Modelling Hen Harrier Dynamics to Inform Human-Wildlife Conflict Resolution : A Spatially-Realistic, Individual-Based Approach

Peer reviewedPublisher PD

Coupled thermo-mechanical damage modelling for structural steel in fire conditions

This paper aims at developing a coupled thermo-mechanical damage model for structural 6 steel at elevated temperatures. The need for adequate modelling of steel deterioration behaviour 7 remains a challenging task in structural fire engineering because of the complexity inherent in 8 the damage states of steel under combined actions of mechanical and fire loading. A fully three9 dimensional damage-coupled constitutive model is developed in this work based on the hypothesis 10 of effective stress space and isotropic damage theory. The new coupling model, adapted from 11 an enhanced Lemaitre’s ductile damage equation and taking into account temperature-dependent 12 thermal degradation, is a phenomenological approach where the underlying mechanisms that govern 13 the damage processes have been retained. The proposed damage model comprises a limited number 14 of parameters that could be identified using unloading slopes of stress-strain relationships through 15 tensile coupon tests. The proposed damage model is successfully implemented in the finite element 16 software ABAQUS and validated against a comprehensive range of experimental results. The 17 damage-affected structural response is accurately reproduced under various loading conditions and 18 a wide temperature range, demonstrating that the proposed damage model is a useful tool in giving a 19 realistic representation of steel deterioration behaviour for structural fire engineering applications

A study on performance measures for auto-scaling CPU-intensive containerized applications

Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Physiological drought responses improve predictions of live fuel moisture dynamics in a Mediterranean forest.

The moisture content of live fuels is an important determinant of forest flammability. Current approaches for modelling live fuel moisture content typically focus on the use of drought indices. However, these have mixed success partly because of species-specific differences in drought responses. Here we seek to understand the physiological mechanisms driving changes in live fuel moisture content, and to investigate the potential for incorporating plant physiological traits into live fuel moisture models. We measured the dynamics of leaf moisture content, access to water resources (through stable isotope analyses) and physiological traits (including leaf water potential, stomatal conductance, and cellular osmotic and elastic adjustments) across a fire season in a Mediterranean mixed forest in Catalonia, NE Spain. We found that differences in both seasonal variation and minimum values of live fuel moisture content were a function of access to water resources and plant physiological traits. Specifically, those species with the lowest minimum moisture content and largest seasonal variation in moisture (Cistus albidus: 49–137% and Rosmarinus officinalis: 47–144%) were most reliant on shallow soil water and had the lowest values of predawn leaf water potential. Species with the smallest variation in live fuel moisture content (Pinus nigra: 96–116% and Quercus ilex: 56–91%) exhibited isohydric behaviour (little variation in midday leaf water potential, and relatively tight regulation of stomata in response to soil drying). Of the traits measured, predawn leaf water potential provided the strongest predictor of live fuel moisture content (R2 = 0.63, AIC = 249), outperforming two commonly used drought indices (both with R2 = 0.49, AIC = 258). This is the first study to explicitly link fuel moisture with plant physiology and our findings demonstrate the potential and importance of incorporating ecophysiological plant traits to investigating seasonal changes in fuel moisture and, more broadly, forest flammability.This study was made possible thanks to the collaboration of and the staff from the Natural Park of Poblet, P Sopeña, and the technical staff from MedForLab. This study was funded by the Spanish Government (RYC-2012-10970, AGL2015-69151-R). R. H. Nolan was supported with funding from the New South Wales Office of Environment and Heritage, via the Bushfire Risk Management Research Hub. We benefitted from critical comments from J Voltas, JM Moreno and L Serrano and instrument loans from R Savín

Toward a better understanding of tool wear effect through a comparison between experiments and SPH numerical modelling of machining hard materials

The aim of this study is to improve the general understanding of tungsten carbide (WC–Co) tool wear under dry machining of the hard-to-cut titanium alloy Ti6Al4V. The chosen approach includes experimental and numerical tests. The experimental part is designed to identify wear mechanisms using cutting force measurements, scanning electron microscope observations and optical profilometer analysis. Machining tests were conducted in the orthogonal cutting framework and showed a strong evolution of the cutting forces and the chip profiles with tool wear. Then, a numerical method has been used in order to model the machining process with both new and worn tools. The use of smoothed particle hydrodynamics model (SPH model) as a numerical tool for a better understanding of the chip formation with worn tools is a key aspect of this work. The redicted chip morphology and the cutting force evolution with respect to the tool wear are qualitatively compared with experimental trends. The chip formation mechanisms during dry cutting process are shown to be quite dependent from the worn tool geometry. These mechanisms explain the high variation of the experimental and numerical feed force between new and worn tools

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations