• 제목/요약/키워드: Probabilistic model

검색결과 1,254건 처리시간 0.026초

A Study on the Fuzzy ELDC of Composite Power System Based on Probabilistic and Fuzzy Set Theories

  • Park, Jaeseok;Kim, Hongsik;Seungpil Moon;Junmin Cha;Park, Daeseok;Roy Billinton
    • KIEE International Transactions on Power Engineering
    • /
    • 제2A권3호
    • /
    • pp.95-101
    • /
    • 2002
  • This paper illustrates a new fuzzy effective load model for probabilistic and fuzzy production cost simulation of the load point of the composite power system. A model for reliability evaluation of a transmission system using the fuzzy set theory is proposed for considering the flexibility or ambiguity of capacity limitation and overload of transmission lines, which are subjective matter characteristics. A conventional probabilistic approach was also used to model the uncertainties related to the objective matters for forced outage rates of generators and transmission lines in the new model. The methodology is formulated in order to consider the flexibility or ambiguity of load forecasting as well as capacity limitation and overload of transmission lines. It is expected that the Fuzzy CMELDC (CoMposite power system Effective Load Duration Curve) proposed in this study will provide some solutions to many problems based on nodal and decentralized operation and control of an electric power systems in a competitive environment in the future. The characteristics of this new model are illustrated by some case studies of a very simple test system.

Bayesian model updating for the corrosion fatigue crack growth rate of Ni-base alloy X-750

  • Yoon, Jae Young;Lee, Tae Hyun;Ryu, Kyung Ha;Kim, Yong Jin;Kim, Sung Hyun;Park, Jong Won
    • Nuclear Engineering and Technology
    • /
    • 제53권1호
    • /
    • pp.304-313
    • /
    • 2021
  • Nickel base Alloy X-750, which is used as fastener parts in light-water reactor (LWR), has experienced many failures by environmentally assisted cracking (EAC). In order to improve the reliability of passive components for nuclear power plants (NPP's), it is necessary to study the failure mechanism and to predict crack growth behavior by developing a probabilistic failure model. In this study, The Bayesian inference was employed to reduce the uncertainties contained in EAC modeling parameters that have been established from experiments with Alloy X-750. Corrosion fatigue crack growth rate model (FCGR) was developed by fitting into Paris' Law of measured data from the several fatigue tests conducted either in constant load or constant ΔK mode. These parameters characterizing the corrosion fatigue crack growth behavior of X-750 were successfully updated to reduce the uncertainty in the model by using the Bayesian inference method. It is demonstrated that probabilistic failure models for passive components can be developed by updating a laboratory model with field-inspection data, when crack growth rates (CGRs) are low and multiple inspections can be made prior to the component failure.

Machine learning-based probabilistic predictions of shear resistance of welded studs in deck slab ribs transverse to beams

  • Vitaliy V. Degtyarev;Stephen J. Hicks
    • Steel and Composite Structures
    • /
    • 제49권1호
    • /
    • pp.109-123
    • /
    • 2023
  • Headed studs welded to steel beams and embedded within the concrete of deck slabs are vital components of modern composite floor systems, where safety and economy depend on the accurate predictions of the stud shear resistance. The multitude of existing deck profiles and the complex behavior of studs in deck slab ribs makes developing accurate and reliable mechanical or empirical design models challenging. The paper addresses this issue by presenting a machine learning (ML) model developed from the natural gradient boosting (NGBoost) algorithm capable of producing probabilistic predictions and a database of 464 push-out tests, which is considerably larger than the databases used for developing existing design models. The proposed model outperforms models based on other ML algorithms and existing descriptive equations, including those in EC4 and AISC 360, while offering probabilistic predictions unavailable from other models and producing higher shear resistances for many cases. The present study also showed that the stud shear resistance is insensitive to the concrete elastic modulus, stud welding type, location of slab reinforcement, and other parameters considered important by existing models. The NGBoost model was interpreted by evaluating the feature importance and dependence determined with the SHapley Additive exPlanations (SHAP) method. The model was calibrated via reliability analyses in accordance with the Eurocodes to ensure that its predictions meet the required reliability level and facilitate its use in design. An interactive open-source web application was created and deployed to the cloud to allow for convenient and rapid stud shear resistance predictions with the developed model.

