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| 2. |
- Dahlquist, Erik, 1951-, et al.
(författare)
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AI Overview : Methods and Structures
- 2021. - 1
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Ingår i: AI and Learning Systems - Industrial Applications and Future Directions. - : IntechIntechOpen. - 9781789858778 - 9781839686016
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Bokkapitel (refereegranskat)abstract
- This paper presents an overview of different methods used in what is normally called AI-methods today. The methods have been there for many years, but now have built a platform of methods complementing each other and forming a cluster of tools to be used to build “learning systems”. Physical and statistical models are used together and complemented with data cleaning and sorting. Models are then used for many different applications like output prediction, soft sensors, fault detection, diagnostics, decision support, classifications, process optimization, model predictive control, maintenance on demand and production planning. In this chapter we try to give an overview of a number of methods, and how they can be utilized in process industry applications.
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| 3. |
- Gkoutzamanis, V. G., et al.
(författare)
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Thermal Management System Considerations for a Hybrid-Electric Commuter Aircraft
- 2022
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Ingår i: Journal of thermophysics and heat transfer. - : AIAA International. - 0887-8722 .- 1533-6808. ; 36:3, s. 650-666
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Tidskriftsartikel (refereegranskat)abstract
- When it comes to novel aircraft concepts, thermal management system (TMS) design is a ubiquitous task, even at the conceptual design phase. This is owing to its impact on the total weight of the aircraft, cooling drag, and overall performance. The commuter air transportation has recently regained attention and is seen as a solution to employ partial or full electrification in the upcoming decades due to its low power requirement and potential benefit of faster “door-to-door” traveling. This work examines the TMS characteristics to cool a battery-powered aft-fan engine. A literature review is initially performed on other research associated with TMS design. The development and weight evaluation of the baseline TMS for this type of propulsive technology are then presented, including the characterization of system redundancy effects on the overall TMS weight. Results show that the TMS design is a function of the selected propulsive configuration and energy management throughout the mission. Primarily, this relates to the cooling method selected, the heat exchangers as the major mass contributors of the TMS, the positioning of components used for the propulsive configuration, and the imposed certification constraints. Finally, the selected TMS design is calculated to have a combined specific cooling of 0.79 kW∕kg.
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| 4. |
- Martinsen, Madeleine, et al.
(författare)
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Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future
- 2023
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Ingår i: Applied Energy. - : ELSEVIER SCI LTD. - 0306-2619 .- 1872-9118. ; 339
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Tidskriftsartikel (refereegranskat)abstract
- Initially, Artificial Intelligence (AI) focused on diagnostics during the 70s and 80s. Unfortunately, it did not gain trust and few industries embraced it, mostly due to the extensive manual programming effort that AI required for interpreting data and act. In addition, the computer capacity, for handling the amounts of data necessary to train AI, was lacking the disc dimensions we are used to today, which made it go slowly. Not until the 2000 s con-fidence in AI was established in parallel with the introduction of new tools that was paving the way for PLS, PCA, ANN and soft sensors. Year 2011, IBM Watson (an AI application) was developed and won over the jeopardy champion. Today's machine learning (ML) such as "deep learning" and artificial neural networks (ANN) have created interesting use cases. AI has therefore regained confidence and industries are beginning to embrace where they see appropriate uses. Simultaneously, Internet of Things (IoT) tools have been introduced and made it possible to develop new capabilities such as virtual reality (VR), augmented reality (AR), mixed reality (MR) and extended reality (XR). These technologies are maturing and could be used in several application areas for the industries and form part of their digitalization journey. Furthermore, it is not only the industries that could benefit from introducing these technologies. Studies also show several areas and use cases where augmented reality has a positive impact, such as on students' learning ability. Yet few teachers know or use this technology. This paper evaluates and analyze AR, remote assistance tool for industrial purposes. The potential of the tool is discussed for frequent maintenance cases in the mining industry. Further on, if we look into the future, it is not surprising if we will be able to see that today's concepts of reality tools have evolved to become smarter by being trained by multimedia recognition and from people who have thus created an AI expert. Where the AI expert will support customers and be able to solve simple errors but also those that occur rarely and thus be a natural part of the solution for future completely autonomous processes for the industry. The article demonstrates a framework for creating smarter tools by combining AR, ML and AI and forms part of the basis creating the smarter industry of the future. Natural Language Processing (NLP) toolbox has been utilized to train and test an AI expert to give suitable resolutions