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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Structure-based design of broadly protective group a streptococcal M protein-based vaccines

Here, a major obstacle to the development of broadly protective M protein-based group A streptococcal (GAS) vaccines is the variability within the N-terminal epitopes that evoke potent bactericidal antibodies. The concept of M type-specific protective immune responses has recently been challenged based on the observation that multivalent M protein vaccines elicited cross-reactive bactericidal antibodies against a number of non-vaccine M types of GAS. Additionally, a new “cluster-based” typing system of 175 M proteins identified a limited number of clusters containing closely related M proteins. In the current study, we used the emm cluster typing system, in combination with computational structure-based peptide modeling, as a novel approach to the design of potentially broadly protective M protein-based vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

WE-G-BRF-07: Non-Circular Scanning Trajectories with Varian Developer Mode

Purpose: Cone-beam CT (CBCT) in image-guide radiation therapy (IGRT) typicallyacquires scan data via the circular trajectory of the linearaccelerator's (linac) gantry rotation. Though this lends itself toanalytic reconstruction algorithms like FDK, iterative reconstructionalgorithms allow for a broader range of scanning trajectories. Weimplemented a non-circular scanning trajectory with Varian's TrueBeamDeveloper Mode and performed some preliminary reconstructions toverify the geometry. Methods: We used TrueBeam Developer Mode to program a new scanning trajectorythat increases the field of view (FOV) along the gantry rotation axiswithout moving the patient. This trajectory consisted of moving thegantry in a circle, then translating the source and detector along theaxial direction before acquiring another circular scan 19 cm away fromthe first. The linear portion of the trajectory includes an additional4.5 cm above and below the axial planes of the source's circularrotation. We scanned a calibration phantom consisting of a lucite tubewith a spiral pattern of CT spots and used the maximum-likelihoodalgorithm to iteratively reconstruct the CBCT volume. Results: With the TrueBeam trajectory definition, we acquired projection dataof the calibration phantom using the previously described trajectory.We obtained a scan of the treatment couch for log normalization byscanning with the same trajectory but without the phantom present.Using the nominal geometric parameters reported in the projectionheaders with our iterative reconstruction algorithm, we obtained acorrect reconstruction of the calibration phantom. Conclusion: The ability to implement new scanning trajectories with the TrueBeamDeveloper Mode enables us access to a new parameter space for imagingwith CBCT for IGRT. Previous simulations and simple dual circle scanshave shown iterative reconstruction with non-circular trajectories canincrease the axial FOV with CBCT. Use of Developer Mode allowsexperimentally testing these and other new scanning trajectories. Support was provided in part by the University of Chicago Research Computing Center, Varian Medical Systems, and NIH Grants 1RO1CA120540, T32EB002103, S10 RR021039 and P30 CA14599. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the supporting organizations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Modeling Information Multiplexing in the Hippocampus

Modern computers are constantly faced with the challenge of processing ever-growing quantities of data that span a wide range of modalities. Of particular relevance to national security interests is the ability to integrate multimodal data for the purpose of fast decision making. The brain is a biological system that is specialized for high performance at this task, suggesting that understanding the mechanisms by which neural circuits integrate multimodal data may lead to improved man-made detection systems. This research focused on understanding these neural algorithms, and specifically tested the hypothesis that hippocampal neurons multiplex information from two different input streams. Specifically, we compare the spiking behavior of a computational model of hippocampal circuitry with neurophysiological data recorded from rodent hippocampus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Developing machine-learning potentials to study properties of the tritium formation and diffusivity in pure and defective Zircaloy-4 getters

Objective of this work was to study the formation of Sn impurity and hydride phases in Zr and study their impacts on the diffusion kinetics of 3 H by using DFT based ML approach. By implementing deep neural potential (DNP) technique, we developed potential for Zr-H and Sn impurity systems and validated the DNP by using DFT results. We found that by implementing machine learning approach, it is possible to achieve accuracy comparable to DFT level for the more realistic models by using less computational time and resources.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

