Christiane Adcock
- Program Years: 2018-2022
- Academic Institution: Stanford University
- Field of Study: Computational and Mathematical Engineering
- Academic Advisor: Gianluca Iaccarino
- Practicum(s):
National Renewable Energy Laboratory (2021)
National Renewable Energy Laboratory (2022) - Degree(s):
B.S. Mechanical Engineering, Massachusetts Institute of Technology, 2018
M.S. Computational and Mathematical Engineering, Stanford, 2021
PhD Computational and Mathematical Engineering, Stanford, 2023
Current Status
- Research Area: Computational and Mathematical Engineering
Publications
Biagioni, D., Zhang, X., Adcock, C., Graf, P., King, J., From Model-Based to Model-Free: Learning Building Control for Demand Response, Applied Energy, 2022, under review.Adcock C., Henry de Frahan M., Melvin J., Ananthan S., Vijayakumar G., Iaccarino G, Moser R., Sprague M., Hybrid RANS-LES of the Atmospheric Boundary Layer for Wind Farm Simulations, AIAA Science and Technology Forum and Exposition, 2022.
Adcock C., Henry de Frahan M., Melvin J., Vijayakumar G., Iaccarino G, Moser R., Sprague M., SST k-omega Simulations of the Atmospheric Boundary Layer Including the Coriolis Effect, APS Division of Fluid Dynamics, 2021.
Adcock C., Yinyu Y., Jofre L. Iaccarino G., Multilevel Monte Carlo Sampling on Heterogeneous Computer Architectures, International Journal for Uncertainty Quantification, 2020.
Adcock C., Iaccarino G., Jofre L, Papadakis M., Torres H., Multilevel Monte Carlo Sampling on Heterogeneous Computer Architectures, SIAM Conference on Uncertainty Quantification, 2020, accepted.
Annoni J., Fleming P., Scholbrock A., Roadman J., Dana S., Adcock C., Raach S., Haizmann F., Schlipf D., Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results, Wind Energy Science, 2018.
Adcock C., King R., Data-Driven Wind Farm Optimization Incorporating Effects of Turbulence Intensity. American Control Conference, 2018.
King R.N., Adcock C., Annoni J., Dykes K., Data-Driven Machine Learning for Wind Plant Flow Modeling, The Science of Making Torque from Wind 2018.
King R.N., Quick J., Adcock C., Dykes K., Active Subspaces for Wind Plant Surrogate Modeling, AIAA Science and Technology Forum and Exposition, 2018.
Adcock, C., Evaluating Trends in Light-Duty Vehicle Technologies to Project Fuel Economy, Bachelor's Thesis, Massachusetts Institute of Technology, 2018.
Adcock C., King R., Data-Driven Wind Plant Flow Modeling for Atmospheric Stability, Rocky Mountain Fluid Mechanics Symposium, 2017.
Awards
Stanford Knight-Hennessy Scholar, 2018-2021NSF Graduate Research Fellowship, 2018 (declined)
Tau Beta Pi (Engineering Honor Society), 2017-present
Pi Tau Sigma (Mechanical Engineering Honor Society), 2016-present