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김철우,허완욱,한기현 대한금속재료학회(대한금속학회) 1984 대한금속·재료학회지 Vol.22 No.4
In hot metal, effects of temperature, basicity, and oxygen source on the reaction of desiliconization and demanganization were studied. The results are as follows; 1. The temperature of hot metal decreased slowly because of the reaction of desiliconization and demanganization. 2. Lower degree of desiliconization and demanganization was obtained at 1450℃ compared with 1350℃. 3. For the effective desiliconization and reducing Mn-loss, it was desirable that the basicity was about 1.0. 4. It was considered that the desiliconization treatment of hot metal should be conducted by mixture oxidizer (gas-solid) to reduce the Mn loss.
박진욱(J. W. Park),허종철(J. C. Huh),이철형(C. H. Lee),박완순(W. S. Park) 한국유체기계학회 2005 유체기계 연구개발 발표회 논문집 Vol.- No.-
The Performance of in-line duct fan depends on the design parameters of impeller and guide vane. such as sweep back angle of impeller, the number of blades, outlet blade angle, guide vane angle etc. In this experimental study total four kinds of impellers having different sweep back angles, 90°, 72.5°, 55°, 37.5° with 8 guide vanes, different the number of blades, 6ea, 8ea, 10ea, 12ea, different kinds of outlet blade angles, 30˚, 45˚, 60˚ and different kinds of guide vane angles, 15°, 30°, 45° were selected and their performance measured to investigate the effects of them. The results were non-dimensionalized to compare their performance.
합성곱 오토인코더 기반 메탄 제트 화염에 대한 차수축소모델 적용
이우진(W.J. Lee),허강열(K.Y. Huh) 한국전산유체공학회 2021 한국전산유체공학회지 Vol.26 No.1
In this work, the convolutional autoencoder is applied to the reduced order model for a turbulent methane jet flame. Autoencoder is a machine learning algorithm, which reduces the problem dimension by non-linear projection. It has an advantage in reconstruction of data with significant non-linearity. Additionally, with a convolutional layer the characteristics of original data can be trained with a relatively small number of hyper-parameters. To check accuracy of the reduced order model using the convolutional autoencoder, we applied it to surrogate model and sparse reconstruction problem, and compared it with other dimension reduction algorithms. For model training, five parameters are selected as the model training parameters and 20 and 40 sensor data are extracted for the sparse reconstruction problem. The proposed convolutional autoencoder shows better accuracy than the linear projection-based dimension reduction algorithm.