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

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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An Update on the Geothermal Data Repository's Data Standards and Pipelines: Geospatial Data and Distributed Acoustic Sensing Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.

cloud-optimized↗
NASA NTRS1985

Evaluation of existing and proposed computer architectures for future ground-based systems

Parallel processing architectures and techniques used in current supercomputers are described and projections are made of future advances. Presently, the von Neumann sequential processing pattern has been accelerated by having separate I/O processors, interleaved memories, wide memories, independent functional units and pipelining. Recent supercomputers have featured single-input, multiple data stream architectures, which have different processors for performing various operations (vector or pipeline processors). Multiple input, multiple data stream machines have also been developed. Data flow techniques, wherein program instructions are activated only when data are available, are expected to play a large role in future supercomputers, along with increased parallel processor arrays. The enhanced operational speeds are essential for adequately treating data from future spacecraft remote sensing instruments such as the Thematic Mapper.

Schulbach, C.↗
DOE OSTI2023

New Tables for Infrasound Event Catalogs with a Focus on Machine Learning

Natural and anthropogenic sources such as volcanoes, earthquakes, auroral processes, chemical and nuclear explosions, rocket launches, and aircraft can generate infrasound, sound with frequencies less than 20 Hz. Both the availability of infrasound data and interest in machine learning (ML) applications have grown in recent years. Large, open-source datasets are essential to solving complex ML problems, but the field of infrasound is lacking in this arena. To increase the utility of ML for infrasound, here we present new tables for infrasound event catalogs. It is the aim that these tables be incorporated into both existing and future infrasound processing pipelines to generate large datasets ripe for use with ML/DL methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2011

Automated X-ray and Optical Analysis of the Virtual Observatory and Grid Computing

We are developing a system to combine the Web Enabled Source Identification with X-Matching (WESIX) web service, which emphasizes source detection on optical images,with the XAssist program that automates the analysis of X-ray data. XAssist is continuously processing archival X-ray data in several pipelines. We have established a workflow in which FITS images and/or (in the case of X ray data) an X-ray field can be input to WESIX. Intelligent services return available data (if requested fields have been processed) or submit job requests to a queue to be performed asynchronously. These services will be available via web services (for non-interactive use by Virtual Observatory portals and applications) and through web applications (written in the Django web application framework). We are adding web services for specific XAssist functionality such as determining .the exposure and limiting flux for a given position on the sky and extracting spectra and images for a given region. We are improving the queuing system in XAssist to allow for "watch lists" to be specified by users, and when X-ray fields in a user's watch list become publicly available they will be automatically added to the queue. XAssist is being expanded to be used as a survey planning 1001 when coupled with simulation software, including functionality for NuStar, eRosita, IXO, and the Wide Field Xray Telescope (WFXT), as part of an end to end simulation/analysis system. We are also investigating the possibility of a dedicated iPhone/iPad app for querying pipeline data, requesting processing, and administrative job control.

Ptak, A.↗
DOE OSTI2025

Code Generators for Floating-Point Unit Design in Integrated Circuits (OpenFloat) v1.0

This IP provides a comprehensive set of code generators for various floating-point units (FPUs) essential for integrated circuit design and integration, targeting a broad spectrum of applications, including machine learning and scientific computing. The suite includes FP adders, multipliers, subtractors, dividers, reciprocals, exponentials, square roots, trigonometric functions (sine, cosine, arctangent), and more. It supports customizable hardware design parameters, such as precision (16, 32, 64, and 128 bits) and pipeline depths, offering users enhanced flexibility and productivity. The generated code is in an industry-standard hardware description language, ensuring compatibility with standard design flows, including simulation, verification, synthesis, and implementation on both field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs).

Shalf, JohnM. [Lawrence Berkeley National Laborato↗
DOE OSTI2025

Undergraduate Women in STEM Internship Program

In collaboration with the Universities Research Association, Inc. (URA), establishment of the Undergraduate Women in STEM Internship pilot program assists Fermi Research Alliance, LLC (FRA) with honoring its commitment to increasing gender, racial and ethnic workforce diversity. This focused initiative is intended to invite, support and yield a qualified pipeline of alumnae underrepresented in FRA’s science and technology management and technical staff positions. Interns in this program spend 10-12 weeks at Fermilab working under the direct leadership, guidance and supervision of scientists, engineers, computing professionals, technicians and staff across all directorates performing hands-on research. In addition, the intern cohort meets with mentors and advisors to grow their professional networks, to take part in engaging and inclusive intellectual discussions, to directly benefit from workplace resources, and to participate in professional development and social engagement opportunities. The design of each research project and the supervision, mentorship and evaluation is conducted by a scientific, engineering, computing professional at Fermilab.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2023

Automated sUAS Inspection Capability for NASA’s Mission Critical Testing Facilities

The wind tunnels at Ames are crucial to NASA and industry but pose unique inspection challenges. Current inspection processes are highly manual, and as such are labor and schedule intensive. These needs can be better met with emerging technology such as sUAS (drones), computer vision, and machine learning. This work seeks to bring the drone based inspection workflow into production-ready status and integrate into existing facility inspections. This includes operation of a drone platform, establishing a photogrammetry pipeline, development of procedures and streamlining flight approval processes, and establishing a base of experience at Ames for this work. We have worked with the NFAC, unitary, and Arcjet facilities to identify use cases and have performed test flights at Ames (both indoors and outdoors). The system is being readied for incorporation into routine inspection operations.

