Mathematical Modelling of Energy Systems and Fluid Machinery

The ongoing digitalization of the energy sector, which will make a large amount of data available, should not be viewed as a passive ICT application for energy technology or a threat to thermodynamics and fluid dynamics, in the light of the competition triggered by data mining and machine learning techniques. These new technologies must be posed on solid bases for the representation of energy systems and fluid machinery. Therefore, mathematical modelling is still relevant and its importance cannot be underestimated. The aim of this Special Issue was to collect contributions about mathematical modelling of energy systems and fluid machinery in order to build and consolidate the base of this knowledge

Vertical annular gas–liquid two-phase flow in large diameter pipes

Gas–liquid annular two phase flow in pipes is important in the oil and gas, nuclear and the process industries. It has been identified as one of the most frequently encountered flow regimes and many models (empirical and theoretical) for the film flow and droplet behaviour for example have been developed since the 1950s. However, the behaviour in large pipes (those with diameter greater than 100 mm) has not been fully explored. As a result, the two- phase flow characteristics, data, and models specifically for such pipes are scarce or non-existent such that those from smaller pipes are extrapolated for use in design and operation. Many authors have cautioned against this approach since multiphase pipe flow behaviour is different between small and large pipes. For instance the typical slug flows seem not to occur in vertical upwards flows when the pipe diameter exceeds 100 mm. It is therefore imperative that theoretical models and empirical correlations for such large diameter pipes are specifically developed. ...[cont.

An Integrated Approach to Performance Monitoring and Fault Diagnosis of Nuclear Power Systems

In this dissertation an integrated framework of process performance monitoring and fault diagnosis was developed for nuclear power systems using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault detection and isolation. In the applications to nuclear power systems, on the one hand, historical data are often not able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components such as steam generators and heat exchangers may experience degradation. On the other hand, first-principle models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of first principle models in modeling a wide range of anticipated operation conditions and the strength of data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using the first principle models and the model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed robust fault diagnosis method was able to eliminate false alarms due to model uncertainty and deal with changes in operating conditions throughout the lifetime of nuclear power systems. Multiple methods of robust data driven model based fault diagnosis were developed in this dissertation. A complete procedure based on causal graph theory and data reconciliation method was developed to investigate the causal relationships and the quantitative sensitivities among variables so that sensor placement could be optimized for fault diagnosis in the design phase. Reconstruction based Principal Component Analysis (PCA) approach was applied to deal with both simple faults and complex faults for steady state diagnosis in the context of operation scheduling and maintenance management. A robust PCA model-based method was developed to distinguish the differences between fault effects and model uncertainties. In order to improve the sensitivity of fault detection, a hybrid PCA model based approach was developed to incorporate system knowledge into data driven modeling. Subspace identification was proposed to extract state space models from thermal hydraulic simulations and a robust dynamic residual generator design algorithm was developed for fault diagnosis for the purpose of fault tolerant control and extension to reactor startup and load following operation conditions. The developed robust dynamic residual generator design algorithm is unique in that explicit identification of model uncertainty is not necessary. Finally, it was demonstrated that the developed new methods for the IRIS Helical Coil Steam Generator (HCSG) system. A simulation model was first developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was then developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operation conditions. This dissertation bridges the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power systems. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors

Physical properties of alkanes and their mixtures

Alkanes and their mixtures are some of the physically simplest molecules and are widely used in industry, yet the connection between their structure and physical properties is still poorly understood. To make progress, we study the properties of pure alkanes with neural networks and molecular dynamics, while we develop a new theoretical framework to study the properties of mixtures of alkanes. We first encode alkanes’ structure into five non-negative integers and use them as neural network inputs. Then, we utilize the neural networks to study the boiling point, vapor pressure, heat capacity, and melting point of light alkanes, as well as flash point and kinematic viscosity of linear alkanes. Neural networks model all these properties more accurately than the competing statistical and physico-chemical methods, while the cross-validation results indicate that they can confidently and accurately extrapolate the boiling point, heat capacity, and vapor pressure models to heavy alkanes. Still, due to a lack of experimental data for non-linear alkanes, neural network flash point and kinematic viscosity models cannot extrapolate to heavy alkanes, while the comparatively low accuracy of melting point models relative to other properties’ models suggests that additional physical effects need to be incorporated into them. To obtain synthetic data as a supplement for the experimental kinematic viscosity dataset, we perform molecular dynamics simulations for density and non-equilibrium molecular dynamics (NEMD) simulations for dynamic viscosity. Density simulation results are corrected through a data-driven approach to increase their accuracy, and we develop a sampling algorithm that automatically selects the shear rates at which to perform the viscosity simulations.The sampling algorithm is tested on linear alkanes, and simulation results are in excellent agreement with the experiments, encouraging applications to more complex alkanes. Then, we use neural networks with molecular structure as inputs to model the molecular dynamics density simulation values and extrapolate to 11-heptyltricosane, 8,11-dipentyloctadecane, and 8,14-dipentylhenicosane at 40°C and 100°C. These extrapolated density values are used as state points for the NEMD viscosity simulations, which are performed with the help of the shear rate sampling algorithm. While the accuracy of neural network models is high, and the usefulness and reliability of the sampling algorithm is further established, viscosity simulation results are not in a good agreement with the experiment due to systematic error in the force field. Finally, to model properties of mixtures of alkanes, we develop a theory of mixtures whose molecules’ positions have a uniform spatial distribution. We apply this theory to molar volume, isentropic compressibility, surface tension, and dynamic viscosity of mixtures of alkanes, first by fitting to experimental data, and then by using the best fit parameters for viscosity to predict viscosity of further mixtures. Best fits and predictions show excellent agreement with the experiments, and our theory shows promise for further applications to mixtures of alkanes, while its conceptual basis has the potential to be applied to other types of mixtures as well.BP-ICAM 5

