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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Simulation Data

We present a novel visualization framework—EnsembleGraph— for analyzing ensemble simulation data, in order to help scientists understand behavior similarities between ensemble members over space and time. A graph-based representation is used to visualize individual spatiotemporal regions with similar behaviors, which are extracted by hierarchical clustering algorithms. A user interface with multiple-linked views is provided, which enables users to explore, locate, and compare regions that have similar behaviors between and then users can investigate and analyze the selected regions in detail. The driving application of this paper is the studies on regional emission influences over tropospheric ozone, which is based on ensemble simulations conducted with different anthropogenic emission absences using the MOZART-4 (model of ozone and related tracers, version 4) model. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations. Positive feedbacks from domain experts and two case studies prove efficiency of our method.

ensemble simulation↗
DOE OSTI2021

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

In this work, we present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models

We describe a new framework that allows users to explore and steer ensembles of energy systems simulations by coupling multiple energy models and interactive visualization through a dataflow API. Through the visual interface, users can interactively explore complex parameter spaces populated by hundreds, or thousands, of simulation runs and interactively spawn new simulations to 'fill in' regions of interest in the parameter space. The computational and visualization capabilities reside within a general-purpose dataflow architecture for connecting producers of multidimensional timeseries data, such as energy simulations, with consumers of that data, whether they be visualizations, statistical analyses, or datastores. Fast computation and agile dataflow can enhance the engagement with energy simulations, allowing users to populate the parameter space in real time. However, many energy simulations are far too slow to provide an interactive response. To support interactive feedback, we are creating reduced-form simulations developed through machine learning techniques, which provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost. These reduced-form simulations have response times on the order of seconds, suitable for real-time human-in-the-loop design and analysis. The approximation methods apply to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. Such reduced-form representations do not replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and exploration for large ensembles of simulations. The improved understanding, facilitated by the reduced-form models, dataflow API, and visualization tools, allows researchers to better allocate computational resources to capture informative relationships within the system as well as provide a low-cost method for validating and quality-checking large-scale modeling efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine learning visualization tool for exploring parameterized hydrodynamics

We are interested in the computational study of shock hydrodynamics, i.e. problems involving compressible solids, liquids, and gases that undergo large deformation. These problems are dynamic and nonlinear and can exhibit complex instabilities. Due to advances in high performance computing it is possible to parameterize a hydrodynamic problem and perform a computational study yielding $\mathscr{O} (TB)$ of simulation state data. We present an interactive machine learning tool that can be used to compress, browse, and interpolate these large simulation datasets. This tool allows computational scientists and researchers to quickly visualize 'what-if' situations, perform sensitivity analyses, and optimize complex hydrodynamic experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models: Preprint

We have developed a framework for the exploration, design, and planning of energy systems that combines interactive visualization with machine-learning based approximations of simulations through a general purpose dataflow API. Our system provides a visual inter- face allowing users to explore an ensemble of energy simulations representing a subset of the complex input parameter space, and spawn new simulations to 'fill in' input regions corresponding to new enegery system scenarios. Unfortunately, many energy simula- tions are far too slow to provide interactive responses. To support interactive feedback, we are developing reduced-form models via machine learning techniques, which provide statistically sound esti- mates of the full simulations at a fraction of the computational cost and which are used as proxies for the full-form models. Fast com- putation and an agile dataflow enhance the engagement with energy simulations, and allow researchers to better allocate computational resources to capture informative relationships within the system and provide a low-cost method for validating and quality-checking large-scale modeling efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Managing Low-Level Waste at Dounreay - 16269

