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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Neural conditional reweighting

There is a growing use of neural network classifiers as unbinned, high-dimensional (and variable-dimensional) reweighting functions. To date, the focus has been on marginal reweighting, where a subset of features are used for reweighting while all other features are integrated over. There are some situations, though, where it is preferable to condition on auxiliary features instead of marginalizing over them. Here, we introduce neural conditional reweighting, which extends neural marginal reweighting to the conditional case. This approach is particularly relevant in high-energy physics experiments for reweighting detector effects conditioned on particle-level truth information. Furthermore we leverage a custom loss function that not only allows us to achieve neural conditional reweighting through a single training procedure, but also yields sensible interpolation even in the presence of phase space holes. As a specific example, we apply neural conditional reweighting to the energy response of high-energy jets, which could be used to improve the modeling of physics objects in parametrized fast simulation packages.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Online Detection of Low-Quality Synchrophasor Data Considering Frequency Similarity

Here, this letter proposes a new approach for online detection of low-quality synchrophasor data under both normal and event conditions. The proposed approach utilizes the features of synchrophasor data in time and frequency domains to distinguish multiple regional PMU signals and detect low-quality synchrophasor data. It is more effective to detect low-quality data with apparently indistinguishable profiles. Case studies from recorded synchrophasor measurements verify the effectiveness of the proposed approach for detecting low-quality synchrophasor data in frequency, voltage magnitude and voltage angle.

42 ENGINEERING↗
DOE OSTI2024

SyPro Poplar: Improving Poplar Biomass Production under Abiotic Stress Conditions: an Integrated Omics, Bioinformatics, Synthetic Biology and Genetic Engineering Approach (Final Report)

SyPro Poplar: Improving Poplar Biomass Production under Abiotic Stress Conditions: an Integrated Omics, Bioinformatics, Synthetic Biology and Genetic Engineering Approach In the SyPro Poplar project, we aimed to integrate omics, bioinformatics, synthetic biology, and genetic engineering approaches to develop transgenic poplar trees with sustained photosynthetic activity and increased biomass production under individual and the simultaneous occurrence of water deficit, increased soil salinity, and elevated temperatures. Specifically, we intended to (i) study the functions of selected stress-responsive genes at tissue and cell type-specific levels, (ii) discover novel motifs and construct stress-responsive synthetic promoters, and (iii) use these promoters to drive the expression of genes shown to confer abiotic stress tolerance in a variety of crops and develop abiotic stress-tolerant poplars in a coordinated fashion.

09 BIOMASS FUELS↗
DOE OSTI2025

Redox poise in R. rubrum phototrophic growth drives large-scale changes in macromolecular pathways

During photoheterotrophic growth on organic substrates, purple nonsulfur photosynthetic bacteria like Rhodospirillum rubrum can acquire electrons by multiple means, including oxidation of organic substrates, oxidation of inorganic electron donors (e.g., H 2 ), and by reverse electron flow from the photosynthetic electron transport chain. These electrons are stored as reduced electron-carrying cofactors (e.g., NAD(P)H and ferredoxin). The overall ratio of oxidized to reduced cofactors (e.g., NAD(P)+:NAD(P)H), or ’redox poise’, is difficult to understand or predict, as are the cellular processes for dissipating these reducing equivalents. Using physics-based models that capture mass action kinetics consistent with the thermodynamics of reactions and pathways, a range of redox conditions for heterophototrophic growth are evaluated, from conditions in which the NADP+/NADPH levels approach thermodynamic equilibrium to conditions in which the NADP+/NADPH ratio is far above the typical physiological values. Modeling predictions together with experimental measurements indicate that the redox poise of the cell results in large-scale changes in the activity of biosynthetic pathways and, thus, changes in cell macromolecule levels (DNA, RNA, proteins, and fatty acids). Furthermore, modeling predictions indicate that during phototrophic growth, reverse electron flow from the quinone pool is a minor contributor to the production of reduced cofactors (e.g., NAD(P)H) compared to other oxidative processes (H 2 and carbon substrate oxidation). Instead, the quinone pool primarily operates to aid ATP production. The high level of ATP, in turn, drives reduction processes even when NADPH levels are relatively low compared to NADP+ by coupling ATP hydrolysis to the reductive processes. The model, in agreement with experimental measurements of macromolecule ratios of cells growing on different carbon substrates, indicates that the dynamics of nucleotide versus lipid and protein production is likely a significant mechanism of balancing oxidation and reduction in the cell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Redox Poise during Rhodospirillum rubrum Phototrophic Growth Drives Large-scale Changes in Macromolecular Synthesis Pathways

