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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multifidelity multiobjective optimization for wake-steering strategies

Abstract. Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of perpendicular alignment with the incoming wind, thereby “steering” wakes away from downstream turbines. However, trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present a multifidelity multiobjective optimization approach for exploring the Pareto front of trade-offs between power and loading during wake steering. A large eddy simulation is used as the high-fidelity model, where an actuator line representation is used to model wind turbine blades and a rainflow-counting algorithm is used to compute damage equivalent loads. A coarser simulation with a simpler loads model is employed as a supplementary low-fidelity model. Multifidelity Bayesian optimization is performed to iteratively learn both a surrogate of the low-fidelity model and an additive discrepancy function, which maps the low-fidelity model to the high-fidelity model. Each optimization uses the expected hypervolume improvement acquisition function, weighted by the total cost of a proposed model evaluation in the multifidelity case. The multifidelity approach is able to capture the logit function shape of the Pareto frontier at a computational cost only 30 % that of the single-fidelity approach. Additionally, we provide physical insights into the vortical structures in the wake that contribute to the Pareto front shape.

17 WIND ENERGY↗
DOE OSTI2012

ISOTROPY IN THE TWO-POINT ANGULAR CORRELATION FUNCTION OF THE COSMIC MICROWAVE BACKGROUND

We study the directional dependence of the angular two-point correlation function in maps of the cosmic microwave background (CMB). We propose two new statistics: one which measures the correlation of each point in the sky with a ring of points separated an angle {theta} away, and a second one that measures the missing angular correlation above 60 deg as a function of direction. Using these statistics, we find that most of the low power in cut-sky maps measured by the Wilkinson Microwave Anisotropy Probe experiment comes from unusually low contributions from the directions of the lobes of the quadrupole and the octupole. These findings may aid a future explanation of why the CMB exhibits low power at large angular scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

1 × 1 km maps of abundances of eight enzyme functional classes for soil C, N, and P cycling across the CONUS

This dataset includes eight 1 × 1 km maps of the abundances of eight enzyme functional classes (EFC) for soil C, N, and P cycling across the CONUS. These mappings are predicted by the machine learning model trained using metagenomics and the corresponding environmental data. This item corresponds to our article: Fan, C., Song, Y., Mishra, U., Gautam, S., & Mayes, M. A. (2025). Harnessing the Power of Machine Learning and Omics to Identify Environmental Regulation on Microbial Functional Composition for Soil C, N, and P Cycling. Journal of Geophysical Research: Biogeosciences, 130(10).

1 × 1 km↗
DOE OSTI2018

Formation of Supported Graphene Oxide: Evidence for Enolate Species

Graphene oxides are promising materials for novel electronic devices or anchoring of the active sites for catalytic applications. Here we focus on understanding the oxygen binding on different regions of graphene (Gr) on Ru(0001). Differences in the Gr/Ru lattices result in the superstructure, which offers an array of distinct adsorption sites. We employ scanning tunneling microscopy and density functional theory to map out the chemical identity and stability of prepared oxygen functionalities in different Gr regions. We demonstrate that in the regions that are close to the metal substrate, the terminally-bonded enolate groups are strongly preferred over bridge-bonded epoxy configurations. No oxygen species are observed on the graphene regions that are far from the underlying Ru, indicating their low relative stability. This study provides a clear fundamental basis for understanding the structural and electronic factors that affect the stability of enolate and epoxy species as a function of Gr/Ru interactions.

Supported Graphene, Graphene Functionalization, Gr↗
DOE OSTI2017

Electron tomography and fractal aspects of MoS 2 and MoS 2 /Co spheres

A study was made by a combination of 3D electron tomography reconstruction methods and N 2 adsorption for determining the fractal dimension for nanometric MoS 2 and MoS 2 /Co catalyst particles. DFT methods including Neimarke-Kiselev’s method allowed to determine the particle porosity and fractal arrays at the atomic scale for the S-Mo-S(Co) 2D- layers that conform the spherically shaped catalyst particles. A structural and textural correlation was sought by further characterization performed by x-ray Rietveld refinement and Radial Distribution Function (RDF) methods, electron density maps, computational density functional theory methods and nitrogen adsorption methods altogether, for studying the structural and textural features of spherical MoS 2 and MoS 2 /Co particles. Neimark-Kiselev’s equations afforded the evaluation of a pore volume variation from 10 to 110 cm 3 /g by cobalt insertion in the MoS 2 crystallographic lattice, which induces the formation of cavities and throats in between of less than 29 nm, with a curvature radius r k < 14.4 nm; typical large needle-like arrays having 20 2D layers units correspond to a model consisting of smooth surfaces within these cavities. Decreasing D P , D B , D I and D M values occur when Co atoms are present in the MoS 2 laminates, which promote the formation of smoother edges and denser surfaces that have an influence on the catalytic properties of the S-Mo-S(Co) system.

