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The two primary goals for this report were the design, contruction and modeling of parallel disk arrays for scientific visualization and animation, and a study of the IO requirements of highly parallel applications. In addition, further work in parallel display systems required to project and animate the very high-resolution frames resulting from our supercomputing simulations in ocean circulation and compressible gas dynamics.
Two experiments examined perceived spatial orientation in a small environment as a function of experiencing that environment under three conditions: real-world, desktop-display (DD), and head-mounted display (HMD). Across the three conditions, participants acquired two targets located on a perimeter surrounding them, and attempted to remember the relative locations of the targets. Subsequently, participants were tested on how accurately and consistently they could point in the remembered direction of a previously seen target. Results showed that participants were significantly more consistent in the real-world and HMD conditions than in the DD condition. Further, it is shown that the advantages observed in the HMD and real-world conditions were not simply due to nonspatial response strategies. These results suggest that the additional idiothetic information afforded in the real-world and HMD conditions is useful for orientation purposes in our presented task domain. Our results are relevant to interface design issues concerning tasks that require spatial search, navigation, and visualization.
Conveying spacecraft health and status information to mission engineering personnel during various mission phases, including mission operations, is a requirement to achieve a successful mission. For NASA/JPL spacecraft, that often means displaying hundreds of telemetry channels from a variety of sensors and components emitting data at rates varying from 1hz-100hz (and faster) in a way that allows the operations team to quickly evaluate the health of the vehicle, identify any off-nominal states and resolve any issues. In this paper we will discuss the system design, requirements and use cases of three telemetry processing and visualization systems recently developed and deployed by our team for NASA's Low Density Supersonic Decelerator (LDSD) test vehicle, NASA's Soil Moisture Active/Passive (SMAP) orbiter, and JPL's Sampling Lab Universal Robotic Manipulator (SLURM) test bed.
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigrid-based hierarchical data representations hold promise as a solution to this problem, allowing for flexible conversion between different fidelities so that, for example, data can be created at high fidelity and then transferred or stored at lower fidelity via logically simple and mathematically sound operations. However, the effective use of such representations has been hindered until now by the relatively high costs of creating, accessing, reducing, and otherwise operating on such representations. We describe here highly optimized data refactoring kernels for GPU accelerators that enable efficient creation and manipulation of data in multigrid-based hierarchical forms. We demonstrate that our optimized design can achieve up to 250 TB/s aggregated data refactoring throughput—83% of theoretical peak—on 1024 nodes of the Summit supercomputer. We showcase our optimized design by applying it to a large-scale scientific visualization workflow and the MGARD lossy compression software.
The D and D Knowledge Management Information tool (D and D KM- IT) is a web-based knowledge management information tool built for the D and D user community. A feasibility study is being conducted on cross-platform application frameworks for their viability to the D and D KM-IT mobile needs. Currently, three main Operating Systems comprise the majority of the mobile market IOS, Android, and Windows, requiring a separate application for each device. Cross-development software allows the development of one application using one language, that can be ported or deployed on multiple devices with little effort. Using Xamarin, a native cross-platform framework, create a shared mobile application using Visual Studio. Build a D and D KMIT mobile application designed for the three major mobile platforms. Use Xamarin to compile the application into native applications for the three target platforms. Build and deploy the application on all three mobile platforms. Perform benchmark tests of the developed applications to analyze the performance on the three platforms. A large portion of the business logic of the application has been shared, eliminating development redundancy and minimizing planning, design, and validation time. The user interface development could not fully be shared cross-platform and some platform specific code must be individually implemented for each device. Maintenance, testing and design phases of the software engineering process were reduced from one for each device to having just one shared cycle processes. A less diverse skill-set for developers is required with cross-platform development. Some processes within the development cycle still require redundant activities.
