and the multipulse derivation of the single phase IDC have been analysed and some simulation results have been presented. It has been shown in a recent publication [1] that the single phase IDC offers a buck-boost operation over wide range without transformer, bidirectional power flow, and complementary commutation of the switches. The topologies examined in this paper have additional features such as lower device and component stresses, and smaller filter requirements, resulting in smaller size and weight. Some performance and possible applications are also examined. Finally the IDCs for serial and parallel power distribution, and ac tapping of the IDC are discussed.

Artificial Neural Network(ANN) techniques are used to develop a system to detect High Impedance Faults(HIFs) in electric power distribution lines. Encouraging results were observed with a simple Multi-layer Perceptron(MLP) trained with the backpropagation learning algorithm. Although the results are not significantly better than those reported with other algorithmic approaches, ANN techniques have potential advantages over the other approaches; namely, ability to train the system easily to accommodate different feeder characteristics, ability to adapt and so become a better detector with experience and better fault tolerance. When these features are incorporated, the system is expected to perform better than existing systems. The system we developed for the current phase, the training strategies used, the tests conducted and the results obtained are discussed in this paper. Also background discussions on existing HIF detection techniques, and ANN techniques can be found in this paper.

The possible use of a dual-loop, model-based adaptive control system for load-following in static space nuclear power systems is investigated. The objective of the fault-tolerant, autonomous control system is to deliver the demanded electric power at the desired voltage level, by appropriately manipulating the neutron power through the control drums. As a result sufficient thermal power is produced to meet the required demand in the presence of dynamically changing system operating conditions and potential sensor failures. Even though the proposed approach has thus far been applied only to a thermoelectric space nuclear power system, it is equally applicable to other static space nuclear power systems, such as thermionic systems. This is because of the considerable similarities in the underlying operational issues and in the dynamics of these systems from a control engineering viewpoint.