Dounreay Site Restoration Limited (DSRL) is the site licence company responsible for the clean-up and demolition of Britain's former centre of fast reactor research and development. The Dounreay site requires facilities to dispose of up to 175,000 cubic metres of low level waste (LLW) that has been and will continue to be generated during the decommissioning and restoration of the site. In May 2014, construction of two new vaults for the disposal of LLW at Dounreay was completed, including the necessary roads, infrastructures and services, followed by an encapsulation plant in September 2014. The vaults are located immediately to the northeast of the Dounreay licensed site, on NDA-owned land. The facilities are for disposal of the solid LLW generated at Dounreay and the adjacent Vulcan site only. In 2004, following consultation with stakeholders and members of the public, Dounreay issued the 'Best Practicable Environmental Option (BPEO) Report' on the management of Dounreay LLW. This report assessed potential management options for LLW against a range of criteria, including technical, environmental, cost, health and safety issues. The study was conducted in line with best practice and involved consultation with a range of people, including the local community and organisations with an interest in radioactive waste management. The recommendations and conclusions from the study provided the basis for the 'Dounreay Solid LLW Overall Strategy', which was published in March 2005. A fundamental component of this strategy was the development of new below-ground disposal vaults for LLW at Dounreay. Siting the vaults on land at Dounreay avoids any need to transport the LLW away from Dounreay on public roads. Disposal at Dounreay therefore satisfies the proximity principle of managing the waste at source. Disposal at Dounreay is also consistent with UK Government Policy on LLW management, the Scottish Government's Radioactive Waste Management Policy and the Nuclear Decommissioning Authority's (NDA's) strategy on management of LLW from the UK nuclear industry. A planning application was initially lodged with the Highland Council in June 2006. The period for the determination of this application was extended to allow the Scottish Environment Protection Agency (SEPA) sufficient time to assess fully the supporting Environmental Safety Case (ESC). The planning application was re-activated in May 2008, and planning permission was granted by Highland Council in April 2009. The planning permission included 26 conditions on DSRL, covering a wide range of environmental issues such as construction noise/dust, traffic, visual impact and also a condition to develop a community fund aimed at socio-economic development of the area around the Dounreay site. After undertaking the necessary detailed design work, construction of the phase one vaults commenced in November 2011 by principal contractor, Graham Construction. Work was undertaken on both vaults simultaneously, excavating around 243,000 m{sup 3} of rock. When excavation was complete, steel reinforcement was placed and concrete poured to construct the floor slabs. This was followed by the construction of the reinforced walls and then the installation of the steel support structure and materials that form the roof. Mechanical and electrical installation then followed, in parallel with the construction of the vault apron, ramps and access roads. A pumping system and control building was also constructed to remove groundwater from around the vaults to allow the vaults to be kept dry while they are being filled with waste. A layer of excavated material, approximately 3 m thick, was placed across the existing ground surface between the vaults and the coast, to ensure the water table lies well below the new ground surface after closure of the vaults, and that any future trace contaminated groundwater flows out to the sea rather than to the soil zone. This layer is referred to in project documents as the 'enhanced geosphere'. After disposals are completed and the vaults are closed, this layer will be joined to the capping over the vaults so that the ensemble merges visually with the surrounding landscape. The original vegetation and soil mix will be returned as far as is practicable. An encapsulation plant has been constructed to fill the LLW containers with grout prior to disposal in the vault, similar to the process used at the national Low-Level Waste Repository facility (LLWR), near Drigg in Cumbria. The plant has the capacity to grout up to four containers per day. The grout fills voids in the container and helps provide chemical conditions in the vault that limit the release of radionuclides in the far future, after the vault has been penetrated by groundwater. On April 24 2015, active commissioning began at the Encapsulation Plant and LLW Vaults. Half-height ISO containers packed with low level waste are currently being filled with grout and disposed of in the LLW Vault. Also packaged Demolition Low Level Waste (DLLW) is being processed and disposed of in the Demolition Low Level Waste Vault. To date over 2500 packages of DLLW and 70 containers LLW have been disposed of. Completion of construction and the start of operating the Phase One vaults and encapsulation plant are fundamental to the successful and efficient decommissioning of the Dounreay site, upon which many millions of pounds of investment and hundreds of jobs rely.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Open-Source Integrated Design-Analysis Environment For Nuclear Energy Advanced Modeling & Simulation Final Scientific/Technical Report