Core simulation tools utilizing Monte Carlo transport methods represent the 'Holy Grail' for nuclear reactor analysis. However, deterministic transport methods pose a number of advantages to Monte Carlo from a computational perspective, which seems likely to make these methods more viable in the near to intermediate term. Deterministic methods almost universally discretize the energy domain in a few to hundreds of energy groups. One of the key challenges for deterministic transport-based methods to be viable is accurate, reactor agnostic multi-group cross section generation. This paper describes tools for data processing and visualization of multigroup cross sections generated on high-fidelity spatial meshes from the OpenMC Monte Carlo code. Next generation deterministic transport reactor core modeling tools will require multi-group cross section (MGXS) generation on high-fidelity spatial meshes. High-fidelity MGXS are necessary for the accurate simulation of neutron physics in nuclear reactors with a high degree of spectral heterogeneity - e.g., large localized neutron flux gradients throughout a reactor core. Although many local (e.g., pin cell) approximations have been developed to model the scalar flux to compute spectral-averaged MGXS, these techniques cannot account for complex intra-pin and intra-assembly spatial self-shielding effects. Monte Carlo presents the most accurate method for reactor agnostic multi-group cross section generation since it does not require the use of local approximations to the flux. Monte Carlo has been proposed as a highly desirable framework for reactor agnostic cross section generation for many years. Monte Carlo methods can effectively embed all of the relevant spectral effects into the MGXS through the random particle tracking and interaction sampling processes. Previous work has applied MCNP and Serpent to compute few-group constants for diffusion calculations, and OpenMC to compute multi-group cross sections for transport-based methods. The primary difficulty with using Monte Carlo for MGXS generation is the tremendous computational resources required to adequately converge multi-group cross sections in each spatial region and energy group. Recent development of the OpenMC Monte Carlo code for high-performance computing platforms has made multigroup cross section generation from Monte Carlo more tractable in the foreseeable future. The recent release of OpenMC v0.7.1 includes a new Python-based module for MGXS generation. The MGXS generation module leverages many of the algorithms and features for 'big data' analysis of large tally datasets on complex spatial meshes. This paper describes the data management workflow and visualization tools which have been developed to explore the plethora of MGXS data generated with OpenMC. It is the authors' hope that the development of the visualization tool described in this paper will ultimately enable the design of machine learning algorithms to accelerate the convergence rate of high-fidelity multi-group cross section generated with Monte Carlo. (authors)
Radon daughter plate-out may pose a fundamental limitation to realizing large-scale superconducting quantum computers. Radon daughters plated-out on a superconducting qubit can decay, releasing energy into the substrate, producing phonons. Phonon-induced quasiparticles then generate bit-flip errors and reduce coherence times. We seek to increase plate-out on chips to enable future study of the impact of radon on coherence times. In this project, we utilize COMSOL Multiphysics simulations to visualize electric field configuration and model ion transport to finalize design of a device to increase radon plate-out on qubit chips.
A procedure and software to implement the procedure have been devised to enable conversion from a tree representation to a graph representation of the requirements governing the development and design of an engineering system. The need for this procedure and software and for other requirements-management tools arises as follows: In systems-engineering circles, it is well known that requirements- management capability improves the likelihood of success in the team-based development of complex systems involving multiple technological disciplines. It is especially desirable to be able to visualize (in order to identify and manage) requirements early in the system- design process, when errors can be corrected most easily and inexpensively.
Visualizing the dynamical response of material heterointerfaces is increasingly important for the design of hybrid materials and structures with tailored properties for use in functional devices. In situ characterization of nanoscale heterointerfaces such as metal-semiconductor interfaces, which exhibit a complex interplay between lattice strain, electric potential, and heat transport at subnanosecond time scales, is particularly challenging. Here in this work, we use a laser pump/X-ray probe form of Bragg coherent diffraction imaging (BCDI) to visualize in three-dimension the deformation of the core of a model core/shell semiconductor-metal (ZnO/Ni) nanorod following laser heating of the shell. We observe a rich interplay of radial, axial, and shear deformation modes acting at different time scales that are induced by the strain from the Ni shell. We construct experimentally informed models by directly importing the reconstructed crystal from the ultrafast experiment into a thermo-electromechanical continuum model. The model elucidates the origin of the deformation modes observed experimentally. Our integrated imaging approach represents an invaluable tool to probe strain dynamics across mixed interfaces under operando conditions.
In this study two waverider configurations are analyzed numerically and experimentally. The geometries are designed using an inverse design and optimization code with the optimization procedure driven by maximized L/D and limited by geometric constraints. Euler and Navier-Stokes numerical simulations are performed with a partially flux-split finite-difference code developed at NASA Ames, F3D. Experimental data is obtained at NASA Langley in the Unitary Plan Wind Tunnel (UPWT) and the 20-Inch Mach 6 wind tunnel for flow visualization, force/moment, and surface pressure measurements at both on- and off-design conditions. Euler simulations are compared with analytic results obtained from conical flow theory, and Navier-Stokes simulations are compared with experimental results both qualitatively and quantitatively for on- and off-design conditions.
