ERI: hybrid physics-informed and data-driven modeling and vibration control of variable-mass rotating machinery

The National Science Foundation Division of Civil, Mechanical, and Manufacturing Innovation awarded $199,977 to President and Board of Trustees of Santa Clara College on September 1, 2026, under the Engineering program (CFDA 47.041) to develop hybrid physics-informed and data-driven mathematical models and vibration suppression methods for high-speed variable-mass centrifuges.

The research addresses vibration challenges in industrial centrifuges used for vaccine production, biological therapy manufacturing, protein purification, and microplastic separation from water. These machines continuously add or remove fluid during operation, causing mass to change over time and generating complex, evolving vibrations that degrade performance, cause premature mechanical failure, and limit safe operating speeds. The project constructs a physics-informed candidate function library using the Lagrange-Cayley formulation for variable-mass mechanical systems, then applies machine learning and sparse regression techniques to identify physically meaningful terms from experimental measurements collected on a custom-built laboratory apparatus. The work includes partnership with an industrial collaborator that manufactures such machines and incorporates graduate and undergraduate student training as well as a summer engineering outreach program for high school students.

Period of performance is September 1, 2026, through August 31, 2028. Work is performed in Santa Clara, California.

Abstract

This engineering research initiation (ERI) grant will fund research related to high-speed industrial centrifuges that are essential machines for modern public health and environmental protection. They are used to rapidly produce vaccines and biological therapies, to process and purify proteins and other life-saving medicines, and to separate harmful microplastic and nanoplastic pollutants from drinking water and natural waterways. A critical challenge is that many of these machines continuously add or remove fluid during operation, causing their mass to change over time. This variable-mass condition generates complex, evolving vibrations that can degrade machine performance, cause premature mechanical failure, and limit the speeds at which these centrifuges can safely operate. Improving the understanding and control of these vibrations would directly advance the reliability and throughput of machines that protect public health and the environment. This project addresses this challenge by developing new mathematical models and vibration suppression methods for high-speed variable-mass centrifuges, in partnership with an industrial collaborator that manufactures such machines. The project will train graduate and undergraduate students in advanced research methods and will engage high school students through a summer engineering outreach program, inspiring the next generation of innovators. This project pursues two interconnected research objectives. The first is to discover accurate governing equations for variable-mass rotating machines by constructing a physics-informed candidate function library derived from the lagrange-cayley formulation for variable-mass mechanical systems, and then applying machine learning and sparse regression techniques to identify the most physically meaningful terms from experimental measurements collected on a custom-built laboratory test RIG. The second objective is to design novel active vibration control algorithms that suppress lateral vibrations under continuously changing mass conditions. This involves using finite element analysis and particle swarm optimization to identify the best locations for sensors and actuators, and then developing a nonsingular terminal sliding mode control law that maintains closed-loop stability and performance across the full range of mass variation encountered during centrifuge operation. All models and control algorithms will be validated experimentally on the laboratory test RIG and on an industrial centrifuge prototype at the collaborating company?S facility. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.