• 제목/요약/키워드: Generation Prediction

검색결과 803건 처리시간 0.025초

온도측정에 의한 히트파이프의 수명예측 (Prediction of Life of Heat Pipes by Measuring Temperature Distribution)

  • 신흥태;;이윤표
    • 대한기계학회논문집B
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    • 제23권7호
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    • pp.856-863
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    • 1999
  • The thermal performance degradation of heat pipes is caused by the non-condensable gas generation mainly due to the electrochemical corrosion which results from the reaction of working fluids with tube materials. In this study, a simplified method described below was proposed to estimate the life of heat pipes concerning the non-condensable gas generation. The temperature distributions at the outer surface of heat pipes was measured, and based on them the amount of non-condensable gas of hydrogen was estimated. Applying it to the Arrhenius model, the mass generation of hydrogen and the volume occupied by the gas In heat pipes could be estimated for an operating temperature and time. Moreover, this simplified method was applied to the accelerated life test of nine methanol-stainless steel heat pipe samples.

PAIVS: prediction of avian influenza virus subtype

  • Park, Hyeon-Chun;Shin, Juyoun;Cho, Sung-Min;Kang, Shinseok;Chung, Yeun-Jun;Jung, Seung-Hyun
    • Genomics & Informatics
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    • 제18권1호
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    • pp.5.1-5.5
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    • 2020
  • Highly pathogenic avian influenza (HPAI) viruses have caused severe respiratory disease and death in poultry and human beings. Although most of the avian influenza viruses (AIVs) are of low pathogenicity and cause mild infections in birds, some subtypes including hemagglutinin H5 and H7 subtype cause HPAI. Therefore, sensitive and accurate subtyping of AIV is important to prepare and prevent for the spread of HPAI. Next-generation sequencing (NGS) can analyze the full-length sequence information of entire AIV genome at once, so this technology is becoming a more common in detecting AIVs and predicting subtypes. However, an analysis pipeline of NGS-based AIV sequencing data, including AIV subtyping, has not yet been established. Here, in order to support the pre-processing of NGS data and its interpretation, we developed a user-friendly tool, named prediction of avian influenza virus subtype (PAIVS). PAIVS has multiple functions that support the pre-processing of NGS data, reference-guided AIV subtyping, de novo assembly, variant calling and identifying the closest full-length sequences by BLAST, and provide the graphical summary to the end users.

내연기관엔진의 가스혼소발전 경제성 예측모델 개발 (Development of Economic Prediction Model for Internal Combustion Engine by Dual Fuel Generation)

  • 허광범;장혁준;이형원
    • 한국수소및신에너지학회논문집
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    • 제31권4호
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    • pp.380-386
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    • 2020
  • This paper represents an analysis of the economic impact of firing natural gas/diesel and natural gas/by-product oil mixtures in diesel engine power plants. The objects of analysis is a power plant with electricity generation capacity (300 kW). Using performance data of original diesel engines, the fuel consumption characteristics of the duel fuel engines were simulated. Then, economic assessment was carried out using the performance data and the net present value method. A special focus was given to the evaluation of fuel cost saving when firing natural gas/diesel and natural gas/by-product oil mixtures instead of the pure diesel firing case. Analyses were performed by assuming fuel price changes in the market as well as by using current prices. The analysis results showed that co-firing of natural gas/diesel and natural gas/by-product oil would provide considerable fuel cost saving, leading to meaningful economic benefits.

초기 선형 설계를 위한 자동화 툴 개발 (Automatic Tool Development for Initial Hull Form Design)

  • 이주현;이신형;전동수;지혜련;김용수
    • 대한조선학회논문집
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    • 제47권6호
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    • pp.763-769
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    • 2010
  • Thanks to the rapid advancement of computational power and development of numerical methods, Computational fluid dynamics techniques are being used widely for the prediction of ship resistance performance. In the present study, an automatic tool was developed to facilitate hull form modification, consequent mesh generation, and flow analysis for parametric study. It is a tedious job to go back and forth between geometry modification and mesh generation for every hull form variation. With the developed tool, users can make multiple hull form variation and their hull form performance prediction easily in a few simple steps. The verification of the developed tool was done by applying it to resistance performance parametric study of a generic POD propulsion cruise ship with different lengths of bow and stern. It is believed that the tool can be extended to more sophisticated hull form variation and help optimize the ship performance more efficiently.

