Guglielmo Scovazzi

Associate Professor in the Department of Civil and Environmental Engineering

Guglielmo Scovazzi received B.S/M.S. in aerospace engineering (summa cum laude) from Politecnico di Torino (Italy); and M.S. and Ph.D. in mechanical engineering from Stanford University. Before coming to Duke, he was a Senior Member of the Technical Staff in the Computer Science Research Institute at Sandia National Laboratories (Albuquerque, NM).

Dr. Scovazzi’s research interests include finite element and advanced numerical methods for computational fluid and solid mechanics. His research emphasizes accurate computational methods aimed at reducing the overall design/analysis costs in multiphase porous media flows, highly transient compressible and incompressible flows, turbulent flows, complex geometry systems in solid mechanics, and fluid/structure interaction problems.

Appointments and Affiliations

• Associate Professor in the Department of Civil and Environmental Engineering
• Associate Professor in the Department of Mechanical Engineering and Materials Science
• Faculty Network Member of The Energy Initiative

Contact Information

• Office Location: 121 Hudson Hall, Box 90287, Durham, NC 27708
• Office Phone: (919) 660-5075
• Email Address: guglielmo.scovazzi@duke.edu
• Websites:
  • Curriculum Vitae
  • Elsevier Scopus
  • Google scholar
  • Research Gate
  • https://cee.duke.edu/faculty/guglielmo-scovazzi

Education

• Ph.D. Stanford University, 2004

Research Interests

Finite element methods, computational fluid and solid mechanics, multiphase porous media flows, computational methods for fluid and solid materials under extreme load conditions, turbulent flow computations, instability phenomena.

Awards, Honors, and Distinctions

• Kavli Fellow. National Academy of Sciences & Kavli Foundation. 2018
• Presidential Early Career Award for Scientist and Engineers (PECASE). US Executive Office of the President (The White House). 2017
• Early Career Award. U.S. Department of Energy (DOE), Advanced Scientific Computing Research (ASCR) Program. 2014

Courses Taught

• CEE 421L: Matrix Structural Analysis
• CEE 531: Finite Element Methods for Problems in Fluid Mechanics
• CEE 630: Nonlinear Finite Element Analysis
• CEE 690: Advanced Topics in Civil and Environmental Engineering
• CEE 780: Internship
• EGR 201L: Mechanics of Solids
• ME 525: Nonlinear Finite Element Analysis
• ME 555: Advanced Topics in Mechanical Engineering
• ME 592: Research Independent Study in Mechanical Engineering or Material Science
• ME 758S: Curricular Practical Training

In the News

• Scovazzi Named Kavli Fellow by the US Academy of Science (Feb 22, 2018)
• Scovazzi Wins Presidential Early Career Award for Scientists and Engineers (Jan 10, 2017)
• Pratt's Guglielmo Scovazzi Wins DOE Early Career Award (May 13, 2014)

Representative Publications

• Karatzas, EN; Stabile, G; Nouveau, L; Scovazzi, G; Rozza, G, A reduced basis approach for PDEs on parametrized geometries based on the shifted boundary finite element method and application to a Stokes flow, Computer Methods in Applied Mechanics and Engineering, vol 347 (2019), pp. 568-587 [10.1016/j.cma.2018.12.040] [abs].
• Klein, M; Scovazzi, G; Germano, M, On the richardson extrapolation of the reynolds stress with the systematic grid and model variation method, vol 25 (2019), pp. 143-149 [10.1007/978-3-030-04915-7_20] [abs].
• Colomés, O; Scovazzi, G; Guilleminot, J, On the robustness of variational multiscale error estimators for the forward propagation of uncertainty, Computer Methods in Applied Mechanics and Engineering, vol 342 (2018), pp. 384-413 [10.1016/j.cma.2018.07.041] [abs].
• Main, A; Scovazzi, G, The shifted boundary method for embedded domain computations. Part I: Poisson and Stokes problems, Journal of Computational Physics, vol 372 (2018), pp. 972-995 [10.1016/j.jcp.2017.10.026] [abs].
• Main, A; Scovazzi, G, The shifted boundary method for embedded domain computations. Part II: Linear advection–diffusion and incompressible Navier–Stokes equations, Journal of Computational Physics, vol 372 (2018), pp. 996-1026 [10.1016/j.jcp.2018.01.023] [abs].