Engineering topics

Guillen, Zoe C.

Publications and source records attributed to Guillen, Zoe C..

5 records

DOE OSTI2015

A Collaborative Extensible User Environment for Simulation and Knowledge Management

In scientific simulation, scientists use measured data to create numerical models, execute simulations and analyze results from advanced simulators executing on high performance computing platforms. This process usually requires a team of scientists collaborating on data collection, model creation and analysis, and on authorship of publications and data. This paper shows that scientific teams can benefit from a user environment called Akuna that permits subsurface scientists in disparate locations to collaborate on numerical modeling and analysis projects. The Akuna user environment is built on the Velo framework that provides both a rich client environment for conducting and analyzing simulations and a Web environment for data sharing and annotation. Akuna is an extensible toolset that integrates with Velo, and is designed to support any type of simulator. This is achieved through data-driven user interface generation, use of a customizable knowledge management platform, and an extensible framework for simulation execution, monitoring and analysis. This paper describes how the customized Velo content management system and the Akuna toolset are used to integrate and enhance an effective collaborative research and application environment. The extensible architecture of Akuna is also described and demonstrates its usage for creation and execution of a 3D subsurface simulation.

collaborative science tools↗

DOE OSTI2015

Towards adaptive, streaming analysis of x-ray tomography data

Temporal and spatial resolution of chemical imaging methodologies such as x-ray tomography are rapidly increasing, leading to more complex experimental procedures and fast growing data volumes. Automated analysis pipelines and big data analytics are becoming essential to effectively evaluate the results of such experiments. Offering those data techniques in an adaptive, streaming environment can further substantially improve the scientific discovery process, by enabling experimental control and steering based on the evaluation of emerging phenomena as they are observed by the experiment. Pacific Northwest National Laboratory (PNNL)’ Chemical Imaging Initiative (CII - http://imaging.pnnl.gov/ ) has worked since 2011 towards developing a framework that allows users to rapidly compose and customize high throughput experimental analysis pipelines for multiple instrument types. The framework, named ‘Rapid Experimental Analysis’ (REXAN) Framework [1], is based on the idea of reusable component libraries and utilizes the PNNL developed collaborative data management and analysis environment ‘Velo’, to provide a user friendly analysis and data management environment for experimental facilities. This article will, discuss the capabilities established for X-Ray tomography, discuss lessons learned, and provide an overview of our more recent work in the Analysis in Motion Initiative (AIM - http://aim.pnnl.gov/ ) at PNNL to provide REXAN capabilities in a streaming environment.

analysis framework↗

DOE OSTI2014

Quality Assurance Procedures for ModCat Database Code Files

The Quality Assurance procedures used for the initial phase of the Model Catalog Project were developed to attain two objectives, referred to as “basic functionality” and “visualization.” To ensure the Monte Carlo N-Particle model input files posted into the ModCat database meet those goals, all models considered as candidates for the database are tested, revised, and re-tested.

MCNP↗

DOE OSTI2014

DNDO Analysis Cell Concept

The Domestic Nuclear Detection Office (DNDO) has a mission of implementing rad/nuc interdiction capabilities for a managed and coordinated response to threats, integration of federal nuclear forensics programs, and coordinating the development of the global nuclear detection and reporting architecture. In the process of executing this mission, DNDO has generated substantial information, data, technical results, operational workflows and analytical tools. The effective utilization of these resources is an overarching goal of the organization. After nearly a decade of performing work, DNDO faces a challenge in capitalizing on the large amount of data, reports, processes, tools, and people. As new work is being planned, managers and researchers need to have an understating of what information has been collected, what tools are available, the collaborations which can be utilized to propel the work forward, processes to plan and execute, and how to present conclusions and results that can assist the government in making decisions. This type of challenge can be met through the use of a series of organized and connected elements which form a broader structure (cell) that promotes cross utilization of elements such that they can be tailored (analyzed) to fit the context of the problem to be solved. The development of an analysis cell for DNDO will address the challenges of utilizing existing elements, identifying gaps, annually reporting the performance of rad/nuc interdiction instrumentation, and planning the execution of future work.

Pagh, Richard T.↗

DOE OSTI2014

Nationwide Buildings Energy Research enabled through an integrated Data Intensive Scientific Workflow and Advanced Analysis Environment

Modern workflow systems enable scientists to run ensemble simulations at unprecedented scales and levels of complexity, allowing them to study system sizes previously impossible to achieve, due to the inherent resource requirements needed for the modeling work. However as a result of these new capabilities the science teams suddenly also face unprecedented data volumes that they are unable to analyze with their existing tools and methodologies in a timely fashion. In this paper we will describe the ongoing development work to create an integrated data intensive scientific workflow and analysis environment that offers researchers the ability to easily create and execute complex simulation studies and provides them with different scalable methods to analyze the resulting data volumes. The integration of simulation and analysis environments is hereby not only a question of ease of use, but supports fundamental functions in the correlated analysis of simulation input, execution details and derived results for multi-variant, complex studies. To this end the team extended and integrated the existing capabilities of the Velo data management and analysis infrastructure, the MeDICi data intensive workflow system and RHIPE the R for Hadoop version of the well-known statistics package, as well as developing a new visual analytics interface for the result exploitation by multi-domain users. The capabilities of the new environment are demonstrated on a use case that focusses on the Pacific Northwest National Laboratory (PNNL) building energy team, showing how they were able to take their previously local scale simulations to a nationwide level by utilizing data intensive computing techniques not only for their modeling work, but also for the subsequent analysis of their modeling results. As part of the PNNL research initiative PRIMA (Platform for Regional Integrated Modeling and Analysis) the team performed an initial 3 year study of building energy demands for the US Eastern Interconnect domain, which they are now planning to extend to predict the demand for the complete century. The initial study raised their data demands from a few GBs to 400GB for the 3year study and expected tens of TBs for the full century.

99 GENERAL AND MISCELLANEOUS↗