Dynamic load balancing of distributed memory parallel computational mechanics using unstructured meshes for multi-physical modelling

As the complexity of parallel applications increase, the performance limitations resulting from computational load imbalance become dominant. Mapping the problem space to the processors in a parallel machine in a manner that balances the workload of each processors will typically reduce the run-time. In many cases the computation time required for a given calculation cannot be predetermined even at run-time and so static partition of the problem returns poor performance. For problems in which the computational load across the discretisation is dynamic and inhomogeneous, for example multi-physics problems involving fluid and solid mechanics with phase changes, the workload for a static subdomain will change over the course of a computation and cannot be estimated beforehand. For such applications the mapping of loads to process is required to change dynamically, at run-time in order to maintain reasonable efficiency. The issue of dynamic load balancing are examined in the context of PHYSICA, a three dimensional unstructured mesh multi-physics continuum mechanics computational modelling code.

Shipping noise in a dynamic sea: a case study of grey seals in the Celtic Sea

Shipping noise is a threat to marine wildlife. Grey seals are benthic foragers, and thus experience acoustic noise throughout the water column, which makes them a good model species for a case study of the potential impacts of shipping noise. We used ship track data from the Celtic Sea, seal track data and a coupled ocean-acoustic modelling system to assess the noise exposure of grey seals along their tracks. It was found that the animals experience step changes in sound levels up to ~20dB at a frequency of 125Hz, and ~10dB on average over 10-1000Hz when they dive through the thermocline, particularly during summer. Our results showed large seasonal differences in the noise level experienced by the seals. These results reveal the actual noise exposure by the animals and could help in marine spatial planning.

Real-time analytics on large dynamic graphs

In today's fast-paced and interconnected digital world, the data generated by an increasing number of applications is being modeled as dynamic graphs. The graph structure encodes relationships among data items, while the structural changes to the graphs as well as the continuous stream of information produced by the entities in these graphs make them dynamic in nature. Examples include social networks where users post status updates, images, videos, etc.; phone call networks where nodes may send text messages or place phone calls; road traffic networks where the traffic behavior of the road segments changes constantly, and so on. There is a tremendous value in storing, managing, and analyzing such dynamic graphs and deriving meaningful insights in real-time. However, a majority of the work in graph analytics assumes a static setting, and there is a lack of systematic study of the various dynamic scenarios, the complexity they impose on the analysis tasks, and the challenges in building efficient systems that can support such tasks at a large scale. In this dissertation, I design a unified streaming graph data management framework, and develop prototype systems to support increasingly complex tasks on dynamic graphs. In the first part, I focus on the management and querying of distributed graph data. I develop a hybrid replication policy that monitors the read-write frequencies of the nodes to decide dynamically what data to replicate, and whether to do eager or lazy replication in order to minimize network communication and support low-latency querying. In the second part, I study parallel execution of continuous neighborhood-driven aggregates, where each node aggregates the information generated in its neighborhoods. I build my system around the notion of an aggregation overlay graph, a pre-compiled data structure that enables sharing of partial aggregates across different queries, and also allows partial pre-computation of the aggregates to minimize the query latencies and increase throughput. Finally, I extend the framework to support continuous detection and analysis of activity-based subgraphs, where subgraphs could be specified using both graph structure as well as activity conditions on the nodes. The query specification tasks in my system are expressed using a set of active structural primitives, which allows the query evaluator to use a set of novel optimization techniques, thereby achieving high throughput. Overall, in this dissertation, I define and investigate a set of novel tasks on dynamic graphs, design scalable optimization techniques, build prototype systems, and show the effectiveness of the proposed techniques through extensive evaluation using large-scale real and synthetic datasets.

On the study of train-track dynamic interactions caused by rail welds on discrete supported rails

The paper studies the influence of rail weld dip on wheel-rail contact dynamics, with particular reference to freight trains where it is important to increase the operating speed and also the load transported. This has produced a very precise model, albeit simple and cost-effective, which has enabled train-track dynamic interactions over rail welds to be studied to make it possible to quantify the influence on dynamic forces and displacements of the welding geometry; of the position of the weld relative to the sleeper; of the vehicle's speed; and of the axle load and wheelset unsprung mass. It is a vertical model on the spatial domain and is drawn up in a simple fashion from vertical track receptances. For the type of track and vehicle used, the results obtained enable the quantification of increases in wheel-rail contact forces due to the new speed and load conditions.