The framework created through the Open-Source Integrated Design-Analysis Environment (IDAE) for Nuclear Energy Advanced Modeling & Simulation grant has simplify and democratize advanced modeling and simulation in the nuclear energy industry that works on a range of nuclear engineering applications. It leverages millions of investment dollars from the Department of Energy's Office of Nuclear Energy for modeling and simulation of light water reactors and the Office of Nuclear Energy's research and development. The IDEA framework enhanced Kitware’s Computational Model Builder (CMB) while leveraging existing open-source toolkits and creating a graphical end-to-end umbrella guiding end-users and developers through the nuclear energy advanced modeling and simulation lifecycle. In addition, the work deliver strategic advancements in meshing and visualization for ensembles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Dynamics of threading dislocations in porous heteroepitaxial GaN films

Behavior of threading dislocations in porous heteroepitaxial gallium nitride (GaN) films has been studied using computer simulation by the two-dimensional discrete dislocation dynamics approach. A computational scheme, where pores are modeled as cross sections of cylindrical cavities, elastically interacting with unidirectional parallel edge dislocations, which imitate threading dislocations, is used. Time dependences of coordinates and velocities of each dislocation from dislocation ensembles under investigation are obtained. Visualization of current structure of dislocation ensemble is performed in the form of a location map of dislocations at any time. It has been shown that the density of appearing dislocation structures significantly depends on the ratio of area of a pore cross section to area of the simulation region. In particular, increasing the portion of pores surface on the layer surface up to 2% should lead to about a 1.5-times decrease of the final density of threading dislocations, and increase of this portion up to 15% should lead to approximately a 4.5-times decrease of it.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constraints. However, in situ visualization approaches limit the flexibility of post-hoc exploration because the raw simulation data are no longer available. Although multiple image-based approaches have been proposed to mitigate this limitation, those approaches lack the ability to explore the simulation parameters. Our approach allows flexible exploration of parameter space for large-scale ensemble simulations by taking advantage of the recent advances in deep learning. Specifically, we design InSituNet as a convolutional regression model to learn the mapping from the simulation and visualization parameters to the visualization results. With the trained model, users can generate new images for different simulation parameters under various visualization settings, which enables in-depth analysis of the underlying ensemble simulations. We demonstrate the effectiveness of InSituNet in combustion, cosmology, and ocean simulations through quantitative and qualitative evaluations.

deep learning↗
DOE OSTI2021

Visual HPC Workflows for the Analysis of System Dynamics Models

Visual analytics supported by high performance computing (HPC) accelerates and enhances the discovery, exploration, and analysis of causal patterns in complex system dynamics (SD) models. We present a suite of visualization-assisted ensemble-based techniques for hypothesis generation and testing, and for sensitivity analysis. By employing HPC to provide parallel, on-demand simulation of SD models, one can “steer” an ensemble of simulated scenarios in real time as one first formulates and then informally tests those hypotheses: this provides rapid feedback for analysts to refine their understanding of the causal relationships emergent from a model. Such understandings can be followed and augmented by rigorous application of statistical methods, namely global variance-based sensitivity analysis, Monte-Carlo filtering, adaptive regional sensitivity analysis, and self-organized maps: here timely computation relies on HPC, while effective presentation emphasizes high-dimensional multivariate data visualization. Immersive visualization in virtual 3D environments provides an excellent adjunct to the traditional 2D graphics typically used for SD models, as it generates an embodied understanding of model behavior and facilitates an active, collaborative critique of model structure and output. Finally, we summarize prospects for HPC-enabled visual analytics applied to SD modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

DragonView: Toward Understanding Network Interference in Dragonfly-based Supercomputers

