Post-test simulation of a PLOFA transient test in the CIRCE-HERO facility

CIRCE is a lead–bismuth eutectic alloy (LBE) pool facility aimed to simulate the primary system of a heavy liquid metal (HLM) cooled pool-type fast reactor. The experimental facility was implemented with a new test section, called HERO (Heavy liquid mEtal pRessurized water cOoled tubes), which consists of a steam generator composed of seven double-wall bayonet tubes (DWBT) with an active length of six meters. The experimental campaign aims to investigate HERO behavior, which is representative of the tubes that will compose ALFRED SG. In the framework of the Horizon 2020 SESAME project, a transient test was selected for the realization of a validation benchmark. The test consists of a protected loss of flow accident (PLOFA) simulating the shutdown of primary pumps, the reactor scram and the activation of the DHR system. A RELAP5-3D© nodalization scheme was developed in the pre-test phase at DIAEE of “Sapienza” University of Rome, providing useful information to the experimentalists. The model consisted to a mono-dimensional scheme of the primary flow path and the SG secondary side, and a multi-dimensional component simulating the large LBE pool. The analysis of experimental data, provided by ENEA, has suggested to improve the thermal–hydraulic model with a more detailed nodalization scheme of the secondary loop, looking to reproduce the asymmetries observed on the DWBTs operation. The paper summarizes the post-test activity performed in the frame of the H2020 SESAME project as a contribution of the benchmark activity, highlighting a global agreement between simulations and experiment for all the primary circuit physical quantities monitored. Then, the attention is focused on the secondary system operation, where uncertainties related to the boundary conditions affect the computational results

Aerosol transport by coughing in a depressurized and air-conditioned chamber

Expiratory droplets from human coughing in an air-conditioned environment have always been considered to be potential carriers of pathogens, responsible for respiratory disease transmission. The air movement/circulation and ambient conditions such as pressure and temperature are all key factors of aerosol transport. To study the transmission of disease by human coughing in a depressurized air-conditioned chamber, there are many technical challenges, including the following: 1) the study of simulating human coughing; 2) the collection of aerosol generated by coughing; 3) the CFD simulation of coughing-induced aerosol transport in an air-conditioned chamber; 4) the validation of such a CFD simulation by experiment; 5) the depressurization of the air-conditioned chamber and 6) the mechanistic study of droplet evaporation. Accordingly, this work provides the following to study the aerosol transmission in a depressurized chamber: a transient repeatable bimodal cough simulator is designed and built; a chamber with air-conditioning and circulation is built; a breathing simulator is designed and built as well as measurement validation is done; a full field three dimensional CFD simulation of the aerosol transport in the air-conditioned chamber is established; a droplet evaporation model is built as well as the technology of depressurization for an open flow system. The cough and breath simulators are designed for purposes of aerosol generation and collection. The CFD simulation is used to calculate various conditions of the aerosol transport and deposition, especially the effect of the air movement/circulation and ambient conditions. The experiment chamber is for validation of CFD simulation. The droplet evaporation is built to better simulate one of the most important factors for the droplets, the evaporation effect, and can be implemented into the CFD model by User-Defined Functions. The depressurization technology is to provide the vacuum environment for the experiment air-conditioned chamber. This study also seeks a breakthrough of a heat transfer model of latent heat partition, which would be a critical factor for the droplet evaporation. The whole project lays down the foundation of the study of aerosol transport in a depressurized air-conditioned chamber, for its inhalation by human, contamination into AC system and deposition on the environment surfaces. It also initiates the coupling with medical model by providing critical input conditions for coughing-induced disease transmission to study the disease transmission as well as decontamination. Future studies would include the calibration and measurement of the breath simulator, the aerosol transport measurement in the air-conditioned chamber, more parametric study of CFD simulation, a more sophisticated multi-component evaporation model and the implementation of this evaporation model in the CFD simulation through User-Defined Functions. If possible, one could couple the more realistic latent heat partition model with the droplet evaporation model and also include the depressurization effect for at least the CFD simulation if not for the experiment

Adaptive swarm optimisation assisted surrogate model for pipeline leak detection and characterisation.