Dynamic mental representations of habitual behaviours: Food choice on a web-based environment

Aim: Rather than being rigid, habitual behaviours may be determined by dynamic mental representations that can adapt to context changes. This adaptive potential may result from particular conditions dependent on the interaction between two sources of mental constructs activation: perceived context applicability and cognitive accessibility . Method: T wo web-shopping simulations of fering the choice between habitually chosen and non-habitually chosen food products were presented to participants. This considered two choice contexts dif fering in the habitual behaviour perceived applicability (low vs. high) and a measure of habitual behaviour chronicity . Results: Study 1 demonstrated a perceived applicability ef fect, with more habitual (non-organic) than non-habitual (organic) food products chosen in a high perceived applicability (familiar) than in a low perceived applicability (new) context. The adaptive potential of habitual behaviour was evident in the habitual products choice consistency across three successive choices, despite the decrease in perceived applicability . Study 2 evidenced the adaptive potential in strong habitual behaviour participants – high chronic accessibility – who chose a habitual product (milk) more than a non-habitual product (orange juice), even when perceived applicability was reduced (new context). Conclusion: Results portray consumers as adaptive decision makers that can flexibly cope with changes in their (inner and outer) choice contexts.

THE IMPACT OF UPPER-LEVEL PROCESSES ON THE INTENSITY AND STRUCTURAL CHANGES OF HURRICANE SANDY (2012)

The first part of this study examines the relative roles of frontogenesis and tropopause undulation in determining the intensity and structural changes of Hurricane Sandy (2012) using a high-resolution cloud-resolving model. A 138-h simulation reproduces Sandy’s four distinct development stages: (i) rapid intensification, (ii) weakening, (iii) steady maximum surface wind but with large continued sea-level pressure (SLP) falls, and (iv) re-intensification. Results show typical correlations between intensity changes, sea-surface temperature and vertical wind shear during the first two stages. The large SLP falls during the last two stages are mostly caused by Sandy’s moving northward into lower-tropopause regions associated with an eastward-propagating midlatitude trough, where the associated lower-stratospheric warm air wraps into the storm and its surrounding areas. The steady maximum surface wind occurs because of the widespread SLP falls with weak pressure gradients lacking significant inward advection of absolute angular momentum (AAM). Meanwhile, there is a continuous frontogenesis in the outer region during the last three stages. Cyclonic inward advection of AAM along each frontal rainband accounts for the continued expansion of the tropical-storm-force wind and structural changes, while deep convection in the eyewall and merging of the final two survived frontal rainbands generate a spiraling jet in Sandy’s northwestern quadrant, leading to its re-intensification prior to landfall. The physical, kinematic and dynamic aspects of an upper-level outflow layer and its possible impact on the re-intensification of Sandy are examined in the second part of this study. Above the outflow layer isentropes are tilted downward with radius as a result of the development of deep convection and an approaching upper-level trough, causing weak subsidence. Its maximum outward radial velocity is located above the cloud top, so the outflow channel experiences cloud-induced long-wave cooling. Because Sandy has two distinct convective regions (an eyewall and a frontal rainband), it has multiple outflow layers, with the eyewall’s outflow layer located above that of the frontal rainband. During the re-intensification stage, the eyewall’s outflow layer interacts with a jet stream ahead of the upper-level trough axis. Because of the presence of inertial instability on the anticyclonic side of the jet stream and symmetric instability in the inner region of the outflow layer, Sandy’s secondary circulation intensifies. Its re-intensification ceases when these instabilities disappear. The relationship between the intensity of the secondary circulation and dynamic instabilities of the outflow layer suggests that the re-intensification occurs in response to these instabilities. Additionally, it is verified that the long-wave cooling in the outflow layer helps induce symmetric instability by reducing static stability.

Reasoning on the Risks of Dynamic Manufacturing Networks through Cognitive Mapping

Part 17: Risk Analysis

Changes in phytoplankton composition in response to tides, wind-induced mixing conditions, and freshwater outflows in an urbanised estuarine complex

