Views from Intelligent Light
Earl Duque

AIAA AVIATION 2015 – Working with large data of today and tomorrow

Turbo image/animation from AIAA Aviation 2015

This turbine blade simulation result was awarded “Most Quantitatively Descriptive Flow Visualization Animation” in the Visualization Showcase at AIAA Aviation 2015. The achromatic colormap enhances the presentation of the numerical differences.
–click image for animation

I attended the AVIATION 2015 meeting in Dallas last month. I had a great time meeting with colleagues, listening in on great papers and presenting my own work. The week started with my presentation for the CFD Visualization Showcase session where I was awarded the “Most Quantitatively Descriptive Flow Visualization Animation” which highlighted the animations and images from my paper “EPIC – An Extract Plug-In Components Toolkit for In situ Data Extracts Architecture“. The paper was presented at the “Post-Processing and Model Reduction” session.


In both the animations and the paper, I made use of FieldView’s achromatic colormaps. I’ve found that the “Achromatic Vision 1″ colormap, easily selected from the new colormap selector in the colormap tab (no more hunting around for user defined colormaps!!!) does a much better job at highlighting flow features that I didn’t see using the default Spectrum colormaps. I use the Achromatic Vision 1 almost exclusively now for all my visualizations.


In addition, I took part in a panel discussion “The Path to CFD Visualization in 2030″ where we discussed our ideas regarding “Facing the Knowledge Extraction and Visualization Challenges of the NASA CFD 2030 Vision”.  During this panel, I described how CFD analysts require the ability to simultaneously compute both very large simulations and large numbers of simulations. Code verification/validation and uncertainty quantification studies also drive the need for unsteady solutions consisting of  billions of grid points and large ensembles of non-deterministic solutions. These types of studies are enabled by: In situ data processing where the solver directly outputs FieldView surface extracts,  FieldView XDB workflow and the use of XDBview.


In order to extract actionable knowledge and create visualizations of these extensive datasets, my Applied Research Group is developing new capabilities for CFDers through our DOE sponsored research with the VisIt code and the Air Force Research Lab EPISODE project (the paper I presented at AVIATION2015).  In the coming months, I will be working with the other panelists on a paper that we’ll present at SciTech2016.


XDBs files and XDBview were critical to this work.


Learn more about in-situ post-processing with XDB workflows:

Earl Duque

Unraveling the Mysteries of Turbulence Transport in a Wind Farm

A joint paper with Prof. Sven Schmitz was just issued in the ”Wind Turbine 2015″ special issue of the online journal Energies.


This paper entitled “Unraveling the Mysteries of Turbulence Transport in a Wind Farm” is co-authored with Pankaj K. Jha 1, Earl P. N. Duque 2, Jessica L. Bashioum 1 and Sven Schmitz 1,*


For this project, we used FieldView XDB workflows to enable the investigation of “mysteries involved in the recovery process of the wake momentum deficit, downstream of utility-scale wind turbines in the atmosphere.”  The “High-resolution surface data extracts provide new insight into the complex recovery process of the wake momentum deficit governed by turbulence transport phenomena. “

Earl Duque

PAR CFD 2015 Meeting – Reporting on the push toward extreme scale CFD

Movie Still

The image shows volume rendering of AVF-LESLIE results for a turbulent planar flame front.
Click to view animation


Last month at the PAR CFD 2015 meeting in Montreal, Canada, I presented a paper entitled “The Impact of In Situ Data Processing and Analytics upon Scaling of CFD Solvers and Workflows“.


This work is based on research under our Department of Energy Phase II grant (Award Number DE-SC0007548) and also work under a DOE grant, through the Office of Advanced Scientific Computing Research (Award number DE-SC0012449).


The latter grant is an effort led by Wes Bethel at Lawrence Berkeley National Laboratory in collaboration with Argonne National Laboratory, Georgia Tech and Kitware. In this work we used the AVF-LESLIE code from Prof. Suresh Menon’s lab at Georgia Tech, instrumenting it with VisIt/Libsim.


The goal is to determine the overhead associated with in situ processing in comparison to conventional file based volumetric post-processing at scale. For this paper, ‘at scale’ means on the order of 60,000 cores.


Next year we plan to be at 120,000 cores. To date, AVF-LESLIE has been instrumented with VisIt/Libsim and is now able to directly output FieldView XDB files using 40,000 cores and we’re working on new in situ data analysis pipelines that can only be performed in situ.


I enjoyed the meeting and learned a lot about the challenges as we all work toward exa-scale CFD simulations.


IL Announcement

HPCwire report

Earl Duque

ASME Verification and Validation Symposium

I had the pleasure of attending this year’s ASME Verification and Validation Symposium last month.  Verification, Validation and Uncertainty Quantification is an ongoing focus area for me, so in addition to the Symposium, I attended the 2 day course taught by Bill Oberkampf and Chris Roy, authors of the book, Verification and Validation for Scientific Computing. The course gave me a deeper understanding of the techniques and issues that we need to address to ensure that our simulations are accurate and reliable.  I look forward to bringing what I’ve learned to my work leading the Applied Research Group.

Earl Duque

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

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.


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

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: