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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Thermoreversible Morphology and Conductivity of a Conjugated Polymer Network Embedded in Block Copolymer Self-Assemblies

Self-assembly of block copolymers provides numerous opportunities to create functional materials, utilizing self-assembled microdomains with a variety of morphology and periodic architectures as templates for functional nanofillers. Here new progress is reported toward the fabrication of thermally responsive and electrically conductive polymeric self-assemblies made from a water-soluble poly(thiophene) derivative with short poly(ethylene oxide) side chains and Pluronic L62 block copolymer solution in water. The structural and electrical properties of conjugated polymer-embedded self-assembled architectures are investigated by combining small-angle neutron and X-ray scattering, coarse-grained molecular dynamics simulations, and impedance spectroscopy. The L62 solution template organizes the conjugated polymers by stably incorporating them into the hydrophilic domains thus inhibiting aggregation. The changing morphology of L62 during the micellarto- lamellar phase transition defines the embedded conjugated polymer network. As a result, the conductivity is strongly coupled to the structural change of the templating L62 phase and exhibits thermally reversible behavior with no signs of quenching of the conductivity at high temperature. In conclusion, this study shows promise for enabling more flexibility in processing and utilizing water-soluble conjugated polymers in aqueous solutions for self-assembly based fabrication of stimuli-responsive nanostructures and sensory materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Time-Sequenced Flow Field Prediction in an Optima Spark-Ignition Direct-Injection Engine Using Bidirectional Recurrent Neural Network (bi-RNN) with Long Short-Term Memory

To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from mull-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.

Bi-RNN model↗
DOE OSTI2021

On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

Physics-informed neural networks (PINNs) are demonstrating remarkable promise in integrating physical models with gappy and noisy observational data, but they still struggle in cases where the target functions to be approximated exhibit high-frequency or multi-scale features. Here in this work we investigate this limitation through the lens of Neural Tangent Kernel (NTK) theory and elucidate how PINNs are biased towards learning functions along the dominant eigen-directions of their limiting NTK. Using this observation, we construct novel architectures that employ spatio-temporal and multi-scale random Fourier features, and justify how such coordinate embedding layers can lead to robust and accurate PINN models. Numerical examples are presented for several challenging cases where conventional PINN models fail, including wave propagation and reaction–diffusion dynamics, illustrating how the proposed methods can be used to effectively tackle both forward and inverse problems involving partial differential equations with multi-scale behavior.

42 ENGINEERING↗
DOE OSTI2019

Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks

Generative Adversarial Networks (GANs) are becoming popular choices for unsupervised learning. At the same time there is a concerted effort in the machine learning community to expand the range of tasks in which learning can be applied as well as to utilize methods from other disciplines to accelerate learning. With this in mind, in the current work we suggest ways to enforce given constraints in the output of a GAN both for interpolation and extrapolation. The two cases need to be treated differently. For the case of interpolation, the incorporation of constraints is built into the training of the GAN. The incorporation of the constraints respects the primary game-theoretic setup of a GAN so it can be combined with existing algorithms. However, it can exacerbate the problem of instability during training that is well-known for GANs. We suggest adding small noise to the constraints as a simple remedy that has performed well in our numerical experiments. The case of extrapolation (prediction) is more involved. First, we employ a modified interpolation training process that uses noisy data but does {\it not} necessarily enforce the constraints during training. Second, the resulting modified interpolator is used for extrapolation where the constraints are enforced after each step through projection on the space of constraints.

Stinis, Panagiotis↗
DOE OSTI2021

Delivery drone route planning over a battery swapping network

Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.

drone battery swapping↗
DOE OSTI2016

Odd–Even Structural Sensitivity on Dynamics in Network-Forming Ionic Liquids

As a compelling case of sensitive structure–property relationship, an odd–even effect refers to the alternating trend of physical or chemical properties on odd/even number of repeating structural units. In crystalline or semicrystalline materials, such odd–even effects emerge as manifestations of differences in the periodic packing patterns of molecules. Therefore, due to the lack of long-range order, such an odd–even phenomenon is not expected for dynamic properties in amorphous state. Herein, we report the discovery of a remarkable odd–even effect of dynamical properties in the liquid phase. In a class of glass-forming diammonium citrate ionic liquids, using incoherent quasi-elastic neutron scattering measurements, we measured the dynamical properties including diffusion coefficient and rotational relaxation time. These directly measured molecular dynamics showed pronounced alternating trends with increased number of methylene (-CH2-) groups in the backbone. Meanwhile, the structure factor S(Q) showed no long-range periodic packing of molecules, while the pair distribution function G(r) revealed subtle differences in the local molecular morphology. The observed dynamical odd–even phenomenon in liquids showed that profound dynamical changes originate from subtle local structural differences.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. Here we show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Stress-Induced Orientation of Cocontinuous Nanostructures within Randomly End-Linked Copolymer Networks

