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.

This paper presents a methodology for optimizing an aeronautical propeller to minimize power consumption. A multi-objective approach using blade element momentum (BEM) theory and evolutionary algorithms is employed to optimize propeller design by minimizing power consumption during takeoff and top-of-climb. Three different evolutionary algorithms generated a Pareto front, from which the optimal propeller design is selected. The selected propeller design is evaluated under optimal operational conditions for a specific mission. In this context, two operational approaches for the optimized propellers during flight missions are evaluated. The first approach considers the possibility of only three values for the propeller rotation, while the second allows continuous changes in the rotational speed and pitch angle values, known as the multi-rotational-speed approach. In the second approach, a modal analysis of the propeller is performed using rotating beam theory. The natural frequencies of vibration, constrained by the Campbell diagram, enable an operational analysis and ensure structural integrity by preventing resonance between propeller blades and the rotational procedures. The multi-rotational approach is conducted with and without frequency constraints, resulting in general flight energy reductions of 1.40% and 1.47%, respectively. However, substantial power savings are achieved, namely up to $10\%$ during critical flight states, which can have a significant impact on future engine design and operability. The main contributions of the research lie in analyzing the multi-rotational approach with vibrational constraints of the optimized propeller. This research advances sustainable aviation practices by focusing on reducing power consumption while maintaining performance.

Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design exploration. This paper introduces a data-driven surrogate modeling framework based on a feedforward neural network to predict natural vibration frequencies of propeller blades with high accuracy and a dramatically reduced runtime. A dataset of 1364 airfoil geometries was parameterized, meshed, and analyzed in ANSYS 2024 R2 across a range of rotational speeds and boundary conditions to generate modal responses. A TensorFlow/Keras model was trained and optimized via randomized search cross-validation over network depth, neuron counts, learning rate, batch size, and optimizer selection. The resulting surrogate achieves $R^2>0.90$ and $NRMSE<0.08$ for the second and higher-order modes, while reducing prediction time by several orders of magnitude compared to full finite-element workflows. The proposed approach seamlessly integrates with CAD/CAE pipelines and supports rapid, iterative optimization and real-time decision support in propeller design.

Higher education has been crucially impacted by the pandemic during the past years. Despite the associated challenges, a wide portfolio of digital literacies has been developed for the delegates. This work evaluates the introduction of digital tools into in-person education. The “Process Optimization” course at Mälardalen University (MDU) is reformed to operate in a digitally enhanced classroom mode. The course covers a variety of optimization methods applied on thermal and fluid machinery such as systems of compressors, pumps and heat exchangers, heat and power plants, aircraft trajectories, and propulsion systems. The constructive alignment is presented to illustrate links between learning objectives, learning activities, and assessment tasks. A series of digital tools is introduced to elevate learning experience prior, during, and after class time. Those comprise digital quizzes, a video channel, polls, a digital whiteboard, and a digital forum. The course is systematically instrumented, yielding a vast set of statistics for evaluating the effectiveness of digital tools as well as engagement levels for learners. The contribution of digitalization into standardizing the formative and summative assessment is discussed. It is observed that digital tools complement the participation into pre- and postclassroom activities. An interactive and digitalized course evaluation activity is also designed. This allowed learners and educators to productively exchange feedback in an inclusive manner. The accrued data provide insight into the impact of digitalization on the delivery of an applied engineering course. Lessons learnt comprise quantitative and qualitative outcomes arising from the perspectives of both learners and teachers. Guidelines and recommended practices are provided for the penetration of digital tools into synchronous and asynchronous learning activities. This paper identifies opportunities as well as space for improvement arising from the penetration of digital tools into the new era for education.

The evolution of district heating and cooling systems into sophisticated energy networks is essential for global decarbonization. However, a fundamental tension exists between rapid innovation in advanced control algorithms and the slow replacement cycle of physical infrastructure, making intelligent system-wide control the primary enabler of network modernization. Despite this critical role, the existing literature remains fragmented and lacks a comprehensive synthesis of control strategies across the production, distribution, and end-user levels. This analysis confirmed a definitive shift from isolated, single-level control to holistic frameworks that unlock system-wide flexibility. This review establishes that successful implementation requires addressing distinct objectives at each operational level, from multisource management in production to occupant-centric control at the end-user level. A critical finding is the credibility gap between the demonstrated potential of advanced control and its limited practical application. This disparity is rooted in systemic challenges, including intensive modeling requirements, computational scalability limits, and unresolved human-in-the-loop problems. To bridge this gap, this review presents a structured framework that synthesizes the current state of district heating and cooling control and proposes a forward-looking research roadmap. This roadmap prioritizes the development of hybrid intelligent controllers that integrate learning-based methods with model predictive control, creating persistent digital twins for human-centric applications, and designing secure and decentralized coordination architectures for next-generation thermal networks.