• Smart Structures and Systems
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• 제15권6호
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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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• 제35권2호
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• pp.175-181
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• 2011

본 연구에서는 선박용 폐열회수 발전시스템에 적용 가능한 100kW급 구심터빈의 설계 및 CFD 해석기법을 이용하여 열사이클 시스템 및 구심터빈 최적화를 위한 설계자료를 확보하는 것이다. 구심터빈은 스크롤 케이싱, 18개의 베인노즐, 13개의 로터 블레이드로 구성되며, 해석격자는 격자테스트를 통해 약 230만개 정도의 최적격자를 구성하였다. 질량유량 0.5kg/s, 회전속도는 75,000rpm, 입구압력은 195~620kPa 범위 내에서 8가지 조건으로 설정하였다. 베인노즐 내부로 증기가 유입된 후 출구로 갈수록 노즐의 압력면과 흡입면의 압력이 비슷해지면서 마하수가 거의 같은 값을 보였다. 입구온도와 압력이 $250^{\circ}C$, 352kPa 일 때 등엔트로피 효율은 74%, 기계동력은 108kW의 해석결과를 보이고 있다.

• 한국항공우주학회지
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• 제40권2호
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• pp.184-191
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• 2012

최적설계 프레임워크를 구성하여 미사일 개념설계 단계에 필요한 공력해석(DATCOM)과 RCS 해석(POFACETS) 프로세스를 통합하였다. 미사일 형상정의는 제작과 설계의 동시성과 형상정보 산출 등을 목적으로 CAD(CATIA)를 기반으로 하였다. 정의된 형상정보가 자동적으로 해석 프로세스에 입력되도록 ModelCenter를 이용하여 프로세스들을 연결 하였다. 군요구도 정립부터 요구도 평가를 거쳐 미사일 설계 기준형상을 선정하였고, 양항비를 망대 구속조건으로 하여 RCS 최소화 최적설계를 실시하였다. 본 논문에서 구성한 최적설계 프레임워크를 이용하여 미사일 개념설계 단계에서 여러 미사일 형상들에 대한 효율적인 분석과 다양한 설계 전략을 구현할 수 있음을 확인하였다.

• Wind and Structures
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• 제36권6호
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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.