to a specific maintenance request. The motivation for AR is the possible energy savings and reduction of CO2 emissions in the maintenance field for all business trips that can be avoided. At the same time saving money for the industries and expert manhours that are spent on traveling and finally enhancing the productivity for the industries. Tests cases have verified that with AR, the resolution time could be significantly reduced, minimizing production stoppages by more than 50% of the time, which ultimately has a positive effect on a country's GDP. How much energy can be saved is predicted by the fact that 50% of all the world's business flights are replaced by one of the reality concepts and are estimated to amount to at least 50 Mton CO2 per year. This figure is probably slightly higher as business trips also take place by other means of transport such as trains, buses, and cars. With today's volatile employees changing jobs more frequently, industry experts are becoming fewer and fewer. Since new employee stays for a maximum of 3-5 years per workplace, they will not stay long enough to become experts. Introducing an AI expert trained by today's experts, there is a chance that this knowledge can be maintained.
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| 5. |
- Olsson, Tomas, et al.
(författare)
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A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines
- 2021
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Ingår i: Energy and AI. - : Elsevier BV. - 2666-5468. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Predictive health monitoring of micro gas turbines can significantly increase the availability and reduce the operating and maintenance costs. Methods for predictive health monitoring are typically developed for large-scale gas turbines and have often focused on single systems. In an effort to enable fleet-level health monitoring of micro gas turbines, this work presents a novel data-driven approach for predicting system degradation over time. The approach utilises operational data from real installations and is not dependent on data from a reference system. The problem was solved in two steps by: 1) estimating the degradation from time-dependent variables and 2) forecasting into the future using only running hours. Linear regression technique is employed both for the estimation and forecasting of degradation. The method was evaluated on five different systems and it is shown that the result is consistent (r>0.8) with an existing method that computes corrected values based on data from a reference system, and the forecasting had a similar performance as the estimation model using only running hours as an input.
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| 6. |
- Sahoo, Smruti, et al.
(författare)
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A Review of Concepts, Benefits, and Challenges for Future Electrical Propulsion-Based Aircraft
- 2020
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Ingår i: Aerospace. - : MDPI AG. - 2226-4310. ; 7:4
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Forskningsöversikt (refereegranskat)abstract
- Electrification of the propulsion system has opened the door to a new paradigm of propulsion system configurations and novel aircraft designs, which was never envisioned before. Despite lofty promises, the concept must overcome the design and sizing challenges to make it realizable. A suitable modeling framework is desired in order to explore the design space at the conceptual level. A greater investment in enabling technologies, and infrastructural developments, is expected to facilitate its successful application in the market. In this review paper, several scholarly articles were surveyed to get an insight into the current landscape of research endeavors and the formulated derivations related to electric aircraft developments. The barriers and the needed future technological development paths are discussed. The paper also includes detailed assessments of the implications and other needs pertaining to future technology, regulation, certification, and infrastructure developments, in order to make the next generation electric aircraft operation commercially worthy.
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| 7. |
- Sielemann, M., et al.
(författare)
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Introduction to multi-point design strategies for aero engines
- 2020
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Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791884157 ; 6
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Konferensbidrag (refereegranskat)abstract
- Classic gas turbine design relies on the definition of a design point, and the subsequent assessment of the design on a range of off-design conditions. On the design point, both component sizing (e.g., in terms of physical dimensions or in terms of map scaling parameters) and a solution to the off-design governing equations are established. With this approach, it is however difficult to capture the contradicting requirements on the full operating envelope. Thus, practical design efforts rely on various multi-point design approaches. This paper introduces a simplified notation of such multi-point approaches via synthesis matching tables. It then summarizes two academic state-of-the-art multi-point design schemes using such tables in a comprehensible fashion. The target audience are students and engineers familiar with the basics of classic cycle design and analysis looking for a practical introduction to such multi-point design approaches. Application examples are given in terms of a simple turbojet and a typical geared turbofan as modeled in state-of-the-art academic cycle design and analysis efforts. The results of the classic design point approach are compared to those of multi-point approaches. Copyright © 2020 ASME
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| 8. |
- Skvaril, Jan, 1982-, et al.
