Electrification of the propulsion system has opened the door to a new paradigm of propulsion system configurations and novel aircraft designs, which was never envisioned before. Despite lofty promises, the concept must overcome the design and sizing challenges to make it realizable. A suitable modeling framework is desired in order to explore the design space at the conceptual level. A greater investment in enabling technologies, and infrastructural developments, is expected to facilitate its successful application in the market. In this review paper, several scholarly articles were surveyed to get an insight into the current landscape of research endeavors and the formulated derivations related to electric aircraft developments. The barriers and the needed future technological development paths are discussed. The paper also includes detailed assessments of the implications and other needs pertaining to future technology, regulation, certification, and infrastructure developments, in order to make the next generation electric aircraft operation commercially worthy.

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.

Biomass used in energy conversion processes is typically characterized by high variability, making its utilization challenging. Therefore, there is a need for a fast and non-destructive method to determine feedstock/product properties and directly monitor process reactors. The near-infrared spectroscopy (NIRS) technique together with advanced data analysis methods offers a possible solution. This review focuses on the introduction of the NIRS method and its recent applications to physical, thermochemical, biochemical and physiochemical biomass conversion processes represented mainly by pelleting, combustion, gasification, pyrolysis, as well as biogas, bioethanol, and biodiesel production. NIRS has been proven to be a reliable and inexpensive method with a great potential for use in process optimization, advanced control, or product quality assurance.

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.

Reduction in $CO2$ emissions is strongly linked with the improvement of engine specific fuel consumption, as well as the reduction in engine nacelle drag and weight. Conventional turbofan designs, however, that reduce $CO2$ emissions—such as increased overall pressure ratio designs—can increase the production of $NOx$ emissions. In the present work, funded by the European Framework 6 collaborative project NEW Aero engine Core concepts (NEWAC), an aero-engine multidisciplinary design tool, Techno-economic, Environmental, and Risk Assessment for 2020 (TERA2020), has been utilized to study the potential benefits from introducing heat-exchanged cores in future turbofan engine designs. The tool comprises of various modules covering a wide range of disciplines: engine performance, engine aerodynamic and mechanical design, aircraft design and performance, emissions prediction and environmental impact, engine and airframe noise, as well as production, maintenance and direct operating costs. Fundamental performance differences between heat-exchanged cores and a conventional core are discussed and quantified. Cycle limitations imposed by mechanical considerations, operational limitations and emissions legislation are also discussed. The research work presented in this paper concludes with a full assessment at aircraft system level that reveals the significant potential performance benefits for the intercooled and intercooled recuperated cycles. An intercooled core can be designed for a significantly higher overall pressure ratio and with reduced cooling air requirements, providing a higher thermal efficiency than could otherwise be practically achieved with a conventional core. Variable geometry can be implemented to optimize the use of the intercooler for a given flight mission. An intercooled recuperated core can provide high thermal efficiency at low overall pressure ratio values and also benefit significantly from the introduction of a variable geometry low pressure turbine. The necessity of introducing novel lean-burn combustion technology to reduce $NOx$ emissions at cruise as well as for the landing and take-off cycle, is demonstrated for both heat-exchanged cores and conventional designs. Significant benefits in terms of $NOx$ reduction are predicted from the introduction of a variable geometry low pressure turbine in an intercooled core with lean-burn combustion technology.

The design space of an intercooled recuperated aero-engine has been explored using detailed engine and aircraft performance, weight, and dimensions modeling. The design parameters of the engine fan, core, intercooler, recuperator, cooling-air ratio, and variable-geometry settings for the low-pressure turbine have been optimized for minimum mission fuel. Analysis shows that the improvement achieved in terms of performance against the datum design can be attributed primarily to an increase in thermal efficiency. A parametric study has also been carried out around the optimal design to understand the impact of the chosen design parameters on mission fuel burn. The study demonstrates in detail the substantially more complex interrelationship that the different fan design parameters have in terms of engine performance compared to what is typical for conventional turbofan designs. Furthermore, the optimal pressure ratio split between the low-pressure compressor and the high-pressure compressor aligns well with ...