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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Multi-scale entropic depletion phenomena in polymer liquids

We apply numerical polymer integral equation theory to study the entropic depletion problem for hard spheres dissolved in flexible chain polymer melts and concentrated solutions over an exceptionally wide range of polymer radius of gyration to particle diameter ratios (R{sub g}/D), particle-monomer diameter ratios (D/d), and chain lengths (N) including the monomer and oligomer regimes. Calculations are performed based on a calibration of the effective melt packing fraction that reproduces the isobaric dimensionless isothermal compressibility of real polymer liquids. Three regimes of the polymer-mediated interparticle potential of mean force (PMF) are identified and analyzed in depth. (i) The magnitude of the contact attraction that dominates thermodynamic stability scales linearly with D/d and exhibits a monotonic and nonperturbative logarithmic increase with N ultimately saturating in the long chain limit. (ii) A close to contact repulsive barrier emerges that grows linearly with D/d and can attain values far in excess of thermal energy for experimentally relevant particle sizes and chain lengths. This raises the possibility of kinetic stabilization of particles in nanocomposites. The barrier grows initially logarithmically with N, attains a maximum when 2R{sub g} ∼ D/2, and then decreases towards its asymptotic long chain limit as 2R{sub g} ≫ D. (iii) A long range (of order R{sub g}) repulsive, exponentially decaying component of the depletion potential emerges when polymer coils are smaller than, or of order, the nanoparticle diameter. Its amplitude is effectively constant for 2R{sub g} ≤ D. As the polymer becomes larger than the particle, the amplitude of this feature decreases extremely rapidly and becomes negligible. A weak long range and N-dependent component of the monomer-particle pair correlation function is found which is suggested to be the origin of the long range repulsive PMF. Implications of our results for thermodynamics and miscibility are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Entropy production of doubly stochastic quantum channels

We study the entropy increase of quantum systems evolving under primitive, doubly stochastic Markovian noise and thus converging to the maximally mixed state. This entropy increase can be quantified by a logarithmic-Sobolev constant of the Liouvillian generating the noise. We prove a universal lower bound on this constant that stays invariant under taking tensor-powers. Our methods involve a new comparison method to relate logarithmic-Sobolev constants of different Liouvillians and a technique to compute logarithmic-Sobolev inequalities of Liouvillians with eigenvectors forming a projective representation of a finite abelian group. Our bounds improve upon similar results established before and as an application we prove an upper bound on continuous-time quantum capacities. In the last part of this work we study entropy production estimates of discrete-time doubly stochastic quantum channels by extending the framework of discrete-time logarithmic-Sobolev inequalities to the quantum case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Axion dark matter: strings and their cores

Axions constitute a well-motivated dark matter candidate, and if PQ symmetry breaking occurred after inflation, it should be possible to make a clean prediction for the relation between the axion mass and the axion dark matter density. We show that axion (or other global) string networks in 3D have a network density that depends logarithmically on the string separation-to-core ratio. This logarithm would be about 10 times larger in axion cosmology than what we can achieve in numerical simulations. We simulate axion production in the early Universe, finding that, for the separation-to-core ratios we can achieve, the changing density of the network has little impact on the axion production efficiency.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Critical gravities in d ≥ 3

Critical gravities originated in the context of three-dimensional massive gravities. They were conjectured to describe gravitational duals of two-dimensional logarithmic conformal field theories. In this talk, we show that critical gravities can also be studied in dimensions d > 3. As in three dimensions, higher-dimensional critical gravities exhibit logarithmic modes. We argue that the existence of these logarithmic modes leads one to conjecture that higher-dimensional critical gravities are dual to logarithmic conformal field theories in more than two dimensions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

The extrapolation of wind speeds measured at a meteorological mast to wind turbine rotor heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power-law and logarithmic profiles. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power-law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the rotor-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power-law and log profiles, which, by contrast, are not highly tuned to rotor-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where winds at the heights relevant for wind energy production are known; rather, its performance at nearby locations (i.e., across a wind farm site) without rotor-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random-forest machine-learning model is trained and evaluated at different sites and then compared against the power-law and logarithmic profiles. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50 and 100 km apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power-law and logarithmic profiles, respectively. Using the round-robin approach proposed here, this improvement drops to 20 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based wind speed extrapolation methods.

17 WIND ENERGY↗
NASA NTRS1974

Granulometric characteristics of lunar surface material from the Sea of Fertility

The particle size distributions in the 1-1000 micrometer range along the length of the core of lunar surface material returned by Luna 16 were recorded by electrical impulse as well as sieve methods. The measurements are represented in the form of cumulative functions of the logarithm of size versus the logarithm of particle number, and also as the logarithm of size versus particle mass on a probability scale. The former functions at all depths consist of the super-positioning of two straight lines with slopes from 2.10 to 4.05. The second functions are near linear, which together with the closeness of the calculated asymmetries of the distribution to zero and the nearness of the calculated excess of the distributions to unity indicate the closeness of the recorded distributions to the logarithmically normal law. This agrees with the assumption that regolith particles were formed in a process of intensive mechanical grinding.

