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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Understanding interfacial segregation in polymer blend films with random and mixed side chain bottlebrush copolymer additives

Bottlebrush polymers are complex macromolecules with tunable physical properties dependent on the chemistry and architecture of both the side chains and the backbone. Prior work has demonstrated that bottlebrush polymer additives can be used to control the interfacial properties of blends with linear polymers but has not specifically addressed the effects of bottlebrush side chain microstructures. Here, using a combination of experiments and self-consistent field theory (SCFT) simulations, we investigated the effects of side chain microstructures by comparing the segregation of bottlebrush additives having random copolymer side chains with bottlebrush additives having a mixture of two different homopolymer side chain chemistries. Specifically, we synthesized bottlebrush polymers with either poly(styrene-ran-methyl methacrylate) side chains or with a mixture of polystyrene (PS) and poly(methyl methacrylate) (PMMA) side chains. Furthermore, the bottlebrush additives were matched in terms of PS and PMMA compositions, and they were blended with linear PS or PMMA chains that ranged in length from shorter to longer than the bottlebrush side chains. Experiments revealed similar behaviors of the two types of bottlebrushes, with a slight preference for mixed side-chain bottlebrushes at the film surface. SCFT simulations were qualitatively consistent with experimental observations, predicting only slight differences in the segregation of bottlebrush additives driven by side chain microstructures. Specifically, these slight differences were driven by the chemistries of the bottlebrush polymer joints and side chain end-groups, which were entropically repelled and attracted to interfaces, respectively. Using SCFT, we also demonstrated that the interfacial behaviors were dominated by entropic effects with high molecular weight linear polymers, leading to enrichment of bottlebrush near interfaces. Surprisingly, the SCFT simulations showed that the chemistry of the joints connecting the bottlebrush backbones and side chains played a more significant role compared with the side chain end groups in affecting differences in surface excess of bottlebrushes with random and mixed side chains. This work provides new insights into the effects of side chain microstructure on segregation of bottlebrush polymer additives.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-performance multiblock PEMs containing a highly acidic fluorinated-hydrophilic domain for water electrolysis

The present paper describes the design and evaluation of novel hydrophilic–hydrophobic poly(arylene ether sulfone) (PAES) multiblock copolymers for their synergistic effects upon transport properties and their potential use in proton exchange membrane water electrolysis. The multiblock copolymers are prepared via a coupling reaction between (i) a hydrophilic segment consisting of a disulfonated quinone fluorinated biphenyl group that contains fluorine moieties next to the sulfonated groups to increase the acidity, and (ii) hydrophobic segments composed of non-sulfonated biphenyl sulfone to provide dimensional stability. Two different lengths (molecular weights; 5k and 10k, where k represents 10 3 g mol -1 ) of hydrophobic segments are used to investigate the effects of the membrane properties compared with those of Nafion® and PAES random copolymer (i.e., BPSH40). Atomic force microscopy images of the BPSH40 and multiblock membranes are shown to agree closely with a mesoscale simulation, thus confirming the importance of the morphological effect upon the transport properties. Furthermore, the multiblock copolymer with a higher proportion of hydrophilic segments (10 k–5k) was shown to provide enhanced performance (3.41 A cm -2 at 1.9 V) compared to the multiblock copolymer with equal proportions of hydrophilic and hydrophobic segments (10 k–10 k) due to the greater continuity of nano-sized ionic channels.

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

Poor reliability of public charging stations can impede the growth of the electric vehicle market

How does the reliability of public charging infrastructure affect electric vehicle (EV) adoption? Substantial public and private investments are expanding EV charging networks, but concerns are growing about the poor reliability of existing chargers and its potential impacts on EV adoption. Using data from a nationwide survey, we employ a choice model to quantify the effects of perceived charging reliability on Americans’ intentions to purchase new or used EVs. By randomly assigning participants to receive information characterizing public charging as either very reliable or very unreliable, we show a causal effect of reliability perceptions on EV purchase intentions. In conclusion, we find that differences in perceived reliability are equivalent to changing price by 32 % of purchasing budget or changing range by 366 miles, underscoring the importance of reliable public charging.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Tensor Decompositions for Count Data that Leverage Stochastic and Deterministic Optimization

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the global maximum likelihood estimator from local minima. Simultaneously, a recent trend in theoretical computer science and numerical linear algebra leverages randomization to solve very large, hard problems. The typical approach is to use randomization for a fast approximation and determinism for refinement to yield effective algorithms with theoretical guarantees. Two popular algorithms for Poisson CPD reflect that emergent dichotomy: CP Alternating Poisson Regression is a deterministic algorithm and Generalized Canonical Polyadic decomposition makes use of stochastic algorithms in several variants. This work extends recent work to develop two new methods that leverage randomized and deterministic algorithms for improved accuracy and performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Local Order in Liquid Gallium–Indium Alloys

