• Title/Summary/Keyword: Semi-Empirical Analysis Tool

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Study on the Correction of a Wing-tail Interference Effect in a Semi-empirical Aerodynamic Analysis Tool (반경험적 공력 해석도구의 주날개-꼬리날개 간섭 효과 보정에 대한 연구)

  • Lee, Dae-Yeon;Kim, Jae-Hyun;Kang, Dong-Gi
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.49 no.2
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    • pp.85-93
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    • 2021
  • In this paper, the aerodynamic characteristics of general tail controlled missile were predicted and corrected the result using semi-empirical analysis tool. The cause of the error was confirmed by comparing the aerodynamic characteristics prediction result of the semi-empirical analysis tool with the wind tunnel test result, and the main error factor of the semi-empirical analysis tool was the interference component between the main wing and the tail wing. The semi-empirical analysis results were corrected using the wind tunnel test results and the computational analysis results, and it was confirmed that the corrected data agrees well with the wind tunnel test results. Through this study, it was confirmed that the wing-tail interference component correction is needed when predicting the aerodynamic characteristics of a general tail controlled missile using a semi-empirical analysis tool.

BASE DRAG PREDICTION OF A SUPERSONIC MISSILE USING CFD (CFD를 이용한 초음속 유도탄 기저항력 예측)

  • Lee Bok-Jik
    • Journal of computational fluids engineering
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    • v.11 no.3 s.34
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    • pp.59-63
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    • 2006
  • Accurate prediction of a supersonic missile base drag continues to defy even well-rounded CFD codes. In an effort to address the accuracy and predictability of the base drags, the influence of grid system and competitive turbulence models on the base drag is analyzed. Characteristics of some turbulence models is reviewed through incompressible turbulent flow over a flat plate, and performance for the base drag prediction of several turbulence models such as Baldwin-Loman(B-L), Spalart-Allmaras(S-A), k-$\varepsilon$, k-$\omega$ model is assessed. When compressibility correction is injected into the S-A model, prediction accuracy of the base drag is enhanced. The NSWC wind tunnel test data are utilized for comparison of CFD and semi-empirical codes on the accuracy of base drag predictability: they are about equal, but CFD tends to perform better. It is also found that, as angle of attack of a missile with control fins increases, even the best CFD analysis tool we have lacks the accuracy needed for the base drag prediction.

Development of SWAT SD-HRU Pre-processor Module for Accurate Estimation of Slope and Slope Length of Each HRU Considering Spatial Topographic Characteristics in SWAT (SWAT HRU 단위의 경사도/경사장 산정을 위한 SWAT SD-HRU 전처리 프로세서 모듈 개발)

  • Jang, Wonseok;Yoo, Dongsun;Chung, Il-moon;Kim, Namwon;Jun, Mansig;Park, Younshik;Kim, Jonggun;Lim, Kyoung-Jae
    • Journal of Korean Society on Water Environment
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    • v.25 no.3
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    • pp.351-362
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    • 2009
  • The Soil and Water Assessment Tool (SWAT) model, semi-distributed model, first divides the watershed into multiple subwatersheds, and then extracts the basic computation element, called the Hydrologic Response Unit (HRU). In the process of HRU generation, the spatial information of land use and soil maps within each subwatershed is lost. The SWAT model estimates the HRU topographic data based on the average slope of each subwatershed, and then use this topographic datum for all HRUs within the subwatershed. To improve the SWAT capabilities for various watershed scenarios, the Spatially Distributed-HRU (SD-HRU) pre-processor module was developed in this study to simulate site-specific topographic data. The SD-HRU was applied to the Hae-an watershed, where field slope lengths and slopes are measured for all agricultural fields. The analysis revealed that the SD-HRU pre-processor module needs to be applied in SWAT sediment simulation for accurate analysis of soil erosion and sediment behaviors. If the SD-HRU pre-processor module is not applied in SWAT runs, the other SWAT factors may be over or under estimated, resulting in errors in physical and empirical computation modules although the SWAT estimated flow and sediment values match the measured data reasonably well.

Uncertainties In Base Drag Prediction of A Supersonic Missile (초음속 유도탄 기저항력 예측의 불확실성)

  • Ahn H. K.;Hong S. K.;Lee B. J.;Ahn C. S.
    • 한국전산유체공학회:학술대회논문집
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    • 2004.10a
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    • pp.47-51
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    • 2004
  • Accurate Prediction of a supersonic missile base drag continues to defy even well-rounded CFD codes. In an effort to address the accuracy and predictability of the base drags, the influence of grid system and competitive turbulence models on the base drag is analyzed. Characteristics of some turbulence models is reviewed through incompressible turbulent flow over a flat plate, and performance for the base drag prediction of several turbulence models such as Baldwin-Lomax(B-L), Spalart-Allmaras(S-A), $\kappa-\epsilon$, $\kappa-\omega$ model is assessed. When compressibility correction is injected into the S-A model, prediction accuracy of the base drag is enhanced. The NSWC wind tunnel test data are utilized for comparison of CFD and semi-empirical codes on the accuracy of base drag predictability: they are about equal, but CFD tends to perform better. It is also found that, as angle of attack of a missile with control (ins increases, even the best CFD analysis tool we have lacks the accuracy needed for the base drag prediction.

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Prediction of Wave Breaking Using Machine Learning Open Source Platform (머신러닝 오픈소스 플랫폼을 활용한 쇄파 예측)

  • Lee, Kwang-Ho;Kim, Tag-Gyeom;Kim, Do-Sam
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.32 no.4
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    • pp.262-272
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    • 2020
  • A large number of studies on wave breaking have been carried out, and many experimental data have been documented. Moreover, on the basis of various experimental data set, many empirical or semi-empirical formulas based primarily on regression analysis have been proposed to quantitatively estimate wave breaking for engineering applications. However, wave breaking has an inherent variability, which imply that a linear statistical approach such as linear regression analysis might be inadequate. This study presents an alternative nonlinear method using an neural network, one of the machine learning methods, to estimate breaking wave height and breaking depth. The neural network is modeled using Tensorflow, a machine learning open source platform distributed by Google. The neural network is trained by randomly selecting the collected experimental data, and the trained neural network is evaluated using data not used for learning process. The results for wave breaking height and depth predicted by fully trained neural network are more accurate than those obtained by existing empirical formulas. These results show that neural network is an useful tool for the prediction of wave breaking.

A Study on Settlement Prediction of Concrete-faced Rockfill Dam Using Measured Data During Construction and After Impounding (시공 중 및 담수 후 계측데이터를 이용한 CFRD의 침하량 예측 연구)

  • Lee, Chungwon;Kim, Yongseong
    • Journal of the Korean GEO-environmental Society
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    • v.16 no.2
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    • pp.5-13
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    • 2015
  • In the present study, the prediction methods of the crest settlement after impounding and the maximum internal settlement during dam construction were proposed through the analysis on settlement data at 38 monitored points of 36 Concrete-Faced Rockfill Dams (CFRDs). The results from this analysis provided that the crest settlement and the maximum internal settlement are increased in proportion to the dam height and the void ratio. However, the relationship between internal settlement and dam height for each void-ratio range plotted in semi-logarithmic scale is the nearly same. Also, the prediction of the crest settlement of the CFRD is possible through the maximum internal settlement during dam construction. In addition, it seems that the valley shape highly affects the dense dam body with high construction modulus. The results of this study will provide the useful tool for the design, construction and management of CFRDs.