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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Role of interfaces on the mechanical response of accumulative roll bonded nanometallic laminates investigated via dislocation dynamics simulations

Unraveling the effects of continuous dislocation interactions with interfaces, particularly at the nanometer length scales, is key to a broader understanding of plasticity, to material design and to material certification. To this end, this work proposes a novel discrete dislocation dynamics-based model for dislocation interface interactions tracking the fate of residual dislocation on interfaces. This new approach is used to predict the impact of dislocation/interface reactions on the overall mechanical behavior of accumulative roll bonded nanometallic laminates. The framework considers the dynamic evolution of the interface concurrent with a large network of dislocations, thus, accounting for the local short and long range effects of the dislocations under the external boundary conditions. Specifically, this study focuses on two-phase Fe/Cu nanometallic laminates, and investigates the role of the underlying elastic and plastic contrast of the Fe and the Cu layers on the composite response of the material. Moreover, the role of initial microstructures, resulting from processing is also investigated. Subsequently, the model is used to examine the effect of layer thickness and interface orientation relationship on the residual stresses of the relaxed microstructure. The associated mechanical response of these laminates are compared when loaded under normal direction compression, as well as shear compression. Finally, this work predicts a dominant effect of the layer thickness, as compared to the interface orientation relationship, on the macroscopic response and on the residual stresses of these nanolaminates, while the local dislocation transmission propensity through the interface is significantly influenced by the corresponding orientation relationship.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Allometric Trophic Networks From Individuals to Socio-Ecosystems: Consumer–Resource Theory of the Ecological Elephant in the Room

A well-known parable is that of the blind men studying an elephant each of which assert the elephant is the part they first hold in their hands, e.g., “rope!” says the tail holder while the leg holder asserts “tree!” The various subdisciplines of ecology appear similar in that we each engage in our enthusiastic but at least somewhat myopic study with remarkably limited agreement or even discussion about the overall system which we all study. Allometric trophic network (ATN) theory offers a path out of this dilemma by integrating across scales, taxa, habitats and organizational levels from physiology to ecosystems based on consumer-resource interactions among co-existing organisms. The network architecture and the metabolic and behavioral processes that determine the structure and dynamics of these interactions form the first principles of ATN theory, which in turn provides a synthetic overview and powerfully predictive framework for ecology from organisms to ecosystems. Beyond ecology, ATN theory also synthesizes eco-evolutionary and socio-ecological research still largely based on consumer-resource mechanisms but respectively integrated with different processes including natural selection and market mechanisms. This paper briefly describes foundations, advances, and future directions of ATN theory including predicting an ecosystem’s phenotype from its community’s genotype in order to accelerate more predictive and unified understanding of the complex systems studied by ecologists and other environmental scientists.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Towards Quantum-Chemical Level Calculations of SARS-CoV-2 Spike Protein Variants of Concern by First Principles Density Functional Theory

The spike protein (S-protein) is a crucial part of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with its many domains responsible for binding, fusion, and host cell entry. In this review we use the density functional theory (DFT) calculations to analyze the atomic-scale interactions and investigate the consequences of mutations in S-protein domains. We specifically describe the key amino acids and functions of each domain, which are essential for structural stability as well as recognition and fusion processes with the host cell; in addition, we speculate on how mutations affect these properties. Such unprecedented large-scale ab initio calculations, with up to 5000 atoms in the system, are based on the novel concept of amino acid–amino acid-bond pair unit (AABPU) that allows for an alternative description of proteins, providing valuable information on partial charge, interatomic bonding and hydrogen bond (HB) formation. In general, our results show that the S-protein mutations for different variants foster an increased positive partial charge, alter the interatomic interactions, and disrupt the HB networks. We conclude by outlining a roadmap for future computational research of biomolecular virus-related systems.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2021

Large-scale space use of large juvenile and adult smalltooth sawfish Pristis pectinata: implications for management

The smalltooth sawfish Pristis pectinata is an endangered species endemic to the Atlantic Ocean. The only known viable populations occur in the USA along both coasts of Florida and in the western Bahamas. Little is known about habitat use and movement ecology of large juvenile and adult smalltooth sawfish. Although Critical Habitat — a management designation in the USA — has been identified for small juveniles, it has yet to be identified for these life stages. Between May 2016 and April 2019, we used passive acoustic telemetry and 3 large data sharing networks of receivers to track movements of 43 large juvenile and adult smalltooth sawfish. During this study, 24 females and 19 males were implanted with transmitters with estimated 4 or 10 yr battery lives. These tagged individuals were detected off the southeastern USA on 461 receivers ranging from off the coast of Brunswick, Georgia, to the lower Florida Keys, and along the Gulf coast to Apalachee Bay, Florida. Seasonal migrations were undertaken by 58% (43% mature; 57% immature) of the tagged individuals, with the remainder being apparent residents of their tagging locations. Tagged sawfish from both size classes and of both sexes migrated, which indicates that neither sex nor length is a predictor of whether a sawfish will migrate or not. Although both coasts of Florida were used for migration, most individuals consistently used the same coast when they migrated. The areas surrounding Boca Grande, Cape Canaveral, and the lower Florida Keys were heavily visited sites that could be further evaluated as potential Critical Habitat for these life stages. Understanding the movement patterns of this Critically Endangered species is essential for creating policies to protect areas important for promoting growth of the population.