During photoheterotrophic growth on organic substrates, purple nonsulfur photosynthetic bacteria like Rhodospirillum rubrum can acquire electrons by multiple means, including oxidation of organic substrates, oxidation of inorganic electron donors (e.g., H2), and by reverse electron flow from the photosynthetic electron transport chain. These electrons are stored as reduced electron-carrying cofactors (e.g., NAD(P)H and ferredoxin). The overall ratio of oxidized to reduced cofactors (e.g., NAD(P)+:NAD(P)H), or ’redox poise’, is difficult to understand or predict, as are the cellular processes for dissipating these reducing equivalents. Using physics-based models that capture mass action kinetics consistent with the thermodynamics of reactions and pathways, a range of redox conditions for heterophototrophic growth are evaluated, from conditions in which the NADP+/NADPH levels approach thermodynamic equilibrium to conditions in which the NADP+/NADPH ratio is far above the typical physiological values. Modeling predictions together with experimental measurements indicate that the redox poise of the cell results in large-scale changes in the activity of biosynthetic pathways and, thus, changes in cell macromolecule levels (DNA, RNA, proteins, and fatty acids). Furthermore, modeling predictions indicate that during phototrophic growth, reverse electron flow from the quinone pool is a minor contributor to the production of reduced cofactors (e.g., NAD(P)H) compared to other oxidative processes (H2 and carbon substrate oxidation). Instead, the quinone pool primarily operates to aid ATP production. The high level of ATP, in turn, drives reduction processes even when NADPH levels are relatively low compared to NADP+ by coupling ATP hydrolysis to the reductive processes. The model, in agreement with experimental measurements of macromolecule ratios of cells growing on different carbon substrates, indicates that the dynamics of nucleotide versus lipid and protein production is likely a significant mechanism of balancing oxidation and reduction in the cell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Systems and methods for randomized energy draw or supply requests

The present disclosure can provide a distributed and anonymous approach to demand response of an electricity system. The approach can conceptualize energy consumption and production of distributed-energy resources (DERs) via discrete energy packets that are coordinated by a cyber computing entity that grants or denies energy packet requests from the DERs. The approach leverages a condition of a DER, which is particularly useful for (1) thermostatically-controlled loads, (2) non-thermostatic conditionally-controlled loads, and (3) bi-directional distributed energy storage systems, among others. In a first aspect of the present approach, each DER independently requests the authority to switch on for a fixed amount of time (i.e., packet duration). The coordinator determines whether to grant or deny each request based electric grid and/or energy or power market conditions. In a second aspect, bi-directional DERs, such as distributed-energy storage systems (DESSs) are further able to request to supply energy to the grid.

Frolik, Jeff↗
DOE OSTI2021

Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence

We use a stable parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs). The parallel training reduces the risk of mode collapse and enhances scalability by using multiple generators that are concurrently trained, each one of them focusing on a single data label. The use of the Wasserstein metric reduces the risk of cycling by stabilizing the training of each generator. We apply the approach on the CIFAR10 and the CIFAR100 datasets, two standard benchmark datasets with images of the same resolution, but different number of classes. Performance is assessed using the inception score, the Fréchet inception distance, and image quality. An improvement in inception score and Fréchet inception distance is shown in comparison to previous results obtained by performing the parallel approach on deep convolutional conditional generative adversarial neural networks (DC-CGANs). Weak scaling is attained on both datasets using up to 100 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

Lupo Pasini, Massimiliano↗
DOE OSTI2020

TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion

Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for reconstructing the internal structure of materials at high spatial resolutions from tens of micrometers to a few nanometers. In order to resolve sample features at smaller length scales, however, a higher radiation dose is required. Therefore, the limitation on the achievable resolution is set primarily by noise at these length scales. We present TomoGAN, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions. We evaluate our approach in two photon-budget-limited experimental conditions: (1) sufficient number of low-dose projections (based on Nyquist sampling), and (2) insufficient or limited number of high-dose projections. In both cases, the angular sampling is assumed to be isotropic, and the photon budget throughout the experiment is fixed based on the maximum allowable radiation dose on the sample. Evaluation with both simulated and experimental datasets shows that our approach can significantly reduce noise in reconstructed images, improving the structural similarity score of simulation and experimental data from 0.18 to 0.9 and from 0.18 to 0.41, respectively. Furthermore, the quality of the reconstructed images with filtered back projection followed by our denoising approach exceeds that of reconstructions with the simultaneous iterative reconstruction technique, showing the computational superiority of our approach.

