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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Probing flow-induced nanostructure of complex fluids in arbitrary 2D flows using a fluidic four-roll mill (FFoRM)

Abstract Engineering flow processes to direct the microscopic structure of soft materials represents a growing area of materials research. In situ small-angle neutron scattering under flow (flow-SANS) is an attractive probe of fluid microstructure under simulated processing conditions, but current capabilities require many different sample environments to fully interrogate the deformations a fluid experiences in a realistic processing flow. Inspired by recent advances in microfluidics, we present a fluidic four-roll mill (FFoRM) capable of producing tunable 2D flow fields for in situ SANS measurements, that is intended to allow characterization of complex fluid nanostructure under arbitrary complex flows within a single sample environment. Computational fluid dynamics simulations are used to design a FFoRM that produces spatially homogeneous and sufficiently strong deformation fields. Particle tracking velocimetry experiments are then used to characterize the flows produced in the FFoRM for several classes of non-Newtonian fluids. Finally, a putative FFoRM-SANS workflow is demonstrated and validated through the characterization of flow-induced orientation in a semi-dilute cellulose nanocrystal dispersion under a range of 2D deformations. These novel experiments confirm that, for steady state straining flows at moderate strain rates, the nanocrystals orient along the principal strain-rate axis, in agreement with theories for rigid, rod-like Brownian particles in a homogeneous flow.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data Readiness for Scientific AI at Scale

This paper examines how Data Readiness for AI (DRAI) principles apply to leadership-scale scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, bio/health, and materials—to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readiness framework that combines canonical preprocessing patterns with a five-level operational readiness scale, both tailored to high-performance computing (HPC) environments. This framework helps outline key challenges in transforming large-scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross-domain support for scalable and reproducible AI for science.

Brewer, Wes [ORNL] (ORCID:0000000236393956)↗
DOE OSTI2022

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

In carbon capture and sequestration (CCS), developing rapid and effective imaging techniques is crucial for real-time monitoring of the spatial and temporal dynamics of CO 2 propagation during/after injection. With continuing improvements in computational power and data storage, data-driven techniques based on machine learning (ML) have been effectively applied to seismic inverse problems. In particular, ML helps alleviate the ill-posedness and high computational cost of full-waveform inversion (FWI). However, such data-driven inversion techniques require massive high-quality training data sets to ensure prediction accuracy, which hinders their application to time-lapse monitoring of CO 2 sequestration. We propose an efficient “hybrid” time-lapse workflow that combines physics-based FWI and data-driven ML inversion. The scarcity of the available training data is addressed by developing a new data-generation technique with physics constraints. The method is vali dated on a synthetic CO 2 -sequestration model based on the Kimberlina storage reservoir in California. The proposed approach is shown to synthesize a large volume of high-quality, physically realistic training data, which is critically important in accurately characterizing the CO 2 movement in the reservoir. In conclusion, the developed hybrid methodology can also simultaneously predict the variations in velocity and saturation and achieve high spatial resolution in the presence of realistic noise in the data.

58 GEOSCIENCES↗
DOE OSTI2019

Continuous Integration! You Keep Saying Those Words. I Do Not Think They Mean What You Think They Mean [Slides]

Continuous Integration (CI) is a best practice in the software community. Often though, there are several ideas about the meaning of continuous integration. With this uncertainty of definition often comes a loss of benefit to the practice. It is good to stop and reflect on how and why CI has been (and still is) practiced in software community. So, presented here is a historical perspective of CI along with associated practices, anti-patterns, and benefits. A subsequent presentation will discuss the workflows and controversy that had arised as CI has “changed” with time, especially in regards to feature-branch development.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Cyber-Informed Engineering Requirements Framework Use Cases

The requirement analysis use case effort leverages the Cyber-Informed Engineering (CIE) requirements framework to examine two real-world scenarios: Battery Energy Storage System (BESS) installation at the Flatirons Campus, NLR; SCADA improvement program. This work evaluates the existing requirements for each use case, applies the CIE requirements framework, and assesses the benefits and enhancements gained compared to the current requirements. The results will advance CIE from concept to practical application by identifying key opportunities for integrating CIE principles into established engineering workflows during the requirements phase.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