SpackDev: Multi-Package Development with Spack

High Energy Physics (HEP) software environments often involve ~ hundreds of external packages and libraries, and similar numbers of experiment-specific, science-critical packages—many under continuous development. Managing coherent releases of the external software stack is challenging enough, but managing the highly-collaborative—and distributed—development of a large body of code against such a stack adds even more complexity and room for error. Spack is a popular Python-based package management tool with a specific focus on the needs of High Performance Computing (HPC) systems and system administrators whose strength is orchestrating the discrete download, build, testing, and installation of pre-packaged or tagged third-party software against similarly stable dependencies. As such it is becoming increasingly popular within HEP as that community makes increasing use of HPC facilities, and as efforts to develop future HPC systems utilize Spack to provide scientific software on those platforms. SpackDev is a system to facilitate the simultaneous development of interconnected sets of packages. Intended to handle packages without restriction to one internal build system, SpackDev is integrated with Spack as a command extension in order to leverage features such as dependency calculations and build system configuration, and is generally applicable outside HEP. We describe SpackDev’s features and development over the last two years, initial experience using SpackDev in the context of the LArSoft liquid argon detector toolkit, and work remaining before it can be considered a fully-functional multi-package build system for HEP experiments utilizing Spack.

Green, Chris↗
DOE OSTI2017

Singularity: Scientific containers for mobility of compute

Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Multi-Scale Inference, Estimation, and Prediction Engine for Earth System Modeling

We posit that AI methods can be leveraged to significantly enhance the predictive skill of forward Earth system modeling (ESM) activities. A hybrid framework incorporating traditional ESM modeling, inference methods, and AI techniques could make better use of both measured and computed information as well as computational resources by targeting inference tasks at program priorities, such as the predictability of precipitation extremes. This runtime pathway to closing the simulation/analysis-data/model improvement loop will streamline the traditional offline pathway to model improvement, which is based on domain science expertise, while suggesting guidance for further observations and measurements.

58 GEOSCIENCES↗
DOE OSTI2017

Pre-Ionization Controlled Laser Plasma Formation for Ignition Applications

The presented research explored new physics and ignition schemes based on laser induced plasmas that are fundamentally distinct from past laser ignition research focused on single laser pulses. Specifically, we consider the use of multiple laser pulses where the first pulse provides pre-ionization allowing controlled absorption of the second pulse. In this way, we can form tailored laser plasmas in terms of their ionization fraction, gas temperature (e.g. to achieve elevated temperature of ~2000 K ideally suited for an ignition source), reduced energy loss to shock waves and radiation, and large kernel size (e.g. length ~1-10 cm). The proposed research included both experimental and modeling efforts, at Colorado State University, Princeton University and University of Tennessee, towards the basic science of the new laser plasma approach with emphasis on tailoring the plasmas to practical propulsion systems. Experimental results (CSU) show that the UV beam produces a pre-ionized volume which assists in breakdown of the NIR beam, leading to reduction in NIR breakdown threshold by factor of >2. Numerical modeling is performed to examine the ionization and breakdown of both beams. The main theoretical and computational parts of the work were done at Princeton University. The modeled breakdown threshold of the NIR, including assist by pre-ionization, is in reasonable agreement with the experimental results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

SU-F-J-47: Inherent Uncertainty in the Positional Shifts Determined by a Volumetric Cone Beam Imaging System

Purpose: To quantify inherent uncertainty associated with a volumetric imaging system in its determination of positional shifts. Methods: The study was performed on an Elekta Axesse™ linac’s XVI cone beam computed tomography (CBCT) system. A CT image data set of a Penta- Guide phantom was used as reference image by placing isocenter at the center of the phantom.The phantom was placed arbitrarily on the couch close to isocenter and CBCT images were obtained. The CBCT dataset was matched with the reference image using XVI software and the shifts were determined in 6-dimensions. Without moving the phantom, this process was repeated 20 times consecutively within 30 minutes on a single day. Mean shifts and their standard deviations in all 6-dimensions were determined for all the 20 instances of imaging. For any given day, the first set of shifts obtained was kept as reference and the deviations of the subsequent 19 sets from the reference set were scored. Mean differences and their standard deviations were determined. In this way, data were obtained for 30 consecutive working days. Results: Tabulating the mean deviations and their standard deviations observed on each day for the 30 measurement days, systematic and random errors in the determination of shifts by XVI software were calculated. The systematic errors were found to be 0.03, 0.04 and 0.03 mm while random errors were 0.05, 0.06 and 0.06 mm in lateral, craniocaudal and anterio-posterior directions respectively. For rotational shifts, the systematic errors were 0.02°, 0.03° and 0.03° and random errors were 0.06°, 0.05° and 0.05° in pitch, roll and yaw directions respectively. Conclusion: The inherent uncertainties in every image guidance system should be assessed and baseline values established at the time of its commissioning. These shall be periodically tested as part of the QA protocol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Performance analysis and implementation details of the Energy Conserving Semi-Implicit Method code (ECsim)

