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Come See Us at AIAA SciTech 2019

Join us at AIAA SciTech in conference sessions and in our booth to learn how we are advancing CFD with innovations in Uncertainity Quantificaton (UQ), Analytics for CFD and large CFD results handling. We have an eye on your largest problems today with HPC FieldView and FieldView 18 and are looking toward 2030 to ensure we will meet the needs of the CFD community tomorrow.

Visit booth #207 for a one-on-one demo of the new FieldView 18.

Request a meeting

Conference Sessions

Application of a CFD Uncertainty Quantification Framework for Industrial-Scale Aerodynamic Analysis

Wed, Jan 09, 5:00pm - 5:30pm, Torrey Hills

John Schaefer and Andrew Cary of The Boeing Company and Earl Duque and Seth Lawrence of Intelligent Light

In a joint effort between the Applied Research Group (Intelligent Light) and Boeing Research and Technology (The Boeing Company), the application of the latest developments in Uncertainty Quantification (UQ) are demonstrated as case studies for industrial scale CFD problems. Starting with a 2D NACA 0012 airfoil at zero lift, then moving to a 3D full-scale aircraft in high lift configuration, the latest UQ frameworks are implemented to determine numerical and model input uncertainty. This effort provides a step towards standardizing the use of UQ as a design and engineering tool to build confidence and reliability in scientific computational engineering and design.

Assessment of Model Validation and Calibration Approaches in the Presence of Uncertainty

Thu, Jan 10, 4:00pm - 4:30pm, Harbor H

Nolan Whiting and Christopher Roy of Virginia Polytechnic Institute and State University, and Earl Duque and Seth Lawrence of Intelligent Light

This paper discusses the investigation and implementation of various methods for quantifying Model Form Uncertainty​ (MFU) in scientific computing. Metrics were obtained through rigorous implementation of several MFU methods to produce comparison studies that quantify the conservativeness of each method. The findings include which methods perform best under different circumstances and provide a guide to best practices when choosing a method for quantifying the model form uncertainty in a computational UQ study.

Summary of 2017 SciTech Computational Environments Special Session Toward the CFD Vision 2030

Fri, Jan 11, 9:30am - 10:00am, Vista B

Andrew Lofthouse, CREATE AV Team, Earl Duque, Intelligent Light, Roger Davis, University of California‚ Davis

Computational Environments can be defined as the hardware and software required to efficiently use high performance computing resources and to easily extract knowledge from that data. As such, they underlie nearly all large-scale simulations. The CFD Vision 2030 report published recommendations for advancements in all areas of computational fluid dynamics that are needed in order to take advantage of Exascale hardware that is expected to be in operation by the year 2030. This paper summarized a special session held at SciTech 2017 sponsored by the Meshing, Visualization and Computational Environments technical committee that looked in more detail at the current status of computational environments and where additional research is required in order to efficiently use Exascale machines.

The session consisted of technical presentations and a question and answer period with a panel of invited experts. Computational environments are vital for efficient use of heterogeneous hardware architectures, large-scale data management, interfaces between multidisciplinary domains, uncertainty quantification and error analysis, as well as automation of complex simulations.

Spectre: A Computational Environment for Managing Total Uncertainty Quantification of CFD Studies

Fri, Jan 11, 10:00am - 10:30am, Vista B

Earl Duque and Seth Lawrence of Intelligent Light

The future of uncertainty quantification as a tool for computational engineering and design hinges on the management, execution and visualization of complex data-driven workflows. Whether the data is historic experiments or modern computational simulations, UQ workflows require onerous dedication to the management and synchronization of all available data. To address the needs of complex UQ workflows, the Applied Research Group at Intelligent Light has developed Spectre: A Computational Environment for Managing Total Uncertainty Quantification of CFD Studies. By encompassing a database, server and user-interface into a RESTful web deployed platform, Spectre provides seamless integration of data management, solver execution and visualization.

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HPC, In Situ and Data Analytics at SC18

Advances in the use of HPC, In Situ and Data Analytics for CFD dominate our contribution to SC18. Join us at events, in workshops and technical sessions and on the exhibit floor in Booth #826 to learn how these advances will help you.

