2004 — 2010 |
Epureanu, Bogdan |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Career: Next-Generation High-Sensitivity Damage Detection and Sensing Based On Enhancing Nonlinear Dynamics and Phase Space Pattern Recognition @ University of Michigan Ann Arbor
CAREER: Next-Generation High-Sensitivity Damage Detection and Sensing Based on Enhancing Nonlinear Dynamics and Phase Space Pattern Recognition
Most vibration-based damage detection methods are designed for linear vibrations while far fewer apply to nonlinear systems. Many of the current nonlinear methods have important limitations, e.g. difficulty tackling high-dimensional systems. This integrated research and educational program eliminates several of these limitations by using a radically different approach.
The main research goal is to develop robust and highly sensitive nonlinear vibration-based techniques for sensing and detecting the location and level of multiple simultaneous damages in high-dimensional systems such as complex fluid-structural systems.
The main educational goal is the development of a program focused on enhancing engineering intuition with emphasis on nonlinear dynamics. The educational effort integrates research with a broad spectrum of educational and outreach activities, and includes: (a) integrating nonlinear dynamics into undergraduate courses in engineering, (b) developing advanced undergraduate and graduate courses on nonlinear dynamics, (c) implementing outreach activities for K-12 students.
This integrated research and educational program will provide a new and comprehensive methodology for damage detection in high-dimensional nonlinear complex systems. This research will make significant contributions to the fields of structural dynamics, fluid-structure interactions, and sensing, and it has immediate potential to impact industry because it addresses important practical engineering problems in, for instance, aerospace, civil and sensing technologies. Also, this research represents a potential benefit to society at large, and it is strengthened by a broad dissemination to enhance scientific understanding.
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0.915 |
2006 — 2012 |
Epureanu, Bogdan |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Micro-Fluid-Structural Sensing Based On Sensitivity Vector Fields and Morphing Modes Created by Nonlinear Feedback Excitation @ University of Michigan Ann Arbor
This research develops the next generation of high-sensitivity micro-fluid-structural sensors and enables radically novel sensing capabilities. The focus applications are bio-detection, label-free bio-chemical analysis, the measurement of micro-mechanical properties, the identification and characterization of micro-fluid-structural phenomena, and homeland defense applications. The novel sensing techniques developed are based on nonlinear vibrations by identifying and exploiting attractor and bifurcation morphing modes, and sensitivity vector fields, and by applying innovative pattern recognition methods to characterize attractor shapes. Also, this research raises the sensitivity of vibration- and acoustic-based sensors by enhancing nonlinearities and exploiting micro-fluid-structure interaction phenomena.
Fundamentally novel sensing capabilities are created, which provide high sensitivity, adaptivity, robustness, as well as multi-functional sensing capabilities. This research has broader impacts as it makes significant contributions to the field of structural dynamics, and has immediate potential impact because it addresses important problems in bio-detection, medical technologies, and homeland defense.
Hence, this research is a potential benefit to society at large, and is strengthened by a broad dissemination to enhance scientific understanding.
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0.915 |
2008 — 2013 |
Epureanu, Bogdan Meyhofer, Edgar (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Cooperative Nonlinear Dynamics of Motor Proteins @ University of Michigan Ann Arbor
During the last three decades both modern engineering and bioscience research have witnessed experimental and theoretical advances which have led to the emergence of nano- to microscale engineering and biomolecular research as major focus for fundamental exploration and applications. This research is concerned with the properties and interactions of biomolecular motors, ubiquitous nanoscale protein machines that are principally responsible for nano- to microscale transport processes insides eukaryotic cells. The hallmark of these systems is their complex dynamics and organization which are widely believed to be crucial for understanding, manipulating, and finally controlling these systems for (bio-) technological use. The main goal of the work is to characterize and exploit the nonlinear dynamic mechanisms which govern the cooperative behavior of multiple molecular motors carrying a common cargo.
