| 1. |
- Åkerberg, Johan, et al.
(författare)
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Design challenges and objectives in industrial wireless sensor networks
- 2017
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Ingår i: Industrial Wireless Sensor Networks: Applications, Protocols, and Standards. - Boca Raton : CRC Press. - 9781466500525 - 9781466500518 ; , s. 79-100
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In recent years the advances in wireless sensor networks have grown exponentially and WSNs have been deployed in diverse application areas such as agriculture, disaster management, intelligent transport systems, and industrial automation. In industrial automation, wireless sensor networks have so far mostly been considered within building automation, factory automation, and process automation in order to save cost in cable reduction and maintenance but also improved flexibility [14]. Recently, wireless sensor networks for smart grid applications have been discussed. Several market forecasts have recently predicted exponential growths in the sensor market over the next few years, resulting in a multi-billion dollar market in the near future. For instance, ABI research [18] predicts that in 2015 around 645 million IEEE 802.15.4 chipsets will be shipped and that the worldwide market for automation systems in process industries will grow to roughly $150 billion.
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| 2. |
- Kyprianidis, Konstantinos G.
(författare)
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Multi-Disciplinary Conceptual Design of Future Jet Engine Systems
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis describes various aspects of the development of a multi-disciplinary aero engine conceptual design tool, TERA2020 (Techno-economic, Environmental and Risk Assessment for 2020), based on an explicit algorithm that considers: engine performance, engine aerodynamic and mechanical design, aircraft design and performance, emissions prediction and environmental impact, engine and airframe noise, and production, maintenance and direct operating costs.As part of this research effort, a newly-derived semi-empirical NOx correlation for modern rich-burn single-annular combustors is proposed. The development of a numerical methods library is also presented, including an improved gradient-based algorithm for solving non-linear equation systems. Common assumptions made in thermo-fluid modelling for gas turbines and their effect on caloric properties are investigated, while the impact of uncertainties on performance calculations and emissions predictions at aircraft system level is assessed. Furthermore, accuracy limitations in assessing novel engine core concepts as imposed by current practice in thermo-fluid modelling are identified.The TERA2020 tool is used for quantifying the potential benefits from novel technologies for three low pressure spool turbofan architectures. The impact of failing to deliver specific component technologies is quantified, in terms of power plant noise and CO2 emissions. To address the need for higher engine thermal efficiency, TERA2020 is again utilised; benefits from the potential introduction of heat-exchanged cores in future aero engine designs are explored and a discussion on the main drivers that could support such initiatives is presented. Finally, an intercooled core and conventional core turbofan engine optimisation procedure using TERA2020 is presented. A back-to-back comparison between the two engine configurations is performed and fuel optimal designs for 2020 are proposed.Whilst the detailed publications and the work carried out by the author, in a collaborative effort with other project partners, is presented in the main body of this thesis, it is important to note that this work is supported by 20 conference and journal papers.
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| 3. |
- 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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| 4. |
- Campeanu, Gabriel, 1982-
(författare)
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GPU Support for Component-based Development of Embedded Systems
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- One pressing challenge of many modern embedded systems is to successfully deal with the considerable amount of data that originates from the interaction with the environment. A recent solution comes from the use of GPUs. Equipped with a parallel execution model, the GPU excels in parallel processing applications, providing an improved performance compared to the CPU.Another trend in the embedded systems domain is the use of component-based development. This software engineering paradigm that promotes construction of applications through the composition of software components, has been successfully used in the development of embedded systems. However, the existing approaches provide no specific support to develop embedded systems with GPUs. As a result, components with GPU capability need to encapsulate all the required GPU information in order to be successfully executed by the GPU. This leads to component specialization to specific platforms, hence drastically impeding component reusability.Our main goal is to facilitate component-based development of embedded systems with GPUs. We introduce the concept of flexible component which increases the flexibility to design embedded systems with GPUs, by allowing the system developer to decided where to place the component, i.e., either on the CPU or GPU. Furthermore, we provide means to automatically generate the required information for flexible components corresponding to their hardware placement, and to improve component communication. Through the introduced support, components with GPU capability are platform-independent, being capable to be executed on a large variety of hardware (i.e., platforms with different GPU characteristics). Furthermore, an optimization step is introduced, which groups connected flexible components into single entities that behave as regular components. Dealing with components that can be executed either by the CPU or GPU, we also introduce an allocation optimization method. The proposed solution, implemented using a mathematical solver, offers alternative options in optimizing particular system goals (e.g., memory and energy usage).
