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The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.

Digital transformation in manufacturing processes such as the ones used in investment casting empowers organizations to simulate, monitor, and enhance simulations, paving the way for factory automation. This paper explores the impact of digitalization on an industrial Robot Cell environment that is used for constructing ceramic casings to create investment casting products. The application of Digital Twin technology into a physical production Robot Cell is presented. Emphasis is given to the relevant technical aspects of creating the virtual space with 3D scanning technology, establishing the Digital Twin system, and simulating the robot path for adaptation to the new requirements. The Digital Twin enables offline simulation and modeling, allowing adaptability, limiting production disruptions for manual changes, and ensuring the safety of testing, while enabling digital access to the Robot Cell's operational system. This work demonstrates how a Digital Twin for an industrial robot can improve the investment casting process by allowing for virtual tests and the flexibility to adapt to new items with previously unseen geometries.

The main goal of an engineering course is for the students to achieve the defined educational goals, enhance their problem- solving capabilities and develop the essential engineering mindset. The continuous improvement of a course is essential to maintain its challenging nature while improving the course quality. Adapting the teaching methods used to new types of students can provide a significant improvement in student learning. 

In that context, a digital tool is employed in an advanced course in Aeroengine Technology. A smartphone application that calculates gas turbine performance is introduced in the course to help students understand some of the key concepts. The purpose of the application is to provide the students with an interactive tool to understand the gas turbine thermodynamic cycle. An exercise regarding this application is assigned to note the performance of different engine technologies used in aircraft propulsion through the years. The assignment with the application is combined with a larger assignment on gas turbine performance. The application is also employed in the final exams of the course. 

The purpose of this paper is to present the use of the application in the course and to address any challenges that arise in the implementation of the app in the learning process. The employed teaching methods received positive feedback from the students who indicated that the app assignment helped them understand some of the key concepts in the course. After all, the main aim of the use of novel teaching methods should be to make learning more interesting, so that students get more involved in a course. 

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.

This paper presents an experimental investigation of the concept of using the combustor transition duct wall to shield the nozzle guide vane leading edge. The new vane is tested in a high-speed experimental facility, demonstrating the improved aerodynamic and thermal performance of the shielded vane. The new design is shown to have a lower average total pressure loss than the original vane, and the heat transfer on the vane surface is overall reduced. The peak heat transfer on the vane leading edge–endwall junction is moved further upstream, to a region that can be effectively cooled as shown in previously published numerical studies. Experimental results under engine-representative inlet conditions showed that the better performance of the shielded vane is maintained under a variety of inlet conditions.

In gas turbines with can combustors the trailing edge of the combustor transition duct wall is found upstream of ev- ery second vane. This paper presents an experimental and numerical investigation of the effect of the combustor wall trailing edge on the aerothermal performance of the nozzle guide vane. In the measurements carried out in a high speed experimental facility, the wake of this wall is shown to in- crease the aerodynamic loss of the vane. On the other hand, the wall alters secondary flow structures and has a protective effect on the heat transfer in the leading edge-endwall junc- tion, a critical region for component life. The different clock- ing positions of the vane relative to the combustor wall are tested experimentally and are shown to alter the aerothermal field. The experimental methods and processing techniques adopted in this work are used to highlight the differences be- tween the different cases studied. 

The remarkable developments in gas turbine materials and cooling technologies haveallowed a steady increase in combustor outlet temperature and, hence, in gas turbine efficiencyover the last half century. However, the efficiency benefits of higher gas temperature,even at the current levels, are significantly offset by the increased losses associatedwith the required cooling. Additionally, the advancements in gas turbine cooling technologyhave introduced considerable complexities into turbine design and manufacture.Therefore, a reduction in coolant requirements for the current gas temperature levels isone possible way for gas turbine designers to achieve even higher efficiency levels. Theleading edges of the first turbine vane row are exposed to high heat loads. The high coolantrequirements and geometry constraints limit the possible arrangement of the multiplerows of film cooling holes in the so-called showerhead region. In the past, investigatorshave tested many different showerhead configurations by varying the number of rows, inclinationangle, and shape of the cooling holes. However, the current leading edge coolingstrategies using showerheads have not been shown to allow a further increase inturbine temperature without the excessive use of coolant air. Therefore, new coolingstrategies for the first vane have to be explored. In gas turbines with multiple combustorchambers around the annulus, the transition duct walls can be used to shield, i.e., to protect,the first vane leading edges from the high heat loads. In this way, the stagnationregion at the leading edge and the showerhead of film cooling holes can be completelyremoved, resulting in a significant reduction in the total amount of cooling air that is otherwiserequired. By eliminating the showerhead the shielding concept significantly simplifiesthe design and lowers the manufacturing costs. This paper numerically analyzes the potentialof the leading edge shielding concept for cooling air reduction. The vane shape wasmodified to allow for the implementation of the concept and nonrestrictive relative movementbetween the combustor and the vane. It has been demonstrated that the coolant flowthat was originally used for cooling the combustor wall trailing edge and a fraction of thecoolant air used for the vane showerhead cooling can be used to effectively cool both thesuction and the pressure surfaces of the vane.