42 ENGINEERING↗
DOE OSTI2021

Feedstock and Catalyst Impact on Bio-Oil Production and FCC Co-Processing to Fuels

NREL’s thermochemical biomass conversion research is focused on ex-situ upgrading of biomass fast-pyrolysis (FP) vapors as an efficient and economical route to completely biogenic pyrolysis-based fuel precursors, potential fuels, and value-added chemicals depending on catalyst choice and process conditions. In this approach, biomass vapors are generated via fast pyrolysis with destabilizing vapor components (char, inorganics, tar aerosols) removed by hot gas filtration to produce conditioned vapors more responsive to catalytic upgrading. A Davison Circulating Riser (DCR), a petroleum industry standard for fluidized catalytic cracking (FCC) catalyst evaluation, was coupled with a custom pyrolyzer system designed to produce consistent composition pyrolysis vapors as feed to the DCR. The coupled system is shown below (Figure 1). Pyrolysis vapors, derived from pure hardwood and softwood, were upgraded using commercially available and laboratory modified zeolite-based catalysts. Upgraded oils were analyzed via 31P and 13C NMR spectroscopy, GCxGC-TOF/MS, carbonyl and ultimate analysis (CHNO), and micro distillation to assess both oil chemistry and distillation behavior as they relate to catalyst and feedstock type for producing fungible hydrocarbon product liquids. Measured process parameters identified from technoeconomic process analysis included achieving oil oxygenate contents of 20% or less, carbon-conversion efficiencies of 30% or more, and catalyst coking rates of 10% or less. A proprietary modified zeolite catalyst from Johnson Matthey produced oils that met these requirements. Using the DCR equipped with a proprietary nozzle system, a second approach focused on reducing the carbon intensity of petroleum-derived hydrocarbon fuels by FCC co-processing these CFP oils with petroleum vacuum gas oil (VGO) to produce biogenic carbon containing fuels. The vapor-phase upgrading results demonstrated for the first time the feasibility of producing refinery-compatible hydrocarbon fuel intermediates entirely from biomass-derived fast-pyrolysis vapors using an industry-accepted DCR system for catalytic upgrading. The co-processing results demonstrated the feasibility of using CFP oils with VGO feeds in FCC refinery operations to produce biogenic carbon containing fuels.

bio-oil↗
DOE OSTI2021

A consistent and conservative model and its scheme for N -phase- M -component incompressible flows

Here, we propose a consistent and conservative model for multiphase and multicomponent incompressible flows, where there can be arbitrary numbers of phases and components. Each phase has a background fluid called the pure phase, each pair of phases is immiscible, and components are dissolvable in some specific phases. The model is developed based on the multiphase Phase-Field model including the contact angle boundary condition, the diffuse domain approach, and the analyses on the proposed consistency conditions for multiphase and multicomponent flows. The model conserves the mass of individual pure phases, the amount of each component in its dissolvable region, and thus the mass of the fluid mixture, and the momentum of the flow. It ensures that no fictitious phases or components can be generated and that the summation of the volume fractions from the Phase-Field model is unity everywhere so that there is no local void or overfilling. It satisfies a physical energy law and it is Galilean invariant. A corresponding numerical scheme is developed for the proposed model, whose formal accuracy is 2nd-order in both time and space. It is shown to be consistent and conservative and its solution is demonstrated to preserve the Galilean invariance and energy law. Numerical tests indicate that the proposed model and scheme are effective and robust to study various challenging multiphase and multicomponent flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Accurate cloud type identification and coverage analysis are crucial in understanding the Earth’s radiative budget. Traditional computer vision methods rely on low-level visual features of clouds for estimating cloud coverage or sky conditions. Several handcrafted approaches have been proposed; however, scope for improvement still exists. Newer deep neural networks (DNNs) have demonstrated superior performance for cloud segmentation and categorization. These methods, however, need expert engineering intervention in the preprocessing steps—in the traditional methods—or human assistance in assigning cloud or clear sky labels to a pixel for training DNNs. Such human mediation imposes considerable time and labor costs. We present the application of a new self-supervised learning approach to autonomously extract relevant features from sky images captured by ground-based cameras, for the classification and segmentation of clouds. We evaluate a joint embedding architecture that uses self-knowledge distillation plus regularization. We use two datasets to demonstrate the network’s ability to classify and segment sky images—one with ~85,000 images collected from our ground-based camera and another with 400 labeled images from the WSISEG database. We find that this approach can discriminate full-sky images based on cloud coverage, diurnal variation, and cloud base height. Additionally, it semantically segments the cloud areas without labels. The approach shows competitive performance in all tested tasks, suggesting a new alternative for cloud characterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Imposition of local boundary conditions in peridynamics without a fictitious layer and unphysical stress concentrations

