Catherine Gorle
Associate Professor of Civil and Environmental Engineering and, by courtesy, of Mechanical Engineering
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
-
Associate Professor, Civil and Environmental Engineering
-
Associate Professor (By courtesy), Mechanical Engineering
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)
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.
2024-25 Courses
- Studio 4: Integrated Architecture and Engineering
CEE 133D, CEE 233D (Win) - Uncertainty Quantification
CEE 362A (Spr) - WindWise: CFD for civil engineers and architects
CEE 161C, CEE 261C (Win) -
Independent Studies (8)
- Advanced Engineering Problems
CEE 399 (Aut, Win, Spr, Sum) - Directed Reading or Special Studies in Civil Engineering
CEE 198 (Aut, Win, Spr, Sum) - Independent Project in Civil and Environmental Engineering
CEE 199L (Aut, Win, Spr, Sum) - Independent Project in Civil and Environmental Engineering
CEE 299L (Aut, Win, Spr, Sum) - Independent Study in Civil Engineering for CEE-MS Students
CEE 299 (Aut, Win, Spr, Sum) - Report on Civil Engineering Training
CEE 398 (Aut, Win, Spr, Sum) - Undergraduate Honors Thesis
CEE 199H (Aut, Win, Spr, Sum) - Undergraduate Research in Civil and Environmental Engineering
CEE 199 (Aut, Win, Spr, Sum)
- Advanced Engineering Problems
-
Prior Year Courses
2023-24 Courses
- Physics of Wind
CEE 261A (Spr) - Uncertainty Quantification
CEE 362A, ME 470 (Win)
2022-23 Courses
- Environmental Engineering Seminar
CEE 269C (Spr) - Physics of Wind
CEE 261A (Win) - Uncertainty Quantification
CEE 362A, ME 470 (Aut) - Wind Engineering for Sustainable Cities
CEE 261C (Spr)
2021-22 Courses
- Mechanics of Fluids
CEE 101B (Aut) - Physics of Wind
CEE 261A (Win)
- Physics of Wind
Stanford Advisees
-
Wenyuan Xue -
Doctoral Dissertation Reader (AC)
Mark Benjamin, Ipshita Dey -
Postdoctoral Faculty Sponsor
Mattia Ciarlatani -
Doctoral Dissertation Advisor (AC)
Nicholas Bachand, Max Beeman, Michael LoCascio, Themistoklis Vargiemezis -
Master's Program Advisor
Antoine Alary, Ariana Carmody, Xingyi Du, Jessica Fairlie, Pietro Gottardo, Jerry Hong, Maisha Mumtaz, Michael Nazari, Alex Wu, Liwei Yang -
Doctoral (Program)
Nicholas Bachand, Michael LoCascio -
Postdoctoral Research Mentor
Hanul Hwang, Jianyu Wang
All Publications
-
FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization
WIND ENERGY
2024
View details for DOI 10.1002/we.2954
View details for Web of Science ID 001342155600001
-
Design and demonstration of a sensing network for full-scale wind pressure measurements on buildings
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
2024; 250
View details for DOI 10.1016/j.jweia.2024.105760
View details for Web of Science ID 001240540500001
-
Comparison of measured and LES-predicted wind pressures on the Space Needle
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
2024; 249
View details for DOI 10.1016/j.jweia.2024.105749
View details for Web of Science ID 001235501300001
-
Data-driven wake model parameter estimation to analyze effects of wake superposition
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
2023; 15 (6)
View details for DOI 10.1063/5.0163896
View details for Web of Science ID 001123550800001
-
Large-eddy simulations to define building-specific similarity relationships for natural ventilation flow rates
FLOW
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 modeling night-time ventilation in Stanford's Y2E2 building.
Energy and Buildings
2018; 168: 319-330
View details for DOI 10.1016/j.enbuild.2018.03.022
-
Optimizing turbulent inflow conditions for large-eddy simulations of the atmospheric boundary layer.
Journal of Wind Engineering and Industrial Aerodynamics
2018; 177: 32-44
View details for DOI 10.1016/j.jweia.2018.04.004
-
Uncertainty quantification for microscale CFD simulations based on input from mesoscale codes.
Journal of Wind Engineering and Industrial Aerodynamics
2018; 176: 87-97
View details for DOI 10.1016/j.jweia.2018.03.011
-
RAMS sensitivity to grid spacing and grid aspect ratio in Large-Eddy Simulations of the dry neutral Atmospheric Boundary Layer
COMPUTERS & FLUIDS
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
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
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
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
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
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
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
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
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)
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
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
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
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
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