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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Editors’ Choice—Perspective—Challenges in Moving to Multiscale Battery Models: Where Electrochemistry Meets and Demands More from Math

There has been significant recent interest in studying multiscale characteristics of current and next-generation batteries, including lithium-metal and lithium-sulfur batteries. Advances in computing power make researchers believe that the detailed multiscale models can be efficiently simulated to arrive at the insights for the degradation and performance loss; however, this is not true and special attention needs to be paid to local singularities, boundary layers, moving boundaries, etc. This article presents 2D examples that illustrate the importance of grid convergence studies, provides well-defined detailed models to test the efficiency of numerical schemes, and discusses the associated simulation challenges.

Shah, Krishna (ORCID:0000000278026361)↗
DOE OSTI2020

BEEAM (Building Electrical Efficiency Analysis Model) [SWR-20-107]

Modern high-performance buildings exhibit an increasing number of building loads that use direct current (DC) electricity internally, rather than alternating current (AC), due to the advent of low-cost computing and advanced power electronics. Powering DC devices from the AC grid requires AC/DC power conversion, which introduces energy losses and reduces efficiency. As DC loads proliferate, the cumulative wasted energy associated with hundreds of millions of AC/DC converters has become one of the broadest energy savings opportunities in buildings. DC power distribution systems have been proposed as an elegant and transformative solution to the problem of DC devices. In a DC distribution system, the building's wires carry DC electricity, rather than AC; and a few centralized, highly efficient AC/DC converters replace the many smaller, less efficient converters that serve individual DC loads. Unfortunately, the trade-offs associated with DC distribution systems are not well understood. Reported energy savings associated with DC power distribution differ widely and have not been well validated. The Building Electrical Efficiency Analysis Model (BEEAM) is a Modelica library that simulates the efficiency of building electrical distribution systems using harmonic power flow. BEEAM can model a wide variety of building distribution topologies, including three-phase AC, single-phase AC, unipolar DC, bipolar DC, and hybrid networks under both balanced and unbalanced load conditions. BEEAM accurately models power electronic converter losses, provides granular estimates of losses throughout the distribution system, and properly models efficiency at part load conditions. Users can package BEEAM within a functional mockup unit (FMU), enabling co-simulation with other modeling platform, such as EnergyPlus. In summary, BEEAM provides a tool suite for fair and accurate comparison of the efficiency of building electrical distribution systems, including AC, DC, and hybrid systems.

Frank, Stephen↗
DOE OSTI2026

JACC.jl: Julia for Accelerators

CPU/GPU portable array, parallel_for/parallel_reduce in Julia for productive science. Funded by the US DOE Advanced Scientific Computing Research (ASCR).

Fackler, Philip [Oak Ridge National Laboratory (OR↗
DOE OSTI2024

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data

Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional neural networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4 km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are 1) it learns and estimates complex precipitation systems directly from raw measurements in near–real time, 2) it uses the automatic spatial neighborhood feature extraction approach, and 3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Designing an Optimal Sensor Network via Minimizing Information Loss

Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that “minimize information loss” from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Redirector: Designing Cell Factories by Reconstructing the Metabolic Objective

Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

We report while existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real world settings. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. We consider a setup where robustness is expected over an unseen test domain that is not i.i.d. but deviates from the training domain. While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes. We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain. Our adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial perturbations, and the outer minimization finding model parameters by optimizing the loss on adversarial perturbations generated from the inner maximization. We demonstrate the applicability of our approach on three types of naturally occurring perturbations --- object-related shifts, geometric transformations, and common image corruptions. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations. We demonstrate the usefulness of the proposed approach by showing the robustness gains of deep neural networks trained using our adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Plasma Simulation Program

Many others in the fusion energy and advanced scientific computing communities participated in the development of this plan. The core planning team is grateful for their important contributions. This summary is meant as a quick overview the Fusion Simulation Program's (FSP's) purpose and intentions. There are several additional documents referenced within this one and all are supplemental or flow down from this Program Plan. The overall science goal of the DOE Office of Fusion Energy Sciences (FES) Fusion Simulation Program (FSP) is to develop predictive simulation capability for magnetically confined fusion plasmas at an unprecedented level of integration and fidelity. This will directly support and enable effective U.S. participation in International Thermonuclear Experimental Reactor (ITER) research and the overall mission of delivering practical fusion energy. The FSP will address a rich set of scientific issues together with experimental programs, producing validated integrated physics results. This is very well aligned with the mission of the ITER Organization to coordinate with its members the integrated modeling and control of fusion plasmas, including benchmarking and validation activities. [1]. Initial FSP research will focus on two critical Integrated Science Application (ISA) areas: ISA1, the plasma edge; and ISA2, whole device modeling (WDM) including disruption avoidance. The first of these problems involves the narrow plasma boundary layer and its complex interactions with the plasma core and the surrounding material wall. The second requires development of a computationally tractable, but comprehensive model that describes all equilibrium and dynamic processes at a sufficient level of detail to provide useful prediction of the temporal evolution of fusion plasma experiments. The initial driver for the whole device model will be prediction and avoidance of discharge-terminating disruptions, especially at high performance, which are a critical impediment to successful operation of machines like ITER. If disruptions prove unable to be avoided, their associated dynamics and effects will be addressed in the next phase of the FSP.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

