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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Impulse Approximation Scattering Function and Its Use in Monte Carlo Photon Transport Simulations

The viability of using the impulse approximation scattering function in Monte Carlo photon transport simulations is explored. This scattering function can be constructed from the double differential incoherent scattering cross section developed by Ribberfors and Berggren. A commonly used method for modeling photon Doppler broadening, which is referred to as the hybrid Doppler broadening method, can also be derived from this cross section. A new photon Doppler broadening method, called the consistent Doppler broadening method, is derived and discussed. This method eliminates some of the commonly employed approximations in the hybrid Doppler broadening method, in part, by using the impulse approximation scattering function. Integrated incoherent cross sections generated using the impulse approximation scattering function and the widely used Waller-Hartree scattering function are in good agreement above 20 keV. Below 20 keV, differences as high as 70% are observed, which differs from the roughly 5% differences observed by Ribberfors for some of the materials. Integral and spectral quantities for two problems are also generated using the Monte Carlo photon transport capabilities of the Framework for REsearch in Nuclear ScIence and Engineering (FRENSIE). Due to the small, relative result differences observed when using the impulse approximation scattering function, it is considered a viable alternative to the Waller-Hartree scattering function. In addition, some small, but expected, differences in spectral fluxes at low energies can be avoided by adopting the consistent Doppler broadening method.

42 ENGINEERING↗
DOE OSTI2020

Discovery and characterization of a neutralizing pan-ELR+CXC chemokine monoclonal antibody

CXCR1 and CXCR2 signaling play a critical role in neutrophil migration, angiogenesis, and tumorigenesis and are therefore an attractive signaling axis to target in a variety of indications. In human, a total of seven chemokines signal through these receptors and comprise the ELR + CXC chemokine family, so named because of the conserved ELRCXC N-terminal motif. To fully antagonize CXCR1 and CXCR2 signaling, an effective therapeutic should block either both receptors or all seven ligands, yet neither approach has been fully realized clinically. In this work, we describe the generation and characterization of LY3041658, a humanized monoclonal antibody that binds and neutralizes all seven human and cynomolgus monkey ELR + CXC chemokines and three of five mouse and rat ELR+CXC chemokines with high affinity. LY3041658 is able to block ELR + CXC chemokine-induced Ca 2+ mobilization, CXCR2 internalization, and chemotaxis in vitro as well as neutrophil mobilization in vivo without affecting other neutrophil functions. In addition to the in vitro and in vivo activity, we characterized the epitope and structural basis for binding in detail through alanine scanning, crystallography, and mutagenesis. Together, these data provide a robust preclinical characterization of LY3041658 for which the efficacy and safety is being evaluated in human clinical trials for neutrophilic skin diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Sheath constraints on turbulent magnetised plasmas

Abstract A solid target in contact with a plasma charges (negatively) to reflect the more mobile species (electrons) and thus keep the bulk plasma quasineutral. To shield the bulk plasma from the charged target, there is an oppositely (positively) charged sheath with a sharp electrostatic potential variation on the Debye length scale λ D . In magnetised plasmas where the magnetic field is inclined at an oblique angle α with the target, some of the sheath potential variation occurs also on the ion sound gyroradius length scale ρ S cos ⁡ α , caused by finite ion gyro-orbit distortion and losses. We consider a collisionless and steady-state magnetised plasma sheath whose thickness l ms ∼ max ( λ D , ρ S cos ⁡ α ) is smaller than the characteristic length scale L of spatial fluctuations in the bulk plasma, such that the limit l ms / L → 0 is appropriate. Spatial structures are assumed to be magnetic field-aligned. In the case of small magnetic field angle α ∼ δ ≡ ρ S / L ≪ 1 , electric fields tangential to the target transport ions towards the target via E × B drifts at a rate comparable to the one from parallel streaming. A generalised form of the kinetic Bohm–Chodura criterion at the sheath entrance is derived by requiring that the sheath electric field have a monotonic spatial decay far from the target. The criterion depends on tangential gradients of potential and ion distribution function, with additional nontrivial conditions.

kinetic Bohm–Chodura condition↗
DOE OSTI2022

Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation

Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density field. Since all the correlators of biased tracers can be computed with arbitrary precision for these displacements, pairing the Zel'dovich `simulation' with the N-body realization allows hundredfold reductions in sample variance for power spectrum or correlation function estimation. Additionally, Zel'dovich control variates can accurately extend matter or tracer field emulators to larger scales than previously possible, as well as improving measurements of statistics in simulations which are inherently limited to small volumes, such as hydrodynamical simulations of galaxy formation and reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A transmission hologram for slitless spectrophotometry on a convergent telescope beam. 1. Focus and resolution

