Statistical analysis of fuel failures in large break loss-of-coolant accident (LBLOCA) in EPR type nuclear power plant

Arkoma, Asko; Hänninen, Markku; Rantamäki, Karin; Kurki, Joona; Hämäläinen, Anitta

doi:10.1016/j.nucengdes.2014.12.023

Cited by 28 publications

(4 citation statements)

References 18 publications

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“…In the recent literature, APROS models have been usually used to simulate thermal power plants. Most studies performed validation of the numerical results based on the experimental measurement for the accuracy and readability of the developed models including combined-cycle power plants (Alobaid et al, 2008;Mertens et al, 2016a;Mertens et al, 2016b;Angerer et al, 2017;Bany Ata et al, 2020), coal-fired power plants (Schuhbauer et al, 2014;Starkloff et al, 2015;Starkloff et al, 2016;Hentschel et al, 2017;Peters et al, 2020), circulating fluidized bed (Lappalainen et al, 2014;Mikkonen et al, 2015), concentrated solar power plant (Henrion et al, 2013;Al-Maliki et al, 2016a;Al-Maliki et al, 2016b), nuclear power plant (Arkoma et al, 2015), municipal waste incinerator (Alobaid et al, 2018).…”

Section: Modelling Approachmentioning

confidence: 99%

Numerical and experimental study of co-combustion of refuse-derived fuels in a circulating fluidized bed during load change

et al. 2022

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This study presents a comprehensive dynamic process simulation model of a 1 MWth circulating fluidized bed test facility applied for lignite and refuse-derived fuel co-combustion. The developed dynamic process simulation model describes the circulating fluidized bed riser and the supplying system with a high level of detail considering heat transfer, gas-solid interaction, combustion, and fluid dynamics. The model was first tuned at two steady-state operation points and was then validated by the measured data from a long-term test campaign of the 1 MWth circulating fluidized bed test facility at various loads (60%–80% to 100%). During the load changes, the simulated pressure and temperature profiles along the combustor as well as the flue gas concentrations agree very well with the measurement data. Finally, increasing the proportion of waste-derived fuel in the co-combustion process was investigated to evaluate the flexibility of its use in power generation to further reduce CO2 emissions.

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Section: Modelling Approachmentioning

confidence: 99%

Numerical and experimental study of co-combustion of refuse-derived fuels in a circulating fluidized bed during load change

et al. 2022

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“…So far, it is rare to find a scientific, standardized, and easy-to-use test tool for English grammar discrimination, which is suitable for English majors in China. Because of this, trying to prepare the ability to distinguish between English learners' English grammar training scale (hereinafter referred to as the English grammar to distinguish ability training scale) [13,14] was to understand English major junior learners' English grammar knowledge and skill development and test the ability of using their knowledge of English grammar and syntax and strengths and weaknesses for English professional basic course teachers and learners of English to take timely remedial strategy to provide targeted intervention measurement tools. It also provides a research tool for Chinese foreign language education researchers to explore the development trend, performance characteristics, individual differences, and the potential relationship between college English learners' English grammar discrimination ability training and English language skills.…”

Section: Related Workmentioning

confidence: 99%

English Grammar Discrimination Training Network Model and Search Filtering

Zhao

2021

Complexity

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The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory.

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“…Geometries, material properties, and characteristic curves of electrical machines can be inserted into the model. APROS is often used in literature for the modeling of thermal processes, especially thermal power plants, such as pulverized-coal fired power plants [20,[29][30][31], municipal waste incinerators [32], circulating fluidized beds [21,33], concentrated solar power plants [34][35][36], and nuclear power plants [37,38].…”

Section: Model Descriptionmentioning

confidence: 99%

Operational Flexibility of a CFB Furnace during Fast Load Change—Experimental Measurements and Dynamic Model

2020

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The share of power from fluctuating renewable energies such as wind and solar is increasing due to the ongoing climate change. It is therefore essential to use technologies that can compensate for these fluctuations. Experiments at 1 MWth scale were carried out to evaluate the operational flexibility of a circulating fluidized bed (CFB) combustor during transient operation from 60% to 100% load. A typical load following sequence for fluctuating electricity generation/demand was reproduced experimentally by performing 4 load changes. The hydrodynamic condition after a load change depends on if the load change was in positive or negative direction due to the heat stored in the refractory/bed material at high loads and released when the load decreases. A 1.5D-process simulation model was created in the software APROS (Advanced Process Simulation) with the target of showing the specific characteristics of a CFB furnace during load following operation. The model was tuned with experimental data of a steady-state test point and validated with the load cycling tests. The simulation results show the key characteristics of CFB combustion with reasonable accuracy. Detailed experimental data is presented and a core-annulus approach for the modeling of the CFB furnace is used.

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Statistical analysis of fuel failures in large break loss-of-coolant accident (LBLOCA) in EPR type nuclear power plant

Cited by 28 publications

References 18 publications

Numerical and experimental study of co-combustion of refuse-derived fuels in a circulating fluidized bed during load change

Numerical and experimental study of co-combustion of refuse-derived fuels in a circulating fluidized bed during load change

English Grammar Discrimination Training Network Model and Search Filtering

Operational Flexibility of a CFB Furnace during Fast Load Change—Experimental Measurements and Dynamic Model

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