| 1. |
- Rabhi, Achref, 1991-
(författare)
-
Numerical Modelling of Subcooled Nucleate Boiling for Thermal Management Solutions Using OpenFOAM
- 2021
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Two-phase cooling solutions employing subcooled nucleate boiling flows e.g. thermosyphons, have gained a special interest during the last few decades. This interest stems from their enhanced ability to remove extremely high heat fluxes, while keeping a uniform surface temperature. Consequently, modelling and predicting boiling flows is very important, in order to optimise the two-phase cooling operation and to increase the involved heat transfer coefficients. In this work, a subcooled boiling model is implemented in the open-source code OpenFOAM to improve and extend its existing solver reactingTwoPhaseEulerFoam dedicated to model boiling flows. These flows are modelled using Computational Fluid Dynamics (CFD) following the Eulerian two-fluid approach. The simulations are used to evaluate and analyse the existing Active Nucleation Site Density (ANSD) models in the literature. Based on this evaluation, the accuracy of the CFD simulations using existing boiling sub-models is determined, and features leading to improve this accuracy are highlighted. In addition, the CFD simulations are used to perform a sensitivity analysis of the interfacial forces acting on bubbles during boiling flows. Finally, CFD simulation data is employed to study the Onset of Nucleate Boiling (ONB) and to propose a new model for this boiling sub-model, with an improved prediction accuracy and extended validity range.It is shown in this work that predictions associated with existing boiling sub-models are not accurate, and such sub-models need to take into account several convective boiling quantities to improve their accuracy. These quantities are the thermophysical properties of the involved materials, liquid and vapour thermodynamic properties and the heated surface micro-structure properties. Regarding the interfacial momentum transfer, it is shown that all the interfacial forces have considerable effects on boiling, except the lift force, which can be neglected without influencing the simulations' output. The new proposed ONB model takes into account convective boiling features, and it able to predict the ONB with a very good accuracy with a standard deviation of 2.7% or 0.1 K. This new ONB model is valid for a wide range of inlet Reynolds numbers, covering both regimes, laminar and turbulent, and a wide range of inlet subcoolings and applied heat fluxes.
|
|
| 2. |
- Zimmerman, Nathan, 1983-
(författare)
-
Modelling Towards Control of Dynamic Systems : Applications on RDF Fired CFB Performance and DHN Distribution
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The combination of global warming along with increasing energy demand necessitates the importance of improving processes pertaining to the production and consumption of energy in combined heat and power plants. This thesis brings to light transient factors currently burdening process performance for circulating fluidized bed boilers (CFBs) combusting refuse derived fuels (RDFs) and district heating networks (DHN). These two domains are not completely disconnected from one another, which is the case for Northern European countries. Heat can be generated from a central location to be distributed through a network of customers to meet a heating demand. Results show that first-principle modelling techniques have the capacity to capture transients factors associated within the aforementioned entwined energy systems.On the production side, obtaining real-time information pertaining to the lower heating value of refuse derived fuel affords the ability to implement feed-forward model predictive control. Therefore, feed-forward model predictive control has the potential to minimize combustion temperature swings by making the necessary controls moves before changes in the fuel’s composition are actualized by the process. On the consumption side, attaining a deeper understanding of district heating network dynamics, e.g. heat propagation, network losses, distribution delays, and end-user requirements, introduces the possibility to analyse network performance and reduce peak load production. The perspective of quick network performance can be achieved by an automated approach to building and simulating district heating networks. Nonconventional end-user heating configurations, e.g. homes utilizing district heating and a heat pump, has the potential of illustrating how heating consumption patterns may change over time. Peak load reduction is achievable in district heating networks when it is possible to reduce network supply temperature. This can be achieved by predicting end-user heating requirements and using this information for feed-forward model predictive control.The overall observations made in this thesis demonstrates that process improvements are obtainable for transient energy systems. Despite the presented work focusing on only one type of energy production and one type of consumption, the approach described unlocks a flexibility that eliminates the need for unambiguous modelling and simulations by allowing for the reusability of model components. The exportability of these models further distinguishes them, as they can be used to test new control approaches within an energy system as real-time predictions within each energy sub-system become more accessible.
