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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

SDSS-IV MaNGA: a catalogue of spectroscopically detected strong galaxy–galaxy lens candidates

ABSTRACT We spectroscopically detected candidate emission-lines of 8 likely, 17 probable, and 69 possible strong galaxy–galaxy gravitational lens candidates found within the spectra of $\approx 10\, 000$ galaxy targets contained within the completed Mapping of Nearby Galaxies at Apache Point Observatory survey. This search is based upon the methodology of the Spectroscopic Identification of Lensing Objects project, which extends the spectroscopic detection methods of the BOSS Emission-Line Lensing Survey and the Sloan Lens ACS Survey. We scanned the co-added residuals that we constructed from stacks of foreground subtracted row-stacked-spectra so a sigma-clipping method can be used to reject cosmic rays and other forms of transients that impact only a small fraction of the combined exposures. We also constructed narrow-band images from the signal to noise of the co-added residuals to observe signs of lensed source images. We also use several methods to compute the probable strong lensing regime for each candidate lens to determine which candidate background galaxies may reside sufficiently near the galaxy centre for strong lensing to occur. We present the spectroscopic redshifts within a value-added catalogue (VAC) for data release 17 (DR17) of SDSS-IV. We also present the lens candidates, spectroscopic data, and narrow-band images within a VAC for DR17. High resolution follow-up imaging of these lens candidates are expected to yield a sample of confirmed grade-A lenses with sufficient angular size to probe possible discrepancies between the mass derived from a best-fitting lens model, and the dynamical mass derived from the observed stellar velocities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

End-to-end orientation estimation from 2D cryo-EM1images

Cryo-electron microscopy (cryo-EM) is a Nobel Prize-winning technique for deter-mining high-resolution 3D structures of biological macromolecules. A 3D structure is reconstructed from hundreds of thousands of noisy 2D projection images. However, existing 3D reconstruction methods are still time-consuming, and one of the major computational bottlenecks is to recover the unknown orientation of the particle in16each 2D image. The dominant methods typically exploit expensive global search on each image to estimate the missing orientations. Here, a novel end-to-end supervised learning method is introduced to directly recover the missing orientations from 2D cryo-EM images. A neural network is used to approximate the mapping from images to orientations. Furthermore, a robust loss function is proposed for optimizing the parameters of the network, which can handle both asymmetric and symmetric 3D structures. Experiments on synthetic datasets with various symmetry types confirm that the neural network is capable of recovering orientations from 2D cryo-EM images, and the results on one real cryo-EM dataset further demonstrate its potential in more challenging imaging conditions.

3D reconstruction↗
DOE OSTI2022

A Subspace-Inclusive Sampling Method for the Computational Design of Compositionally Graded Alloys

Abstract Compositionally graded alloys, a subclass of functionally graded materials (FGMs), utilize localized variations in composition with a single metal part to achieve higher performance than traditional single material parts. In previous work [Kirk, T., Galvan, E., Malak, R., and Arroyave, R., 2018, “Computational Design of Gradient Paths in Additively Manufactured Functionally Graded Materials,” J. Mech. Des., 140, p. 111410. 10.1115/1.4040816], the authors presented a computational design methodology that avoids common issues which limit a gradient alloy’s feasibility, such as deleterious phases, and optimizes for performance objectives. However, the previous methodology only samples the interior of a composition space, meaning designed gradients must include all elements in the space throughout the gradient. Because even small amounts of additional alloying elements can introduce new deleterious phases, this characteristic often neglects potentially simpler solutions to otherwise unsolvable problems and, consequently, discourages the addition of new elements to the state space. The present work improves upon the previous methodology by introducing a sampling method that includes subspaces with fewer elements in the design search. The new method samples within an artificially expanded form of the state space and projects samples outside the true region to the nearest true subspace. This method is evaluated first by observing the sample distribution in each subspace of a 3D, 4D, and 5D state space. Next, a parametric study in a synthetic 3D problem compares the performance of the new sampling scheme to the previous methodology. Lastly, the updated methodology is applied to design a gradient from stainless steel to equiatomic NiTi that has practical uses such as embedded shape memory actuation and for which the previous methodology fails to find a feasible path.

