The energy generation landscape is changing, pushed by stricter regulations for emissions control and green energy generation. The limitations of renewable energy sources, however, require flexible energy production sources to supplement them. Micro gas turbine based combined heat and power plants, which are used for domestic applications, can fill this gap if they become more reliable. This can be achieved with the use of an engine monitoring and diagnostics system: real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. In order to allow the step change in the connection of small engines to the grid, a fleet monitoring system for micro gas turbines is required. A proposed framework combines a physics-based model and a data-driven model with machine learning capabilities for predicting system behavior, and includes a purpose-developed diagnostic tool for anomaly detection and classification for a multitude of engines. The framework has been implemented on a fleet of micro gas turbines and some of the lessons learned from the demonstration of the concept as well as key takeaways from the general literature are presented in this paper. The extension of fleet monitoring to optimal operation and production planning in relation to the needs of the grid will allow the micro gas turbines to fit in the future green energy system, connect to the grid, and trade in the energy market. The requirements on the system level for the widespread use of micro gas turbines in the energy system are addressed in the paper. A review of the current solutions in fleet monitoring and diagnostics, generally developed for larger engines, is included, with an outlook into a sustainable future.
As a consequence of globalization and advances in digital tools, synchronous or asynchronous distance courses are becoming an integral part of universities’ educational offers. The design of an online course introduces more challenges compared to a traditional on campus course with face to face lectures. This is true especially for engineering subjects where problem or project-based courses may be preferred to stimulate critical thinking and engage the learners with real-life problems. However, realizing this with distance learning implies that a similar study pace should be kept by the learners involved. This may not be easy, since individual pace is often a motivation for choosing a distance course. Student engagement in group projects, collaborations, and the proper design of examination tasks are only some of the challenges in designing a distance course for an engineering program.
A series of web-based courses on measurement techniques, control, and diagnostics were developed and delivered to groups of learners. Each course comprised short modules covering key points of the subject and aimed at getting learners to understand both the fundamental concepts that they do not typically learn or understand in the respective base courses and to build on that knowledge to reach a more advanced cognitive level.
The experience obtained in the courses on what strategies worked better or worse for the learners is presented in this paper. A comparison between the courses provides an interesting outlook on how the learners reacted to slightly different requirements and incentives in each course. The results from the evaluation of the courses are also used as a base for discussion.
The background and availability of the learners is closely linked to how a course should be designed to optimally fit the learning group, without compromising on the achievement of the learning outcomes. This series of courses is a good example of continuous professional development courses in the field of control, diagnostics, and instrumentation (CDI), and brings with it a number of challenges and opportunities for the development of online courses.
This paper addresses the teaching of gas turbine technology in a third-year undergraduate course in Sweden and the challenges encountered. The improvements noted in the reaction of the students and the achievement of the learning outcomes is discussed. The course, aimed at students with a broad academic education on energy, is focused on gas turbines, covering topics from cycle studies and performance calculations to detailed design of turbomachinery components. It also includes economic aspects during the operation of heat and power generation systems and addresses combined cycles as well as hybrid energy systems with fuel cells. The course structure comprises lectures from academics and industrial experts, study visits, and a comprehensive assignment. With the inclusion of all of these aspects in the course, the students find it rewarding despite the significant challenges encountered. An important contribution to the education of the students is giving them the chance, stimulation, and support to complete an assignment on gas turbine design. Particular attention is given on striking a balance between helping them find the solution to the design problem and encouraging them to think on their own. Feedback received from the students highlighted some of the challenges and has given directions for improvements in the structure of the course, particularly with regards to the course assignment. This year, an application developed for a mobile phone in the Aristotle University of Thessaloniki for the calculation of engine performance will be introduced in the course. The app will have a supporting role during discussions and presentations in the classroom and help the students better understand gas turbine operation. This is also expected to reduce the workload of the students for the assignment and spike their interest.
