Bio

Gorlé received her BSc (2002) and MSc (2005) degrees in Aerospace Engineering from the Delft University of Technology, and her PhD (2010) from the von Karman Institute for Fluid Dynamics in cooperation with the University of Antwerp. Afterwards she was a Postdoctoral Fellow at the Center for Turbulence Research at Stanford University and a Research Professor at the von Karman Institute funded by a Pegasus Marie Curie fellowship. Before joining the Civil & Environmental Engineering Department at Stanford she was an Assistant Professor in the Department of Civil Engineering & Engineering Mechanics at Columbia University.

Gorle's research focuses on the development of predictive flow simulations to support the design of sustainable buildings and cities. Specific topics of interest are the coupling of large- and small-scale models and experiments to quantify uncertainties related to the variability of boundary conditions, the development of uncertainty quantification methods for low-fidelity models using high-fidelity data, and the use of field measurements to validate and improve computational predictions.

Academic Appointments

Professional Education

  • BSc, Delft University of Technology, Aerospace Engineering (2002)
  • MSc, Delft University of Technology, Aerospace Engineering (2005)
  • PhD, Von Karman Institute for Fluid Dynamics, Environmental and Applied Fluid Dynamics (2010)

Contact

  • Academic
    gorle@stanford.edu
    University - Faculty Department: Civil and Environmental Engineering Position: Assoc Professor

Additional Info

Links

Current Research and Scholarly Interests

Gorle's research focuses on the development of predictive flow simulations to support the design of sustainable buildings and cities. Specific topics of interest are the coupling of large- and small-scale models and experiments to quantify uncertainties related to the variability of boundary conditions, the development of uncertainty quantification methods for low-fidelity models using high-fidelity data, and the use of field measurements to validate and improve computational predictions.

2023-24 Courses

Stanford Advisees

All Publications

  • Large-eddy simulations to define building-specific similarity relationships for natural ventilation flow rates FLOW Hwang, Y., Gorle, C. 2023; 3

    View details for DOI 10.1017/flo.2023.4

    View details for Web of Science ID 001037257100001

  • Investigation of peak wind loading on a high-rise building in the atmospheric boundary layer using large-eddy simulations JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS Ciarlatani, M., Huang, Z., Philips, D., Gorle, C. 2023; 236

    View details for DOI 10.1016/j.jweia.2023.105408

    View details for Web of Science ID 001025948700001

  • Characterizing spatial variability in the temperature field to support thermal model validation in a naturally ventilated building JOURNAL OF BUILDING PERFORMANCE SIMULATION Chen, C., Wai Chew, L., Gorle, C. 2023

    View details for DOI 10.1080/19401493.2023.2179115

    View details for Web of Science ID 000933549000001

  • Full-scale validation of CFD simulations of buoyancy-driven ventilation in a three-story office building BUILDING AND ENVIRONMENT Chen, C., Gorle, C. 2022; 221

    View details for DOI 10.1016/j.buildenv.2022.109240

    View details for Web of Science ID 000823191800003

  • Improving thermal model predictions for naturally ventilated buildings using large eddy simulations BUILDING AND ENVIRONMENT Chew, L., Chen, C., Gorle, C. 2022; 220

    View details for DOI 10.1016/j.buildenv.2022.109241

    View details for Web of Science ID 000823148500006

  • Large-Eddy Simulations of Wind-Driven Cross Ventilation, Part 2: Comparison of Ventilation Performance Under Different Ventilation Configurations FRONTIERS IN BUILT ENVIRONMENT Hwang, Y., Gorle, C. 2022; 8

    View details for DOI 10.3389/fbuil.2022.911253

    View details for Web of Science ID 000827419100001

  • Large-Eddy Simulations of Wind-Driven Cross Ventilation, Part1: Validation and Sensitivity Study FRONTIERS IN BUILT ENVIRONMENT Hwang, Y., Gorle, C. 2022; 8

    View details for DOI 10.3389/fbuil.2022.911005

    View details for Web of Science ID 000827472000001

  • Improving the predictive capability of building simulations using uncertainty quantification SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT Gorle, C. 2022; 28 (5): 575-576

    View details for DOI 10.1080/23744731.2022.2079261

    View details for Web of Science ID 000807365000001

  • Conceptual model to quantify uncertainty in steady-RANS dissipation closure for turbulence behind bluff bodies PHYSICAL REVIEW FLUIDS Hao, Z., Gorle, C. 2022; 7 (1)

