Austrian High-Performance-Computing meeting (AHPC2020)

This booklet is a collection of abstracts presented at the AHPC conference

A combined model for tsunami wave propagation, dispersion, breaking and fluid-structure interaction

In this work, a fully combined tsunami model was developed, by coupling a sequence of 3 distinct numerical models, with different characteristics, for particular phases of the tsunami lifecycle. The computational codes that compose the fully combined tsunami model are the GeoClaw code, the FUNWAVE-TVD code and the OpenFOAM code, via the olaFlow solver. The coupling of GeoClaw with FUNWAVE-TVD was designated as the combined model 1 (CM1) and the combination of FUNWAVE-TVD/CM1 with the CFD code was designated as the combined model 2 (CM2). The full combination of both CM1 and CM2 resulted in the fully combined tsunami model CM. To achieve the coupling between numerical models, individual coupling methodologies were approached, tested and analysed. For the CM1, we choose a refined covered gauge domain coupling methodology and for the CM2 a timeSeries condition coupling methodology was used, which applied waveType wavemaker and the waveTheory tveta, from the olaFlow module. The validation of the individual numerical codes and of the combined model patches was performed with both numerical and physical test cases. Several physical experiments were carried out to generate both solitary and N-waves and a novel first-order theoretical formulation, necessary to generate N-waves experimentally, by means of a piston wave generating system, was developed and detailed in this work. The large-scale physical experiments were performed in the wave basin and in a beach composed by a 1:15 plane slope and a 1:30 plane slope. The generated solitary and N-waves were classified according to their Stokes number. Experimental free surface elevation, run-in, run-up and pressure measurements were retrieved from the physical experiments. Run-in, run-up and pressure laws were proposed for solitary waves and N-waves respectively. The experimental measurements were compared with numerical simulation results. The objectives of the development of the fully combined tsunami model were (1) to join the advantages of the individual models in a single one, attempting to increase the accuracy, efficiency and regime of validity, and (2) to bring a contribution in the tackling of some of the existing problems and challenges of tsunami science, such as the frequency dispersion in long distance tsunami propagation, the complex tsunami on land propagation and fluid flow interactions with river courses and with the coastal and urban areas. The fully combined tsunami model CM simulation results for a Mω 8.5 Earthquake and Tsunami hitting the Portuguese coast showed the ability of the combined model to cover all the tsunami stages. We show that with a 2DV simulation of the CFD code for the Marina of Cascais bathymetric and topographic profile it was possible to observe the vortices behind the breakwater. The analysis of the free surface elevation, velocities and pressure of the tsunami waves was performed. This allowed us to understand the consequence of three diferent tsunami waves scenarios after the breakwater zone. It was possible to draw some brief conclusions considering the tsunami impact. The fully combined tsunami model achieved in this work is a novelty, since it is composed by a sequence of distinct numerical models, including the three-dimensional component granted by the CFD code. With this combined model, it is possible to perform the simulation of real case tsunami events and hypothetical scenarios, applying real or synthetic tsunami-type wave profiles, studying and researching the impact and the tsunami interaction with the coastal areas

THE INFLUENCE OF CLIMATE ON WATERSHED TRANSIT TIMES, WITH IMPLICATIONS FOR NITRATE TRANSPORT IN THE CHESAPEAKE BAY WATERSHED

The ability to accurately model the timing and quantity of contaminant transport from landscapes to surface waters under different climate conditions is vital to the development of climate-resilient watershed management tools and strategies. Although hydrologic transport cannot be directly measured at the full range of relevant scales, a measurable proxy at catchment scale is the integrated transit time distribution (TTD). The TTD is the time-varying, probabilistic distribution of water travel times or, equivalently, water ages in catchment outflow. This dissertation presents advances in hydrologic theory and catchment-scale modeling and uses them to learn about the influence of climate on TTD behavior at multiple sites. The specific contributions of this work include (1) the first benchmarking of the sensitivity of catchment transit times to present and projected climate conditions, which shows that climate change could significantly shift the phenology of stream age; (2) the introduction of a computationally efficient approach to calibrating integrated surface-subsurface hydrology models (ISSHMs) under realistic climate forcing to both discharge and stream age, and subsequent virtual experiments suggesting that the age of baseflow is significantly influenced by upper soil properties due to dynamic hydrologic partitioning, which is not captured in steady-state simulations; (3) a novel analysis using flowpath decomposition in an ISSHM to understand the influence of climate on catchment TTD dynamics, which reveals a complex relationship between flowpath and water age that belies suggestions in the literature of a one-to-one mapping; and (4) proof-of-concept of an enhancement to the popular Soil and Water Assessment Tool (SWAT) that allows users to calculate groundwater TTDs, calibrate TTDs to available data, and more realistically simulate groundwater nitrate transport with variable recharge rates. The dissertation concludes with a brief discussion of its implications on our understanding of groundwater nitrate transport in the Chesapeake Bay watershed under present and future climates

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts