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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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NASA NTRSDate not supplied

Beyond Fair: Engagement, Data Usability, and Open Community Productivity through the NASA Open Science Data Repository

The FAIR principle (findable, accessible, interoperable, and reusable) governs the storage and sharing of NASA space biology and health data[1]. These guiding principles maximize reuse of data and the reproducibility of scientific findings. The NASA Open Science Data Repository (OSDR; an expansion of NASA GeneLab) was built on the FAIR principles and houses over 500 studies and close to 1000 datasets from decades of space life sciences experiments. OSDR embodies the FAIR principles through data governance that includes mediated, embargoed, and fully open access data. The FAIR data governance principles were recently proposed to be expanded to encompass a FAIREST framework for assessing research data repositories (FAIR + Engagement, Social connections, and Trust)[2]. FAIREST emphasizes the importance of data repositories engaging with the scientific community and gaining the trust of researchers regarding data quality. Trust also refers to the TRUST principles developed for assessment of digital repositories: Transparency, Responsibility, User Focus, Sustainability, Technology[3]. We present the “Open Science for Life in Space” Analysis Working Groups (AWGs) as evidence regarding the power of engagement, social connections, and trust which has enhanced OSDR’s capabilities and productivity. AWG members engage in two main activities. One, members provide feedback on OSDR scientific standards for data ingestion, curation, and reuse (study, subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability). Two, AWG members collaborate to mine-reuse OSDR data to conduct scientific analysis. With nearly 800 active members, the AWGs have resulted in 32 publications re-using OSDR data and contributed many papers in two major special issues in Cell (2020) and Nature (2024). AWGs also serve as networking groups, facilitate social connections between researchers at all levels of experience, and also have a social online ‘Forum’ used to keep members informed on projects and opportunities. This community-centric, productive, and trustworthy data culture has resulted in a broader effect with international space agencies, academics, and the commercial space sector wanting to submit their data to OSDR. Ten studies of Inspiration 4 data were recently publicly released by OSDR, as were some JAXA human data. Coming up soon in OSDR are data submissions from the European Space Agency, Virgin Galactic PIs, and SpaceX Polaris Dawn. A major benefit of OSDR is the array of standardized and uniformly formatted data (which was developed through AWG member consensus), from which visualization tools, analysis tools, and machine learning models can be built or trained. This talk will cover the Multi-Study Visualization Tool, the Environmental Data Application, RadLab, and a UCSF-NSF funded knowledge graph biomedical health discovery tool ‘SPOKE’ currently being integrated with OSDR. OSDR also provides training programs in bioinformatics and machine learning to improve the scientific community’s awareness of data availability and to boost their ability to perform data analysis. The increasing engagement of the scientific community and the public with technologies powered by artificial intelligence (AI) heightens the need for data analysis to be transparent. The AI for Life in Space initiative leverages the data products provided in OSDR to train AI models, with an emphasis on explainable and trustworthy AI, which would not be possible without FAIR data and metadata. Overall, here we will demonstrate the importance for NASA life sciences data repositories to adhere to the FAIREST framework, by providing examples and success stories from different aspects of OSDR.

data↗
NASA NTRS2011

Toward Intelligent Software Defect Detection

Source code level software defect detection has gone from state of the art to a software engineering best practice. Automated code analysis tools streamline many of the aspects of formal code inspections but have the drawback of being difficult to construct and either prone to false positives or severely limited in the set of defects that can be detected. Machine learning technology provides the promise of learning software defects by example, easing construction of detectors and broadening the range of defects that can be found. Pinpointing software defects with the same level of granularity as prominent source code analysis tools distinguishes this research from past efforts, which focused on analyzing software engineering metrics data with granularity limited to that of a particular function rather than a line of code.

