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Kyprianidis, KonstantinosORCID iD iconorcid.org/0000-0002-8466-356X
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Publikasjoner (10 av 184) Visa alla publikasjoner
Hashmi, M. B., Mansouri, M., Fentaye, A. D., Ahsan, S. & Kyprianidis, K. (2024). An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines. Energies, 17(3), Article ID 719.
Åpne denne publikasjonen i ny fane eller vindu >>An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines
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2024 (engelsk)Inngår i: Energies, E-ISSN 1996-1073, Vol. 17, nr 3, artikkel-id 719Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible to health degradation in the form of steam-induced corrosion and erosion in the hot gas path. In this context, the fault diagnosis of hydrogen-fueled gas turbines becomes indispensable. To the authors' knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN)-based fault diagnosis model using the MATLAB environment. Prior to the fault detection, isolation, and identification modules, physics-based performance data of a 100 kW micro gas turbine (MGT) were synthesized using the GasTurb tool. An ANN-based classification algorithm showed a 96.2% classification accuracy for the fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing, and validation accuracies in terms of the root mean square error (RMSE). The study revealed that the presence of hydrogen-induced corrosion faults (both as a single corrosion fault or as simultaneous fouling and corrosion) led to false alarms, thereby prompting other incorrect faults during the fault detection and isolation modules. Additionally, the performance of the fault identification module for the hydrogen fuel scenario was found to be marginally lower than that of the natural gas case due to assumption of small magnitudes of faults arising from hydrogen-induced corrosion.

sted, utgiver, år, opplag, sider
MDPI, 2024
Emneord
hydrogen fuel, micro gas turbines, health degradation, steam-induced corrosion, fault detection, diagnostics
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-66085 (URN)10.3390/en17030719 (DOI)001160097200001 ()2-s2.0-85184656336 (Scopus ID)
Tilgjengelig fra: 2024-02-20 Laget: 2024-02-20 Sist oppdatert: 2024-02-20bibliografisk kontrollert
Zhou, Y., Aslanidou, I., Karlsson, M. & Kyprianidis, K. (2024). An explainable AI model for power plant NOx emission control. Energy and AI, 15, Article ID 100326.
Åpne denne publikasjonen i ny fane eller vindu >>An explainable AI model for power plant NOx emission control
2024 (engelsk)Inngår i: Energy and AI, ISSN 2666-5468, Vol. 15, artikkel-id 100326Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In recent years, developing Artificial Intelligence (AI) models for complex system has become a popular research area. There have been several successful AI models for predicting the Selective Non-Catalytic Reduction (SNCR) system in power plants and large boilers. However, all these models are in essence black box models and lack of explainability, which are not able to give new knowledge. In this study, a novel explainable AI (XAI) model that combines the polynomial kernel method with Sparse Identification of Nonlinear Dynamics (SINDy) model is proposed to find the governing equation of SNCR system based on 5-year operation data from a power plant. This proposed model identifies the system's governing equation in a simple polynomial format with polynomial order of 1 and only 1 independent variable among original 68 input variables. In addition, the explainable AI model achieves a considerable accuracy with less than 21 % deviation from base-line models of partial least squares model and artificial neural network model.

