Hardware qualification for scientific ballooning missions

The European Stratospheric Balloon Observatory (ESBO) initiative aims at simplifying the access to stratospheric balloon missions. We plan to provide platforms and support with instrument design in order to support scientists. During the design process, the inevitable question of qualification for the harsh flight conditions arises. Unfortunately, there is no existing standard for qualification of stratospheric ballooning hardware. Thus, we developed a qualification procedure for use within ESBO and similar projects.In this paper, we present our analysis of the environmental conditions in the stratosphere. While conditions at typical balloon float altitudes are similar to the space environment, there are also some relevant differences. For example, the thermal environment is dominated by radiation and thermal conduction, but the remaining atmosphere still supports a certain amount of convection. The remaining atmospheric pressure in the stratosphere also leads to reduced arcing distances. Vibrational loads are far less than for space missions, but quasi-static or shock loads may occur. The criticality of radiation increases with mission duration.Based on the environmental conditions, we present the qualification procedures for ESBO, which are based on the European Cooperation for Space Standardization (ECSS) standards for space systems. Overtesting against too high requirements leads to overengineering, driving mission cost and mitigating the advantages of balloons over space missions. Therefore, we modified the ECSS standards to fit typical scientific ballooning missions over several days at altitudes up to 40 km. Furthermore, we analyzed design rules for space systems with regard to their relevance for scientific ballooning, including material and component selection. We present the experience from the hardware qualification process for the ESBO prototype STUDIO (Stratospheric UV Demonstrator of an Imaging Observatory). Even though boundary conditions are different for each individual mission, we aimed for a broader approach: We investigated more general requirements for scientific ballooning missions to support future flights.     

An architecture decomposition method of pneumatic catapult system based on OPM and DSM

The architecture strategy of the Unmanned Aerial Vehicle (UAV) pneumatic launch system should continue to evolve to adapt to complex and variable operating environments. Architecture representation, decomposition perspective, and cluster analysis play a vital role in the early phase of system architecture development. In order for the system to emerge anticipated and desirable intrinsic functional properties, an architecture decomposition method based on the Object-Process Methodology (OPM) and Design Structure Matrix (DSM) is put forward in this paper. The OPM is proposed to model the UAV launch process formally, and the matrix representation of the architecture of the pneumatic launch system is established. After the extension of the definition and operations of DSM, with the Idicula-Gutierrez-Thebeau Algorithm plus (IGTA + ) clustering algorithm, the transformation of the pneumatic launch system architecture from process decomposition to function decomposition is demonstrated in this paper. The analysis shows that the architecture decomposition of the pneumatic launch system meets the functional requirements of stakeholders.     

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.     

Downwash modelling for three-lifting-surface aircraft configuration design

This paper introduces a semi-empirical model to predict the downwash gradient at the horizontal tail of a three-lifting-surface aircraft. The superposition principle applied to well established formulations valid for two lifting surfaces is not a reasonable approach to calculate the downwash of a canard-wing-tail layout, and this paper demonstrates that such a basic technique leads to incorrect results. Therefore, an ad hoc prediction model is proposed that considers the combined nonlinear effec...