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. 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. 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).
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.
Figure 1. Screenshot of the current work-in-progress real-time Xxxxxx asset in Unreal Engine
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. The Contractor shall construct multilevel models to answer the research questions and to identify and minimise any bias introduced through the non-random selection of trial schools. The Contractor shall agree with the Department the outcome measures to be used in the final analyses. The anticipated measures to be used in the modelling are: · Exclusions. Logistic modelling using Sample F shall include schools’ average past exclusion rate to address possible pre-existing differences in exclusions policies. Analysis shall explore both fixed-term and permanent exclusions. · KS4 attainment. Models shall use Sample F (NPD and survey data) and include controls for pupils’ background characteristics and prior attainment. The choice of outcome measures may include: · Proportion of pupils achieving Level 1 and Level 2 at KS4; · KS4 total points score; · Numbers of pupils exceeding given points score thresholds; · KS2-KS4 progression (either continuous or threshold measures). · Post-16 destination. Logistic/multinomial categorical models based on Sample F. This analysis shall consider a number of classifications of post-16 destinations, for example whether or not a young person is Not in Employment, Education and Training (NEET), and more detailed categorisation such as school, further education, work based learning, etc. · Attendance. The Contractor’s analysis of pupil attendance shall be based solely on the ‘at risk’ group from Sample F who are still in mainstream education (i.e. those who have not been excluded). The selection for this group is likely to be endogenous to the policy change so the Contractor shall explore corrections to allow for this. The analysis shall consider both authorised and unauthorised absence rates. · Pupil behaviour and other PPF data. This analysis shall be based on data provided by schools, and shall only include pupils from Sample A who remain in mainstream education.
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.
b) To account for coupling between parallel and adjacent conductors, Kinetrics must use the appropriate CDEGS engineering module. For example, in CDEGS, the HIFREQ module is the appropriate choice.
c) Kinectrics shall consider the effect of surrounding metallic infrastructure, which must be modelled as accurately as possible in a module capable of calculating the inductive coupling.
d) Kinetrics shall use the existing wet stone resistivity (from stone testing performed on site) in its design of the ground grid of a substation, and assume 2-3 inches of insulating gravel (3,000 Ohm meter), putting emphasis on the performance of the ground grid and insulation gravel.
e) Kinetrics shall assign a soil model to the model after interpreting the soil resistivity data taken from each site.
f) Kinetrics shall record the GPS coordinate of any test probe location during onsite tests, and import the entire current injection arrangement into the model.
g) Kinetrics shall validate the onsite test results against the model and make corrections based on proximity between the probes.
h) After obtaining acceptable agreement between the model and the test results, Kinetrics will perform a detailed fault current analysis to identify the worst fault conditions in terms of remote zero sequence contribution. Kinetrics shall use this fault to assess the per unit step and touch potentials measurement on site and estimated values in the CDEGS model.
i) Kinetrics will then compare the scaled step and touch potentials to the allowable limits which Kinetrics will calculate based on the fault clearing time (provided by City of Palo Alto) and the stone resistivity (onsite test results or new gravel).
j) Kinetrics shall identify any deficiencies during the onsite testing and/or design phase and prepare a proposed mitigation/recommendation. Kinetrics shall update and revise the ground grid drawings and include them in the Design Report.
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. 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
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.