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-65124 (URN)10.1016/j.egyai.2023.100326 (DOI)001132419000001 ()2-s2.0-85178644436 (Scopus ID)
Tilgjengelig fra: 2023-12-20 Laget: 2023-12-20 Sist oppdatert: 2024-01-17bibliografisk kontrollert
Ghilardi, A., Frate, G. F., Kyprianidis, K., Tucci, M. & Ferrari, L. (2024). Brayton pumped thermal energy storage: Optimal dispatchment in multi-energy districts. Energy Conversion and Management, 314, Article ID 118650.
Åpne denne publikasjonen i ny fane eller vindu >>Brayton pumped thermal energy storage: Optimal dispatchment in multi-energy districts
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2024 (engelsk)Inngår i: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 314, artikkel-id 118650Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Energy storage systems play a crucial role in supporting the integration of renewable energy sources. In this framework, Brayton Pumped Thermal Energy Storage is an emerging technology thanks to many positive features, including geographical and raw materials independence, long lifetime, and peculiar sector-coupling capabilities. By storing electric energy as thermal exergy, this technology offers the flexibility to discharge energy directly for heating or cooling applications or convert it back into electricity as needed by the grid. This dual functionality fits well with the multi-energy intrinsic nature of urban districts in which electrical and thermal energy carriers are involved. This paper aims then to evaluate the potential economic benefit due to the usage of a Brayton based Pumped Thermal Energy Storage as multi-energy device instead of a solely electric-to-electric. An urban district with thermal and electric requirements is used as a case study to investigate the techno-economic performance of the mentioned storage capacity when coupled to photo-voltaic plants to simulate deep-decarbonization scenarios. The system day-ahead optimization, performed through a Mixed Integer Linear Programming approach, aims to minimize the operational cost computed over a 24-h horizon. The results highlight that operational yearly cost savings are 5–10 % when using the multi-energy storage functionalities compared to the standard electric-to-electric operation. Despite the cost reduction, allowing only direct heating causes unavoidable thermal curtailment losses in the 6–10 % range. However, these losses can be reduced to 3 % by introducing the additional direct cooling functionality, bringing the best performances from the economic and thermodynamic standpoints.

sted, utgiver, år, opplag, sider
Elsevier, 2024
Emneord
Mixed integer linear programming, Multi-energy storage, Pumped thermal energy storage, Sector-coupling, Cost reduction, Electric discharges, Electric energy storage, Electric load dispatching, Heat storage, Integer programming, Renewable energy, Brayton, Integer Linear Programming, Mixed integer linear, Multi energy, Thermal, Thermal energy storage, Thermal energy
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-67707 (URN)10.1016/j.enconman.2024.118650 (DOI)2-s2.0-85195295184 (Scopus ID)
Tilgjengelig fra: 2024-06-20 Laget: 2024-06-20 Sist oppdatert: 2024-06-20bibliografisk kontrollert
Netzell, P., Kazmi, H. & Kyprianidis, K. (2024). Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden. Energies, 17(10), Article ID 2246.
Åpne denne publikasjonen i ny fane eller vindu >>Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden
2024 (engelsk)Inngår i: Energies, E-ISSN 1996-1073, Vol. 17, nr 10, artikkel-id 2246Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the "weekly Na & iuml;ve" benchmark with a relative Root Mean Square Errors of 32-34% and 39-40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.

sted, utgiver, år, opplag, sider
Multidisciplinary Digital Publishing Institute (MDPI), 2024
Emneord
short-term load forecasting, electrical grid, machine learning, multiple linear regression, light gradient boosting machine, explanatory variables
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-67184 (URN)10.3390/en17102246 (DOI)001232160200001 ()2-s2.0-85194279875 (Scopus ID)
Tilgjengelig fra: 2024-06-05 Laget: 2024-06-05 Sist oppdatert: 2024-06-05bibliografisk kontrollert
Fentaye, A. D. & Kyprianidis, K. (2024). Gas turbine prognostics via Temporal Fusion Transformer. Aeronautical Journal
Åpne denne publikasjonen i ny fane eller vindu >>Gas turbine prognostics via Temporal Fusion Transformer
2024 (engelsk)Inngår i: Aeronautical Journal, ISSN 0001-9240Artikkel i tidsskrift (Fagfellevurdert) Epub ahead of print
Abstract [en]

Gas turbines play a vital role in various industries. Timely and accurately predicting their degradation is essential for efficient operation and optimal maintenance planning. Diagnostic and prognostic outcomes aid in determining the optimal compressor washing intervals. Diagnostics detects compressor fouling and estimates the trend up to the current time. If the forecast indicates fast progress in the fouling trend, scheduling offline washing during the next inspection event or earlier may be crucial to address the fouling deposit comprehensively. This approach ensures that compressor cleaning is performed based on its actual health status, leading to improved operation and maintenance costs. This paper presents a novel prognostic method for gas turbine degradation forecasting through a time-series analysis. The proposed approach uses the Temporal Fusion Transformer model capable of capturing time-series relationships at different scales. It combines encoder and decoder layers to capture temporal dependencies and temporal-attention layers to capture long-range dependencies across the encoded degradation trends. Temporal attention is a self-attention mechanism that enables the model to consider the importance of each time step degradation in the context of the entire degradation profile of the given health parameter. Performance data from multiple two-spool turbofan engines is employed to train and test the method. The test results show promising forecasting ability of the proposed method multiple flight cycles into the future. By leveraging the insights provided by the method, maintenance events and activities can be scheduled in a proactive manner. Future work is to extend the method to estimate remaining useful life.

