Martian Buildings: Design loading

Multiplanetary life has been studied by scientists as a way to supply energy or sustain human life in the future. Mars is likely to be man’s first destination, colonization using onsite structural construction would be one of the main options. The first step to designing a reliable building is to know the applied structural loads and to have an accurate design load combination. Due to lack of complete knowledge, short span of recorded data, Martian environment, and hazardous environment that Martian structures face, constructed Martian structures should behave appropriately under the highest likely live, dead and environmental loads either simultaneously or as a worst-case scenario. The present study evaluated and calculated probable Martian structural loads, including live, internal pressure, snow, gravity (dead), dust accumulation, thermal stress, wind, marsquake, asteroid, and meteoroid impact loads and their effects. Information was gathered from previous studies and valid data from Martian landers, rovers and orbiters. Wind loads were calculated based on the over 6.5 years of data recorded by Vikings 1 and 2, temperature and winds for InSight (TWINS) sensor. A wind shear exponent and wind profile have been proposed for a Martian flat terrain construction site. Marsquake lateral loads, frequency content and seismicity were assessed using data from the seismic experiment for interior structure (SEIS) and the Viking 2 seismometer. Considering the high influx of micrometeoroids, their penetration distance, impact loads and their effects on structures were calculated. The annual probability of an asteroid impact on a settlement was assessed for a 30-year mission. A load map for Martian residential buildings that considers the worst-case scenario in which a Martian structure should be designed based on them has been proposed.     

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...