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Business Analytics Meets Post Processing

Thu, 08/11/2011 - 5:11am
Scott Imlay, PhD, CTO, Tecplot Inc., Bellevue, Wash.

Simulation analytics software combines the best of business analytics tools and post-processing tools for better designs based on CAE simulations.

Tecplot Image

The Tecplot 360 CFD visualization tool, which is integrated within Tecplot Chorus, can be used to explore the underlying surface and volume data associated with points within the design space.

Increasingly complex design challenges, coupled with the pressure to deliver more innovative products, are driving engineers to find better analytical processes that can help them quickly identify trends or anomalies and make better design decisions.

As part of this process, the rapidly expanding capabilities of computer systems are allowing the increased use of computer-aided engineering (CAE) simulation codes like computational fluid dynamics (CFD) and finite element analysis (FEA). For example, in the aerodynamic design phase of a jet wing, or the cooling system design of a turbine blade, engineers rely heavily on data from large collections of CFD runs. It's not uncommon for a single project to involve more than 1,000 cases, with each case generating more than 10 GB of data. That adds up to 10 TB of data, roughly equivalent to the amount of raw data found in the entire United States Library of Congress.

It is impossible for an engineer to analyze this amount of information in detail with today's tools. Unearthing anomalies in such large data fields is equivalent to finding a needle in a very large haystack.

Business analytics tools
Companies have historically used business analytics tools based on statistical techniques, data mining, and knowledge discovery to assist in the decision-making process. At first glance, it appears that these tools could be applied to large collections of simulation runs. Unfortunately, they are ineffective because simulation data is different. This data has two layers: low-level simulation field data—such as velocity, pressure, and temperature—that is defined at millions of points within the solution domain; and the higher-level metadata, such as lift, drag, and pitching moment. Business analytics tools lack the ability to analyze the low-level field data in a way that gives engineers an understanding of the underlying physical causes of trends in the metadata.

Post-processing tools
In comparison to business analytics, traditional post-processing tools offer engineers a detailed understanding of the physics for a particular case, by handling very large datasets and using the same grid structure as the simulation codes. The tools provide a full set of visualization and analysis capabilities that allow engineers to explore the variation of the field data throughout the domain, and integrate results over subdomains to generate metadata. However, post-processing tools cannot simultaneously analyze large collections of simulation metadata the way business analytics tools can.

Simulation analytics
Simulation analytics software, like Tecplot Chorus from Tecplot Inc., Bellevue, Wash., encompass the capabilities of both business analytics tools and traditional post-processors. Simulation analytics is the application of visualization, data management, statistics, and data mining to related collections of datasets generated by CAE codes. It involves the simultaneous analysis of the detailed field data and the associated metadata for the collection of datasets.

When anomalies are detected in the metadata the engineer can dive down into the associated field data to identify the root cause, which may be a numerical problem, such as an inaccurate or poorly converged solution or the result of an unexpected physical phenomenon, such as shock-induced boundary separation on a wing. The ability to identify the cause of such an anomaly early in the design process can provide more visibility into the overall problem, and can lead to better-designed, more efficient, and more capable products.

For industries striving for more R&D efficiency, simulation analytics holds the promise of significantly improving the quality and speed of engineering design decisions. As computing power continues to increase, the use of CAE analysis tools grow as well, leading to increasingly large collections of simulation results. Ultimately, simulation analytics allows engineers to tap into the wealth of information hidden within those collections.

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