sted, utgiver, år, opplag, sider
CAMBRIDGE UNIV PRESS, 2024
Emneord
gas turbines prognostics, remaining useful life, Temporal Fusion Transformer, compressor washing, predictive maintenance, maintenance optimisation
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-66545 (URN)10.1017/aer.2024.40 (DOI)001207525400001 ()2-s2.0-85191409334 (Scopus ID)
Tilgjengelig fra: 2024-05-08 Laget: 2024-05-08 Sist oppdatert: 2024-05-08bibliografisk kontrollert
Pettinari, M., Frate, G. F., Tran, A. P., Oehler, J., Stathopoulos, P., Kyprianidis, K. & Ferrari, L. (2024). Impact of the Regulation Strategy on the Transient Behavior of a Brayton Heat Pump. Energies, 17(5), Article ID 1020.
Åpne denne publikasjonen i ny fane eller vindu >>Impact of the Regulation Strategy on the Transient Behavior of a Brayton Heat Pump
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2024 (engelsk)Inngår i: Energies, E-ISSN 1996-1073, Vol. 17, nr 5, artikkel-id 1020Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

High-temperature heat pumps are a key technology for enabling the complete integration of renewables into the power grid. Although these systems may come with several variants, Brayton heat pumps are gaining more and more interest because of the higher heat sink temperatures and the potential to leverage already existing components in the industry. Because these systems utilize renewable electricity to supply high-temperature heat, they are particularly suited for industry or energy storage applications, thus prompting the development of various demonstration plants to evaluate their performance and flexibility. Adapting to varying load conditions and swiftly responding to load adjustments represent crucial aspects for advancing such systems. In this context, this study delves into assessing the transient capabilities of Brayton heat pumps during thermal load management. A transient model of an emerging prototype is presented, comprising thermal and volume dynamics of the components. Furthermore, two reference scenarios are examined to assess the transient performance of the system, namely a thermal load alteration due to an abrupt change in the desired heat sink temperature and, secondly, to a sudden variation in the sink mass flow rate. Finally, two control methodologies—motor/compressor speed variation and fluid inventory control—are analyzed in the latter scenario, and a comparative analysis of their effectiveness is discussed. Results indicate that varying the compressor speed allows for a response time in the 8–20 min range for heat sink temperature regulation (first scenario). However, the regulation time is conditioned by the maximum thermal stress sustained by the heat exchangers. In the latter scenario, regulating the compressor speed shows a faster response time than the inventory control (2–5 min vs. 15 min). However, the inventory approach provides higher COPs in part-load conditions and better stability during the transient phase.

sted, utgiver, år, opplag, sider
Multidisciplinary Digital Publishing Institute (MDPI), 2024
Emneord
Brayton heat pump, control system, dynamic modeling, high-temperature heat pump, transient simulation, Electric energy storage, Electric loads, Heat pump systems, High temperature applications, Inventory control, Pumps, Thermal load, Brayton, Dynamics models, Heat pumps, Heat sink temperature, High temperature heat pump, Integration of renewables, Key technologies, Transient behavior, Heat storage
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-66281 (URN)10.3390/en17051020 (DOI)001182671500001 ()2-s2.0-85187468466 (Scopus ID)
Merknad

Article; Export Date: 20 March 2024; Cited By: 0; Correspondence Address: M. Pettinari; Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Pisa, 56122, Italy; email: matteo.pettinari@phd.unipi.it; L. Ferrari; Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Pisa, 56122, Italy; email: lorenzo.ferrari@unipi.it

Tilgjengelig fra: 2024-03-20 Laget: 2024-03-20 Sist oppdatert: 2024-03-27bibliografisk kontrollert
Soibam, J., Aslanidou, I., Kyprianidis, K. & Bel Fdhila, R. (2024). Inverse flow prediction using ensemble PINNs and uncertainty quantification. International Journal of Heat and Mass Transfer, 226
Åpne denne publikasjonen i ny fane eller vindu >>Inverse flow prediction using ensemble PINNs and uncertainty quantification
2024 (engelsk)Inngår i: International Journal of Heat and Mass Transfer, ISSN 0017-9310, E-ISSN 1879-2189, Vol. 226Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