The emergence of technologies in which rare-earth elements provide critical functionality has increased the demand for these materials, with important implications for supply security. Recycling provides an option for mitigating supply risk and for creating economic value from the resale of recovered materials. While solvent extraction is a proven technology for rare-earth recovery and separation, its application often requires extensive trial-and-error experimentation to estimate parameter values and determine experimental design configurations. We describe a modeling strategy based on Gibbs energy minimization that incorporates parameter estimation for required thermodynamic properties as well as process design for solvent extraction and illustrate its applicability to rare earths separation. Visualization analysis during parameter estimation revealed a linear relationship between the standard enthalpies of the extractant and respective organo-metal complexes, analogous to the additivity principle for predicting molar volumes of organic compounds. Establishing this relationship reduced the size of the parameter estimation problem and yielded good agreement between model predictions and reported equilibrium extraction data, validating the property estimates for the organic phase species. Design exploration and optimization results map the space of feasible solvent extraction column configurations and identify the set of optimal design parameter values that meet recovery and purity targets.
Heuristics have been used since the early 1990's in the Human Computer Interaction (HCI) domain, for evaluating user interfaces. Most notably, heuristics developed by Nielsen and Molich (1990) have been used by the HCI community. Forsell and Johansson (2010) used the same methodology as Nielsen and Molich and developed a set of 10 heuristics for information visualization. Currently, neither the information visualization or the visual analytics community has adopted these heuristics. However, several studies have been conducted using heuristics. Hearst et al. (2015) found that using the heuristics for visualizations along with asking questions about the data resulted in complementary results as the questions about the data could compensate for heuristics that were difficult to understand and apply. Heli Väätäjä et al. (2016) found that heuristics related to interaction, veracity, and aesthetics needed to be added to the Forsell and Johansson (2010) set. They also noted that a lack of domain knowledge caused the evaluators issues in conducting a heuristic evaluation. We conducted a controlled experiment to determine what factors might impact the expert evaluators' capability to conduct a heuristic evaluation of a static information visualization. In particular, we wanted to understand how their familiarity with the visualizations and their experience in conducting heuristic evaluations impacted their evaluations of visualizations. We recruited 10 participants who were domain experts in the visualization field each having experience with one of more of the following: user experience design or research, user interface development, and human computer interaction research. We used a heuristic set proposed by Forsell and Johansson. Participants were also asked to complete a demographic survey in which they rated their experience with each visualization type generally, their experience conducting heuristic evaluations, their experience using each visualization type to extract information, and their experience developing and creating each visualization type. The participants were shown a series of 5 common visualizations (Scatterplots, Sunbursts, Tree Maps, Parallel Sets, and Area Graphs) all displaying the VAST 2008 Mini Challenge Two- Migrant Boats data . Participants were given a suggested analytical question related to each visualization to keep in mind as they applied the 10 heuristics to each visualization. They were asked if they could find any usability problems in the visualizations and which heuristic(s) helped them to identify these problems. The participants were asked to rate how relevant each heuristic was for evaluating each visualization along with rating how confident they felt in their ability to apply the heuristic to evaluate each visualization. At the conclusion of the study, participants provided qualitative feedback on the usability issues of each visualization. Additionally, we collected eye tracking data on 6 of the 10 participants. We will be using this data to determine how long participants looked at the various visualizations and what parts of the visualizations they focused on. We had two hypotheses for this empirical evaluation: 1.The Forsell and Johansson heuristics of Orientation and help, Data set reduction, and Prompting are less useful for static visualizations because they require some interaction in order to test completely. 2.Participants that do not have experience with heuristic evaluation will provide lower evaluation scores for the heuristics compared to those that have experience. Related to hypothesis 1, as our study used only static visualizations, interaction-related usability challenges should be harder to identify. This should result in lower usefulness ratings for those heuristics. Preliminary results indicate that participants found it more difficult to apply heuristics to unfamiliar visualizations.