|
|
| 3. |
- Pan, Tianyao, 1994-
(författare)
-
Development of a Novel Gas Turbine Simulator for Hybrid Solar-Brayton Systems
- 2022
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Hybrid solar-Brayton systems utilize both solar thermal energy and supplementary renewable fuels to provide controllable and dispatchable power output, which renders them a promising way to meet the growing energy demand and reduce the carbon footprints. However, existing testing facilities for key components in such hybrid systems often fail to accomplish the testing requirements, hence impeding the improvement of the renewable energy share and the overall efficiency. A novel testing facility is urgently needed in order to thoroughly stimulate and analyze the component characteristics.This research work focuses on the development of a gas turbine simulator as an innovative testing facility for hot, pressurized components in hybrid solar-Brayton systems. The dual-flow choked nozzle based flow control has been proposed, explained, and analyzed in comparison to the single-flow layout. The basic idea of gas turbine simulator has been experimentally implemented and validated on a prototype, verifying its functionality. By incorporating a PLC-based control system, an automated gas turbine simulator has been designed and modified based on the prototype. Its performance with regard to stabilizing boundaries and tracking trajectories has been evaluated by experiments.Based on the experimental results, the gas turbine simulator prototype has proven its ability to establish controllable boundary conditions and migrate operating points for the impinging receiver. Through manual adjustments, excellent quasi-steady state performance has been obtained, with the precision for pressure control reaching ±0.005 bar at ambient temperature and ±0.015 bar at high temperature of 797.1-931.5 °C. The manual operation time has been identified at 23.1 s for establishing the receiver boundaries, and at 70 s for changing operating points.With the help of the proposed control strategy, the automated gas turbine simulator has eliminated the need for manual adjustments, and demonstrated the ability to maintain the safe and convergent operation for the receiver. The performance in boundary condition stabilization has been satisfactory, with enhanced steady-state accuracy comparing to the prototype by virtue of the PID controller. The transient-state fluctuations in pressure control have been effectively restrained within an acceptable region with deviations of ±0.018 bar to ±0.076 bar from the desired 2.400 bar operating pressure. The capability of tracking linear and nonlinear trajectories has also been testified, with the precision level between ±0.023 bar and ±0.037 bar.Finally, in view of the good stability, high precision, and rapid response manifested in the experimental studies, the gas turbine simulator has validated its ability to imitate the steady and transient characteristics of gas turbines on the boundaries of the test section. It also grants the possibilities to conduct control variable studies and wide-range transition studies. The gas turbine simulator is a suitable testing facility for the key components in hybrid solar-Brayton systems.
|
|
| 4. |
- Soibam, Jerol
(författare)
-
Data-Driven Techniques for Fluid Mechanics and Heat Transfer
- 2022
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- One of the main challenges in fluid mechanics and heat transfer is the need for detailed studies andfast computational speed to monitor and optimise a system. These fluid/heat flows comprise time-dependent velocity, multi-scale, pressure, and energy fluctuations. Although there has been major advancements in computational power and technology, modelling detailed physical problems is currently falling short. The fluid mechanics and heat transfer domains are rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatio temporal scales. Such an increase in the volume of data unlocks the possibility of using techniques like machine learning. These machine learning algorithms offer a wealth of techniques to extract information from data that can be translated into knowledge about the underlying physics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimisation. A significant milestone in the area of machine learning is the rise of deep learning, which is a powerful tool which can handle large data sets describing complex nonlinear dynamics that are commonly encountered in heat transfer and fluidflows.Therefore, this thesis aims to investigate data obtained from numerical simulations with deep learning techniques to reproduce the underlying physics present in data and considerably speed up the process. In this study, subcooled boiling transfer data has been used to train the deep neural network model then the trained model is validated using a validation dataset. The performance of the model is further evaluated using a set of interpolation and extrapolation datasets for different operating conditions outside the training and validation data. Furthermore, to highlight the robustness and reliability of the deep learning model, uncertainty quantification techniques such as Monte Carlo dropout and Deep Ensemble are implemented.This study demonstrates how a data-driven model can be used for subcooled boiling heat transfer and highlights why uncertainty quantification is important for such a model. The analysis and discussion in this thesis serve as the basis for further extending the potential use of data-driven methods for system optimisation, control and monitoring, diagnostic, and industrial applications.
|
|
| 5. |
- Stenfelt, Mikael, 1983-
(författare)
-
On model based aero engine diagnostics
- 2023
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Maintenance and diagnostics play a vital role in the aviation sector. This is especially true for the engines, being one of the most vital components. Lack of maintenance, or poor knowledge of the current health status of the engines, may lead to unforeseen disruptions and possibly catastrophic effects. To keep track of the health status, and thereby supporting maintenance planning, model based diagnostics is a key factor. In the work going into this thesis, various aspects of model based gas turbine diagnostics, focused on aero engines, are covered. First, the importance of knowing what health parameters may be derived from a set of measurements is addressed. The selected combination is herein denoted as a matching scheme. A framework is proposed where the most suitable matching scheme is selected for a numerically robust diagnostic system. If a sensor malfunction is detected, the system automatically adapts.The second subject is a system for detecting a burn-through of an afterburner inner liner. This kind of burn-through event has a very small impact on available on-board measurements, making it difficult to detect numerically. A method is proposed performing back-to-back testing after each engine start. The method has shown potential to detect major burn-through events under the preconditions, regarding data collection time and frequency. Increasing these will allow for more accurate estimations.The third subject covers the importance of knowing the airplane installation effects. These are generally the intake pressure recovery, bleed and shaft power extraction. Just like inaccurate measurements affect diagnostic results, so does erroneous installation effects. A method for estimating said effects in the presence of gradual degradation has been proposed by using neural networks. By retraining the networks throughout the degradation process, the estimation errors is reduced, ensuring relevant estimations even at severe degradations.Finally, an issue related to the general lack of on-board measurements for diagnostics is addressed. Due to lack of measurements, the diagnostic model tend to be underdetermined. A least square solver working without a priori information has been implemented and evaluated. Results from the solver is very much dependent on available instrumentation. In well instrumented components, such as the compressors, good diagnostic accuracy was achieved while the turbine health estimations suffer from smeared out results due to poor instrumentation.