Benson, Markland J.↗
NASA NTRS2022

A High-fidelity Performance and Sensitivity Analysis of X-ray Pulsar Navigation in Near-Earth and Cislunar Orbits

Millisecond X-ray pulsars are rapidly rotating neutron stars that emit broadband electromagnetic radiation, including X-rays. These emissions can appear as pulsations to distant observers with long-term stabilityrivaling laboratory atomic clocks. The concept of using X-ray pulsars for navigation, commonly referred to as XNAV, has a long history in the research literature. Notably, in 2017, NASA’s Station Explorer for X-ray Timing and Navigation (SEXTANT) mission,a technology enhancement to the Neutron Star Interior Composition Explorer, made a successful on-orbit demonstration of XNAV on the International Space Station (ISS) in Low-Earth-Orbit. This paper investigates the performance of XNAV in Earth and lunar regimes using a new high-fidelity XNAV analysis tool that builds on tools developed for SEXTANT and uses recent updates to the Goddard Enhanced Onboard Navigation System flight software. Specifically, we study the use of XNAV in an ISS-like orbit for comparison with SEXTANT results, in a highly-inclined geosynchronous orbit, and in an Earth-Moon L1 libration point Near Rectilinear Halo Orbit similar to that proposed for the planned lunar Gateway space station. This paper provides a high-level description of the XNAV analysis tool, baseline predicted performance results, selected sensitivity analyses, and conclusions.

Luke B Winternitz↗
NASA NTRS2019

Automatic Incorporation of Flaws into Cad and FEA Models Based On Measured NDE Data

Automatic incorporation of non-destructive evaluation (NDE) data into CAD and finite element (FE) models for structural analysis is essential for accurate residual-life and strength analyses of composite structures. Currently, two significant but independent challenges exist for incorporating NDE data in such analyses: interpretation of the NDE signals to locate and characterize the structural defects and the incorporation of that information into structural analysis tools. The first challenge will be addressed through linear inversion of calculated synthetic responses generated by a simulation engine to evaluate flaw size, location, and other characteristics. The second challenge will be addressed by building on prior and parallel defect modeling efforts to integrate suitable flaw models into the CAD/finite-element representation of a specimen. Ultrasonic NDE information is usually represented using C-Scans of time-of-flight or intensity information. Other modalities such as thermography and computed tomography (CT) could also be used to characterize the structure. This information will first be projected into material coordinates and then aligned with the specimen’s finite element mesh. The NDE data can then be incorporated by inserting flaw geometry into the CAD and FE analysis model and automatic modification of local material properties and boundary conditions. This process will enable the use of structural analysis tools even for practical modalities such as ultrasound and thermography which may not directly give accurate-enough geometric information to fully define the flaw. The full flaw characteristics will be inferred using prior expert and modeling knowledge to categorize flaw signatures. Outputs for this project will include demonstrated methods for creating structural analysis geometric models that incorporate flaw characterization information; these models can then be used directly in popular commercial FEA software. In addition, the primary deliverable for the project will include a software that will be able to automatically incorporate different kinds of flaws into an existing FE models. The models will be generated in standard file formats to enable direct import into the analysis software.

Adarsh Krishnamurthy↗
NASA NTRS2005

A Method for Aircraft Concept Selection Using Multicriteria Interactive Genetic Algorithms

The problem of aircraft concept selection has become increasingly difficult in recent years as a result of a change from performance as the primary evaluation criteria of aircraft concepts to the current situation in which environmental effects, economics, and aesthetics must also be evaluated and considered in the earliest stages of the decision-making process. This has prompted a shift from design using historical data regression techniques for metric prediction to the use of physics-based analysis tools that are capable of analyzing designs outside of the historical database. The use of optimization methods with these physics-based tools, however, has proven difficult because of the tendency of optimizers to exploit assumptions present in the models and drive the design towards a solution which, while promising to the computer, may be infeasible due to factors not considered by the computer codes. In addition to this difficulty, the number of discrete options available at this stage may be unmanageable due to the combinatorial nature of the concept selection problem, leading the analyst to arbitrarily choose a sub-optimum baseline vehicle. These concept decisions such as the type of control surface scheme to use, though extremely important, are frequently made without sufficient understanding of their impact on the important system metrics because of a lack of computational resources or analysis tools. This paper describes a hybrid subjective/quantitative optimization method and its application to the concept selection of a Small Supersonic Transport. The method uses Genetic Algorithms to operate on a population of designs and promote improvement by varying more than sixty parameters governing the vehicle geometry, mission, and requirements. In addition to using computer codes for evaluation of quantitative criteria such as gross weight, expert input is also considered to account for criteria such as aeroelasticity or manufacturability which may be impossible or too computationally expensive to consider explicitly in the analysis. Results indicate that concepts resulting from the use of this method represent designs which are promising to both the computer and the analyst, and that a mapping between concepts and requirements that would not otherwise be apparent is revealed.

