NSF Org:	CMMI Div Of Civil, Mechanical, & Manufact Inn
Awardee:	NORTH DAKOTA STATE UNIVERSITY
Initial Amendment Date:	March 23, 2021
Latest Amendment Date:	May 20, 2021
Award Number:	2045538
Award Instrument:	Standard Grant
Program Manager:	Siddiq Qidwai sqidwai@nsf.gov  (703)292-2211 CMMI  Div Of Civil, Mechanical, & Manufact Inn ENG  Directorate For Engineering
Start Date:	July 1, 2021
End Date:	June 30, 2026 (Estimated)
Total Intended Award Amount:	$535,141.00
Total Awarded Amount to Date:	$535,141.00
Funds Obligated to Date:	FY 2021 = $535,141.00
History of Investigator:	Ravi  Yellavajjala (Principal Investigator) ravi.kiran@ndsu.edu  (701)231-8045
Awardee Sponsored Research Office:	North Dakota State University Fargo Dept 4000 - PO Box 6050 FARGO ND  US  58108-6050 (701)231-8045
Sponsor Congressional District:	00
Primary Place of Performance:	North Dakota State University Fargo Dept 4000 - PO Box 6050 FARGO ND  US  58108-6050
Primary Place of Performance Congressional District:	00
DUNS ID:	803882299
Parent DUNS ID:	803882299
NSF Program(s):	ECI-Engineering for Civil Infr, Mechanics of Materials and Str, EPSCoR Co-Funding
Primary Program Source:	040100 NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	022E, 026E, 1045, 9150
Program Element Code(s):	073Y, 1630, 9150
Award Agency Code:	4900
Fund Agency Code:	4900
CFDA Number(s):	47.041

ABSTRACT

This Faculty Early Career Development (CAREER) grant will support research to extend the applicability of reduced-scale physical models to quantify the failure behavior of full-scale structural steel castings under seismic loads. The modular construction of steel structures using castings reduces the structural weight, construction time, and erection costs. Steel castings also offer exceptional architectural freedom and high seismic and blast resistance. However, they are often large and geometrically complex, hence traditional mechanical testing is not commonly feasible, which prevents structural engineers from exploring their full potential. The outcome of this project will enable engineers to perform multiple tests on scaled-models using testing laboratories with limited capabilities instead of fewer tests on full-scale prototypes in sprawling facilities with expensive equipment, providing a better understanding of system-level behavior at a relatively meager cost. The research will also facilitate the development of design guidelines for structural steel castings, increase their market share, create new jobs in the US manufacturing sector, and reduce the carbon footprint. As part of the grant, an extensive set of education and outreach activities will also be pursed to increase the participation of Native Americans in STEM disciplines and improve their retention and graduation rates, including undergraduate internships, multi-day workshops, and competitions and debates.

Satisfying the geometrical and material strength similitude requirements and deriving scaling relationships are the two challenges that accurate reduced-scale physical models are required to overcome. This research aims to quantify the fundamental scaling relationships between strength, ultra-low cycle fatigue fracture (ULCF) initiation strain, and life of additively manufactured, reduced-scale physical models and full-scale steel castings subject to seismic loads. The approach is centered on the additive manufacturing of reduced-scale models with the help of data-driven process and build parameters and post-heat treatments to satisfy geometric and material strength similitudes. It also comprises development of a deep neural network framework to be trained with large quantities of fracture data, numerically generated from realistic microstructures, to learn the damage scaling relationship that will relate the fatigue fracture behavior of additive manufactured models to full-scale steel castings. The experimental validation plan includes characterization of the microscopic fatigue damage using x-ray tomography and structural testing of two full-scale components. Overall, the similitude theory based on mechanics, additively manufactured physical models, and machine-learned scaling relationships will advance the use of reduced-scale physical modeling beyond large deformations into the realm of fatigue fracture.

This project is jointly funded by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) and the Established Program to Stimulate Competitive Research (EPSCoR).

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.

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