Stakheyev, Y. I.↗
NASA NTRS2006

Robust Bioinformatics Recognition with VLSI Biochip Microsystem

A microsystem architecture for real-time, on-site, robust bioinformatic patterns recognition and analysis has been proposed. This system is compatible with on-chip DNA analysis means such as polymerase chain reaction (PCR)amplification. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function based on error backpropagation (EBP) algorithm is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized.

bioinformatics↗
DOE OSTI2020

How Should Machine Learning Be Successfully Used for Wind Speed Vertical Extrapolation?

An accurate characterization of the wind resource available at hub-height is required for an efficient and bankable wind farm project. However, direct measurement of wind speed at the constantly increasing height of the hub of commercial wind turbines is oftentimes challenging and expensive, so that it is common practice to vertically extrapolate the wind resource from lower and more easily accessible levels. Conventional techniques for wind speed vertical extrapolation include the use of a power law and a logarithmic profile. While simple, the limits in accuracy of these methods have been shown in various studies. Recently, machine learning has been proposed as a new method to vertically extrapolate winds. All the published studies on the topic assess the performance of machine learning techniques in vertically extrapolating the wind resource at the same location where the algorithm has been trained. However, in real-world applications, the wind resource is measured at the instrument location, but it then needs to be extrapolated at hub height at the location of the wind turbines within the find farm. To be able to fully recommend the use of machine learning techniques over the simple power law and logarithmic law, the spatial variability of the performance improvements of the machine learning approaches needs to be assessed. Here, we propose a round-robin validation of a machine learning-based method for wind speed extrapolation. We use 20 months of observations at four locations spanning a 100 km wide region at the Southern Great Plains (SGP) atmospheric observatory, in north-central Oklahoma. At each location, we train a random forest to predict 30-min average wind speed at 143 m AGL. We use as input features lidar wind speed at 65 m AGL, time of day, sonic anemometer wind speed at 4 m AGL, turbulent kinetic energy, and Obukhov length. First, we perform a same-site comparison of the performance of the proposed random forest against the conventional techniques for wind speed extrapolation (namely power law and logarithmic profile, with widely accepted stability corrections). We find that the random forest outperforms the power law in vertically extrapolating wind speed in all the considered stability regimes, with a 33% reduction in MAE for stable conditions, and a 31% reduction in unstable conditions. Similar results are found when comparing predictions of extrapolated winds from the logarithmic profile and the random forest with the observed values. Next, we propose a round-robin validation, to use the random forest trained at each site to extrapolate wind speed at the remaining three sites. We find that the performance of the random forest approach degrades when the algorithm is tested at a site different than the training one. However, even under those circumstances, the machine learning-based approach still outperforms the conventional techniques for wind speed extrapolation, with, on average, a reduction in mean absolute error between 15 and 20% over the conventional methods, with the largest benefits obtained under stable conditions.

Monte Carlo↗
DOE OSTI2022

Comment on Comment on “Anomalous structural recovery in the near glass transition range in a polymer glass: Data revisited in light of temperature variability in vacuum oven‐based experiments”*

Abstract Cangialosi, Alegría, and Colmenero have made a comment on a paper of ours [Polym. Eng. Sci. 2022:1–13], in which we discussed the concern that the enthalpy recovery data reported by Cangialosi and co‐workers [Phys. Rev. Lett. 2013;111(9):095701] for polystyrene aged up to 15 K below glass transition temperature was anomalous and contradicted existing experimental results from the literature over a similar range of aging conditions. Their response shifts the focus away from the raised questions about their experimental results and attempts to invalidate the data that we cited in support of our argument. Here we respond to the comment and add additional analysis that suggests the structural recovery response of glassy materials exhibits smooth behavior over the full range of measurements, up to 7 or 8 logarithmic decades. We do this by referring to the work on the intrinsic isotherm down‐jump and memory responses of poly(vinyl acetate) between 40°C and 15°C over six logarithmic decades by Kovacs [Fortsch. Hochpolym. Fo. 1963;3(1/2):394–508], the small‐strain tensile creep of poly(vinyl chloride) quenched from 90°C to 40°C (approximately 40°C below T g ) over seven logarithmic decades from Struik [Polym Eng. Sci., 1977;17:165–173], and the volume recovery behavior for aging times up to 3 months in a temperature range between 95°C and −50°C by Greiner and Schwarzl [Rheol. Acta. 1984;23(4):378–395]. We also add discussion that an isothermal aging procedure using a vacuum oven is highly vulnerable to temperature errors due to the problem of good temperature control when the heat transfer mechanism is primarily radiative.