Liquid metals such as eutectic Ga–In alloys have low melting points and low toxicity and are used in catalysis and micro-robotics. This study investigates the local atomic structure of liquid gallium-indium alloys by a combination of density measurements, diffraction data, and Monte-Carlo simulation via the empirical potential structure refinement approach. A high-Q shoulder observed in liquid Ga is related to structural rearrangements in the second coordination shell. Structure analysis found coordination environments close to a random distribution for eutectic Ga–In alloy, while electronic effects appear to dominate the mixing enthalpy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantum Metrology of Noisy Spreading Channels

We provide the optimal measurement strategy for a class of noisy channels that reduce to the identity channel for a specific value of a parameter (spreading channels). We provide an example that is physically relevant: the estimation of the absolute value of the displacement in the presence of phase randomizing noise. Surprisingly, this noise does not affect the effectiveness of the optimal measurement. We show that, for small displacement, a squeezed vacuum probe field is optimal among strategies with same average energy. A squeezer followed by photodetection is the optimal detection strategy that attains the quantum Fisher information, whereas the customarily used homodyne detection becomes useless in the limit of small displacements, due to the same effect that gives Rayleigh’s curse in optical superresolution. There is a quantum advantage: a squeezed or a Fock state with N average photons allow to asymptotically estimate the parameter with a N better precision than classical states with same energy.

Górecki, Wojciech↗
DOE OSTI2024

Quantifying Quantum Chaos through Microcanonical Distributions of Entanglement

A characteristic feature of “quantum chaotic” systems is that their eigenspectra and eigenstates display universal statistical properties described by random matrix theory (RMT). However, eigenstates of local systems also encode structure beyond RMT. To capture this feature, we introduce a framework that allows us to compare the properties of eigenstates in local systems with those of pure random states. In particular, our framework defines a notion of distance between quantum state ensembles that utilizes the Kullback-Leibler divergence to compare the microcanonical distribution of entanglement entropy (EE) of eigenstates with a reference RMT distribution generated by pure random states (with appropriate constraints). This notion gives rise to a quantitative metric for quantum chaos that not only accounts for averages of the distributions but also higher moments. The differences in moments are compared on a highly resolved scale set by the standard deviation of the RMT distribution, which is exponentially small in system size. As a result, the metric can distinguish between chaotic and integrable behaviors and, in addition, quantify and compare the of chaos (in terms of proximity to RMT behavior) between two systems that are assumed to be chaotic. We implement our framework in local, minimally structured, Floquet random circuits, as well as a canonical family of many-body Hamiltonians, the mixed-field Ising model (MFIM). Importantly, for Hamiltonian systems, we find that the reference random distribution must be appropriately constrained to incorporate the effect of energy conservation in order to describe the ensemble properties of midspectrum eigenstates. The metric captures deviations from RMT across all models and parameters, including those that have been previously identified as strongly chaotic, and for which other diagnostics of chaos such as level spacing statistics look strongly thermal. In Floquet circuits, the dominant source of deviations is the second moment of the distribution, and this persists for all system sizes. For the MFIM, we find significant variation of the KL divergence in parameter space. Notably, we find a small region where deviations from RMT are minimized, suggesting that “maximally chaotic” Hamiltonians may exist in fine-tuned pockets of parameter space. Published by the American Physical Society 2024

Physics↗
DOE OSTI2021

Transfer Learning using Denoising Auto-Encoders for Cellular-Level Annotation of Tumor in Pathology Slides

Adversarial examples can produce altered classifications using only seemingly innocuous, imperceptible perturbations to the original image. The imperceptibility of adversarial perturbations suggests that the corresponding classifiers use decision criteria different than those of a human. In a medical setting, inexplicable decision criteria confound a pathologist’s willingness to trust machine-generated annotations. Here, we analyze denoising tumor detection models to see if they are robust to imperceptible adversarial perturbations. Moreover, to be more fully trusted by pathologists, we require tumor detectors that generate interpretable annotations which segment pathology slides into tumorous and normal regions at the cellular level. We therefore compare transfer learning based on two different autoencoder architectures, one derived from a deep denoising bottleneck autoencoder and one from an over-complete sparse autoencoder. Both autoencoders were first trained in an unsupervised manner on a set of pathology slides drawn from the Camelyon16 dataset. The latent representations produced by each autoencoder were then passed to separate neural networks that were trained in a supervised manner on binary tumor-normal masks generated by pathologists at cellular resolution. Both tumor detectors supported better than 90% AUC PR as measured by the area under the precision/recall curve on a held-out pathology slide. To assess the underlying decision criteria used by both tumor detectors, we constructed imperceptible adversarial examples which reduced the AUC PR of both models to less than 70%. Random noise of the same amplitude had almost no effect on the AUC PR of either model. Additionally, each tumor detector was resistant to adversarial “transfer” attacks targeting the other. The adversarial perturbations showed strong characteristic differences: the deep denoising models perturbations were a very diffuse, seemingly unrecognizable pattern while the sparse coding models perturbations showed traces of tissue cells.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

