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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Microbial macroecology: In search of mechanisms governing microbial biogeographic patterns

Abstract Introduction Rapidly advancing technologies and accumulating information about microbial communities across the globe allow the quantification of microbial properties and functions at a macro‐scale. These emerging microbial biogeographic patterns call for a practical macroecological approach to investigate their underlying mechanisms. Aims The primary aims of this paper are to review the advancements of microbial macroecology in seeking mechanisms governing microbial biogeographic patterns, and to further lay out a roadmap for microbial macroecology in 10 years. Methods We reviewed the progress of microbial macroecology and demonstrated the application of the microbial macroecological approach to microbial biogeographic patterns with three case studies. Results Microbial macroecology provides a platform for understanding microbial abundance, community structure, and functioning across space, time, and taxonomic hierarchy. It emphasizes the integral effects of environmental filtering, microbial responses, diversification, dispersal, and local extinction that drive the microbial biogeographic patterns. The microbial macroecological approach emphasizes the last two stages of the four‐stage scientific method applied to microbial ecology: (a) describing microbial traits across scales to reveal patterns, (b) mathematically representing these patterns, (c) developing and testing conceptual models to build a mechanistic understanding of these patterns from a macroecological perspective, (d) plugging the new knowledge into the theoretical advancements. Three case studies were used to demonstrate the microbial macroecological approach for understanding the global patterns of microbial biomass carbon, microbial composition (fungi : bacteria ratio), and microbial carbon use efficiency. Conclusions Microbial macroecology offers a platform for understanding the mechanisms that drive biogeographic patterns of microbial abundance, diversity and functions. It is likely that these patterns and mechanisms will be increasingly incorporated into predictive models that link climate, carbon dynamics, and biogeochemical processes. A roadmap is outlined for the growing microbial macroecology field; we expect significant progress will be made in five research directions over the next 10 years.

Xu, Xiaofeng↗
DOE OSTI2020

Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America

The behavior of wildlife varies seasonally, and that variation can have substantial demographic consequences. This is especially true for long-distance migrants where the use of landscapes varies by season and, sometimes, age cohort. In this study, we tested the hypothesis that distributional patterns of Golden Eagles (Aquila chrysaetos) wintering in eastern North America are age-structured (i.e., birds of similar ages winter together) through the analysis of 370,307 images collected by motion-sensitive trail cameras set over bait during the winters of 2012–2013 and 2013–2014. At nine sites with sufficient data for analysis, we documented 145 eagle visits in 2012–2013 and 146 in 2013–2014. We found significant between-year variation in age structure of wintering eastern Golden Eagles, driven largely by annual differences in the proportion of first-winter birds. However, although many other species show spatial structure in wintering behavior, our analysis revealed no latitudinal organization among age cohorts of wintering eastern Golden Eagles. The lack of age-related latitudinal segregation in wintering behavior does not exclude the possibility that these eagles have sex-based or other types of dominance hierarchies that could result in spatial or temporal segregation. Alternatively, other mechanisms such as food availability or habitat structure may determine the distribution and abundance of Golden Eagles in winter.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Network Uncertainty Quantification for Analysis of Multi-Component Systems

To impact physical mechanical system design decisions and realize the full promise of high-fidelity computational tools, simulation results must be integrated at the earliest stages of the design process. This is particularly challenging when dealing with uncertainty and optimizing for system-level performance metrics, as full-system models (often notoriously expensive and time-consuming to develop) are generally required to propagate uncertainties to system-level quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in real-world applications. These methods avoid the need for time consuming mesh generation of full-system geometries when changes are made to components or subassemblies. Additionally, they explicitly tie full-system model predictions to component/subassembly validation data which is valuable for qualification. These methods work by leveraging the fact that many engineered systems are inherently modular, being comprised of a hierarchy of components and subassemblies that are individually modified or replaced to define new system designs. By doing so, these methods enable rapid model development and the incorporation of uncertainty quantification earlier in the design process. The resulting formulation of the uncertainty propagation problem is iterative. We express the system model as a network of interconnected component models, which exchange solution information at component boundaries. We present a pair of approaches for propagating uncertainty in this type of decomposed system and provide implementations in the form of an open-source software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problem-specific details on the performance and accuracy of the resulting UQ analysis. This work represents the most comprehensive investigation of these network uncertainty propagation methods to date.

