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Ioanna Aslanidou; Valentina Zaccaria; Amare Fentaye; Konstantinos Kyprianidis. Development of Web-Based Short Courses on Control, Diagnostics, and Instrumentation. 2021, 1 .
AMA StyleIoanna Aslanidou, Valentina Zaccaria, Amare Fentaye, Konstantinos Kyprianidis. Development of Web-Based Short Courses on Control, Diagnostics, and Instrumentation. . 2021; ():1.
Chicago/Turabian StyleIoanna Aslanidou; Valentina Zaccaria; Amare Fentaye; Konstantinos Kyprianidis. 2021. "Development of Web-Based Short Courses on Control, Diagnostics, and Instrumentation." , no. : 1.
Mario Luigi Ferrari; Valentina Zaccaria; Konstantinos Kyprianidis. Pressurized SOFC System Fuelled by Biogas: Control Approaches and Degradation Impact. 2021, 1 .
AMA StyleMario Luigi Ferrari, Valentina Zaccaria, Konstantinos Kyprianidis. Pressurized SOFC System Fuelled by Biogas: Control Approaches and Degradation Impact. . 2021; ():1.
Chicago/Turabian StyleMario Luigi Ferrari; Valentina Zaccaria; Konstantinos Kyprianidis. 2021. "Pressurized SOFC System Fuelled by Biogas: Control Approaches and Degradation Impact." , no. : 1.
Manuel A. Rend\xe3\xb3N; Konstantinos Kyprianidis; Yipsy Roque Benito; Daniel De A. Fernandes; Ariele T. Ferraz; Luan R. C. Vieira. Energy Management of a Hybrid-Electric Aeronautical Propulsion System to Be Used in a Stationary Test Bench. 2021, 1 .
AMA StyleManuel A. Rend\xe3\xb3N, Konstantinos Kyprianidis, Yipsy Roque Benito, Daniel De A. Fernandes, Ariele T. Ferraz, Luan R. C. Vieira. Energy Management of a Hybrid-Electric Aeronautical Propulsion System to Be Used in a Stationary Test Bench. . 2021; ():1.
Chicago/Turabian StyleManuel A. Rend\xe3\xb3N; Konstantinos Kyprianidis; Yipsy Roque Benito; Daniel De A. Fernandes; Ariele T. Ferraz; Luan R. C. Vieira. 2021. "Energy Management of a Hybrid-Electric Aeronautical Propulsion System to Be Used in a Stationary Test Bench." , no. : 1.
Vasilis Gkoutzamanis; Arjun Srinivas; Mavroudis Kavvalos; Doukaini Mavroudi; George Korbetis; Konstantinos Kyprianidis; Anestis Kalfas. Conceptual Design and Energy Storage Positioning Aspects for a Hybrid-Electric Light Aircraft. 2021, 1 .
AMA StyleVasilis Gkoutzamanis, Arjun Srinivas, Mavroudis Kavvalos, Doukaini Mavroudi, George Korbetis, Konstantinos Kyprianidis, Anestis Kalfas. Conceptual Design and Energy Storage Positioning Aspects for a Hybrid-Electric Light Aircraft. . 2021; ():1.
Chicago/Turabian StyleVasilis Gkoutzamanis; Arjun Srinivas; Mavroudis Kavvalos; Doukaini Mavroudi; George Korbetis; Konstantinos Kyprianidis; Anestis Kalfas. 2021. "Conceptual Design and Energy Storage Positioning Aspects for a Hybrid-Electric Light Aircraft." , no. : 1.
Luca Mantelli; Valentina Zaccaria; Konstantinos Kyprianidis; Mario Luigi Ferrari. A Degradation Diagnosis Method for Gas Turbine \u2013 Fuel Cell Hybrid Systems Using Bayesian Networks. 2021, 1 .
AMA StyleLuca Mantelli, Valentina Zaccaria, Konstantinos Kyprianidis, Mario Luigi Ferrari. A Degradation Diagnosis Method for Gas Turbine \u2013 Fuel Cell Hybrid Systems Using Bayesian Networks. . 2021; ():1.
Chicago/Turabian StyleLuca Mantelli; Valentina Zaccaria; Konstantinos Kyprianidis; Mario Luigi Ferrari. 2021. "A Degradation Diagnosis Method for Gas Turbine \u2013 Fuel Cell Hybrid Systems Using Bayesian Networks." , no. : 1.
