SEARCH · Engineering Papers
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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This session will help you tune up your skills and knowledge on the latest advances in network design and management, to keep your agency's data communications running at peak performance, with minimal cost and effort.
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Bioresorbable implantable electronics require power sources that are also bioresorbable with controllable electrical output and lifetime. In this paper, we report a bioresorbable zinc primary battery anode filament based on a zinc microparticle (MP) network coated with chitosan and Al 2 O 3 double shells. When discharged in 0.9% NaCl saline, a Zn MP filament with a 0.17 × 2 mm 2 cross-sectional area exhibited a stable voltage output of 0.55 V at a current of 0.01 mA. Covered by chitosan and Al 2 O 3 double shells, the zinc MP filament exhibited a directional dissolution behavior with a tunable lifetime approximately linear to its length. A stable 200 h discharging time was achieved with a 15 mm Zn MP filament. The maximum output power was found to be 12 μW at 0.03 mA for one filament. The linearity relationship between the current output and the filament cross-sectional area suggested a facile strategy to raise the power output at constant discharging voltage. The filaments could also be connected in series and in parallel to boost its overall voltage and current output, demonstrating their excellent integration capability. Furthermore, this work presents a promising pathway toward bioresorbable transient batteries with controllable lifetime and power output, demonstrating a great potential for powering transient implantable biomedical devices.
Optical synthesis of lossless double-pass networks using birefringent crystals
We present a technique that derives the link activation schedule (transmit/receive mode and communications neighbor selection) and routes used for efficient traffic relay through the network.
Speaker and Presenter at the Lincoln Laboratory Communications Workshop on April 5, 2016 at the Massachusetts Institute of Technology Lincoln Laboratory in Lexington, MA. A visual presentation titled Earth Regimes Network Evolution Study (ERNESt).
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)
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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.
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.
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.
This state of the art book discusses current trends in sensor networks, systems, and security. This proceedings contains a collection of articles authored by outstanding researchers in the field, providing an excellent reference for graduate students and researchers in this area.
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.
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.
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