Data Science Meets CFD - Steve Legensky, Invited speaker at In Situ Analysis and Visualization Workshop

VisIt Users and Developers Reception - Get an update on VisIt 3.0 and VisIt Prime

SENSEI Tutorial: Cross-Platform View of In Situ Analytics

Data from Juan D. Colmenares, Svetlana Poroseva, Yulia T. Peet, and Scott M. Murman. "Analysis of uncertainty sources in DNS of a turbulent mixing layer using Nek5000", 2018 Fluid Dynamics Conference, AIAA AVIATION Forum, (AIAA 2018-3226). Simulations were performed on the Pleiades Computer system at NASA Ames Research Center. Images created by Intelligent Light.

Invited talk: Data Science Meets CFD, Steve Legensky, Intelligent Light

Presentation to the ISAV 2018 Workshop: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization

Monday, November 12, 3:30-4:25PM, Room D168

Advances in computational fluid dynamics (CFD) and high-performance computing (HPC) have allowed an amazing increase in model fidelity. For CFD practitioners, these new capabilities present challenges with the analysis of highly unsteady flows of ever-increasing complexity. Simultaneous advances in data science offer promising techniques for resolving these issues that are just starting to be applied to CFD post-processing and knowledge extraction workflows.

ISAV 2018: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization

Session: Monday, November 12, 9:00-5:30PM Room D168

The considerable interest in the HPC community regarding in situ analysis and visualization is due to several factors. First is an I/O cost savings, where data is analyzed and visualized while being generated, without first storing to a file system. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU's and accelerators, in the computation of analysis products.

The workshop brings together researchers, developers and practitioners from industry, academia and government laboratories developing, applying and deploying in situ methods in extreme-scale, high-performance computing. 

Data from Juan D. Colmenares, Svetlana Poroseva, Yulia T. Peet, and Scott M. Murman. "Analysis of uncertainty sources in DNS of a turbulent mixing layer using Nek5000", 2018 Fluid Dynamics Conference, AIAA AVIATION Forum, (AIAA 2018-3226). Simulations were performed on the Pleiades Computer system at NASA Ames Research Center. Images created by Intelligent Light.

Tutorial: SENSEI Cross-Platform View of In Situ Analytics
Presenters: E. Wes Bethel, David Thompson, Burlen Loring, Silvio Rizzi, Brad Whitlock, Matthew Wolf, Patrick O'Leary

Sunday, November 11, 1:30-5:00PM Room C147

This tutorial covers the design and use of SENSEI, a platform for in situ visualization and analysis. Attendees will learn the basics of in situ analysis and visualization — which eliminates the need for writing large simulation state files or other data that prevents scaling to large machines — while being exposed to advanced analysis such as autocorrelation, interactive monitoring and computational steering.

Image courtesy of Michael Brazell and Prof. Dimitri Mavriplis, University of Wyoming

Spend time with our experts:

Come by Booth #826 or request a meeting, to learn and explore how we can help you advance your CFD.

In-booth presentation: Data Science Meets CFD, Steve Legensky

Tuesday, 4:00PM in Booth #826, followed by reception including beer and hors d'oeuvres

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Come See Us at Overset Grid Symposium 2018!

We are happy to be sponsors of the symposium again this year and look forward to seeing you there.

There are three opportunities to meet us and learn more about our work with FieldView, VVUQ and ​Data Analytics:

  1. Tutorial on Monday: FieldView: Parallel Calculation, Remote Visualization, Automation and Extract Based Workflows (See below for details)
  2. Presentation during symposium: Spectre: A Computational Environment for Managing Total Uncertainty Quantification of OVERFLOW based CFD studies  (See below for details)
  3. Visit our table in the exhibit area
We look forward to seeing you there!
Turbulent Wakes of the Lillgrund Wind Farm, simulated with W2A2KE3D, in situ processing with VisIt / Libsim image rendered in FieldView
Data Courtesy: M. Brazell, A. Kirby, and D. Mavriplis University of Wyoming   Image by: Intelligent Light

Earl Duque, PhD Manager of Applied Research, Intelligent Light

Spectre: A Computational Environment for Managing Total Uncertainty Quantification of OVERFLOW based CFD studies

Dr. Earl P.N. Duque, Manager of Applied Research Group, Intelligent Light

The uncertainty in CFD based simulation results may be quantified through rigorous verification, validation and uncertainty quantification (VVUQ) procedures. Procedures and frameworks such as the ASME V&V 20, and Oberkampf and Roy Uncertainty frame work require extensive simulations, post-processing workflows and management of the simulation results to quantify the contributions of numerical uncertainty, model input uncertainty and model form uncertainty to the total uncertainty for any given study or design. To date, the application of VVUQ procedures were ad hoc implementations. Spectre is a new computational environment designed to enable CFD practitioners to easily quantify the total uncertainty in their computational study and campaigns. This presentation will present how Spectre makes use of "Wizard-based" user interfaces to guide the user through all the steps needed to arrive at the total uncertainty using the OVERFLOW2 code. Work in progress case studies based upon a UQ study of a NACA0012 airfoil at zero lift, a multi-element airfoil and the AIAA High Lift Prediction workshop Common Research Model will be presented.