Modelling, simulation and multi-objective optimization of industrial hydrocrackers

Ph.DDOCTOR OF PHILOSOPH

Internal Combustion Engine Heat Transfer and Wall Temperature Modeling: An Overview

[EN] Internal combustion engines are now extremely optimized, in such ways improving their performance is a costly task. Traditional engine improvement by experimental means is aided by engine thermodynamic models, reducing experimental and total project costs. For those models, accuracy is mandatory in order to offer good prediction of engine performance. Modelling of the heat transfer and wall temperature is an important task concerning the accuracy and the predictions of any engine thermodynamic model, although it is many times an overcome task. In order to perform good prediction of engine heat transfer and wall temperature, models are required for accomplish heat transfer from hot gases to engine parts, heat transfer inside each engine part, and also heat transfer to coolant and lubricating oil. This paper presents an overview about engine heat transfer and wall temperature modelling, with main purpose to aid engine thermodynamic modelling and offer more accurate predictions of engine performance, consumption and emission parameters. The most important correlation are reviewed for three engine heat transfer approaches: gas to wall, wall to wall and wall to liquid heat transfer models. In order to obtain good prediction of wall temperature, those three approaches must be coupled, which may imply convection-conduction-convection problems, although for some applications in diesel engines, radiation problems must be considered.This study was partially funded by CAPES - DEMANDA SOCIAL Ph.D. level scholarship, from CAPES (Coordination for the Improvement of Higher Education Personnel).Fonseca, L.; Novella Rosa, R.; Olmeda, P.; Valle, RM. (2019). Internal Combustion Engine Heat Transfer and Wall Temperature Modeling: An Overview. 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Nuclear Power - System Simulations and Operation

At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the system-thinking approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners

Multiphase flow measurement using gamma-based techniques

The oil and gas industry need for high performing and low cost multiphase meters is ever more justified given the rapid depletion of conventional oil reserves. This has led oil companies to develop smaller/marginal fields and reservoirs in remote locations and deep offshore, thereby placing great demands for compact and more cost effective soluti8ons of on-line continuous multiphase flow measurement. The pattern recognition approach for clamp-on multiphase measurement employed in this research study provides one means for meeting this need. Cont/d

HYDRODYNAMIC AND MASS TRANSFER PARAMETERS IN LARGE-SCALE SLURRY BUBBLE COLUMN REACTORS

The design, modeling, optimization and scaleup of slurry bubble column reactors (SBCRs) require, among others, the knowledge of the kinetics, hydrodynamics, and mass as well as heat transfer characteristics in larger-scale reactors, operating under typical industrial conditions. In this study, the hydrodynamic (gas holdup, ƒÕG, bubble size distribution, dB, and the Sauter-mean bubble diameter, d32), gas solubility (C*) and mass parameters (gas-liquid interfacial area, a, and volumetric liquid-side mass transfer coefficient, kLa) were measured for various gases (H2, CO, N2, CH4 and He) in an organic liquid (Isopar-M) in the absence and presence of two different solids (glass beads and alumina powder) in two large-scale SBCRs. The data for the five gases were obtained in a cold SBCR (0.301 m ID) under wide ranges pressures (P = 1-8 bar), temperatures (T = 293-305 K), superficial gas velocities (UG = 0.08-0.20 m/s), and solid concentrations (CV = 0-36 vol.%); and the data for He and N2 were obtained in a hot SBCR (0.29 m ID) under wide ranges pressures (P = 7-25 bar), temperatures (T = 323-453 K), superficial gas velocities (UG = 0.08-0.30 m/s), and solid concentrations (CV =0-20 vol.%). All the experiments and the operating ranges were selected following the Central Composite Statistical Design (CCSD) approach. The experimental data obtained showed that the gas holdup and volumetric liquid-side mass transfer coefficients increased with pressure due to the increase of small gas bubbles holdup; increased with superficial gas velocity due to the increase of the gas momentum; and significantly decreased with solid concentration due to a reduction of small gas bubble population. The gas holdup and volumetric liquid-side mass transfer coefficients were found to increase with temperature due to the decrease of the Sauter mean bubble diameter and increase of the mass transfer coefficient (kL). The gas holdup, however, was found to decrease with temperature when the solid concentration was greater or equal 15 vol.% due to the reduction of froth stability under such conditions.Empirical and back propagation neural network (BPNN) models were developed to correlate the hydrodynamic and mass transfer parameters in BCRs and SBCRs obtained in our laboratory and those from the literature. The developed models were then used to predict the effects of pressure, superficial gas velocity, temperature and catalyst loading on the total syngas holdup and mass transfer coefficients for the Low-Temperature Fischer-Tropsch (LTFT) synthesis carried out in a 5 m ID SBCR with iron oxides and cobalt-based catalysts. The predicted total syngas holdup and mass transfer coefficients appeared to increase with reactor pressure, superficial gas velocity and the number of orifices in the gas sparger. The predicted values, however, were found to decrease with catalyst loading and reactor temperature. Also, under similar LTFT operating conditions (P = 30 bar, T = 513 K, CW = 30 and 50 wt%), the total syngas holdup and mass transfer coefficients predicted for H2/CO ratio of 2:1 with cobalt-based catalyst were consistently lower than those obtained for H2/CO ratio of 1:1 with iron oxide catalyst in the superficial gas velocity range from 0.005 to 0.4 m/s

Center for Advanced Space Propulsion Second Annual Technical Symposium Proceedings

The proceedings for the Center for Advanced Space Propulsion Second Annual Technical Symposium are divided as follows: Chemical Propulsion, CFD; Space Propulsion; Electric Propulsion; Artificial Intelligence; Low-G Fluid Management; and Rocket Engine Materials