NASA NTRS · 20240002372
Creating a rubberized engine model and exploring different approaches for converging over multiple design points.
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Caroline Kuhnle, Tom Lavelle. Parametric Subsonic Engine Model. https://ntrs.nasa.gov/citations/20240002372
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NASA is supporting the development of Electrified Aircraft Propulsion (EAP) technology due to its potential to reduce aircraft fuel burn, emissions, and noise as well as improving safety and performance. One focus of this research is the electrification of conventional turbomachinery propulsion systems, which offers ways to improve the performance and operability of turbine-engine powered aircraft through the addition of electro-mechanical systems. These hybrid-electric turbine engines provide additional actuation and energy management control opportunities for improving stability and transient response behavior. This paper summarizes the results of a Hardware-in-the-Loop (HIL) test performed at the NASA Electric Aircraft Testbed (NEAT) during the summer of 2022. The test demonstrates the feasibility and performance of an advanced energy management control strategy by integrating a simulated turbofan engine with scaled electro-mechanical hardware. A full-scale real-time reference model of a geared turbofan was run alongside a scaled electro-mechanical system representing the electrified turbofan components operating at a megawatt-scale power level. The model was interfaced with the hardware through a novel closed-loop control and scaling algorithm that emulated the dynamic speed and torque response of the turbofan shafts. The control strategy was implemented on the electrical machines connected to the emulated turbomachinery shafts. The results from the testbed are compared against simulations that predict the testbed and geared turbofan model operation. The energy management control strategy successfully changed the operating point of the engine model and improved its stability during throttle transients. These results also demonstrate the success of the novel closed loop control and scaling approach for emulating turbomachinery and elevate the Technology Readiness Level (TRL) of the energy management control strategy.
A Data & Reasoning Fabric (DRF) is envisioned to enable the full potential of air mobility by providing all data and reasoning where they are needed. The DRF provides a marketplace based on an open foundational ecosystem of data and reasoning exchange between the many systems that must seamlessly interplay to manage the complex and dense airspace operations required to achieve advanced air mobility goals. The DRF marketplace is decentralized and will not be owned by any single party. To deliver reference designs and development environments that catalyze broad private and public sector buy-in and self-sustaining development of DRF and associated standards, DRF activities will collaboratively test these technologies, open standards and architectures, and an integrated framework with end-users.
This paper describes a novel class of robots specifically adapted to climb periodic lattices, which we call 'Relative Robots'. These robots use the regularity of the structure to simplify the path planning, align with minimal feedback, and reduce the number of degrees of freedom (DOF) required to locomote. They can perform vital inspection and repair tasks within the structure that larger truss construction robots could not perform without modifying the structure. We detail a specific type of relative robot designed to traverse a cuboctahedral (CubOct) cellular solids lattice, show how the symmetries of the lattice simplify the design, and test these design methodologies with a CubOct relative robot that traverses a 76.2 mm (3 in.) pitch lattice, MOJO (Multi-Objective JOurneying robot). We perform three locomotion tasks with MOJO: vertical climbing, horizontal climbing, and turning, and find that, due to changes in the orientation of the robot relative to the gravity vector, the success rate of vertical and horizontal climbing is significantly different.