This work is a feasibility study of a 19-passenger hybrid-electric aircraft, to serve the short-haul segment within the 200–600 nautical miles. Its ambition is to answer some dominating research questions, during the evaluation and design of aircraft based on alternative propulsion architectures. The potential entry into service (EIS) is foreseen beyond 2030. A literature review is performed to identify similar concepts under research and development. After the requirements' definition, the first level of conceptual design is employed. The objective of design selections is driven by the need to reduce CO2 emissions and accommodate aircraft electrification with boundary layer ingestion engines. Based on a set of assumptions, a methodology for the sizing of the hybrid-electric aircraft is described to explore the basis of the design space, incorporating a parametric analysis for the consideration of boundary layer ingestion effects. Additionally, a methodology for the energy storage positioning is provided to highlight the multidisciplinary aspects between the sizing of an aircraft, the selected architecture (series/parallel partial hybrid), and the storage characteristics. The results show that it is not possible to fulfill the initial design requirements (600 nmi) with a fully-electric aircraft configuration, due to the far-fetched battery necessities. It is also highlighted that compliance with airworthiness standards is favored by switching to hybrid-electric aircraft configurations and relaxing the design requirements (targeted range, payload, battery technology). Finally, the lower degree of hybridization (40%) is observed to have a higher energy efficiency (−12% energy consumption) compared to the higher degree of hybridization (50%) and greater CO2 reduction, with respect to the conventional configuration.
Vasilis G. Gkoutzamanis; Mavroudis D. Kavvalos; Arjun Srinivas; Doukaini Mavroudi; George Korbetis; Konstantinos G. Kyprianidis; Anestis I. Kalfas. Conceptual Design and Energy Storage Positioning Aspects for a Hybrid-Electric Light Aircraft. Journal of Engineering for Gas Turbines and Power 2021, 143, 1 .
AMA StyleVasilis G. Gkoutzamanis, Mavroudis D. Kavvalos, Arjun Srinivas, Doukaini Mavroudi, George Korbetis, Konstantinos G. Kyprianidis, Anestis I. Kalfas. Conceptual Design and Energy Storage Positioning Aspects for a Hybrid-Electric Light Aircraft. Journal of Engineering for Gas Turbines and Power. 2021; 143 (9):1.
Chicago/Turabian StyleVasilis G. Gkoutzamanis; Mavroudis D. Kavvalos; Arjun Srinivas; Doukaini Mavroudi; George Korbetis; Konstantinos G. Kyprianidis; Anestis I. Kalfas. 2021. "Conceptual Design and Energy Storage Positioning Aspects for a Hybrid-Electric Light Aircraft." Journal of Engineering for Gas Turbines and Power 143, no. 9: 1.
The present study describes the development of a preliminary design of a rotor for a radial turbine operating in an organic Rankine cycle. An optimization algorithm is applied to the preliminary design in order to obtain a better configuration of the geometric parameters that provides good quantification of the efficiency in the turbine, a priori, since the application of optimization processes applied to three-dimensional problems consume a lot of computational resources. The strategy makes it possible to obtain an optimized geometry to obtain flow field analyzes by applying computational fluid dynamics techniques. The working fluid R236fa was used for comparison with the literature, as it presents a positive slope of the saturation curve, and thus it is possible to work with lower temperatures. The R245fa working fluid is more suitable to the operating conditions of the proposed cycle, allows an overpressure in the condenser and allows higher levels of system efficiency. The losses at the rotor nozzle were initially modeled using a mean line design approach. The preliminary design was implemented in a commercial code Matlab®, as well as the optimization algorithm, CRSA (Controlled Random Search Algorithm), and the real gas formulations were used based on the NIST REFPROP® database. The present study is presented under three work routes: i) Development of the preliminary design methodology for a radial turbine that operates with ORC producing 50 kW of power, in order to compare with other methodologies presented in the literature. The results were compared with results observed in the literature, and demonstrate agreement between the reference geometry and the thermodynamic parameters. The total-total efficiencies of the reference turbine designs were 76.23% (R236fa) and 79.28% (R245fa); ii) Optimization by CRSA of the preliminary design of a radial turbine developed on the basis of flow coefficient and load coefficient correlations. A three-dimensional analysis of the flow through the blade section using computational fluid dynamics was performed in the final optimized design to confirm the preliminary design and subsequently analyze its characteristics. The optimization focused on the R245fa working fluid. Although several optimized preliminary designs are available in the literature with efficiency levels of up to 90%, the preliminary design choices made will only be valid for machines operating with ideal gases, that is, exhaust gases typical of an air-breathing combustion engine. For machines operating with real gases, such as organic working fluids, the design options need to be rethought and a preliminary design optimization process must be introduced. As an important result observed, an efficiency of 82.4% was obtained in the final design of the radial turbine operating with R245fa after the optimization process.