Estudos recentes na baía de Santos (sudeste do Brasil), localizada em um sistema estuarino altamente urbanizado, mostraram o aumento de espécies fitoplanctônicas potencialmente nocivas. Apesar da importância da previsão das florações algais nocivas, é difícil determinar a estrutura da comunidade fitoplanctônica em ambientes extremamente dinâmicos. O presente estudo analisa florações dominadas pelo microfitoplâncton e sua relação com variáveis físicas e meteorológicas, a fim de determinar padrões associados às marés e às estações do ano. Foram comparadas oito situações e obtidos cinco cenários de dominância relacionados aos ventos, marés e pluviosidade: i) Surfers, diatomáceas associadas à zona de surfe, de alta energia; ii) Sinkers, diatomáceas de tamanho grande que ocorrem nas marés de sizígia, depois de períodos de alta pluviosidade; iii) Opportunistic mixers, diatomáceas pequenas ou alongadas, formadoras de cadeia, que ocorrem durante períodos de quadratura; iv) Local mixers, diatomáceas e dinoflagelados microplanctônicos que foram abundantes em todas as 298 estações amostradas, e v) Mixotrophic dinoflagellates, que ocorrem após intensas descargas estuarinas. Os resultados sugerem uma alteração no padrão temporal de algumas espécies formadoras de florações, enquanto outras apresentaram abundâncias superiores aos valores seguros para a saúde publica. Esta abordagem ilustra também os possíveis impactos de variações na descarga de água doce em estuários altamente eutrofizados.

The Sensing Enterprise: Towards the Next Generation Dynamic Virtual Organisations

Part 6: Engineering and Implementation of Collaborative Networks

Development and testing of a Fourier trasform based experimental system for Dynamic Mechanical Thermal Analysis tests

Altough nowadays DMTA is one of the most used techniques to characterize polymers thermo-mechanical behaviour, it is only effective for small amplitude oscillatory tests and limited to a single frequency analysis (linear regime). In this thesis work a Fourier transform based experimental system has proven to give hint on structural and chemical changes in specimens during large amplitude oscillatory tests exploiting multi frequency spectral analysis turning out in a more sensitive tool than classical linear approach. The test campaign has been focused on three test typologies: Strain sweep tests, Damage investigation and temperature sweep tests.

Random walk with restart over dynamic graphs

Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|V|^3) time and O(|V|^2) memory to compute all (|V|^2) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|V|^3) to O(|delta|) time for each update without loss of accuracy, where |delta| (<<|V|^2) is the number of affected proximities. (2) To avoid O(|V|^2) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(l|V|) memory and O(|V|/l) I/O costs, where 1<=l<=|V| is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1–2 orders of magnitude faster than other competitors while securing scalability and exactness.

Using an infra-red sensor to measure the dynamic behaviour of N2O gas escaping through different sized holes

An anastomosis is a surgical procedure that consists of the re-connection of two parts of an organ and is commonly required in cases of colorectal cancer. Approximately 80% of the patients diagnosed with this problem require surgery. The malignant tissue located on the gastrointestinal track must be resected and the most common procedure adopted is the anastomosis. Studies made with 2,980 patients that had this procedure, show that the leakage through the anastomosis was 5.1%. This paper discusses the dynamic behavior of N2O gas through different sized leakages as detected by an Infra-Red gas sensor and how the sensors response time changes depending on the leakage size. Different sized holes were made in the rigid tube to simulate an anastomostic leakage. N2O gas was injected into the tube through a pipe and the leakage rate measured by the infra-red gas sensor. Tests were also made experimentally also using a CFD (Computational Fluid Dynamics) package called FloWorks. The results will be compared and discussed in this paper.

A pro-active and dynamic prediction assistance using BaranC framework

Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.

Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian Semiarid region, for use in Dynamic Global Vegetation Models.

The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga

Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2.

Dynamic global vegetation models (DGVMs) simulate surface processes such as the transfer of energy, water, CO2, and momentum between the terrestrial surface and the atmosphere, biogeochemical cycles, carbon assimilation by vegetation, phenology, and land use change in scenarios of varying atmospheric CO2 concentrations. DGVMs increase the complexity and the Earth system representation when they are coupled with atmospheric global circulation models (AGCMs) or climate models. However, plant physiological processes are still a major source of uncertainty in DGVMs. The maximum velocity of carboxylation (Vcmax), for example, has a direct impact over productivity in the models. This parameter is often underestimated or imprecisely defined for the various plant functional types (PFTs) and ecosystems. Vcmax is directly related to photosynthesis acclimation (loss of response to elevated CO2), a widely known phenomenon that usually occurs when plants are subjected to elevated atmospheric CO2 and might affect productivity estimation in DGVMs. Despite this, current models have improved substantially, compared to earlier models which had a rudimentary and very simple representation of vegetation?atmosphere interactions. In this paper, we describe this evolution through generations of models and the main events that contributed to their improvements until the current state-of-the-art class of models. Also, we describe some main challenges for further improvements to DGVMs.