David Daisuke Murakami↗
DOE OSTI2024

The "PVLib" of Degradation: PVDeg

The Photovoltaic (PV) industry constantly aims for lower costs through higher-efficiency cells, improved module designs, and improvements in durability. This leads to the use of new materials, designs, and manufacturing processes, and not always with a sufficient amount of durability testing. To help drive down costs there is a desire to create modules that will last for up to 50 years of service life. To accomplish this, every degradation mode and mechanism must be identified and either eliminated or otherwise mitigated. This involves the extrapolation of laboratory results to the field conditions. There is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. While the basic equations used to model degradation are sometimes very simple, the full analysis involves calculations are cumbersome but ubiquitous for many degradation processes. A simplified, modeling framework to accomplish these repetitive processes will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this talk, we will describe our progress creating the open-source tool PVDeg. This tool can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. PVDeg simplifies many of the common foundational computational operations for obtaining meteorological data and using it to generate a model of the PV deployment. This prediction tool repository also contains various degradation models as well as a library of material parameters suitable for estimating the durability assessment of materials and components. We use an integration pipeline approach that allows us to leverage weather data from the National Solar Radiation Database, and other weather sources, to perform geospatial degradation analysis in the US and worldwide. We hope to become a repository that can be used for weathering and degradation analysis for various applications beyond the PV industry. During the talk, we will provide the PVPMC attendees the opportunity to interact with the tool via a Google Collab tutorial they can run on their phones or laptops.

durability↗
DOE OSTI2022

Computational models of direct and indirect X‐ray breast imaging detectors for in silico trials

Abstract Background To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed. Purpose The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems. Methods We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open‐source GPU‐accelerated Monte Carlo x‐ray imaging simulation code (MC‐GPU). We build on the previous version of the MC‐GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)‐approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x‐ray photon till its first interaction within the x‐ray detector. On the other hand, the signal spread in the x‐ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron–hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth‐dependent spread and collection of optical photons and relies on a pre‐computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x‐ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data. Results We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors. Conclusions We have presented computational models of three commercially available FDA‐approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC‐GPU codes that can be used to replicate three systems are available in open source format through GitHub.

Sengupta, Aunnasha↗
NASA NTRSDate not supplied

Optical Time Transfer for Bistatic SAR Spacecraft

A spacecraft-to-spacecraft optical time-transfer simulation has been developed as a tool for informing NASA’s Surface Deformation and Change (SDC) mission architecture. The SDC mission will combine radar images from multiple spacecraft to improve understanding of the Earth’s sea-level and landscape changes. Spacecraft must be precisely synchronized in order to create sharp radar images. Simulation of multiple spacecraft time-synchronizing via laser communication can inform technology choices of a mission by providing a picosecond-precision level estimate of clock error. This timing and ranging simulation has been combined with a radar system performance analysis pipeline. The simulated timing errors are used to predict performance of bistatic SAR systems in the presence of oscillator noise and time synchronization in accuracy. This analysis includes both analytic approximation equations from existing literature, and a numerical radar simulation to extract key system performance parameters like phase error and signal-to-noise ratio (SNR)degradation. Precision time-transfer techniques facilitate the accurate synchronization of clocks between any combination of terminals. Most time-transfer technology for comparing two clocks at different terminals use radio frequencies (RF) to measure the time delay between the sending and receiving of signals. Laser technology offers the capability to transmit high data rates with systems that are of smaller size and lower power than comparable RF systems. The clocks on independent spacecraft will have some phase and frequency errors between them that result in clock drift. The two clock models that are included in this bi-directional MATLAB simulation are a cesium-based Chip-Scale Atomic Clock (CSAC) and a rubidium-based Miniature Atomic Clock (MAC). The CSAC has flown as hardware for small satellite missions such as the University of Florida’s CHOMPTT mission. A study of example orbits, including that of NASA NASA-ISRO Synthetic Aperture Radar Mission (NISAR) mission, and lasing rates demonstrate the impact of flight configuration parameters on the synchronization error between two spacecraft. The MATLAB timing simulation uses a Runge-Kutta 4th-order method to propagate spacecraft orbits and computes the light-travel time estimate between them. The simulation outputs the estimated range and estimated clock error based on a user-defined spacecraft cluster configuration. The radar simulation and analytic approximations are applied to evaluate a potential future NASA bistatic SAR constellation architecture. In the proposed architecture, satellites follow each other in the same orbit at 800 km altitude, with a 210 km baseline. We also baseline the CSAC as an ultra stable oscillator, and use NASA’s NISAR for baseline radar system parameters to compute a clock-system introduced phase error of 5.6 degrees without synchronization by frequent time transfer. We build on this base case with a sensitivity analysis of radar performance over a proposed range of constellation and radar system parameters. With this analysis pipeline, we comment on which radar parameters should or should not be changed to minimize synchronization requirements. This analysis technique could be extended or modified to evaluate the timing requirements of other geometries for other future multistatic SAR missions, or other interferometric satellite missions.