Pristis pectinata Habitat use · Acoustic monitorin↗
DOE OSTI2025

A mesoscopic link-transmission-model able to track individual vehicles

Macroscopic traffic flow is a common choice for large-scale traffic simulations. These models do not provide individual-specific metrics as outputs. However, this treatment is necessary in agent-based-models, as in, for example, assigning routes based on personal characteristics. Here, in this paper, we propose an extension of the link-transmission-model, an efficient and yet accurate discretization of the Lighthill-Whitham-Richards (LWR) model, which allow vehicles to be tracked individually while keeping the main features of the underlying model. The extension comprises modifying the link and node models to ensure that the flow between links is always at discrete levels. Therefore, every unit of flow is associated with one individual vehicle moving from its current to its next link. An upper bound of the discretization error is provided. We show that the proposed model resembles its continuous counterpart on lane drop, merge, and diverge cases. In addition, we apply the model into three different networks to validate its applicability in large networks. Finally, we also confirm the parameter transferability between continuous and discrete models and that both can well reproduce field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Nanoparticle Motion in Entangled Melts of Linear and Nonconcatenated Ring Polymers [Nanoparticle Motion in Entangled Melts of Non-Concatenated Ring Polymers].

The motion of nanoparticles (NPs) in entangled melts of linear polymers and non-concatenated ring polymers are compared by large-scale molecular dynamics simulations. The comparison provides a paradigm for the effects of polymer architecture on the dynamical coupling between NPs and polymers in nanocomposites. Strongly suppressed motion of NPs with diameter d larger than the entanglement spacing a is observed in a melt of linear polymers before the onset of Fickian NP diffusion. This strong suppression of NP motion occurs progressively as d exceeds a, and is related to the hopping diffusion of NPs in the entanglement network. In contrast to the NP motion in linear polymers, the motion of NPs with d > a in ring polymers is not as strongly suppressed prior to Fickian diffusion. The diffusion coefficient D decreases with increasing d much slower in entangled rings than in entangled linear chains. NP motion in entangled non-concatenated ring polymers is understood through a scaling analysis of the coupling between NP motion and the self-similar entangled dynamics of ring polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop

Abstract Background Despite their widespread distribution and ecological importance, protists remain one of the least understood components of the soil and rhizosphere microbiome. Knowledge of the roles that protists play in stimulating organic matter decomposition and shaping microbiome dynamics continues to grow, but there remains a need to understand the extent to which biological and environmental factors mediate protist community assembly and dynamics. We hypothesize that protists communities are filtered by the influence of plants on their rhizosphere biological and physicochemical environment, resulting in patterns of protist diversity and composition that mirror previously observed diversity and successional dynamics in rhizosphere bacterial communities. Results We analyzed protist communities associated with the rhizosphere and bulk soil of switchgrass (SG) plants ( Panicum virgatum ) at different phenological stages, grown in two marginal soils as part of a large-scale field experiment. Our results reveal that the diversity of protists is lower in rhizosphere than bulk soils, and that temporal variations depend on soil properties but are less pronounced in rhizosphere soil. Patterns of significantly prevalent protists groups in the rhizosphere suggest that most protists play varied ecological roles across plant growth stages and that some plant pathogenic protists and protists with omnivorous diets reoccur over time in the rhizosphere. We found that protist co-occurrence network dynamics are more complex in the rhizosphere compared to bulk soil. A phylogenetic bin-based null model analysis showed that protists’ community assembly in our study sites is mainly controlled by homogenous selection and dispersal limitation, with stronger selection in rhizosphere than bulk soil as SG grew and senesced. Conclusions We demonstrate that environmental filtering is a dominant determinant of overall protist community properties and that at the rhizosphere level, plant control on the physical and biological environment is a critical driver of protist community composition and dynamics. Since protists are key contributors to plant nutrient availability and bacterial community composition and abundance, mapping and understanding their patterns in rhizosphere soil is foundational to understanding the ecology of the root-microbe-soil system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques

Abstract. Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machine-learning-based surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change, such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly reduces computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between eight model parameters and 42 660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42 660 variables, wherein the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly accurate and fast-to-evaluate surrogate system will greatly enhance the computational efficiency of data–model integration to improve predictions and advance our understanding of the Earth system.