We report in this paper the test of a plane holographic optical element to be used as an aberration-corrected grating for a slitless spectrograph, inserted in a convergent telescope beam. Our long-term objective is the optimization of a specific hologram to switch the auxiliary telescope imager of the Vera Rubin Observatory into an accurate slitless spectrograph, dedicated to the atmospheric transmission measurement. We present and discuss here the promising results of tests performed with prototype holograms at the CTIO $0.9\,$m telescope during a run of 17 nights in 2017 May–June. After their on-sky geometrical characterization, the performances of the holograms as aberration-balanced dispersive optical elements have been established by analysing spectra obtained from spectrophotometric standard stars and narrow-band emitter planetary nebulae. Thanks to their additional optical function, our holographic disperser prototypes allow us to produce significantly better focused spectra within the full visible wavelength domain $[370,1050]\,$nm than a regular grating, which suffers from strong defocusing and aberrations when used in similar conditions. We show that the resolution of our slitless on-axis spectrograph equipped with the hologram approaches its theoretical performance. While estimating the benefits of a hologram for the spectrum resolution, the roadmap to produce a competitive holographic element for the Vera Rubin Observatory auxiliary telescope has been established.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

GUN4 appeared early in cyanobacterial evolution

Photosynthesis relies on chlorophylls, which are synthesized via a common tetrapyrrole trunk pathway also leading to heme, vitamin B12, and other pigmented cofactors. The first committed step for chlorophyll biosynthesis is insertion of magnesium into protoporphyrin IX by magnesium chelatase. Magnesium chelatase is composed of H-, I-, and D-subunits, with the tetrapyrrole substrate binding to the H-subunit. This subunit is rapidly inactivated in the presence of substrate, light, and oxygen, so oxygenic photosynthetic organisms require mechanisms to protect magnesium chelatase from similar loss of function. An additional protein, GUN4, binds to the H-subunit and to tetrapyrroles. GUN4 has been proposed to serve this protective role via its ability to bind linear tetrapyrroles (bilins). In the current work, we probe the origins of bilin binding by GUN4 via comparative phylogenetic analysis and biochemical validation of a conserved bilin-binding motif. Based on our results, we propose that bilin-binding GUN4 proteins arose early in cyanobacterial evolution and that this early acquisition represents an ancient adaptation for maintaining chlorophyll biosynthesis in the presence of light and oxygen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The JA‐responsive MYC2‐ BADH ‐ like transcriptional regulatory module in Poncirus trifoliata contributes to cold tolerance by modulation of glycine betaine biosynthesis

Summary Glycine betaine (GB) is known to accumulate in plants exposed to cold, but the underlying molecular mechanisms and associated regulatory network remain unclear. Here, we demonstrated that PtrMYC2 of Poncirus trifoliata integrates the jasmonic acid (JA) signal to modulate cold‐induced GB accumulation by directly regulating PtrBADH‐l , a betaine aldehyde dehydrogenase (BADH)‐like gene. PtrBADH‐l was identified based on transcriptome and expression analysis in P. trifoliata . Overexpression and VIGS (virus‐induced gene silencing)‐mediated knockdown showed that PtrBADH‐l plays a positive role in cold tolerance and GB synthesis. Yeast one‐hybrid library screening using PtrBADH‐l promoter as baits unraveled PtrMYC2 as an interacting candidate. PtrMYC2 was confirmed to directly bind to two G‐box cis ‐acting elements within PtrBADH‐l promoter and acts as a transcriptional activator. In addition, PtrMYC2 functions positively in cold tolerance through modulation of GB synthesis by regulating PtrBADH‐l expression. Interestingly, we found that GB accumulation under cold stress was JA‐dependent and that PtrMYC2 orchestrates JA‐mediated PtrBADH‐l upregulation and GB accumulation. This study sheds new light on the roles of MYC2 homolog in modulating GB synthesis. In particular, we propose a transcriptional regulatory module PtrMYC2‐PtrBADH‐l to advance the understanding of molecular mechanisms underlying the GB accumulation under cold stress.

Ming, Ruhong↗
DOE OSTI2020

General Correlations for Laminar Flow Friction Loss and Heat Transfer in Plain Rectangular Plate-Fin Cores

In this paper, new generalized correlations for predicting the average fanning friction factor f and average Nusselt number Nu for laminar flow in plain plate-fin compact cores of rectangular cross section are presented. These are based on extended experimental data, as well as three-dimensional computational simulations, obtained for a broad range of fin density and geometrical attributes. The results indicate that while the fully developed forced convection scales only with the interfin channel cross-sectional ratio α (fin spacing by fin height), the entrance region hydrodynamic and thermal performance is additionally a function of the fin-core length L, flow Reynolds number Re, and fluid Prandtl number Pr. The developing flow and convection is further shown to scale as: (fRe)~(L/d h Re) 1/2 , and Nu ~(L/d h Re) 1/2 Pr 1/3 Φ(α), where f, Re, and Nu are all based on the hydraulic diameter dh of the interfin flow channel. Generalized correlations for both (fRe) and Nu are developed by the corresponding scaling of the forced convection behavior and asymptotic matching of the entrance or developing flow (short fin-core flow length) and the fully developed flow (large fin-core flow length) region performance. Finally, the predictions from these correlations are found to be within ±15% of all available experimental data for air, water, and glycol (0.71 ≤ Pr ≤ 10), and fin cores with 0 < α ≤ 1.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Direction-optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis

Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large-diameter graphs, up to 13.2× speedup over the Shiloach-Vishkin algorithm, and up to 1.6× speedup over Afforest on an Intel Xeon processor using 40 threads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Few-shot leaf segmentation

In this work, we use few-shot learning to segment the body and vein architecture of Populus trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy, in some cases exceeding even human-level segmentation. The leaf and vein segmentations are subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the "vein density" trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code, we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes.

Lagergren, John [Oak Ridge National Lab. (ORNL), O↗
DOE OSTI2024

favela3/Maize.N-cycle.Function

Supplemental sequence processing and R statistical analysis for publication which compares the microbiome of 27 Zea cultivars: 12 Inbred maize genotypes, 9 hybrids, and 6 wild teosinte. The project contains amplicon data for various genes: 16S rRNA, ITS, bacterial amoA, Archeal amoA, nirS, nirK, and nosZ. In addition to functional potential assay data, and N2O flux.

Favela, Alonso↗
DOE OSTI2021

Skip-Connected Self-Recurrent Spiking Neural Networks with Joint Intrinsic Parameter and Synaptic Weight Training

As an important class of spiking neural networks (SNNs), recurrent spiking neural networks (RSNNs) possess great computational power and have been widely used for processing sequential data like audio and text. However, most RSNNs suffer from two problems. 1. Due to the lack of architectural guidance, random recurrent connectivity is often adopted, which does not guarantee good performance. 2. Training of RSNNs is in general challenging, bottlenecking achievable model accuracy. To address these problems, we propose a new type of RSNNs called Skip-Connected Self-Recurrent SNNs (ScSr-SNNs). Recurrence in ScSr-SNNs is introduced in a stereotyped manner by adding self-recurrent connections to spiking neurons. The SNNs with self-recurrent connections can realize recurrent behaviors similar to those of more complex RSNNs while the error gradients can be more straightforwardly calculated due to the mostly feedforward nature of the network. The network dynamics is enriched by skip connections between nonadjacent layers. Moreover, we propose a new backpropagation (BP) method called backpropagated intrinsic plasticity (BIP) to further boost the performance of ScSr-SNNs by training intrinsic model parameters. Unlike standard intrinsic plasticity rules that adjust the neuron's intrinsic parameters according to neuronal activity, the proposed BIP method optimizes intrinsic parameters based on the backpropagated error gradient of a well-defined global loss function in addition to synaptic weight training. Here, based on challenging speech, neuromorphic speech, and neuromorphic image datasets, the proposed ScSr-SNNs can boost performance by up to 2.85% compared with other types of RSNNs trained by state-of-the-art BP methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Supporting information for Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

In this work, we use few-shot learning to segment the body and vein architecture of P. trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy, in some cases exceeding even human-level segmentation. The leaf and vein segmentations are subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the vein density trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code (see https://github.com/jlager/few-shot-leaf-segmentation), we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes. The data folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, C_1_1_2_bot.jpeg, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs. The images folder includes the 2,906 high-resolution leaf scans taken in the field. The leaf_masks folder includes 50 ground truth segmentations used for training the leaf tracing algorithm. The leaf_preds folder includes the 2,906 predicted segmentations from the leaf tracing algorithm. The vein_masks folder includes 8 ground truth segmentations used for training the vein growing algorithm. The vein_preds folder includes the 1,453 predicted segmentations from the vein growing algorithm. The vein_probs folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding. The genomes folder includes the set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes with a README file detailing the steps taken. The results folder includes: (i) raw values of the 68 predicted leaf traits in digital_traits.tsv, (ii) manually measured values of petiole length and width in manual_traits.tsv, (iii) thin plate spline (TPS) adjusted values of the vein density trait in vein_density_tps_adj.tsv, (iv) best linear unbiased prediction (BLUP) adjusted values of the vein density trait in vein_density_blups.tsv, and (v) GWAS results for the vein density trait, including chromosome positions and corresponding P values, in gwas_results.csv.

