hsbalance 0.0.32a0

Python tools for Practical Modeling and Solving High Speed Rotor Unbalance Problem

Turbomachinery-Flexible-Rotors-Balancing

Introduction

The purpose of this project is to solve the problem of turbomachinery rotor balancing when more than critical speed is required and where there are a large number of bearings.

Descripting the problem

Back to Basics

Balancing simply is to bring the center of mass of a rotating component to its center of rotation.

Every rotating component such as impellers, discs of a motor, turbine, or compressor has a center of gravity in which the mass is distributed, and it has a center of rotation which is the line between their bearings. At the manufacturing phase, they never coincide. But why?
Simple answer: it's too expensive to machine each component to have the same centerline of mass and rotation. Second, bearings and impellers are usually made by different manufacturers at different places. However, even though the equipment is produced by the same company, their installation setup impacts the balance and thus the center of rotation of the equipment.
Ubalance problem Why should we be concerned about unbalanced rotors?
It generates large centrifugal forces on the rotor and bearings, resulting in high stresses on the bearings and other rotating parts of the machine. They lead to premature failure! Unplanned shutdowns happen, high-risk damages endanger lives and assets.

Flexible Rotors

To increase efficiency, larger machines are often designed with longer shafts and multiple stages, along with higher rotational speeds. As a result, machines are running above their first or second critical levels.
Failure may occur if the machine is run at a critical speed. We can all relate to the Tacoma Narrows Bridge incident.
Two measures are necessary to overcome such a problem. First, to pass the critical speed as fast as possible, and then to balance the critical mode. Otherwise, the machine will never start due to vibration protection controls.
For balancing the turbine at different critical speeds, you must be knowledgeable about the various modes and try to optimize. For example, balancing the first critical will not affect the second critical. This has been the traditional approach which is called “Modal Balancing”.
The second method is to empirically find the balancing weights which give you the best vibration at all critical and running speeds. Commonly known as the “Influence Coefficient Method”.

The Mathmatical Model

Balance of flexible rotors is important in order to get optimal vibration levels at all rotor bearings since balancing weights must be calculated for each balancing plane. Turbines and compressors usually have measuring planes that are more than balancing planes. This creates an over-determined mathematical model that needs optimization methods to get the best results. The optimization problem is set to be convex optimization with constraints regarding balancing weights and maximum vibration allowed for certain locations. The challenge was also to beat the problem of ill-conditioned planes multicollinearity The whole work was a trial to convert Darlow "Balancing of High-Speed Machinery" work published 1989 to a working python script that can be used in the filed.