Modelling. T2.1 Mould modelling Modelling of heat transfer from liquid steel through to cooling water within the mould and heat transfer and solidification in the steel strand T2.2 Secondary cooling modelling Modelling of the effect of online secondary water cooling T2.3 Thermodynamic & microstructural modelling Modelling phase stability and microstructure evolution during solidification and cooling
Modelling. Integration capability with external tools Interoperability and integration of Maenad outcomes and modelling language with external tools is a key issue to have a widespread acceptance in automotive industry, due to the existence within OEMs and supplier of consolidated procedures and methods based on well established toolchain. Maenad plan and is working to establish concrete link with the major development environment in the market (e.g. simulink, modelica,…) to take also advantage of the simulation and analysis capability that they provide. Link is established through model transformation approach, or inserting explicit reference on the Maenad functions element to external model. Possible evaluation metrics could be based on qualitative estimation about the effectiveness of model transformation (to what extend the original model could be automatically exported to other environment). Support for neutral data exchange format - Tools interoperability To enable support by tools vendor for the EAST-ADL modelling language, a neutral exchange format of the meta-model shall be specified in order to provide means for the different platform to implement basic import-export capability and to make possible interoperability between the tools starting from a common basis. A schema for an XML based exchange format, EAXML is automatically generated from the EAST-ADL meta-model. New versions will be generated as the metamodel is enhanced to support the new requirements related to the Maenad project. Effectiveness and scalability of EAXML exchange will be evaluated by exchanging models between different development environment provided by tool vendors involved in the Maenad Project Optimization Automated exploration of design spaces for design solutions One of the goal of the MAENAD project is the development of model-based engineering methods and tool for optimal design of FEV. This will be achieved through the development of tools for automated exploration of different architectural configurations, starting from quality attributes (dependability, performance) and cost, to support the designer to identify the best possible trade-off. Effectiveness of optimization tools and plug-in will be evaluated by means of a quality review of the results obtained applying the MAENAD approach on two variants of the proposed case studies. Throughout the project, the successful design of the case studies applying Maenad methodology and tools will be considered as a fundamental metrics ...
Modelling. As already noted in this document, monitoring both the service and customer behaviour are very important in making sure both current service level agreements are kept to and understanding how additional customer demands can be met. Modelling goes hand in hand with monitoring. Monitoring data can be used to validate a system model, train a model and as input to a model that predicts future requirements. Modelling itself varies from a domain expert making estimates based on experience to pilot studies and prototypes. Models can be used to predict the resources required for an SLA and to predict the affect of a change in the system (hardware or software). There are a huge variety of modelling techniques available. The simplest models may just use trend analysis: taking the historical usage and extrapolating into the future. A domain expert can use this type of information and predict resource requirements for SLAs. Work along these lines was carried out in the SIMDAT project.22 Analytical models can be built to represent system behaviour using mathematical techniques such as queuing theory. Such models can be used to predict response time for instance. Data on expected customer and resource performance can be used to train models such as Bayesian belief networks and artificial neural networks. Stochastic models can then be built. The IRMOS project23 is using models such as these as well as finite state machines to predict resource requirements for SLAs. Finally, simulation modelling may be used to understand the effect of different customer workloads on a real or prototype system. Software can be used to simulate user behaviour (service requests etc), perhaps simulating high workloads not normally reached in day to day operation. In this way the behaviour of a system to workload can be accurately assessed.
Modelling. A conceptual model was developed by Xxxxxxxx et al. to assess possible methane and brine leakage rates from a decommissioned shale gas well into a shallow groundwater aquifer [35]. The simulation results show that hydrodynamic properties of the casing annulus are the key factors that determine the methane arrival time at the base of the aquifer. For the poorest cementation scenarios, the maximum flow rates are reached within one year after the plug- in.
Modelling. Select modelling techniques, build models, optimize the hyperparameters of the models. Sometimes, a combination of modelling techniques can be used as well. Here, both the machine learning algorithms and dimensionality reductions methods will be discussed.
Modelling. 3.4.1 Classification Algorithms The predictive modelling process aims at finding generalizable patterns in the data. To get the most value from machine learning, one needs to know how to pair the best algorithms with the right data (SAS Analytics Insights, 2020). For this master’s dissertation, the decision was made to go for a supervised approach, in particular using multi-class classification. A summary of some possible modelling methods are proposed to be certain that the right algorithm is used. One is inherently multiclass (Random Forest), while the others are inherently binary classifiers (Support Vector Machine and Extreme Gradient Boosting). To make these classifiers multiclass, either the one-vs-rest or one-vs-all will be used (cfr. infra). Each machine learning method that was tested and their hyperparameters are discussed below. A model hyperparameter is a characteristic of a model that is external to the model itself, which means that its value cannot be estimated from the data. The value has to be set before the prediction process can begin. By tuning them, the optimal hyperparameters will result in the most ‘accurate’ predictions (Xxxxxx, 2018).
Modelling. The analysis is carried out daily with data provided by the Ministry of Health and AWS network from AEMET with an assessment of the information in near-real time. ANALYSIS, DIAGNOSIS, PROGNOSIS EARLY WARNING SYSTEM An epidemiological early warning system will be developed at the state level based on the influence of the environmental factors analysed COOPERATION: This line of research remains open at the national and international level with the consideration of other meteorological variables with the support of COPERNICUS (mainly CAMS and C3S), analysing the effect of air pollution as well as the inclusion of other biological and social factors.
Modelling. The base topology has been designed so it can be subdivided to increase the resolution of the mesh. The base mesh is designed with such restrictions in place on polygon count that when subdivided it hits resolutions that fit nicely just under the memory limitations of the various use-cases. This design allows us to use a low subdivision level for background agents, a medium-high subdivision levels for the Unreal agent while retaining the highest subdivision levels for non-realtime use-cases. However, in all cases compatibility is retained.
Modelling. 4.1.1 The Participants are committed to working in partnership to predict the likely impact of COVID-19 and to enable evidence-based decisions on how best to respond across the island of Ireland. This may involve using published evidence and data from outbreaks elsewhere and international work in modelling infectious disease. This will be adapted to and informed by the relevant demographics, healthcare structures and health policies of both jurisdictions.
Modelling a) Kinetrics shall generate all substation ground grid models using the Safe Engineering Service and Technologies (SES) CDEGS program grounding system design software, and include buildings, perimeter fences (both bonded to and isolated from the grounding system), transmission/distribution grounding systems, buried ground grids, ground rods, lightning counterpoise loops, ground xxxxx or buried conductive pipes, ground-bonded concrete footings or structures.