Pipelines are often subject to leakage due to ageing, corrosion and weld defects. It is difficult to avoid pipeline leakage as the sources of leaks are diverse. Various pipeline leakage detection methods, including fibre optic, pressure point analysis and numerical modelling, have been proposed during the last decades. One major issue of these methods is distinguishing the leak signal without giving false alarms. Considering that the data obtained by these traditional methods are digital in nature, the machine learning model has been adopted to improve the accuracy of pipeline leakage detection. However, most of these methods rely on a large training dataset for accurate training models. It is difficult to obtain experimental data for accurate model training. Some of the reasons include the huge cost of an experimental setup for data collection to cover all possible scenarios, poor accessibility to the remote pipeline, and labour-intensive experiments. Moreover, datasets constructed from data acquired in laboratory or field tests are usually imbalanced, as leakage data samples are generated from artificial leaks. Computational fluid dynamics (CFD) offers the benefits of providing detailed and accurate pipeline leakage modelling, which may be difficult to obtain experimentally or with the aid of analytical approach. However, CFD simulation is typically time-consuming and computationally expensive, limiting its pertinence in real-time applications. In order to alleviate the high computational cost of CFD modelling, this study proposed a novel data sampling optimisation algorithm, called Adaptive Particle Swarm Optimisation Assisted Surrogate Model (PSOASM), to systematically select simulation scenarios for simulation in an adaptive and optimised manner. The algorithm was designed to place a new sample in a poorly sampled region or regions in parameter space of parametrised leakage scenarios, which the uniform sampling methods may easily miss. This was achieved using two criteria: population density of the training dataset and model prediction fitness value. The model prediction fitness value was used to enhance the global exploration capability of the surrogate model, while the population density of training data samples is beneficial to the local accuracy of the surrogate model. The proposed PSOASM was compared with four conventional sequential sampling approaches and tested on six commonly used benchmark functions in the literature. Different machine learning algorithms are explored with the developed model. The effect of the initial sample size on surrogate model performance was evaluated. Next, pipeline leakage detection analysis - with much emphasis on a multiphase flow system - was investigated in order to find the flow field parameters that provide pertinent indicators in pipeline leakage detection and characterisation. Plausible leak scenarios which may occur in the field were performed for the gas-liquid pipeline using a three-dimensional RANS CFD model. The perturbation of the pertinent flow field indicators for different leak scenarios is reported, which is expected to help in improving the understanding of multiphase flow behaviour induced by leaks. The results of the simulations were validated against the latest experimental and numerical data reported in the literature. The proposed surrogate model was later applied to pipeline leak detection and characterisation. The CFD modelling results showed that fluid flow parameters are pertinent indicators in pipeline leak detection. It was observed that upstream pipeline pressure could serve as a critical indicator for detecting leakage, even if the leak size is small. In contrast, the downstream flow rate is a dominant leakage indicator if the flow rate monitoring is chosen for leak detection. The results also reveal that when two leaks of different sizes co-occur in a single pipe, detecting the small leak becomes difficult if its size is below 25% of the large leak size. However, in the event of a double leak with equal dimensions, the leak closer to the pipe upstream is easier to detect. The results from all the analyses demonstrate the PSOASM algorithm's superiority over the well-known sequential sampling schemes employed for evaluation. The test results show that the PSOASM algorithm can be applied for pipeline leak detection with limited training datasets and provides a general framework for improving computational efficiency using adaptive surrogate modelling in various real-life applications

Application of A* algorithm for tortuosity and effective porosity estimation of 2D rock images

