Project Summary/Abstract The candidate holds a Ph.D. in Mechanical Engineering from the University of Sheffield UK and is currently a Research Associate at Mayo Clinic. His research interests are in the field of cardiovascular computational biomechanics with his postdoctoral work investigating the mechanics of atherosclerotic arteries and aortic valves with finite element modeling. Through completion of his postdoctoral training the candidate aims to transition from mentored to independent research and begin a tenure track faculty position at a major research university. The K99/R00 mechanism provides the perfect opportunity for accomplishing this goal. The candidate’s primary mentor, Dr. Amir Lerman, has extensive research experience and has operated a research laboratory successfully throughout his career and has made numerous contributions to several fields of cardiovascular medicine. Dr. Melissa Young has extensive industry and academic experience in replacement aortic valve design and testing. Dr. Dan Dragomir-Daescu, a co-mentor on this project, also has both academic and industry experience in the development and evaluation of medical devices. The final co- mentor, Prof. Jeffery Salisbury has vast experience in microscopy techniques and is the director of the Microscopy and Cell Analysis core facility at Mayo Clinic and also has significant academic experience. Working with his mentors, the candidate will train in fatigue mechanics, medical image processing, microscopy, and computational modeling. The candidate will also train in other essential skills including communication of research findings, mentoring, grantsmanship, scientific writing, and project management. Mayo also offers a variety of research resources and facilities to assist this research including core facilities such as the Microscopy and Cell Analysis Core, the X-Ray Imaging Core, and the Materials and Structural Testing Core. The proposed project addresses the important clinical need of TAVR valve longevity through development of a fatigue computational model. The planned work will utilize mechanical testing data and various imaging techniques to provide a link between the macrostructural mechanical properties and the microstructural fiber damage from physiological fatigue loading. This data will then be utilized to develop a fatigue finite element model that more accurately predicts the valve mechanical wear. This validated model can then be utilized clinically with medical imaging of patient vasculature to perform personalized simulations of valve performance to improve valve selection and sizing to improve longevity. Additionally, the proposed model can be used to enhance regulatory approval allowing multiple designs to be evaluated for fatigue performance and longevity in simulated physiological environments. These applications can improve the valve longevity, reducing the frequency of valve replacement significantly reducing the burden of aortic valve disease.