In energy production the characterization of the fuel is a key aspect for modelling and optimizing the operation of a power plant. Near-infrared spectroscopy is a wellestablished method for characterization of different fuels and is widely used both in laboratory environments and in power plants for real-time results. It can provide a fast and accurate estimate of key parameters of the fuel, which for the case of biomass can include moisture content, heating value, and ash content. These instruments provide a chemical fingerprint of the samples and require a calibration model to relate that to the parameters of interest.

A near-infrared spectrometer can provide point data whereas a hyperspectral imaging camera allows the simultaneous acquisition of spatial and spectral information from an object. As a result, an installation above a conveyor belt can provide a distribution of the spectral data on a plane. This results in a large amount of data that is difficult to handle with traditional statistical analysis. Furthermore, storage of the data becomes a key issue, therefore a model to predict the parameters of interest should be able to be updated continuously in an automated way. This makes hyperspectral imaging data a prime candidate for the application of machine learning techniques. This paper discusses the modelling approach for hyperspectral imaging, focusing on data analysis and assessment of machine learning approaches for the development of calibration models.

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. 

With a growing population and increased use of resources, there is an urgent need to transform towards sustainable production in order to stay competitive. Prior studies suggest that circular thinking positively impacts the environmental impact of products. However, few studies have investigated the implications of applying circular thinking to the design of production equipment. We address this research gap by looking at what circularity is and how it can be perceived in the context of production equipment. Our research reveals that different circularity requirements need to be implemented in different phases of the life cycle of the production equipment. However, to succeed the requirements need to be considered already early in the design phase of the production equipment. Further, since the development of production equipment is a co-creation between the equipment with the manufacturing company, i.e. users of the production equipment. The circularity thinking between the two partners needs to be aligned and coordinated. Our findings emphasise the need for a holistic approach with system thinking implemented early in the life cycle of production equipment. 

The manufacturing industry contributes to climate change and must take action to reach its high-set target. Implementation of circularity to reach sustainability has shown to be beneficial for products, but there is a lack of knowledge on how to transfer the concept to the design of factories. The purpose of the paper is to identify and systemically analyse areas of importance to enable circularity in production development to better understand what requires attention to achieve a circular factory. The focus of the study is on production development narrowed down to production equipment. A case study approach was used with interviews as the main method for data collection. Four main themes were identified: competence, collaboration, mindset and time. These were considered as areas of importance to enable circularity and to understand what needs further attention they were analysed in a systemic view of macro-, meso- and microlevel. The findings stress the need for investing in circularity in early phases to achieve circularity and that all levels of the industry need to take part in the transition towards circularity. Further, research within areas with similar complexities could benefit and learn from each other.

This research presents a comprehensive framework for the development, integration, and formulation of 3D simulation models within industrial production systems. It aims to provide the required guidelines for utilizing 3D simulation technology, improving system productivity, assisting with decision-making, and furthering system optimization. The research highlights the considerable influence of 3D simulation technology on production systems, starting with developing and evaluating the system current state and its potential in manufacturing industries. The framework includes elements related to collecting data, the creation of 3D production simulation models, validation, and verification phases; setting objectives for the integration of 3D simulation; and the choice and advancement of customized simulation technologies that meets specific industry demands. The framework is developed with the help of a case study where the authors present the steps and information necessary to facilitate the modelling of the current state version within 3D production simulation model. Through the case study presented, this research illustrates the practical application of this framework, aiming to serve as an exemplary guide for academic and industrial practitioners.

Hydrocracking is an energy-intensive process, and its control system aims at stable product specifications. When the main product is diesel, the quality measure is usually 95% of the true boiling point. Constant diesel quality is hard to achieve when the feed characteristics vary and feedback control has a long response time. This work suggests a feedforward model predictive control structure for an industrial hydrocracker. A state-space model, an autoregressive exogenous model, a support vector machine regression model, and a deep neural network model are tested in this structure. The resulting reactor temperature decisions and final diesel product quality values are compared against each other and against the actual measurements. The results show the importance of the feed character measurements. Significant improvements are shown in terms of product quality as well as energy savings through decreasing the heat duty of the preheating furnace. 

