dataesr/scientific-paragraphs-categorization huggingface dataset:
• System thermal-hydraulic analysis It's the analysis of the behavior of the whole NPP system (primary and secondary circuits, emergency corecooling systems, auxiliary systems, etc. ) from the thermalhydraulic point of view. The resulting parameters include, among others, temperatures, pressures, flow rates, velocities, and heat transfer coefficients in all modeled components and piping. The goal of the system thermalhydraulic calculations is, in addition to the coolant pressure calculation, to give the initial and boundary conditions for thermal-hydraulic mixing calculations. In some cases, the system thermal-hydraulic calculations directly provide the boundary conditions for structural analyses. For this analysis, APAL partners use RELAP5, ATHLET, and TRACE software. • Mixing thermal-hydraulic analysis It's the detailed analysis of coolant mixing inside the reactor, namely in the reactor downcomer.
• Structural and fracture mechanics analyses The structural and fracture-mechanics analyses can be performed using either a deterministic or a probabilistic approach. The software tools for both approaches significantly differ. -Deterministic approach For structural analysis, commercial "general" finiteelement method (FEM) software tools are used. Among many capabilities of general FEM codes, the solutions of heat-transfer problems and mechanical problems (either linear-elastic or elastic-plastic) are used for PTS analysis. The results from thermal-hydraulic mixing analyses (see above) serve as boundary conditions for solving the heat-transfer problem with the FEM code. Results of the heat-transfer problem solution (time-dependent temperature field in the RPV wall) serve as the load for the mechanical problem together with the load due to pressure (which is the result of system TH analyses under point 1).
The mechanical problem is usually solved by the same software and on the same FEM mesh as the heat-transfer problem. The results of the mechanical problem solution are time-dependent displacements, strains, and stresses in the RPV wall (calculated in all nodes of the FEM mesh). The fracture-mechanics analysis is generally performed in two different methods. The first method is based on the formulae from standards, which allow for calculating fracture-mechanics parameters (namely stress intensity factor, K I ) based on stresses exported from the FEM model. In this method, the FEM model of the RPV does not contain any cracks. These fracture-mechanics calculations are usually performed by in-house auxiliary software, together with the final assessment of RPV integrity (comparison of stress intensity factor with its allowable value, establishing the maximum allowable transition temperature, preparing the resulting graphs and tables, etc.
). The second method of performing the fracturemechanics analysis is based on the FEM model of the RPV with the assessed crack included in the FEM mesh. Fracture-mechanics parameters (J-integral or energy release rate, G) are calculated directly in the postprocessor of the commercial FEM software. All major commercial FEM software has this capability. Some inhouse auxiliary codes (or EXCEL spreadsheets) are used only for the final assessment of RPV integrity. In APAL, the codes used for this deterministic approach are ABAQUS, ANSYS, SYSTUS, etc. There is no development of commercial FEM codes expected within APAL. New FEM models will be created by all partners for application within APAL.
-Probabilistic approach The probabilistic approach differs from the deterministic one by taking into account the randomness and uncertainties of input data and calculating probabilities of crack initiation or RPV failure. Because commercial software is not suitable for this type of analysis, a specific software especially for the fracture-mechanics assessment, frequently created in-house, is used. Sometimes commercial FEM software can be used as a pre-processor for structural analysis (calculation of temperature and stress fields). In other cases, the specific or in-house software has its own pre-processor for this purpose. The structural assessment is usually based on a deterministic approach and treatment of uncertainties on temperature and stresses requires additional investigations. The main task of the probabilistic software for fracture-mechanics analysis is sampling some of the input data, which are treated as statistically distributed, and to calculate the conditional probability of crack initiation or RPV failure.