42 ENGINEERING↗
DOE OSTI2021

Illinois Express Quantum Network (IEQNET): metropolitan-scale experimental quantum networking over deployed optical fiber

The Illinois Express Quantum Network (IEQNET) is a program to realize metro-scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Qnodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the results via standard, classical, means. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages software-defined-networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely classical. Issues associated with synchronization, calibration, network monitoring, and scheduling will be discussed. An important goal of IEQNET is demonstrating the extent to which the control plane can coexist with the data plane using the same fiber lines. This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation.

Chung, Joaquin↗
DOE OSTI2021

Self-organization of nanoparticles and molecules in periodic Liesegang-type structures

Chemical organization in reaction-diffusion systems offers a strategy for the generation of materials with ordered morphologies and structural hierarchy. Periodic structures are formed by either molecules or nanoparticles. On the premise of new directing factors and materials, an emerging frontier is the design of systems in which the precipitation partners are nanoparticles and molecules. We show that solvent evaporation from a suspension of cellulose nanocrystals (CNCs) and L -(+)-tartaric acid [ L -(+)-TA] causes phase separation and precipitation, which, being coupled with a reaction/diffusion, results in rhythmic alternation of CNC-rich and L -(+)-TA–rich rings. The CNC-rich regions have a cholesteric structure, while the L -(+)-TA–rich bands are formed by radially aligned elongated bundles. The moving edge of the pattern propagates with a finite constant velocity, which enables control of periodicity by varying film preparation conditions. This work expands knowledge about self-organizing reaction-diffusion systems and offers a strategy for the design of self-organizing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function

The evolutionary rates of functionally related genes often covary. We present a gene coevolution network inferred from examining nearly 3 million orthologous gene pairs from 332 budding yeast species spanning ~400 million years of evolution. Network modules provide insight into cellular and genomic structure and function. Examination of the phenotypic impact of network perturbation using deletion mutant data from the baker’s yeast Saccharomyces cerevisiae, which were obtained from previously published studies, suggests that fitness in diverse environments is affected by orthologous gene neighborhood and connectivity. Mapping the network onto the chromosomes of S. cerevisiae and Candida albicans revealed that coevolving orthologous genes are not physically clustered in either species; rather, they are often located on different chromosomes or far apart on the same chromosome. The coevolution network captures the hierarchy of cellular structure and function, provides a roadmap for genotype-to-phenotype discovery, and portrays the genome as a linked ensemble of genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Knots are not for naught: Design, properties, and topology of hierarchical intertwined microarchitected materials

Lightweight and tough engineered materials are often designed with three-dimensional hierarchy and interconnected structural members whose junctions are detrimental to their performance because they serve as stress concentrations for damage accumulation and lower mechanical resilience. We introduce a previously unexplored class of architected materials, whose components are interwoven and contain no junctions, and incorporate micro-knots as building blocks within these hierarchical networks. Tensile experiments, which show close quantitative agreements with an analytical model for overhand knots, reveal that knot topology allows a new regime of deformation capable of shape retention, leading to a ~92% increase in absorbed energy and an up to ~107% increase in failure strain compared to woven structures, along with an up to ~11% increase in specific energy density compared to topologically similar monolithic lattices. Our exploration unlocks knotting and frictional contact to create highly extensible low-density materials with tunable shape reconfiguration and energy absorption capabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exceptional hardness in multiprincipal element alloys via hierarchical oxygen heterogeneities

Refractory multiprincipal element alloys (RMPEAs) are potential successors to incumbent high-temperature structural alloys, although efforts to improve oxidation resistance with large additions of passivating elements have led to embrittlement. RMPEAs containing group IV and V elements have a balance of properties including moderate ductility, low density, and the necessary formability. We find that oxidation of group IV-V RMPEAs induces hierarchical heterogeneities, ranging from nanoscale interstitial complexes to tertiary phases. This microstructural hierarchy considerably enhances hardness without indentation cracking, with values ranging between 12.1 and 22.6 GPa from the oxide-adjacent metal to the surface oxides, a 3.7 to 6.8× increase over the interstitial-free alloy. Our fundamental understanding of the oxygen influence on phase formation informs future alloy design to enhance oxidation resistance and obtain exceptional hardness while preserving plasticity.