확률적 LCC분석기법을 활용한 수도시설물의 설계VA모델에 관한 연구 (A Study on the Design Value Analysis Model Using Probabilistic LCC Analysis of Water Supply System Project)

  • 정평기;서종원;임종권
    • 한국건설관리학회논문집
    • /
    • 제5권2호
    • /
    • pp.181-193
    • /
    • 2004
  • 수도건설사업은 공용이후단계에서 소요되는 $운영\cdot유지관리비용$ 중에서 기계설비 및 관로시설이 대부분을 차지하는 대표적인 플랜트시설로 구성되므로 일반적인 토목시설물의 LCC모델과 차별화 되어야 할 것이다. 따라서 본 연구에서는 수도건설사업에 적합하도록 비용분류체계를 제시하고, 이에 따라 수도시설의 확률적 LCC분석 모델을 개발하였다. 또한, 설계VE 활동시 기초가 되는 설계VA의 절차를 실무활용도 측면을 고려하여 개선된 설계VA절차를 제시하였다. 제시된 설계VA절차와 확률적 LCC분석모델을 사용하여 실제 건설사업의 설계VE활동에 있어 송수관로의 적정 선형 선정에 적용하였다. 제안된 수도건설사업의 설계VA 및 확률적 LCC분석모델은 향후 수도건설사업의 $경제적\cdot가치혁신적$ 대안선정과 유지관리비 예산추정 및 적정예산 배정에 매우 유용하게 활용될 수 있을 것이다.

Probabilistic Bilinear Transformation Space-Based Joint Maximum A Posteriori Adaptation

  • Song, Hwa Jeon;Lee, Yunkeun;Kim, Hyung Soon
    • ETRI Journal
    • /
    • 제34권5호
    • /
    • pp.783-786
    • /
    • 2012
  • This letter proposes a more advanced joint maximum a posteriori (MAP) adaptation using a prior model based on a probabilistic scheme utilizing the bilinear transformation (BIT) concept. The proposed method not only has scalable parameters but is also based on a single prior distribution without the heuristic parameters of the previous joint BIT-MAP method. Experiment results, irrespective of the amount of adaptation data, show that the proposed method leads to a consistent improvement over the previous method.

콘크리트 구조물의 확률적 응답특성을 이용한 손상평가모델 (Probabilistic Damage Assessment of Concrete Structures)

  • 오병환;이성로;윤철호;이성규
    • 한국콘크리트학회:학술대회논문집
    • /
    • 한국콘크리트학회 1991년도 가을 학술발표회 논문집
    • /
    • pp.119-123
    • /
    • 1991
  • The concrete structures subjected to strong earthquakes may undergo hysteretic behavior and result in severe damage. The inelastic behavior and steffness degradation due to seismic loading must be properly modeled. The present study proposes a realistic model to assess the structural damage of concrete structures under seismic loadings. The present model also takes into account the probabilistic nature of seimic loading and thus the randomness of motion.

  • PDF

A Probabilistic Order Level System When Delay in Payment Is Permissible

  • Shah, Mita H.
    • 한국경영과학회지
    • /
    • 제18권2호
    • /
    • pp.175-182
    • /
    • 1993
  • The probabilistic order level inventory model is developed when a supplier allows some credit period T for settling the accounts for purchase quantity. The credit period T is known constant. Mathematical models are derived for both the cases i) T'.leq. T and ii) T'>T. Expressions are derived for average expected total cost of the system, the optimum cycle time and for obtaining optimum order level S = S$_{0}$ in each case. The exmaples are given to illustrate the model.

  • PDF

Integrated Level 1-Level 2 decommissioning probabilistic risk assessment for boiling water reactors

  • Mercurio, Davide;Andersen, Vincent M.;Wagner, Kenneth C.
    • Nuclear Engineering and Technology
    • /
    • 제50권5호
    • /
    • pp.627-638
    • /
    • 2018
  • This article describes an integrated Level 1-Level 2 probabilistic risk assessment (PRA) methodology to evaluate the radiological risk during postulated accident scenarios initiated during the decommissioning phase of a typical Mark I containment boiling water reactor. The fuel damage scenarios include those initiated while the reactor is permanently shut down, defueled, and the spent fuel is located into the spent fuel storage pool. This article focuses on the integrated Level 1-Level 2 PRA aspects of the analysis, from the beginning of the accident to the radiological release into the environment. The integrated Level 1-Level 2 decommissioning PRA uses event trees and fault trees that assess the accident progression until and after fuel damage. Detailed deterministic severe accident analyses are performed to support the fault tree/event tree development and to provide source term information for the various pieces of the Level 1-Level 2 model. Source terms information is collected from accidents occurring in both the reactor pressure vessel and the spent fuel pool, including simultaneous accidents. The Level 1-Level 2 PRA model evaluates the temporal and physical changes in plant conditions including consideration of major uncertainties. The goal of this article is to provide a methodology framework to perform a decommissioning Probabilistic Risk Assessment (PRA), and an application to a real case study is provided to show the use of the methodology. Results will be derived from the integrated Level 1-Level 2 decommissioning PSA event tree in terms of fuel damage frequency, large release frequency, and large early release frequency, including uncertainties.

복합재 압력 용기의 신뢰도 예측 (Reliability Evaluation of a Composite Pressure Vessel)

  • 황태경;박재범;김형근;도영대;문순일
    • Composites Research
    • /
    • 제19권3호
    • /
    • pp.7-14
    • /
    • 2006
  • 본 논문에서는 내압 하중을 받는 복합재 압력 용기의 신뢰도를 구하기 위해 확률적 강도 해석이 수행되었다. 이때 확률적 강도 해석은 점진적 파손 모델과 몬테카를로 시뮬레이션으로 구성된 확률 연속 파손 모델과 상용 유한 요소 해석 코드인 ABAQUS가 연계한 형태로서 복잡한 형상 및 경계 조건을 갖는 복합재 구조물의 확률적 파손 해석을 수행하게 된다. 설계확률 변수로서 복합재 층의 각 방향 별 강도가 고려되었다. 최종적으로, 확률 강도 해석을 통해 복합재 압력 용기의 파열 압력 분산 현상이 설명되었고, 복합재 압력 용기의 각 부위별 신뢰도 값이 제시되었다. 양산 중인 복합재 구조물인 경우, 재료 및 제작 공정의 불확실성이 구조물 성능에 미치는 영향이 더욱 커지게 되어 확률 강도 해석을 이용한 구조 설계가 필수적이다.

DOProC-based reliability analysis of structures

  • Janas, Petr;Krejsa, Martin;Sejnoha, Jiri;Krejsa, Vlastimil
    • Structural Engineering and Mechanics
    • /
    • 제64권4호
    • /
    • pp.413-426
    • /
    • 2017
  • Probabilistic methods are used in engineering where a computational model contains random variables. The proposed method under development: Direct Optimized Probabilistic Calculation (DOProC) is highly efficient in terms of computation time and solution accuracy and is mostly faster than in case of other standard probabilistic methods. The novelty of the DOProC lies in an optimized numerical integration that easily handles both correlated and statistically independent random variables and does not require any simulation or approximation technique. DOProC is demonstrated by a collection of deliberately selected simple examples (i) to illustrate the efficiency of individual optimization levels and (ii) to verify it against other highly regarded probabilistic methods (e.g., Monte Carlo). Efficiency and other benefits of the proposed method are grounded on a comparative case study carried out using both the DOProC and MC techniques. The algorithm has been implemented in mentioned software applications, and has been used effectively several times in solving probabilistic tasks and in probabilistic reliability assessment of structures. The article summarizes the principles of this method and demonstrates its basic possibilities on simple examples. The paper presents unpublished details of probabilistic computations based on this method, including a reliability assessment, which provides the user with the probability of failure affected by statistically dependent input random variables. The study also mentions the potential of the optimization procedures under development, including an analysis of their effectiveness on the example of the reliability assessment of a slender column.