B17
SDM 8

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13:05
conference time (CEST, Berlin)
The Basics of Successful Simulation Data Management. Part 1: Planning and Recording Simulation Data
28/10/2021 13:05 conference time (CEST, Berlin)
Room: B
S. Howell, P. Middha (Abercus Limited, GBR)
S. Howell, P. Middha (Abercus Limited, GBR)
As the term implies, simulation data management (SDM) is essentially the managed recording of simulation data, specifically the metadata that describes a simulation so that its purpose can be immediately understood without having to load it up in its associated simulation software. Ignoring for now the business aspects of a simulations study (for example, who is the client and its representative), since this is not specifically simulation data, there are two aspects to SDM: 1) planning what to do before the start of the analysis, and 2) recording what was done during the course of the analysis. It is important to clearly set out what the aim of the analysis is at the start through appropriate planning and then subsequently to check whether the analysis has addressed the initial aim. For routine analyses, the intended approach and actual approach may be very similar or indeed identical, and this may lend itself to a template driven study that, once developed, can be used by less experienced users – so called democratization. For novel analyses, however, the planned analysis will often differ from the actual analysis as limitations in the simulation software become apparent, or the solver parameters need to be adjusted in order to achieve a solution. A successful SDM system should be capable of easily enabling the planning and recording of simulation data for both routine and novel analyses, and for the spectrum in between – for example, capturing modifications to previously routine workflows such that processes can be improved for future use. In its simplest form, an SDM system can (and often does) store metadata in the form of a spreadsheet. This is at least a start and simulation metadata is recorded. A more powerful approach, however, is to use a database system since this allows the simulation data to be stored more robustly and searched more easily, and it enables further opportunities such as the automated creation and execution of simulation activities which can significantly reduce effort and improve quality. With traditional database thinking, however, the difficulty is: how to design a data structure that is sufficiently flexible to allow different metadata to be stored depending upon the type of study, and even evolve during the course of a novel study where the precise metadata to store may not be clear even to the most experienced user a priori. This presentation discusses precisely how to set up the flexible database structure required for successful SDM, and how useful information can be retrieved in an easily understood format. Specifically, the database structure is based upon the principal of inheritance and captures only the changes in metadata from one simulation to another. Everything presented is programmed within Microsoft Office, and is therefore easily accessible to many simulation users.
13:25
conference time (CEST, Berlin)
How to Get Started with Simulation Data Management - a Value-focussed Approach
28/10/2021 13:25 conference time (CEST, Berlin)
Room: B
M. Norris (the SDMConsultancy, GBR)
M. Norris (the SDMConsultancy, GBR)
This presentation introduces the publication "How to get started with SDM" which was developed for the SDM Working Group to assist members who need to initiate a project to manage their simulation data in an information system. It describes the key features of an SDM solution and summarises twenty years of production experience of SDM in industry. Typically SDM has been deployed for critical processes such as automotive crash simulation or for new simulation processes such as Virtual Testing of critical systems. This ensures that critical simulation processes are secured and has been found to yield productivity and engineering throughput improvements. While SDM solutions have proved highly effective at leading companies, the overall adoption of information systems to manage simulation data by simulation engineers is low. Two reasons for low uptake are the cost and time to implement SDM and negative experiences of software offerings that lack basic SDM capabilities. Best practice to deploy SDM has been to implement one end-to-end process at a time, providing end-to-end process traceability. There are two principal disadvantages with this sequential approach: -Firstly, simulation engineers working in less critical domains have to wait potentially years to gain benefit from SDM. -Secondly, digital traceability of important simulation data-sets is incomplete until all domains have been implemented in SDM. This is unsatisfactory to support regulatory compliance in safety critical industries. it proposes a different, value-focussed approach to Simulation Data Management: to first deploy a System of Record (SoR) for simulation data on an SDM platform for all simulation domains. This delivers digital traceability of important simulation data-sets and provides value to all simulation engineers and to the enterprise immediately. Further benefits of this approach are that the repository of simulation information is valuable input to the deployment team and will accelerate the roll-out of full Simulation Data Management to all domains.
Simulation Data Management, System of Record, SDM, Simulation Process and Data Management, SPDM, Simulation Management, Simulation Lifecycle Management, SLM
13:45
conference time (CEST, Berlin)
The Basics of Successful Simulation Data Management, Part 2: Practical Automation of Simulation Activities
28/10/2021 13:45 conference time (CEST, Berlin)
Room: B
S. Howell, P. Middha (Abercus Limited, GBR)
S. Howell, P. Middha (Abercus Limited, GBR)
One of the major opportunities for a simulation data management (SDM) system is that the activities for routine simulation applications can be automated, which can significantly reduce effort and improve quality. Indeed, this can lend itself to a template driven workflow approach that, once developed, can enable simulation analysis to be accessible and used by less experienced users – so called democratization. For successful automation, the SDM data structure must be flexible enough to evolve with the requirements of the analysis, and the metadata must be easily retrieved in a useful form. This presentation demonstrates a simple SDM tool in the context of an automated CFD dispersion study. Specifically, it covers the following aspects: 1) Democratization through a template driven workflow approach, as part of an integrated SDM system. Democratization does not mean a free for all. With a template driven approach a workflow can be robustly designed and rigorously tested by expert users, such that less experienced users can subsequently undertake analysis using the template. This has the benefit that the work is unsupervised at the point of use, but since all simulation data is recorded within the SDM system, every simulation can be easily checked and scrutinized to the same degree as if the experienced user had undertaken the simulation themselves. 2) Deviations from the workflow. Regardless of how well a template may have been designed, it is always possible (or even probable) that in some instances there will need to be some deviation from the intended workflow. By having a flexible data structure that can capture such deviations, intermediate users are empowered to modify the workflow and, since this deviation is captured in the SDM system, there is a trigger for this to be brought to the attention of the experienced user/owner of the workflow for discussion and future improvement of the workflow. 3) Automated reporting. Manual reporting of simulation predictions is often a laborious, repetitive task involving the copying and pasting of simulation plots into a report to be delivered to the end client. Such repetitive tasks are well executed with an automated reporting tool as part of an SDM system. The SDM tool is programmed within Microsoft Office, and is therefore easily accessible to many simulation users.
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