Buonanno, Michael↗
NASA NTRS2021

Lunar Terrain Coverage Analysis Data Delivery Workflow

In this work, we are developing a lunar terrain database to enable fast rendering of sun illumination and earth visibility for a proposed coverage analysis tool. This development will advance lunar mission design and formulation for current and future communications architectures, and will aid in lunar surface mission planning and communications/navigation operations. Our effort can be described in three steps: (1) we parallelize a brute force algorithm, which computes elevation masks from laser altimetry data acquired by the Lunar Reconnaissance Orbiter’s (LRO) Lunar Orbiter Laser Altimeter (LOLA); (2) we investigate parallel I/O methods to store terrain mask information from step (1) into a parallel file system; and (3) we finally deliver data to the terrain coverage analysis tool.

Michels, Dominik↗
NASA NTRS2018

Helium Pressurization Modeling of the EDU Submerged Diffuser Using Thermal Desktop

NASA is currently working on developing its predictive modeling capabilities to support future missions that require long term storage of cryogenic propellants. Through the Evolvable Cryogenics project, Development and Validation of Analysis Tools (DVAT) task, both CFD and multi-node fluid modeling tools are being validated against to increase the following cryogenic fluid management technologies: Self-Pressurization, Pressure Control, Helium Pressurization, de-stratification, jet mixing, Tank and transfer line chill-down, tank filling and transfer. NASA is using previous and future micro-gravity experiments to validate both types of models. Recent activities include: Validation of the Tank Pressure Control Experiment (TPCE), Hydrogen and Helium pressurization, and Completion of the Zero Boil Off Tank (ZBOT) experiment on ISS. Future activities include validation of the ZBOT experiment with both CFD and multi-node analysis tools, validation of the Cryogenic Demonstration System of the Robotic Refueling Mission (RRM3).

low-gravity fluid modeling↗
NASA NTRS2018

SLS Navigation Model-Based Design Approach

The SLS Program chose to implement a Model-based Design and Model-based Requirements approach for managing component design information and system requirements. This approach differs from previous large-scale design efforts at Marshall Space Flight Center where design documentation alone conveyed information required for vehicle design and analysis and where extensive requirements sets were used to scope and constrain the design. The SLS Navigation Team has been responsible for the Program-controlled Design Math Models (DMMs) which describe and represent the performance of the Inertial Navigation System (INS) and the Rate Gyro Assemblies (RGAs) used by Guidance, Navigation, and Controls (GN&C). The SLS Navigation Team is also responsible for the navigation algorithms. The navigation algorithms are delivered for implementation on the flight hardware as a DMM. For the SLS Block 1-B design, the additional GPS Receiver hardware is managed as a DMM at the vehicle design level. This paper provides a discussion of the processes and methods used to engineer, design, and coordinate engineering trades and performance assessments using SLS practices as applied to the GN&C system, with a particular focus on the Navigation components. These include composing system requirements, requirements verification, model development, model verification and validation, and modeling and analysis approaches. The Model-based Design and Requirements approach does not reduce the effort associated with the design process versus previous processes used at Marshall Space Flight Center. Instead, the approach takes advantage of overlap between the requirements development and management process, and the design and analysis process by efficiently combining the control (i.e. the requirement) and the design mechanisms. The design mechanism is the representation of the component behavior and performance in design and analysis tools. The focus in the early design process shifts from the development and management of design requirements to the development of usable models, model requirements, and model verification and validation efforts. The models themselves are represented in C/C++ code and accompanying data files. Under the idealized process, potential ambiguity in specification is reduced because the model must be implementable versus a requirement which is not necessarily subject to this constraint. Further, the models are shown to emulate the hardware during validation. For models developed by the Navigation Team, a common interface/standalone environment was developed. The common environment allows for easy implementation in design and analysis tools. Mechanisms such as unit test cases ensure implementation as the developer intended. The model verification and validation process provides a very high level of component design insight. The origin and implementation of the SLS variant of Model-based Design is described from the perspective of the SLS Navigation Team. The format of the models and the requirements are described. The Model-based Design approach has many benefits but is not without potential complications. Key lessons learned associated with the implementation of the Model Based Design approach and process from infancy to verification and certification are discussed

Oliver, T. Emerson↗
DOE OSTI2020

Power Grid Contingency Analysis with Machine Learning: A Brief Survey and Prospects

We briefly review previous applications of machine learning (ML) in power grid analyses and introduce our ongoing effort toward developing a generative-adversarial (GA) model for fast and reliable grid contingency analyses. According to our review, the persisting limitation of traditional ML techniques in grid analyses is the need for an exhaustive amount of training data for model generalization and accurate predictions. GA models overcome this limitation by first learning true data distribution from a small training set, from which new samples assimilating true data are generated with some variations. Subsequently, GA models can transfer learn or super-generalize with increased accuracy, that is, accurately predict n - (k + 2) contingencies from a small n - k training set and generated n - (k + 1) data. The joint effort between Idaho National Lab and Florida State University strives to develop a zero-shot and deep learning-based contingency analysis tool, named Smart Contingency Analysis Neural Network (SCANN), by leveraging the aforementioned advantages of GA models. The basic architecture of SCANN stems from the Latent Encoding of Atypical Perturbations network combined with an adversarial network, and it is designed to generate imbalanced power flow data from learned true data distributions for prediction purposes. Here we also introduce the abstract concept of resilience-chaos plots, a new resilience characterization tool proposed to complement SCANN by aiding in the assessment of large amounts of high-order contingency predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Performance analysis and data reduction for exascale scientific workflows

Chimbuko is the first in situ, scalable, workflow-level performance analysis tool for trace-level analysis and visualization of application performance. This tool was developed by the Co-design Center for Online Data Analysis and Reduction and funded by the U.S. Department of Energy’s Exascale Computing Project. We provide a detailed description of Chimbuko’s architecture and illustrate our online and offline visualization with multiple use cases. We also present results for the deployment and scalability of the tool as applied to a high-energy physics workflow running at large scale on the Frontier supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cloud based tool for analysis of chemical kinetic mechanisms

The size and complexity of chemical kinetic models used to simulate combustion of hydrocarbon fuels continue to increase with our improved understanding of the underlying physical mechanisms, along with the improved ability to utilize such models as the result of increased computational power and efficiency. As mechanisms grow beyond thousands of species and tens of thousands of reactions, it becomes increasingly difficult to manually check for errors and physical inconsistencies. In this work, we present several automated methods to check for such issues in the specification of chemical reaction models. First, we demonstrate how discontinuities in thermodynamic data can cause simulation difficulties manifested as long wall-clock times and convergence failures. To correct this type of problem, we describe an automated method for refitting thermodynamic parameters. We also outline several methods to check the timescales of reaction rate coefficients to ensure physical consistency. All the methods are made available through a web-based tool (https://combustiontools.llnl.gov) intended to aid mechanism developers and users to improve the accuracy and performance of fuel models used by the combustion community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Two-Dimensional Non-Linear Magnetic Equivalent Circuit Model to Facilitate the Preliminary Design of a Normal Conducting Quadrupole