Real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. Prediction of faults can change the maintenance model of a system from a fixed maintenance interval to a condition based maintenance interval, further decreasing the total cost of ownership of a system. Technologies developed for engine health monitoring and advanced diagnostic capabilities are generally developed for larger gas turbines, and generally focus on a single system; no solutions are publicly available for engine fleets. This paper presents a concept for fleet monitoring finely tuned to the specific needs of micro gas turbines. The proposed framework includes a physics-based model and a data-driven model with machine learning capabilities for predicting system behaviour, combined with a diagnostic tool for anomaly detection and classification. The integrated system will develop advanced diagnostics and condition monitoring for gas turbines with a power output under 100 kW.
The main outcomes of an engineering course should be for the students to achieve the educational goals, enhance their problem solving capabilities and develop essential skills for their future career. In that context, it is important to understand what motivates the students and what helps them develop an engineering mindset. This paper discusses the improvement of a course with the use of student feedback to motivate students and help them develop essential skills. The purpose of the paper is to provide insight into how different aspects of the course are linked to the students’ growth. Different activities have been integrated in the course over the past years. The effect these have on the student motivation to follow the course and develop skills, knowledge and interest in the subject is discussed through the analysis of student performance, student feedback and the experience of the lecturers. The improvements in the course based on the student feedback were received positively by the students, whose learning experience improved, even though the workload of the course was high. Their motivation to successfully complete the course has also increased through the changes in the delivery of the course and the support by the teachers. The combination of student feedback and teacher experience is key for the improvement of a course, while ensuring that the students develop their engineering knowledge. Therefore, the teachers should strike a balance between helping the students find the solution and encouraging them to think on their own in order to develop essential skills.
Aircraft electrification for propulsion is a promising way to alleviate the negative environmental impact of conventional carbon-powered aviation. Inclusion of the electrical powertrain aims to enhance design freedom allowing for more efficient power systems and operational schemes. In this work a design space exploration is performed aiming to derive power management guidelines based on aircraft environmental performance. A 19-passenger commuter aircraft employing the series/parallel partial hybrid-electric architecture is examined. Two underwing-mounted turboprop engines are combined with a boundary layer ingestion fan mounted in the aircraft aft and powered by an electrical drive. The primary electrical energy source is a battery system. A multi-disciplinary framework is utilized, comprising modelling approaches for multi-point thermal engine design, physics-based electrical component sizing and performance, aircraft sizing, mission design, and environmental assessment. The investigation revealed that the reference designed hybrid-electric configuration with entry-into-service 2035-assumed technologies yields roughly 18% improvement in block consumption and emissions, but an 8% increase in maximum take-off weight, compared to its 2014 conventional counterpart. The design space exploration for an optimal power management scheme indicated a minimum ratio of 1:1.35 between cruise and design point hybridization power. However, even the optimally operated hybrid aircraft showcases worse environmental performance compared to the conventional design of same entry-into-service date. The investigation has revealed that the complex powertrain and hybrid architecture selected may be more suitable for larger class aircraft, with the accumulated performance benefits reaching the order of 5% for the hybrid designs explored under relaxed top-level constraints.
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.
Hybrid-electric propulsion is recognized as one of the enabling technologies 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 modelling accuracy of propulsion systems is aimed for by development of an integrated framework. A multi-point synthesis scheme for the gas turbine and electric fan is combined with physics-based analytical modelling 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 in mission-significant segments. A threshold system voltage of 1kV, yielding 32% mass reduction of electrical branches and performance improvements of 1-2%, is defined. Above the indicated threshold, benefits are limited, and system design complexity increases unfavorably. 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.
Distributed generation, and active distribution networks constitute an economic and technically viable alternative for reducing green house gases emissions and increase the use of renewable energy sources in local distribution grids. These active networks allow replacing large generators, usually located far from the consumption loads, thus considerably minimizing distribution losses and increase renewable energy penetration. However, designing and successfully controlling these complex networks, becomes a great engineering challenge; most computational modeling and simulation tools available for these systems are either focused on the individual generation components themselves, or the economic dispatch of multiple generators. Moreover, these tools often rely on closed source commercial software that use manufacturers' data for predefining the parameters of the models' components. This approach does not provide enough flexibility to users, since often is not possible to adjust these parameters. This paper presents object- oriented, component-based, open software components for simulating and optimizing the operation of active distribution networks, including multiple distributed generators and energy using the Modelica open-source modeling language.