    View details for DOI 10.1103/PhysRevFluids.7.014607

    View details for Web of Science ID 000747749300001

  • Optimal temperature sensor placement in buildings with buoyancy-driven natural ventilation using computational fluid dynamics and uncertainty quantification BUILDING AND ENVIRONMENT Chen, C., Gorle, C. 2022; 207

    View details for DOI 10.1016/j.buildenv.2021.108496

    View details for Web of Science ID 000779413700003

  • Wind tunnel pressure data analysis for peak cladding load estimation on a high-rise building JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS Pomaranzi, G., Amerio, L., Schito, P., Lamberti, G., Gorle, C., Zasso, A. 2022; 220

    View details for DOI 10.1016/j.jweia.2021.104855

    View details for Web of Science ID 000789221100002

  • The ICECool Fundamentals Effort on Evaporative Cooling of Microelectronics IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY Bar-Cohen, A., Asheghi, M., Chainer, T. J., Garimella, S., Goodson, K., Gorle, C., Mandel, R., Maurer, J. J., Ohadi, M., Palko, J. W., Parida, P. R., Peles, Y., Plawsky, J. L., Schultz, M. D., Weibel, J. A., Joshi, Y. 2021; 11 (10): 1546-1564

    View details for DOI 10.1109/TCPMT.2021.3111114

    View details for Web of Science ID 000712564200007

  • A multi-fidelity machine learning framework to predict wind loads on buildings JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS Lamberti, G., Gorle, C. 2021; 214

    View details for DOI 10.1016/j.jweia.2021.104647

    View details for Web of Science ID 000663781000004

  • Confronting Grand Challenges in environmental fluid mechanics PHYSICAL REVIEW FLUIDS Dauxois, T., Peacock, T., Bauer, P., Caulfield, C. P., Cenedese, C., Gorle, C., Haller, G., Ivey, G. N., Linden, P. F., Meiburg, E., Pinardi, N., Vriend, N. M., Woods, A. W. 2021; 6 (2)

    View details for DOI 10.1103/PhysRevFluids.6.020501

    View details for Web of Science ID 000616272500001

  • Sensitivity of LES predictions of wind loading on a high-rise building to the inflow boundary condition JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS Lamberti, G., Gorle, C. 2020; 206

    View details for DOI 10.1016/j.jweia.2020.104370

    View details for Web of Science ID 000580638300047

  • Quantifying turbulence model uncertainty in Reynolds-averaged Navier-Stokes simulations of a pin-fin array. Part 1: Flow field COMPUTERS & FLUIDS Hao, Z., Gorle, C. 2020; 209

    View details for DOI 10.1016/j.compfluid.2020.104641

    View details for Web of Science ID 000556841200006

  • Quantifying turbulence model uncertainty in Reynolds-averaged Navier-Stokes simulations of a pin-fin array. Part 2: Scalar transport COMPUTERS & FLUIDS Hao, Z., Gorle, C. 2020; 209

    View details for DOI 10.1016/j.compfluid.2020.104642

    View details for Web of Science ID 000556841200007

  • Comparison of high resolution pressure measurements on a high-rise building in a closed and open-section wind tunnel JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS Lamberti, G., Amerio, L., Pomaranzi, G., Zasso, A., Gorle, C. 2020; 204

    View details for DOI 10.1016/j.jweia.2020.104247

    View details for Web of Science ID 000566881700003

  • Pressure scrambling effects and the quantification of turbulent scalar flux model uncertainties PHYSICAL REVIEW FLUIDS Hao, Z., Gorle, C. 2020; 5 (8)

    View details for DOI 10.1103/PhysRevFluids.5.082501

    View details for Web of Science ID 000554828000002

  • Large eddy simulations of forced heat convection in a pin-fin array with a priori examination of an eddy-viscosity turbulence model INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW Hao, Z., Gorle, C. 2019; 77: 73–83

    View details for DOI 10.1016/j.ijheatfluidflow.2019.03.006

    View details for Web of Science ID 000471084900007

  • Computational urban flow predictions with Bayesian inference: Validation with field data BUILDING AND ENVIRONMENT Sousa, J., Gorle, C. 2019; 154: 13–22