Characterization and understanding of fluid flow phenomena in un-derground porous media at the micro and macro scales is fundamental in reser-voir engineering for the definition of the optimal reservoir exploitation strategy. Laboratory analyses on rock cores provide fundamental macroscale parameters such as porosity, absolute and relative permeability and capillary pressure curves. In turn, macroscale parameters as well as flow behavior, are strongly af-fected by the micro geometrical features of the rock, such as pore structure, tor-tuosity and pore size distribution. Therefore, a thorough comprehension of sin-gle and multiphase flow phenomena requires analyses, observations and charac-terization at the micro scale. In this paper we focus on the analysis of a 2D bina-ry image of a real rock thin section to characterize the pore network geometry and to estimate tortuosity, effective porosity and pore size distribution. To this end, a geometrical analysis of the pore structure, based on the identification and characterization of the set of the shortest geometrical pathways between inlets and outlets pairs, is implemented. The geometrical analysis is based on the A* path-finding algorithm derived from graph theory. The results provided by the geometrical analysis are validated against hydrodynamic numerical simulation via the Lattice Boltzmann Method (LBM), which is well suited for simulating fluid flow at the pore-scale in complex geometries. The selected rock for this analysis is Berea sandstone, which is recognized as a standard rock for various applications such as core analysis and flooding experiment. Results show that the path-finding approach provides reasonable and reliable estimates of tortuos-ity and can be successfully applied for analyzing the distribution of effective pore radius, as well as for estimating the effective porosity

DEVELOPMENT OF INDUSTRY ORIENTED CFD CODE FOR ANALYSIS / DESIGN OF FACE VENTILATION SYSTEMS

Two of the main safety and health issues recognized during deep cut coal mining are methane and dust hazards. Advances in continuous miner technology have improved safety and productivity. However, these advances have created some environmental problems, notably more dust and methane being generated at the face during coal extraction. Results of studies performed in the last three decades concerning the face ventilation for deep cut mining showed very complicated airflow behavior. The specifics of flow patterns developed by the face ventilation systems presents significant challenge for analytical description even for equipment-free entry. Fortunately, there are methods, such as numerical simulations that could be used to provide an engineering solution to the problem. Computational Fluid Dynamics (CFD) codes have been successfully applied during the last decade using the power of Supercomputers. Although significant progress has been made, a benchmark industry oriented CFD code dedicated to face ventilation is still not available. The goal of this project is to provide the mining industry a software for CFD analysis and design of face ventilation systems. A commercial CFD system SC/Tetra Thermofluid Analysis System with Unstructured Mesh Generator, copyright © Cradle Co, was selected for a development platform. A number of CFD models were developed for the needs of this study including methane release, dust generation, 3D models of commonly used continuous mining machines, scrubbers and water spray systems. The developed models and the used CFD code were successfully validated in the part for methane dilution, using available data from small scale and full scale experiments. The developed models for simulation of dust control systems need to be validated in the future. The developed code automates all necessary steps needed for simulation of face ventilation systems, starting with the construction of a 3D model, generation of the computational mesh, solving and monitoring the calculations, to post-processing and graphical representation of the obtained results. This code shall allow mining engineers to design better and safer face ventilation systems while providing the Mine Safety and Health Administration (MSHA) a tool to check and approve the industry’ proposed ventilation plans

Appl Therm Eng

Carbon monoxide (CO) poisoning is a leading cause of mine fire fatalities in underground mines. To reduce the hazard of CO poisoning in underground mines, it is important to accurately predict the spread of CO in underground mine entries when a fire occurs. This paper presents a study on modeling CO spread in underground mine fires using both the Fire Dynamics Simulator (FDS) and the MFIRE programs. The FDS model simulating part of the mine ventilation network was calibrated using CO concentration data from full-scale mine fire tests. The model was then used to investigate the effect of airflow leakage on CO concentration reduction in the mine entries. The inflow of fresh air at the leakage location was found to cause significant CO reduction. MFIRE simulation was conducted to predict the CO spread in the entire mine ventilation network using both a constant heat release rate and a dynamic fire source created from FDS. The results from both FDS and MFIRE simulations are compared and the implications of the improved MFIRE capability are discussed.CC999999/Intramural CDC HHS/United States2016-05-05T00:00:00Z27069400PMC482605