Pumped hydro storage (PHS) is the most mature and widely used technology for large-scale energy storage. Hydropower plants are in fact also employed for this aim. However, most hydraulic sites suitable for this purpose have been already exploited. Therefore, the use of abandoned mines represents an alternative solution to take advantage of the availability of underground volumes as hydro storages. This paper investigates the potential of PHS plants integrated within a power generation system that comprises both programmable (e.g., hydropower and nuclear power plants) and non-programmable (e.g., wind and solar power plants) energy systems. All systems are connected with the power grid. To this purpose, this paper develops a methodology aimed at identifying the optimal sizing of the PHS plant as well as the optimal operation of the whole power generation system at Country level, with the goal of minimizing the imported energy. The methodology is validated by using Sweden as the case study, to assess the energy and economic feasibility of PHS plants in 2050. Different future scenarios of electricity production, demand, and cost are analyzed. The analyses carried out in this paper demonstrate that PHS plants are highly recommended if the cost of imported energy is expected to increase. In such a scenario, PHS is mainly employed to meet domestic electricity demand.
A methodology and tool that allows evaluating and quantifying aero engines design trade-offs originated as a consequence of addressing conflicting objectives such as low environmental impact and low operating costs is presented, and applied to a general case study to assess the feasibility of using new highly efficient engine configurations: intercooled- recuperated (ICR) engines. The case study results show that according to the ICR systems performance (heat exchangers effectiveness, pressure losses, and weight penalty) they could find usage in practical applications.
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.
Hydrogen energy has been considered as one of the solutions to achieve the net-zero emission scenario by 2050. Steam methane reforming is a widely used industrial process for producing hydrogen from natural gas or methane nowadays. Considering that methane could be utilized as a suitable carrier for hydrogen energy, it is anticipated that steam methane reforming will still play an important role in the future energy sector when it comes to hydrogen production, storage, and transportation. In this work, a one-imensional dynamic model is established to simulate the performance of an auto-thermal reforming reactor, which allows for capturing the localized phenomena inside the reactor over time. A set of input parameters is selected based on the Latin Hypercube Sampling method to generate the training data for the surrogate model development. Singular value decomposition and Gaussian Process regression are then implemented on the training data to construct a surrogate model of the reformer. This surrogate model is subsequently utilized in the optimization process to enhance hydrogen production and lower the maximum catalyst temperature within the reactor. The results show that the surrogate model, developed by using singular value decomposition and Gaussian Process, exhibits a high level of accuracy when compared to the physics-based reformer model. Furthermore, the optimization framework built upon surrogate modelling offers the potential to substantially reduce the computational expenses associated with the optimization process, while preserving the precision of the optimization results. This method could efficiently serve as a tool for parameters optimization of such reactors and could be used to guide the operation of these systems toward improved performance.
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.
The use of hydrogen in transportation is considered a promising solution to reduce CO2 emissions and combat climate change. Among the various technologies, PEM fuel cells represent a consolidated choice for hydrogen propulsion given their lightweight and relatively low cost. However, despite a few cases of successful applications of PEM fuel cells in road and rail transport, there are still barriers hindering the full exploitation of this technology, especially in the aerospace sector. Research is needed to assess the long-term viability, and modeling tools are of utmost importance for this task. The highly complex architectures necessary for hybrid propulsion require new methods for design and optimization, for which accurate but computationally fast models are critical. This paper presents a new methodology of using surrogate-based optimization to assist the design of the power system for a regional electric aircraft. A multi-objective optimization framework has been developed considering the weight, and efficiency of the power system, as well as the operating current density of the fuel cell stack. A surrogate model based on Gaussian Process regression and Singular Value Decomposition is employed to reduce the computational cost. The results show that the surrogate-based optimization approach could accelerate the optimization process without significantly affecting the prediction accuracy. This approach could be used to guide the design of experiments/high-fidelity simulations for the optimal design and energy management of power systems for electric aircraft.