The developments of these codes have been initiated several years ago and are achieved outside the CAMIVVER framework. Within the CAMIVVER scope, essential adaptations of APOLLO3 R required for VVER applications are implemented in WP4, focusing on the industrialization of the lattice part, and in WP5, by coupling neutronics with thermal-hydraulics models. In that respect, CAMIVVER will include a demonstration of the feasibility of the APOLLO3 R /CATHARE3 coupling. CATHARE3 development and validation are also involved regarding VVER plant system thermalhydraulics. Within CAMIVVER that code is benchmarked with updated RELAP5 and TRACE codes on some selected transients: MCP (main circulation pipeline) start-up, LOCA (loss-of-coolant accident), and MSLB (main streamline break). CFD modeling of VVER is addressed in WP6 of CAMIVVER especially as regards mixing phenomena in the primary vessel.
The progress Half the way of CAMIVVER has just been crossed recently. It is worth noticing that the project is proceeding normally despite the pandemic. Detailed information can be found in project deliverables, the majority of them being public and available on the project website (www. camivver-h2020. eu). The first technical activities consisted in collecting all necessary reference data to be used by project partners. Those essential tasks have been conducted within WP3 and have been achieved since the summer of 2021. In WP4, the activities are dedicated to setting up the framework (first steps) for the industrialization of a computing platform capable of performing lattice neutronic analysis and generating multi-parameter neutron data libraries based on the new lattice code APOLLO3 R , up to now developed mostly as a research computational tool.
Codes and methods description • The THERMAL-HyDraulic SYSTEM CODE ATHLET The thermal-hydraulic system code ATHLET  [9, 10]  is developed by the Gesellschaft fuer Anlagen-und Reaktorsicherheit gGmbH (GRS). The highly modular code structure of ATHLET includes advanced thermal-hydraulics as well as physical and numerical models. ATHLET is able to calculate single or two-phase flows. To simulate the thermo-fluid-dynamic behavior of the fluid, the user has to specify the system configuration by connecting basic fluid dynamic elements, the so-called thermo-fluid dynamic objects (TFOs). These TFOs can be divided into several segments, the so-called control volumes (CVs), to refine the simulation's resolution. The user can choose between the classic 5-equation model, also called the 1-M model, (with drift flux approach) and a two-fluid model with separate momentum balance equations for liquid and vapor, also called the 6-equation model or 2-M model.
The ATHLET code system performs spatial integrations based on a finite volume approach, which leads to a set of first-order differential equations, stemming from two mass and energy conservation equations per control volume and one or two momentum conservation equations per junction. The code also distinguishes homogenous CVs, where all phases are distributed uniformly and no directional quantities exist, and mixture-level CVs, which contain a horizontal mixture level. Below the mixture level, only pure fluid with vapor bubbles may exist, and above, only vapor with liquid droplets.
• The Thermal-Hydraulic Code CATHARE The CATHARE (Code Avancé de THermohydraulique pour les Accidents de Réacteurs à Eau) code is a French thermal-hydraulics system code developed since 1979 and extensively validated in collaboration between CEA, EDF, IRSN, and FRAMATOME  [11]. It is internationally used in plant simulators and for nuclear power plant safety analysis and licensing. It was originally devoted to best estimate calculations of thermal-hydraulic transients in water-cooled reactors. The CATHARE 2 has then been extended to gas applications in the 2000s. However, the gas was then considered ideal in this version of the code. This is one of the reasons for the development of a new version of the code CATHARE 3  [12]  which enables the consideration of real gas instead of an ideal one in simulations.
Therefore, the CATHARE 3 code allows expanding CATHARE 2 ability to model nuclear reactors such as new generation reactors with sodium application and closed Brayton cycles  [13] [14] [15] [16]. Its major feature is the possibility to choose field numbers from one (pure monophasic) to four: two continuous fields (liquid and vapor) and two dispersed fields (droplets and bubbles). Furthermore, CATHARE 3 can use the Equation Of State (EOS) component which enables the calculation of gas properties from several libraries especially the NIST Reference Fluid Thermodynamic and Transport Properties Database (REFPROP). The CATHARE 3 solves a two-phase six equation model including additional equations for non-condensable gases and radio-chemical components transport. These equations correspond to the local instantaneous mass, momentum, and energy conservation.
