Use Case. The purpose of tokens in 'read' and 'bring online' primarily is to steer data streams or better to steer the location of files. The space reservation aspect of tokens is of minor interest. An example is that the same dataset may be needed by the reprocessing system as well as for FTS export or user analysis. It would be envisioned that this file is served to the various competing processes by different locations in the system mainly not to interfere or slowdown expensive reprocessing.
Use Case. In this section, we develop a realistic use case to illustrate the application of the energy aware SLA and the VNFD extension.
Use Case. The Contextualising Tool Xxxxx is an editor at Deutsche Welle’s (DW) Online News Desk. In general, she is reporting about energy-related topics and events. Today she has to report about “fracking”, also known as shale gas extraction. After the nuclear accident in Fukushima, the German Parliament decided to reverse its nuclear power facilities. Since then, energy transformation is an ever-growing topic on the political, economical and cultural media agenda. Fracking is a controversial topic around the world, especially in Germany. Therefore, Xxxxx has to consider all perspectives, opinions and involved entities since the debate began. First she has to get a general idea about all media items (articles, videos clips, audio clips) Deutsche Welle produced in the past. For this purpose, Xxxxx uses EUMSSI. EUMSSI indexes, analyses, links, enriches and contextualises all these items based on the latest research algorithms combining knowledge from three major research areas (audio, text and video analysis).
Use Case. Second Screen The following text is partially extracted from the DoW. Key statements derived from the guided interviews supplement our existing assumptions. There is an increasing popularity of video on-demand. On the one hand, users want to choose the programs they like. On the other hand, they also like some guidance to find out “what’s on, what’s new, what’s cool, what’s for me”. “The perfect second-screen service supplies me with entertaining and informative content.”
Use Case. MTQIP shall share MTQIP aggregated data set data with the Anesthesiology Performance Improvement and Reporting Exchange (ASPIRE) for the purpose of perioperative care quality improvement. ASPIRE may use MTQIP data for identification of patient outcomes. In exchange for sharing MTQIP data for the purpose(s) articulated ASPIRE will in turn share ASPIRE data with MTQIP to assist MTQIP to understand the relationship between anesthesia variables and outcome(s) to improve care and will be used in accordance with all uses enumerated in the Agreement. This Amendment, coupled with the underlying terms and conditions of the Agreement, contains and merges all of the terms and conditions between the parties with respect to the subject matter hereof without modifications. Participant: By: Print name: Title: Date of Signature: Address for Notice: Regents of the University of Michigan: By: Print name: _Jeanne Xxxxxxxxxx Title: Chief Compliance Officer Date of Signature: Address for Notice: University of Michigan NCRC MTQIP Building 16, room 139E 0000 Xxxxxxxx Xxxx Xxx Xxxxx, XX 00000-0000
Use Case. MTQIP shall share MTQIP aggregated data set data with MSQC for quality improvement. MSQC shall share MSQC aggregated data set data with MTQIP for quality improvement. The MSQC data set contains a comprehensive cadre of perioperative variables for patients who undergo an operative intervention. The MTQIP data set contains perioperative variables for patients who may or may not undergo an operative intervention. MSQC-MTQIP data aggregation allows for identification of potential care pathways for patients at high risk of mortality and/or complications in both operative and non-operative instances. This information can be used to aid clinicians in providing the safest care at the safest time. This Amendment, coupled with the underlying terms and conditions of the Agreement, contains and merges all of the terms and conditions between the parties with respect to the subject matter hereof without modifications.
Use Case. 3 – Heat transfer from three particles This use case has been selected to test the solver’s ability to model multiple particles that are separated by a comparably large distance (see Figure 3). Also, for this specific flow configuration, reference data from literature was available. As can be seen from Figure 4, the agreement with both sources of literature data agree very well with our predictions for the force and heat flux experienced by the individual particles. Figure 3: Streamlines predicted using the new HFD-IBM for flow around three spheres aligned with the main flow direction. Force (compared to Xxxxxxxxxx et al.) Heat Flux, d = 2.dp Figure 4: Relative deviations of the predicted force (left panel) and heat flux (right panel) when using the HFD-IBM solver from literature data.
Use Case. 4 – Heat transfer from two particles The three-particle test case discussed in Chapter 2.3 contains particles that are located at relatively large distance. However, for a typical situation (i.e., a randomly-oriented particle ensemble), particles may often be located very close to each other. Initial test runs showed that this situation is especially difficult to handle for Immersed Boundary Method-based solvers. Hence, we have included a use case that considers two particles (with their connecting axis perpendicular to the main flow) at a relatively small distance to each other (see Figure 5). Fortunately, this setup is simple enough to allow us to obtain a reference solution using a body-fitted mesh (the mesh is fine enough to fully resolve all details of the flow, data not shown). Our analysis of the force and heat flux data indicates, that the predictions of the new HFD-IBM solver is within ca. 6 % of the solution obtained with a body- fitted mesh. This suggests that our HFD-IBM solver is indeed able to correctly predict the flow and the temperature (or other scalar) fields in dense fluid-particle suspensions. HFD-IBM Body-Fitted Mesh Figure 5: Comparison the dimensionless temperature field (color contours), as well as the streamlines (black lines) for the new HFD-IBM solver (left panel), and a solution obtained with a body-fitted mesh (right panel, flow is from left to right). Heat Flux Drag Force Figure 6: Relative error of the heat flux (left panel), as well as the force (right panel) when using the HFD-IBM solver (the reference is the solution obtained with the body-fitted mesh).
Use Case. 5 – Heat transfer in a fixed periodic particle bed This case is the most relevant one, since it is closest to the final application of the DNS solver, i.e., the prediction of heat and mass transfer in a randomly-arranged particle bed (mono-sized particles). Unfortunately, for this situation, it was very challenging to generate a body-fitted mesh. Hence, the results are only compared to literature data (see Figure 7). As can be seen, the agreement with the results of Xxxx is excellent, whereas the more recent results of Deen et al. are somewhat below our predictions. Figure 7: Comparison of the predicted Nusselt number (red crosses, each data points represents the mean value obtained from multiple realizations for identical system parameters), as well as the newly developed correlation (red lines) with literature data (black and blue lines). Also, the newly developed correlation (see next Chapter for details) approximates the predicted Nusselt numbers reasonably well, indicating that the chosen functional form for the Nusselt numbers seems to be appropriate.
Use Case. This section demonstrates the application of the proposed ID-based key agreement protocols in the blockchain-powered smart home scenario.
