Results for “impedance-based modeling”

NASA NTRSDate not supplied

Electric Aircraft Propulsion Power System Impedance Modelling Methodology

This paper describes techniques to be used to model megawatt Electrified Aircraft Propulsion (MW EAP) components, specifically motor/generators and inverters, DC-DC converters, and long power leads; resulting in impedance-based models which require no information from the component manufacturers. A custom curve fitting method is described, and an example power supply model is fit using measured impedance data. A method called subscale measurement, employing measurement equipment which has much lower power capability than the MW EAP system under test, is described, simulated, and used to generate an impedance transfer function model of a load. Finally, stability analysis using a Nyquist based approach is performed on the combined supply and load system, using the generated models.

Megawatt electrified aircraft propulsion↗

NASA NTRS1992

Active structural control for damping augmentation and compensation of thermal distortion

A large space-based Focus Mission Interferometer is used as a testbed for the NASA Controls and Structures Interaction Program. Impedance-based adaptive structural control and control of thermal disturbances are demonstrated using an end-to-end simulation of the system's optical performance. Attention is also given to integrated optical/structural modeling and a hierarchical, layered control strategy.

Sirlin, S. W.↗

NASA NTRS2024

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Yang Zhang↗