The proposed work will answer several crucial scientific questions, and will impact a large number of applications. Understanding how motor proteins function and cooperate to transport cargoes is a key engineering element in applications such as drug delivery and design, and has a strong potential to impact medicine (e.g. clinical paradigms and treatments of an array of diseases such as Parkinson's and cancer). Such understanding can be obtained by an approach such as proposed herein, where quantitative, nonlinear dynamics approaches are used to solve this important problem in cell biology. Also, the proposed work will provide fundamental understanding and analysis tools for bio-mechano-chemical processes. The proposal includes strong integrated educational components and outreach activities.
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0.915 |
2012 — 2016 |
Epureanu, Bogdan |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Complex Bio-Nano-Dynamics of Motor Proteins in Dynamically Controlled Fluids @ University of Michigan Ann Arbor
The recent breathtaking advances in modern engineering and bioscience research have enabled the identification of properties and roles of biomolecules that power cellular processes. The hallmark of these nanoscale systems is their multi-scale organization. In neurodegenerative diseases for example, axonal degeneration occurs because the neurotransmitters do not reach the synapse correctly. This is because binding of kinesins on microtubules is hindered. Hence, interrogating the properties of the nanotransport done by kinesins on microtubules is crucial for early/presymptomatic diagnosis of neurodegenerative diseases. Such early diagnosis will be enabled the novel models developed in this work.
The goal of this work is to create an innovative approach to model and to actively interrogate the complex bio-nanotransport of groups of kinesins interacting with microtubules in controlled fluids. Specific aims are: (a) novel models: develop new models which bridge the gap between nano/pico length/time scales and macroscales through combined atomistic/continuum modeling and experiments, and (b) novel detection: create novel methods to interrogate kinesin-MT systems about their properties by monitoring the nanotransport done by these systems in controlled fluids. Uniquely combined theoretical and experimental methods from atomistic simulations, nonlinear dynamics and bioengineering are used.
This effort will answer important scientific questions, and will impact applications spanning from cell science to biomolecular nanotechnology and engineered biodevices. For example, understanding how to model motor proteins in controlled fluids and how to identify properties of nanotransport in vivo is a key element in the next generation of drug technology and delivery, and can profoundly impact medicine. This work contributes to such understanding by using nonlinear dynamic approaches for solving very important questions in medicine and cell biology. If successful, this research can radically transform the way neurodegenerative diseases are diagnosed. Also, this work will provide fundamental understanding of bio-mechanochemical processes which affect the development of lab-on-a-chip bio-applications.
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0.915 |
2013 — 2017 |
Pascual, Mercedes (co-PI) [⬀] D'souza, Kiran (co-PI) [⬀] Epureanu, Bogdan |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Anticipating Bifurcations For Identifying Dynamic Characteristics of Nonlinear Systems @ University of Michigan Ann Arbor
The main goal of this research project is to create a novel method to quantitatively forecast bifurcations as well as the pre- and post-bifurcation dynamics of large dimensional nonlinear systems with a low dimensional inertial manifold. Dramatic changes in the dynamics of complex systems, from ecosystems to engineered systems, occur. Forecasting such events using advanced nonlinear techniques is of major importance. The behavior of such complex systems is commonly characterized by nonlinearities that can lead to regime shifts or bifurcations from a stable to an unstable dynamics. A method that can quantitatively predict bifurcations as well as the pre- and post-bifurcation dynamics for large dimensional nonlinear systems would have a significant impact in a variety of fields, from the analysis of nano-systems to the design of disease eradication campaigns. The three key tasks are to: (1) develop novel techniques to differentiate the dynamics along the inertial manifold from the overall dynamics and to handle noise using a robust signal processing methodology, (2) develop innovative methods to forecast stable/unstable branches of bifurcation diagrams, and (3) refine the general methods for application to complex nonlinear systems including population dynamics and aeroelastic systems.
This project has broader impacts on the society at large. This effort will answer important scientific questions, and will impact applications spanning from computational dynamics to population dynamics. For example, there is an acute need for reliable methods to predict catastrophic events in populations of plants and/or animals because such events can lead to irreversible consequences such as extinction of species. The potential impact of this method is even higher when applied to disease eradication (populations of infectious diseases). While the dynamics of diseases is a very complex system and the method may not be perfect, it can prove to outperform most other methods because of its ability to filter out noise and the ability to provide forecasts without the need for an accurate model.
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0.915 |