Edna Raimunda da Silva; Konstantinos G. Kyprianidis; Ramiro G. Ramirez Camacho; Michael Säterskog; Tania Marie Arispe Angulo. Preliminary design, optimization and CFD analysis of an organic rankine cycle radial turbine rotor. Applied Thermal Engineering 2021, 195, 117103 .
AMA StyleEdna Raimunda da Silva, Konstantinos G. Kyprianidis, Ramiro G. Ramirez Camacho, Michael Säterskog, Tania Marie Arispe Angulo. Preliminary design, optimization and CFD analysis of an organic rankine cycle radial turbine rotor. Applied Thermal Engineering. 2021; 195 ():117103.
Chicago/Turabian StyleEdna Raimunda da Silva; Konstantinos G. Kyprianidis; Ramiro G. Ramirez Camacho; Michael Säterskog; Tania Marie Arispe Angulo. 2021. "Preliminary design, optimization and CFD analysis of an organic rankine cycle radial turbine rotor." Applied Thermal Engineering 195, no. : 117103.
This paper shows control approaches for managing a pressurized solid oxide fuel cell (SOFC) system fuelled by biogas. This is an advanced solution to integrate the high efficiency benefits of a pressurized SOFC with a renewable source. The operative conditions of these analyses are based on the matching with an emulator rig including a T100 machine for tests in cyber-physical mode. So, this paper presents a real-time model including the fuel cell, the off-gas burner (OFB), and the recirculation lines. Although the microturbine components are planned to be evaluated with the hardware devices, the model includes also the T100 expander for machine control reasons. The simulations shown in this paper regard the assessment of an innovative control tool based on the model predictive control (MPC) technology. This controller and an additional tool based on the coupling of MPC and proportional integral derivative (PID) approaches were assessed against the application of PID controllers. The control targets consider both steady-state and dynamic aspects. Moreover, different control solutions are presented to operate the system during fuel cell degradation. The results include the system response to load variations, and SOFC voltage decrease. Considering the simulations including SOFC degradation, the MPC was able to decrease the thermal stress, but it was not able to compensate the degradation. On the other hand, the tool based on the coupling of the MPC and the PID approaches produced the best results in terms of set-point matching, and SOFC thermal stress containment.
Mario L. Ferrari; Valentina Zaccaria; Konstantinos G. Kyprianidis. Pressurized SOFC System Fuelled by Biogas: Control Approaches and Degradation Impact. Journal of Engineering for Gas Turbines and Power 2021, 143, 1 .
AMA StyleMario L. Ferrari, Valentina Zaccaria, Konstantinos G. Kyprianidis. Pressurized SOFC System Fuelled by Biogas: Control Approaches and Degradation Impact. Journal of Engineering for Gas Turbines and Power. 2021; 143 (6):1.
Chicago/Turabian StyleMario L. Ferrari; Valentina Zaccaria; Konstantinos G. Kyprianidis. 2021. "Pressurized SOFC System Fuelled by Biogas: Control Approaches and Degradation Impact." Journal of Engineering for Gas Turbines and Power 143, no. 6: 1.
This paper aims to develop and test Bayesian belief network-based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor, and fuel cell (FC) in a hybrid system based on different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks are generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks (BBNs) to fuel cell—gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in a gas turbine, fuel cell and sensors in a fuel cell—gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady-state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.
Luca Mantelli; Valentina Zaccaria; Mario Luigi Ferrari; Konstantinos G. Kyprianidis. A Degradation Diagnosis Method for Gas Turbine—Fuel Cell Hybrid Systems Using Bayesian Networks. Journal of Engineering for Gas Turbines and Power 2021, 143, 1 .
AMA StyleLuca Mantelli, Valentina Zaccaria, Mario Luigi Ferrari, Konstantinos G. Kyprianidis. A Degradation Diagnosis Method for Gas Turbine—Fuel Cell Hybrid Systems Using Bayesian Networks. Journal of Engineering for Gas Turbines and Power. 2021; 143 (5):1.
