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At least 37 records · Page 2

In situ feature analysis for large-scale multiphase flow simulations

The study of multiphase flow is essential for designing chemical reactors such as fluidized bed reactors (FBR), as a detailed understanding of hydrodynamics is critical for optimizing reactor performance and stability. An FBR allows scientists to conduct different types of chemical reactions involving multiphase materials, especially interaction between gas and solids. During such complex chemical processes, the formation of void regions in the reactor, generally termed as bubbles, is an important phenomenon. The study of these bubbles has a deep implication in predicting the reactor’s overall efficiency. But physical experiments needed to understand bubble dynamics are costly and non-trivial due to the technical difficulties involved and harsh working conditions of the reactors. Therefore, to study such chemical processes and bubble dynamics, a state-of-the-art computational simulation MFIX-Exa is being developed. Despite the proven accuracy of MFIX-Exa in modeling bubbling phenomena, the large-scale output data prohibits the use of traditional post hoc analysis capabilities in both storage and I/O time. Herein, to address these issues and allow the application scientists to explore the bubble dynamics in an efficient and timely manner, we have developed an end-to-end analytics pipeline that enables in situ detection of bubbles, followed by a flexible post hoc visual exploration methodology of bubble dynamics. The proposed method enables interactive analysis of bubbles, along with quantification of several bubble characteristics, enabling experts to understand the bubble interactions in detail. Positive feedback from the experts has indicated the efficacy of the proposed approach for exploring bubble dynamics in very-large-scale multiphase flow simulations.

97 MATHEMATICS AND COMPUTING↗

Toward a Smart Metaverse City: Immersive Realism and 3D Visualization of Digital Twin Cities

Metaverse and its related extended reality technologies can enable immersive, realistic, and participatory visualization of 3D data, and their use and the potential within a smart city can be effective for supporting urban research and urban operations management. This book chapter describes a vision for prototyping a “Smart Metaverse City” to combine the unique advantage of the Metaverse technology with the two-way connectivity of a digital twin city application. This synergy aims to create a virtual environment for immersive geovisualization to help researchers and the public understand the complex urban system through science-based and data-driven approaches. This book chapter selectively reviews past technological and paradigm advancements for collecting, analyzing, and visualizing 3D urban big data. Then we present a prototyping Geographic Information System (GIS) framework, together with some relevant data sources and open-source web technologies, to help researchers create a smart Metaverse city. We demonstrate our vision and discuss its application opportunities through a real-world example, a digital twin city developed at the Oak Ridge National Laboratory, to facilitate participatory, smart, and sustainable campus management.

Xu, Haowen↗

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. Lastly, this survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.

3D crop modeling↗

Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions

Big data has a potential to unlock novel groundbreaking opportunities in the power grid sector that enhances a multitude of technical, social, and economic gains. The currently untapped potential of applying the science of big data for better planning and operation of the power grid is a very challenging task and needs significant efforts all-around. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data sets from diverse sources. In particular, computational complexity, data security, and operational integration of big data into utility decision frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. Moreover, due to the complex nature of power grids along with the need to balance power in real time, seamless integration of big data into utility operations is very critical. In this context, big data analytics combined with grid visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into electric utility decision framework. Detailed information for utilities looking to apply big data analytics and details insights on how utilities can enhance revenue streams and bring disruptive innovation in the industry are discussed. More importantly, general guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.

13 HYDRO ENERGY↗

NeuralCubes: Deep Representations for Visual Data Exploration

Visual exploration of large multi-dimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are some of the most promising approaches. However, these techniques usually require a large memory footprint for large datasets. To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes. NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Our models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network connection. Here, we demonstrate the effectiveness of NeuralCubes through extensive experiments on a variety of datasets and discuss how NeuralCubes opens up opportunities for new types of visualization and interaction.