17 WIND ENERGY↗
DOE OSTI2021

Probabilistic Voltage Sensitivity based Preemptive Voltage Monitoring in Unbalanced Distribution Networks

With increasing penetration of renewable energy and active consumers, control and management of power distribution networks has become challenging. Renewable energy sources can cause random voltage fluctuations as their output power depends on weather conditions. Conventional voltage control schemes such as tap changers and capacitor banks lack the foresight required to quickly alleviate voltage violations. Thus, there is an urgent need for effective approaches for predicting and mitigating voltage violations as a result of random fluctuations in power injections. This work proposes a novel voltage monitoring approach based on low-complexity, data-driven probabilistic voltage sensitivity analysis. The usefulness of this work is not only in predicting voltage violations in unbalanced distribution grids, but also in opening up the door for optimal voltage control. Using system data and forecasts, the proposed approach predicts the distribution of system node voltages which is then used to to identify nodes that may violate the nominal operational limits with high probability. The method is tested on the IEEE 37 node distribution system considering integrated distributed solar energy sources. The method is validated against the classic load flow based method and offers over 95% accuracy in predicting voltage violations.

Abujubbeh, Mohammad↗
DOE OSTI2023

Order Parameter Engineering for Random Systems

The chemical short-range order (CSRO) in crystalline materials influences the properties, and its effect is significant in the context of multicomponent materials. Here, we propose a scheme for the CSRO parameter or Δ-parameter in terms of the number of like and unlike bonds in the multicomponent systems. The OPERA or Order Parameter Engineering for RAndom Systems scheme for semi-canonical and canonical ensembles is proposed. The proposed framework of Δ-parameter with OPERA framework can generate the single-phase supercell with desired CSRO without explicit energy calculations and provides a computationally efficient scheme for exploration of the CSRO. We demonstrate the applicability of the Δ-parameter as a scalar quantity for describing the CSRO in multicomponent alloys and oxides (FCC-CoCrNi, BCC-MoNbTaW, and (CoCuMgNiZn)O).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analytical homogenization techniques applied to the Fickian diffusion: Effective diffusivity coefficient

For multiple applications in nuclear energy, the ability to accurately represent material behavior with a simplified model is important to facilitate practical engineering-scale simulations. In this work, we focus on the homogenized thermal response of a medium containing spherical inclusions, similar to a fuel form (compact or pebble) containing TRISO particles. An extensive survey on effective thermal conductivity modeling was performed in our previous study, considering a random distribution of mono-sized spherical inclusions in a continuous matrix. Using the analogy between heat conduction and the simplified Fickian diffusion (or fission product species conservation), we can use the same analytical homogenization methods to obtain ETC as for the effective diffusivity coefficient (EDC). We performed several numerical experiments at varying conditions to assess the validity of our hypothesis for EDC calculations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Quasiparticle self-consistent GW band structures and phase transitions of LiAlO 2 in tetrahedrally and octahedrally coordinated structures

In this work, a first-principles computational study is presented of various phases of LiAlO 2 . The relative total energies and equations of state of the α, β, and γ phases are determined after structural relaxation of each phase. The β and γ tetrahedral phases are found to be very close in energy and lattice volume with the γ phase having the lowest energy. The octahedral α phase is a high-pressure phase and the transition pressure from the γ and β phases to α is determined to be about 1 GPa. The electronic band structures of each phase at their own equilibrium volume are determined using the quasiparticle self-consistent (QS) GW method as well as using the 0.8 Σ approach in which the QS GW self-energy is reduced by a factor of 0.8 to correct for the underscreening of W in QS GW. The effective masses of the band edges and the nature of the band gaps are presented. The lowest energy γ phase is found to have a pseudodirect gap of 7.69 eV. The gap is direct at Γ but corresponds to a dipole forbidden transition. The imaginary part of the dielectric function and the absorption coefficient are calculated in the long-wavelength limit and the random phase approximation, without local field or electron-hole interaction effects for each phase, and their anisotropies are discussed. Si doping on the Al site is investigated as a possible n-type dopant in γ-LiAlO 2 using a 128-atom supercell corresponding to 3.125% Si on the Al sublattice in the generalized gradient approximation and a smaller 16-atom cell with 25% Si in the QS GW approximation. The Si is found to significantly perturb the conduction band and lower the gap but a clearly separated deep donor defect level is not found. However, the donor binding energy is still expected to be relatively deep, on the order of a few tenths eV in the hydrogenic effective mass approximation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

