• Smart Structures and Systems
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• v.15 no.6
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• pp.1393-1410
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• 2015

An original sensor system based on Fiber Bragg Gratings (FBG) for the strain monitoring of an adaptive wing element is presented in this paper. One of the main aims of the SARISTU project is in fact to measure the shape of a deformable wing for performance optimization. In detail, an Adaptive Trailing Edge (ATE) is monitored chord- and span-wise in order to estimate the deviation between the actual and the desired shape and, then, to allow attaining a prediction of the real aerodynamic behavior with respect to the expected one. The integration of a sensor system is not trivial: it has to fit inside the available room and to comply with the primary issue of the FBG protection. Moreover, dealing with morphing structures, large deformations are expected and a certain modulation is necessary to keep the measured strain inside the permissible measure range. In what follows, the mathematical model of an original FBG-based structural sensor system is presented, designed to evaluate the chord-wise strain of an Adaptive Trailing Edge device. Numerical and experimental results are compared, using a proof-of-concept setup. Further investigations aimed at improving the sensor capabilities, were finally addressed. The elasticity of the sensor structure was exploited to enlarge both the measurement and the linearity range. An optimisation process was then implemented to find out an optimal thickness distribution of the sensor system in order to alleviate the strain level within the referred component.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.2
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• pp.175-181
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• 2011

The purpose of this study is to secure the design data for the optimization of the radial turbine and heat cycle system, by using the CFD analysis technique and the design of 100kW class radial turbine applicable to waste heat recovery generation system for ship. Radial turbine was comprised of scroll casing, vane nozzle with 18 blades and rotor with 13 blades, and analysis grid was used to about 2.3 million. Mass flow rate and rotational speed was 0.5kg/s, 75,0000rpm, respectively. Eight kinds of inlet pressure was set between 195 and 620kPa. As the flow accelerated through the nozzle passage to the throat, the pressure level at the pressure and suction sides becomed similar to about Mach number of 0.35. When the inlet temperature and pressure was $250^{\circ}C$, 352kPa respectively, the isentropic efficiency and mechanical power showed the analysis results of 74% and 108kW.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.2
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• pp.184-191
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• 2012

Aerodynamic analysis(DATCOM) and radar cross section(RCS) analysis(POFACETS) were integrated for the air-to-surface missile concept design using a design framework. The missile geometry was defined based on the CAD(CATIA) for synchronizing the manufacturing with design processes. Aero/RCS analyses were linked with the CAD process under the ModelCenter framework in order to receive the geometry data automatically. The missile design baseline configuration was selected from ROC(requirement of capability). Then the RCS minimization was performed subject to thelargerthebetter constraint of the missile lift-to-drag ratio. This study demonstrated that various design strategies can be performed efficiently about many missile configurations using this design framework in the missile conceptual design phase.

• Wind and Structures
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• v.36 no.6
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• pp.393-404
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• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.