58 GEOSCIENCES↗
DOE OSTI2024

Architecture for Web-Based Visualization of Large-Scale Energy Domains: Preprint

With the growing penetration of inverter-based distributed energy resources and increased loads through electrification, power systems analyses are becoming more important and more complex. Moreover, these analyses increasingly involve the combination of interconnected energy domains with data that are spatially and temporally increasing in scale by orders of magnitude, surpassing the capabilities of many existing analysis and decision-support systems. We present the architectural design, development, and application of a high-resolution web-based visualization environment capable of cross-domain analysis of tens of millions of energy assets, focusing on scalability and performance. Our system supports the exploration, navigation, and analysis of large data from diverse domains such as electrical transmission and distribution systems, mobility and electric vehicle charging networks, communications networks, cyber assets, and other supporting infrastructure. We evaluate this system across multiple use cases, describing the capabilities and limitations of a web-based approach for high-resolution energy system visualizations.

grid modernization↗
DOE OSTI2019

A Real-Time Big Data Control-Theoretical Framework for Cyber-Physical-Human Systems [Book Chapter]

It is known that cyber-physical-human systems naturally arise from interdependent infrastructure systems and smart connected communities. Such applications require ubiquitous information sensing and processing, intelligent machine-to-machine communication for a seamless coordination, as well as intelligent interactions between humans and machines. This chapter presents a control-theoretical framework to model heterogeneous physical dynamic systems, information and communication, as well as cooperative controls and/or distributed optimization of such interconnected systems. It is shown that efficient analytical and computational algorithms can be modularly designed and hierarchically implemented to operate and optimize cyber-physical-human systems, first to quantify individually the input–output relationship of nonlinear dynamic behaviors of every physical subsystem, then to coordinate locally both cyber-physical interactions of neighboring agents as well as physical-human interactions, and finally to dynamically model and optimize the overall networked system. The hierarchical structure makes the overall optimization and control problem scalable and solvable. Moreover, the three levels integrate individual designs and optimization, distributed cooperative optimization, and decision-making through real-time, data-driven, model-based learning and control. Specifically, one of the contributions of the chapter is to demonstrate how the combination of dissipativity theory and cooperative control serves as a natural framework and promising tools to analyze, optimize, and control such large-scale system. Application to digital grid is investigated as an illustrative example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Developing an integrated hub location and revenue management model considering multi-classes of customers in the airline industry

The hub location and revenue management problems are two interesting research fields in the transportation and network design studies. The hub location model designs the transportation network structure and the revenue management model allocates the network’s capacity for customer classes based on their sensitivity to prices. In this paper, we consider the integrated hub location and revenue management problem in the airline industry to maximize the revenue of transportation network and minimize hub installation costs. p hubs have been chosen from a set of n nodes and connected to a central node based on star–star network design. The capacity of the installed links between the hubs and the central node and the hubs and non-hub nodes is limited. This limited capacity has been allocated to the stochastic demands of customer classes using the revenue management approach. A two-stage stochastic model has been derived to determine the locations of hubs and protection levels in the ticket sale process, and an efficient modification of genetic algorithm has been proposed for the large-scale problems. Numerical experiments have been carried out to assess the effectiveness of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computationally efficient models for aqueous organic redox flow batteries

The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.

Analytical model↗
DOE OSTI2025

Electromagnetic energy calibration of the SoLid detector with horizontal muons

SoLid is a neutrino experiment at very-short baselinesearching for active-to-sterile oscillations of reactorantineutrinos. The detection principle is based on the pairing oftwo types of solid scintillators: polyvinyl toluene and $^{6}$Li:ZnS(Ag), which is a new technology used in this field ofPhysics. In addition to good neutron-gamma discrimination, thissetup allows the detector to be highly segmented; the basicdetection unit is a 5 cm cube. High segmentation provides numerousadvantages including precise localisation of the Inverse Beta Decay(IBD) products, the derivation of an antineutrino energy estimatorbased on the isolated positron energy, and a powerful backgroundreduction tool that relies on the topological signature of thesignal. Finally, the system is read out by a network ofwavelength-shifting fibres coupled to photosensors. A relativeelectromagnetic calibration is performed with horizontal cosmicmuons. This source poses the simplest calibration problem in which asingle detection unit is involved. In addition, large muon energydeposits allow us to perform a calibration at the most detailedlevel (i.e. per fibre) and to accurately define the fraction ofenergy escaping to neighbouring detection cells. A statisticalprecision at the sub-percent level is reached. The paper alsodiscusses two methods to calibrate the absolute energy scale andpresents their implementation and results. The first method relieson horizontal muons, though the precision is limited to around 10%because of the uncertainty in the energy distribution of suchmuons. A novel, alternative method based on the radioactiveamericium-beryllium source is proposed. It takes advantage of theelectron-positron pair-production process and provides a calibrationpoint at 3.4 MeV (i.e. in the core of the IBD positronspectrum). The paper is concluded with various cross-check includinga determination of the energy spectrum of the standard cosmogenicbackground candle: $^{12}$B.