지형효과를 고려한 강원지역의 태양광 발전지도 개발 (The Development of Photovoltaic Resources Map Concerning Topographical Effect on Gangwon Region)

  • 지준범;조일성;이규태;이원학
    • 한국태양에너지학회 논문집
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    • 제31권2호
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    • pp.37-46
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    • 2011
  • The GWNU (Gangnung-Wonju national university) solar radiation model was developed with radiative transfer theory by Iqbal and it is applied the NREL (National Research Energy Laboratory). Input data were collected and accomplished from the model prediction data from RDAPS (Regional Data Assimilated Prediction Model), satellite data and ground observations. And GWNU solar model calculates not only horizontal surface but also complicated terrain surface. Also, We collected the statistical data related on photovoltaic power generation of the Korean Peninsula and analyzed about photovoltaic power efficiency of the Gangwon region. Finally, the solar energy resource and photovoltaic generation possibility map established up with 4 km, 1 km and 180 m resolution on Gangwon region based on actual equipment from Shinan solar plant,statistical data for photovoltaic and complicated topographical effect.

View Factor를 고려한 마이크로그리드 적용용 고효율 P-Type Si 양면형 태양광 모듈의 출력량 예측 (Power Prediction of P-Type Si Bifacial PV Module Using View Factor for the Application to Microgrid Network)

  • 최진호;김광순;차혜림;김규광;방병관;박소영;안형근
    • 한국전기전자재료학회논문지
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    • 제31권3호
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    • pp.182-187
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    • 2018
  • In this study, 20.8% of a p-type Si bifacial solar cell was used to develop a photovoltaic (PV) module to obtain the maximum power under a limited installation area. The transparent back sheet material was replaced during fabrication with a white one, which is opaque in commercial products. This is very beneficial for the generation of more electricity, owing to the additional power generation via absorption of light from the rear side. A new model is suggested herein to predict the power of the bifacial PV module by considering the backside reflections from the roof and/or environment. This model considers not only the frontside reflection, but also the nonuniformity of the backside light sources. Theoretical predictions were compared to experimental data to prove the validity of this model, the error range for which ranged from 0.32% to 8.49%. Especially, under $700W/m^2$, the error rate was as low as 2.25%. This work could provide theoretical and experimental bases for application to a distributed and microgrid network.

진동수주 파력발전장치를 위한 머신러닝 기반 압력 예측모델 설계 및 분석 (A Design and Analysis of Pressure Predictive Model for Oscillating Water Column Wave Energy Converters Based on Machine Learning)

  • 서동우;허태상;김명일;오재원;조수길
    • 한국산학기술학회논문지
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    • 제21권11호
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    • pp.672-682
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    • 2020
  • 최근 다양한 산업/제조 현장에서 운영 효율화를 위한 디지털 트윈(digital twin) 기술 연구가 활발하게 수행 중이고, 화석 연료의 점진적 고갈과 환경오염 문제는 파력발전소와 같은 신재생/친환경 발전방식을 요구한다. 하지만, 파도의 에너지에 의해서 전기를 생산하는 파력발전에서 변동성이 높은 파도에너지에 의해서 발전량과 고장 등의 운영효율화 요소가 밀접하게 관련되어 있어 이들 사이의 관계를 이해하고 예측하는 것이 매우 중요하다. 따라서 첫 번째로 파고 데이터, 진동수주(OWC: Oscillating Water Column, 이하 OWC) 챔버의 센서 데이터 등과 같은 변동성이 높은 데이터 간에 의미 있는 상관관계 도출이 필요하다. 두 번째로 도출된 상관관계를 기반으로 추출된 데이터로 예측 상황을 학습함으로써 원하는 정보를 예측할 수 있는 방법론 연구가 이루어져야 한다. 본 연구에서는 파력발전 시스템의 디지털 트윈으로 스마트 운용 및 유지보수가 가능하도록 실제 파력발전소의 IoT 센서 데이터를 이용하여 OWC의 압력 예측을 위해 머신러닝 프레임워크를 활용한 워크플로우 기반의 학습모델을 설계하고, 검증 및 평가 데이터셋을 통한 압력 예측분석의 유효성을 확인한다.