Stephen Makinen, PhD Customer Application Engineer, Intelligent Light

FieldView: Parallel Calculation, Remote Visualization, Automation and Extract Based Workflows

Dr. Stephen Makinen, Customer Application Engineer, Intelligent Light

Synopsis: Modern high-fidelity physics simulation methods generate massive amounts of data, so a challenging scenario exists for efficiently processing results to create new knowledge. The rate of new data generation has far surpassed data rates for disk read/write operations and transfer from remote computational facilities. FieldView post-processing enables scientists and engineers to efficiently create new knowledge with scalable methods that navigate around these limitations. The FieldView tutorial will cover the following topics.

  • Overview of FieldView including new and upcoming features
  • Parallel calculations for efficient post-processing
  • Remote visualizations to reduce data transfer
  • Extract based workflows to minimize disk read/write operations
  • Data analytics for solution decomposition and insight into the underlying state-variables
  • Automation with Scripting
  • Discussion
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Live Webinar - Easy Access to CFD Data for Analysis

CFD Data Analysis with FieldView 17

LIVE Webinar

Thursday, May 3
12 noon ET (1600 GMT)

If you rely on Excel, Matlab, Octave and NumPy in your daily work, FieldView 17 allows fast I/O of CFD data for further analysis through MAT-File and CSV.

Query your CFD results at super-high speed (200,000 points/sec and more) from a list of points provided in *.mat, *.csv or plain text.

FieldView surfaces can also be exported to MAT-File and CSV to perform Reduced Order Analysis (DMD, POD, etc.), Fourier transforms and many more analyses.

Because performance matters, FieldView 17 also provides:

  • New built in Q-criterion function - easy to use and 10 X faster than previous FieldView
  • 10 X faster reading of arbitrary polyhedral cells
  • 2 X faster Surface Flow calculation
  • 40% faster reading of XDB extracts
Please join us for this live web event. If you would like to watch a recording, register now, and we will send you a note when the recording is available.

The 40 minute presentation will be followed by a live Q&A.

Yves-Marie Lefebvre, FieldView Product Chief
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Making Collaborative CFD Fly

"This method of aggregating data and standardizing the interrogation of the data, especially large scale, full 3D multiphyisics data is very, very necessary and very, very useful. This partnership is exciting and we are exploring the boundaries of what we can achieve with this."

Nathan Hariharan
Chair, AIAA Helicopter Hover Prediction Workshop

The world's helicopter community came together to study the complex physics of rotorcraft in hover using CFD. They needed a HPC collaboration hub that minimized researchers' efforts to collaborate in full 3D. We made it work for the AIAA Hover Prediction Workshop and we can make it work for you.

Join Dr. Nathan Hariharan, Chair, AIAA Helicopter Hover Prediction Workshop, Dr. Earl Duque, Manager of Applied Research at Intelligent Light, and Michael Senizaiz, ‎Chief Technology Officer at R Systems, as they explore how work done to support the Hover Prediction Workshop is yielding valuable lessons for any organization looking to streamline its CFD workflow.

Learn how Intelligent Light and R Systems provide the CFD expertise and on-demand HPC capability to make high-performance CFD workflow a reality:

  • Enable data sharing among many disparate members of the organization: CFD Engineers, Designers, Managers and Customers
  • Compare many different cases, even those with different meshes or from different solvers, etc.
  • Derive higher order features, like vortex path, automatically for comparison
  • Standardize the workflow so that input from many groups can be compared side by side, both visually and numerically
  • Utilize on-demand HPC resources to run automated post-processing workflows across all the datasets producing standardized outputs and enabling collaboration

A live Q & A with the speakers will follow the presentation.

When: Tuesday, May 9, 2017, 2:00 PM Eastern

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