Normal conducting quadrupoles have been used to focus charged particle beams in synchrotrons, beam transfer lines, medical linacs, etc. for a long time. Optimization techniques based on analytical expressions combined with the use of a numerical field analysis tool exist in the literature for the design of an optimal pole tip shape. However, the initial shape and dimensions of the remaining yoke (including the pole itself and yoke base that act as the return path) are usually less well defined. This article discusses a design methodology for a normal conducting quadrupole based on a two-dimensional magnetic equivalent circuit. Here, this approach considers the geometry of the entire magnet and the non-linear behavior of the yoke material, thereby eliminating the initial iterations. The design outcome of this exercise serves as a competent starting point and can then be used to refine the pole tip, pole taper, yoke size, and add other geometrical features to achieve the required field quality, gradient, coil considerations, etc. by employing a finite element analysis tool. An example design study to demonstrate the proposed methodology is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1983

Manipulator interactive design with interconnected flexible elements

This paper describes the development of an analysis tool for the interactive design of control systems for manipulators and similar electro-mechanical systems amenable to representation as structures in a topological chain. The chain consists of a series of elastic bodies subject to small deformations and arbitrary displacements. The bodies are connected by hinges which permit kinematic constraints, control, or relative motion with six degrees of freedom. The equations of motion for the chain configuration are derived via Kane's method, extended for application to interconnected flexible bodies with time-varying boundary conditions. A corresponding set of modal coordinates has been selected. The motion equations are imbedded within a simulation that transforms the vector-dyadic equations into scalar form for numerical integration. The simulation also includes a linear, time-invariant controler specified in transfer function format and a set of sensors and actuators that interface between the structure and controller. The simulation is driven by an interactive set-up program resulting in an easy-to-use analysis tool.

Singh, R. P.↗
NASA NTRS1989

A zonal method for modeling powered-lift aircraft flow fields

A zonal method for modeling powered-lift aircraft flow fields is based on the coupling of a three-dimensional Navier-Stokes code to a potential flow code. By minimizing the extent of the viscous Navier-Stokes zones the zonal method can be a cost effective flow analysis tool. The successful coupling of the zonal solutions provides the viscous/inviscid interations that are necessary to achieve convergent and unique overall solutions. The feasibility of coupling the two vastly different codes is demonstrated. The interzone boundaries were overlapped to facilitate the passing of boundary condition information between the codes. Routines were developed to extract the normal velocity boundary conditions for the potential flow zone from the viscous zone solution. Similarly, the velocity vector direction along with the total conditions were obtained from the potential flow solution to provide boundary conditions for the Navier-Stokes solution. Studies were conducted to determine the influence of the overlap of the interzone boundaries and the convergence of the zonal solutions on the convergence of the overall solution. The zonal method was applied to a jet impingement problem to model the suckdown effect that results from the entrainment of the inviscid zone flow by the viscous zone jet. The resultant potential flow solution created a lower pressure on the base of the vehicle which produces the suckdown load. The feasibility of the zonal method was demonstrated. By enhancing the Navier-Stokes code for powered-lift flow fields and optimizing the convergence of the coupled analysis a practical flow analysis tool will result.

Roberts, D. W.↗
NASA NTRS2004

Results of an Advanced Development Zero Boil-Off Cryogenic Propellant Storage Test

A zero boil-off (ZBO) cryogenic propellant storage concept was recently tested in a thermally relevant low-earth orbit environment, an important development in the effort to apply this concept to flight projects. Previous efforts documented the benefits of ZBO for launch vehicle upper stages in a low-earth orbit (LEO). Central to that analysis is a ZBO Cryogenic Analysis Tool that estimates the performance of each component and the ZBO system. This test is essential to the validation of that tool, and was the first flight representative configuration tested in a thermally representative environment. The test article was comprised of a spherical 1.4 m diameter insulated propellant tank, with a submerged mixer, a cryogenic heat pipe, flight design cryocooler, and a radiator. All were enclosed in a thermal shroud and inserted into and tested in a vacuum chamber that simulated an LEO thermal environment. Thermal and pressure control tests were performed at sub-critical LN2 temperatures and approximately 2 atmospheres pressure. The cold side of the ZBO system performed well. In particular, the heat pipe performed better than expected, which suggests that the cryocooler could be located further from the tank than anticipated, i.e. on a spacecraft bus, while maintaining the desired efficiency. Also, the mixer added less heat than expected. The tank heating rate through the insulation was higher than expected; also the temperatures on the cryocooler hot side were higher than planned. This precluded the cryocooler from eliminating the boil-off. The results show the cryocooler was successful at removing 6.8 W of heat at approximately 75 K and 150 W of input power, with a heat rejection temperature of 311 K. The data generated on the ZBO components is essential for the upgrade of the ZBO Cryogenic Analysis Tool to more accurately apply the concept to future missions.

