## 1 Introduction  
A personal air vehicle (PAV), a type of future transportation, is an emerging aviation market that may provide on-demand aviation services to resolve traffic congestion. Because the desire for rapid door-to-door air travel that also avoids traffic jams is drawing attention to new possible transportation systems, it is expected that PAVs will play a key role as one such transportation system in the near future. In response to this paradigm shift, the U.S. government has initiated several projects, such as the Personal Air Vehicle Exploration (PAVE) and the Next Generation Air Transportation System (NextGen), focusing especially on the Small Aircraft Transportation System (SATS). In addition, certain aviation companies such as Terafugia, AeroMobil, and PAL-V are pioneering new markets for roadable PAVs. A team of researchers at the Georgia Institute of Technology has conducted research to develop promising roadable aircraft concepts that meet a set of potential design requirements. In their research, a notional roadable aircraft concept was designed and analyzed to meet a generic set of roadable aircraft performance requirements. Subsequently, a number of different propulsion system architectures were considered and quantitatively compared. Finally, requirement and technology trade-off studies were conducted in relation to a notional roadable aircraft concept [1]. Although several roadable aircraft including PAVs have been developed and completed their flight tests successfully, the commercialization of roadable aircraft has been delayed due to well-known safety and certification problems of interest to both regulators and industry. Even if these issues are resolved in the very near future, the prices of roadable aircraft should be competitive before they can enter the aviation market. Moreover, it is necessary to consider an optimization process, widely used in a variety of industries, for the design of the complex systems needed during the PAV development process in order to maximize the efficiency of these vehicles [2]. To make these aircraft less sensitive to both economic and environmental factors, it is essential to employ optimization techniques such as fuzzy clustering analysis in the conceptual design. Moreover, it is necessary to consider an uncertainty analysis. Traditional uncertainty analyses need many sampling points to simulate uncertain models. These methods include a large number of calculations to achieve the required level of accuracy. A method with dynamic surrogate models based on a fuzzy clustering analysis can increase the efficiency of an uncertainty analysis and reduce the effects of error propagation on uncertainty models. Such a method should show that the calculated uncertainty from an uncertainty analysis can be reduced effectively while optimizing the performance to meet reliability and robustness targets [3]. Aircraft design requires collaborative work among experts in various fields to make the proper decisions and therefore achieve the expected performance outcomes at a lower cost and with less risk. An agile decision support system (ADSS) for aircraft design that comprehensively integrates and applies modeling and simulation (M&S), artificial intelligence, data mining, and group decision-making technology was proposed. The ADSS provides a rich set of simulation, assessment, and optimization tools for decision makers, offering an important objective reference for effectively improving the accuracy of decisions [4]. The development of unmanned aerial vehicles (UAVs) has attracted much attention in the aviation industry for decades. [PERSON] et al. presented the design, manufacturing, and flight testing of an electric-powered experimental flying wing unmanned aerial vehicle (UAV) [5]. The design process began by defining the performance requirements, including the stall speed, maximal speed, cruise altitude, absolute ceiling, as well as turn radius and speed. [PERSON] et al. developed a methodology for sizing the electric propulsion subsystems of UAVs [6].