An XML Representation for Crew Procedures

NASA ensures safe operation of complex systems through the use of formally-documented procedures, which encode the operational knowledge of the system as derived from system experts. Crew members use procedure documentation on the ground for training purposes and on-board space shuttle and space station to guide their activities. Investigators at JSC are developing a new representation for procedures that is content-based (as opposed to display-based). Instead of specifying how a procedure should look on the printed page, the content-based representation will identify the components of a procedure and (more importantly) how the components are related (e.g., how the activities within a procedure are sequenced; what resources need to be available for each activity). This approach will allow different sets of rules to be created for displaying procedures on a computer screen, on a hand-held personal digital assistant (PDA), verbally, or on a printed page, and will also allow intelligent reasoning processes to automatically interpret and use procedure definitions. During his NASA fellowship, Dr. Simpson examined how various industries represent procedures (also called business processes or workflows), in areas such as manufacturing, accounting, shipping, or customer service. A useful method for designing and evaluating workflow representation languages is by determining their ability to encode various workflow patterns, which depict abstract relationships between the components of a procedure removed from the context of a specific procedure or industry. Investigators have used this type of analysis to evaluate how well-suited existing workflow representation languages are for various industries based on the workflow patterns that commonly arise across industry-specific procedures. Based on this type of analysis, it is already clear that existing workflow representations capture discrete flow of control (i.e., when one activity should start and stop based on when other activities start and stop), but do not capture the flow of data, materials, resources or priorities. Existing workflow representation languages are also limited to representing sequences of discrete activities, and cannot encode procedures involving continuous flow of information or materials between activities.

Simpson, Richard C.↗
NASA NTRS2019

An Integrated Data Analytics Platform

An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources which subsequently enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA technology integration project to establish a cloud-based Integrated Ocean Science Data Analytics Platform at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) for big ocean science. It focuses on advancement and maturity by bringing together several NASA open-source, big data projects for parallel analytics, anomaly detection, in-situ to satellite data matchup, quality-screened data subsetting, search relevancy, and data discovery. Our communities are relying on data distributed through data centers such as the PO.DAAC, COAPS, NCAR, and many others to conduct their research. In typical investigations, scientists would engage in: search for data, evaluate the relevance of that data, download it, and then apply algorithms to identify trends. Such workflow cannot scale if the research involves a massive amount of data or multi-variate measurements. NASA’s Surface Water and Ocean Topography (SWOT) mission is expected to produce massive amount of observational data during its 3-year nominal mission. Collections like SWOT challenges all existing Earth Science data archival, distribution and analysis paradigms. In this paper, we will discuss how OceanWorks enhances the analysis of physical ocean data where the computation is done on an elastic cloud platform next to the archive to deliver fast, web-accessible services for working with oceanographic measurements.

Yang, Chaowei↗
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↗
DOE OSTI2017

Python and HPC for High Energy Physics Data Analyses

High level abstractions in Python that can utilize computing hardware well seem to be an attractive option for writing data reduction and analysis tasks. In this paper, we explore the features available in Python which are useful and efficient for end user analysis in High Energy Physics (HEP). A typical vertical slice of an HEP data analysis is somewhat fragmented: the state of the reduction/analysis process must be saved at certain stages to allow for selective reprocessing of only parts of a generally time-consuming workflow. Also, algorithms tend to to be modular because of the heterogeneous nature of most detectors and the need to analyze different parts of the detector separately before combining the information. This fragmentation causes difficulties for interactive data analysis, and as data sets increase in size and complexity (O10 TiB for a “small” neutrino experiment to the O10 PiB currently held by the CMS experiment at the LHC), data analysis methods traditional to the field must evolve to make optimum use of emerging HPC technologies and platforms. Mainstream big data tools, while suggesting a direction in terms of what can be done if an entire data set can be available across a system and analysed with high-level programming abstractions, are not designed with either scientific computing generally, or modern HPC platform features in particular, such as data caching levels, in mind. Our example HPC use case is a search for a new elementary particle which might explain the phenomenon known as “Dark Matter”. Here, using data from the CMS detector, we will use HDF5 as our input data format, and MPI with Python to implement our use case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Sci-Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques - 08: Retrospective Dose Accumulation Workflow in Head and Neck Cancer Patients Using RayStation 4.5.2

Purpose: We have developed a semi-automated dose accumulation workflow for Head and Neck Cancer (HNC) patients to evaluate volumetric and dosimetric changes that take place during radiotherapy. This work will be used to assess how dosimetric changes affect both toxicity and disease control, hence inform the feasibility and design of a prospective HNC adaptive trial. Methods: RayStation 4.5.2 features deformable image registration (DIR), where structures already defined on the planning CT image set can be deformably mapped onto cone-beam computed tomography (CBCT) images, accounting for daily treatment set-up shifts and changes in patient anatomy. The daily delivered dose can be calculated on each CBCT and mapped back to the planning CT to allow dose accumulation. The process is partially automated using Python scripts developed in collaboration with RaySearch. Results: To date we have performed dose accumulation on 18 HNC patients treated at our institution during 2013–2015 under REB approval. Our semi-automated process establishes clinical feasibility. Generally, dose accumulation for the entire treatment course of one case takes 60–120 minutes: importing all CBCTs requires 20–30 minutes as each patient has 30 to 40 treated fractions; image registration and dose accumulation require 60–90 minutes. This is in contrast to the process without automated scripts where dose accumulation alone would take 3–5 hours. Conclusions: We have developed a reliable workflow for retrospective dose tracking in HNC using RayStation. The process has been validated for HNC patients treated on both Elekta and Varian linacs with CBCTs acquired on XVI and OBI platforms respectively.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Streamlining the Design-to-Build Transition with Build-Optimization Software Tools