Crude oil has different characteristics according to its origin, and this difference causes suboptimal operation if not considered. Similar to other refinery operations, hydrodesulfurization suffers from lacking this knowledge. Information on the true boiling point curve of the feed, next to its sulfur concentration, can be used to optimize the operating temperature. In this work, an optimization problem is demonstrated for two manipulated temperatures of the system and solved by using a gradient-based and a gradient-free algorithm. While the gradient based solution has a single objective of minimum sulfur content, the gradient-free solution has three objectives: minimum sulfur, inlet temperature, and secondary hydrogen flow rate. A continuous lumping model is used to predict the temperature and sulfur responses of a real hydrodesulfurization plant. An adaptive approach is preferred for the model to cope with the catalyst deactivation interference on the product sulfur content constraint. The effect of changing feed on optimality is demonstrated by using eight types of feeds with varying true boiling point and sulfur content. In addition to that, the impact of catalyst age is shown on similar feed processed on different dates.

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.

Several sensors are installed in the majority of chemical reactors and storage tanks to monitor temperature profiles for safety and decision-making processes such as heat demand or flow rate calculations. These sensors fail occasionally and generate erroneous measurement data that need to be detected and excluded from the calculations. However, due to the high number of process variables displayed in the chemical plants, this task is not trivial. In this work, a Bayesian network approach to detect faulty temperature sensors is proposed. By comparing the sensor measurements with each other, the faulty sensor is detected. A modular approach is preferred, and networks are created for 10 K temperature intervals to increase flexibility and sensitivity. Created networks can be adjusted for the operating temperature ranges; hence, they can be used for any catalyst and entire life cycle. The developed method is demonstrated on an industrial scale hydrocracker unit with 92 sensor couples installed in a series of reactors. From the investigated sensors, 16 of them showed a greater difference than the 2 K threshold chosen for the fault. In addition to that, 13 sensors showed an increasing temperature difference that may lead to a fault. Two scenarios were created to calculate the energy loss due to a faulty measurement, and a 5.5 K offset error was found to cause a 5.79 TJ energy loss every year for a small scale hydrocracker.

Model-based approaches are essential for the operation, optimization, and control of applications in the process industry. Different structures are often investigated to build representative and robust models, and a set of parameters with the same attributes are required to utilize them effectively. Parameter estimation gets arduous with the increasing complexity of the process, the model, and the size of the parameter space. In this work, a parameter-estimation problem based on a steady-state model of diesel hydrodesulfurization is investigated using gradient-based and gradient-free optimizers. The optimal parameter sets obtained are then assessed in terms of performance and computational time for the different optimizers. Furthermore, the sensitivity of the various parameters is also investigated. Due to the catalytic reactions in this process, some parameters have to be updated depending on the catalyst activity. In addition to the initial estimation, the updated parameters are also studied, and instead of a time-based one, a tolerance-based recalculation schedule is suggested. Finally, the robustness of the final model is analyzed by giving different operating conditions and feed characteristics. The adaptive parameter approach proved better data fitting capabilities by improving the coefficient of determination for temperature predictions.

Electricity grid flexibility is vital for renewable energy to be used effectively. Power-to-gas technologies are investigated to connect electricity grid to gas grid and to tackle capacity challenges. Grid management expenses consist of redispatch and feed-in management. These management procedures, next to being costly, cause a significantenergy loss. Proton-exchange membrane electrolyzer installations were studied to reduce these expenses and recover energy. The change in the levelized cost of hydrogen production with varying electrolyzer capacities was presented. The sensitivity of the levelized cost and net presentvalue with respect to installation costs, maintenance costs, and electricity prices were investigated. While the electricity prices have the most significant effect on the levelized cost of hydrogen production, the net present value was affected considerably by the hydrogen selling price. Possible energy savings were calculated between 2 – 23 GWh for 2, 5, 10, 20 MW installations. The annual gridmanagement expense savings were in the range of 0.2– 2.3 million Euros, increasing with the increasing electrolyzer capacity.

This paper presents the development and application of a modelling approach of variable geometry conceptsfor low pressure ratio fans; namely Variable Area Nozzle and Variable Pitch Fan. An enhanced approachfor Outlet Guide Vane pressure loss predictions and an aerothermodynamic analysis of variable pitchconcept are developed and integrated into a multi-disciplinary conceptual engine design framework. Astreamline curvature algorithm is deployed for the derivation of the off-design fan performance map,alleviating scaling issues from higher pressure ratio fan designs. Correction deltas are derived through thevariable pitch analysis for calculating the re-shaped off-design fan performance map. The aforementionedvariable geometry concepts are evaluated in terms of surge margin at engine and aircraft level for a lowpressure ratio aft-fan of a hybrid-electric configuration. Performance assessments carried out suggest thata +8° closing of fan blade cascades leads to a 33% surge margin improvement (with reference being thesurge margin without variable geometry) compared to a 25% improvement achieved by +20% opening thenozzle area at end of runway take-off conditions. Although weight and complexity implications of variablegeometry are not considered, the integrated modelling approach is shown to be able to assess and comparesuch novel engine technologies for low pressure ratio fans in terms of operability.