Engineering↗
DOE OSTI2024

In-Situ and Ex-Situ Studies on the Morphology Changes of Polymer Pipeline Materials for Use in Hydrogen Gas Environments

The US natural gas infrastructure is a national asset that could be used to deliver hydrogen and hydrogen blends of natural gas as a pathway to reduce carbon emissions. The distribution system comprises nearly 50% plastic pipe composed of medium- and high-density polyethylene materials (MDPE and HDPE). While these materials perform adequately for natural gas, research on their hydrogen compatibility is essential to understand if any immediate and long-term risks are associated with hydrogen addition. The Blended Gas CRADA, a HyBlend project, has established a comprehensive test method for evaluating MDPE and HDPE of various plastic resin compositions of pipeline material in pure hydrogen and 20% hydrogen/80% methane blends. Both in-situ and ex-situ measurements were performed to capture hydrogen-induced changes in the polyethylene material's crystalline, amorphous and their interphase regions. We investigated MDPE and HDPE pipeline materials made from different polymer resin systems to evaluate the effects of hydrogen gas. The materials were characterized by their density, diffusion coefficient, free volume ratio, and degree of crystallinity. Various advanced characterization methods, including in situ NMR, ex situ XRD, ex situ DSC, and ex situ TDA, were used to analyze the effects of changes in crystalline, amorphous, and interphase regions due to gas exposure. Time-dependent post-decompression quasi-static tensile tests were conducted to explore the effects of gas exposure time on the mechanical behavior of the pipe materials. This work will highlight the time sensitivities during and after gas exposure. The correlation between gas-induced polyethylene morphology changes and the associated material performance will be addressed for the intended applications. These studies will show that polyethylene resin composition and material exposure are important factors when considering whether hydrogen gas affects pipeline materials positively or negatively.

Simmons, Kevin L.↗
DOE OSTI2023

SwitchX : Gmin-Gmax Switching for Energy-efficient and Robust Implementation of Binarized Neural Networks on ReRAM Xbars

Memristive crossbars can efficiently implement Binarized Neural Networks (BNNs) wherein the weights are stored in high-resistance states (HRS) and low-resistance states (LRS) of the synapses. We propose SwitchX mapping of BNN weights onto ReRAM crossbars such that the impact of crossbar non-idealities, that lead to degradation in computational accuracy, are minimized. Essentially, SwitchX maps the binary weights in such a manner that a crossbar instance comprises of more HRS than LRS synapses. We find BNNs mapped onto crossbars with SwitchX to exhibit better robustness against adversarial attacks than the standard crossbar mapped BNNs, the baseline. Finally, we combine SwitchX with state-aware training (that further increases the feasibility of HRS states during weight mapping) to boost the robustness of a BNN on hardware. We find that this approach yields stronger defense against adversarial attacks than adversarial training, a state-of the-art software defense. We perform experiments on a VGG16 BNN with benchmark datasets (CIFAR-10, CIFAR-100 and TinyImagenet) and use Fast Gradient Sign Method (ϵ = 0.05 to 0.3) and Projected Gradient Descent (ϵ = $\frac{2}{255}$ to $\frac{32}{255}$, α = $\frac{2}{255}$) adversarial attacks. We show that SwitchX combined with state-aware training can yield upto ~35% improvements in clean accuracy and ~6–16% in adversarial accuracies against conventional BNNs. Furthermore, an important by-product of SwitchX mapping is increased crossbar power savings, owing to an increased proportion of HRS synapses, which is furthered with state-aware training. We obtain upto ~21–22% savings in crossbar power consumption for state-aware trained BNN mapped via SwitchX on 16 × 16 and 32 × 32 crossbars using the CIFAR-10 and CIFAR-100 datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Exploring Sustainability in Scientific Software through Code Quality & Test Coverage Metrics