09 BIOMASS FUELS↗
DOE OSTI2022

ORNL Progress Report on Microreactor Functional Containment Economics

A summary of Oak Ridge National Laboratory’s (ORNL’s) initial contributions to the economic and functional containment evaluation approach for microreactors is presented. A final report will be completed at the end of 2022. This initial progress report does not represent a final product or an expression of a complete technical analysis. Initial contributions from ORNL can be categorized into two parts: (1) description of functional containment as a design optimization tool, and (2) summary of high level technology concepts for an evaluation of economic and functional containment analysis. Additionally, the initial portion of a functional containment evaluation is presented for a generic high temperature gas reactor technology. Remaining tasks include completing the functional containment evaluation by connecting economic and consequence metrics, as well as performing the same effort for other select reactor technologies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Addressing Chemical and Thermomechanical Instabilities in Metal Halide Perovskites Across Length Scales

The chemical and thermomechanical instabilities of metal halide perovskites represent barriers to long operational lifetimes of perovskite-based devices. There is no single magic bullet to address these instabilities, and so multiple complementary strategies must be undertaken. I will discuss the work by our research group to address these instabilities at multiple length scales, including the effects of the molecular structure and functionality of additives on their interactions with perovskite surfaces, the enhancement of mechanical toughness of perovskite layers by compositing with polymeric nanofibers, and the use of confined volume printing to rapidly produce perovskite films with large grains and reduced surface area of grain boundaries detrimental to stability. Together, these approaches provide one possibly pathway toward significantly increasing the operational lifetimes of perovskite-based devices.

Printz, Adam↗
DOE OSTI2022

In-Situ Powder-Directed Energy Process Control for Additively Manufactured Multi-Layer, Functionally Graded Components

Coatings and cladding can experience damage in harsh environments: Erosion Oxidation Delamination External mechanical damage Radiation induced damage Functionally Graded Materials (FGMs) were primarily designed to reduce thermal stress in thermal barrier coatings (TBCs), but can produce coatings which resist a variety of material damage (corrosion, surface damage, diffusion) Powder-fed DED with multiple independent feeders allows for AM FGMs, and provides robust control over process parameters to optimize microstructure and performance. FGMs have a great opportunity for applications in energy applications as a strategy to increase plant efficiency and lifetime.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Hardware-Based Demonstration of Temperature Control Functions for Reactor Systems

Establishing autonomy in reactor control systems has become essential for the expansion of nuclear technologies. Thermal regulation in particular remains crucial for maintaining stable operation and ensuring the integrity of fuel. To alleviate public skepticism of the safety of nuclear reactors, demonstrating control over this key factor is pivotal. Utilizing electric heat pads to simulate the heat released in a reactor core, thermocouples for temperature monitoring, and an Arduino micro programmable logic controller (PLC) for control, a hardware-based demonstration of a reactor heating system validates the efficacy of reactor control over this key parameter. To improve precision, a proportional-integral-derivative (PID) algorithm was implemented in the heating control loop to ensure meticulous control of reactor functions. In addition, the integration of this physical system with a digital simulator tool such as RELAP5-3D establishes a foundation for a comprehensive testing environment. This allows for a refinement of temperature control under various simulated reactor conditions, bringing another layer of reliability to the operation of the system. By facilitating a physical demonstration of reactor thermal management and control strategies, this project provides a foundation for expanded testing and educational outreach. Ultimately, this system advances the broader goal of demonstrating the safety and viability of autonomous reactor operations, contributing to public trust and future reactor deployment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Designing Imidazolium Poly(amide-amide) and Poly(amide-imide) Ionenes and Their Interactions with Mono- and Tris(imidazolium) Ionic Liquids

Here we introduce the synthesis and thermal properties of a series of sophisticated imidazolium ionenes with alternating amide-amide or amide-imide backbone functionality, and investigate the structural effects of mono(imidazolium) and unprecedented tris(imidazolium) ionic liquids (ILs) in these ionenes. The new set of poly(amide-amide) (PAA) and poly(amide-imide) (PAI) ionenes represent the intersection of conventional high-performance polymers with the ionene archetype–presenting polymers with alternating functional and ionic elements precisely sequenced along the backbone. The effects of polymer composition on the thermal properties and morphology were analyzed. Five distinct polymer backbones were synthesized and combined with a stoichiometric equivalent of the IL 1-benzyl-3-methylimidazolium bistriflimide ([Bnmim][Tf 2 N]), which were studied to probe the self-assembly, structuring, and contributions of intermolecular forces when IL is added. Furthermore, three polyamide (PA) or polyimide (PI) ionenes with simpler xylyl linkages were interfaced with [Bnmim][Tf 2 N] as well as a novel amide-linked tris(imidazolium) IL, to demonstrate the structural changes imparted by the inclusion of functional, ionic additives dispersed within the ionene matrix. This work highlights the possibilities for utilizing concepts from small molecules which exhibit supramolecular self-assembly to guide creative design and manipulate the structuring of ionenes.

36 MATERIALS SCIENCE↗