A key trend facing extreme-scale computational science is the widening gap between computational and I/O rates, and the challenge that follows is how to best gain insight from simulation data when it is increasingly impractical to save it to persistent storage for subsequent visual exploration and analysis. One approach to this challenge is centered around the idea of in situ processing, where visualization and analysis processing is performed while data is still resident in memory. This paper examines several key design and performance issues related to the idea of in situ processing at extreme scale on modern platforms: Scalability, overhead, performance measurement and analysis, comparison and contrast with a traditional post hoc approach, and interfacing with simulation codes. We illustrate these principles in practice with studies, conducted on large-scale HPC platforms, that include a miniapplication and multiple science application codes, one of which demonstrates in situ methods in use at greater than 1M-way concurrency.
Micro-Helm is a PC based mission visualization system that can be employed for comprehensive monitoring of mission system states during operation. Micro-Helm was designed as a cost-effective system solution that can be adapted to multiple missions in various mission lifecycle phases. It has been successfully applied to DS1 mission during its entire operation phases and Mars Odyssey mission during its launch and orbit-insertion phase, providing scenario design and validation.
Metal anode instability, including dendrite growth, metal corrosion, and hetero-ions interference, occurring at the electrolyte/electrode interface of aqueous batteries, are among the most critical issues hindering their widespread use in energy storage. Herein, a universal strategy is proposed to overcome the anode instability issues by rationally designing alloyed materials, using Zn-M alloys as model systems (M = Mn and other transition metals). An in-situ optical visualization coupled with finite element analysis is utilized to mimic actual electrochemical environments analogous to the actual aqueous batteries and analyze the complex electrochemical behaviors. The Zn-Mn alloy anodes achieved stability over thousands of cycles even under harsh electrochemical conditions, including testing in seawater-based aqueous electrolytes and using a high current density of 80 mA cm –2 . The proposed design strategy and the in-situ visualization protocol for the observation of dendrite growth set up a new milestone in developing durable electrodes for aqueous batteries and beyond.
Visualizing large document collections using a spatial layout of terms can enable quick overviews of information. However, these metaphors (e.g., word clouds, tag clouds, etc.) often lack interactivity to explore the information and the location and rendering of the terms are often not based on mathematical models that maintain relative distances from other information based on similarity metrics. Further, transitioning between levels of detail (i.e., from terms to full documents) can be challanging. In this paper, we present Typograph, a multi-scale spatial exploration visualization for large document collections. Based on the term-based visualization methods, Typograh enables multipel levels of detail (terms, phrases, snippets, and full documents) within the single spatialization. Further, the information is placed based on their relative similarity to other information to create the “near = similar” geography metaphor. This paper discusses the design principles and functionality of Typograph and presents a use case analyzing Wikipedia to demonstrate usage.
A high-temperature optical window is essential to the optical diagnostics of high-temperature combustion rigs. Laser Doppler velocimetry, schlieren photography, light sheet visualization, and laser-induced fluorescence spectroscopy are a few of the tests that require optically clear access to the combustor flow stream. A design was developed for a high-temperature window that could withstand the severe environment of the NASA Lewis 3200 F Lean Premixed Prevaporized (LPP) Flame Tube Test Rig. The development of this design was both time consuming and costly. This report documents the design process and the lessons learned, in an effort to reduce the cost of developing future designs for high-temperature optical windows.
In recent years, an increasing number of safety-relevant structures are designed and manufactured from carbon fiber reinforced polymers (CFRP) in order to reduce weight of airplanes by taking the advantage of their specific strength into account. Non-destructive testing (NDT) methods for quantitative defect analysis of damages are liquid- or air-coupled ultrasonic testing (UT), phased array ultrasonic techniques, and active thermography (IR). The advantage of these testing methods is the applicability on large areas. However, their quantitative information is often limited on impact localization and size. In addition to these techniques, Airbus Group Innovations operates a micro x-ray computed tomography (μ-XCT) system, which was developed for CFRP characterization. It is an open system which allows different kinds of acquisition, reconstruction, and data evaluation. One main advantage of this μ-XCT system is its high resolution with 3-dimensional analysis and visualization opportunities, which enables to gain important quantitative information for composite part design and stress analysis. Within this study, different NDT methods will be compared at CFRP samples with specified artificial impact damages. The results can be used to select the most suitable NDT-method for specific application cases. Furthermore, novel evaluation and visualization methods for impact analyzes are developed and will be presented.