Jin, Shuang↗
DOE OSTI2023

Mixed-state entanglement and information recovery in thermalized states and evaporating black holes

We study the universal behavior of quantum information-theoretic quantities in thermalized isolated quantum many-body systems and evaporating black holes. In particular, we study a genuine mixed-state entanglement measure called the logarithmic negativity, other correlation measures including the Renyi negativities and the mutual information, and a signature of multipartite entanglement called the reflected entropy. We also probe the feasibility of recovering quantum information from subsystems of a thermalized quantum many-body system or from the radiation of an evaporating black hole, using quantities such as relative entropy and Petz map fidelity. A recently developed technique called the equilibrium approximation allows us to probe these quantities at finite temperature. We find striking qualitative differences from the infinite temperature case, which has been the topic of previous studies using Haar-random states. In particular, we find regimes where the logarithmic negativity is extensive but the mutual information is sub-extensive, indicating a large amount of undistillable, bound entanglement in thermalized states. For evaporating black holes at finite temperature, both the logarithmic negativity and the Petz map fidelity reveal an important new time scale t b , which is earlier than the Page time t p by a finite fraction of the total evaporation time. We find that t b , as opposed to t p , is the time scale at which quantum entanglement between different parts of the radiation becomes extensive, and the fidelity of information recovery for a large diary thrown into the black hole starts to grow.

2D Gravity↗
DOE OSTI2018

Factorization and resummation for groomed multi-prong jet shapes

Observables which distinguish boosted topologies from QCD jets are playing an increasingly important role at the Large Hadron Collider (LHC). These observables are often used in conjunction with jet grooming algorithms, which reduce contamination from both theoretical and experimental sources. In this paper we derive factorization formulae for groomed multi-prong substructure observables, focusing in particular on the groomed D 2 observable, which is used to identify boosted hadronic decays of electroweak bosons at the LHC. Our factorization formulae allow systematically improvable calculations of the perturbative D 2 distribution and the resummation of logarithmically enhanced terms in all regions of phase space using renormalization group evolution. They include a novel factorization for the production of a soft subjet in the presence of a grooming algorithm, in which clustering effects enter directly into the hard matching. We use these factorization formulae to draw robust conclusions of experimental relevance regarding the universality of the D 2 distribution in both e+e- and pp collisions. In particular, we show that the only process dependence is carried by the relative quark vs. gluon jet fraction in the sample, no non-global logarithms from event-wide correlations are present in the distribution, hadronization corrections are controlled by the perturbative mass of the jet, and all global color correlations are completely removed by grooming, making groomed D 2 a theoretically clean QCD observable even in the LHC environment. Lastly, we compute all ingredients to one-loop accuracy, and present numerical results at next-to-leading logarithmic accuracy for e + e - collisions, comparing with parton shower Monte Carlo simulations. Results for pp collisions, as relevant for phenomenology at the LHC, are presented in a companion paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

The soft drop groomed jet radius at NLL

We present results for the soft drop groomed jet radius R g at next-to-leading logarithmic accuracy. The radius of a groomed jet which corresponds to the angle between the two branches passing the soft drop criterion is one of the characteristic observables relevant for the precise understanding of groomed jet substructure. We establish a factorization formalism that allows for the resummation of all relevant large logarithms, which is based on demonstrating the all order equivalence to a jet veto in the region between the boundaries of the groomed and ungroomed jet. Non-global logarithms including clustering effects due to the Cambridge/Aachen algorithm are resummed to all orders using a suitable Monte Carlo algorithm. We perform numerical calculations and find a very good agreement with Pythia 8 simulations. We provide theoretical predictions for the LHC and RHIC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Jet shapes in dijet events at the LHC in SCET

Here, we consider the class of jet shapes known as angularities in dijet production at hadron colliders. These angularities are modified from the original definitions in e + e- collisions to be boost invariant along the beam axis. These shapes apply to the constituents of jets defined with respect to either k T -type (anti-k T , C/A, and k T ) algorithms and cone-type algorithms. We present an SCET factorization formula and calculate the ingredients needed to achieve next-to-leading-log (NLL) accuracy in kinematic regions where non-global logarithms are not large. The factorization formula involves previously unstudied “unmeasured beam functions,” which are present for finite rapidity cuts around the beams. We derive relations between the jet functions and the shape-dependent part of the soft function that appear in the factorized cross section and those previously calculated for e + e - collisions, and present the calculation of the non-trivial, color-connected part of the soft-function to O(αs) . This latter part of the soft function is universal in the sense that it applies to any experimental setup with an out-of-jet p T veto and rapidity cuts together with two identified jets and it is independent of the choice of jet (sub-)structure measurement. In addition, we implement the recently introduced soft-collinear refactorization to resum logarithms of the jet size, valid in the region of non-enhanced non-global logarithm effects. While our results are valid for all 2 → 2 channels, we compute explicitly for the qq' → qq' channel the color-flow matrices and plot the NLL resummed differential dijet cross section as an explicit example, which shows that the normalization and scale uncertainty is reduced when the soft function is refactorized. For this channel, we also plot the jet size R dependence, the p$cut\atop{T}$ dependence, and the dependence on the angularity parameter a.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Non-linear evolution in QCD at high-energy beyond leading order