They are averaged for each phase over a cross-section over time and allow to represent mechanical and thermal disequilibrium between phases. The system of the equations is closed by physical closure laws of momentum and energy transfers between phases. Finally, the global non-linear system is solved using a Newton-Raphson iterative method. CATHARE 3 allows modeling the coolant circuits of any reactor by assembling axial (1-D), volume (0-D), and three-D (3-D) hydraulic modules. Thermal and hydraulic sub-modules such as thermal walls, heat exchangers, pumps, valves, turbines, a fluid source, and a sink can be added to these main modules.
• DYMOLA modeling and simulation tool To build and simulate the sCO 2 -loop numerical model, the Dymola [17] modeling and simulation tool was used by the project partner CVR. Supporting the simulation of the models that are written in the Modelica programming language, the sCO 2 -loop Dymola model was prepared using the Modelica library and ClaRaPlus, TSMedia, ClaRa DCS Modelica language-based libraries developed by XRG [18] and TLK [19] companies. In this way, the sCO 2 -loop Dymola model is a 1D model capable of capturing basic characteristics of the non-steady fluid flow and heat transfer based on the finite volume approach. In a case of a two-phase flow, the fluid is treated as a homogenous mixture of liquid and steam. TS Media library also allows for subcooled CO 2 and water to be modeled.
• Developments and results for CATHARE -Extensions for the simulation of sCO 2 cycles Many developments have been made in CATHARE 2 to represent all kinds of possible Brayton cycles with turbomachinery  [16] , a perfect gas turbomachinery module was developed from the CATHARE 2 pump module to describe either a compressor or a turbine that can be coupled to a shaft and an alternator. The behavior of the code has been validated on four real loops. This has therefore demonstrated the ability of the code to accurately represent the dynamic behavior of Brayton cycles in particular the turbomachinery behavior. But the Brayton cycles modeling was then only available for ideal gases. Therefore, in the framework of the NEPTUNE project  [34] , REFPROP has been implemented as a new library in the EOS component to use several real gas equations of state in CATHARE-3 such as supercritical CO 2.
As for turbomachinery, the non-dimensional performance curves were implemented to consider changing inlet conditions. When modeling heat exchangers, the 2D nature of the real design was addressed by a proper connection of the hot and cold side heat connectors. The Dymola sCO 2 -loop model needs to be coupled with some external code if we want to simulate the nuclear power plant SBO scenario. Such an external code was ATHLET with the Temelin VVER-1000 nuclear power plant model prepared by UJV. An interface between the Dymola and ATHLET codes was the wall between the CO 2 (Dymola) and steam/water (ATHLET) side of the CHX. To a couple, the Dymola and ATHLET models, the TISC Suite software package (developed by TLK company) was used together with the supervisor code  developed previously by UJV for coupling of ATHLET and FLUENT.
This supervisor code was adapted for the current application with the help of XRG company. The block diagram of the coupling is depicted in Figure  8. The Dymola sCO 2 -loop model basic structure is shown in Figure  9. Here, the basic components are as follows: radial turbocompressor (1), CHX CO 2 side (3), radial turbine (5), and UHS  (8). As the decay heat gradually decreases with time, so does the number of the sCO 2 -loops needed to dissipate the decay heat to the atmosphere. As only one sCO 2 -loop was modeled in the Dymola, the information about the number of the sCO 2 -loops currently in operation was also transferred between the two codes. For each constant number of running sCO 2 -loops, the loop thermal capacity was accommodated by the turbomachinery speed of revolution control. Two transient Dymola-ATHLET coupled simulations were performed including that with changing ambient air temperature during the decay heat removal campaign.
