Scale-resolving simulations of the flow in the Francis-99 turbine at part-load condition

In this paper, we investigate the formation of the Rotating Vortex Rope (RVR) using scale-resolving methods, SAS and Wall-Modeled LES (WMLES). We compare the results from these simulation methods with the experimental data of the Francis-99 workshop. This comparison shows that the general features of the RVR can be captured with both methods. However, using WMLES methods would lead to a better quantitative agreement between the velocity profiles in the draft tube in the simulation and the experiment. The reasons for this better agreement are discussed in detail. A comparison of the pressure fluctuations in the draft tube captured in the simulations and the experiment is also presented. This comparison shows that all simulations under-predict the Root Mean Square (RMS) of these pressure fluctuations, although the RMS values predicted by the WMLES simulation are closer to the experimental values

Cavitation Simulations of a Low Head Contra-rotating Pump-turbine

To meet the demands of a larger share of the electrical energy produced by intermittent renewable energy sources, an increasing amount of plannable energy sources is needed. One solution to handle this is to increase the amount of energy storage in the electrical grids. The most widespread energy storage technology today is by far pumped hydro storage (PHS). In an attempt to enable PHS at low-head sites, the ALPHEUS (augmenting grid stability through low head pumped hydro energy utilization and storage) EU Horizon 2020 research project was formed. In ALPHEUS, new axial flow, low-head, contra-rotating pump-turbine (CRPT) designs are investigated. A CRPT has two individual runners rotating in opposite directions.CRPTs developed within the ALPHEUS project have already been thoroughly analysed at stationary and transient operating conditions by the authors. However, the effects on the CPRT\u27s performance due to potential cavitation on the runner blade surfaces have previously not been investigated. For that reason, the current study focuses on running cavitation simulations on a model scale CRPT using the OpenFOAM computational fluid dynamics (CFD) software. In the CFD simulations, cavitation is modelled as a two-phase liquid-vapour mixture using the interPhaseChangeDyMFoam solver. The two runner domains have a prescribed solid body rotation. Condensation and evaporation processes are handled with the Schnerr-Sauer model. Turbulence is managed with the k-omega shear stress transport-scale adaptive simulation (kOmegaSSTSAS) model. Flow-driving pressure differences over the computational domain are achieved with the headLossPressure boundary condition to emulate a larger experimental test facility of which the CRPT is part.Figure 1 shows a snapshot in time of an iso-surface (light blue) of cavitating cloud with alpha_liquid=0.9 in turbine mode. At this operating point, a small amount of cavitating flow is found by the suction side of the leading edges of the left runner, which is facing a lower reservoir. In Figure 2, the same type of iso-surface is shown, however now in pump mode. It is seen that the pump mode operating condition is much worse than the turbine mode. The cavitating cloud covers most of the suction side of the left runner, additionally, the tip-clearance region is also exposed to cavitation. Furthermore, traces of cavitation are found on the leading edges of the right runner as well as on the left small-support struts. It is thus important to, at least, analyse the pump mode to determine if and how much cavitation affects the CRPS\u27s operating performance

A comparison of reinforcement learning techniques for fuzzy cloud auto-scaling

A goal of cloud service management is to design self-adaptable auto-scaler to react to workload fluctuations and changing the resources assigned. The key problem is how and when to add/remove resources in order to meet agreed service-level agreements. Reducing application cost and guaranteeing service-level agreements (SLAs) are two critical factors of dynamic controller design. In this paper, we compare two dynamic learning strategies based on a fuzzy logic system, which learns and modifies fuzzy scaling rules at runtime. A self-adaptive fuzzy logic controller is combined with two reinforcement learning (RL) approaches: (i) Fuzzy SARSA learning (FSL) and (ii) Fuzzy Q-learning (FQL). As an off-policy approach, Q-learning learns independent of the policy currently followed, whereas SARSA as an on-policy always incorporates the actual agent's behavior and leads to faster learning. Both approaches are implemented and compared in their advantages and disadvantages, here in the OpenStack cloud platform. We demonstrate that both auto-scaling approaches can handle various load traffic situations, sudden and periodic, and delivering resources on demand while reducing operating costs and preventing SLA violations. The experimental results demonstrate that FSL and FQL have acceptable performance in terms of adjusted number of virtual machine targeted to optimize SLA compliance and response time