This study aims to describe a design methodology for supersonic rotor blade geometry, depending on the working fluid, for a low enthalpy Organic Rankine Cycle (ORC) system. Thus, the working fluid is a non-ideal gas with low impact of real flow effects. An innovate algorithm was developed, in order to generate the two-dimensional geometry of the rotor blade, for various working media. A design method, based on the principle of vortex flow field, was used for the blading design and, for the design of supersonic blades, the method of characteristics was selected as the most optimum. The geometry was tested using a commercial simulation software that uses a pressure-based solving algorithm named SIMPLE (Semi-implicit Method for Pressure-Linked Equations). Key advantages of this procedure are both its simplicity and precision of the results.

The above procedure was applied for three working fluids, indicatively isobutane (R-600a), tetrafluroethane (R134a) and a mixture of 15% isobutane – 85% isopentane. Considering the ratio of specific heat capacities as constant, which is a realistic assumption for the operating conditions of these systems, the algorithm produces three different blade geometries. Results comparison indicates that every working fluid, for the same operating conditions and for the same design options, has a significantly differentiated geometry of the two-dimensional blade. Finally, the calculated total to total isentropic efficiency, for these rotor blades, is almost 92%. 

Near InfraRed Spectroscopy (NIRS) offers rapid on-line analysis of biomass feedstocks and can be utilized for process control of biomass- based combined heat and power plants. Within the OPtiC-NIRS project we have carried out a full-scale on-site testing of different NIRS for online powerplant control at the facilities of Mälarenergi and Eskilstuna Strängnäs Energi och Miljö. 

The project has been focused on developing and testing robust NIRS soft-sensors for fuel higher heating value and composition (incl. moisture, components such as recycle wood and glass, different type of plastics and ash) and combining them with dynamic models for on-line feed-forward process monitoring and control. Expected benefits include reduced risk of agglomeration and pollutant emissions formation as well as improved production control. A longer-term potential and ambition is to be able to identify the fossil content in municipal waste fuel, which can hopefully be addressed in a follow-up study. 

This paper describes a new modular experimental facility that was purpose-built to investigateflow interactions between the combustor and first stage nozzle guide vanes (NGVs)of heavy duty power generation gas turbines with multiple can combustors. The first stageturbine NGV is subjected to the highest thermal loads of all turbine components andtherefore consumes a proportionally large amount of cooling air that contributes detrimentallyto the stage and cycle efficiency. It has become necessary to devise novel coolingconcepts that can substantially reduce the coolant air requirement but still allow theturbine to maintain its aerothermal performance. The present work aims to aid this objectiveby the design and commissioning of a high-speed linear cascade, which consists oftwo can combustor transition ducts and four first stage NGVs. This is a modular nonreactiveair test platform with engine realistic geometries (gas path and near gas path), coolingsystem, and boundary conditions (inlet swirl, turbulence level, and boundary layer).The paper presents the various design aspects of the high pressure (HP) blow down typefacility, and the initial results from a wide range of aerodynamic and heat transfermeasurements under highly engine realistic conditions.

This paper describes an investigation into the off-design performance comparison of single and two-shaft gas turbine engines. A question that has been asked for a long time is which gas turbine delivers a better thermal efficiency at part load. The authors, notwithstanding their intensive searches, were unable to find a comprehensive answer to this question. A detailed investigation was carried out using a state-of-the-art performance evaluation method and the answer was found to be: It depends! In this work, the performance of two engine configurations is assessed. In the first one, the single-shaft gas turbine operates at constant shaft rotational speed. Thus, the shape of the compressor map rotational speed line will have an important influence on the performance of the engine. To explore the implications of the shape of the speed line, two single-shaft cases are examined. The first case is when the speed line is curved and as the compressor pressure ratio falls, the nondimensional mass flow increases. The second case is when the speed line is vertical and as the compressor pressure ratio falls, the nondimensional mass flow remains constant. In the second configuration, the two-shaft engine, the two shafts can be controlled to operate at different rotational speeds and also varying relationships between the rotational speeds. The part-load operation is characterized by a reduction in the gas generator rotational speed. The tool, which was used in this study, is a 0-D whole engine simulation tool, named Turbomatch. It was developed at Cranfield and it is based on mass and energy balance, carried out through an iterative method, which is based on component maps. These generic, experimentally derived maps are scaled to match the design point of a particular engine before an off-design calculation is performed. The code has been validated against experimental data elsewhere, it has been used extensively for academic purposes and the research activities that have taken place at Cranfield University. For an ideal cycle, the single-shaft engine was found to be a clear winner in terms of part-load thermal efficiency. However, this picture changed when realistic component maps were utilized. The basic cycle and the shape of component maps had a profound influence on the outcome. The authors explored the influence of speed line shapes, levels of component efficiencies, and the variation of these component efficiencies within the operating range. This paper describes how each one of these factors, individually, influences the outcome.