97 MATHEMATICS AND COMPUTING↗

An overview of visualization and visual analytics applications in water resources management

Recent advances in information, communication, and environmental monitoring technologies have increased the availability, spatiotemporal resolution, and quality of water-related data, thereby leading to the emergence of many innovative big data applications. Among these applications, visualization and visual analytics, also known as the visual computing techniques, empower the synergy of computational methods (e.g., machine learning and statistical models) with human reasoning to improve the understanding and solution toward complex science and engineering problems. These approaches are frequently integrated with geographic information systems and cyberinfrastructure to provide new opportunities and methods for enhancing water resources management. Here, we present a comprehensive review of recent hydroinformatics applications that employ visual computing techniques to (1) support complex data-driven research problems, and (2) support the communication and decision-makings in the water resources management sector. Then, we conduct a technical review of the state-of-the-art web-based visualization technologies and libraries to share our experiences on developing shareable, adaptive, and interactive visualizations and visual interfaces for water resources management applications. We close with a vision that applies the emerging visual computing technologies and paradigms to develop the next generation of hydroinformatics applications.

54 ENVIRONMENTAL SCIENCES↗

Database Management Module Framework for Dynamic Contingency Analysis and Visualization

The Dynamic Contingency Analysis Tool (DCAT) is being developed to mimic the response of generation dynamics, protection, and load components to extreme contingencies in power systems. Currently, DCAT generates a huge data set, with the analysis results stored in multiple subfolders using a great many files in different formats (power flow cases, time series, figures, spreadsheets). To help power system engineers thoroughly understand and analyze system behavior under many scenarios and contingencies, we developed a database management module (DBM) that enables future integration of DCAT with industry-grade Big Data analytical and interactive visualization platforms. In this paper, we demonstrate the DBM framework with interactive analytics and visualization for DCAT on the synthetic South Carolina 500-bus power system model using hurricane contingencies that capture possibilities for an extreme contingency. DCAT’s advanced data management and interactive visualization can help planning engineers to visually explore and analyze system behavior, all in one view.

Cascading, dynamic simulations, database managemen↗

BactoGeNIE: A large-scale comparative genome visualization for big displays

The volume of complete bacterial genome sequence data available to comparative genomics researchers is rapidly increasing. However, visualizations in comparative genomics--which aim to enable analysis tasks across collections of genomes--suffer from visual scalability issues. While large, multi-tiled and high-resolution displays have the potential to address scalability issues, new approaches are needed to take advantage of such environments, in order to enable the effective visual analysis of large genomics datasets. In this paper, we present Bacterial Gene Neighborhood Investigation Environment, or BactoGeNIE, a novel and visually scalable design for comparative gene neighborhood analysis on large display environments. We evaluate BactoGeNIE through a case study on close to 700 draft Escherichia coli genomes, and present lessons learned from our design process. In conclusion, BactoGeNIE accommodates comparative tasks over substantially larger collections of neighborhoods than existing tools and explicitly addresses visual scalability. Given current trends in data generation, scalable designs of this type may inform visualization design for large-scale comparative research problems in genomics.

59 BASIC BIOLOGICAL SCIENCES↗

Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic

This article presents the design, implementation, and use cases of the Chattanooga Digital Twin (CTwin) towards the vision for next-generation smart city applications for urban mobility management. CTwin is an end-to-end web-based platform that incorporates various aspects of the decision-making process for optimizing urban transportation systems in Chattanooga, Tennessee, to reduce traffic congestion, incidents, and vehicle fuel consumption. The platform serves as a cyberinfrastructure to collect and integrate multi-domain urban mobility data from various online repositories and Internet of Things (IoT) sensors, covering multiple urban aspects (e.g., traffic, natural hazards, weather, and safety) that are relevant to urban mobility management. The platform enables advanced capabilities for: (a) real-time situational awareness on traffic and infrastructure conditions on highways and urban roads, (b) cyber-physical control for optimizing traffic signal timing, and (c) interactive visual analytics on big urban mobility data and various metrics for traffic prediction and transportation performance evaluation. The platform is designed using a multi-level componentization paradigm and is implemented using modular and adaptive architecture, rendering it as a generalizable and extendable prototype for other urban management applications. We present several use cases to demonstrate CTwin's core capabilities for supporting decision-making in smart urban mobility management.