Chicago/Turabian StyleLuca Mantelli; Valentina Zaccaria; Mario Luigi Ferrari; Konstantinos G. Kyprianidis. 2021. "A Degradation Diagnosis Method for Gas Turbine—Fuel Cell Hybrid Systems Using Bayesian Networks." Journal of Engineering for Gas Turbines and Power 143, no. 5: 1.
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.
Tomas Olsson; Enislay Ramentol; Moksadur Rahman; Mark Oostveen; Konstantinos Kyprianidis. A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines. Energy and AI 2021, 4, 100064 .
AMA StyleTomas Olsson, Enislay Ramentol, Moksadur Rahman, Mark Oostveen, Konstantinos Kyprianidis. A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines. Energy and AI. 2021; 4 ():100064.
Chicago/Turabian StyleTomas Olsson; Enislay Ramentol; Moksadur Rahman; Mark Oostveen; Konstantinos Kyprianidis. 2021. "A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines." Energy and AI 4, no. : 100064.
Despite that mechanistic and accurate correlations predicting the Onset of Nucleate Boiling (ONB) for pool boiling are widely presented in the literature, models for forced convective boiling remain few. These models do not provide the desired quality, principally because they do not consider important features of convective boiling. In this work, numerical investigations of the ONB for water boiling flow at atmospheric pressure upward a narrow rectangular channel (3 mm × 100 mm × 400 mm) are carried out based on Computational Fluid Dynamics (CFD) simulations. The predictions of the CFD calculations are validated with the available experimental data. A new ONB model incorporating the convective boiling features is developed and proposed. This model is derived based on several CFD simulation data, covering wide operating conditions. The flow Reynolds number ranges from 959 to 13500, inlet subcooling from 2.5 to 30 K and applied heat flux from 5 to 90 kW/m2. The new model predictions have a standard deviation of 2.7% where its performance is better than ±0.3 K when compared with additional simulation data that are provided for validation.
Achref Rabhi; Ioanna Aslanidou; Konstantinos Kyprianidis; Rebei Bel Fdhila. Onset of Nucleate Boiling Model for Rectangular Upward Narrow Channel: CFD Based Approach. International Journal of Heat and Mass Transfer 2020, 165, 120715 .
AMA StyleAchref Rabhi, Ioanna Aslanidou, Konstantinos Kyprianidis, Rebei Bel Fdhila. Onset of Nucleate Boiling Model for Rectangular Upward Narrow Channel: CFD Based Approach. International Journal of Heat and Mass Transfer. 2020; 165 ():120715.
Chicago/Turabian StyleAchref Rabhi; Ioanna Aslanidou; Konstantinos Kyprianidis; Rebei Bel Fdhila. 2020. "Onset of Nucleate Boiling Model for Rectangular Upward Narrow Channel: CFD Based Approach." International Journal of Heat and Mass Transfer 165, no. : 120715.
Subcooled flow boiling occurs in many industrial applications where enormous heat transfer is needed. Boiling is a complex physical process that involves phase change, two-phase flow, and interactions between heated surfaces and fluids. In general, boiling heat transfer is usually predicted by empirical or semiempirical models, which are horizontal to uncertainty. In this work, a data-driven method based on artificial neural networks has been implemented to study the heat transfer behavior of a subcooled boiling model. The proposed method considers the near local flow behavior to predict wall temperature and void fraction of a subcooled minichannel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The outputs of the models are based on the quantities of interest in a boiling system wall temperature and void fraction. To train the network, high-fidelity simulations based on the Eulerian two-fluid approach are carried out for varying heat flux and inlet velocity in the minichannel. Two classes of the deep learning model have been investigated for this work. The first one focuses on predicting the deterministic value of the quantities of interest. The second one focuses on predicting the uncertainty present in the deep learning model while estimating the quantities of interest. Deep ensemble and Monte Carlo Dropout methods are close representatives of maximum likelihood and Bayesian inference approach respectively, and they are used to derive the uncertainty present in the model. The results of this study prove that the models used here are capable of predicting the quantities of interest accurately and are capable of estimating the uncertainty present. The models are capable of accurately reproducing the physics on unseen data and show the degree of uncertainty when there is a shift of physics in the boiling regime.
