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At least 1,189 records · Page 66

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Liquid crystalline epoxy networks with exchangeable disulfide bonds

In this study, a liquid crystalline epoxy network (LCEN) with exchangeable disulfide bonds is synthesized by polymerizing a biphenyl-based epoxy monomer with an aliphatic dicarboxylic acid curing agent containing a disulfide bond. The effect of disulfide bonds on curing behavior and liquid crystalline (LC) phase formation of the LCEN is investigated. The presence of the disulfide bonds results in an increase in the reaction rate, leading to a reduction in liquid crystallinity of the LCEN. In order to promote LC phase formation and stabilize the self-assembled LC domains, a similar aliphatic dicarboxylic acid without the disulfide bond is used as a co-curing agent to reduce the amount of exchangeable disulfide bonds in the system. After optimizing the molar ratio of the two curing agents, the resulting LCEN exhibits improved reprocessability and recyclability because of the disulfide exchange reactions, while preserving LC properties, such as the reversible LC phase transition and macroscopic LC orientation, for shape memory applications.

36 MATERIALS SCIENCE↗

NASA NTRS1989

A summary of precise orbit computation for the Geosat Exact Repeat Mission

Recent efforts to recompute orbits for the Geosat Exact Repeat Mission have resulted in a substantial reduction in the satellite's orbit error. The improved orbits are based on Doppler tracking and on the new earth gravity models being developed at the Goddard Space Flight Center in support of requirements for the TOPEX/Poseidon mission. The first set of orbit solutions based on the Goddard Earth Model (GEM T1) gravity model and tracking data from the U.S. Navy's Operational Network system are accurate to about 85 cm root mean square (rms) in height. Preliminary tests of orbits computed with the GEM-T2 model, along with tracking data from an augmented network consisting of the Navy's Operational Network and selected Tracking Network sites, indicate that radial accuracies of 35 cm rms can be achieved.

Haines, Bruce J.↗

NASA NTRS2000

Application of Energy Integration Techniques to the Design of Advanced Life Support Systems

Exchanging heat between hot and cold streams within an advanced life support system can save energy. This savings will reduce the equivalent system mass (ESM) of the system. Different system configurations are examined under steady-state conditions for various percentages of food growth and waste treatment. The scenarios investigated represent possible design options for a Mars reference mission. Reference mission definitions are drawn from the ALSS Modeling and Analysis Reference Missions Document, which includes definitions for space station evolution, Mars landers, and a Mars base. For each scenario, streams requiring heating or cooling are identified and characterized by mass flow, supply and target temperatures and heat capacities. The Pinch Technique is applied to identify good matches for energy exchange between the hot and cold streams and to calculate the minimum external heating and cooling requirements for the system. For each pair of hot and cold streams that are matched, there will be a reduction in the amount of external heating and cooling required, and the original heating and cooling equipment will be replaced with a heat exchanger. The net cost savings can be either positive or negative for each stream pairing, and the priority for implementing each pairing can be ranked according to its potential cost savings. Using the Pinch technique, a complete system heat exchange network is developed and heat exchangers are sized to allow for calculation of ESM. The energy-integrated design typically has a lower total ESM than the original design with no energy integration. A comparison of ESM savings in each of the scenarios is made to direct future Pinch Analysis efforts.

Levri, Julie↗

NASA NTRS2020

Prochlorococcus phage ferredoxin: structural characterization and electron transfer to cyanobacterial sulfite reductases

Marine cyanobacteria are infected by phages whose genomes encode ferredoxin (Fd) electron carriers. These Fds are thought to redirect the energy harvested from light to phage-encoded oxidoreductases that enhance viral fitness, but it is unclear how the biophysical properties and partner specificities of phage Fds relate to those of photosynthetic organisms. Here, results of a bioinformatics analysis using a sequence similarity network revealed that phage Fds are most closely related to cyanobacterial Fds that transfer electrons from photosystems to oxidoreductases involved in nutrient assimilation. Structural analysis of myovirus P-SSM2 Fd (pssm2-Fd), which infects the cyanobacterium Prochlorococcus marinus, revealed high levels of similarity to cyanobacterial Fds (root mean square deviations of ≤0.5 Å). Additionally, pssm2-Fd exhibited a low midpoint reduction potential (–336 mV versus a standard hydrogen electrode), similar to other photosynthetic Fds, although it had lower thermostability (Tm = 28 °C) than did many other Fds. When expressed in an Escherichia coli strain deficient in sulfite assimilation, pssm2-Fd complemented bacterial growth when coexpressed with a P. marinus sulfite reductase, revealing that pssm2-Fd can transfer electrons to a host protein involved in nutrient assimilation. The high levels of structural similarity with cyanobacterial Fds and reactivity with a host sulfite reductase suggest that phage Fds evolved to transfer electrons to cyanobacterially encoded oxidoreductases.