Acknowledgements Albannie Cagnac  as coordinator of the sCO2-4-NPP project would like to thank all the contributors who participated in the work of this article:  Frybort O. ,  Kriz D. ,  Melichar T. ,  Vlcek P. ,  Hakl V. ,  Meca R. ,  Vyskocil L. ,  Sobecki N. ,  Delmaere T. ,  Prusek T. ,  Hofer M. ,  Buck M. ,  Starflinger J. ,  Schuster S. Denis Verrier  as coordinator of the CAMIVVER project would like to thank all the contributors who participates in the works behind this paper through WP Leaders:  Groudev P. ,  Mosca P. ,  Mercatali L. ,  Bernard O. ,  Hashymov A. ,  Forgione N. Vladislav PI ŠTORA  as coordinator of the APAL project would like to thank all the contributors who participated in the work of this article:  Král P. ,  Lauerová D. ,  Trewin R. ,  Costa O. ,  Dillström P.
Funding The  APAL  Project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 945253. The  CAMIVVER  project has received funding from the  Euratom research  and training program  2019-2020  under grant agreement No. 945081. The  sCO2-4 -NPP  Project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 847606. The work of the  University of Stuttgart  was also funded by the  German Ministry for Economic Affairs and Energy (BMWi  Project No. 1501557 ) on basis of a decision by the  German Bundestag.
The French coast of the Eastern English Channel (ECC) is classified as potential eutrophication zone by the Paris and Oslo Convention (OSPAR), and as moderate to poor according to phytoplankton quality element of the Water Framework Directive (WFD). Indeed, the French part of the EEC is regularly affected by Phaeocystis globosa bloom events, which have detrimental effects on the marine ecosystem, economy as well as public health. Since phytoplankton is an important indicator of water quality, the MAREL Carnot oceanographic multi-sensor station was installed in the Eastern English Channel at the Carnot wall in Boulogne sur Mer in 2004. The aim of this station was to monitor water quality and phytoplankton in order to complement results from existing more conventional low resolution monitoring programs, with high frequency data (sampling every 20 minutes).
The purpose of this paper is to introduce the MAREL Carnot dataset and show how it can be used for several research objectives. MAREL Carnot collects high frequency, multi-parameter observations from surface water, as well as meteorological measurements, and send data almost immediately to an inshore data center. In this paper, we present several physiochemical and biological parameters measured by this station. In addition, we demonstrated, based on previous research activities, that the MAREL Carnot dataset is useful for evaluating environmental or ecological statuses, marine phytoplankton ecology, physical oceanography, turbulence, as well as public policy. Most importantly, we showed its contribution to Marine Strategy Framework Directive (MSFD) and other regional or universal conventions.
It was until 2004, when MAREL Carnot monitoring station has been installed in the French part of the English Channel. The MAREL (Mesures Automatisées en Réseau pour l'Environnement Littoral) Carnot station, developed and implemented by Ifremer (French Research Institute for Sea Exploitation), is a moored buoy protected by a tube and equipped with physicochemical and biological measuring devices and sensors that operate continuously and autonomously. This multi-sensor station is located in the Boulogne-sur-Mer harbor (Eastern English Channel) which is influenced by both marine and fresh waters. It is equipped with high-performance systems for seawater analysis and data transmission in near real time. It measures the following parameters with a high frequency resolution (20 minutes): estimated sea level, gust wind speed, wind direction relative to true north, horizontal wind speed, relative humidity, light irradiance surface PAR, sea temperature, practical salinity, pH, dissolved oxygen, oxygen saturation, fluorescence, and turbidity.
Objectives The purpose of this article is to introduce the MAREL Carnot dataset and provide an overview of its variability. We will provide a detailed description of the MAREL Carnot station, including its deployment and measurements, for any future users of the associated dataset. Based on previous research publications, we aim to demonstrate that the MAREL Carnot dataset is useful for evaluating environmental or ecological statuses, marine phytoplankton ecology, physical oceanography, turbulence, as well as public policy.
However, such an infrastructure would be very huge and expensive, so the tube was the best solution. Figure  2  shows MAREL Carnot measuring station, which consists of the tube, along with the light house. Its sensors are placed on a float inside the tube in order to follow tidal movements. Until 2014, it was made of a measurement cell containing several sensors. The seawater was pumped upwards to be analysed. During periods when there are no measurement cycles, the system was chlorinated via electrolysis to prevent biofouling. In other words, water was taken from the sub-surface at a depth of 1. 5 meters, and then sent to the passage chamber to be redistributed to different sensors. In order to make measurements directly in-situ using a multi-parameter probe, the system was updated in 2014, and water circulation in the chamber was removed to avoid air intake, which would compromise measurements and data quality.