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| 5. |
- 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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| 6. |
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| 7. |
- 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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| 8. |
- Anbalagan, Anbarasan, 1988-
(författare)
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A passage to wastewater nutrient recovery units : Microalgal-Bacterial bioreactors
- 2018
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Konstnärligt arbete (övrigt vetenskapligt/konstnärligt)abstract
- In recent years, the microalgal–bacterial process has been considered to be a very attractive engineering solution for wastewater treatment. However, it has not been widely studied in the context of conventional wastewater treatment design under Swedish conditions. The technology holds several advantages: as a CO2 sink, ability to withstand cold conditions, ability to grow under low light, fast settling without chemical precipitation, and reducing the loss of valuable nutrients (CO2, N2, N2O, PO4). The process also provides the option to be operated either as mainstream (treatment of municipal wastewater) or side stream (treatment of centrate from anaerobic digesters) to reduce the nutrient load of the wastewater. Furthermore, the application is not only limited to wastewater treatment; the biomass can be used to synthesise platform chemicals or biofuels and can be followed by recovery of ammonium and phosphate for use in agriculture.In the present study, the feasibility of applying the process in Swedish temperature and light conditions was investigated by implementing microalgae within the activated sludge process. In this context, the supporting operational and performance indicators (hydraulic retention time (HRT), sludge retention time (SRT) and nutrients removal) were evaluated to support naturally occurring consortia in photo-sequencing and continuous bioreactor configuration. Furthermore, CO2 uptake and light spectrum-mediated nutrient removal were investigated to reduce the impact on climate and the technical challenges associated with this type of system.The results identified effective retention times of 6 and 4 days (HRT = SRT) under limited lighting to reduce the electrical consumption. From the perspective of nitrogen removal, the process demands effective CO2 input either in the mainstream or side stream treatment. The incorporation of a vertical absorption column demonstrated effective CO2 mass transfer to support efficient nitrogen and phosphorus removal as a side stream treatment. However, the investigation of a continuous single-stage process as the mainstream showed a requirement for a lower SRT in comparison to semi-continuous operation due to faster settlability, regardless of inorganic carbon. Furthermore, the process showed an effective reduction of influent phosphorus and organic compounds (i.e. COD/TOC) load in the wastewater as a result of photosynthetic aeration. Most importantly, the operation was stable at the temperature equivalent of wastewater (12 and 13 ˚C), under different lighting (white, and red-blue wavelengths) and retention times (6 and 1.5 d HRT) with complete nitrification. Additionally, the biomass production was stable with faster settling properties without any physiochemical separation.The outcomes of this thesis on microalgal–bacterial nutrient removal demonstrates that (1) photosynthesis-based aeration at existing wastewater conditions under photo-sequential and continuous photobioreactor setup, (2) flocs with rapid settling characteristics at all studied retention times, (3) the possibility of increasing carbon supplementation to achieve higher carbon to nitrogen balance in the photobioreactor, and (4) most importantly, nitrification-based microalgal biomass uptake occurred at all spectral distributions, lower photosynthetic active radiation and existing wastewater conditions.
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| 9. |
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Developments in Near-Infrared Spectroscopy
- 2017
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Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- Over the past few decades, exciting developments have taken place in the field of near-infrared spectroscopy (NIRS). This has been enabled by the advent of robust Fourier transform interferometers and diode array solutions, coupled with complex chemometric methods that can easily be executed using modern microprocessors. The present edited volume intends to cover recent developments in NIRS and provide a broad perspective of some of the challenges that characterize the field. The volume comprises six chapters overall and covers several sectors. The target audience for this book includes engineers, practitioners, and researchers involved in NIRS system design and utilization in different applications. We believe that they will greatly benefit from the timely and accurate information provided in this work.
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| 10. |
- Hosain, Md Lokman, 1984-, et al.
(författare)
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Smoothed Particle Hydrodynamics modeling of transient conduction and convection heat transfer
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Smoothed Particle Hydrodynamics (SPH) is a mesh-free particle method that has been widely used in the last years to model some complex flows. SPH was mainly used to investigate problems related to hydrodynamics and maritime engineering where heat transfer is of no importance. Thermal problems have seldom been addressed due to the limitation of the main commercial and open-source SPH codes.In this article, the energy equation is implemented in the SPH based open-source code DualSPHysics to solve conduction and forced convection heat transfer problems. Laminar flow cases are simulated as the first validation cases of the implemented model. The studied cases include conduction in an aluminum block, conduction in still water in a cavity, laminar water flow between two infinite parallel plates and tube bank heat exchanger. The thermal solutions obtained from SPH are benchmarked with the solutions from Finite Volume Method (FVM) and also validated using available analytical solutions. The obtained results are in good agreement with FVM and available analytical models, which combined with the advantages of the meshless approach, show the high potential for industrial heat transfer applications.This development is an important step towards thermal optimization of several industrial applications that can’t benefit from the conventional FVM approach due to geometry or process complexities. The demonstrated SPH simulation and visualization capabilities contribute to build the future reliable energy-saving solutions.
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