Context: Scientific open-source software (SciOSS) plays a foundational role in research and engineering, yet its long-term sustainability has often been overlooked and remains a significant concern. Objective: This study investigates the long-term sustainability of SciOSS through code and test quality metrics. Method: We analyze CASS Software Portfolio projects, classifying them by sustainability and comparing their code structure, test coverage, and links between code quality and testing across the dataset. Results: Sustainable projects show higher, more consistent test coverage and clearer code-test correlations, while unsustainable ones show weaker patterns. Overall, test coverage is low in scientific software, and high complexity and coupling reduce testability. Conclusion: In this study, we present a practical, data-driven approach for assessing sustainability in scientific software, offering a foundation for evaluating long-term software health and supporting future efforts in quality assurance and sustainability monitoring.

Md mushfiqur rahman, Sheikh [University of Tenness↗
DOE OSTI2023

Wcomp (Wind Farm Wake Comparison Framework) [SWR-23-72]

The Wind Farm Wake Comparison Framework (Wcomp) is a software tool to facilitate the comparison of a specific collection of wind farm wake modeling tools: Python-based, steady-state, analytical wake modeling utilities. Wcomp integrates another software project, windIO, to create a consistent method for describing a wind farm flow control problem. Additionally, a data structure is included to represent the outputs a wind farm flow control simulation. Well-described interfaces allow existing wake modeling tools to plug into this framework.

Mudafort, Rafael↗
DOE OSTI2022

ESS-DIVE Reporting Format for Dataset Package Metadata

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats.Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE.Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Machine Learning for Memory Reduction in the Implicit Monte Carlo Simulations of Thermal Radiative Transfer [Slides]

Project Goal: Use parametric Machine Learning methods in order to reduce memory requirements at checkpointing & restarting in the IMC simulations of Thermal Radiative Transfer using: Expectation Maximization and Weighted Gaussian Mixture Model-based approach for `particle-data compression', introduced in Plasma Physics to model Maxwellian particle distributions by Luis Chacon and Guangye Chen; Expectation Maximization with Weighted Hyper-Erlang Model in order to compress isotropic IMC particle data in the frequency domain; and Expectation Maximization and von Mises-Fisher Mixture Model for compression of anisotropic IMC particle data in the angular domain (work-in-progress).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Probing Coherent States of Light and Matter in Two-Dimensional Semiconductors. Final Report

The ability to enhance light-matter interactions in engineered optical environments is well-established in micro- and nano-photonics. New classes of materials bring new rich correlations between spin, momentum, and light polarization that can be exploited in these photonic systems for information processing and low-power electronics. Interfacing these novel material features with strong optical interactions suggests the compelling capability to create hybrid light-matter systems harnessing low-energy and protected material properties such as spin. The long-term objective of this program was to investigate the interplay between pseudospin in 2D materials and light, revealing new coherent phenomena. The materials studied are the monolayer transition metal dichalcogenides (TMDs), which support polarization-sensitive optical transitions when isolated to a single sub-nanometer crystal layer in thickness. In particular, monolayer TMD semiconductors such as MoS 2 exhibit degenerate valleys in momentum space with distinct spin character. These valleys can be separately addressed by circularly polarized light, providing a tool for manipulation. Integrating this valley pseudospin of excitons in monolayer TMDs with engineered strong optical excitations to create novel coherent phenomena was a key long-term goal of this research. The scope of this research primarily focused on studying light-matter interactions of valley-sensitive excitations in monolayer TMDs. The scope also included the integration of monolayer materials that support valley-polarized emission into photonic devices. This approach extends prior work on exciton-polaritons in 2D materials toward a platform with far more control and improved light input and output characteristics to aid scalability of these polarization-sensitive phenomena to many-body, long-range optical networks. During the course of the five-year Early Career program, the approaches led to successful progress on each proposed aim. Initial successful work seeded additional research that developed from the main themes, while this program focus evolved toward coherent manipulation of valley polaritons and integration with photonics. Both of these advanced topics set the stage for new capabilities in future innovative research. The results of this project provide advances in materials and methods relevant for new visions of opto-electronics and photonics that can improve information processing in an era of increased focus on quantum and coherent phenomena. The research has also contributed to the training of two postdoctoral scholars and five graduate students.