This paper describes an investigation into the off-design performance comparison of single and two-shaft gas turbine engines. A question that has been asked for a long time in which gas turbine delivers a better thermal efficiency at part load. The authors, notwithstanding their intensive searches, were unable to find a comprehensive answer to this question. A detailed investigation was carried out using a state of the art performance evaluation method and the answer was found to be: It depends!

In this work, the performance of two engine configurations is assessed. In the first one, the single shaft gas turbine operates at constant shaft rotational speed. Thus, the shape of the rotational speed line will have an important influence on the performance of the engine. To explore the implications of the shape of the speed line, two single shaft cases are examined. The first case is when the speed line is curved and as the compressor pressure ratio falls, the non-dimensional mass flow increases. The second case is when the speed line is vertical and as the compressor pressure ratio falls, the non-dimensional mass flow remains constant. 

In the second configuration, one shaft couples a compressor and a turbine (often called the gas generator shaft). The second shaft couples the power turbine to the driven equipment. The two shafts can be controlled to operate at different rotational speeds and also varying relationships between the rotational speeds. The part-load operation is characterised by a reduction in the gas generator rotational speed.

The tool, which was used in this study, is a 0-D whole engine simulation tool, named Turbomatch. It was developed at Cranfield and it is based on mass and energy balance, carried out through an iterative method, which is based on component maps. These generic, experimentally derived maps are scaled to match the design point of a particular engine before an off-design calculation is performed. The code has been validated against experimental data, it has been used extensively for academic purposes and the research activities taken place at Cranfield University.

For an ideal cycle, the single shaft engine was found to be a clear winner in terms of part-load thermal efficiency. However, this picture changed when realistic component maps were utilised. The basic cycle and the shape of component maps had a profound influence on the outcome.

The authors explored the influence of speed line shapes, levels of component efficiencies and the variation of these component efficiencies within the operating range. This paper describes how each one of these factors, individually, influences the outcome.

A conceptual design of a hybrid Gas Turbine - Solid Oxide Fuel Cell (SOFC) system is presented for civil aviation applications. The system operates using hydrogen as fuel, for the aircraft’s propulsion, while at the same time produces electrical energy in the fuel cell. Hydrogen is produced during flight by reformation of methane. The motivation of the study is to investigate hydrogen’s use for aviation purposes, so the hybrid system’s operation characteristics need to be examined. A configuration is designed, where a SOFC and the burner is modeled as one and simulated, in a modern multidisciplinary programming environment, in order to analyze the thermodynamic characteristics of the hybrid system. The fuel cell sets into motion when the aircraft reaches top of climb. During operation, liquefied natural gas is converted to hydrogen in the fuel cell and part of it is used to produce electrical energy while the rest for combustion. To determine the efficiency of the system, its performance was simulated using two scenarios, one for longhaul flights and one for short-haul flights. Comparing the results, for long-haul flights, the hybrid system presents a reduction in fuel consumption and an increase in thermal efficiency. For flights of a short range, the existing conditions in the fuel cell inlet were found to be prohibitive for it’s operation and the use of the hybrid system ineffective. For the system’s efficiency, the larger the pressure in the SOFC’s inlet the better. However, SOFC’s pressure limits restrict the pressure range and the cell’s use only during flight. Concluding, according to the study’s results, the hybrid system can operate in flight conditions, making the use of hydrogen in civil aviation possible. As a result, a 12% and 35% benefit is achieved, in fuel saving and thermal efficiency respectively.

This paper presents the development of AeroEngineS (Aircraft Engine Simulation), a multi-platform app with graphical user interface for aero engine simulation and compressor map operating point prediction. Gas turbine performance simulation is a crucial part of the design process. It provides information about the required operating conditions of all the components and the overall performance of the engine so that engineers can determine whether the current engine configuration meets the performance requirements. Some gas turbine simulation programs have been developed in the last decades, however, there was a lack of an open-source, lightweight, user-friendly, but still very accurate, application which would be easily accessible from all platforms. AeroEngineS can be used as a user-friendly preliminary design tool, since, during this design phase, details about the geometry are not known yet. The main aim is to calculate simply and quickly the basic parameters of the thermodynamic cycle and the performance, in order to determine which design is able to meet the required specifications. AeroEngineS constitutes a free and simple app which can primarily serve educational purposes as it is easily accessible by students from any platform to assist them in aero engine technology courses. Secondarily, it has the potential to be used even by engineers as a quick tool accessible from all devices. The app consists of two basic stand-alone functions. The first function is aero engine simulation at Design Point which solves thermodynamic calculations. The second function is compressor map operating point prediction using a novel method of combining scaling techniques and Artificial Neural Networks.

The interest in using Two-Phase Loop Thermosyphons(TPLT) for heat recovery and energy saving within different in-dustrial processes has been in rise on the last few decades. Thesedevices are characterized by geometrical flexibility, as well asenhanced heat exchange rates. However, TPLT operation in-volves complex physical mechanisms, where different flow andheat transfer regimes are encountered. These regimes are crucialto be assessed and understood, in order to successfully predictand optimize the TPLT operation.

In this paper, a comprehensive one-dimensional thermo-hydraulic modelling approach is developed and presented in or-der to simulate the TPLT operation. The novelty of this modellies in the exhibition of the different experienced complex flowpatterns, heat transfer regimes and physical mechanisms, includ-ing the dry-out prediction and reporting. This modelling frame-work is based on the separated two-fluid model coupled withmass, momentum and energy balances as well as relevant ther-modynamic constraints. The obtained results are compared to theavailable experimental measurements from literature, and a goodagreement is found with a maximum prediction error of 7%.

Furthermore, a sensitivity analysis is performed aiming todetermine the effect of the operating saturation temperature, andtherefore the filling ratio, on the average heat transfer coefficientof the TPLT’s evaporator. Optimal values leading to enhance theheat removal are proposed and discussed at the end of this paper.

Boiling flows are widely encountered in several engineering and industrial processes. They have a special interest in nuclear industry, where a Computational Fluid Dynamic(CFD) thermohydraulic investigation becomes very popular for design and safety. Many attempts to model numerically subcooled nucleate boiling flows can be found in the literature, where several interfacial forces acting on bubbles which are interacting on the bulk fluid were neglected, due to the hard convergence of the calculations, or to the bad accuracy of the obtained results. In this paper, a sensitivity analysis is carried out for the interfacial forces acting on bubbles during subcooled nucleate boiling flows. For this purpose, 2D CFD axisymmetric simulations based on an Eulerian approach are performed. The developed models aim to mimic the subcooled nucleate boiling flows in concentric pipes, operating at high pressure. The predicted spatial fields of boiling quantities of interest are presented and commented. The numerical results are compared against the available experimental data, where it is shown that neglecting some interfacial forces like the lift or the wall lubrication forces will yield to good predictions for some quantities but will fail the prediction for others. The models leading to the best predictions are highlighted and proposed as recommendations for future CFD simulations of subcooled nucleate boiling flows.

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. © 2020 Elsevier Ltd

Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail.

Leveraging the potential of digital twins is of utmost importance to support smart production. Digital twin research has principally focused on defining digital twin concepts and applications and proposing various frameworks for their implementation. Less is known about using simplified models to overcome many challenges related to digital twin models. Based on a longitudinal case study at a multinational manufacturing company engaged in digital twins in manufacturing efforts, this paper identifies the main challenges encountered related to people, processes, and technology, as well as requirements placed on a digital twin. This study also presents the opportunities of applying simplified models for digital twins to overcome the identified challenges and fulfill the defined requirements. The present study provides theoretical and practical implications of the development of digital twins in manufacturing, focusing attention on the challenges and requirements that affect the outcome of the manufacturing company to drive digital twin efforts.

Digital twins have emerged as a critical technology to enable smart production. Digital twins can enhance the current production system by optimizing the current setup and facilitating decision-making based on facts rather than gut feeling. Despite the numerous benefits explored, digital twins have faced many challenges in developing and implementing production systems. Their complexity is causing a lack of digital twin implementations in the production system. This complexity can be traced back to physical and virtual entities and the digital twin development process. By conducting a case study in a global manufacturing company, this publication explores the sources of complexity when developing digital twins. The findings are organized around the digital twin development steps and their corresponding complexity. The number of different types of entities being modeled, the choice of the modeling approach, modeling low-frequency events, emergent phenomena, and the unpredictability and variability of the manufacturing process are all examples of structural and dynamic complexity that have been found to impede success in digital twin applications. This research has implications for managers who are involved in the development of digital twins in their organizations. It can help with methodological guidance when dealing with an undefined and complicated process of digital twin development.

