Homochiral Dy(III) complexes: by changing the ligand-to-metal ratio, enantiomeric pairs of a Dy(III) complex of different nuclearity could be obtained. The mono- and dinuclear complexes exhibit characteristics of single-molecule magnets and different slow magnetic relaxation processes. In addition, the dinuclear complexes exhibit ferroelectric behavior, thus representing the first chiral polynuclear lanthanide-based single-molecule magnets with ferroelectric properties.

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Accurate gas turbine performance models are crucial in many gas turbine performance analysis and gas path diagnostic applications. With current thermodynamic performance modeling techniques, the accuracy of gas turbine performance models at off-design conditions is determined by engine component characteristic maps obtained in rig tests and these maps may not be available to gas turbine users or may not be accurate for individual engines. In this paper, a nonlinear multiple point performance adaptation approach using a genetic algorithm is introduced with the aim to improve the performance prediction accuracy of gas turbine engines at different off-design conditions by calibrating the engine performance models against available test data. Such calibration is carried out with introduced nonlinear map scaling factor functions by “modifying” initially implemented component characteristic maps in the gas turbine thermodynamic performance models. A genetic algorithm is used to search for an optimal set of nonlinear scaling factor functions for the maps via an objective function that measures the difference between the simulated and actual gas path measurements. The developed off-design performance adaptation approach has been applied to a model single spool turbo-shaft aero gas turbine engine and has demonstrated a significant improvement in the performance model accuracy at off-design operating conditions.

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A nonlinear multiple point genetic algorithm based performance adaptation developed earlier by the authors using a set of nonlinear scaling factor functions has been proven capable of making accurate performance predictions over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain the optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of the trial and error process. In this paper, an improvement on the present adaptation method is presented using a least square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the least square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

In this paper, a first-state contractive (FSC) model predictive control (MPC) algorithm is developed for the trajectory tracking and point stabilization problems of nonholonomic mobile robots. Different from other stabilizing MPC methods, which address stability by adding terminal state penalties in the performance index and imposing constraints on the terminal state at the end of the prediction horizon, the proposed MPC algorithm guarantees its stability by adding a contractive constraint on the first state at the beginning of the prediction horizon. The resulting MPC scheme is denoted as first-state contractive MPC (FSC-MPC). In the absence of disturbances, it can be shown that the proposed algorithm is exponentially stable. Simulation results are provided to verify the effectiveness of the method. Moreover, it is shown that the FSC-MPC algorithm has simultaneous tracking and point stabilization capability.