33 ADVANCED PROPULSION SYSTEMS↗

Big Data as a Service from an Urban Information System

Big Data has already proven itself as a valuable tool that lets geographers and urban researchers utilize large data re- sources to generate new insights. However, wider adoption of Big Data techniques in these areas is impeded by a number of difficulties in both knowledge discovery and data and sci- ence production. Typically users face such problems as dis- parate and scattered data, data management, spatial search- ing, insufficient computational capacity for data-driven anal- ysis and modelling, and the lack of tools to quickly visual- ize and summarize large data and analysis results. Here we propose an architecture for an Urban Information Sys- tem (UrbIS) that mitigates these problems by utilizing the Big Data as a Service (BDaaS) concept. With technolog- ical roots in High-performance Computing (HPC), BDaaS is based on the idea of outsourcing computations to dif- ferent computing paradigms, scalable to super-computers. UrbIS aims to incorporate federated metadata search, in- tegrated modeling and analysis, and geovisualization into a single seamless workflow. The system is under active devel- opment and is built around various emerging technologies that include hybrid and NoSQL databases, massively par- allel systems, GPGPU computing, and WebGL-based geo- graphic visualization. UrbIS is designed to facilitate using Big Data across multiple cities to better understand how ur- ban areas impact the environment and how climate change and other environmental change impact urban areas.

Sorokine, Alexandre↗

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗

Design Concepts for an Outage Control Center Information Dashboard

The nuclear industry, and the business world in general, is facing a rapidly increasing amount of data to be dealt with on a daily basis. In the last two decades, the steady improvement of data storage devices and means to create and collect data along the way influenced the manner in which we deal with information. Most data is still stored without filtering and refinement for later use. Many functions at a nuclear power plant generate vast amounts of data, with scheduled and unscheduled outages being a prime example of a source of some of the most complex data sets at the plant. To make matters worse, modern information and communications technology is making it possible to collect and store data faster than our ability to use it for making decisions. However, in most applications, especially outages, raw data has no value in itself; instead, managers, engineers and other specialists want to extract the information contained in it. The complexity and sheer volume of data could lead to information overload, resulting in getting lost in data that may be irrelevant to the task at hand, processed in an inappropriate way, or presented in an ineffective way. To prevent information overload, many data sources are ignored so production opportunities are lost because utilities lack the ability to deal with the enormous data volumes properly. Decision-makers are often confronted with large amounts of disparate, conflicting and dynamic information, which are available from multiple heterogeneous sources. Information and communication technologies alone will not solve this problem. Utilities need effective methods to exploit and use the hidden opportunities and knowledge residing in unexplored data resources. Superior performance before, during and after outages depends upon the right information being available at the right time to the right people. Acquisition of raw data is the easy part; instead, it is the ability to use advanced analytical, data processing and data visualization methods to turn the data into reliable information and comprehensible, actionable information. Techniques like data mining, filtering and analysis only work reliably for well-defined and well-understood problems. The path from data to decision is more complex. The ability to communicate knowledge during outages and emergent issues is crucial. This paper presents an approach to turn the unused data into an opportunity: applying principles from semiotics, human factors and visual analytics to transform the traditional way of processing outage data into media that will improve the collective situation awareness, knowledge, decisions, actions and overall performance of the entire outage team, and also support the reliability, quality and overall effectiveness of maintenance work. The application of the proposed visualization methods will become the medium of a semi-automated analytical process where humans and machines cooperate using their respective, distinct capabilities for the most effective results.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Ultrascale Visualization of Climate Data

To support interactive visualization and analysis of complex, large-scale climate data sets, UV-CDAT integrates a powerful set of scientific computing libraries and applications to foster more efficient knowledge discovery. Connected through a provenance framework, the UV-CDAT components can be loosely coupled for fast integration or tightly coupled for greater functionality and communication with other components. This framework addresses many challenges in the interactive visual analysis of distributed large-scale data for the climate community.