Emneord
Heat transfer, mixed convection, physics informed neural network, optimal sensor placement, transient simulation, inverse method
HSV kategori
Forskningsprogram
energi- och miljöteknik
Identifikatorer
urn:nbn:se:mdh:diva-64897 (URN)10.1016/j.ijheatmasstransfer.2024.125480 (DOI)001226062100001 ()2-s2.0-85189514108 (Scopus ID)
Tilgjengelig fra: 2023-11-29 Laget: 2023-11-29 Sist oppdatert: 2024-05-29bibliografisk kontrollert
Chen, H., Sandberg, A. H., Biancini, G., Dahlquist, E. & Kyprianidis, K. (2024). Profitability Analysis of Integrating Fast Pyrolysis into Existing Combined Heat and Power Plants for Biofuel Production. In: Energy Proceedings: . Paper presented at 15th International Conference on Applied Energy, ICAE 2023. Doha. 3 December 2023 through 7 December 2023. Scanditale AB, Article ID 310669.
Åpne denne publikasjonen i ny fane eller vindu >>Profitability Analysis of Integrating Fast Pyrolysis into Existing Combined Heat and Power Plants for Biofuel Production
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2024 (engelsk)Inngår i: Energy Proceedings, Scanditale AB , 2024, artikkel-id 310669Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Existing combined heat and power plants are seeking additional heat sinks to address challenges arising from the declining district heating demand and the increasing share of renewable energy in primary energy use in the coming decades. In the meantime, the world’s demand for sustainable fuel production keeps increasing due to the need to reduce carbon emissions and mitigate the effects of climate change. Fast pyrolysis, as a thermochemical conversion process based on widely available feedstocks such as lignocellulosic biomass, is promising to provide a long‐term supply of sustainable fuels, and could be integrated into existing combined heat and power plants due to the scalability and maturity of this method. This work focuses on techno‐economic analysis of integrating fast pyrolysis into existing combined heat and power plants for biofuel production. A process model of fast pyrolysis and bio‐oil upgrading is established in Aspen Plus to simulate the integration process. In this work, particular attention is given to the profitability analysis based on different final fuel products(crude pyrolysis oil and upgraded bio‐oil). Different hydrogen generation solutions (electrolysis, and gasification) for onsite bio‐oil upgrading are also examined. This study also performs an analysis of several economic indicators, such as payback period, net present value, and internal rate of return to provide insights for the future business model development for such systems. Sensitivity analysis is also carried out to further reveal the impacts of key variables in the economic evaluation process on the system’s profitability.

sted, utgiver, år, opplag, sider
Scanditale AB, 2024
Emneord
biofuel production, combined heat and power, fast pyrolysis, profitability analysis, uncertainty quantification
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-66576 (URN)10.46855/energy-proceedings-11014 (DOI)2-s2.0-85190879508 (Scopus ID)
Konferanse
15th International Conference on Applied Energy, ICAE 2023. Doha. 3 December 2023 through 7 December 2023
Tilgjengelig fra: 2024-05-08 Laget: 2024-05-08 Sist oppdatert: 2024-05-08bibliografisk kontrollert
Chen, H., Dahlquist, E. & Kyprianidis, K. (2024). Retrofitting Biomass Combined Heat and Power Plant for Biofuel Production-A Detailed Techno-Economic Analysis. Energies, 17(2), Article ID 522.
Åpne denne publikasjonen i ny fane eller vindu >>Retrofitting Biomass Combined Heat and Power Plant for Biofuel Production-A Detailed Techno-Economic Analysis
2024 (engelsk)Inngår i: Energies, E-ISSN 1996-1073, Vol. 17, nr 2, artikkel-id 522Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Existing combined heat and power plants usually operate on part-load conditions during low heating demand seasons. Similarly, there are boilers designated for winter use that remain inactive for much of the year. This brings a concern about the inefficiency of resource utilization. Retrofitting existing CHP plants (especially for those with spare boilers) for biofuel production could increase revenue and enhance resource efficiency. This study introduces a novel approach that combines biomass gasification and pyrolysis in a polygeneration process that is based on utilizing existing CHP facilities to produce biomethane, bio-oil, and hydrogen. In this work, a detailed analysis was undertaken of retrofitting an existing biomass combined heat and power plant for biofuel production. The biofuel production plant is designed to explore the polygeneration of hydrogen, biomethane, and bio-oil via the integration of gasification, pyrolysis, and renewable-powered electrolysis. An Aspen Plus model of the proposed biofuel production plant is established followed by a performance investigation of the biofuel production plant under various design conditions. An economic analysis is carried out to examine the profitability of the proposed polygeneration system. Results show that the proposed polygeneration system can achieve 40% carbon efficiency with a payback period of 9 years and an internal rate of return of 17.5%, without the integration of renewable hydrogen. When integrated with renewable-power electrolysis, the carbon efficiency could be significantly improved to approximately 90%; however, the high investment cost associated with the electrolyzer system makes this integration economically unfavorable.