Science & Technology - Other Topics↗
DOE OSTI2021

A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates

This work develops a new multi delity ensemble Kalman lter (MFEnKF) algorithm based on linear control variate framework. The approach allows for rigorous multi delity extensions of the EnKF, where the uncertainty in coarser delities in the hierarchy of models represent control variates for the uncertainty in ner delities. Small ensembles of high delity model runs are com- plemented by larger ensembles of cheaper, lower delity runs, to obtain much improved analyses at only small additional computational costs. We investigate the use of reduced order models as coarse delity control variates in the MFEnKF, and provide analyses to quantify the improvements over the traditional ensemble Kalman lters. We apply these ideas to perform data assimilation with a quasi-geostrophic test problem, using direct numerical simulation and a corresponding POD-Galerkin reduced order model. Numerical results show that the two- delity MFEnKF provides better analyses than existing EnKF algorithms at comparable or reduced computational costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multiscale Control of Generic Second Order Traffic Models by Driver-Assist Vehicles

We study the derivation of generic high order macroscopic traffic models from a follow-the-leader particle description via a kinetic approach. First, we recover a third order traffic model as the hydrodynamic limit of an Enskog-type kinetic equation. Next, we introduce in the vehicle interactions a binary control modeling the automatic feedback provided by driver-assist vehicles and we upscale such a new particle description by means of another Enskog-based hydrodynamic limit. The resulting macroscopic model is now a generic second order model (GSOM), which contains in turn a control term inherited from the microscopic interactions. We show that such a control may be chosen so as to optimize global traffic trends, such as the vehicle flux or the road congestion, constrained by the GSOM dynamics. By means of numerical simulations, we investigate the effect of this control hierarchy in some specific case studies, which exemplify the multiscale path from the vehiclewise implementation of a driver-assist control to its optimal hydrodynamic design.

GSOM↗
DOE OSTI2023

Multigrid Reduction in Time for Chaotic Dynamical Systems

As CPU clock speeds have stagnated and high performance computers continue to have ever higher core counts, increased parallelism is needed to take advantage of these new architectures. Traditional serial time-marching schemes can be a significant bottleneck, as many types of simulations require large numbers of time-steps which must be computed sequentially. Parallel-in-time schemes, such as the Multigrid Reduction in Time (MGRIT) method, remedy this by parallelizing across time-steps and have shown promising results for parabolic problems. However, chaotic problems have proved more difficult, since chaotic initial value problems (IVPs) are inherently ill-conditioned. MGRIT relies on a hierarchy of successively coarser time-grids to iteratively correct the solution on the finest time-grid, but due to the nature of chaotic systems, small inaccuracies on the coarser levels can be greatly magnified and lead to poor coarse-grid corrections. Here we introduce a modified MGRIT algorithm based on an existing quadratically converging nonlinear extension to the multigrid Full Approximation Scheme (FAS), as well as a novel time-coarsening scheme. Together, these approaches better capture long-term chaotic behavior on coarse-grids and greatly improve convergence of MGRIT for chaotic IVPs. Further, we introduce a novel low-memory variant of the algorithm for solving chaotic PDEs with MGRIT which not only solves the IVP, but also provides estimates for the unstable Lyapunov vectors of the system. Finally, we provide supporting numerical results for the Lorenz system and demonstrate parallel speedup for the chaotic Kuramoto–Sivashinsky PDE over a significantly longer time-domain than in previous works.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Constrained Local Approximate Ideal Restriction for Advection-Diffusion Problems

Herein this paper focuses on developing a reduction-based algebraic multigrid (AMG) method that is suitable for solving general (non)symmetric linear systems and is naturally robust from pure advection to pure diffusion. Initial motivation comes from a new reduction-based AMG approach, $\ell \text{AIR}$ (local approximate ideal restriction), that was developed for solving advection-dominated problems. Though this new solver is very effective in the advection-dominated regime, its performance degrades in cases where diffusion becomes dominant. This is consistent with the fact that in general, reduction-based AMG methods tend to suffer from growth in complexity and/or convergence rates as the problem size is increased, especially for diffusion-dominated problems in two or three dimensions. Motivated by the success of $\ell \text{AIR}$ in the advective regime, our aim in this paper is to generalize the AIR framework with the goal of improving the performance of the solver in diffusion-dominated regimes. To do so, we propose a novel way to combine mode constraints as used commonly in energy-minimization AMG methods with the local approximation of ideal operators used in $\ell \text{AIR}$. The resulting constrained $\ell \text{AIR}$ algorithm is able to achieve fast scalable convergence on advective and diffusive problems. In addition, it is able to achieve standard low complexity hierarchies in the diffusive regime through aggressive coarsening, something that was previously difficult for reduction-based methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Characterization of cubic Li$_{2}$$^{100}$MoO$_4$ crystals for the CUPID experiment

