Modeling and Simulation of a Fluttering Cantilever in Channel Flow

Characterizing the dynamics of a cantilever in channel flow is relevant to applications ranging from snoring to energy harvesting. Aeroelastic flutter induces large oscillating amplitudes and sharp changes with frequency that impact the operation of these systems. The fluid-structure mechanisms that drive flutter can vary as the system parameters change, with the stability boundary becoming especially sensitive to the channel height and Reynolds number, especially when either or both are small. In this paper, we develop a coupled fluid-structure model for viscous, two-dimensional channel flow of arbitrary shape. Its flutter boundary is then compared to results of two-dimensional direct numerical simulations to explore the model's validity. Provided the non-dimensional channel height remains small, the analysis shows that the model is not only able to replicate DNS results within the parametric limits that ensure the underlying assumptions are met, but also over a wider range of Reynolds numbers and fluid-structure mass ratios. Model predictions also converge toward an inviscid model for the same geometry as Reynolds number increases

Node-level resilience loss in dynamic complex networks

In an increasingly connected world, the resilience of networked dynamical systems is important in the fields of ecology, economics, critical infrastructures, and organizational behaviour. Whilst we understand small-scale resilience well, our understanding of large-scale networked resilience is limited. Recent research in predicting the effective network-level resilience pattern has advanced our understanding of the coupling relationship between topology and dynamics. However, a method to estimate the resilience of an individual node within an arbitrarily large complex network governed by non-linear dynamics is still lacking. Here, we develop a sequential mean-field approach and show that after 1-3 steps of estimation, the node-level resilience function can be represented with up to 98% accuracy. This new understanding compresses the higher dimensional relationship into a one-dimensional dynamic for tractable understanding, mapping the relationship between local dynamics and the statistical properties of network topology. By applying this framework to case studies in ecology and biology, we are able to not only understand the general resilience pattern of the network, but also identify the nodes at the greatest risk of failure and predict the impact of perturbations. These findings not only shed new light on the causes of resilience loss from cascade effects in networked systems, but the identification capability could also be used to prioritize protection, quantify risk, and inform the design of new system architectures

Machine-learning-corrected quantum dynamics calculations

Quantum scattering calculations for all but low-dimensional systems at low energies must rely on approximations. All approximations introduce errors. The impact of these errors is often difficult to assess because they depend on the Hamiltonian parameters and the particular observable under study. Here, we illustrate a general, system and approximation-independent, approach to improve the accuracy of quantum dynamics approximations. The method is based on a Bayesian machine learning (BML) algorithm that is trained by a small number of rigorous results and a large number of approximate calculations, resulting in ML models that accurately capture the dependence of the dynamics results on the quantum dynamics parameters. Most importantly, the present work demonstrates that the BML models can generalize quantum results to different dynamical processes. Thus, a ML model trained by a combination of approximate and rigorous results for a certain inelastic transition can make accurate predictions for different transitions without rigorous calculations. This opens the possibility of improving the accuracy of approximate calculations for quantum transitions that are out of reach of rigorous scattering calculations.Comment: 6 pages, 4 figure

The effects of sedimentary basins on the dynamics of the East Antarctic Ice Sheet from enhanced groundwater and geothermal heat flow

It is well known that ice sheets heavily influence groundwater systems, however, the impact of groundwater on ice sheet dynamics is not. This poorly understood aspect of ice-sheet hydrology is relevant to the subglacial hydrology of ice sheets lacking surface or englacial meltwater such as the East Antarctic Ice Sheet (EAIS). How groundwater systems redistribute geothermal heat at the base of an ice sheet is also largely unknown. Geothermal heat and subglacial hydrology are important basal processes controlling ice flow. Large sedimentary basins underlie the EAIS, which likely play host to many groundwater systems. I hypothesized that groundwater systems in these sedimentary basins may be the main water transport mechanism over water sheets (or films) at large scales in the interior of the ice sheet where basal melt rates are very low. I also hypothesized that these groundwater systems are likely important to the basal processes (specifically heat flux) and dynamics of the EAIS (particularly in rheological and sliding behavior). To test these, I created various one- and two-dimensional numerical models incorporating relevant datasets and conservative assumptions about the subsurface. The models ranged from simple groundwater and thermal simulations to a complex subsurface fluid and thermal model coupled to a fully dynamic ice sheet simulator. The models suggest that groundwater most likely has measurable effects on the dynamics of ice sheets like the EAIS. I have shown that probable groundwater systems underneath the interior of the EAIS can likely transport most of the meltwater produced and that groundwater can strongly affect the heat flux (positively, as well as, negatively) at the ice base under kilometers of relatively slow-moving ice. I have also not only shown that groundwater systems under the EAIS are strongly controlled by the ice sheet’s dynamics but that groundwater systems have a feedback to the ice dynamics, mostly through enhanced basal sliding and changes to the ice rheology. These results provide the justification to include groundwater in future simulations of the EAIS as well as a call to collect more data to better delineate its subsurface sedimentary basins – a critical input for groundwater and heat transport modeling.Geological Science