    View details for DOI 10.1016/j.buildenv.2019.02.028

    View details for Web of Science ID 000464358100003

  • Epistemic uncertainty quantification for Reynolds-averaged Navier-Stokes modeling of separated flows over streamlined surfaces PHYSICS OF FLUIDS Gorle, C., Zeoli, S., Emory, M., Larsson, J., Iaccarino, G. 2019; 31 (3)

    View details for DOI 10.1063/1.5086341

    View details for Web of Science ID 000462915800037

  • Improving Predictions of the Urban Wind Environment Using Data Technology Architecture and Design Gorlé, C. 2019; 3 (2): 137-141

    View details for DOI 10.1080/24751448.2019.1640522

  • Predictive large eddy simulations for urban flows: Challenges and opportunities BUILDING AND ENVIRONMENT Garcia-Sanchez, C., van Beeck, J., Gorle, C. 2018; 139: 146–56

    View details for DOI 10.1016/j.buildenv.2018.05.007

    View details for Web of Science ID 000435063700014

  • Improving urban flow predictions through data assimilation BUILDING AND ENVIRONMENT Sousa, J., Garcia-Sanchez, C., Gorle, C. 2018; 132: 282–90

    View details for DOI 10.1016/j.buildenv.2018.01.032

    View details for Web of Science ID 000428484400026

  • Uncertainty quantification for microscale CFD simulations based on input from mesoscale codes. Journal of Wind Engineering and Industrial Aerodynamics Garcia-Sanchez, C., Gorlé, C. 2018; 176: 87-97

    View details for DOI 10.1016/j.jweia.2018.03.011

  • Optimizing turbulent inflow conditions for large-eddy simulations of the atmospheric boundary layer. Journal of Wind Engineering and Industrial Aerodynamics Lamberti, G., Garcia-Sanchez, C., Sousa, J., Gorle, C. 2018; 177: 32-44

    View details for DOI 10.1016/j.jweia.2018.04.004

  • Uncertainty Quantification for modeling night-time ventilation in Stanford's Y2E2 building. Energy and Buildings Lamberti, G., Gorlé, C. 2018; 168: 319-330

    View details for DOI 10.1016/j.enbuild.2018.03.022

  • RAMS sensitivity to grid spacing and grid aspect ratio in Large-Eddy Simulations of the dry neutral Atmospheric Boundary Layer COMPUTERS & FLUIDS Ercolani, G., Gorle, C., Corbari, C., Mancini, M. 2017; 146: 59-73

    View details for DOI 10.1016/j.compfluid.2017.01.010

    View details for Web of Science ID 000395218100005

  • Quantifying inflow uncertainties in RANS simulations of urban pollutant dispersion Atmospheric Environment Garcia-Sanchez, C., Van Tendeloo, G., Gorle, C. 2017; 161: 263-273

    View details for DOI 10.1016/j.atmosenv.2017.04.019

  • Thermal Modeling of Extreme Heat Flux Microchannel Coolers for GaN-on-SiC Semiconductor Devices JOURNAL OF ELECTRONIC PACKAGING Lee, H., Agonafer, D. D., Won, Y., Houshmand, F., Gorle, C., Asheghi, M., Goodson, K. E. 2016; 138 (1)

    View details for DOI 10.1115/1.4032655

    View details for Web of Science ID 000372735400008

  • RAMS and WRF sensitivity to grid spacing in large-eddy simulations of the dry convective boundary layer COMPUTERS & FLUIDS Ercolani, G., Gorle, C., Garcia-Sanchez, C., Corbari, C., Mancini, M. 2015; 123: 54-71

    View details for DOI 10.1016/j.compfluid.2015.09.009

    View details for Web of Science ID 000365367500006

  • Quantifying inflow and RANS turbulence model form uncertainties for wind engineering flows JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS Gorle, C., Garcia-Sanchez, C., Iaccarino, G. 2015; 144: 202-212

    View details for DOI 10.1016/j.jweia.2015.03.025

    View details for Web of Science ID 000360874900023

  • Quantifying inflow uncertainties for CFD simulations of the flow in downtown Oklahoma City BUILDING AND ENVIRONMENT Garcia-Sanchez, C., Philips, D. A., Gorle, C. 2014; 78: 118-129