Here, this study introduces a general approach for the imposition of local boundary conditions in non- ordinary state-based peridynamics (NOSB PD) to eliminate the displacement kinks near the boundary without a fictitious layer under quasi-static loading conditions. It identifies the underlying reason for the unphysical displacement kinks. Under an imposed linear displacement field, the NOSB PD equilibrium equation is not satisfied near the boundary due to the unsymmetric horizon of material points. However, the equilibrium equation derived by using the PD differential operator is satisfied at such material points. Therefore, the material body is divided into three regions to satisfy the equilibrium equations and to impose displacement and tractions boundary conditions. This approach does not deviate from the original NOSB PD; however, it provides a simple solution to eliminate the displacement kink near the boundary, which leads to unphysical stress concentrations. Its efficacy is demonstrated by considering elastic rectangular and square plates subjected to various types of boundary conditions leading to homogeneous as well as nonhomogeneous deformations. The creep response of a rectangular plate further proves the robustness of the present approach. Also, a quasi-static crack propagation from a pre-existing crack in a square plate under mode-I, mode-II and mixed-mode loading conditions demonstrates its capability for failure prediction based on the critical stretch criteria. Finally, its applicability for 3D analysis is demonstrated by considering a rectangular prism under applied stretch and normal stress.

42 ENGINEERING↗
DOE OSTI2024

Weather effects on the lifecycle of U.S. Department of Defense equipment replacement (WELDER)

Extreme weather has a direct and significant impact on buildings and infrastructure, resulting in billions of dollars of damage each year. This problem continues to grow as climate patterns change and buildings are exposed to new and different hazards than what they were designed to withstand. In order to better plan for the long-range sustainment, restoration, modernization, and eventual recapitalization of these buildings, organizations with large building portfolios, such as the U.S. Department of Defense (DoD), must have an awareness of the risks that these extreme weather events present. This research aimed to develop an approach to estimate condition loss and reduction in service life for the components of a building due to extreme weather hazards, to understand the risks that may be present in certain buildings and building systems. To achieve this objective, a damage association matrix was developed that categorizes climate hazards, the damage modes that they produce, and the individual component types impacted. This damage matrix formally links state-of-the-art climate model output, which provides projections of the probability of various climate hazards with a damage effects model that quantifies the consequence on component-level condition and service life. This method is applied to an actual portfolio of buildings in a particular geographic location and with a pre-defined component inventory that comprises the building. This approach can be aggregated to the system-, facility-, and site-level thus helping support billions of dollars in recapitalization decisions related to restoration/modernization of facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fast charging of Li-ion cells: Effect of separator membranes and mapping of “safe lines” to avoid Li plating

An experimental method is introduced that uses microprobe Li/Cu reference electrodes to map "safe" charging regimes to avoid Li metal plating on graphite anodes at rates to 8C. With this method, a Li-ion cell closely approaches the Li plating condition but avoids descending into this regime. This "safe" approach is used to demonstrate effects of the microporous separator on Li plating in cells consisting of a layered nickel-manganese -cobalt oxide cathode and graphite anode. We argue that most of the difference in behaviors for different separators arises from time delayed Li + ion percolation through the membrane. In conclusion, our study suggests that explicitly treating kinetics of phase transitions in the lithiated graphite anode is essential for electrochemical models predicting "safe" lines for fast charging cells.

