Masakatsu Nakane

Feasibility Study on Single Stage to Orbit Space Plane with RBCC Engine

In this study, a Turbine-Based Combined-Cycle (TBCC) engine propelling a Single-Stage To Orbit (SSTO) space plane was replaced with a Rocket-Based Combined-Cycle (RBCC) engine for two purposes: to examine whether it would be capable of achieving orbital injection as a SSTO, and to determine what influence the engine replacement would have on the guidelines governing design of the airframe and planning of the flight path. By definition, a multidisciplinary optimization is necessary in order to obtain exact answers to these questions. For this study, the airframe weight and aerodynamics were estimated and only the airframe design and flight path were optimized in order to reduce the calculation. The following findings were established. In order to achieve a minimal structural weight for the SSTO, ultimately, a RBCC-powered craft was calculated to have clear advantages over a TBCC-powered craft. Secondly, the wings should be designed with as small an area as possible for takeoff, in both TBCCand RBCC-powered space planes. It was also found to be essential for the TBCC-powered craft to operate in an equivalent dynamic pressure flight regime, while the time history of the angle of attack of the RBCC-powered craft must incorporate a steep climb in the ducted rocket mode and horizontal flight in the ramjet mode.

Applying Autonomous Control method for Material Circulation to Advanced Life Support System

An Advanced Life Support System (ALS) recycles and circulates substances within a living environment, and makes it possible to sustain life in space. Among those systems, this study addressed a system for recycling the elements C, H, and O in the system, which includes waste processing and food production. We proposed a procedure for such a system that combines automatic generation of scheduling and multi-agent reinforcement learning, based on a hierarchical control method. In this report, a procedure was applied to a simple model and a computer simulation was performed. Specifically, an automatic scheduling procedure was used to create an overall plan for operation of a model that assumed closed circulation. The tanks, processors, and module subsystems were controlled by multi-agent reinforcement learning (MARL) in a decentralized autonomous system. Currently, the calculations are complete for the simplest system, that with two tanks. These results indicate that MARL requires preliminary learning, particularly for emergency events, but otherwise, all learning can be done by MARL on-line.

System Structure Modification in Advanced Life Support System using Autonomous Control Method

An Advanced Life Support System (ALS) recycles and circulates materials within a living environment, and will eventually make it possible to sustain life in outer space. This study addresses the system subset that will recycle the elements of carbon, hydrogen and oxygen, with planned functions that include waste recycling and food production. We have previously proposed a procedure for such a system that combines automatic generation of scheduling and multi-agent reinforcement learning (MARL), based on a hierarchical control method. This paper reports the application of this procedure during a material circulation simulation that includes modifications to the system. Specifically, a model with two installed tanks each for oxygen (O2) and carbon dioxide (CO2) is expanded by the installation of three additional tanks after scheduling calculations have been performed, thus providing a total of five tanks for each gas, at which point the calculation is repeated. It was confirmed that the increase in tanks caused no disturbances in the scheduling-based upper control layer, or any problems in the MARL-based lower layer.

Autonomous Control Method for Material Circulation in Advanced Life Support System

An Advanced Life Support System (ALS) conducts recycling and circulation of materials within the system, and is the component ensuring the survival of living beings during missions. The present research examines schemes for recycling the elements carbon, hydrogen and oxygen inside a system to process wastes and produce food. The circulations of materials become very complicated in this kind of ALS, so it is difficult to manually draw up comprehensive control principles. It is also difficult to handle emergency conditions. Therefore, it would be preferable to carry out environmental control automatically, and to provide for autonomous handling when an emergency arises. We applied methods for automatically creating schedules in an attempt to enable the system to continue degenerate operation automatically during emergencies; one control method employed multi-agent reinforcement learning, and the other employed Lagrangian decomposition and coordination method. The two procedures were found to offer complementary advantages and weaknesses. A system was then proposed under which the two procedures would compensate for each other’s weaknesses.