Utah Valley University is currently seeking a Postdoctoral Researcher! Work with a professor from the Mechanical Engineering program of the Engineering Department in the area of reliability prediction of wind turbine blades based on fatigue damage. The project involves developing an automated drone-based inspection system for online condition monitoring, fault diagnosis, and predictive maintenance of wind turbine blades. The hired postdoc will develop fluid-structure interaction models of horizontal axis wind turbine blades under various wind loads using ANSYS Fluent. In addition, the postdoc must develop a predictive maintenance model based on finite element modeling.  The postdoctoral is also expected to lead several teams of undergraduate students in the project and work with the involved faculty on publishing the results in related journals and conference proceedings.  

• (25%) Develop CFD models for a horizontal axis wind turbine blades using ANSYS Fluent 
• (20%) Develop finite element models of damaged wind turbine blades under cyclic loadings based on fracture mechanics and fatigue 
• (30%) Use detected cracks through an autonomous inspection using drones and develop a predictive maintenance model for damaged wind turbine blades under field loading conditions 
• (15%) Publish results in conference proceedings and journals. 
• (10%) Perform other job-related duties as assigned 

PhD in Mechanical Engineering or related discipline 

Knowledge:
The applicant is required to have solid knowledge on computational fluid dynamics, fracture and fatigue, mechanical behavior of composite materials, finite element modeling, and reliability analysis. 
Skills:
Must have extensive experience in the following areas. 

• SolidWorks or any CAD tool 
• Highly skilled in CFD and FEA using ANSYS Fluent and Workbench 
• Good knowledge of fracture mechanics and fatigue 
• Data acquisition and analysis. 
• Reliability analysis 

EEO Statement:

UVU employment decisions are made on the basis of an applicant’s qualifications and ability to perform the job without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, gender expression, age (40 and over), disability, veteran status, pregnancy, childbirth, or pregnancy-related conditions, genetic information, or other bases protected by applicable federal, state, or local law.