In the upcoming space revolutions aiming at the implementation of automated, smart, and self-aware crewless vehicles and reusable spacecraft, sensors play a significant role in the control systems. In particular, fiber optic sensors, with their small footprint and electromagnetic immunity, represent a great opportunity in aerospace. The radiation environment and the harsh conditions in which these sensors will operate represent a challenge for the potential user in the aerospace vehicle design and the fiber optic sensor specialist. We present a review that aims to be a primer in the field of fiber optic sensors in radiation environments for aerospace. We review the main aerospace requirements and their relationship with fiber optics. We also present a brief overview of fiber optics and sensors based on them. Finally, we present different examples of applications in radiation environments for aerospace applications.

In this paper, a complete conjugate heat transfer analysis of a scaled-sized prototype of an integrated air-cooled surface-mounted permanent-magnet generator for the “more-electric engine” application is presented. The selected integration position inside the aircraft's main gas turbine engine imposes the use of an unconventional cooling system, where the coolant directly flows through the machine air gap. To predict and prevent the critical working conditions of the prototype, the adopted cooling system has been investigated using a complete fluid-thermal analysis. Due to the capabilities of computational fluid dynamic software, it has been possible to analyze the temperature and flow fields inside the machine, giving an idea about the distribution of the thermal quantities both inside the solid materials and above the surfaces. The numerical model validation has been provided, comparing computed and experimental results, i.e., imposing the same working conditions, the predicted temperatures satisfactorily agree with the measured temperatures.

Additive manufacturing has introduced a great step in the manufacturing process of consumer goods. Fused Deposition Modeling (FDM) and in particular 3D printers for home desktop applications are employed in the construction of prototypes, models and in general in non-structural objects. The aim of this new work is to characterize this process in order to apply this technology in the construction of aeronautical structural parts when stresses are not excessive. An example is the construction of the PoliDrone UAV, a multicopter patented, designed and realized by researchers at Politecnico di Torino. For this purpose, a statistical characterization of the mechanical properties of ABS (Acrylonitrile Butadiene Styrene) specimens in compression tests is proposed in analogy with the past authors’ work about the tensile characterization of ABS specimens. A desktop 3D printer, including ABS filaments as the material, has been employed. ASTM 625 has been considered as the reference normative. A capability analysis has also been used as a reference method to evaluate the boundaries of acceptance for both mechanical and dimensional performances. The statistical characterization and the capability analysis are here proposed in an extensive form in order to validate a general method that will be used for further tests in a wider context.

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.