We present in this work the implementation of the Energy Conserving Semi-Implicit Method in a parallel code called ECsim. This new code is a three-dimensional, fully electromagnetic particle in cell (PIC) code. It is written in C/C++ and uses MPI to allow massive parallelization. ECsim is unconditionally stable in time, eliminates the finite grid instability, and has the same cycle scheme as the explicit code with a computational cost comparable to other semi-implicit PIC codes. All these features make it a very valuable tool to address situations which have not been possible to analyze until now with other PIC codes. In this work, we show the details of the algorithm implementation and we study its performance in different systems. ECsim is compared with another semi-implicit PIC code with different time and spectral resolution, showing its ability to address situations where other codes fail.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CIGS Technology Advancement via Fundamental Modeling of Defect/Impurity Interactions (Final Technical Report)

The primary goals of the proposed work were to provide modeling tools (and the associated insight which comes along with model development) for design and optimization of CuIn x Ga 1-x Se 2 (CIGS) and CdSeTe (CST) solar cell manufacturing processes and to establish the foundation for comprehensive end-to-end predictive modeling tools to enable optimization of thin film photovoltaic technology for performance, cost, yield, and reliability. The initial focus of efforts within this project was to develop coupled process/optical/device models for CIGS PV technology and to work with Siva Power to apply that TCAD (technology computer-aided design) system to improve the efficiency and reduce manufacturing costs for CIGS solar cells. Our approach to that end was to generate an extensive database of DFT calculations and to use those calculations via statistical thermodynamics methods and Monte Carlo simulation to develop and characterize models for the behavior of native defects as well as intentional and unintentional impurities, including the redistribution of the primary components of CIGS films. Increased effort went toward coupling those models for defect behavior and composition evolution to the performance of multicrystalline CIGS solar cells via prediction of doping level and recombination lifetime as function of manufacturing process. In the second budget period, the project pivoted to developing a similar system for the CdSeTe system, focused especially on understanding the role of Se/Te alloy concentration. Execution of the project resulted in the successful development of TCAD systems for both CIGS and CdSeTe thin film PV within the Synopsys Sentaurus framework by utilizing the Alagator interface. In the first budget period of the project, we developed quantitative models for the major components of CIGS PV and implemented them within a framework that couples process, optical, and device simulation. From the insights we have gained, we identified novel opportunities for enhancing CIGS solar cell performance and have laid the groundwork to further optimize the layer structure, composition profile, and thermal cycles for substantially improved efficiency and lower manufacturing costs. For the CIGS system, process changes to achieve greater than 1% absolute enhancement in efficiency were identified, but testing of those approaches was stymied by lack of a domestic CIGS manufacturing partner after the closure of Siva Power as well as Miasole. For CdSeTe, a fully capable TCAD system only became ready to apply near the end of the project period, so substantial opportunities remain to apply those models to enhance the leading thin film PV technology.

14 SOLAR ENERGY↗
DOE OSTI2025

UnigeneFinder: An Automated Pipeline for Gene Calling From Transcriptome Assemblies Without a Reference Genome

ABSTRACT For most species, transcriptome data are much more readily available than genome data. Without a reference genome, gene calling is cumbersome and inaccurate because of the high degree of redundancy in de novo transcriptome assemblies. To simplify and increase the accuracy of de novo transcriptome assembly in the absence of a reference genome, we developed UnigeneFinder. Combining several clustering methods, UnigeneFinder substantially reduces the redundancy typical of raw transcriptome assemblies. This pipeline offers an effective solution to the problem of inflated transcript numbers, achieving a closer representation of the actual underlying genome. UnigeneFinder performs comparably or better, compared with existing tools, on plant species with varying genome complexities. UnigeneFinder is the only available transcriptome redundancy solution that fully automates the generation of primary transcript, coding region, and protein sequences, analogous to those available for high‐quality reference genomes. These features, coupled with the pipeline’s cross‐platform implementation, focus on automation, and an accessible, user‐friendly interface, make UnigeneFinder a useful tool for many downstream sequence‐based analyses in nonmodel organisms lacking a reference genome, including differential gene expression analysis, accurate ortholog identification, functional enrichments, and evolutionary analyses. UnigeneFinder also runs efficiently both on high‐performance computing (HPC) systems and personal computers, further reducing barriers to use.

