School of Engineering
Showing 1-10 of 17 Results
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Eric Darve
Professor of Mechanical Engineering
Current Research and Scholarly InterestsThe research interests of Professor Darve span across several domains, including machine learning for engineering, surrogate and reduced order modeling, stochastic inversion, anomaly detection for engineering processes and manufacturing, numerical linear algebra, high-performance and parallel computing, and GPGPU.
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Stefan P. Domino
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioDr. Domino’s research interest rests within low-Mach fluid mechanics methods development for complex systems that drive the coupling of mass, momentum, species and energy transport. His core research resides within the intersection of physics model development, numerical methods research, V&V techniques exploration, and high performance computing and coding methods for low-Mach turbulent flow applications. Stefan also supports the co-teaching of ME469, Computational Methods in Fluid Mechanics, while continuing his primary career at Sandia National Laboratories as a Distinguished Member of the Technical Staff.
Education:
University of Utah
Ph.D. Department of Chemical Engineering, 1999
"Methods towards improved simulations for the oxides of nitrogen in pulverized-coal furnaces"
Professor Philip J. Smith, Advisor
Select Recent Publications:
* Scott, S., Domino, S. P., "A computational examination of large-scale pool fires: variations in crosswind velocity and pool shape", Flow, 2022, https://doi.org/10.1017/flo.2022.26.
* Domino, S. P., Horne, W., "Development and deployment of a credible unstructured, six-DOF, implicit low-Mach overset simulation tool for wave energy applications", Renew. Energy, 2022, https://doi.org/10.1016/j.renene.2022.09.005.
* Hubbard, J., Hansen, M., Kirsch, J., Hewson, J., Domino, S. P., “Medium scale methanol pool fire model validation”, J. Heat Transfer, 2022, https://doi.org/10.1115/1.4054204.
* Barone, M., Ray, J., Domino, S. P., "Feature selection, clustering, and prototype placement for turbulence datasets", AIAA Journal, 2021, https://doi.org/10.2514/1.J060919.
* Domino, S. P., Hewson, J., Knaus, R., Hansen, M., "Predicting large-scale pool fire dynamics using an unsteady flamelet- and large-eddy simulation-based model suite", Physics of Fluids, 2021, https://doi.org/10.1063/5.0060267 (Editor's pick: August 4, 2021).
* Domino, S. P., "A case study on pathogen transport, deposition, evaporation and transmission: linking high-fidelity computational fluid dynamics simulations to probability of infection", Int. J. CFD, 2021, https://doi.org/10.1080/10618562.2021.1905801.
* Domino, S. P., Pierce, F., Hubbard, J., "A multi-physics computational investigation of droplet pathogen transport emanating from synthetic coughs and breathing", Atom. Sprays, 2021, https://doi.org/10.1615/AtomizSpr.2021036313.
* Jofre, L., Domino, S. P., Iaacarino, G., "Eigensensitivity analysis of subgrid-scale stresses in large-eddy simulation of a turbulent axisymmetric jet", Int. J. Heat Mass, 2019, https://doi.org/DOI:10.1016/J.IJHEATFLUIDFLOW.2019.04.014.
* Domino, S. P., Sakievich, P., Barone, M., "An assessment of atypical mesh topologies for low-Mach large-eddy simulation", Comp. Fluids, 2019, https://doi.org/10.1016/j.compfluid.2018.12.002.
* Domino, S. P., "Design-order, non-conformal low-Mach fluid algorithms using a hybrid CVFEM/DG approach ", J. Comput. Physics, 2018, https://doi.org/10.1016/j.jcp.2018.01.007.
* Jofre, L., Domino, S. P., Iaacarino, G., "A Framework for Characterizing Structural Uncertainty in Large-Eddy Simulation Closures", Flow Turb. Combust., 2018, https://doi.org/10.1007/s10494-017-9844-8.
CV: https://github.com/spdomin/Present/blob/master/cv/dominoCV.pdf -
David Donoho
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences
BioDavid Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to our understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description.
Research Statement:
My theoretical research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. My applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing, and inverse problems.
