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Small Business Innovation Research/Small Business Tech Transfer

Reaction Wheel Disturbance Model Extraction Software, Phase II

Completed Technology Project

Project Description

Reaction Wheel Disturbance Model Extraction Software, Phase II
Reaction wheel mechanical noise is one of the largest sources of disturbance forcing on space-based observatories. Such noise arises from mass imbalance, bearing imperfections, and other sources. It takes the form of a number of discrete harmonics of the wheel speed, often also with a broadband noise component. Jitter problems can arise when harmonics sweep across observatory modes, and can be exacerbated by gyroscopically coupled spin-rate-dependent wheel structural modes that dynamically amplify the tonal and broadband disturbances. For a well-balanced wheel, higher harmonic forces can be on the same order as the fundamental, therefore when there is a jitter problem it can occur at very low wheel speed. These higher harmonics are generally less well-characterized than the fundamental. The proposed Reaction Wheel Disturbance Model Extraction Software (RWDMES) is a tool for fitting a hybrid physical/empirical model to wheel induced-vibration data. The physical model captures the wheel structure including gyroscopic effects, while the empirical model captures the harmonic forcing and broadband noise. The Phase I effort demonstrated the ability to fit a highly accurate harmonic/broadband/structural model, including 43 harmonics up to 14.63 times the fundamental, to measured wheel disturbance data in a point-and-click environment in about 2 hours. The benefits of the technology include reduced program effort to produce wheel disturbance models, leading to more accurate jitter prediction earlier in a mission. This in turn allows jitter problems to be mitigated at the design stage when changes are relatively inexpensive. More »

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This is a historic project that was completed before the creation of TechPort on October 1, 2012. Available data has been included. This record may contain less data than currently active projects.