Xue, Bo [Plant Resilience Institute Michigan State↗
DOE OSTI2015

Biological responses to engineered nanomaterials: Needs for the next decade

In this study, the interaction of nanomaterials with biomolecules, cells, and organisms is an enormously vital area of current research, with applications in nanoenabled diagnostics, imaging agents, therapeutics, and contaminant removal technologies. Yet the potential for adverse biological and environmental impacts of nanomaterial exposure is considerable and needs to be addressed to ensure sustainable development of nanomaterials. In this Outlook four research needs for the next decade are outlined: (i) measurement of the chemical nature of nanomaterials in dynamic, complex aqueous environments; (ii) real-time measurements of nanomaterial-biological interactions with chemical specificity; (iii) delineation of molecular modes of action for nanomaterial effects on living systems as functions of nanomaterial properties; and (iv) an integrated systems approach that includes computation and simulation across orders of magnitude in time and space.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The effect of protein mutations on drug binding suggests ensuing personalised drug selection

The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein–ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Addressing quantum’s “fine print” with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks

Abstract Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones. Otherwise, the quantum advantage carefully attained through algorithmic ingenuity can be entirely negated. Previous work addressing quantum algorithms for geologic fracture flow has illustrated core algorithmic approaches while incrementally removing assumptions. This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms: efficient system state preparation and efficient information extraction. Our approach to addressing each is consistent with an overall exponential speed-up.

58 GEOSCIENCES↗
DOE OSTI2021

Tailoring magnetization reversal of a single-domain bar nanomagnet via its end geometry

Nanoscale single-domain bar magnets are building blocks for a variety of fundamental and applied mesoscopic magnetic systems, such as artificial spin ices, magnetic shape-morphing microbots, and magnetic majority logic gates. The magnetization reversal switching field of the bar nanomagnets is a crucial parameter that determines the physical properties and functionalities of their constituted artificial systems. Previous methods on tuning the magnetization reversal switching field of a bar nanomagnet usually relied on modifying its aspect ratio, such as its length, width, and/or thickness. Here, we show that the switching field of a bar nanomagnet saturates when extending its length beyond a certain value, preventing further tailoring of the magnetization reversal via aspect ratios. We showcase a highly tunable switching field of a bar nanomagnet by tailoring its end geometry without altering its size. This provides an easy method to control the magnetization reversal of a single-domain bar nanomagnet. It would enable new research and/or applications, such as designing artificial spin ices with additional tuning parameters, engineering magnetic microbots with more flexibility, and developing magnetic quantum-dot cellular automata systems for low power computing.

36 MATERIALS SCIENCE↗
DOE OSTI2014

SU-E-T-407: Evaluation of Four Commercial Dosimetry Systems for Routine Patient-Specific Tomotherapy Delivery Quality Assurance

Purpose: The purpose of this project was to evaluate the performance of four commercially available dosimetry systems for Tomotherapy delivery quality assurance (DQA). Methods: Eight clinical patient plans were chosen to represent a range of treatment sites and typical clinical plans. Four DQA plans for each patient plan were created using the TomoTherapy DQA Station (Hi-Art version 4.2.1) on CT images of the ScandiDose Delta4, IBA MatriXX Evolution, PTW Octavius 4D and Sun Nuclear ArcCHECK phantoms. Each detector was calibrated following the manufacture-provided procedure. No angular response correction was applied. All DQA plans for each detector were delivered on the Tomotherapy Hi-Art unit in a single measurement session but on different days. The measured results were loaded into the vendor supplied software for each QA system for comparison with the TPS-calculated dose. The Gamma index was calculated using 3%/3mm, 2%/2mm with 10% dose threshold of maximum TPS calculated dose. Results: Four detector systems showed comparable gamma pass rates for 3%/3m, which is recommended by AAPM TG119 and commonly used within the radiotherapy community. The averaged pass rates ± standard deviation for all DQA plans were (98.35±1.97)% for ArcCHECK, (99.9%±0.87)% for Matrix, (98.5%±5.09)% for Octavius 4D, (98.7%±1.27)% for Delata4. The rank of the gamma pass rate for individual plans was consistent between detectors. Using 2%/2mm Gamma criteria for analysis, the Gamma pass rate decreased on average by 9%, 8%, 6.6% and 5% respectively. Profile and Gamma failure map analysis using the software tools from each dosimetry system indicated that decreased passing rate is mainly due to the threading effect of Tomo plan. Conclusion: Despite the variation in detector type and resolution, phantom geometry and software implementation, the four systems demonstrated similar dosimetric performance, with the rank of the gamma pass rate consistent for the plans considered.

60 APPLIED LIFE SCIENCES↗