Uncertainty quantification of dynamic earthquake rupture simulations

© 2021 The Authors. We present a tutorial demonstration using a surrogate-model based uncertainty quantification (UQ) approach to study dynamic earthquake rupture on a rough fault surface. The UQ approach performs model calibration where we choose simulation points, fit and validate an approximate surrogate model or emulator, and then examine the input space to see which inputs can be ruled out from the data. Our approach relies on the mogp_emulator package to perform model calibration, and the FabSim3 component from the VECMA toolkit to streamline the workflow, enabling users to manage the workflow using the command line to curate reproducible simulations on local and remote resources. The tools in this tutorial provide an example template that allows domain researchers that are not necessarily experts in the underlying methods to apply them to complex problems. We illustrate the use of the package by applying the methods to dynamic earthquake rupture, which solves the elastic wave equation for the size of an earthquake and the resulting ground shaking based on the stress tensor in the Earth. We show through the tutorial results that the method is able to rule out large portions of the input parameter space, which could lead to new ways to constrain the stress tensor in the Earth based on earthquake observations. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.EPSRC grant no. EP/N510129/1HA to the Alan Turing Institute; European Union Horizon 2020 research and innovation programme under grant agreement no. 800925 (VECMA) and 824115 (HiDALGO)

Sensitivity-driven simulation development: a case study in forced migration

© 2021 The Authors. This paper presents an approach named sensitivity-driven simulation development (SDSD), where the use of sensitivity analysis (SA) guides the focus of further simulation development and refinement efforts, avoiding direct calibration to validation data. SA identifies assumptions that are particularly pivotal to the validation result, and in response model ruleset refinement resolves those assumptions in greater detail, balancing the sensitivity more evenly across the different assumptions and parameters. We implement and demonstrate our approach to refine agent-based models of forcibly displaced people in neighbouring countries. Over 70.8 million people are forcibly displaced worldwide, of which 26 million are refugees fleeing from armed conflicts, violence, natural disaster or famine. Predicting forced migration movements is important today, as it can help governments and NGOs to effectively assist vulnerable migrants and efficiently allocate humanitarian resources. We use an initial SA iteration to steer the simulation development process and identify several pivotal parameters. We then show that we are able to reduce the relative sensitivity of these parameters in a secondary SA iteration by approximately 54% on average. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.European Union Horizon 2020 research and innovation programme VECMA and HiDALGO projects under grant agreement nos 800925 and 824115

FabSim3: An automation toolkit for verified simulations using high performance computing

A common feature of computational modelling and simulation research is the need to perform many tasks in complex sequences to achieve a usable result. This will typically involve tasks such as preparing input data, pre-processing, running simulations on a local or remote machine, post-processing, and performing coupling communications, validations and/or optimisations. Tasks like these can involve manual steps which are time and effort intensive, especially when it involves the management of large ensemble runs. Additionally, human errors become more likely and numerous as the research work becomes more complex, increasing the risk of damaging the credibility of simulation results. Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers. FabSim3 helps to prepare, submit, execute, retrieve, and analyze simulation workflows. By providing a suitable level of abstraction, FabSim3 reduces the complexity of setting up and managing a large-scale simulation scenario, while still providing transparent access to the underlying layers for effective debugging. The tool also facilitates job submission and management (including staging and curation of files and environments) for a range of different supercomputing environments. Although FabSim3 itself is application-agnostic, it supports a provably extensible plugin system where users automate simulation and analysis workflows for their own application domains. To highlight this, we briefly describe a selection of these plugins and we demonstrate the efficiency of the toolkit in handling large ensemble workflows.EPSRC under grant agreement EP/W007711/1, as well as by the VECMA and HiDALGO projects, which have received funding from the European Union Horizon 2020 research and innovation programme under grant agreement nos 800925 and 824115. In addition, FabFlee was supported by the ITFLOWS project and FabCovid19 by the STAMINA project, both of which have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 882986 and No 883441 respectivel

Fair Resource Sharing for Dynamic Scheduling of Workflows on Heterogeneous Systems

International audienceScheduling independent workflows on shared resources in a way that satisfy users Quality of Service is a significant challenge. In this study, we describe methodologies for off-line scheduling, where a schedule is generated for a set of knownworkflows, and on-line scheduling, where users can submit workflows at any moment in time. We consider the on-line scheduling problem in more detail and present performance comparisons of state-of-the-art algorithms for a realistic model of a heterogeneous system