Nowadays thermal management becomes a challenge as it implies high power density with high lossesconverted to large heat release. For low power levels, natural or forced single-phase convection could besufficient. For a much higher heat release nucleate boiling can be the alternative solution since it can dissipate the heat more efficiently, thanks to the latent heat effect present during the phase change. Its performance depends on many parameters that enable potential control and make system integration often very complex. The transition towards nucleate boiling, called Onset of Nucleate Boiling requires better estimation, and the mechanism still lacks understanding, especially in mini-channels. This study aims to characterize nucleate boiling in a rectangular mini-channel experimental set-up, built at Mälardalenuniversity, to better characterize the onset of nucleate boiling and the fully developed bubbly flow. The experiment allows full control of single-phase and two-phase regimes by varying the thermo-hydraulic and heat transfer conditions. With the use of a high-speed camera, bubble dynamics and their principal characteristics such as size, shape, propagation, and nucleation site location are determined with a digital image analysis technique developed within this work. The image processing has proved to be successful even on noisy images due to shadows or background changes. The reconstruction of segmented bubbles enabled flexible and automated bubble and path detection with a statistical approach, especially at the Onset of Nucleate Boiling. Local Reynolds numbers are then estimated to determine the drag coefficient in the flow during bubble growth, or their coalescence.

In subcooled flow boiling, heat transfer mechanism involves phase change between liquid phase to the vapour phase. During this phase change, a large amount of energy is transferred, and it is one of the most effective heat transfer methods. Subcooled boiling heat transfer is an attractive trend for industrial applications such as cooling electronic components, supercomputers, nuclear industry, etc. Due to its wide variety of applications for thermal management, there is an increasing demand for a faster and more accurate way of modelling. 

In this work, a supervised deep neural network has been implemented to study the boiling heat transfer in subcooled flow boiling heat transfer. The proposed method considers the near local flow behaviour to predict wall temperature and void fraction of a sub-cooled mini-channel. The input of the network consists of pressure gradients, momentum convection, energy con- vection, turbulent viscosity, liquid and gas velocities, and surface information. The output of the model is based on the quantities of interest in a boiling system i.e. wall temperature and void fraction. The network is trained from the results obtained from numerical simulations, and the model is used to reproduce the quantities of interest for interpolation and extrapolation datasets. To create an agile and robust deep neural network model, state-of-the-art methods have been implemented in the network to avoid the overfitting issue of the model. The results obtained from the deep neural network model shows a good agreement with the numerical data, the model has a maximum relative error of 0.5 % while predicting the temperature field, and for void fraction, it has approximately 5 % relative error in interpolation data and a maximum 10 % relative error for the extrapolation datasets. 

The thermal boundary conditions in a numerical simulation for heat transfer are often imprecise. This leads to poorly defined boundary conditions for the energy equation. The lack of accurate thermal boundary conditions in real-world applications makes it impossible to effectively solve the problem, regardless of the advancement of conventional numerical methods. 

This study utilises a physics-informed neural network to tackle ill-posed problems for unknown thermal boundaries with limited sensor data. The network approximates velocity and temperature fields while complying with the Navier-Stokes and energy equations, thereby revealing unknown thermal boundaries and reconstructing the flow field around a square cylinder. The method relies on optimal sensor placement determined by the QR pivoting technique, which ensures the effective capture of the dynamics, leading to enhanced model accuracy. In an effort to increase the robustness and generalisability, an ensemble physics-informed neural network is implemented. This approach mitigates the risks of overfitting and underfitting while providing a measure of model confidence. As a result, the ensemble model can identify regions of reliable prediction and potential inaccuracies. Therefore, broadening its applicability in tackling complex heat transfer problems with unknown boundary conditions.

While simulating heat transfer problems using a numerical method, the thermal boundary conditions are never known precisely, which leads to ill-posed boundary conditions for the energy equation. The lack of knowledge of accurate thermal boundary conditions in a practical application makes it impossible to solve this problem no matter how sophisticated the conventional numerical method is. Hence, the current work addresses this ill-posed problem using physics informed neural network by assuming that the thermal boundary near the source is unknown and only a few measurements of temperature are known in the domain. Physics-informed neural network is employed to represent the velocity and temperature fields, while simultaneously enforcing the Navier-Stokes and energy equations at random points in the domain. This work serves as an inverse problem since the goal here is to reproduce the global flow field and temperature profile in the domain with few measurement data points. Furthermore, the work focuses on using transfer learning for different parameters such as the position and size of the source term inside the enclosure domain. These parameters are of particular interest while designing a thermal system and being able to predict the flow and thermal behaviour instantly will allow for better design of the system. For this study, the sensors' data are extracted from numerical simulation results. The placement of the sensors in the domain plays a vital role in accuracy hence, sensors were optimized using the residual of the energy equation. The results obtained from this work demonstrate that the proposed method is in good agreement with the underlying physics represented by the numerical results.

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.

A sound understanding of the critical heat flux is of prime importance for any industrial boiling system design and safety. From the literature, the majority of the critical heat flux studies are based on empirical knowledge, often supported by ex- perimental investigations which are performed under specific conditions difficult to be generalized. Consequently, most of the available correlations have ±30% predictive error when com- pared to measurement data. Hence, accurate prediction of this quantity remains an open challenge for the thermal engineering community. The present study aims to investigate the hidden features that exist in experimental data using a machine learning technique. Firstly, a literature survey is carried out to collect experimental data for boiling flows in tubes under low pressure and low flow conditions. These experimental data consist of the following parameters: system pressure, mass flux, characteristic dimensions, thermodynamic quality, inlet subcooling, and critical heat flux. A parametric Gaussian process regression model is used to predict the critical heat flux. The prediction obtained from the model is then compared with experimental measurements and the values obtained from the critical heat flux look-up table. The model used in this study is capable of predicting the critical heat flux with better accuracy along with the information of prediction uncertainty. Moreover, it provides insights on the relevance of the different input parameters to the prediction of the critical heat flux and aligns well with the underlying physics. The model used in this study shows a good level of robustness which can be further extended for other geometries, datasets, and operating conditions. 

The present work focuses on designing a robust deep-learning model to track bubble dynamics in a vertical rectangular mini-channel. The rectangular mini-channel is heated from one side with a constant heat flux, resulting in the creation of bubbles. Images of the bubbles are recorded using a high-speed camera, which serve as the input data for the deep learning model. The raw image data acquired from the high-speed camera is inherently noisy due to the presence of shadows, reflections, background noise, and chaotic bubbles. The objective is to extract the mask of the bubble given all these challenging factors. Transfer learning is adopted to eliminate the need for a large dataset to train the deep learning model and also to reduce computational costs. The trained model is then validated against the validation datasets, demonstrating an accuracy of 98% while detecting the bubbles. The model is then evaluated on different experimental conditions, such as lighting, background, and blurry images with noise. The model demonstrates high robustness to different conditions and is able to detect the edges of the bubbles and classify them accurately. Moreover, the model achieves an average intersection over union of 85%, indicating a high level of accuracy in predicting the masks of the bubbles. The method enables accurate recognition and tracking of individual bubble dynamics, capturing their coalescence, oscillation, and collisions to estimate local parameters by proving the bubble masks. This allows for a comprehensive understanding of their spatial-temporal behaviour, including the estimation of local Reynolds numbers.

This paper investigates how glass contamination in refuse-derived fuel can be quantitatively detected using near-infrared spectroscopy. Near-infrared spectral data of glass in four different background materials were collected, each material chosen to represent a main component in municipal solid waste; actual refuse-derived fuel was not tested. The resulting spectra were pre- processed and used to develop multi-variate predictive models using partial least squares regression. It was shown that predictive models for coloured glass content are reasonably accurate, while models for mixed glass or clear glass content are not; the validated model for coloured glass content had a coefficient of determination of 0.83 between the predicted and reference data, and a root- mean-square error of validation of 0.64. The methods investigated in this paper show potential in predicting coloured glass content in different types of background material, but a different approach would be needed for predicting mixed type glass contamination in refuse-derived fuel. 

In the past decade, aircraft fuel burn has been continually decreased, mainly by improving thermal and propulsion efficiencies with consequent decrement in specific fuel consumption. In view of future emission specifications, the requirements for SFC in the forthcoming years are expected to become more stringent. In this paper, a preliminary design of a turbofan engine for entry in service in 2025 was performed. The design of a baseline 2010 EIS engine was improved according to 2025 specifications. A thermodynamic analysis was carried out to select optimal jet velocity ratio, pressure ratio, and temperatures with the goal of minimizing specific fuel consumption. A gas path layout was generated and an aerodynamic analysis was performed to optimize the engine stage by stage design. The optimization resulted in a 3-shaft turbofan jet engine with a 21% increase in fan diameter, a 2.2% increment in engine length, and a fuel burn improvement of 11% compared to the baseline engine, mainly due to an increment in propulsive efficiency. A sensitivity analysis was also conducted to highlight what the focus of technology development should be.