Special Analysis of the Area 3 Radioactive Waste Management Site at the Nevada National Security Site, Nye County, Nevada

This report describes the methods and results of a special analysis (SA) of the Area 3 Radioactive Waste Management Site (RWMS) on the Nevada National Security Site (NNSS). The purpose of the SA is to determine if the approved performance assessment (PA) and composite analysis (CA) (Shott et al., 2001) remain valid. The Area 3 RWMS PA and CA were prepared as a single document and received conditional approval on October 6, 1999. A conditional Disposal Authorization Statement (DAS) for the Area 3 RWMS was issued on October 20, 1999. Since preparation of the approved PA and CA, new information and additional environmental monitoring data have been used to update the PA and CA. At the same time, continual advancements in computer processors and software have allowed improvement to the PA and CA models. Annual reviews of the PA and CA required by U.S. Department of Energy (DOE) Order DOE O 435.1 have documented multiple changes occurring since preparation of the PA and CA. Potentially important changes include: Development of a new and improved baseline PA and CA model implemented in the probabilistic GoldSim simulation platform. A significant increase in the waste inventory disposed at the site. Revision and updating of model parameters based on additional years of site monitoring data and new research and development results. Although changes have occurred, many important PA/CA issues remain unchanged, including the site conceptual model, important features, events, and processes, and the points of compliance. The SA is performed to document the current status of the PA/CA model and to quantitatively assess the impact of cumulative changes on the PA and CA results. The results of the SA are used to assess the validity of the approved PA/CA and make a determination if revision of the PA or CA is necessary. The SA was performed using the Area 3 RWMS, version 2.102, GoldSim model, the current baseline PA/CA model. Comparison of the maximum SA results with the PA performance objectives indicates that there continues to be a reasonable expectation of compliance. The resident exposure scenario was evaluated for compliance with the air pathway and all-pathways annual total effective dose (TED) performance objectives. The maximum mean air pathway TED, 7E-6 millisievert (mSv) at 1,000 years (y) has decreased relative to the approved PA and is significantly less than the 0.1 mSv limit. The maximum mean all-pathways annual TED, 7E-5 mSv at 1,000 y has increased but remains a small fraction of the 0.25 mSv limit. The SA maximum mean radon-222 (222Rn) flux density, 0.03 becquerel per square meter per second (Bq m-2 s-1), has increased relative to the PA results but is significantly less than the 0.74 Bq m-2 s-1 limit. The SA results continue to support a conclusion that the disposed waste inventory is protective of intruders and groundwater resources. The maximum mean intruder TED, 0.01 mSv for an acute construction scenario at the U-3ah/at disposal unit, was less than the 5 mSv performance measure. Site monitoring data and research results continue to support a conclusion that a groundwater pathway will not exist within the 1,000 y compliance period. Projected releases to the environment are a small fraction of the performance objectives. Cost-effective options for reducing releases further are unlikely to exist. Therefore, releases from the Area 3 RWMS are judged to be as low as reasonably achievable. Comparison of the maximum CA result with the 0.3 mSv CA dose constraint indicates that no action is required to reduce the dose from the Area 3 RWMS and all interacting sources of residual radioactive contamination. The SA maximum mean CA annual TED, 0.02 mSv at 1,000 y, has increased from the approved CA result but remains less than 10% of the dose constraint. The CA TED continues to be due predominantly to inhalation of plutonium-239 resuspended from soils contaminated by nuclear weapons tests conducted near the Area 3 RWMS. The SA results estimated with the Area 3 RWMS version 2.102 model indicate that changes to the PA and CA do not significantly alter the PA/CA results or conclusions. Although increases occur for the all-pathways annual TED, the 222Rn flux density, and the CA annual TED, all PA and CA results are a small fraction of the performance objectives or dose constraints. The same conclusion was reached for the approved PA and CA. The SA results continue to support a conclusion that there is a reasonable expectation of meeting all performance objectives and the CA dose constraint. There is no need to revise the DAS at this time. The SA results support a conclusion that the Area 3 RWMS PA and CA remain valid, and revision is not necessary at this time.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