Abreu, Y. [Antwerp U.]↗
DOE OSTI2016

On the Strong Scaling of the Spectral Element Solver Nek5000 on Petascale Systems. In: EASC '16 Proceedings of the Exascale Applications and Software Conference 2016, Article No. 5

The present work is targeted at performing a strong scaling study of the high-order spectral element fluid dynamics solver Nek5000. Prior studies such as [5] indicated a recommendable metric for strong scalability from a theoretical viewpoint, which we test here extensively on three parallel machines with different performance characteristics and interconnect networks, namely Mira (IBM Blue Gene/Q), Beskow (Cray XC40) and Titan (Cray XK7). The test cases considered for the simulations correspond to a turbulent flow in a straight pipe at four different friction Reynolds numbers Reτ = 180, 360, 550 and 1000. Considering the linear model for parallel communication we quantify the machine characteristics in order to better assess the scaling behaviors of the code. Subsequently sampling and profiling tools are used to measure the computation and communication times over a large range of compute cores. We also study the effect of the two coarse grid solvers XXT and AMG on the computational time. Super-linear scaling due to a reduction in cache misses is observed on each computer. The strong scaling limit is attained for roughly 5000-10,000 degrees of freedom per core on Mira, 30,000 - 50,0000 on Beskow, with only a small impact of the problem size for both machines, and ranges between 10,000 and 220,000 depending on the problem size on Titan. This work aims at being a reference for Nek5000 users and also serves as a basis for potential issues to address as the community heads towards exascale supercomputers.

Offermans, Nicolas↗
DOE OSTI2023

Design Considerations and Analysis of Multi-Level Erasure Coding in Large-Scale Data Centers

Multi-level erasure coding (MLEC) has seen large deployments in the field, but there is no in-depth study of design considerations for MLEC at scale. In this paper, we provide comprehensive design considerations and analysis of MLEC at scale. We introduce the design space of MLEC in multiple dimensions, including various code parameter selections, chunk placement schemes, and various repair methods. We quantify their performance and durability, and show which MLEC schemes and repair methods can provide the best tolerance against independent/correlated failures and reduce repair network traffic by orders of magnitude. To achieve this, we use various evaluation strategies including simulation, splitting, dynamic programming, and mathematical modeling. We also compare the performance and durability of MLEC with other EC schemes such as SLEC and LRC and show that MLEC can provide high durability with higher encoding throughput and less repair network traffic over both SLEC and LRC.

Wang, Meng↗
DOE OSTI2018

Biologically inspired approaches for biosurveillance anomaly detection and data fusion

This study developed and tested biologically inspired computational methods to detect anomalous signals in data streams that could indicate a pending outbreak or bio-weapon attack. Current large-scale biosurveillance systems are plagued by two principal deficiencies: (1) timely detection of disease-indicating signals in noisy data and (2) anomaly detection across multiple channels. Anomaly detectors and data fusion components modeled after human immune system processes were tested against a variety of natural and synthetic surveillance datasets. A pilot scale immune-system-based biosurveillance system performed at least as well as traditional statistical anomaly detection data fusion approaches. Machine learning approaches leveraging Deep Learning recurrent neural networks were developed and applied to challenging unstructured and multimodal health surveillance data. Within the limits imposed of data availability, both immune systems and deep learning methods were found to improve anomaly detection and data fusion performance for particularly challenging data subsets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Center for Computing Research Highlights

Sandia has a legacy of leadership in the advancement of high performance computing (HPC) at extreme scales. First-of-a-kind scalable distributed-memory parallel platforms such as the Intel Paragon, ASCI Red (the world’s first teraflops computer), and Red Storm (co-developed with Cray) helped form the basis for one of the most successful supercomputer product lines ever: the Cray XT series. Sandia also has pioneered system software elements—including lightweight operating systems, the Portals network programming interface, advanced interconnection network designs, and scalable I/O— that are critical to achieving scalability on large computing systems.

97 MATHEMATICS AND COMPUTING↗