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In this project we considered a number of variants of parallel computations of data analysis and visualization techniques for large-scale simulations and their ensembles with the goal of achieving in situ analytics.
Presentation outline: Introduction; Data Science at Scale; Feature Analysis; Tools and Frameworks; Comparative Data Reduction; Comparative Visualization; and, Conclusion.
Extreme-scale systems for compute- and data-centric science are pushing the boundaries of system architectures in order to achieve ambitious goals in terms of computing capability, resilience, and power efficiency. Future systems necessitate emerging designs that incorporate new technologies, system organizations, and computer science techniques, and these changes are not yet well understood. Discrete-event-driven simulation of computing system architectures and subsystems has emerged as a productive and cost-effective means to evaluating potential designs, along with capabilities for executing these simulations of extreme-scale systems. However, techniques for analyzing the behavior of these simulations have not kept pace with our ability to perform the simulations, preventing us from extracting the most value from this promising approach. The objective of this project is to support the codesign of extreme-scale system architectures for compute and data-centric science through research and development of novel methods for analysis and visualization of large-scale event-driven simulations.
Within the Transactive Systems Program (TSP) at Pacific Northwest National Laboratory (PNNL) the need to incorporate a valuation analysis design early within the research process of transactive energy systems led to development of a valuation methodology. This methodology allows the modeling of economic exchanges within a complex system and supports the evaluation of individual stakeholder economic outcomes in addition to systemwide costs and benefits. The use of visual modeling practices enables the research team to reach common understanding and agreement on the analysis design within the complex system. While this methodology was developed for the valuation of transactive energy systems, it can be applied to any complex system where a granular economic analysis is desired. It allows for the inclusion of equity analyses and ties individual activities and microeconomic outcomes with the systemwide macroeconomic impacts. This document serves as implementation guidance for analysts planning to deploy the methodology within a research study. The appendixes provide specific guidance on how this methodology is deployed within the TSP at PNNL for analysts seeking guidance for deployment within that context.
Mechanochromic photonic materials present promising prospects for enhancing diverse facets of human life. Cholesteric liquid crystal elastomers (CLCEs) present immediate mechanochromic properties, allowing their utility in various fields, including camouflages, textiles, and anticounterfeiting. Nonetheless, it is essential to provide differentiated color information corresponding to diverse types and degrees of deformation for practical use in real life. Herein, we investigate the mechanochromic responses of CLCEs using their optical rotation under compression and uniaxial stretching. The CLCEs exhibit the same reflection colors indistinguishable for compression and uniaxial stretching due to their contracted pitches. In contrast, the linearly polarized light passing through deformed CLCEs can be modulated by optical rotation, resulting in distinct transmission colors that can be discerned through mechanical deformations. We demonstrate a visual signaling system with color information derived from optical rotation. By comprehending and manipulating the mechanoresponsive behavior of CLCEs, we can advance these materials into innovative solutions with enhanced functionality and performance, thereby broadening their applications for human life enhancement.
This project aims to drastically enhance the usability of in situ analysis and visualization for extreme-scale scientific simulations. Current exascale computing capabilities promise to offer greater predictive ability of simulations and to further push the frontiers of science and technology. However, to validate the simulation output at extreme scale, examine the modeled phenomena, and discover previously unknowns from the output data, the output must be reduced or transformed in situ as it is being generated during the simulation such that the amount of data to examine and store is kept to a minimum. Such in situ approaches allow us to process and analyze the data and any embedded geometry to an extent that would be prohibitively expensive, if not impossible, to perform as a post hoc task. While in situ processing has been demonstrated to be a feasible and promising approach, its full potential has not yet been leveraged. In this project, we have developed comprehensive enhancements to in situ technology based on probability distributions in data. Our research focuses on jointly developing new ways of interacting with massive statistical samples while creatively utilizing new state-of-the-art computational resources to push the boundaries of in situ exploration. Moreover, we have developed new time-dependent techniques to enable previously unattainable capabilities in areas such as intelligent simulation steering and precise feature identification. We have experimentally studied our design and implementation at NERSC and OLCF, and are able to leverage existing in situ infrastructures whenever possible. While the exemplar in this project is combustion, many other fields for which turbulent transport is important, e.g., fusion, climate, astrophysics among others, encounter similar issues as simulations scale up to the exascale. This project shows its potential to generate high impact on DOE missions since the resulting technology promises to improve scientists’ ability to rapidly and correctly interpret and tune extreme-scale simulations, leading to new scientific understanding and advancements.