Center for Plasma Edge Simulation (CPES). Final Technical Report

The Center for Plasma Edge Simulation (CPES) project was a multi-institutional research effort funded jointly by the Office of Advanced Scientific Computing Research (OASCR) and the Office of Fusion Energy Sciences (OFES) within the Department of Energy's Office of Science. The effort was led by our Principal Investigator, CS Chang, at the Courant Institute for Mathematical Sciences at New York University. The Center included participants from Oak Ridge National Laboratory, Princeton Plasma Physics Laboratory, Lawrence Berkeley National Laboratory, California Institute of Technology, Columbia University, Lehigh University, Rutgers University, University of Colorado, Massachusetts Institute of Technology, University of California at Davis, University of California at Irvine, North Carolina State University, and Georgia Institute of Technology. This report concerns the work performed by Dr. Julian C. Cummings, who was the institutional Principal Investigator for the CPES project at Caltech.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

Development of Computational Approaches for Simulation and Advanced Controls for Hybrid Combustion-Gasification Chemical Looping

This document provides the results of the project through September 2009. The Phase I project has recently been extended from September 2009 to March 2011. The project extension will begin work on Chemical Looping (CL) Prototype modeling and advanced control design exploration in preparation for a scale-up phase. The results to date include: successful development of dual loop chemical looping process models and dynamic simulation software tools, development and test of several advanced control concepts and applications for Chemical Looping transport control and investigation of several sensor concepts and establishment of two feasible sensor candidates recommended for further prototype development and controls integration. There are three sections in this summary and conclusions. Section 1 presents the project scope and objectives. Section 2 highlights the detailed accomplishments by project task area. Section 3 provides conclusions to date and recommendations for future work.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2013

Aerial Measuring System (AMS)/Israel Atomic Energy Commission (IAEC) Joint Comparison Study Report

Under the 13th Bilateral Meeting to Combat Nuclear Terrorism conducted on January 8–9, 2013, the committee approved the development of a cost-effective proposal to conduct a Comparison Study of the Aerial Measuring System (AMS) of the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA) and Israel Atomic Energy Commission (IAEC). The study was to be held at the Remote Sensing Laboratory (RSL), Nellis Air Force Base, Las Vegas, Nevada, with measurements at the Nevada National Security Site (NNSS). The goal of the AMS and the IAEC joint survey was to compare the responses of the two agencies’ aerial radiation detection systems to varied radioactive surface contamination levels and isotopic composition experienced at the NNSS, and the differing data processing techniques utilized by the respective teams. Considering that for the comparison both teams were using custom designed and built systems, the main focus of the short campaign was to investigate the impact of the detector size and data analysis techniques used by both teams. The AMS system, SPectral Advanced Radiological Computer System, Model A (SPARCS-A), designed and built by RSL, incorporates four different size sodium iodide (NaI) crystals: 1" × 1", 2" × 4" × 4", 2" × 4" ×16", and an “up-looking” 2" × 4" × 4". The Israel AMS System, Air RAM 2000, was designed by the IAEC Nuclear Research Center – Negev (NRCN) and built commercially by ROTEM Industries (Israel) and incorporates two 2" diameter × 2" long NaI crystals. The operational comparison was conducted at RSL-Nellis in Las Vegas, Nevada, during week of June 24–27, 2013. The Israeli system, Air RAM 2000, was shipped to RSL-Nellis and mounted together with the DOE SPARCS on a DOE Bell-412 helicopter for a series of aerial comparison measurements at local test ranges, including the Desert Rock Airport and Area 3 at the NNSS. A 4-person Israeli team from the IAEC NRCN supported the activity together with 11 members of the RSL team, which consisted of pilots, mechanics, scientists, a data analyst, equipment operators, and operation specialists. All planned flight activities followed by scientific discussions on the collected data were completed. For IAEC, the joint survey provided an opportunity to characterize their system’s response to extended sources of various fission products at the NNSS. As both systems play an important role in their respective countries’ (United States and Israel) national framework of radiological emergency response and are subject to multiple mutual cooperation agreements, it was important for each country to obtain more thorough knowledge of how they would employ these important assets and define the roles that they would each play in an actual response.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