SANS characterization of time dependent, slow molecular exchange in an SDS micellar system

Here, we have investigated the molecular exchange of sodium dodecyl sulfate (SDS) micelles in aqueous solution by time-resolved small angle neutron scattering (TR-SANS) measurements as a function of the surfactant and salt concentration. Starting with deuterated (d-SDS) and protonated (h-SDS) SDS micelles, surfactant exchange across the micelles leads to a randomized distribution of d-SDS and h-SDS within each micelle. By employing the contrast matching technique, we have studied this randomization process which is a direct measure of the molecular exchange of this system. Our results show that the randomization of the pure h-SDS and d-SDS micelles occurs in two steps: first, an almost instantaneous drop in the scattering intensity is observed where ~80% of the micelles are randomized (contrast matched). After this, micelle randomization progresses slowly spanning over ~100 hours. Importantly, we show that the kinetics in the second step are dominated by the formation of domains rich in either h-SDS, d-SDS and randomized (50 : 50 h-SDS : d-SDS). The slow exchange step is modeled via a phenomenological approach by drawing analogy to the Langmuir adsorption theory. Finally, the effects of the surfactant and salt concentrations on the instantaneous, and the time dependent randomization of SDS micelles are discussed.

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

A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms

Abstract Increasing occurrence of harmful algal blooms across the land–water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

On the Investigation of Phase Fault Classification in Power Grid Signals: A Case Study for Support Vector Machines, Decision Tree and Random Forest

In monitoring the power grid, an ability to differentiate between fault types is essential to ensuring electrical safety. Accordingly, this study introduces a fault detection and classification method by considering different machine learning (ML) and feature extraction (FE) methods combinations. Specifically, the proposed method is established in two classification layers; the first layer determines the fault, and the second layer distinguishes the type of fault. Based on the proposed system model, this study seeks to determine the influential data attributes in a power grid signal using FE methods, including fast Fourier transform, power spectral density (PSD), auto-correlation, and wavelet transform (WT). A cross-comparison of the effectiveness of the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) is also performed to accomplish the classification layers of the proposed method. The designed algorithm is analyzed under the various combinations of FE and ML methods, and outcomes are presented by considering the trade-off between computational complexity and prediction accuracy. The results reveal that the RF-based ML algorithm shows the most accurate classification performance with PSD, and the most time-saving of the models is the DT WT. Also, SVM emerges superior on a subsequent test of the simulated models on real-world signals.

Galbraith, Kelli↗
DOE OSTI2022

Random strain induced correlations in materials with intertwined nematic and magnetic orders

Electronic nematicity is rarely observed as an isolated instability of a correlated electron system. Instead, in iron pnictides and in certain cuprates and heavy-fermion materials, nematicity is intertwined with an underlying spin-stripe or charge-stripe state. As a result, random strain, ubiquitous in any real crystal, creates both random-field disorder for the nematic degrees of freedom and random-bond disorder for the spin or charge ones. Here, we put forward an Ashkin-Teller model with random Baxter fields to capture the dual role of random strain in nematic systems for which nematicity is a composite order arising from a stripe state. Using Monte Carlo to simulate this random Baxter-field model, we find not only the expected break-up of the system into nematic domains, but also the emergence of nontrivial disorder-promoted magnetic correlations. Such correlations enhance and tie up the fluctuations associated with the two degenerate magnetic stripe states from which nematicity arises, leaving characteristic signatures in the spatial profile of the magnetic domains, in the configurational space of the spin variables, and in the magnetic noise spectrum. We discuss possible experimental manifestations of these effects in iron-pnictide superconductors. Furthermore, our work establishes the random Baxter-field model as a more complete alternative to the random-field Ising model to describe complex electronic nematic phenomena in the presence of disorder.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Random Knotting in Fractal Ring Polymers

Many ring polymer systems of physical and biological interest exhibit both pronounced topological effects and nontrivial self-similarity, but the relationship between these two phenomena has not yet been clearly established. Here, we use theory and simulation to formulate such a connection by studying a fundamental topological property—the random knotting probability—for ring polymers with varying fractal dimension, d f . Using straightforward scaling arguments, we generalize a classic mathematical result, showing that the probability of a trivial knot decays exponentially with chain size, N, for all fractal dimensions: P 0 (N) ∝ exp(–N/N 0 ). However, no such simple considerations can account for the dependence of the knotting length, N 0 , on d f , necessitating a more involved analytical calculation. This analysis reveals a complicated double-exponential dependence, which is well supported by numerical data. By contrast, functional forms typical of simple scaling theories fail to adequately describe the observations. These findings are equally valid for two-dimensional ring polymer systems, where “knotting” is defined as the intersection of any two segments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