The CUPID Collaboration is designing a tonne-scale, background-free detector to search for double beta decay with sufficient sensitivity to fully explore the parameter space corresponding to the inverted neutrino mass hierarchy scenario. One of the CUPID demonstrators, CUPID-Mo, has proved the potential of enriched Li$_{2}$$^{100}$MoO$_4$ crystals as suitable detectors for neutrinoless double beta decay search. In this work, we characterised cubic crystals that, compared to the cylindrical crystals used by CUPID-Mo, are more appealing for the construction of tightly packed arrays. We measured an average energy resolution of (6.7$\pm$0.6) keV FWHM in the region of interest, approaching the CUPID target of 5 keV FWHM. We assessed the identification of $\alpha$ particles with and without a reflecting foil that enhances the scintillation light collection efficiency, proving that the baseline design of CUPID already ensures a complete suppression of this $\alpha$-induced background contribution. We also used the collected data to validate a Monte Carlo simulation modelling the light collection efficiency, which will enable further optimisations of the detector.

100Mo↗
DOE OSTI2020

Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning

Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade “after-burner”. Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Scalar and tensor resonances in $J/\psi $ radiative decays

We perform a systematic analysis of the J/ψ → γπ 0 π 0 and → γ$K$$^{0}_{S}$$K$$^{0}_{S}$ partial waves measured by BESIII. We use a large set of amplitude parametrizations to reduce the model bias. We determine the physical properties of seven scalar and tensor resonances in the 1–2.5GeV mass range. These include the well known f 0 (1500) and f 0 (1710), that are considered to be the primary glueball candidates. The hierarchy of resonance couplings determined from this analysis favors the latter as the one with the largest glueball component.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Naturalness: past, present, and future

We assess the state of naturalness in high-energy physics and summarize recent approaches to the three major naturalness problems: the cosmological constant problem, the electroweak hierarchy problem, and the strong CP problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Toward learning Lattice Boltzmann collision operators

In this work, we explore the possibility of learning from data collision operators for the Lattice Boltzmann Method using a deep learning approach. We compare a hierarchy of designs of the neural network (NN) collision operator and evaluate the performance of the resulting LBM method in reproducing time dynamics of several canonical flows. In the current study, as a first attempt to address the learning problem, the data were generated by a single relaxation time BGK operator. We demonstrate that vanilla NN architecture has very limited accuracy. On the other hand, by embedding physical properties, such as conservation laws and symmetries, it is possible to dramatically increase the accuracy by several orders of magnitude and correctly reproduce the short and long time dynamics of standard fluid flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Status of weak scale supersymmetry after LHC Run 2 and ton-scale noble liquid WIMP searches

After completion of LHC Run 2, the ATLAS and CMS experiments had collected of order 139 fb –1 of data at √s = 13 TeV. While discovering a very Standard Model-like Higgs boson of mass m h ≃ 125 GeV, no solid signal for physics beyond the Standard Model has emerged so far at LHC. In addition, no WIMP signals have emerged so far at ton-scale noble liquid WIMP search experiments. For the case of weak scale supersymmetry (SUSY), which is touted as a simple and elegant solution to the gauge hierarchy problem and likely low energy limit of compactified string theory, LHC has found rather generally that gluinos are beyond about 2.2 TeV whilst top squark must lie beyond 1.1 TeV. These limits contradict older simplistic notions of naturalness that emerged in the 1980s–1990s, leading to the rather pessimistic view that SUSY is now excluded except for perhaps some remaining narrow corners of parameter space. Yet, this picture ignores several important developments in SUSY/string theory that emerged in the 21st century: 1. the emergence of the string theory landscape and its solution to the cosmological constant problem, 2. a more nuanced view of naturalness including the notion of “stringy naturalness”, 3. the emergence of anomaly-free discrete R-symmetries and their connection to R-parity, Peccei-Quinn symmetry, the SUSY μ problem and proton decay and 4. the importance of including a solution to the strong CP problem. Rather general considerations from the string theory landscape favor large values of soft terms, subject to the vacuum selection criteria that electroweak symmetry is properly broken (no charge and/or color breaking (CCB) minima) and the resulting magnitude of the weak scale is not too far from our measured value. Then stringy naturalness predicts a Higgs mass m h ~ 125 GeV whilst sparticle masses are typically lifted beyond present LHC bounds. Lastly, in light of these refinements in theory perspective confronted by LHC and dark matter search results, we review the most likely LHC, ILC and dark matter signatures that are expected to arise from weak scale SUSY as we understand it today.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