While aircraft environmental performance has been important since the beginnings of commercial aviation, continuously increasing passenger traffic and a rise in public awareness have made aircraft noise and emissions two of the most pressing issues hampering commercial aviation growth today. The air transportation for the new millennium will require revolutionary solutions to meeting public demand for improving safety, reliability, environmental compatibility, and affordability. The objective of this research is to assess the trade-off between operating costs and environmental requirements of the future aero engines for short range commercial aircrafts. This involves optimising the engines’ design point to minimise the block fuel and evaluating the economic and environmental impact. A high by-pass ratio turbofan engine with performance characteristics and technology from the year 2000 was set up as a baseline and compared to very high by-pass ratio turbofans. The results present a great potential benefit of the geared turbofan compared to high BPR one (baseline) to reduce cruise CO2 emissions and noise; however this may involve NOx penalties, that is an increase of 5.1% in comparison to the baseline. The CRTF engine seems to be, at least according to the simulations, a very promising solution in terms of environmental and economical performance. This is one on the series of work that would be carried out on the cycles being assessed in this paper (feasibility study). Further work on the specific technical issues — such as: technological implications — would be published when completed.
Nowadays, the reduction of fuel consumption and pollutant emissions has become a top priority for society and economy. In the past decades, some of the environmental advantages of the gas turbine (such as inherently low CO and unburned HC) have led some car manufacturers to evaluate the potential of this type of engine as prime mover. This paper suggests a strategy to assess the fuel consumption of gas turbines applied in road vehicles. Based on a quasistatic approach, a model was created that can simulate road vehicles powered by gas turbines, and thereafter a comparison was established with reciprocating engines. Within this study, material and turbomachinery technology developments that have taken place in micro gas turbines since the 1960’s have been considered. A 30% efficiency improvement target has been identified with respect to making the gas turbine fuel competitive to a diesel engine powering an SUV. It is the authors’ view that several technologies that could mature sufficiently within the next 10–15 years exist, such as uncooled ceramic turbines. Such technologies could help bridge the fuel efficiency gap in micro gas turbines and make them commercially competitive in the future for low-emissions vehicular applications. Furthermore, the system developed also allows the simulation of hybrid configurations using gas turbines as range extenders, a solution that some car manufacturers consider to be the most promising in the coming years.
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.
The present study describes the application of a preliminary design approach for the optimization of an organic Rankine cycle radial turbine. Losses in the nozzle the rotor have initially been modelled using a mean-line design approach. The work focuses on a typical small-scale application of 50 kW, and two working fluids, R245fa (1,1,1,3,3,-pentafluoropropane) and R236fa (1,1,1,3,3,3-hexafluoropropane) are considered for validation purposes. Real gas formulations have been used based on the NIST REFPROP database. The validation is based on a design from the literature, and the results demonstrate close agreement the reference geometry and thermodynamic parameters. The total-to-total efficiencies of the reference turbine designs were 72% and 79%. Following the validation exercise, an optimization process was performed using a controlled random search algorithm with the turbine efficiency set as the figure of merit. The optimization focuses on the R245fa working fluid since it is more suitable for the operating conditions of the proposed cycle, enables an overpressure in the condenser and allows higher system efficiency levels. The R236fa working fluid was also used for comparison with the literature, and the reason is the positive slope of the saturation curve, somehow is possible to work with lower temperatures. Key preliminary design variables such as flow coefficient, loading coefficient, and length parameter have been considered. While several optimized preliminary designs are available in the literature with efficiency levels of up to 90%, the preliminary design choices made will only hold true for machines operating with ideal gases, i.e. typical exhaust gases from an airbreathing combustion engine. For machines operating with real gases, such as organic working fluids, the design choices need to be rethought and a preliminary design optimization process needs to be introduced. The efficiency achieved in the final radial turbine design operating with R245fa following the optimization process was 82.4%. A three-dimensional analysis of the flow through the blade section using computational fluid dynamics was carried out on the final optimized design to confirm the preliminary design and further analyze its characteristics.
There is a potential to integrate biomass gasification with pulp & paper and CHP plants in order to complement the existing systems with production of chemicals, such as methane, hydrogen, and methanol etc. To perform system analysis of such integration, it is important to gain knowledge of relevant input data on expected synthesis gas composition by gasifying different types of feed stock. In this paper, the synthesis gas quality from wood pellets gasification (WPG) has been compared with black liquor gasification (BLG) through modeling and experimental results at pilot scale. In addition, the study develops regression models like Partial Least Squares (PLS) made from the experimental data. The regression models are then combined with dynamic models developed in Modelica for the investigation of dynamic energy and material balances for integrated plants. The data presented in this study could be used as input to relevant analysis using e.g. ASPEN plus and similar system analysis tools.