With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.
Scientific computing is dominated by team-authored legacy code that has evolved over decades with the purpose of capturing the evolving understanding of a scientific discipline. Accumulated deprecated code, various optimization techniques, and evolving algorithms lead to convoluted source code that is impractical to reverse engineer using mainstream methods. This prevents codes from being truly repeatable or understandable, which are two of the most essential needs in scientific computing. We refactored a long-standing implementation of a common biosequence alignment algorithm in an effort to reproduce its salient behaviors in usable form. Because of the sheer size and complexity of this code base, we developed custom tools to visualize and manipulate the source code behavior under a variety of conditions. We present a case study of extracting and refactoring the algorithmic core and a novel process of discovery/prototyping/testing using a combination of openly available and custom-built tools. The result is a reduction in code size of over 2 orders of magnitude while reconstructing the key protein alignment function in BLAST
The Super Identity Model is a collaboration with six United Kingdom universities to develop use cases used to piece together a person's identity across biological, cyber, psychological, and biographical domains. PNNL visualized the model in a web-based application called IdentityMap. This is the first step in a promising new field of research. Interested future collaborators are welcome to find out more by emailing superid@pnnl.gov.
This brochure describes a system dynamics simulation (SD) framework that supports an end-to-end analysis workflow that is optimized for deployment on ESIF facilities(Peregrine and the Insight Center). It includes (I) parallel and distributed simulation of SD models, (ii) real-time 3D visualization of running simulations, and (iii) comprehensive database-oriented persistence of simulation metadata, inputs, and outputs.
This presentation describes a system dynamics simulation (SD) framework that supports an end-to-end analysis workflow that is optimized for deployment on ESIF facilities(Peregrine and the Insight Center). It includes (I) parallel and distributed simulation of SD models, (ii) real-time 3D visualization of running simulations, and (iii) comprehensive database-oriented persistence of simulation metadata, inputs, and outputs.
Strategic trade concerns global trade flows of items with strategic military value. Items include goods with exclusive military use, but also dual-use items that can have both military and civil uses. Many countries require licenses for trade in strategic items as a measure of non-proliferation. For example, items listed for trade control by multilateral export control arrangements, such as the Nuclear Suppliers Group (NSG), typically are considered strategic goods. Analysts and officials involved in strategic trade control often lack quantitative global understanding of relevant trade patterns that would help them focus control and enforcement efforts, select companies for outreach and audit, target transactions for verification and inspection, and assess the potential economic impact of trade control policies. Many are unaware of available open source data on international trade that can inform these issues. Many do not know how to make use of such data or lack the tools necessary to do so. A book of charts dubbed a ‘Strategic Trade Atlas’ was developed to illustrate global trade flows of strategic goods and raise awareness of the availability and utility of open source trade data. The Atlas was prepared for the World Customs Organization (WCO) and its Member States to support their planning and execution of Strategic Trade Control Enforcement (STCE) operations. It offers macroscopic graphical representations of global trade flows classified under Harmonized System (HS) codes associated by the WCO with commodities described in its STCE Implementation Guide. While these HS codes are understood to also encompass many non-strategic items, they nevertheless provide a good overview of potentially strategic trade flows. These include HS codes covering nuclear and nuclear-related commodities listed by the NSG, such as uranium, heavy water, graphite, carbon fiber, nickel powder, beryllium, aluminum tubes, vacuum pumps, valves, machine tools, cameras, and many others. The Atlas uses trade data from the Base pour l’Analyse du Commerce International (BACI), a statistical elaboration of public UN Comtrade data. BACI data reconciles trade flows, taking into account asymmetries in import and export trade valuation. The data visualization approach facilitates rapid sense making, pattern recognition, and anomaly detection. Country-based profiles summarize vast amounts of data into readily understandable graphical representations of each country’s primary strategic imports and exports and their main trading partners. Commodity-based profiles provide a global view of major exporters, importers, and trade patterns for each commodity. These insights can inform the risk management approach employed by customs to allocate enforcement and compliance efforts.