(författare)
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Applications of near-infrared spectroscopy (NIRS) in biomass energy conversion processes : A review
- 2017
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Ingår i: Applied spectroscopy reviews (Softcover ed.). - : Informa UK Limited. - 0570-4928 .- 1520-569X. ; 52:8, s. 675-728
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Forskningsöversikt (refereegranskat)abstract
- Biomass used in energy conversion processes is typically characterized by high variability, making its utilization challenging. Therefore, there is a need for a fast and non-destructive method to determine feedstock/product properties and directly monitor process reactors. The near-infrared spectroscopy (NIRS) technique together with advanced data analysis methods offers a possible solution. This review focuses on the introduction of the NIRS method and its recent applications to physical, thermochemical, biochemical and physiochemical biomass conversion processes represented mainly by pelleting, combustion, gasification, pyrolysis, as well as biogas, bioethanol, and biodiesel production. NIRS has been proven to be a reliable and inexpensive method with a great potential for use in process optimization, advanced control, or product quality assurance.
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| 9. |
- Skvaril, Jan, 1982-, et al.
(författare)
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Fast Determination of Fuel Properties in Solid Biofuel Mixtures by Near Infrared Spectroscopy
- 2017
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Ingår i: Energy Procedia. - Amsterdam : Elsevier. - 1876-6102. ; 105, s. 1309-1317
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Tidskriftsartikel (refereegranskat)abstract
- This paper focuses on the characterization of highly variable biofuel properties such as moisture content, ash content and higher heating value by near-infrared (NIR) spectroscopy. Experiments were performed on different biofuel sample mixtures consisting of stem wood chips, forest residue chips, bark, sawdust, and peat. NIR scans were performed using a Fourier transform NIR instrument, and reference values were obtained according to standardized laboratory methods. Spectral data were pre-processed by Multiplicative scatter correction correcting light scattering and change in a path length for each sample. Multivariate calibration was carried out employing Partial least squares regression while absorbance values from full NIR spectral range (12,000–4000 cm-1), and reference values were used as inputs. It was demonstrated that different solid biofuel properties can be measured by means of NIR spectroscopy. The accuracy of the models is satisfactory for industrial implementation towards improved process control.
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| 10. |
- Soibam, Jerol, et al.
(författare)
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Inverse flow prediction using ensemble PINNs and uncertainty quantification
- 2024
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Ingår i: International Journal of Heat and Mass Transfer. - 0017-9310 .- 1879-2189. ; 226
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Tidskriftsartikel (refereegranskat)abstract
- The thermal boundary conditions in a numerical simulation for heat transfer are often imprecise. This leads to poorly defined boundary conditions for the energy equation. The lack of accurate thermal boundary conditions in real-world applications makes it impossible to effectively solve the problem, regardless of the advancement of conventional numerical methods. This study utilises a physics-informed neural network to tackle ill-posed problems for unknown thermal boundaries with limited sensor data. The network approximates velocity and temperature fields while complying with the Navier-Stokes and energy equations, thereby revealing unknown thermal boundaries and reconstructing the flow field around a square cylinder. The method relies on optimal sensor placement determined by the QR pivoting technique, which ensures the effective capture of the dynamics, leading to enhanced model accuracy. In an effort to increase the robustness and generalisability, an ensemble physics-informed neural network is implemented. This approach mitigates the risks of overfitting and underfitting while providing a measure of model confidence. As a result, the ensemble model can identify regions of reliable prediction and potential inaccuracies. Therefore, broadening its applicability in tackling complex heat transfer problems with unknown boundary conditions.
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