The pulley system is placed in a chamber inside the harbor structure, allowing for the management of the cables during the tide's downward and upward movements, as well as the raising of the station for maintenance. The MAREL Carnot automated station is built with 1990s electronic, computer, and mechanical components. The general aging process, which primarily affects marine-exposed systems, necessitates the replacement of a number of elements that are no longer functional and whose maintenance is impossible due to a lack of spare parts. This explains why the measurement system was replaced in 2014 with a new automated measuring probe. Hence, several data for the year 2014 are missing. The core of the system is now composed of the following elements: -a PLC type MAREL ESTRAN -a small in situ circulation pump (pumping of the water on the probe) -a chlorinator for the production of chlorine by electrolysis -a multiparameter probe type MP6 nke -a Systea nutrient analyzer (nitrate, phosphate, silicate) -Seabird PAR Satlantic to measure the Photosynthetic Active Radiation, -A PONCPC-EH-10 probe for pH measurement.
Measured and Calculated Parameters The MAREL Carnot multisensor station measures physiochemical and biological parameters in a continuous and autonomous mode. With a sampling frequency of 20 minutes, it is capable of providing high resolution data for conductivity (S. m -1 ), water and air temperature (°C), pH, fluorescence (FFU), turbidity (NTU), dissolved oxygen concentration (mg. L -1 ), Photosynthetically Active Radiation or P. A. R (µmol of photons. s -1. m -2 ), wind direction (degree), and wind speed (m. s -1 ), as well as sea level (m). On the other hand, nutrient concentration like nitrate, phosphate, and silicate are only measured once every 12 hours due to the limited amount of chemical reagents. As a result, taking measurements twice a day allows chemicals to last longer (3 months).
Quality Code Correction Coastal CORIOLIS, or simply CORIOLIS, is a data portal for all in situ data platforms in Coastal French waters, including MAREL Carnot (https://data. coriolis-cotier. org). CORIOLIS quality control procedure provide the users with the quality of each measurement as a Quality Code (QC). This code is normally given following the completion of quality control procedures, which are a part of the CORIOLIS harmonized method. During this process, the data is automatically verified using fundamental statistics (minimum, maximum, median, and standard deviation), and is subsequently validated or modified by an expert using more sophisticated methods and based on his environmental expertise. Table  2  shows the significance of the quality code utilized with MAREL Carnot dataset.
Sensor and Expert Range Correction The sensor range is an interval of correct values defined by the manufacturer, while the expert range is an interval of correct values defined by a field expert. Indeed, the sensor ranges were obtained from the information provided by the sensor suppliers, whereas the expert ranges were derived from expert judgment based on specific knowledge acquired in the studied area through previous research activities. For all of the parameters, only the values that fall within the sensor and expert ranges are kept. Values that fall outside of the ranges are replaced with missing data (Not Available or NA). The sensor and expert ranges for MAREL Carnot are represented in Table  1. Indeed, the expert range is more precise than the sensor range. For instance, the sensor may give us a salinity value of 38, but our specialists know that salinity can only reach 35 in the Eastern English Channel, so the sensor's result is qualified as false and must be corrected.
Time Alignment Before statistical methods can be applied to the dataset, it must have an identical time interval between each measurement. However, the measurements of the various sensors are not taken at the same time, resulting in a time lag that can range from few seconds to several minutes. In addition, the series may contain duplicates in some cases. Thus, to eliminate potential replicates and synchronize the dataset, we perform a temporal alignment using the average time interval of the measurements. The alignment protocol calculates the average time step. This time step creates a regular/no-replicates time variable. Based on the parameter to be regularized and the goals to be attained, the maximum/minimum/or average of all subsets of our dataset matching to each regular interval of our ideal time variable is then returned.