Surface Deformation and Change↗
NASA NTRS1989

Correlative visualization techniques for multidimensional data

Critical to the understanding of data is the ability to provide pictorial or visual representation of those data, particularly in support of correlative data analysis. Despite the advancement of visualization techniques for scientific data over the last several years, there are still significant problems in bringing today's hardware and software technology into the hands of the typical scientist. For example, there are other computer science domains outside of computer graphics that are required to make visualization effective such as data management. Well-defined, flexible mechanisms for data access and management must be combined with rendering algorithms, data transformation, etc. to form a generic visualization pipeline. A generalized approach to data visualization is critical for the correlative analysis of distinct, complex, multidimensional data sets in the space and Earth sciences. Different classes of data representation techniques must be used within such a framework, which can range from simple, static two- and three-dimensional line plots to animation, surface rendering, and volumetric imaging. Static examples of actual data analyses will illustrate the importance of an effective pipeline in data visualization system.

Treinish, Lloyd A.↗
NASA NTRS2010

Materials Development: Pitfalls, Successes, and Lessons

The incorporation of new or improved materials in aerospace systems, or indeed any systems, can yield tremendous payoffs in the system performance or cost, and in many cases can be enabling for a mission or concept. However, the availability of new materials requires advance development, and too often this is neglected or postponed, leaving a project or mission with little choice. In too many cases, the immediate reaction is to use what was used before; this usually turns out not to be possible and results in large sums of money, and amounts of time, being expended on reinvention rather than development of a material with extended capabilities. Material innovation and development is time consuming, with some common wisdom claiming that the timeline is at least 20 years. This time expands considerably when development is stopped and restarted, or knowledge is lost. Down selection of materials is necessary, especially as the Technical Readiness Level (TRL) increases. However, the costs must be considered and approaches should be taken to retain knowledge and allow for restarting the development process. Regardless of the exact time required, it is clear that it is necessary to have materials, at all stages of development, in a research and development pipeline and available for maturation as required. This talk will discuss some of theses issues, including some of the elements for a development path for materials. Some history of materials developments will be included. The usefulness of computational materials science, as a route to decreasing material development time, will be an important element of this discussion. Collaboration with outside institutions and nations is also critical for innovation, but raises the issues of intellectual property and protections, and national security (ITAR rules, for example).

Johnson, Sylvia M.↗
DOE OSTI2023

Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches.

big scientific dataset↗
DOE OSTI2023

MFA-DVR: direct volume rendering of MFA models

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for large-scale volumetric data, and uniform representation of both structured and unstructured data. Here, in this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured and unstructured volumetric datasets. We demonstrate improved rendering quality using MFA-DVR on both synthetic and real datasets through a comparative study. We show that MFA-DVR not only generates more faithful volume rendering than using local filters but also performs faster on high-order interpolations on structured and unstructured datasets. MFA-DVR is implemented in the existing volume rendering pipeline of the Visualization Toolkit (VTK) to be accessible by the scientific visualization community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Sparse Distributed Gigascale Resolution Material Point Method

In this paper, we present a four-layer distributed simulation system and its adaptation to the Material Point Method (MPM). The system is built upon a performance portable C++ programming model targeting major High-Performance-Computing (HPC) platforms. A key ingredient of our system is a hierarchical block-tile-cell sparse grid data structure that is distributable to an arbitrary number of Message Passing Interface (MPI) ranks. We additionally propose strategies for efficient dynamic load balance optimization to maximize the efficiency of MPI tasks. Our simulation pipeline can easily switch among backend programming models, including OpenMP and CUDA, and can be effortlessly dispatched onto supercomputers and the cloud. Finally, we construct benchmark experiments and ablation studies on supercomputers and consumer workstations in a local network to evaluate the scalability and load balancing criteria. We demonstrate massively parallel, highly scalable, and gigascale resolution MPM simulations of up to 1.01 billion particles for less than 323.25 seconds per frame with 8 OpenSSH-connected workstations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