Acquiring and maintaining high performance computing systems represents a substantial, long term investment. Therefore, it is paramount to optimize their utilization in order to maximize the return on investment and the impact on science and engineering. In this context, network congestion and inter-job interference are a major challenge as they can lead to significant systemwide performance degregation. This is especially pertinent in shared interconnects such as the dragonfly network, in which resources are shared by multiple jobs. Since many large-scale applications are communication-bound, a few communication heavy applications can have a large impact on the performance of all jobs and decrease the overall system throughput. In this paper, we describe a web-based visual analytics environment to study congestion and inter-job interference on dragonfly networks. The system enables network architects, system administrators and application developers to explore the potential impact of machine configuration, job placement policies and routing algorithms by visualizing and comparing ensembles of simulated workloads and real world data. We focus on the design of the system and describe the challenges, design decisions and the final implementation. Finally, we present two detailed case studies of prototypical analysis sessions by our stakeholders aimed at exploring the implications of different workloads and machine configurations using ensembles of network simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Slycat Enables Synchronized 3D Comparison of Surface Mesh Ensembles [Brief]

In support of analyst requests for Mobile Guardian Transport studies, researchers at Sandia National Laboratories have expanded data types for the Slycat ensemble-analysis and visualization tool to include 3D surface meshes. This new capability represents a significant advance in our ability to perform detailed comparative analysis of simulation results. Analyzing mesh data rather than images provides greater flexibility for post-processing exploratory analysis.

36 MATERIALS SCIENCE↗
NASA NTRS1984

Basic experimental study of the coupling between flow instabilities and incident sound

Whether a solid trailing edge is required to produce efficient coupling between sound and instability waves in a shear layer was investigated. The differences found in the literature on the theoretical notions about receptivity, and a need to resolve them by way of well-planned experiments are discussed. Instability waves in the shear layer of a subsonic jet, excited by a point sound source located external to the jet, were first visualized using an ensemble averaging technique. Various means were adopted to shield the sound reaching the nozzle lip. It was found that the low frequency sound couples more efficiently at distances downstream of the nozzle. To substantiate the findings further, a supersonic screeching jet was tested such that it passed through a small opening in a baffle placed parallel to the exit plane. The measured feedback or screech frequencies and also the excited flow disturbances changed drastically on traversing the baffle axially thus providing a strong indication that a trailing edge is not necessary for efficient coupling between sound and flow.

Ahuja, K. K.↗
DOE OSTI2020

eFESTA: Ensemble Feature Exploration with Surface Density Estimates

We propose surface density estimate (SDE) to model the spatial distribution of surface features-isosurfaces, ridge surfaces, and streamsurfaces-in 3D ensemble simulation data. The inputs of SDE computation are surface features represented as polygon meshes, and no field datasets are required (e.g., scalar fields or vector fields). The SDE is defined as the kernel density estimate of the infinite set of points on the input surfaces and is approximated by accumulating the surface densities of triangular patches. We also propose an algorithm to guide the selection of a proper kernel bandwidth for SDE computation. An ensemble Feature Exploration method based on Surface densiTy EstimAtes (eFESTA) is then proposed to extract and visualize the major trends of ensemble surface features. For an ensemble of surface features, each surface is first transformed into a density field based on its contribution to the SDE, and the resulting density fields are organized into a hierarchical representation based on the pairwise distances between them. The hierarchical representation is then used to guide visual exploration of the density fields as well as the underlying surface features. We demonstrate the application of our method using isosurface in ensemble scalar fields, Lagrangian coherent structures in uncertain unsteady flows, and streamsurfaces in ensemble fluid flows.

Computational modeling↗
NASA NTRS1987

Structure of unsteady flows at leading- and trailing-edges: Flow visualization and its interpretation

Unsteady two- and three-dimensional flow structure at leading and trailing edges of bodies can be characterized effectively using recently developed techniques for acquisition and interpretation of flow visualization. The techniques addressed here include: flow image/surface pressure correlations; 3-D reconstruction of flow structure from flow images; and interactive interpretation of flow images with theoretical simulations. These techniques can be employed in conjunction with: visual correlation and ensemble-averaging, both within a given image and between images; recognition of patterns from images; and estimates of velocity eigenfunctions from images.

Rockwell, D.↗