Jerol Soibam; Achref Rabhi; Ioanna Aslanidou; Konstantinos Kyprianidis; Rebei Bel Fdhila. Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model. Energies 2020, 13, 5987 .
AMA StyleJerol Soibam, Achref Rabhi, Ioanna Aslanidou, Konstantinos Kyprianidis, Rebei Bel Fdhila. Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model. Energies. 2020; 13 (22):5987.
Chicago/Turabian StyleJerol Soibam; Achref Rabhi; Ioanna Aslanidou; Konstantinos Kyprianidis; Rebei Bel Fdhila. 2020. "Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model." Energies 13, no. 22: 5987.
Being at the heart of modern pulp mills, continuous pulp digesters have attracted much attention from the research community. In this article, a comprehensive review in the area of modeling, control and diagnostics of continuous pulp digesters is conducted. The evolution of research focus within these areas is followed and discussed. Particular effort has been devoted to identifying the state-of-the-art and the research gap in a summarized way. Finally, the current and future research directions in the areas have been analyzed and discussed. To date, digester modeling following the Purdue approach, Kappa number control using model predictive controllers and health index-based diagnostic approaches by utilizing different statistical methods have dominated the field. While the rising research interest within the field is evident, we anticipate further developments in advanced sensors and integration of these sensors for improving model prediction and controller performance; and the exploration of different AI-based approaches will be at the core of future research.
Moksadur Rahman; Anders Avelin; Konstantinos Kyprianidis. A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters. Processes 2020, 8, 1231 .
AMA StyleMoksadur Rahman, Anders Avelin, Konstantinos Kyprianidis. A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters. Processes. 2020; 8 (10):1231.
Chicago/Turabian StyleMoksadur Rahman; Anders Avelin; Konstantinos Kyprianidis. 2020. "A Review on the Modeling, Control and Diagnostics of Continuous Pulp Digesters." Processes 8, no. 10: 1231.
In a world of fast technological advancements, it is increasingly important to see how hydrocracking applications can benefit from and adapt to digitalization. A review of hydrocracking processes from the perspective of modeling and characterization methods is presented next to an investigation on digitalization trends. Both physics-based and data-based models are discussed according to their scope of use, needs, and capabilities based on open literature. Discrete and continuous lumping, structure-oriented lumping, and single event micro-kinetic models are reported as well as artificial neural networks, convolutional neural networks, and surrogate models. Infrared, near-infrared, ultra-violet and Raman spectroscopic methods are given with their examples for the characterization of feed or product streams of hydrocracking processes regarding boiling point curve, API, SARA, sulfur, nitrogen and metal content. The critical points to consider while modeling the system and the soft sensor are reported as well as the problems to be addressed. Optimization, control, and diagnostics applications are presented together with suggested future directions of interdisciplinary studies. The links required between the models, soft sensors, optimization, control, and diagnostics are suggested to achieve the automation goals and, therefore, a sustainable operation.
Esin Iplik; Ioanna Aslanidou; Konstantinos Kyprianidis. Hydrocracking: A Perspective towards Digitalization. Sustainability 2020, 12, 7058 .
AMA StyleEsin Iplik, Ioanna Aslanidou, Konstantinos Kyprianidis. Hydrocracking: A Perspective towards Digitalization. Sustainability. 2020; 12 (17):7058.
Chicago/Turabian StyleEsin Iplik; Ioanna Aslanidou; Konstantinos Kyprianidis. 2020. "Hydrocracking: A Perspective towards Digitalization." Sustainability 12, no. 17: 7058.
District heating networks have become widespread due to their ability to distribute thermal energy efficiently, which leads to reduced carbon emissions and improved air quality. The characteristics of these networks vary remarkably depending on the urban layout and system amplitude. Moreover, extensive data about the energy distribution and thermal capacity of different areas are seldom available. Design, optimization and control of these systems are enabled by the availability of fast and scalable models of district heating networks. This work addresses this issue by proposing a novel method to develop a scale-free model of large-scale district heating networks. Starting from coarse data available at the main substations, a physics-based model of the system aggregated regions is developed by identifying the heat capacity and heat loss coefficients. The model validation on the network of Västerås, Sweden, shows compatibility with literature data and can therefore be exploited for system design, optimization and control-oriented applications. In particular, the possibility to estimate the heat storage potential of network regions allows new smart management strategies to be investigated.
Costanza Saletti; Nathan Zimmerman; Mirko Morini; Konstantinos Kyprianidis; Agostino Gambarotta. A Scale-Free Dynamic Model for District Heating Aggregated Regions. 2020, 1 .