25 ENERGY STORAGE↗
DOE OSTI2026

AI-driven Neutrino Beam Diagnostics for Next-Generation Neutrino Experiments

The accelerator-driven beam uncertainty limits oscillation measurements in long-baseline neutrino experiments. Spill-resolved beam diagnostics and real-time inference are necessary to address these neutrino flux systematics. As such, we present a machine-learning-based beam monitoring framework developed and validated using data from the T2K experiment. Our approach uses downstream, spill-by-spill muon monitor observables to predict upstream parameters such as proton beam position and width. We achieve high predictive accuracy on nominal runs, demonstrating robust baseline performance whether the model is trained on stable runs or systematically varied conditions. The framework is designed to be robust against domain shifts, allowing the neural network architectures and inference strategies developed with T2K data to be retrained and validated using LBNF simulations, with the goal of eventual deployment under real LBNF/DUNE operating conditions. This scalable approach to real-time beam inference offers a pathway toward reducing flux systematics for next-generation neutrino experiments such as DUNE.

Aney, Noah [Fermilab; U. Chicago (main)]↗
DOE OSTI2023

3′ RNA-seq is superior to standard RNA-seq in cases of sparse data but inferior at identifying toxicity pathways in a model organism

The application of RNA-sequencing has led to numerous breakthroughs related to investigating gene expression levels in complex biological systems. Among these are knowledge of how organisms, such as the vertebrate model organism zebrafish (Danio rerio), respond to toxicant exposure. Recently, the development of 3' RNA-seq has allowed for the determination of gene expression levels with a fraction of the required reads compared to standard RNA-seq. While 3' RNA-seq has many advantages, a comparison to standard RNA-seq has not been performed in the context of whole organism toxicity and sparse data. Here, we examined samples from zebrafish exposed to perfluorobutane sulfonamide (FBSA) with either 3' or standard RNA-seq to determine the advantages of each with regards to the identification of functionally enriched pathways. We found that 3' and standard RNA-seq showed specific advantages when focusing on annotated or unannotated regions of the genome. We also found that standard RNA-seq identified more differentially expressed genes (DEGs), but that this advantage disappeared under conditions of sparse data. We also found that standard RNA-seq had a significant advantage in identifying functionally enriched pathways via analysis of DEG lists but that this advantage was minimal when identifying pathways via gene set enrichment analysis of all genes. These results show that each approach has experimental conditions where they may be advantageous. Our observations can help guide others in the choice of 3' RNA-seq vs standard RNA sequencing to query gene expression levels in a range of biological systems.

3’ RNA-seq↗
DOE OSTI2024

High-throughput genetics enables identification of nutrient utilization and accessory energy metabolism genes in a model methanogen

Archaea are widespread in the environment and play fundamental roles in diverse ecosystems; however, characterization of their unique biology requires advanced tools. This is particularly challenging when characterizing gene function. Here, we generate randomly barcoded transposon libraries in the model methanogenic archaeon Methanococcus maripaludis and use high-throughput growth methods to conduct fitness assays (RB-TnSeq) across over 100 unique growth conditions. Using our approach, we identified new genes involved in nutrient utilization and response to oxidative stress. We identified novel genes for the usage of diverse nitrogen sources in M. maripaludis including a putative regulator of alanine deamination and molybdate transporters important for nitrogen fixation. Furthermore, leveraging the fitness data, we inferred that M. maripaludis can utilize additional nitrogen sources including $\tiny{L}$-glutamine, $\tiny{D}$-glucuronamide, and adenosine. Under autotrophic growth conditions, we identified a gene encoding a domain of unknown function (DUF166) that is important for fitness and hypothesize that it has an accessory role in carbon dioxide assimilation. Finally, comparing fitness costs of oxygen versus sulfite stress, we identified a previously uncharacterized class of dissimilatory sulfite reductase-like proteins (Dsr-LP; group IIId) that is important during growth in the presence of sulfite. When overexpressed, Dsr-LP conferred sulfite resistance and enabled use of sulfite as the sole sulfur source. The high-throughput approach employed here allowed for generation of a large-scale data set that can be used as a resource to further understand gene function and metabolism in the archaeal domain.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Real-time Agent Based Optimization and Control Approach for Residential Building Heating Ventilation and Air Conditioning Systems

The prevalence of the loT (Internet of Things) is fostering the development of new control options and decision making that was not previously available. This is creating a wealth of opportunities for real-time control approaches and systems that can optimize for a common goal. This paper presents a smart residential neighborhood with optimization at the residential level utilizing a system of agents. The optimization utilizes information and modeling to optimize variable speed HVAC real-time operation in actual residential buildings. Data is presented showing the performance of the optimization and conclusions are drawn on next steps for development.

Hall, Joni↗