Hydraulic and pneumatic networks are highly nonlinear and difficult to analyze. This study presents a software application designed to help students, visualize and understand fluid systems' dynamic behaviors. The application uses a combined bond graph and singular perturbation approach for system equation formulation. A standard iterative and adaptive integrator provides online numerical solutions to the system equations. Coupled to the integrator's output are a graphical animation subsystem and an instrumentation subsystem. The animation subsystem is responsible for rendering movable components on screen, at every simulation time-step, creating the illusion of continuous movement. The instrumentation subsystem collects and displays numerical data in numerical and graphical forms. An interesting contribution of this fluid system analyzer is its ''user-in-the-loop'' feature. This feature allows students to become active participants by enabling them to interact with network components while a simulation run is in progress.
The optimization of classification systems is often confronted by the solution over-fit problem. Solution over-fit occurs when the optimized classifier memorizes the training data sets instead of producing a general model. This paper compares two validation strategies used to control the over-fit phenomenon in classifier optimization problems. Both strategies are implemented within the multi-objective NSGA-II and MOMA algorithms to optimize a Projection Distance classifier and a Multiple Layer Perceptron neural network classifier, in both single and ensemble of classifier configurations. Results indicated that the use of a validation stage during the optimization process is superior to validation performed after the optimization process.
The optimization of many engineering systems is challenged by the solution over-fit to the data set used to evaluate potential solutions during the evolutionary process. The solution over-fit phenomenon is hard to detect and is especially prevalent in problems involving example-based training, such as pattern feature selection and pattern classifier design. For these applications, uncontrolled over-fit can lead to biased features being extracted and degraded classifier generalization abilities. This paper details the performance of a solution over-fit control strategy used in the multiobjective evolutionary optimization of a multileveled classification system. This control, embedded within a solution validation procedure, minimizes the over-fit effects without modifying the dominance relation used in the processing of candidate solutions. Extensive experimental analysis using multiobjective genetic and memetic algorithms demonstrates both the need and the efficiency of the proposed over-fit control for pattern classification systems optimization.
This work presents an aerodynamic optimization method for a Droop Nose Leading Edge (DNLE) and Morphing Trailing Edge (MTE) of a UAS-S45 root airfoil by using Bezier-PARSEC parameterization. The method is performed using a hybrid optimization technique based on a Particle Swarm Optimization (PSO) algorithm combined with a Pattern Search algorithm. This is needed to provide an efficient exploitation of the potential configurations obtained by the PSO algorithm. The drag minimization and the endurance maximization were investigated for these configurations individually as two single-objective optimization functions. The aerodynamic calculations in the optimization framework were performed using the XFOIL solver with flow transition estimation criteria, and these results were next validated with a Computational Fluid Dynamics solver using the Transition Shear Stress Transport (SST) turbulence model. The optimization was conducted at different flight conditions. Both the DNLE and MTE optimized airfoils showed a significant improvement in the overall aerodynamic performance, and MTE airfoils increased the efficiency of CL3/2/CD by 10.25%, indicating better endurance performance. Therefore, both DNLE and MTE configurations show promising results in enhancing the aerodynamic efficiency of the UAS-S45 airfoil.
This paper presents the design and wind tunnel testing of a morphing camber system and an estimation of performances on an unmanned aerial vehicle. The morphing camber system is a combination of two subsystems: the morphing trailing edge and the morphing leading edge. Results of the present study show that the aerodynamics effects of the two subsystems are combined, without interfering with each other on the wing. The morphing camber system acts only on the lift coefficient at a 0° angle of attack when morphing the trailing edge, and only on the stall angle when morphing the leading edge. The behavior of the aerodynamics performances from the MTE and the MLE should allow individual control of the morphing camber trailing and leading edges. The estimation of the performances of the morphing camber on an unmanned aerial vehicle indicates that the morphing of the camber allows a drag reduction. This result is due to the smaller angle of attack needed for an unmanned aerial vehicle equipped with the morphing camber system than an unmanned aerial vehicle equipped with classical aileron. In the case study, the morphing camber system was found to allow a reduction of the drag when the lift coefficient was higher than 0.48.
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