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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

IAEA CONNECT: Connecting the network of networks for enhanced communication and training - 15143

To further support the exchange of information and cooperation in the management of radioactive waste, decommissioning and environmental remediation, a new tool has been developed and was officially launched in October 2014: The IAEA CONNECT platform. This paper describes the platform, including its objectives, principles, participation/audience, methods of work, Governance document, current available networks and features, and explains the context it was developed in, as well as the reasons behind its creation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

The Wasatch Environmental Observatory: A mountain to urban research network in the semi‐arid western US

Abstract The 2085 km 2 Jordan River Basin, and its seven sub‐catchments draining the Central Wasatch Range immediately east of Salt Lake City, UT, are home to an array of hydrologic, atmospheric, climatic and chemical research infrastructure that collectively forms the Wasatch Environmental Observatory (WEO). WEO is geographically nested within a wildland to urban land‐use gradient and built upon a strong foundation of over a century of discharge and climate records. A 2200 m gradient in elevation results in variable precipitation, temperature and vegetation patterns. Soil and subsurface structure reflect systematic variation in geology from granitic, intrusive to mixed sedimentary clastic across headwater catchments, all draining to the alluvial or colluvial sediments of the former Lake Bonneville. Winter snowfall and spring snowmelt control annual hydroclimate, rapid population growth dominates geographic change in lower elevations and urban gas and particle emissions contribute to episodes of severe air pollution in this closed‐basin. Long‐term hydroclimate observations across this diverse landscape provide the foundation for an expanding network of infrastructure in both montane and urban landscapes. Current infrastructure supports both basic and applied research in atmospheric chemistry, biogeochemistry, climate, ecology, hydrology, meteorology, resource management and urban redesign that is augmented through strong partnerships with cooperating agencies. These features allow WEO to serve as a unique natural laboratory for addressing research questions facing seasonally snow‐covered, semi‐arid regions in a rapidly changing world and an excellent facility for providing student education and research training.

Follstad Shah, Jennifer J.↗
DOE OSTI2018

Ordered Networks of Gold Nanoparticles Crosslinked by Dithiol-Oligomers

Here, controlled aggregation of nanoparticles into superlattices is a grand challenge in material science, where ligand based self–assembly is the dominant route. Here, the self–assembly of gold nanoparticles (AuNPs) that are crosslinked by water soluble oligo–(ethylene glycol)–dithiol (oEG–dithiol) is reported and their 3D structure by small angle X–ray scattering is determined. Surprisingly, a narrow region is found in the parameter space of dithiol linker–length and nanoparticle size for which the crosslinked networks form short–ranged FCC crystals. Using geometrical considerations and numerical simulations, the stability of the formed lattices is evaluated as a function of dithiol length and the number of connected nearest–neighbors, and a phase diagram of superlattice formation is provided. Identifying the narrow parameter space that allows crystallization facilitates focused exploration of linker chemical composition and medium conditions such as thermal annealing, pH, and added solutes that may lead to superior and more robust crystals.

36 MATERIALS SCIENCE↗
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↗