It is vital to perform system analysis on integrated biomass gasification in chemical recovery systems in pulp and paper and heat and power plants for polygeneration applications. The proposed integration complements existing pulp and paper and heat and power production systems with production of chemicals such as methane and hydrogen. The potential to introduce gasification-based combined cycles comprising gas turbines and steam turbines to utilize black liquors and wood pellets also merits investigation. To perform such analysis, it is important to first build knowledge on expected synthesis gas composition by gasifying at smaller scale different types of feed stock. In the present paper, the synthesis gas quality from wood pellets gasification has been compared with black liquor gasification by means of numerical simulation as well as through pilot-scale experimental investigations. The experimental results have been correlated into partial least squares models to predict the composition of the synthesis gas produced under different operating conditions. The gas quality prediction models are combined with physical models using a generic open-source modelling language for investigating the dynamic performance of large-scale integrated polygeneration plants. The analysis is further complemented by considering potential gas separation using modern membrane technology for upgrading the synthesis gas with respect to hydrogen content. The experimental data and statistical models presented in this study form an important literature source for future use by the gasification and polygeneration research community on further integrated system analysis.
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.
District heating is a well-established technology; however, the use of individual heat pumps has been expanding and is now the main competitor to district heating. The prices for both electricity and district heating often vary over time because of the variation of raw material prices in the marketplace. Consequently, for the building owner it would be cost effective if they had the possibility to integrate both district heating and heat pumps. Aiding in the flexibility to switch between the two systems in order to choose the one with the lowest operating cost throughout the year. In the presented work, the modeling and control of a detached house integrated with both district heating and a heat pump are developed. The operating costs of both systems are computed considering the marketplace prices and the coefficient of performance of the heat pump, related to the external temperature. The results show that heat pumps can be well exploited during the spring and fall to cover base loads, and in the summer can be used for ambient cooling.
The increasing environmental concern during the last years is driving the research community towards reducing aviation’s environmental impact. Several strict goals set by various aviation organizations shifted the research focus towards more efficient and environmentally friendly aircraft concepts. Boundary Layer Ingestion (BLI) is currently investigated as a potential technology to achieve different design goals such as energy efficiency improvement and noise emission reductions in the next generation of commercial aircraft. The technology principle is to place the propulsive unit within the boundary layer generated by the airframe body. Although several studies showed its theoretical benefits, a multidisciplinary nature is introduced in the design phase. This imposes new challenges on the current design tools. An increasing number of publications are focusing on assessing this technology while taking into account interlinks between different disciplines. The goal of this work is to review the current state-of-the-art of BLI evaluation studies. Particular focus is given to the underlying assumptions of each work, the methodology employed, and the level of fidelity of the tools used. By organizing the available work in a comprehensive manner, the up-to-date results are interpreted. The current trends and trade-offs emerging from studies are presented. Through reviewing the ongoing published work, the next steps for further development of the methods that will assess this technology are derived.
During the last years, the aviation industry has shifted its focus towards increasing the aircraft efficiency. The constant drive to search for more efficient systems has led to the introduction of novel concepts. These concepts expand the design space but, at the same time, bring several challenges to the design process. One of the challenges is to develop a conceptual engine design model that can work effectively and provide consistently accurate solutions, even when there are dramatic changes in design constraints. In this work, a multipoint synthesis approach is developed which considers multiple points during the design phase. By incorporating multiple operating points into the design analysis phase, it is ensured that all performance requirements and design constraints are satisfied. A comparison between the traditional engine design approach and the proposed approach is presented to showcase the advantages of the proposed method. A parametric analysis of a geared turbofan configuration is conducted for both design approaches. Then, the multi-point synthesis approach is employed for the design space exploration of a conventional geared turbofan engine and a parallel-hybrid (or boosted) turbofan engine. To enable these studies, surrogate models are developed which utilize machine learning methods in a database of converged engine designs and can ensure the effective and fast operation of the engine model. It is concluded that this surrogate adapted algorithm improves computational efficiency and can be used to evaluate alternative designs.
