Matthew Norman

A concern in the deployment of large wind turbines on ocean floating platforms is the effect of floating-platform motions on their electrical power generation. Further, it is not clear how floating motions influence waking, which might affect the combined power generation of collections of turbines. We examine the average power output of a single and a collection of NREL 5 MW wind turbines mounted on a tension-leg platform (TLP) under the action of fully developed ocean wave motions, coupling floating motions with the large-eddy simulation (LES) of atmospheric and rotor dynamics. The ocean dynamics enter as fully developed waves derived from the Pierson–Moskowitz spectrum. To assess the influence of ocean motions, we performed simulations over the full range of wind speeds in the operational range of the turbine, reporting comparisons of average power output when the platforms are allowed to move to when they are held rigidly in place. In all simulations, we find that the effects of the TLP floating-platform's induced motions have a minor effect on single and multiple turbine power production and wake deficits. Even when using coherent and large amplitude harmonic-floating-induced perturbations, any significant wake modifications from floating motions are confined to the near-wake region, where downstream turbines are unlikely to be located. The relatively small amplitude of TLP motions relative to pre-existing turbulent fluctuations are the primary reason for low wake and power modifications downstream.

In this paper, we study the propagation of data uncertainty through the marching cubes algorithm for isosurface visualization for correlated uncertain data. Consideration of correlation has been shown paramount for avoiding errors in uncertainty quantification and visualization in multiple prior studies. Although the problem of isosurface uncertainty with spatial data correlation has been previously addressed, there are two major limitations to prior treatments. First, there are no analytical formulations for uncertainty quantification of isosurfaces when the data uncertainty is characterized by a Gaussian distribution with spatial correlation. Second, as a consequence of the lack of analytical formulations, existing techniques resort to a Monte Carlo sampling approach, which is expensive and difficult to integrate into visualization tools. To address these limitations, we present a closed-form framework to efficiently derive uncertainty in marching cubes level-sets for Gaussian uncertain data with spatial correlation (MAGIC). To derive closed-form solutions, we leverage the Hinkley's derivation on the ratio of Gaussian distributions. With our analytical framework, we achieve a significant speed-up and enhanced accuracy of uncertainty quantification over classical Monte Carlo methods. We further accelerate our analytical solutions using many-core processors to achieve speed-ups up to $\text{585} \times$585× and integrability with production visualization tools for broader impact. We demonstrate the effectiveness of our correlation-aware uncertainty framework through experiments on meteorology, urban flow, and astrophysics simulation datasets.

Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer architectures requires extremely long, computationally prohibitive token sequences. To address this issue, we propose two novel adaptive tokenization schemes that dynamically adjust patch sizes based on local features: one ensures convergent behavior to uniform patch refinement, while the other offers better computational efficiency. Moreover, we present a set of spatiotemporal attention schemes, where the temporal or axial spatial dimensions are decoupled, to evaluate their baseline computational and data efficiencies and to determine whether adaptive tokenization can improve this performance. We assess the performance of the proposed multiscale adaptive model, MATEY, in a sequence of experiments. Compared to a full spatiotemporal attention scheme or a scheme that decouples only the temporal dimension, we find that fully decoupled axial attention is less efficient and expressive, requiring more training time and model parameters to achieve the same accuracy. The experiments on the adaptive tokenization schemes show that, compared to a uniformly refined model, the proposed schemes achieve comparable or improved accuracy at a much lower cost in the tested two-dimensional settings. While the asymptotic analysis suggests the potential for favorable scaling, empirical validation at substantially longer sequence lengths remains to be performed in future work. Finally, we demonstrate in two fine-tuning tasks featuring different physics that models pretrained on PDEBench data outperform the ones trained from scratch, especially in the low data regime with frozen attention.

A concern in the deployment of large wind turbines on ocean floating platforms is the effect of floating platform motions on their electrical power generation. Further, it is not clear how floating motions influence waking, which might affect the combined power generation of collections of turbines. We examine the average power output of a single and a collection of NREL 5 MW wind turbines, mounted on a Tension-Leg Platform (TLP) under the action of fully developed ocean wave motions, coupling floating motions with Large Eddy Simulation (LES) of atmospheric and rotor dynamics. The ocean dynamics enter as fully developed waves derived from the Pierson-Moskowitz spectrum. To assess the influence of ocean motions we performed simulations under multiple turbulence intensities, reporting comparisons of average power output when the platforms are allowed to move to when they are held rigidly in place. In all simulations, we find that the effects of TLP floating platform’s induced motions have a minor effect on single and multiple turbine power production and wake structure. Even when using coherent and large amplitude harmonic floating induced perturbations, any significant wake modifications from floating motions are confined to the near-wake region, where downstream turbines are unlikely to be located. The relatively small amplitude of TLP motions relative to pre-existing turbulent fluctuations are the primary reason for low wake and power modifications downstream.