2D Materials↗
DOE OSTI2020

Cosmic Growth Rate as Probe of Dark Matter and Gravity. Final Report

Some 95% of the energy content of the universe lies outside the standard model of particle physics, with approximately 70% arising from physics that accelerates cosmic expansion in opposition to normal gravity and about 25% dark matter that clusters under gravity but does not interact directly with light. This project made significant advances in developing methods to extract information on the former – dark energy or modified gravity – and latter – dark matter through their effects on the expansion history of the universe and the cosmic growth of large scale structures under the influence of gravity and dark energy. These results directly benefit experimental/observational cosmic surveys of large scale structure imaging hundreds of millions of galaxies and mapping spectroscopically the 3D positions of tens of millions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Research Priorities and Opportunities in United States Wholesale Electricity Markets

The power system is currently undergoing a number of changes, including a rapidly evolving resource mix, growth of distributed energy resources (DERs), more active consumer participation, increased deployment of energy storage and hybrid resources, and more advanced communication and control requirements. These changes in the power system present numerous technical, economic, implementation, and policy challenges and research opportunities for power system operators. To help address these challenges, a collaboration among five research institutions—Argonne National Laboratory, the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory, the Electric Power Research Institute, and Johns Hopkins University—has been established to provide technical assistance to the seven U.S. Independent System Operators (ISOs) and Regional Transmission Organizations (RTOs). The 3-year project aims to leverage the advanced methods, tools, datasets, and resources of the collaborators to provide robust analytical support to address the high-priority market challenges that will be faced in the 2- to 10-year time horizon.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Extremely Lightweight Optical Structures for Rapid Deployment

The structures that surround and support optical components play a key role in the performance of the overall optical system. For aerospace applications, creating an opto-mechanical structure that is athermal, lightweight, robust, and can be quickly developed from concept through to hardware is challenging. This project demonstrates a design and fabrication method for optical structures using origami-style folded, photo-etched sheetmetal pieces that are micro-welded to each other or to 3d printed metal components. Thin flexures, critical for athermal mounting of optics, can be thinner with sheetmetal than from standard machining, which leads to more compact designs and the ability to mount smaller optics. Building a structure by starting with the thinnest features, then folding that thin material to make the ''thicker'' sections is the opposite of standard machining (cutting thin features from thicker blocks). A design method is shown with mass savings of >90%, and stiffness to weight ratio improvements of 5x to 10x compared to standard methods for space systems hardware. Designs and processes for small, flexured, actively aligned systems are demonstrated as are methods for producing lightweight, structural, Miura-core sandwich panels in both flat and curved configurations. Concepts for deployable panels and component hinges are explored, as is a lens subcell with tunable piston movement with temperature change and an ultralight sunshade.

42 ENGINEERING↗
DOE OSTI2020

Production of Radiohalogens: Bromine and Astatine for Imaging and Therapy

Objective: This collaborative proposal between the University of Alabama at Birmingham (UAB), University of Wisconsin, Madison (UWisc) and the University of Pennsylvania (Penn) aims to research the efficient production of high purity, clinical grade radiohalogens for preclinical and translational applications by optimizing the cyclotron targetry and separation chemistry of 76,77Br and 211At for use in imaging and therapy. Proposed Research and Methods: During the course of this project, faculty and trainees at our three institutions will use the complementary infrastructure available to examine the 76Se(p,n)76Br, 77Se(p,n)77Br, 78Se(p,2n)77Br, natAs(α,2n)77Br ,80Kr(p,α)77Br and 209Bi(α, 2n)211At reactions as routes to the production of these valuable radiohalogens for imaging and therapy. Working together we aim to optimize both solid and gas targetry, including reclamation of expensive enriched material. We will develop high yielding recovery processes based on dry distillation and ion exchange techniques. We will also create a comprehensive set of quality control procedures to ensure we are producing our radioisotopes in the highest radiochemical and chemical purity possible. By bringing three institutions together to perform research and development we provide the unique opportunity to build an everlasting cooperative culture in the field of nuclear medicine research by enabling academic centers to delve into the fields of radiohalogen chemistry. In addition, each institution involved holds different skills and expertise and a collaborative grant will facilitate the cross-pollination of ideas, techniques, and build strong bonds that unite under the DOE mission. Additionally, our work does not stop at the completion of this proposal, rather this proposal allows us to gain the momentum needed to get our research and developmental efforts off the ground thereby creating a network of three institutions dedicated to the advancement of isotope production for medical research.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Profitability, Scalability and Optimized Siting for Waste-to-Energy Supply Chains: A Case Study on Hydrothermal Liquefaction