AMA StyleCostanza Saletti, Nathan Zimmerman, Mirko Morini, Konstantinos Kyprianidis, Agostino Gambarotta. A Scale-Free Dynamic Model for District Heating Aggregated Regions. . 2020; ():1.
Chicago/Turabian StyleCostanza Saletti; Nathan Zimmerman; Mirko Morini; Konstantinos Kyprianidis; Agostino Gambarotta. 2020. "A Scale-Free Dynamic Model for District Heating Aggregated Regions." , no. : 1.
Since aeronautic transportation is responsible for a rising share of polluting emissions, it is of primary importance to minimize the fuel consumption any time during operations. From this perspective, continuous monitoring of engine performance is essential to implement proper corrective actions and avoid excessive fuel consumption due to engine deterioration. This requires, however, automated systems for diagnostics and decision support, which should be able to handle large amounts of data and ensure reliability in all the multiple conditions the engines of a fleet can be found in. In particular, the proposed solution should be robust to engine-to-engine deviations and different sensors availability scenarios. In this paper, a probabilistic Bayesian network for fault detection and identification is applied to a fleet of engines, simulated by an adaptive performance model. The combination of the performance model and the Bayesian network is also studied and compared to the probabilistic model only. The benefit in the suggested hybrid approach is identified as up to 50% higher accuracy. Sensors unavailability due to manufacturing constraints or sensor faults reduce the accuracy of the physics-based method, whereas the Bayesian model is less affected.
Valentina Zaccaria; Amare D. Fentaye; Mikael Stenfelt; Konstantinos G. Kyprianidis. Probabilistic Model for Aero-Engines Fleet Condition Monitoring. Aerospace 2020, 7, 66 .
AMA StyleValentina Zaccaria, Amare D. Fentaye, Mikael Stenfelt, Konstantinos G. Kyprianidis. Probabilistic Model for Aero-Engines Fleet Condition Monitoring. Aerospace. 2020; 7 (6):66.
Chicago/Turabian StyleValentina Zaccaria; Amare D. Fentaye; Mikael Stenfelt; Konstantinos G. Kyprianidis. 2020. "Probabilistic Model for Aero-Engines Fleet Condition Monitoring." Aerospace 7, no. 6: 66.
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.
Smruti Sahoo; Xin Zhao; Konstantinos Kyprianidis. A Review of Concepts, Benefits, and Challenges for Future Electrical Propulsion-Based Aircraft. Aerospace 2020, 7, 44 .
AMA StyleSmruti Sahoo, Xin Zhao, Konstantinos Kyprianidis. A Review of Concepts, Benefits, and Challenges for Future Electrical Propulsion-Based Aircraft. Aerospace. 2020; 7 (4):44.
Chicago/Turabian StyleSmruti Sahoo; Xin Zhao; Konstantinos Kyprianidis. 2020. "A Review of Concepts, Benefits, and Challenges for Future Electrical Propulsion-Based Aircraft." Aerospace 7, no. 4: 44.
Microgas turbine (MGT) engines in the range of 1–100 kW are playing a key role in distributed generation applications, due to the high reliability and quick load following that favor their integration with intermittent renewable sources. Micro-combined heat and power (CHP) systems based on gas turbine technology are obtaining a higher share in the market and are aiming at reducing the costs and increasing energy conversion efficiency. An effective control of system operating parameters during the whole engine lifetime is essential to maintain desired performance and at the same time guarantee safe operations. Because of the necessity to reduce the costs, fewer sensors are usually available than in standard industrial gas turbines, limiting the choice of control parameters. This aspect is aggravated by engine aging and deterioration phenomena that change operating performance from the expected one. In this situation, a control architecture designed for healthy operations may not be adequate anymore, because the relationship between measured parameters and unmeasured variables (e.g., turbine inlet temperature (TIT) or efficiency) varies depending on the level of engine deterioration. In this work, an adaptive control scheme is proposed to compensate the effects of engine degradation over the lifetime. Component degradation level is monitored by a diagnostic tool that estimates performance variations from the available measurements; then, the information on the gas turbine health condition is used by an observer-based model predictive controller to maintain the machine in a safe range of operation and limit the reduction in system efficiency.