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

High Throughput Mapping of Single Molecules’ Redox Potentials on Electrode

By synchronizing electrochemical potential scanning with a single-molecule localization super-resolution fluorescence microscope, kinetic fluorescence changes of hundreds of single molecular redox events were tracked simultaneously with high throughput, and subsequent cross-correlation function analysis mapped single molecules’ redox potentials (times) out on the imaging area from site to site in unprecedented detail by extracting electrochemically induced fluorescence change from apparently random fluorescence on/off blinking. This work paves the way toward mapping redox states at single-molecule levels in high throughput in chemical and biological systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2010

THE HALO MASS FUNCTION FROM EXCURSION SET THEORY. II. THE DIFFUSING BARRIER

In excursion set theory, the computation of the halo mass function is mapped into a first-passage time process in the presence of a barrier, which in the spherical collapse model is a constant and in the ellipsoidal collapse model is a fixed function of the variance of the smoothed density field. However, N-body simulations show that dark matter halos grow through a mixture of smooth accretion, violent encounters, and fragmentations, and modeling halo collapse as spherical, or even as ellipsoidal, is a significant oversimplification. In addition, the very definition of what is a dark matter halo, both in N-body simulations and observationally, is a difficult problem. We propose that some of the physical complications inherent to a realistic description of halo formation can be included in the excursion set theory framework, at least at an effective level, by taking into account that the critical value for collapse is not a fixed constant {delta}{sub c}, as in the spherical collapse model, nor a fixed function of the variance {sigma} of the smoothed density field, as in the ellipsoidal collapse model, but rather is itself a stochastic variable, whose scatter reflects a number of complicated aspects of the underlying dynamics. Solving the first-passage time problem in the presence of a diffusing barrier we find that the exponential factor in the Press-Schechter mass function changes from exp{l_brace}-{delta}{sup 2}{sub c}/2{sigma}{sup 2{r_brace}} to exp{l_brace}-a{delta}{sup 2}{sub c}/2{sigma}{sup 2{r_brace}}, where a = 1/(1 + D{sub B}) and D{sub B} is the diffusion coefficient of the barrier. The numerical value of D{sub B} , and therefore the corresponding value of a, depends among other things on the algorithm used for identifying halos. We discuss the physical origin of the stochasticity of the barrier and, from recent N-body simulations that studied the properties of the collapse barrier, we deduce a value D{sub B} {approx_equal} 0.25. Our model then predicts a {approx_equal} 0.80, in excellent agreement with the exponential fall off of the mass function found in N-body simulations, for the same halo definition. Combining this result with the non-Markovian corrections computed in Paper I of this series, we derive an analytic expression for the halo mass function for Gaussian fluctuations and we compare it with N-body simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Evolution of ʟ–DOPA 4,5–dioxygenase activity allows for recurrent specialisation to betalain pigmentation in Caryophyllales

The evolution of ʟ-DOPA 4,5-dioxygenase activity, encoded by the gene DODA, was a key step in the origin of betalain biosynthesis in Caryophyllales. We previously proposed that ʟ-DOPA 4,5-dioxygenase activity evolved via a single Caryophyllales-specific neofunctionalisation event within the DODA gene lineage. However, this neofunctionalisation event has not been confirmed and the DODA gene lineage exhibits numerous gene duplication events, whose evolutionary significance is unclear. To address this, we functionally characterised 23 distinct DODA proteins for ʟ-DOPA 4,5-dioxygenase activity, from four betalain-pigmented and five anthocyanin-pigmented species, representing key evolutionary transitions across Caryophyllales. By mapping these functional data to an updated DODA phylogeny, we then explored the evolution of ʟ-DOPA 4,5-dioxygenase activity. We find that low ʟ-DOPA 4,5-dioxygenase activity is distributed across the DODA gene lineage. In this context, repeated gene duplication events within the DODA gene lineage give rise to polyphyletic occurrences of elevated ʟ-DOPA 4,5-dioxygenase activity, accompanied by convergent shifts in key functional residues and distinct genomic patterns of micro-synteny. In the context of an updated organismal phylogeny and newly inferred pigment reconstructions, we argue that repeated convergent acquisition of elevated ʟ-DOPA 4,5-dioxygenase activity is consistent with recurrent specialisation to betalain synthesis in Caryophyllales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Scalable Bayesian optimization with randomized prior networks

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2019

Brønsted–Evans–Polanyi relation for CO oxidation on metal oxides following the Mars–van Krevelen mechanism

Scaling relations are widely used to model catalytic reactions on metal surfaces, but they are less commonly applied to metal oxides. Oxygen vacancy formation energies have been suggested as a descriptor for the activity toward oxidation reactions via the Mars-van Krevelen mechanism. However, there is currently no function that maps oxygen vacancy formation energies to CO oxidation barriers. We have compiled a data set for CO oxidation on doped and pristine metal oxide surfaces as well as metal/metal oxide interface sites using density functional theory. Based on this data, we can predict CO oxidation barriers from oxygen vacancy formation energies using a Brønsted–Evans–Polanyi relation with a mean absolute error of 14 kJ/mol. Contrary to what is known for reactions on metal surfaces, we find that the scaling parameter α that describes the lateness of the transition state for CO oxidation on metal oxides is not constant.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize

Abstract Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here, we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions. We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over 200,000 variants, genetic, epigenetic, or both (termed binding quantitative trait loci (bQTL)), linked tocis-element occupancy. Three lines of evidence support the functional significance of bQTL: (1) coincidence with causative loci that regulate traits, includingvgt1,ZmTRE1and the MITE transposon nearZmNAC111under drought; (2) bQTL allelic bias is shared between inbred parents and matches chromatin immunoprecipitation sequencing results; and (3) partitioning genetic variation across genomic regions demonstrates that bQTL capture the majority of heritable trait variation across ~72% of 143 phenotypes. Our study provides an auspicious approach to make functionalcis-variation accessible at scale for genetic studies and targeted engineering of complex traits.