|
|
| 6. |
|
|
| 7. |
- Rahman, Moksadur, 1989-
(författare)
-
On a learning system for industrial automation : Model-based control and diagnostics for decision support
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Access to energy is fundamental to economic and technological advancement. Hence, the more the world develops, the greater the demand for energy becomes. Evidently, the production and consumption of energy alone account for more than 80% of global anthropogenic greenhouse gas (GHG) emissions. There is broad scientific consensus that efficiency improvements in energy production and consumption must come first on the path to reducing global GHG emissions. As the largest producer and consumer of energy, the industrial sector faces tremendous challenges due to stringent environmental regulations, intense price-based global competition, rising operating costs and rapidly changing economic conditions. Therefore, increasing energy and resource efficiency while improving throughput and asset reliability is a matter of utmost importance. Satisfying such demanding objectives requires an optimal operation, control and monitoring of plant assets and processes. This is one of the main driving forces behind developing digital solutions, methods, and frameworks that can be integrated with old and new industrial automation platforms. The main focus of this dissertation is to investigate frameworks, process models, soft sensors, control optimization, and diagnostic techniques to improve the operation, control, and monitoring of industrial plants and processes. In this thesis, a generic architecture for control optimization, diagnostics, and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies, one representing large-scale assets and another representing a fleet of small-scale assets, are considered to demonstrate the genericness of the proposed system architecture. In this thesis, a very energy-intensive chemical pulping process represents the case study of large-scale assets, and a micro gas turbine (MGT) fleet for distributed heat and power generation represent the case study of a fleet of small-scale assets. One of the main challenges in this research arises from the marked differences between the case studies in terms of size, functions, quantity, and structure of the existing automation systems. Typically, only a few pulp digesters are found in a Kraft pulping mill, but there may be hundreds of units in a MGT fleet. The main argument behind the selection of these two case studies is that, if the proposed learning system architecture can be adapted for these significantly different cases, then it can be adapted for many other industrial applications as well. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control, and diagnostics methods are studied for continuous pulp digesters, whereas mathematical modelling, model adaptation, and diagnostics techniques are explored for the MGT fleet. Due to the naturally varying wood quality, significant residence time, insufficient measurements, and complexity of pulping reactions, modelling and controlling a continuous pulp digester is a challenging task. Moreover, process abnormalities due to non-ideal flow in the digester often occur that considerably affect the pulp quality. Within this dissertation, variation of wood-chip quality is identified as one of the main process disturbances. Thereafter, a feedforward model predictive control (MPC) approach is explored by feedforwarding the lignin content of the wood chips to the controller. The result shows that the disturbance rejection and tracking performance of the feedforward MPC are superior to other alternatives, like Proportional–integral–derivative (PID), MPC, and current industrial control. When it comes to diagnostics, a literature gap is identified in the area of modelling digester faults. Hence, the well-known Purdue model, a widely used dynamic model of the digester, is extended to simulate process faults like screen-clogging, hangups, and channelling. The findings suggest that both hangups and channelling considerably affect the pulp quality at the blowline. The impact of channelling is prominent on reaction temperature compared to hangups, while hangups change the residence time of the wood chips significantly. Subsequently, a hybrid diagnostics scheme for pulp digester, combining a physical model and a Bayesian network (BN), is demonstrated. Overall, the results show that fault type and severity can be estimated with acceptable accuracy even in presence of noise. Enabling remote fleet diagnostics is expected to foster the commercialization of distributed micro-combined heat and power (micro-CHP) generators, i.e., MGTs. Even though the modelling and diagnostics of large-scale gas turbines are well researched, studies targeting MGT are limited. In this thesis, a physical model of a commercial MGT system is developed. Subsequently, a hybrid scheme by combining a physics-based gas path analysis with a data-driven approach is used to enable MGT diagnostics. The proposed scheme was tested by simulating case studies corresponding to single and multiple faults. Furthermore, sensitivity studies are performed for different measurement uncertainties (i.e., sensor noise and bias) to evaluate the robustness of the scheme against measurement uncertainties. The findings show that the proposed diagnostics approach performs satisfactorily even under measurement uncertainties. To sum up, the increased availability of data and higher computing power is fostering the development of accurate process models and algorithms necessary for optimal operation, control, and monitoring of industrial processes. With the emergence of new measurement techniques, it is possible to leverage productivity and quality with tighter control of key process parameters. Additionally, studying the underlying mechanism of process degradation and developing diagnostics methods by incorporating these can lead to significant economic benefits. Having said that, to tap the full potential of these digital solutions, an integrated framework like that presented in this thesis, i.e., a learning system is essential.
|
|