The present paper is examining the geometry optimization of a power production turbine, in the range of 100kW(el), for a low enthalpy Organic Rankine cycle system (<= 100 degrees C). In the last years, accelerated consumption of fossil fuels has caused many serious environmental problems such as global warming, ozone layer destruction and atmospheric pollution. It is this reason that a growing trend towards exploiting low-enthalpy content energy sources has commenced and led to a renewed interest in small-scale turbines for Organic Rankine Cycle applications. The design concept for such turbines can be quite different from either standard gas or steam turbine designs. The limited enthalpic content of many energy sources imposes the use of organic working media, with unusual properties for the turbine. A versatile cycle design and optimization requires the parameterization of the main turbine design. There are many potential applications of this power-generating turbine, including geothermal and concentrate solar thermal fields or waste heat of steam turbine exhausts. An integrated model of equations has been developed, thus creating a model to assess the performance of an organic cycle for various working fluids such as R134a and isobutane-isopentane mixture. The most appropriate working fluid has been chosen, taking its influence on both cycle efficiency and the specific volume ratio into consideration. This choice is of particular importance at turbine extreme operating conditions, which are strongly related to the turbine size. In order to assess the influence of various design parameters, a turbine design tool has been developed and applied to define the geometry of blades in a preliminary stage. Finally, as far as the working fluid is concerned, the mixture of 85% isopentane-15% isobutane has been chosen as the most suitable fluid for the low enthalpy ORC system, since its output net power is 10% higher compared to the output net power of R134a.
Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the conventional methods such as the inability to deal with the nonlinear engine behavior, measurement uncertainty, simultaneous faults, and the limited number of sensors available remain the driving force for exploring more advanced techniques. This review aims to provide a critical survey of the existing literature produced in the area over the past few decades. In the first section, the issue of GT degradation is addressed, aiming to identify the type of physical faults that degrade a gas turbine performance, which gas-path faults contribute more significantly to the overall performance loss, and which specific components often encounter these faults. A brief overview is then given about the inconsistencies in the literature on gas-path diagnostics followed by a discussion of the various challenges against successful gas-path diagnostics and the major desirable characteristics that an advanced fault diagnostic technique should ideally possess. At this point, the available fault diagnostic methods are thoroughly reviewed, and their strengths and weaknesses summarized. Artificial intelligence (AI) based and hybrid diagnostic methods have received a great deal of attention due to their promising potentials to address the above-mentioned limitations along with providing accurate diagnostic results. Moreover, the available validation techniques that system developers used in the past to evaluate the performance of their proposed diagnostic algorithms are discussed. Finally, concluding remarks and recommendations for further investigations are provided.
In a gas turbine fault diagnostics, the removal of measurementnoise and data outliers prior to the fault analysis is very essential. Theconventional filtering methods, particularly the linear ones, are notsufficiently accurate, which might possibly lead to the loss of criticallyimportant features in the fault analysis process. Conversely, the recordedaccuracies obtained from the non-linear filters are promising. Recently, thefocus has been shifted to the artificial neural network (ANN) based nonlinearfilters due to their capability of providing a robust identity map between theinput and output data, which can be efficiently exploited in the process offault diagnosis. This paper aims to present combined auto-associative neuralnetwork (AANN) and K-nearest neighbor (KNN) based noise reduction andfault detection method for a gas turbine engine application. The performanceof the developed method has been evaluated using data obtained from amodel simulation. The test results revealed that the developed hybrid methodis more effective and reliable than the conventional methods for the faultdetection of the gas turbine engine with negligible false alarms and misseddetections.
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.
The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.
Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine lifecycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry. © 2021, Prognostics and Health Management Society. All rights reserved.
Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.
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 (a real-time model including components emulated in the rig, operating in parallel with the experimental facility and used to manage some properties in the plant, such as the turbine outlet temperature set-point and the air flow injected in the anodic circuit). The T100 machine is a microturbine able to produce a nominal electric power output of 100 kW. So, the paper presents a real-time model including the fuel cell, the off-gas burner, 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, as detailed presented in the devoted section. 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 PID approaches were assessed against the application of Proportional Integral Derivative (PID) controllers. The control targets consider both steady-state (e.g. high efficiency solutions) and dynamic aspects (stress smoothing in the cell). 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. Special attention is devoted to the fuel cell system constraints, such as temperature and time-dependent thermal gradient. 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.