    View details for DOI 10.1016/j.buildenv.2014.04.013

    View details for Web of Science ID 000338619700013

  • The deviation from parallel shear flow as an indicator of linear eddy-viscosity model inaccuracy PHYSICS OF FLUIDS Gorle, C., Larsson, J., EMORY, M., Iaccarino, G. 2014; 26 (5)

    View details for DOI 10.1063/1.4876577

    View details for Web of Science ID 000337103900002

  • A framework for epistemic uncertainty quantification of turbulent scalar flux models for Reynolds-averaged Navier-Stokes simulations PHYSICS OF FLUIDS Gorle, C., Iaccarino, G. 2013; 25 (5)

    View details for DOI 10.1063/1.4807067

    View details for Web of Science ID 000320001200043

  • A Comprehensive Modelling Approach for the Neutral Atmospheric Boundary Layer: Consistent Inflow Conditions, Wall Function and Turbulence Model BOUNDARY-LAYER METEOROLOGY Parente, A., Gorle, C., van Beeck, J., Benocci, C. 2011; 140 (3): 411-428

    View details for DOI 10.1007/s10546-011-9621-5

    View details for Web of Science ID 000293923800004

  • Improved kappa-epsilon model and wall function formulation for the RANS simulation of ABL flows 5th International Symposium on Computational Wind Engineering (CWE2010) Parente, A., Gorle, C., van Beeck, J., Benocci, C. ELSEVIER SCIENCE BV. 2011: 267–78

    View details for DOI 10.1016/j.jweia.2010.12.017

    View details for Web of Science ID 000290972900012

  • Dispersion in the Wake of a Rectangular Building: Validation of Two Reynolds-Averaged Navier-Stokes Modelling Approaches BOUNDARY-LAYER METEOROLOGY Gorle, C., van Beeck, J., Rambaud, P. 2010; 137 (1): 115-133

    View details for DOI 10.1007/s10546-010-9521-0

    View details for Web of Science ID 000281712500006

  • Stack gas dispersion measurements with Large Scale-PIV, Aspiration Probes and Light Scattering Techniques and comparison with CFD ATMOSPHERIC ENVIRONMENT Nakiboglu, G., Gorle, C., Horvath, I., van Beeck, J., Blocken, B. 2009; 43 (21): 3396-3406

    View details for DOI 10.1016/j.atmosenv.2009.03.047

    View details for Web of Science ID 000267529600013

  • CFD modelling of small particle dispersion: The influence of the turbulence kinetic energy in the atmospheric boundary layer ATMOSPHERIC ENVIRONMENT Gorle, C., van Beeck, J., Rarnbaud, P., Van Tendeloo, G. 2009; 43 (3): 673-681

    View details for DOI 10.1016/j.atmosenv.2008.09.060

    View details for Web of Science ID 000262737900023

  • Flow analyses in the lower airways: Patient-specific model and boundary conditions MEDICAL ENGINEERING & PHYSICS De Backer, J. W., Vos, W. G., Gorle, C. D., Germonpre, P., Partoens, B., Wuyts, F. L., Parizel, P. M., De Backer, W. 2008; 30 (7): 872-879

    Abstract

    Computational fluid dynamics (CFD) is increasingly applied in the respiratory domain. The ability to simulate the flow through a bifurcating tubular system has increased the insight into the internal flow dynamics and the particular characteristics of respiratory flows such as secondary motions and inertial effects. The next step in the evolution is to apply the technique to patient-specific cases, in order to provide more information about pathological airways. This study presents a patient-specific approach where both the geometry and the boundary conditions (BC) are based on individual imaging methods using computed tomography (CT). The internal flow distribution of a 73-year-old female suffering from chronic obstructive pulmonary disease (COPD) is assessed. The validation is performed through the comparison of lung ventilation with gamma scintigraphy. The results show that in order to obtain agreement within the accuracy limits of the gamma scintigraphy scan, both the patient-specific geometry and the BC (driving pressure) play a crucial role. A minimal invasive test (CT scan) supplied enough information to perform an accurate CFD analysis. In the end it was possible to capture the pathological features of the respiratory system using the imaging and computational fluid dynamics techniques. This brings the introduction of this new technique in the clinical practice one step closer.

    View details for DOI 10.1016/j.medengphy.2007.11.002

    View details for Web of Science ID 000259768300009

    View details for PubMedID 18096425

https://orcid.org/0000-0001-8281-6545