This paper introduces “portUrb”: a moist, compressible, non-hydrostatic atmospheric Large Eddy Simulation model that aims for portability, performance, accuracy, simplicity, readability, robustness, extensibility, and ensemble capabilities. Additionally, there is an emphasis on free-slip immersed boundaries with surface friction to account for urban building geometries. Coded in portable C++ with high-order Weighted Essentially Non-Oscillatory (WENO) numerics, this study investigates the behavior of portUrb under atmospheric boundary layer, supercell, and urban scenarios. PortUrb matches experimental observations and model comparisons closely under several test cases in mean and turbulent statistics. It also provides physically realizable flow through complex building geometries from a portion of Manhattan without needing to pre-process or smooth the building geometry.

A concern in the deployment of large wind turbines on ocean floating platforms is the effect of floating platform motions on their electrical power generation. Further, it is not clear how floating motions influence waking, which might affect the combined power generation of collections of turbines. We examine the average power output of a single and a collection of 5 MW wind turbines, mounted on a Tension-Leg Platform (TLP) under the action of fully developed ocean wave motions, coupling floating motions with Large Eddy Simulation (LES) of atmospheric and rotor dynamics. The ocean dynamics enter as fully developed waves derived from the Pierson-Moskowitz spectrum. To assess the influence of ocean motions we performed simulations under multiple turbulence intensities, reporting comparisons of average power output when the platforms are allowed to move to when they are held rigidly in place. In all simulations, we find that the effects of TLP floating platform's induced motions have a minor effect on single and multiple turbine power production and wake structure. Even when using coherent and large amplitude harmonic floating induced perturbations, any significant wake modifications from floating motions are confined to the near-wake region, where downstream turbines are unlikely to be located. The relatively small amplitude of TLP motions relative to pre-existing turbulent fluctuations are the primary reason for low wake and power modifications downstream.

This paper introduces "portUrb": a moist, compressible, non-hydrostatic atmospheric Large Eddy Simulation model that aims for portability, performance, accuracy, simplicity, readability, robustness, extensibility, and ensemble capabilities. Additionally, there is an emphasis on free-slip immersed boundaries with surface friction to account for urban building geometries. Coded in portable C++ with high-order Weighted Essentially Non-Oscilatory (WENO) numerics, this study investigates the behavior of portUrb under atmospheric boundary layer, supercell, and urban scenarios. PortUrb matches experimental observations and model comparisons closely under several test cases in mean and turbulent statistics. It also provides physically realizable flow through complex building geometries from a portion of Manhattan without needing to pre-process or smooth the building geometry.

This paper introduces "portUrb": a moist, compressible, non-hydrostatic atmospheric Large Eddy Simulation model that aims for portability, performance, accuracy, simplicity, readability, robustness, extensibility, and ensemble capabilities. Additionally, there is an emphasis on free-slip immersed boundaries with surface friction to account for urban building geometries. Coded in portable C++ with high-order Weighted Essentially Non-Oscilatory (WENO) numerics, this study investigates the behavior of portUrb under atmospheric boundary layer, supercell, and urban scenarios. PortUrb matches experimental observations and model comparisons closely under several test cases in mean and turbulent statistics. It also provides physically realizable flow through complex building geometries from a portion of Manhattan without needing to pre-process or smooth the building geometry.

This paper introduces "portUrb": a moist, compressible, non-hydrostatic atmospheric Large Eddy Simulation model that aims for portability, performance, accuracy, simplicity, readability, robustness, extensibility, and ensemble capabilities. Additionally, there is an emphasis on free-slip immersed boundaries with surface friction to account for urban building geometries. Coded in portable C++ with high-order Weighted Essentially Non-Oscilatory (WENO) numerics, this study investigates the behavior of portUrb under atmospheric boundary layer, supercell, and urban scenarios. PortUrb matches experimental observations and model comparisons closely under several test cases in mean and turbulent statistics. It also provides physically realizable flow through complex building geometries from a portion of Manhattan without needing to pre-process or smooth the building geometry.