The Temporal Analysis of Products (TAP) Reactor applies transient kinetics to analyze sophisticated chemical reaction mechanisms, rapidly changing temperature, flow rate, pressure or concentration and then monitoring the system’s time dependent response. A simulation tool exists for the TAP reactor, but it requires a visualization platform to interface effectively with researchers. This graphical user interface remedies this problem, fixing many of the difficulties caused by users interacting directly with the simulation code by providing an intuitive web application that satisfies the simulation input parameters for fast generation of data. This tool will ensure robust and repeatable simulation results, expedite the data generating process and enhance the overall ability of TAP to produce significant findings.
Learn how NREL researchers are using advanced modeling and visualization tools to help create the planned net-zero energy district, Peña Station NEXT, near the Denver International Airport in Denver, Colorado. This project will result in tools for smart city design that can be applied across the country.
There are multiple challenges in performing real time simulations and real time experiments. Data storage and visualization are challenges that could be solved easily and when solved provide rich and valuable insights into the experiments. In this presentation, we will talk about the National Renewable Energy Laboratory solutions to tackle these issues while running real time experiments and simulations. The NREL team has made these open-source for the community to utilize with real time simulations and experiments.
NOODLES is a new cross-domain collaborative visualization and analysis capability being developed at NREL. The NOODLES specification is a minimal protocol that can tie different software packages and platforms together. As an example, a simulation code that speaks this protocol could stream iso-surfaces to an immersive VR space and a web browser. Another example is a team (with some members across the country) classifying and discovering features in a statistical data plot, driven from a researcher's existing Python-based workflow. In this talk, we will discuss some background on the problems that this protocol intends to solve, basic principles of the specification, the current state of supporting libraries, and some demonstrations of the protocol in action.
NREL staff (Jal Desai and Cory Chovanec) will introduce the U.S. Department of Energy Weatherization Assistance Program (DOE WAP) Installer Badges Toolkit. The presentation will cover topics such as how this toolkit can be customized and implemented in a program, the potential benefits of using the Badges, and lessons learned from other implementers. This session will also engage participants to share their experiences regarding their existing training and how they are bringing their workforce up to speed in their building envelope-related programs. The Installer Badges Toolkit consists of 25 Badges, each representing different energy efficiency tasks that an installer could perform on a home. Each Badge defines the desired outcome, criteria to verify the work, applicable material requirements, and references to DOE's Standard Work Specifications (SWS) or other relevant standards. Workers earn Badges by completing each task and receiving approval from a qualified supervisor. The Badges provide a consistent approach to training by ensuring that installers are learning the same skills nationwide. Organizations can also customize the Toolkit by choosing only those Badges that are relevant to their program. NREL staff will also showcase new visualization resources like the Single-family and manufactured home job aids and the Single-family 3D house tool.
T-junction is a typical structure in many industrial systems such as power plants, chemical process, and hydrocarbon production industries. The separation of two-phase flow passing through T-junction is a common flow phenomenon. There are lots of investigations regarding two-phase flow separation in branching tees and wyes tees. The characteristic of the two-phase flow between the two outlets are always non-uniform, and this kind of fluid status may contribute to the unpredictable damage of downstream facilities. For the nuclear power plants, severe accidents analysis is important especially for Light-Water Reactors (LWR) when Loss of Coolant Accident (LOCA) occurs. The small break of the pipe may cause the coolant become two-phase flow due to the pressure drop. Under certain conditions, the behavior of T-junction is similar to a gas-liquid separator, as a result of the gas enters the side arm and the liquid flows straight along the run arm. The partial separation of gas-liquid mixtures can reduce the load on the main separator, probably leading to smaller units. The study aims to establish complete visual observations for the benchmark of Computational Fluid Dynamics (CFD) model. As a consequence, a vertical T-junction (D = 26 mm) experimental loop was set up for the purpose of elaborating the complex two-phase fluid phenomena. In this experiment, variables were manipulated under highly controlled conditions and a photographic method was proposed to analyze the images of phase separation. Furthermore, the images obtained by camera have been discussed to receive valuable information about the two phase flow behavior around the T-junction.