Genetics & Heredity↗
DOE OSTI2011

THE FIRST PUBLIC RELEASE OF SOUTH POLE TELESCOPE DATA: MAPS OF A 95 deg{sup 2} FIELD FROM 2008 OBSERVATIONS

The South Pole Telescope (SPT) has nearly completed a 2500 deg{sup 2} survey of the southern sky in three frequency bands. Here, we present the first public release of SPT maps and associated data products. We present arcminute-resolution maps at 150 GHz and 220 GHz of an approximately 95 deg{sup 2} field centered at R.A. 82.{sup 0}7, decl. -55 Degree-Sign . The field was observed to a depth of approximately 17 {mu}K arcmin at 150 GHz and 41 {mu}K arcmin at 220 GHz during the 2008 austral winter season. Two variations on map filtering and map projection are presented, one tailored for producing catalogs of galaxy clusters detected through their Sunyaev-Zel'dovich effect signature and one tailored for producing catalogs of emissive sources. We describe the data processing pipeline, and we present instrument response functions, filter transfer functions, and map noise properties. All data products described in this paper are available for download at http://pole.uchicago.edu/public/data/maps/ra5h30dec-55 and from the NASA Legacy Archive for Microwave Background Data Analysis server. This is the first step in the eventual release of data from the full 2500 deg{sup 2} SPT survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

An unconstrained Lagrangian formulation and conservation laws for the Schrödinger map system

We consider energy-critical Schrödinger maps from R{sup 2} into S{sup 2} and H{sup 2}. Viewing such maps with respect to orthonormal frames on the pullback bundle provides a gauge field formulation of the evolution. We show that this gauge field system is the set of Euler-Lagrange equations corresponding to an action that includes a Chern-Simons term. We also introduce the stress-energy tensor and derive conservation laws. In conclusion we offer comparisons between Schrödinger maps and the closely related Chern-Simons-Schrödinger system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems

We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs) with models governed by nonlinear parametric partial differential equations (PDEs). Neural operators have gained significant attention in recent years for their ability to approximate the parameter-to-solution maps defined by PDEs using as training data solutions of PDEs at a limited number of parameter samples. The computational cost of BIPs can be drastically reduced if the large number of PDE solves required for posterior characterization are replaced with evaluations of trained neural operators. However, reducing error in the resulting BIP solutions via reducing the approximation error of the neural operators in training can be challenging and unreliable. We provide an a priori error bound result that implies certain BIPs can be ill-conditioned to the approximation error of neural operators, thus leading to inaccessible accuracy requirements in training. To reliably deploy neural operators in BIPs, we consider a strategy for enhancing the performance of neural operators: correcting the prediction of a trained neural operator by solving a linear variational problem based on the PDE residual. We show that a trained neural operator with error correction can achieve a quadratic reduction of its approximation error, all while retaining substantial computational speedups of posterior sampling when models are governed by highly nonlinear PDEs. The strategy is applied to two numerical examples of BIPs based on a nonlinear reaction–diffusion problem and deformation of hyperelastic materials. We demonstrate that posterior representations of the two BIPs produced using trained neural operators are greatly and consistently enhanced by error correction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Neurodegenerative disease mutations in TREM2 reveal a functional surface and distinct loss-of-function mechanisms

Genetic variations in the myeloid immune receptor TREM2 are linked to several neurodegenerative diseases. To determine how TREM2 variants contribute to these diseases, we performed structural and functional studies of wild-type and variant proteins. Our 3.1 Å TREM2 crystal structure revealed that mutations found in Nasu-Hakola disease are buried whereas Alzheimer’s disease risk variants are found on the surface, suggesting that these mutations have distinct effects on TREM2 function. Biophysical and cellular methods indicate that Nasu-Hakola mutations impact protein stability and decrease folded TREM2 surface expression, whereas Alzheimer’s risk variants impact binding to a TREM2 ligand. Additionally, the Alzheimer’s risk variants appear to epitope map a functional surface on TREM2 that is unique within the larger TREM family. These findings provide a guide to structural and functional differences among genetic variants of TREM2, indicating that therapies targeting the TREM2 pathway should be tailored to these genetic and functional differences with patient-specific medicine approaches for neurodegenerative disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network.

Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs [6, 7]. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN’s optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques [8]. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.

97 MATHEMATICS AND COMPUTING↗