Intelligent Light and FieldView

ScienceNode: Wind Farm CFD by University of Wyoming and Intelligent Light

In Situ processing enables full wind farm simulation by reducing file size from 234TB to under 1TB

Excerpted from an article originally published on ScienceNode. Read the complete article

Researchers at the University of Wyoming (UW) and Intelligent Light are working hard to improve the efficiency of wind energy. One aspect they're currently investigating is the use of computational fluid dynamics (CFD) to simulate, predict, and improve the performance of wind farms.

CFD uses supercomputers to model the behavior of fluids such as air or water over moving surfaces. In this instance, UW professor of mechanical engineering Dimitri Mavriplis and Earl Duque at Intelligent Light turned to CFD to study how wind currents transform after coming into contact with a moving turbine's blades in the context of a complete wind farm.

Using the Cheyenne supercomputer at the National Center for Atmospheric Research (NCAR)-Wyoming Supercomputer Alliance, Duque and UW researchers Mavriplis, Michael Brazell and Andrew Kirby were able to model wind farms in unique ways. 

What really makes their research unique is the use of in-situ processing. Unlike traditional visualizations produced from stored data, in-situ processing means that visualizations and analysis can be created as a simulation takes place, without writing to disk.

In-situ processing eliminates file transfer bottlenecks, allows increased fidelity, and faster turnaround. 

"In-situ has helped reduce the amount of 3D data that we need to store. Just the restart files take 10 TB on a 12-hour run. Automation has gone way up; we have scripts that go straight from simulation to animation. Also, in-situ has helped with debugging—I don't need to pull down 3D data, and I can just use slices, save those to a FieldView XDB file, and then view that on my local machine."

Michael Brazell, University of Wyoming

Excerpts trom an article was originally published on Read the original article.

CFD images courtesy of Prof. Dimitry Mavripilis and Andrew Kirby, University of Wyoming, an Intelligent Light University Partner.
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Wind Leaders Addressing Future Data Needs - Atmosphere to Electrons Initiative

I had the honor and pleasure to participate in the Atmosphere to Electrons Workshop hosted by the Department of Energy, Office of Energy Efficiency and Renewable Energy.  The focus of the initiative is on the use of computational simulation to improve understanding and performance predictions from the microscale to the mesoscale.


FieldView image published in paper: "Turbulence Transport Phenomena in the Wakes of Wind Turbines", Earl Duque, Intelligent Light; Pankaj Jha and Jessica Bashioum and Sven Schmitz, The Pennsylvania State University

The event brought together leaders from the wind energy community including National Labs, Universities and Industry. The purpose was to map out the direction for simulating the performance of a wind turbine farm; capturing the temporal and spatial scales from meso-scale (kilometer and hours) down to the airfoil boundary layer scales (micron and milliseconds). Morning and afternoon sessions began with a topical plenary talk followed by working groups focused on the computation and modeling needs at different scales such as Park Scale, Turbine Scale and Airfoil Scale.

 Wind Farm - FieldView image as published in "Wind Farm Simulations Using a Full Rotor Model for Wind Turbines", J. Sitaraman, D. Mavriplis, E. Duque
AIAA Paper 2014-1086

For me, it was clear that it will be essential to include in-situ data analysis methods and file I/O standards in order to work with the tremendous volumes of data that will be created and processed. This was recognized by many at the meeting.  The use of in-situ methods with FieldView and VisIt offers solutions to those grappling with the current data analysis bottlenecks. 

With the high-caliber people from government, academia, and industry converging on this challenging problem, the A2E initiative is making progress toward vast improvements in the understanding of the complex physics of wind flowing into and through wind farms.  DOE sees the potential to improve wind farm efficiency by 20% while drastically reducing operating costs for wind energy producers.

Related Research Papers:

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EPISODE: A new user interface to automatically Extract, Reduce and Sort Large CFD Parametric Studies

Intelligent Light R&D continues to produce new tools for large scale CFDParametric Studies. EPISODE is a new large scale data management tool that enables an engineer to readily extract knowledge and insight from their large scale physics based simulations and experimental data. EPISODE provides tools that enable the user to create a relevant subset of their solution results via in-situ data extraction at regions of interest, further reduce the size of that data via proper orthogonal decomposition (POD), and then sort the parametric space of both the input and output solver parameters using self organizing maps (SOMs).

This project consists of new data extracts and compression methods based upon POD (proper orthogonal decomposition) and image compression methods such as JPEG. In addition, we're developing a new UI based upon self organizing maps  which will automatically sort a large number of simulations based upon the parametric inputs and outputs. The result is a set of colored maps that helps to determine the trends in the data.  The user will be able to click on different areas of the map which will then display the results in FieldView. The results may be directly from the CFD solver, reconstructed from the POD or it may be computed from a reduced order model (ROM) that was derived from the CFD and POD results.

The EPISODE project will address limitations of current data analysis tools by:

  • Performing data management and post-processing in-situ, without writing to storage and without direct engineer interaction.

  • Maintain ability to support high-frequency information for maximum temporal fidelity.

  • Handle both experimental and computational data together, supporting automated batch processing.

  • Deliver post-processing capabilities to rapidly collect engineering information such as mass flows through a passage, FFTs, etc.

Key project collaborators include:

  • Robert Haimes from MIT - Haimes' expertise will support the development of a scalable data extracts architecture and plug-in components.

  • Prof. Steven Gorrell from BYU - Professor Gorrell contributes CFD domain expertise in applications for gas turbines.

This work is sponsored by the Air Force Research Laboratory (AFRL) through a Phase II SBIR, Contract FA8650-14-C-2439, and TPOC Michael List.

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