Scaling-up capabilities for the design, build, and test of synthetic biology constructs holds great promise for the development of new applications in fuels, chemical production, or cellular-behavior engineering. Construct design is an essential component in this process; however, not every designed DNA sequence can be readily manufactured, even using state-of-the-art DNA synthesis methods. Current biological computer-aided design and manufacture tools (bioCAD/CAM) do not adequately consider the limitations of DNA synthesis technologies when generating their outputs. Designed sequences that violate DNA synthesis constraints may require substantial sequence redesign or lead to price-premiums and temporal delays, which adversely impact the efficiency of the DNA manufacturing process. In this paper, we have developed a suite of build-optimization software tools (BOOST) to streamline the design-build transition in synthetic biology engineering workflows. BOOST incorporates knowledge of DNA synthesis success determinants into the design process to output ready-to-build sequences, preempting the need for sequence redesign. The BOOST web application is available at https://boost.jgi.doe.gov and its Application Program Interfaces (API) enable integration into automated, customized DNA design processes. Finally, the herein presented results highlight the effectiveness of BOOST in reducing DNA synthesis costs and timelines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Automated identification and calculation of prompt effects in kinetic mechanisms using statistical models

The kinetics of prompt dissociation involves rovibrationally excited species (generally formed by exothermic reactions) which may dissociate or isomerize prior to thermalization via collisions with the bath gas. Treating such rovibrationally excited species (so-called "hot" species) with standard kinetic phenomenology may result in incorrect macroscopic representation of their reactivity. Here this work presents the first fully automated methodology for the calculation of prompt effects of a chosen species in a kinetic mechanism, including (i) reaction selection; (ii) theoretical calculation of rate constants and prompt branching fractions; and (iii) final rate constant fitting. The energy partition between hot fragments is estimated using a variety of statistical models, including a new physically sound microcanonical statistical model based on the rovibrational density of states of the fragments. The methodology is validated against literature data for the prompt dissociations of HCO and C 3 H 7 radicals. The microcanonical statistical model is in better agreement with trajectory simulations for larger species and is thus applicable for practical systems that typically involve large molecules, for which direct dynamics calculations are impractical. The automated workflow is applied to the evaluation of the effects of prompt dissociation for two isomeric radicals C 4 H 7 1-3 (1-methylallyl) and C 4 H 7 1-4 (3-buten-1-yl). Twelve H-atom abstraction reactions are selected and the corresponding rate constants are computed with first principles theory. The microcanonical statistical model predicts that prompt dissociations of C 4 H 7 1-3 and C 4 H 7 1-4 are already significant at 1000K, resulting in differences of up to an order of magnitude at 2000K with respect to the phenomenological thermal rate constants. To illustrate the effects of prompt dissociation on simulations of experimental data, the calculated prompt rate constants are implemented in both CRECK and C3MechV3.3 kinetic mechanisms. Simulations of experimental flame data illustrate the noticeable impact of prompt dissociation kinetics on the high-temperature combustion reactivity of C 4 H 8 -1 and C 4 H 8 -2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

YOLO11 to SAM2 pipeline for feature extraction from nuclear test films

The response to the effects of nuclear detonations is supported by models that describe the evolution of the nuclear fireball and cloud and the associated transport of active debris. Validation of those descriptions relies on data from the nuclear test operations. Video records of those events offer a rich source of information that was exploited to a limited extent in historic analyses. Computer vision and machine learning techniques are powerful tools that can be used to increase the number of measurements that can be obtained from those films. In this work, we apply computer vision techniques to automatically track the temporal evolution of the nuclear fireball. In particular, we apply You Only Look Once 11 (YOLO11) and Segment Anything Model 2 (SAM2) in combination with minimal human intervention to digitized versions of the original nuclear test films. As part of the proposed workflow, the YOLO11 model is applied to films to determine bounding boxes for the fireball within each frame. These are then used as inputs to SAM2, which uses image segmentation to determine the fireball boundaries and their temporal evolution. We assess the accuracy of our approach by using it to determine the energy released during the Trinity nuclear test and comparing the results with previous analyses based on manual measurements.