cyanobacteria↗

DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Comparison of source-location algorithms for atmospheric samplers

Numerous algorithms have been developed to determine the source characteristics for an atmospheric release, e.g., (Bieringer et al., 2017). This study compares three models that have been applied to the data collected by the International Monitoring System operated by the Comprehensive Nuclear-Test-Ban Treaty Preparatory Commission to estimate source event parameters. Each model uses a different approach to estimate the parameters. A deterministic model uses a possible source region (PSR) approach (Ringbom et al., 2014) that is based on the correlation between predicted and measured sample values. An unnamed model developed at Pacific Northwest National Laboratory (herein called BAYEST) uses a Bayesian formulation (Eslinger et al., 2019; Eslinger et al., 2020; Eslinger and Schrom, 2016). The FREAR model uses a different Bayesian formulation (De Meutter and Hoffman, 2020; De Meutter et al., 2021; Pieter De Meutter, 2021). The performance of the three source-location models is evaluated with 100 synthetic release cases for the single xenon isotope, 133 Xe. The release cases resulted in detections in a fictitious network with 120 noble gas samplers. All three source-location models use the same sampling data. The two Bayesian models yield more accurate location estimates than the deterministic PSR model, with FREAR having slightly better location performance than BAYEST. Samplers with collection periods of 3, 6, 8, 12, and 24-h were used. Results from BAYEST show that location accuracy improves with each reduction in sample collection length. The BAYEST model is slightly better for estimating the start time of the release. The PSR model has about the same spread in start times as the FREAR model, but the PSR results have a better average start time. The Bayesian source-location algorithms give more accurate results than the PSR approach, and provide release magnitude estimates, while the base PSR model does not estimate the release magnitude. This investigation demonstrates that a reasonably dense sampling grid will sometimes yield poor location and time estimates regardless of the model. The poor estimates generally coincide with cases where there is a much larger distance between the release point and the first detecting sampler than the average sampler spacing.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2023

Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Direct experimental evidence for differing reactivity alterations of minerals following irradiation. The case of calcite and quartz

Concrete, used in the construction of nuclear power plants (NPPs), may be exposed to radiation emanating from the reactor core. Until recently, concrete has been assumed immune to radiation exposure. Direct evidence acquired on Ar+ -ion irradiated calcite and quartz indicates, on the contrary, that, such minerals, which constitute aggregates in concrete, may be significantly altered by irradiation. More specifically, while quartz undergoes disordering of its atomic structure resulting in a near complete lack of periodicity, calcite only experiences random rotations, and distortions of its carbonate groups. As a result, irradiated quartz shows a reduction in density of around 15%, and an increase in chemical reactivity, described by its dissolution rate, similar to a glassy silica. However, calcite shows little change in dissolution rate - although its density noted to reduce by 9%. These differences are correlated with the nature of bonds in these minerals, i.e., being dominantly ionic or covalent, and the rigidity of the mineral's atomic network that is characterized by the number of topological constraints (nc) that are imposed on the atoms in the network. Our outcomes have major implications on the durability of concrete structural elements formed with calcitic or quartzitic aggregates in nuclear power plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty

Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.

Olson, Daniel↗

DOE OSTI2020

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Crop Model Improvement Reduces the Uncertainty of the Response to Temperature of Multi-Model Ensembles

To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures greater than 24 C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.

damage assessment↗

NASA NTRS2018

Polymer-Filled Metal Foams for Contamination Resistant Aircraft Leading Edges

Adhesion of contaminants has been identified as a ubiquitous issue for aeronautic exterior surfaces. Organic coatings have been demonstrated to enable reduction in insect residue adhesion as well as a reduction in ice adhesion strength. These approaches however, have yet to demonstrate sufficient durability to enable application to commercial aircraft wing leading edges. High durability materials have been developed in parallel to these efforts; contaminant adhesion issues are not addressed. This work describes a first attempt to identify a solution to the seemingly competing issues of contaminant adhesion and durability. A co-continuous network of organic and metallic domains may provide greater durability, while still enabling the changes in surface-contaminant interactions that have been realized. To that end, unique properties have been achieved through fabrication of composite metal foam materials. Likewise, extensive research has been dedicated toward development of polymeric coatings for mitigation of insect residue adhesion.This work describes initial results from infusion of a low surface free energy epoxy resin into a composite metal foam substrate for the purpose of generating a durable contaminant adhesion-resistant material.