Valentina Zaccaria; Mario L. Ferrari; Konstantinos G. Kyprianidis. Adaptive Control of Microgas Turbine for Engine Degradation Compensation. Journal of Engineering for Gas Turbines and Power 2020, 142, 1 .
AMA StyleValentina Zaccaria, Mario L. Ferrari, Konstantinos G. Kyprianidis. Adaptive Control of Microgas Turbine for Engine Degradation Compensation. Journal of Engineering for Gas Turbines and Power. 2020; 142 (4):1.
Chicago/Turabian StyleValentina Zaccaria; Mario L. Ferrari; Konstantinos G. Kyprianidis. 2020. "Adaptive Control of Microgas Turbine for Engine Degradation Compensation." Journal of Engineering for Gas Turbines and Power 142, no. 4: 1.
In this paper, a turbo-electric propulsion system was analyzed, and its performance was assessed. The aircraft considered here was a single-aisle, medium-range configuration targeting a capacity of 150 Pax. The propulsion concept comprised two boosted geared turbofan engines mounted under-wing. Those main engines were supported by an electrically driven aft-propulsor contributing to the thrust generation and by taking advantage of ingesting the boundary layer of the fuselage for potentially higher levels of propulsive efficiency and allowing for the improved operation of the main engines. The performance assessment as carried out in the context of this paper involved different levels: Firstly, based on the reference aircraft and the detailed description of its major components, the engine performance model for both main engines, as well as for the electrically driven aft-propulsor was set up. The methodology, as introduced, has already been applied in the context of hybrid-electric propulsion and allowed for the aforementioned aircraft sizing, as well as the subsequent gas turbine multi-point synthesis (simulation). A geared turbofan architecture with 2035 technology assumptions was considered for the main engine configuration. The present trade study focused on the design and performance analysis of the aft-propulsor and how it affected the performance of the main engines, due to the electric power generation. In order to allow for a more accurate description of the performance of this particular module, the enhanced streamline curvature method with an underlying and pre-optimized profile database was used to design a propulsor tailored to meet the requirements of the aft propulsor as derived from the cycle synthesis and overall aircraft specification; existing design expertise for novel and highly integrated propulsors could be taken advantage of herein. The resulting performance characteristics from the streamline curvature method were then fed back to the engine performance model in a closely coupled approach in order to have a more accurate description of the module behavior. This direct coupling allowed for enhanced sensitivity studies, monitoring different top-level parameters, such as the thrust/power split between the main engines and the aft propulsor. As a result, different propulsor specifications and fan designs with optimal performance characteristics were achieved, which in return affected the performance of all subsystems considered.
Rainer Schnell; Xin Zhao; Efthymios Rallis; Mavroudis Kavvalos; Smruti Sahoo; Markus Schnoes; Konstantinos Kyprianidis. Assessment of a Turbo-Electric Aircraft Configuration with Aft-Propulsion Using Boundary Layer Ingestion. Aerospace 2019, 6, 134 .
AMA StyleRainer Schnell, Xin Zhao, Efthymios Rallis, Mavroudis Kavvalos, Smruti Sahoo, Markus Schnoes, Konstantinos Kyprianidis. Assessment of a Turbo-Electric Aircraft Configuration with Aft-Propulsion Using Boundary Layer Ingestion. Aerospace. 2019; 6 (12):134.
Chicago/Turabian StyleRainer Schnell; Xin Zhao; Efthymios Rallis; Mavroudis Kavvalos; Smruti Sahoo; Markus Schnoes; Konstantinos Kyprianidis. 2019. "Assessment of a Turbo-Electric Aircraft Configuration with Aft-Propulsion Using Boundary Layer Ingestion." Aerospace 6, no. 12: 134.
The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.
Valentina Zaccaria; Moksadur Rahman; Ioanna Aslanidou; Konstantinos Kyprianidis. A Review of Information Fusion Methods for Gas Turbine Diagnostics. Sustainability 2019, 11, 6202 .
AMA StyleValentina Zaccaria, Moksadur Rahman, Ioanna Aslanidou, Konstantinos Kyprianidis. A Review of Information Fusion Methods for Gas Turbine Diagnostics. Sustainability. 2019; 11 (22):6202.
Chicago/Turabian StyleValentina Zaccaria; Moksadur Rahman; Ioanna Aslanidou; Konstantinos Kyprianidis. 2019. "A Review of Information Fusion Methods for Gas Turbine Diagnostics." Sustainability 11, no. 22: 6202.