Randomly end-linked copolymer networks (RECNs) provide a robust route to self-assembled cocontinuous nanostructures. Here, we study the orientation of cocontinuous polystyrene/poly(D,L-lactide) (PS/PLA) RECNs induced by uniaxial stretching above the glass transition temperatures of the components. Small-angle X-ray scattering (SAXS) reveals that the domains initially undergo nonaffine stretching at low strain (ε < 0.4), followed by domain rotation at larger strains, yielding a “soft elastic” response and providing a high degree of orientation. Transmission electron microscopy (TEM) tomography confirms that stretching leads to topological changes in the nanostructure, corresponding to reorganization of domain interfaces. The combination of orientation at the molecular and nanostructural levels provides substantial improvements in yield strength, toughness, and stiffness. In addition to possibilities for improving mechanical properties, cocontinuous nanostructures with controlled levels of orientation have potential in a variety of contexts including directional ion transport and energy absorption.

42 ENGINEERING↗
DOE OSTI2023

Upscaling Wetland Methane Emissions From the FLUXNET–CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data-driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH 4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH 4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH 4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ~0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH 4 emissions of 146 ± 43 TgCH 4 y –1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH 4 y –1 ) and overlaps with top-down atmospheric inversion models (155–200 TgCH 4 y –1 ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH 4 fluxes has the potential to produce realistic extra-tropical wetland CH 4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Intermetallic Pd3Pb Nanowire Networks Boost Ethanol Oxidation and Oxygen Reduction Reaction with Significantly Improved Methanol Tolerance

Intermetallic nanocrystals are currently receiving extensive attentions due to their well- defined crystal structures, highly ordered atomic distribution and superior structural stability that endow them with optimized catalytic activities, stabilities and high selectivity as electrocatalysts for fuel cells. Here, for the first time, we reported the facile synthesis of intermetallic Pd3Pb nanowire networks (IM-Pd3Pb NNs) with a one-step wet-chemical strategy at a relatively low temperature (i.e. 170 °C) in 1 h. The as-prepared IM-Pd3Pb NNs exhibited superior bifunctional catalytic performances toward oxygen reduction reaction (ORR) and ethanol oxidation reaction (EtOR) compared to commercial Pt/C and Pd black, respectively. Significantly, IM-Pd3Pb NNs also showed excellent methanol- and CO-tolerant ability as ORR cathode and EtOR anode electrocatalysts, respectively. The electrochemical active surface area and mass activity of IM-Pd3Pb NNs are about 3.4 times and 2 times higher than Pd black toward EtOR, respectively. As the Pt-free bifunctional electrocatalysts, 3D IM-Pd3Pb architectures with exceptional catalytic performances hold great promise in various applica-tions of energy conversion and storage devices, sensors, electronics, optics and so on.

Shi, Qiurong↗
DOE OSTI2015

Highly efficient nonprecious metal catalyst prepared with metal–organic framework in a continuous carbon nanofibrous network

Fuel cell vehicles, the only all-electric technology with a demonstrated >300 miles per fill travel range, use Pt as the electrode catalyst. The high price of Pt creates a major cost barrier for large-scale implementation of polymer electrolyte membrane fuel cells. Nonprecious metal catalysts (NPMCs) represent attractive low-cost alternatives. However, a significantly lower turnover frequency at the individual catalytic site renders the traditional carbon-supported NPMCs inadequate in reaching the desired performance afforded by Pt. Unconventional catalyst design aiming at maximizing the active site density at much improved mass and charge transports is essential for the next-generation NPMC. We report here a method of preparing highly efficient, nanofibrous NPMC for cathodic oxygen reduction reaction by electrospinning a polymer solution containing ferrous organometallics and zeolitic imidazolate framework followed by thermal activation. The catalyst offers a carbon nanonetwork architecture made of microporous nanofibers decorated by uniformly distributed high-density active sites. In a single-cell test, the membrane electrode containing such a catalyst delivered unprecedented volumetric activities of 3.3 A∙cm -3 at 0.9 V or 450 A∙cm -3 extrapolated at 0.8 V, representing the highest reported value in the literature. Improved fuel cell durability was also observed.

30 DIRECT ENERGY CONVERSION↗