Robbins, Samuel J.↗

DOE OSTI2019

Spatter reduction laser scanning strategy in selective laser melting

An additive manufacturing system wherein a gentle sintering run on heat-fusible particles is followed closely by a selective laser melting run. The laser sintering run slightly sinters the particles and causes the particles to adhere to each other, but not necessarily deform and lose their original shape. The particles become connected via bridges between each other, which holds them in place. During the laser melting run, the powder melts in place, with minimum particle ejection or mobility, since the particles form a network of connected particles.

Khairallah, Saad↗

DOE OSTI2019

Spatter reduction laser scanning strategy in selective laser melting

36 MATERIALS SCIENCE↗

DOE OSTI2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

A Search for Rapidly Pulsating Hot Subdwarf Stars in the GALEX Survey

NASA’s Galaxy Evolution Explorer ( GALEX ) provided near- and far-UV observations for approximately 77% of the sky over a 10-year period; however, the data reduction pipeline initially only released single NUV and FUV images to the community. The recently released Python module gPhoton changes this, allowing calibrated time-series aperture photometry to be extracted easily from the raw GALEX data set. Here we use gPhoton to generate light curves for all hot subdwarf B (sdB) stars that were observed by GALEX , with the intention of identifying short-period, p-mode pulsations. We find that the spacecraft’s short visit durations, uneven gaps between visits, and dither pattern make the detection of hot subdwarf pulsations difficult. Nonetheless, we detect UV variations in four previously known pulsating targets and report their UV pulsation amplitudes and frequencies. Additionally, we find that several other sdB targets not previously known to vary show promising signals in their periodograms. Using optical follow-up photometry with the Skynet Robotic Telescope Network, we confirm p-mode pulsations in one of these targets, LAMOST J082517.99+113106.3, and report it as the most recent addition to the sdBV{sub r} class of variable stars.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Gold-in-copper at low *CO coverage enables efficient electromethanation of CO 2

The renewable-electricity-powered CO 2 electroreduction reaction provides a promising means to store intermittent renewable energy in the form of valuable chemicals and dispatchable fuels. Renewable methane produced using CO 2 electroreduction attracts interest due to the established global distribution network; however, present-day efficiencies and activities remain below those required for practical application. Here we exploit the fact that the suppression of *CO dimerization and hydrogen evolution promotes methane selectivity: we reason that the introduction of Au in Cu favors *CO protonation vs. C-C coupling under low *CO coverage and weakens the *H adsorption energy of the surface, leading to a reduction in hydrogen evolution. We construct experimentally a suite of Au-Cu catalysts and control *CO availability by regulating CO 2 concentration and reaction rate. This strategy leads to a 1.6× improvement in the methane:H 2 selectivity ratio compared to the best prior reports operating above 100 mA cm -2 . We as a result achieve a CO 2 -to-methane Faradaic efficiency (FE) of (56 ± 2)% at a production rate of (112 ± 4) mA cm -2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Canalization of Phenotypes—When the Transcriptome is Constantly but Weakly Perturbed

Abstract Recent studies have increasingly pointed to microRNAs (miRNAs) as the agent of gene regulatory network (GRN) stabilization as well as developmental canalization against constant but small environmental perturbations. To analyze mild perturbations, we construct a Dicer-1 knockdown line (dcr-1 KD) in Drosophila that modestly reduces all miRNAs by, on average, ∼20%. The defining characteristic of stabilizers is that, when their capacity is compromised, GRNs do not change their short-term behaviors. Indeed, even with such broad reductions across all miRNAs, the changes in the transcriptome are very modest during development in stable environment. By comparison, broad knockdowns of other regulatory genes (esp. transcription factors) by the same method should lead to drastic changes in the GRNs. The consequence of destabilization may thus be in long-term development as postulated by the theory of canalization. Flies with modest miRNA reductions may gradually deviate from the developmental norm, resulting in late-stage failures such as shortened longevity. In the optimal culture condition, the survival to adulthood is indeed normal in the dcr-1 KD line but, importantly, adult longevity is reduced by ∼90%. When flies are stressed by high temperature, dcr-1 KD induces lethality earlier in late pupation and, as the perturbations are shifted earlier, the affected stages are shifted correspondingly. Hence, in late stages of development with deviations piling up, GRN would be increasingly in need of stabilization. In conclusion, miRNAs appear to be a solution to weak but constant environmental perturbations.

Lu, Guang-An↗