sted, utgiver, år, opplag, sider
MDPI, 2024
Emneord
biofuel, biomass, existing CHP plants, process modeling, techno-economic analysis
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-65948 (URN)10.3390/en17020522 (DOI)001151936200001 ()2-s2.0-85183319309 (Scopus ID)
Tilgjengelig fra: 2024-02-07 Laget: 2024-02-07 Sist oppdatert: 2024-02-07bibliografisk kontrollert
Bermperis, D., Ntouvelos, E., Kavvalos, M., Vouros, S., Kyprianidis, K. & Kalfas, A. I. (2024). Synergies and Trade-Offs in Hybrid Propulsion Systems Through Physics-Based Electrical Component Modeling. Journal of engineering for gas turbines and power, 146(1), Article ID 011005.
Åpne denne publikasjonen i ny fane eller vindu >>Synergies and Trade-Offs in Hybrid Propulsion Systems Through Physics-Based Electrical Component Modeling
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2024 (engelsk)Inngår i: Journal of engineering for gas turbines and power, ISSN 0742-4795, E-ISSN 1528-8919, Vol. 146, nr 1, artikkel-id 011005Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Hybrid-electric propulsion is recognized as an enabling technology for reducing aviation’s environmental impact. In this work, a serial/parallel hybrid configuration of a 19-passenger commuter aircraft is investigated. Two underwing-mounted turboprop engines are connected to electrical branches via generators. One rear fuselage-mounted electrically driven ducted fan is coupled with an electric motor and respective electrical branch. A battery system completes the selected architecture. Consistency in modeling accuracy of propulsion systems is aimed for by development of an integrated framework. A multipoint synthesis scheme for the gas turbine and electric fan is combined with physics-based analytical modeling for electrical components. Influence of turbomachinery and electrical power system design points on the integrated power system is examined. An opposing trend between electrical and conventional powertrain mass is driven by electric fan design power. Power system efficiency improvements in the order of 2% favor high-power electric fan designs. A trade-off in electrical power system mass and performance arises from oversizing of electrical components for load manipulation. Branch efficiency improvements of up to 3% imply potential to achieve battery mass reduction due to fewer transmission losses. A threshold system voltage of 1 kV, yielding 32% mass reduction of electrical branches and performance improvements of 1–2%, is identified. This work sets the foundation for interpreting mission-level electrification outcomes that are driven by interactions on the integrated power system. Areas of conflicting interests and synergistic opportunities are highlighted for optimal conceptual design of hybrid powertrains.

sted, utgiver, år, opplag, sider
American Society of Mechanical Engineers (ASME), 2024
Emneord
Conceptual design, Economic and social effects, Efficiency, Electric loads, Electric power transmission, Electric propulsion, Engines, Environmental technology, Machine design, Efficiency improvement, Electric fans, Electrical components, Electrical power system, Fan designs, Integrated Power Systems, Mass reduction, Performance, Physics-based, Trade off, Environmental impact
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-64855 (URN)10.1115/1.4063381 (DOI)2-s2.0-85177224156 (Scopus ID)
Tilgjengelig fra: 2023-11-29 Laget: 2023-11-29 Sist oppdatert: 2023-11-29bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-8466-356X