Van Exel, Kimberly [ORNL] (ORCID:0009000877463894)↗
DOE OSTI2016

SU-F-E-13: Design and Fabrication of Gynacological Brachytherapy Shielding & Non Shielding Applicators Using Indigenously Developed 3D Printing Machine

Purpose: In this innovative work we have developed Gynecological Brachytherapy shielding & Non Shielding Applicators and compared with the commercially available applicators by using the indigenously developed 3D Printing machine. Methods: We have successfully indigenously developed the 3D printing machine. Which contain the 3 dimensional motion platform, Heater unit, base plate, ect… To fabricate the Gynecological Brachytherapy shielding & non shielding applicators the 3D design were developed in the computer as virtual design. This virtual design is made in a CAD computer file using a 3D modeling program. Separate programme for the shielding & non shielding applicators. We have also provided the extra catheter insert provision in the applicator for the multiple catheter. The DICOM file of the applicator were then converted to stereo Lithography file for the 3D printer. The shielding & Non Shielding Applicators were printed on a indigenously developed 3D printer material. The same dimensions were used to develop the applicators in the acrylic material also for the comparative study. A CT scan was performed to establish an infill-density calibration curve as well as characterize the quality of the print such as uniformity and the infill pattern. To commission the process, basic CT and dose properties of the printing materials were measured in photon beams and compared against water and soft tissue. Applicator were then scanned to confirm the placement of multiple catheter position. Finally dose distributions with rescanned CTs were compared with those computer-generated applicators. Results: The doses measured from the ion Chamber and X-Omat film test were within 2%. The shielded applicator reduce the rectal dose comparatively with the non shielded applicator. Conclusion: As of submission 3 unique cylinders have been designed, printed, and tested dosimetrically. A standardizable workflow for commissioning custom 3D printed applicators was codified and will be reported.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

dfnWorks, Version 1.0

DFNWorks is a suite of software for generating and solving flow and transport physics on a Discrete Fracture Network (DFN). The suite is composed of three components: DFNGen: Stochastic fracture generation, mesh generation, model setup; DFNFlow: Flow solution using PFLOTRAN or FEHM control volume formulation solver; DFNTrans: Particle tracking transport; DFNGen Algorithm Description: This is a workflow that uses Mathematica software, Python scripts written by the authors and the LaGriT software to generate; DFNFlow; The solution of flow in porous media is accomplished by coupling DFNGen output with the existing software packages FEHM and PFLOTRAN. The DFNWorks workflow involves modules and scripts that translate DFNGen output to formats compatible with FEHM and PFLOTRAN input. FEHM LA-CC-2012-083 PFLOTRAN LA-CC-09-047 LaGriT LA-CC-07-38 DFNTrans Stand alone particle tracking algorithm that takes FEHM/PFLOTRAN flow solution as output and computes velocity field and advects particles.

Makedonska, Nataliia↗
DOE OSTI2026

Self-driving thin film laboratory: autonomous epitaxial atomic-layer synthesis via real-time computer vision analysis of electron diffraction

Emerging materials science platforms with the ability to make autonomous decisions on the fly are fundamentally changing the outlook and protocols for materials optimization and discovery. Because AI-driven self-navigating schemes can effectively reduce the total number of iterations needed to arrive at the "answer" (i.e. the best stochiometric composition for a desired physical property, optimum materials processing parameters, etc.) by significant margins, they have the potential to revolutionize materials and chemical manufacturing processes at large in research laboratory settings as well as in industrial plants. Here, we demonstrate a successful implementation of real-time closed-loop autonomous navigation of a multi-dimensional materials synthesis parameter space for fabricating phase-pure epitaxial films of a metastable phase of a functional oxide in a combinatorial pulsed laser deposition chamber. Sequential epitaxial growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision analysis of reflection high-energy electron diffraction (RHEED) images of the unit-cell level film being deposited. The autonomous scheme regularly resulted in > 30-fold reduction in the number of required experiments compared to a comprehensive mapping of the parameter space. The real-time workflow developed here can be readily extended to a variety of thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous optimization of semiconductor manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2016

dfnWorks, Version 1.0

DFNWorks is a suite of software for generating and solving flow and transport physics on a Discrete Fracture Network (DFN). The suite is composed of three components: DFNGen: Stochastic fracture generation, mesh generation, model setup; DFNFlow: Flow solution using PFLOTRAN or FEHM control volume formulation solver; DFNTrans: Particle tracking transport; DFNGen Algorithm Description: This is a workflow that uses Mathematica software, Python scripts written by the authors and the LaGriT software to generate; DFNFlow; The solution of flow in porous media is accomplished by coupling DFNGen output with the existing software packages FEHM and PFLOTRAN. The DFNWorks workflow involves modules and scripts that translate DFNGen output to formats compatible with FEHM and PFLOTRAN input. FEHM LA-CC-2012-083 PFLOTRAN LA-CC-09-047 LaGriT LA-CC-07-38 DFNTrans Stand alone particle tracking algorithm that takes FEHM/PFLOTRAN flow solution as output and computes velocity field and advects particles.

Makedonska, Nataliia↗