| 1. |
- Soibam, Jerol, et al.
(författare)
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A Data-Driven Approach for the Prediction of Subcooled Boiling Heat Transfer
- 2020
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Ingår i: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020. - : Linköping University Electronic Press. ; , s. 435-442
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In subcooled flow boiling, heat transfer mechanism involves phase change between liquid phase to the vapour phase. During this phase change, a large amount of energy is transferred, and it is one of the most effective heat transfer methods. Subcooled boiling heat transfer is an attractive trend for industrial applications such as cooling electronic components, supercomputers, nuclear industry, etc. Due to its wide variety of applications for thermal management, there is an increasing demand for a faster and more accurate way of modelling. In this work, a supervised deep neural network has been implemented to study the boiling heat transfer in subcooled flow boiling heat transfer. The proposed method considers the near local flow behaviour to predict wall temperature and void fraction of a sub-cooled mini-channel. The input of the network consists of pressure gradients, momentum convection, energy con- vection, turbulent viscosity, liquid and gas velocities, and surface information. The output of the model is based on the quantities of interest in a boiling system i.e. wall temperature and void fraction. The network is trained from the results obtained from numerical simulations, and the model is used to reproduce the quantities of interest for interpolation and extrapolation datasets. To create an agile and robust deep neural network model, state-of-the-art methods have been implemented in the network to avoid the overfitting issue of the model. The results obtained from the deep neural network model shows a good agreement with the numerical data, the model has a maximum relative error of 0.5 % while predicting the temperature field, and for void fraction, it has approximately 5 % relative error in interpolation data and a maximum 10 % relative error for the extrapolation datasets.
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| 2. |
- Holstein, Tobias, et al.
(författare)
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Steps Towards Real-world Ethics for Self-driving Cars: Beyond the Trolley Problem
- 2021
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Ingår i: Machine Law, Ethics, and Morality in the Age of Artificial Intelligence. - Hershey, Pennsylvania : IGI Global. - 9781799848943 - 9781799848950 ; , s. 85-107
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Research on self-driving cars is transdisciplinary and its different aspects have attracted interest in general public debates as well as among specialists. To this day, ethical discourses are dominated by the Trolley Problem, a hypothetical ethical dilemma that is by construction unsolvable. It obfuscates much bigger real-world ethical challenges in the design, development, and operation of self-driving cars. We propose a systematic approach that connects processes, components, systems, and stakeholders to analyze the real-world ethical challenges for the ecology of socio-technological system of self-driving cars. We take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that must be met and that imply novel expectations for engineering in car industry.
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| 3. |
- Soibam, Jerol
(författare)
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Data-Driven Techniques for Fluid Mechanics and Heat Transfer
- 2022
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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.
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| 4. |
- Kyprianidis, Konstantinos, et al.
(författare)
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On-line Powerplant Control using Near-InfraRed Spectroscopy : OPtiC-NIRS, REPORT 2021:746
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Near InfraRed Spectroscopy (NIRS) offers rapid on-line analysis of biomass feedstocks and can be utilized for process control of biomass- based combined heat and power plants. Within the OPtiC-NIRS project we have carried out a full-scale on-site testing of different NIRS for online powerplant control at the facilities of Mälarenergi and Eskilstuna Strängnäs Energi och Miljö. The project has been focused on developing and testing robust NIRS soft-sensors for fuel higher heating value and composition (incl. moisture, components such as recycle wood and glass, different type of plastics and ash) and combining them with dynamic models for on-line feed-forward process monitoring and control. Expected benefits include reduced risk of agglomeration and pollutant emissions formation as well as improved production control. A longer-term potential and ambition is to be able to identify the fossil content in municipal waste fuel, which can hopefully be addressed in a follow-up study.
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| 5. |
- Lavassani, Mehrzad
(författare)
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Evolving Industrial Networks : Data-Driven Network Traffic Modelling and Monitoring
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The concept of Industrial IoT encompasses the joint applicability of operation and information technologies to expand the efficiency expectation of automation to green and flexible processes with innovative products and services. Future industrial networks need to accommodate, manage and guarantee the performance of converged traffic from different technologies. The network infrastructures are transforming to enable data availability for advanced applications and enhance flexibility. Nonetheless, the pace of IT–OT networks development has been slow despite their considered benefits in optimising performance and enhancing information flows. The hindering factors vary from general challenges in performance management of the diverse traffic for greenfield configuration to the lack of outlines for evolving from brownfield installations without interrupting the operation of ongoing processes. One tangible gap is the lack of insight into the brownfield installation in operation. This dissertation explores the possible evolutionary steps from brownfield installations to future industrial networks.The goal is to ensure the uninterrupted performance of brownfield installations on the path of evolving to the envisioned smart factories. It addresses the gap between the state of the art and state of practice, and the technical prerequisites of the integrated traffic classes for the development of an IIoT monitoring mechanism. A novel lightweight learning algorithm at the sensor level for an IIoT compliance monitoring system, together with a case study of traffic collected from a brownfield installation, provides the baseline of comparative analysis between the common assumptions and the state of practice. The identified gaps and challenges to address them directs the research for proposing a two-step aggregated traffic modelling by introducing new measurement method and performance indicator parameters for capturing the communication dynamics. Lastly, the sensor-level learning algorithm is refined with the knowledge gained from practice and research contributions to propose an in-band telemetry mechanism for monitoring aggregated network traffic.
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| 6. |
- Leander, Björn, 1978-
(författare)
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Dynamic Access Control for Industrial Systems
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Industrial automation and control systems (IACS) are taking care of our most important infrastructures, providing electricity and clean water, producing medicine and food, along with many other services and products we take for granted. The continuous, safe, and secure operation of such systems are obviously of great importance. Future iterations of IACS will look quite different from the ones we use today. Modular and flexible systems are emerging, powered by technical advances in areas such as artificial intelligence, cloud computing, and motivated by fluctuating market demands and faster innovation cycles. Design strategies for dynamic manufacturing are increasingly being adopted. These advances have a fundamental impact on industrial systems at component as well as architectural level. As a consequence of the changing operational requirements, the methods used for protection of industrial systems must be revisited and strengthened. This for example includes access control, which is one of the fundamental cybersecurity mechanisms that is hugely affected by current developments within IACS. The methods currently used are static and coarse-grained and therefore not well suited for dynamic and flexible industrial systems. A transition in security model is required, from implicit trust towards zero-trust, supporting dynamic and fine-grained access control. This PhD thesis discusses access control for IACS in the age of Industry 4.0, focusing on dynamic and flexible manufacturing systems. The solutions presented are applicable at machine-to-machine as well as human-to-machine interactions, using a zero-trust strategy. An investigation of the current state of practice for industrial access control is provided as a starting point for the work. Dynamic systems require equally dynamic access control policies, why several approaches on how dynamic access control can be achieved in industrial systems are developed and evaluated, covering strategies for policy formulations as well as mechanisms for authorization enforcement.
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