JPV 소수 생성 알고리즘의 확률적 분석 및 성능 개선 (Probabilistic Analysis of JPV Prime Generation Algorithm and its Improvement)

  • 박희진;조호성
    • 한국정보과학회논문지:시스템및이론
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    • 제35권2호
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    • pp.75-83
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    • 2008
  • Joye와 연구자들은 기존의 조합 소수 판단 검사에서 trial division 과정을 제거한 새로운 소수 생성 알고리즘 (이하 JPV 알고리즘)을 제시하였으며, 이 알고리즘이 기존의 조합 소수 생성 알고리즘에 비해 $30{\sim}40%$ 정도 빠르다고 주장하였다. 하지만 이 비교는 전체 수행시간이 아닌 Fermat 검사의 호출 횟수만을 비교한 것으로 정확한 비교와는 거리가 있다. 기존의 조합 소수 생성 알고리즘에 대해 이론적인 수행시간 예측 방법이 있음에도 불구하고 두 알고리즘의 전체 수행시간을 비교할 수 없었던 이유는 JPV 알고리즘에 대한 이론적인 수행 시간 예측 모델이 없었기 때문이다. 본 논문에서는 먼저 JPV 알고리즘을 확률적으로 분석하여 수행시간 예측 모델을 제시하고, 이 모델을 이용하여 JPV 알고리즘과 기존의 조차 소수 생성 알고리즘의 전체 수행시간을 비교한다. 이 모델을 이용하여 펜티엄4 시스템에서 512비트 소수의 생성 시간을 예측해 본 결과 Fermat 검사의 호출 횟수를 이용한 비교와는 달리 JPV 알고리즘이 기존의 조합 소수 생성 알고리즘보다 느리다는 결론을 얻었다. 이러한 이론적인 분석을 통한 비교는 실제 동일한 환경에서 실험을 통해서 검증되었다. 또한, 본 논문에서는 JPV 알고리즘의 성능 개선 방법을 제시한다. 이 방법을 사용하여 JPV 알고리즘을 개선하면 동일한 공간을 사용할 경우에 JPV 알고리즘이 기존의 조합 소수 생성 알고리즘과 비슷한 성능을 보인다.

Grade 91 강의 장시간 크리프 수명 예측 방법 (Long-term Creep Life Prediction Methods of Grade 91 Steel)

  • 박재영;김우곤;;김선진;장진성
    • 동력기계공학회지
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    • 제19권5호
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    • pp.45-51
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    • 2015
  • Grade 91 steel is used for the major structural components of Generation-IV reactor systems such as a very high temperature reactor (VHTR) and sodium-cooled fast reactor (SFR). Since these structures are designed for up to 60 years at elevated temperatures, the prediction of long-term creep life is very important to determine an allowable design stress of elevated temperature structural component. In this study, a large body of creep rupture data was collected through world-wide literature surveys, and using these data, the long-term creep life was predicted in terms of three methods: Larson-Miller (L-M), Manson-Haferd (M-H) and Wilshire methods. The results for each method was compared using the standard deviation of error. The L-M method was overestimated in the longer time of a low stress. The Wilshire method was superior agreement in the long-term life prediction to the L-M and M-H methods.

PREDICTION OF DIAMETRAL CREEP FOR PRESSURE TUBES OF A PRESSURIZED HEAVY WATER REACTOR USING DATA BASED MODELING

  • Lee, Jae-Yong;Na, Man-Gyun
    • Nuclear Engineering and Technology
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    • 제44권4호
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    • pp.355-362
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    • 2012
  • The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict Pressure Tube (PT) diametral creep employing the previously measured PT diameters and operating conditions. There are twelve bundles in a fuel channel, and for each bundle a linear model was developed by using the dependent variables, such as the fast neutron fluences and the bundle coolant temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3, and 4 of the Wolsung nuclear plant in Korea were used to develop the BPLM. The data from the remaining 10 channels were used to test the developed BPLM. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from Units 2, 3, and 4. Two error components for the BPLM, which are the epistemic error and the aleatory error, were generated. The diametral creep prediction and two error components will be used for the generation of the regional overpower trip setpoint at the corresponding effective full power days. The root mean square (RMS) errors were also generated and compared to those from the current prediction method. The RMS errors were found to be less than the previous errors.