Plachta, David↗
NASA NTRS2026

Developing a Pyrolysis Gas Thermal Blocking Model for Reentry Demise

In NASA’s Object Reentry Survival Analysis Tool (ORSAT), aerodynamic drag and aerothermal heating coefficients are computed for each of the free-molecular, continuum, and transitional flow regimes using analytical and semi-analytical methods. These heating coefficients were derived for typical metallic materials that melt and do not have a strong gas-phase contribution to the flow in the boundary layer. Modern satellites typically feature fiber-reinforced polymer (FRP) components, such as solar array booms, facesheets of sandwich panels, or overwraps for composite-overwrapped pressure vessels (COPV). These FRP materials do not behave the same as metals in the reentry environment, but instead will pyrolyze and develop significant volumes of gas into the boundary layer. Accurately predicting the reentry demise of FRP components is critical to assessing the reentry casualty risk for modern spacecraft. Research in recent years has shown that this demisability can depend heavily on how the expulsion of gaseous pyrolysis products through the outer surface of the material affects the heat flux at the surface. The ODPO has been developing a reduced-order model of the effect of pyrolysis gas blowing on the heat flux based on correlations between a blowing factor and a non-dimensional heat flux to be incorporated in the upcoming version 7.3 of the Object Reentry Survivability Analysis Tool (ORSAT). This presentation discusses the progress of this development project and the challenges remaining for generalizing the model across families of FRP materials.

Benton Greene↗
DOE OSTI2026

Automated ICRF heating surrogate modeling via machine learning

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.

Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗
NASA NTRS1997

Tools for Analysis and Visualization of Large Time-Varying CFD Data Sets

In the second year, we continued to built upon and improve our scanline-based direct volume renderer that we developed in the first year of this grant. This extremely general rendering approach can handle regular or irregular grids, including overlapping multiple grids, and polygon mesh surfaces. It runs in parallel on multi-processors. It can also be used in conjunction with a k-d tree hierarchy, where approximate models and error terms are stored in the nodes of the tree, and approximate fast renderings can be created. We have extended our software to handle time-varying data where the data changes but the grid does not. We are now working on extending it to handle more general time-varying data. We have also developed a new extension of our direct volume renderer that uses automatic decimation of the 3D grid, as opposed to an explicit hierarchy. We explored this alternative approach as being more appropriate for very large data sets, where the extra expense of a tree may be unacceptable. We also describe a new approach to direct volume rendering using hardware 3D textures and incorporates lighting effects. Volume rendering using hardware 3D textures is extremely fast, and machines capable of using this technique are becoming more moderately priced. While this technique, at present, is limited to use with regular grids, we are pursuing possible algorithms extending the approach to more general grid types. We have also begun to explore a new method for determining the accuracy of approximate models based on the light field method described at ACM SIGGRAPH '96. In our initial implementation, we automatically image the volume from 32 equi-distant positions on the surface of an enclosing tessellated sphere. We then calculate differences between these images under different conditions of volume approximation or decimation. We are studying whether this will give a quantitative measure of the effects of approximation. We have created new tools for exploring the differences between images produced by various rendering methods. Images created by our software can be stored in the SGI RGB format. Our idtools software reads in pair of images and compares them using various metrics. The differences of the images using the RGB, HSV, and HSL color models can be calculated and shown. We can also calculate the auto-correlation function and the Fourier transform of the image and image differences. We will explore how these image differences compare in order to find useful metrics for quantifying the success of various visualization approaches. In general, progress was consistent with our research plan for the second year of the grant.

Wilhelms, Jane↗