The standard formulation of the high-energy evolution in perturbative QCD, based on the Balitsky-Kovchegov equation, is known to suffer from severe instabilities associated with radiative corrections enhanced by double transverse logarithms, which occur in all orders starting with the next-to-leading one. Over the last years, several methods have been devised to resum such corrections by enforcing the time-ordering of the successive gluon emissions. We observe that the instability problem is not fully cured by these methods: various prescriptions for performing the resummation lead to very different physical results and thus lack of predictive power. We argue that this problem can be avoided by using the rapidity of the dense target (which corresponds to Bjorken x) instead of that of the dilute projectile as an evolution time. This automatically ensures the proper time-ordering and also allows for a direct physical interpretation of the results. We explicitly perform this change of variables at NLO. We observe the emergence of a new class of double logarithmic corrections, potentially leading to instabilities, which are however less severe, since disfavoured by the typical BK evolution for “dilute-dense” scattering. We propose several prescriptions for resumming these new double-logarithms to all orders and find only little scheme dependence: different prescriptions lead to results which are consistent to each other to the accuracy of interest. We restore full NLO accuracy by completing one of the resummed equations (non-local in rapidity) with the remaining NLO corrections.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Semi-inclusive single-jet production in DIS at next-to-leading order in the Color Glass Condensate

Within the Color Glass Condensate (CGC) effective field theory, we derive the next-to-leading order (NLO) cross-section for the single-jet semi-inclusive cross-section in deep inelastic scattering (DIS) at small x, for both longitudinally and transversely polarized virtual photons. We provide analytic expressions, valid at finite N c and suitable for numerical evaluation, for both the cross-section differential in rapidity and transverse momentum and the cross-section differential in rapidity only. Our NLO formulae demonstrate that the very forward rapidity regime is plagued by large double logarithmic corrections coming from phase space constraints on soft gluons close to the kinematic threshold for jet production. A joint resummation of small-x and threshold logarithms at single logarithmic accuracy is proposed to remedy the instability of the cross-section in this regime. By integrating over the single-jet phase space, we recover known results for the NLO DIS structure functions at small x, previously obtained using the optical theorem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Collinear drop

We introduce collinear drop jet substructure observables, which are unaffected by contributions from collinear radiation, and systematically probe soft radiation within jets. These observables can be designed to be either sensitive or insensitive to process-dependent soft radiation originating from outside the jet. Such collinear drop observables can be exploited as variables to distinguish quark, gluon, and color neutral initiated jets, for testing predictions for perturbative soft radiation in Monte Carlo simulations, for assessing models and universality for hadronization corrections, for examining the efficiency of pileup subtraction methods, and for any other application that leaves an imprint on soft radiation. We discuss examples of collinear drop observables that are based both on clustering and on jet shapes. Using the soft-collinear effective theory we derive factorization expressions for collinear drop observables from QCD jets, and carry out a resummation of logarithmically enhanced contributions at next-to-leading-logarithmic order. We also identify an infinite class of collinear drop observables for which the leading double logarithms are absent.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Not all that is β0 is β-function: the DGLAP resummation and the running coupling in NLO JIMWLK

Abstract We reanalyze the origin of the large transverse logarithms associated with the QCD one loopβfunction coefficient in the NLO JIMWLK Hamiltonian. We show that some of these terms are not associated with the running of the QCD coupling constant but rather with the DGLAP evolution. The DGLAP-like resummation of these logarithms is mandatory within the JIMWLK Hamiltonian, as long as the color correlation length in the projectile is larger than that in the target. This regime in fact covers the whole range of rapidities at which JIMWLK evolution is supposed to be applicable. We derive the RG equation that resums these logarithms to all orders inα s in the JIMWLK Hamiltonian. This is a nonlinear equation for the eikonal scattering matrixS(x). We solve this equation, and perform the DGLAP resummation in two simple cases: the dilute limit, where both the projectile and the target are far from saturation, and the saturated regime, where the target correlation length also determines its saturation momentum.

Physics↗