Waste-to-Energy (WtE) conversion technologies have the potential to simultaneously reduce waste volumes, treatment-related emissions, and carbon intensity while producing valuable energy services. However, questions remain regarding under which conditions emerging WtE technologies are feasible to deploy. In this project, we develop an optimized siting method for WtE technologies to assess cost-effective WtE processor locations, throughput scales, and profitability. We apply the model using Hydrothermal Liquefaction (HTL) as an example WtE technology. We develop calibrated capital and operating expense cost curves based on literature data to study the techno-economic characteristics of WtE supply chains. Prior to solving the siting optimization, we analyze the model parameters to describe organic waste management system behavior under four different HTL deployment configurations: Co-Processing, where HTL plants send biocrude to existing conventional refineries via assumed pipelines; Co-Location, where biorefineries are co-located with HTL plants; and two alternative Standalone Biorefining cases, where distributed HTL plants transport biocrude intermediate via either assumed pipelines or trucking to centralized biorefineries. We also define and apply several feedstock gate fee calculations, representing different revenue distribution options, to investigate how bioproduct value could impact WtE supply chain economics in terms of waste producer and processor profit or cost reduction.

09 BIOMASS FUELS↗
DOE OSTI2024

Cold Spray Technology Applications for SRF Cavity Thermal and Mechanical Stabilization

The overall objective of the project is the development of a method allowing increased thermal and mechanical stabilization of SRF cavities by depositing a thick layer on the outer surface by cold-spray. Coating and testing of bi-metallic Nb/Cu and Nb/CuW samples were carried out to optimize the process parameters. The process developed on samples was applied to a single cell SRF cavity for proof-of-principle demonstration of improved thermal and mechanical stability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Thermally Insulating Transparent Barrier (THINNER) coatings on single pane windows

Conventional silica aerogel monoliths can provide remarkable thermal insulation but the presence of large pores (> 30 nm) tends to scatter visible light and render the material opaque or translucent instead of transparent. In addition, they are prone to cracking during synthesis and handling which makes them difficult to integrate in products and in particular in window solutions. This project developed two new solgel synthesis methods using silica precursors or preformed silica nanoparticles and ambient drying to produce mesoporous organo-silica monoliths. The monoliths were (i) thermally insulating, (ii) optically transparent, (iii) flexible, and (iv) hydrophobic. They feature porosity ranging from 50% to 90% with narrow pore size distribution with pore less than 20 nm resulting in excellent optical clarity (haze < 2%) and very low thermal conductivity (< 30 mW/mK). Interestingly, not only porosity but also pore size and mass fractal dimension were found to affect the thermal conductivity of the mesoporous silica. The superior transparency of the monoliths was shown to be attributed to dependent scattering among silica nanoparticles. Flexibility was achieved through trimethylchlorosilane surface modification. Furthermore, process scale-up and integration of the ambigel monoliths into window solutions using optically clear adhesives were also demonstrated in 6”x6” double-pane windows. The aerogel and the window solution were shown to be durable to accelerated aging under UV, moisture, and/or temperature gradient and thermal cycling. Overall, the window solution achieved the technical performance and the cost target of $10/sqft set for the SHIELD program. However, scaling to industry relevant scale (> 10’x10’) remains a challenge due to requirements on the drying process and fume hood size and to the propensity of large aerogel slab to crack during drying.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