RELAP-7 Numerical Stabilization: Entropy Viscosity Method

The RELAP-7 code is the next generation nuclear reactor system safety analysis code being developed at the Idaho National Laboratory (INL). The code is based on the INL's modern scientific software development framework, MOOSE (Multi-Physics Object Oriented Simulation Environment). The overall design goal of RELAP-7 is to take advantage of the previous thirty years of advancements in computer architecture, software design, numerical integration methods, and physical models. The end result will be a reactor systems analysis capability that retains and improves upon RELAP5's capability and extends the analysis capability for all reactor system simulation scenarios. RELAP-7 utilizes a single phase and a novel seven-equation two-phase flow models as described in the RELAP-7 Theory Manual (INL/EXT-14-31366). The basic equation systems are hyperbolic, which generally require some type of stabilization (or artificial viscosity) to capture nonlinear discontinuities and to suppress advection-caused oscillations. This report documents one of the available options for this stabilization in RELAP-7 -- a new and novel approach known as the entropy viscosity method. Because the code is an ongoing development effort in which the physical sub models, numerics, and coding are evolving, so too must the specific details of the entropy viscosity stabilization method. Here the fundamentals of the method in their current state are presented.

42 ENGINEERING↗
DOE OSTI2014

RELAP-7 Software Verification and Validation Plan

This INL plan comprehensively describes the software for RELAP-7 and documents the software, interface, and software design requirements for the application. The plan also describes the testing-based software verification and validation (SV&V) process—a set of specially designed software models used to test RELAP-7. The RELAP-7 (Reactor Excursion and Leak Analysis Program) code is a nuclear reactor system safety analysis code being developed at Idaho National Laboratory (INL). The code is based on the INL’s modern scientific software development framework – MOOSE (Multi-Physics Object-Oriented Simulation Environment). The overall design goal of RELAP-7 is to take advantage of the previous thirty years of advancements in computer architecture, software design, numerical integration methods, and physical models. The end result will be a reactor systems analysis capability that retains and improves upon RELAP5’s capability and extends the analysis capability for all reactor system simulation scenarios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

RELAP-7 Beta Release: Summary of Capabilities

RELAP-7 is a nuclear systems safety analysis code being developed at the Idaho National Laboratory (INL). Building upon the decades of software development at the INL, we began the development of RELAP-7 in 2011 to support the Risk Informed Safety Margins Characterization (RISMC) Pathway. As part of this development, the first lines of RELAP-7 code were committed to the software revision control repository on November 7th, 2011. The overall design goal of RELAP-7 is to take advantage of the previous thirty years of advancements in computer architecture, software design, numerical methods, and physical models in order to provide capabilities needed for the RISMC methodology and to support nuclear power safety analysis. RELAP-7 is built using the INL’s modern scientific software development framework, MOOSE (Multi-physics Object Oriented Simulation Environment). MOOSE provides improved numerical calculations (including higher-order integration in both space and time, yielding converged second-order accuracy). The RELAP-7 code structure is based on multiple physical component models such as pipes, junctions, pumps, etc. Each component can have options for different fluid models such as single- and two-phase flow. This component-based and physics-based software architecture allows RELAP-7 to adopt different physical models for different applications. A relatively new two-phase hydrodynamic model, termed the ''7-Equation model'' for two phasic pressures, velocities, energies, and volumetric fraction, is incorporated into RELAP-7 for liquid-gas (water-steam) flows. This new model allows second-order integration because it is well-posed, which will reduce the numerical error associated with traditional systems analysis codes. In this paper, we provide a RELAP-7 capability list describing analysis features, range of applicability, and reactor components that will be available for the December 15th, 2014 beta release of the software.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

SciDAC Visualization and Analytics Center for Enabling Technologies

This project focuses on leveraging scientific visualization and analytics software technology as an enabling technology for increasing scientific productivity and insight. Advances in computational technology have resulted in an "information big bang," which in turn has created a significant data understanding challenge. This challenge is widely acknowledged to be one of the primary bottlenecks in contemporary science. The vision for our Center is to respond directly to that challenge by adapting, extending, creating when necessary and deploying visualization and data understanding technologies for our science stakeholders. Using an organizational model as a Visualization and Analytics Center for Enabling Technologies (VACET), we are well positioned to be responsive to the needs of a diverse set of scientific stakeholders in a coordinated fashion using a range of visualization, mathematics, statistics, computer and computational science and data management technologies.

97 MATHEMATICS AND COMPUTING↗