97 MATHEMATICS AND COMPUTING↗

Big Data and Deep data in scanning and electron microscopies: functionality from multidimensional data sets

The development of electron, and scanning probe microscopies in the second half of the twentieth century have produced spectacular images of internal structure and composition of matter with, at nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition and analysis. The progress in imaging technologies in the beginning of the twenty first century has opened the proverbial floodgates of high-veracity information on structure and functionality. High resolution imaging now allows information on atomic positions with picometer precision, allowing for quantitative measurements of individual bond length and angles. Functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this data into physically and chemically relevant information from imaging data.

36 MATERIALS SCIENCE↗

EDX ClaiMM

EDX ClaiMM is a centralized data & analytical platform designed to revolutionize U.S. critical minerals and materials (CMM) activities. By providing a robust digital infrastructure, ClaiMM will accelerate the combination, leveraging, and rapid utilization of vital data, advanced tools, and cutting-edge research advancements in CMM. This adaptive digital research hub connects the CMM community to essential knowledge products and offers access to interoperable datasets, databases, models, software, and tools from the National Energy Technology’s (NETL’s) Energy Data eXchange (EDX) and other authoritative sources, serving both public and private sectors. EDX ClaiMM delivers AI-informed solutions to address fundamental knowledge gaps and fosters the innovation of new techniques for enhanced characterization and recovery of CMMs within the U.S. By leveraging cloud-hosted, scalable digital infrastructure, ClaiMM meets public–private applied energy needs. It equips the CMM community with priority digital resources that harness on-site and cloud compute capabilities, enabling big data storage, advanced processing, analytics, and visualization.

Critical Materials; Critical Minerals; Rare Earth ↗

Hardening DOE R&D Software Tools for Web-based Visualization SBIR Phase I Final Report

Ubiquitous web-based visualization is essential to delivering large-scale data visualization to various stakeholders, from the scientist to the board member. These stakeholders will not tolerate a stalled application or a pop-up window asking them to wait for the processing to complete. They require a responsive and interactive visualization environment with high-quality imagery suitable for detailed analysis and boardroom presentations. At Kitware, Inc., we have accomplished web visualization to this point, leveraging state-of-the-art tools like HTML5, CSS3, SVG, Canvas, and WebGL. Solutions that leverage a combination of these technologies are necessary to handle workloads that vary significantly in data size efficiently. However, it is not always practical to move large data to the web client for visualization. Kitware's ParaView as a Service combines client-side visualization using both distributed processing and remote rendering on big data impractical to move. Existing distributed processing and remote rendering solution's interactivity is below the expectations of web-based applications. Our project examined proposed solutions to the areas outlined above in ParaView as a Service. We have investigated concurrent pipelines, streaming images, progressive rendering, and optimization of algorithms and data movement to address these concerns. For the Phase I project, we completed the proposed work plan. As a result, the project produced three prototypes of essential importance for web visualization and the ParaView as a Service community. We created a simple desktop application for an interactive streamline placement prototype, a web-based interactive streamline placement prototype, and a web-based progressive rendering utilizing raytracing prototype. These prototypes relied on the hardening of emerging software toolkits funded by the Department of Energy (DOE) Advanced Scientific Computing Research (ASCR) program (such as ParaView, VTK-m, and Mochi). We blended these components into web-based visualization prototypes that meet the industry's expectations for interactivity and responsiveness. The Phase I project had four essential focus areas: 1. Develop prototype ParaView as a Service backend server using asynchronous, non-blocking design principles. 2. Develop a prototype web application that uses the ParaView as a Service backend server for remote data visualization. 3. Implement image streaming with encoding/compression and progressive rendering capabilities in the proposed platform. 4. Evaluate the prototype developed and summarize observations, including the challenges and pitfalls of our approach. After our successful completion of Phase I, we are strongly positioned to propose a successful Phase II project.

Geveci, Berk↗

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.

Biochemistry & Molecular Biology↗