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.
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.
This work focuses on the feasibility 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 electric propulsion architectures. The potential entry into service of such aircraft is foreseen in 2030. A literature review is performed, to identify similar concepts that are under research and development. After the requirements definition, the first level of conceptual design is employed. 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. 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 energy storage operational characteristics. The design choices are driven by the aim to reduce CO2 emissions and accommodate boundary layer ingestion engines, with aircraft electrification. 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 farfetched battery necessities. It is also highlighted that compliance with airworthiness certifications 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% lower energy consumption and larger CO2 reduction), compared to the higher degree of hybridization (50%), with respect to the conventional configuration.
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.
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 farfetched 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.
An increase in environmental awareness in both the aviation industry and the wider global setting has led to large bodies of research dedicated to developing more sustainable technology with a lower environmental impact and lower energy usage. The goal of reducing environmental impact has necessitated research into revolutionary new technologies that have the potential to be significantly more energy efficient than their predecessors. However, for innovative technologies in any industry, there is a risk that adoption will be prohibitively expensive for commercial application. It is therefore important to model the economic factors of the new technology or policy at an early stage of development. This research demonstrates the application of a Techno-economic Environmental Risk Assessment framework that may be used to identify the economic impact of an energy-efficient aircraft concept and the impact that environmental policy would have on the viability of the concept. The framework has been applied to a case study aircraft designed to achieve an energy saving of 60% in comparison to a baseline 2005 entry-into-service aircraft. The model compares the green aircraft concept to a baseline conventional aircraft using a sensitivity analysis of the aircraft direct operating cost to changes in acquisition and maintenance cost. The research illustrates an economically viable region for the technology. Cost margins are identified where the increase in operating cost due to expensive novel technology is counterbalanced by the reduction in cost resulting from low energy consumption. Viability was found to be closely linked to fuel price, with a low fuel price limiting the viability of energy-efficient aviation technology. In contrast, a change in environmental taxation policy was found to be beneficial, with the introduction of carbon taxation incentivising the use of an environmentally optimised aircraft.
Within the European research project EnVIronmenTALly Friendly Aero Engines, VITAL, a number of low pressure system component technologies are being investigated. The emerging progress will allow the design of new power plants providing a step change in engine fuel burn and noise. As part of the VITAL project a Technoeconomic and Environmental Risk Assessment tool, the TERA2020, is being developed. Within this tool, means to assess the impact of component technology progress on the engine/aircraft system level has been implemented. Sensitivities relating parameters traditionally used to describe component performance, such as allowable shaft torque, low pressure turbine stage loading, fan blade weight and system level parameters have been established. This allows a direct assessment of the impact of component research progress on the VITAL power plant CO2 and noise emissions.
The performance of an intercooled turbofan engine is analysed by multidisciplinary optimization. A model for making preliminary simplified analysis of the mechanical design of the engine is coupled to an aircraft model and an engine performance model. A conventional turbofan engine with technology representative for a year 2020 entry of service engine is compared to a corresponding intercooled engine. A mission fuel burn reduction of 4.3% is observed. The results are analysed in terms of the relevant constraints such as compressor exit temperature, turbine entry temperature, turbine rotor blade temperature and compressor exit blade height. It is then shown that a separate variable exhaust nozzle mounted in conjunction with the intercooler together with a variable low pressure turbine may further improve the fuel burn benefit to 5.5%. Empirical data and a parametric CFD study is used to verify the intercooler heat transfer and pressure loss characteristics.
