• Title/Summary/Keyword: abs

Search Result 973, Processing Time 0.026 seconds

Structure Analysis and Scale Model Test for Strength Performance Evaluation of Submersible Mooring Pulley Installed on Floating Offshore Wind Turbine (부유식 해상풍력발전기용 반잠수식 계류 풀리의 강도 성능평가를 위한 구조해석과 축소 모형시험)

  • Chang-Yong Song
    • Journal of the Korean Society of Marine Environment & Safety
    • /
    • v.29 no.5
    • /
    • pp.479-487
    • /
    • 2023
  • Recently, the destructive power of typhoons is continuously increasing owing to global warming. In a situation where the installation of floating wind turbines is increasing worldwide, concerns about the huge loss and collapse of floating offshore wind turbines owing to strong typhoons are deepening. A new type of disconnectable mooring system must be developed for the safe operation of floating offshore wind turbines. A new submersible mooring pulley considered in this study is devised to more easily attach or detach the floating of shore wind turbine with mooring lines compared with other disconnectable mooring apparatuses. To investigate the structural safety of the initial design of submersible mooring pulley that can be applied to an 8MW-class floating type offshore wind turbine, scale-down structural models were developed using a 3-D printer and structural tests were performed on the models. For the structural tests of the scale-down models, tensile specimens of acrylonitrile butadiene styrene material that was used in the 3-D printing were prepared, and the material properties were evaluated by conducting the tensile tests. The finite element analysis (FEA) of submersible mooring pulley was performed by applying the material properties obtained from the tensile tests and the same load and boundary conditions as in the scale-down model structural tests. Through the FEA, the structural weak parts on the submersible mooring pulley were reviewed. The structural model tests were conducted considering the main load conditions of submersible mooring pulley, and the FEA and test results were compared for the locations that exceeded the maximum tensile stress of the material. The results of the FEA and structural model tests indicated that the connection structure of the body and the wheel was weak in operating conditions and that of the body and the chain stopper was weak in mooring conditions. The results of this study enabled to experimentally verify the structural safety of the initial design of submersible mooring pulley. The study results can be usefully used to improve the structural strength of submersible mooring pulley in a detailed design stage.

Investigating Data Preprocessing Algorithms of a Deep Learning Postprocessing Model for the Improvement of Sub-Seasonal to Seasonal Climate Predictions (계절내-계절 기후예측의 딥러닝 기반 후보정을 위한 입력자료 전처리 기법 평가)

  • Uran Chung;Jinyoung Rhee;Miae Kim;Soo-Jin Sohn
    • Korean Journal of Agricultural and Forest Meteorology
    • /
    • v.25 no.2
    • /
    • pp.80-98
    • /
    • 2023
  • This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

Review of the Korean Indigenous Species Investigation Project (2006-2020) by the National Institute of Biological Resources under the Ministry of Environment, Republic of Korea (한반도 자생생물 조사·발굴 연구사업 고찰(2006~2020))

  • Bae, Yeon Jae;Cho, Kijong;Min, Gi-Sik;Kim, Byung-Jik;Hyun, Jin-Oh;Lee, Jin Hwan;Lee, Hyang Burm;Yoon, Jung-Hoon;Hwang, Jeong Mi;Yum, Jin Hwa
    • Korean Journal of Environmental Biology
    • /
    • v.39 no.1
    • /
    • pp.119-135
    • /
    • 2021
  • Korea has stepped up efforts to investigate and catalog its flora and fauna to conserve the biodiversity of the Korean Peninsula and secure biological resources since the ratification of the Convention on Biological Diversity (CBD) in 1992 and the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits (ABS) in 2010. Thus, after its establishment in 2007, the National Institute of Biological Resources (NIBR) of the Ministry of Environment of Korea initiated a project called the Korean Indigenous Species Investigation Project to investigate indigenous species on the Korean Peninsula. For 15 years since its beginning in 2006, this project has been carried out in five phases, Phase 1 from 2006-2008, Phase 2 from 2009-2011, Phase 3 from 2012-2014, Phase 4 from 2015-2017, and Phase 5 from 2018-2020. Before this project, in 2006, the number of indigenous species surveyed was 29,916. The figure was cumulatively aggregated at the end of each phase as 33,253 species for Phase 1 (2008), 38,011 species for Phase 2 (2011), 42,756 species for Phase 3 (2014), 49,027 species for Phase 4 (2017), and 54,428 species for Phase 5(2020). The number of indigenous species surveyed grew rapidly, showing an approximately 1.8-fold increase as the project progressed. These statistics showed an annual average of 2,320 newly recorded species during the project period. Among the recorded species, a total of 5,242 new species were reported in scientific publications, a great scientific achievement. During this project period, newly recorded species on the Korean Peninsula were identified using the recent taxonomic classifications as follows: 4,440 insect species (including 988 new species), 4,333 invertebrate species except for insects (including 1,492 new species), 98 vertebrate species (fish) (including nine new species), 309 plant species (including 176 vascular plant species, 133 bryophyte species, and 39 new species), 1,916 algae species (including 178 new species), 1,716 fungi and lichen species(including 309 new species), and 4,812 prokaryotic species (including 2,226 new species). The number of collected biological specimens in each phase was aggregated as follows: 247,226 for Phase 1 (2008), 207,827 for Phase 2 (2011), 287,133 for Phase 3 (2014), 244,920 for Phase 4(2017), and 144,333 for Phase 5(2020). A total of 1,131,439 specimens were obtained with an annual average of 75,429. More specifically, 281,054 insect specimens, 194,667 invertebrate specimens (except for insects), 40,100 fish specimens, 378,251 plant specimens, 140,490 algae specimens, 61,695 fungi specimens, and 35,182 prokaryotic specimens were collected. The cumulative number of researchers, which were nearly all professional taxonomists and graduate students majoring in taxonomy across the country, involved in this project was around 5,000, with an annual average of 395. The number of researchers/assistant researchers or mainly graduate students participating in Phase 1 was 597/268; 522/191 in Phase 2; 939/292 in Phase 3; 575/852 in Phase 4; and 601/1,097 in Phase 5. During this project period, 3,488 papers were published in major scientific journals. Of these, 2,320 papers were published in domestic journals and 1,168 papers were published in Science Citation Index(SCI) journals. During the project period, a total of 83.3 billion won (annual average of 5.5 billion won) or approximately US $75 million (annual average of US $5 million) was invested in investigating indigenous species and collecting specimens. This project was a large-scale research study led by the Korean government. It is considered to be a successful example of Korea's compressed development as it attracted almost all of the taxonomists in Korea and made remarkable achievements with a massive budget in a short time. The results from this project led to the National List of Species of Korea, where all species were organized by taxonomic classification. Information regarding the National List of Species of Korea is available to experts, students, and the general public (https://species.nibr.go.kr/index.do). The information, including descriptions, DNA sequences, habitats, distributions, ecological aspects, images, and multimedia, has been digitized, making contributions to scientific advancement in research fields such as phylogenetics and evolution. The species information also serves as a basis for projects aimed at species distribution and biological monitoring such as climate-sensitive biological indicator species. Moreover, the species information helps bio-industries search for useful biological resources. The most meaningful achievement of this project can be in providing support for nurturing young taxonomists like graduate students. This project has continued for the past 15 years and is still ongoing. Efforts to address issues, including species misidentification and invalid synonyms, still have to be made to enhance taxonomic research. Research needs to be conducted to investigate another 50,000 species out of the estimated 100,000 indigenous species on the Korean Peninsula.