Recent decades have seen remarkable increase in the fidelity of computational fluid dynamics (CFD) models for the simulation of exchange processes between plant canopies and the atmosphere. However, no matter how accurate the selected CFD solver is, model results are found to be affected by an irreducible level of uncertainty that originates from the inability of exactly measuring vegetation (leaf orientation, foliage density, plant reconfiguration) and flow features (incoming wind direction, solar radiation, stratification effects).
Motivated by this consideration, the present PhD thesis proposes a Bayesian uncertainty quantification (UQ) framework for evaluating uncertainty on model parameters and its impact on model results, in the context of CFD for idealized and realistic plant canopy flow. Two problems are considered. First, for the one-dimensional flow within and above the Duke forest near Durham, NC, a one-dimensional Reynolds-averaged Navier--Stokes model is employed. In-situ measurements of turbulence statistics are used to inform the UQ framework in order to evaluate uncertainty on plant geometry and its impact on turbulence statistics and aerodynamic coefficients.
The second problem is characterized by a more realistic setup, with three-dimensional simulations aiming at replicating the flow over a walnut block in Dixon, CA. Due to the substantial computational cost associated with large-eddy simulation (LES), a surrogate model is used for flow simulations. The surrogate is built on top of an exiguous number of LESs over realistic plant canopy, with plant area density derived from LiDAR measurements. Here, the goal is to investigate uncertainty on incoming wind direction and potential repercussions on turbulence statistics. Synthetic data are used to inform the framework.
In both cases, uncertainty on model parameters is characterized via a Markov chain Monte Carlo procedure (inverse problem) and propagated to model results through Monte Carlo sampling (forward problem). In the validation phase, profiles of turbulence statistics with associated uncertainty are compared with the measurements used to inform the framework. By providing an enriched solution for simulation of flow over idealized and realistic plant canopy, this PhD thesis highlights the potential of UQ to enhance prediction of micro-scale exchange processes between vegetation and atmosphere.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/sa4f-6v10 |
| Date | January 2023 |
| Creators | Giacomini, Beatrice |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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