Global energy transition efforts towards decarbonization requires significant advances within the energy sector. In this regard, hydrogen is envisioned as a long-term alternative fuel for gas turbines. Accordingly, the gas turbine industry has expedited their efforts in developing 100% hydrogen compliant burners and associated auxiliary components for retrofitting the existing gas turbines. The utilization of hydrogen in gas turbines has some underlying challenges such as corrosion mainly originating from increased steam content in the hot gas path. In addition to corrosion, the gas turbine compressor is vulnerable to fouling which is the most commonly occurring fault in gas turbine operating over certain time window. Both faults are susceptible to performance and health degradation. To avoid expensive asset loss caused by unexpected downtimes and shutdowns, timely maintenance decision making is required. Therefore, simple, accurate and computationally efficient fault detection and diagnostics models become crucial for timely assessment of health status of the gas turbines. The present study encompassed development of a physics-based performance model of a 100-kWe micro gas turbine running on 100% hydrogen fuel. The model is validated with experimental data acquired from test campaigns at the University of Stavanger. Data synthesized from experimentally validated performance model are utilized further for training machine learning algorithms. To identify an accurate algorithm, various algorithms such as support vector machine, decision tree, random forest algorithm, k-nearest neighbors, and artificial neural network were tested. The findings from fault diagnostics process (classification) revealed that ANN outperformed its counterpart algorithm by giving accuracy of 94.55%. Similarly, ANN also showed higher accuracy in performance degradation estimation process (regression) by showing the MSE of training loss as low as ~0.14. The comparative analysis of all the chosen algorithms in the present study revealed ANN as the most accurate algorithm for fault diagnostics of hydrogen fueled gas turbines. However, there is need to further implement the ensemble machine learning models or deep learning model to explore and expedite the real time fault diagnostic accuracy to avoid false alarms and missed detections in context of hydrogen fuel.
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.
This paper describes the current activities at the German Aerospace Center (DLR) and an associated consortium related to conceptual design studies of an aircraft configuration with hybrid electric propulsion for a typical short range commercial transport mission. The work is implemented in the scope of the European Clean Sky 2 program in the project “Advanced Engine and Aircraft Configurations” (ADEC) and “Turbo electric Aircraft Design Environment” (TRADE). The configuration analyzed incorporates parallel hybrid architecture consisting of gas turbines, electric machines, and batteries that adds electric power to the fans of the engines. A conceptual aircraft sizing workflow built in the DLR’s “Remote Component Environment” (RCE) incorporating tools of DLR that are based on semi-empirical and low level physics based methods. The TRADE consortium developed a simulation and optimization design platform with analysis models of higher fidelity for an aircraft with hybrid electric propulsion architecture. An implementation of the TRADE simulation and optimization design platform into the DLR’s RCE workflow by replacing the DLR models was carried out. The focus of this paper is on the quantitative evaluation of the “Boosted Turbofan” configuration utilizing the resulting workflow. In order to understand the cooperation between the DLR and TRADE consortium, a brief overview of the activities is given. Then the multi-disciplinary overall aircraft sizing workflow for hybrid electric aircraft built in RCE is shown. Hereafter, the simulation and optimization models of the TRADE design platform are described. Subsequently, an overview of the aircraft configurations considered in the scope of this work is given. The design space studies of the “Boosted Turbofan” configuration are presented. Finally, the deviations of the results between the workflows with and without the TRADE modules are discussed. © 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
In Nordic countries, district heating accounts for a large share of the consumers’ heat demand. In Sweden, roughly 50% of the total heat demand is attributed to district heating. Which, over the past few years, is equivalent to around 50 TWh, and imposes a difficult balance between supply and demand for the suppliers of district heating. For large networks the propagation of heat from supplier to end-user can vary several hours. Further complexities of large networks, which can consist of multiple overlapping rings, is that during transient conditions the flow can actually change direction. A dynamic modeling library has been developed in Modelica using OpenModelica for district heating networks. Methods for modeling, handling data, simulating and the visualization of results has been developed using Matlab. The model has been validated using data from Mälarenergi AB, a local provider of district heating in Västerås, Sweden. The model provides to an acceptable degree in predicting the heat propagation and temperature distribution in a localized case study. Adding a higher level of robustness, the model has the capacity to handle bi-directional and reversing flows in complex ring structures. Through this work, the combination of OpenModelica and Matlab, a framework for automating the building and simulation of district heating networks is obtainable. The implications of automating network modeling from computer-aided design drawings allows for a quick robust overview of how the network is working and how prospective additions to the network could impact the end-users. Furthermore, incorporating visual aspects for heat propagation in a network contributes to a higher understanding of complex network structures.