The purpose of our research is to formulate a drone delivery problem (DDP) as a constrained multi-objective optimization problem and evaluate the cost-reduction effect of a drone delivery service using the provisional-ideal-point (PIP) method proposed in this paper. The original PIP method is a genetic algorithm-based (GA-based) optimization method that can efficiently generate a preferred solution for a decision-maker. However, there are two problems occur when this method is applied to the DDP. The first problem is that there exist some cases wherein the evaluation function becomes infinite in the search process, making it impossible to sort the generated solutions. The second problem is that a long time is needed for the solution search to converge. Accordingly, the process had to be aborted at the halfway point. We present an improved PIP method to overcome these two problems. The proposed method is a solution search comprising a GA combined with tabu search. It converts the DDP into a single-objective optimization problem of a delivery cost using conversion factors. This paper presents several things understood regarding the cost-reduction effect on drone delivery services using our newly proposed method.
The prediction accuracy of arcjet flow using a computer code named ARCFLO3+ is examined by comparing the arc heater operational characteristic data, pitot pressure and cold-wall heat flux data obtained from a segmented constrictor-type arc-heated wind tunnel at JAXA. Results are mainly presented to discuss how the discrepancy between the calculated and measured arc heater operational characteristic data obtained impact the core of the arcjet flow in the test section. Results show that the present computational approach gives a conservative estimation of the arcjet flow core properties within the test section when the mass-averaged enthalpy value obtained through the arc heater is replicated.
The AQua Thruster-Demonstrator (AQT-D) is a 3U CubeSat for demonstrating the water resistojet propulsion system developed by the University of Tokyo. The AQT-D was launched to rendezvous with the International Space Station (ISS) in the middle of 2019. This spacecraft will also be the world’s first ISS-deployed CubeSat equipped with a water propulsion system. AQT-D is comprised of a 1U propulsion system and 2U bus systems. The bus systems of the AQT-D were designed and developed based on the TRICOM-1R, also known as "Tasuki," was launched using a SS-520 nanosatellite launcher and was in operation during 2017-2018. The AQUA ResIstojet propUlsion System (AQUARIUS-1U) has one Delta-V-Thruster (F: 4 mN) for orbital maneuver and four Reaction-Control-Thruster (F: 1 mN) for reaction control. AQUARIUS-1U is a resistojet propulsion system comprised of a tank, a vaporization chamber and nozzles. It uses water as a propellant (i.e., propellant mass was less than 400 g. Ultimate green propellant: water (H2O) enables ISS-deployed CubeSat to install a propulsion system. It is expected that use of the CubeSat deployed by the ISS will expand drastically as the propulsion system lengthens the satellite lifetime, which is one of the bottlenecks for the low-earth-orbit CubeSat. This paper discusses the mission overview of the AQT-D and the ground test results of the propulsion system installed in it.
Conventional rockets are faced with several problems such as high launching cost. Therefore, in Japan, a reusable vertical-takeoff-and-vertical-landing (VTVL) rocket vehicle is being developed. This vehicle utilizes nose entry as the return flight system including the attitude change (turnover) due to aerodynamic forces. To safely achieve turnover, it is necessary to reduce the difference between the maximum value and minimum value of Cm (i.e., pitching-moment coefficient). In this study, a delta-wing with vortex flaps (developed for the aircraft industry) is attached to the aft of the vehicle with the expectation of improving the Cm characteristics during the turnover process. Consequently, when the flap deflection angle is 0°, the nose-up Cm can be reduced at forward angles (i.e., AOA 0° - 90°) because vortices generated by the fins result in a nose-down Cm and cancel the nose-up Cm. Moreover, when the flap deflection angle is -30°, the nose-down Cm is enhanced at the backward angles (i.e., AOA 90° - 180°) because the flaps reduce the vortices generated by fins. Hence, setting the flap deflection angle at 0° for the forward angles and -30° for the backward angles reduced the difference between the maximum and minimum values of Cm (i.e., 12% smaller than a conventional model).