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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probabilistic locked mode predictor in the presence of a resistive wall and finite island saturation in tokamaks

We present a framework for estimating the probability of locking to an error field in a rotating tokamak plasma. This leverages machine learning methods trained on data from a mode-locking model, including an error field, resistive magnetohydrodynamics modeling of the plasma, a resistive wall, and an external vacuum region, leading to a fifth-order ordinary differential equation (ODE) system. It is an extension of the model without a resistive wall introduced by Akçay et al. [Phys. Plasmas 28, 082106 (2021)]. Tearing mode saturation by a finite island width is also modeled. We vary three pairs of control parameters in our studies: the momentum source plus either the error field, the tearing stability index, or the island saturation term. The order parameters are the time-asymptotic values of the five ODE variables. Normalization of them reduces the system to 2D and facilitates the classification into locked (L) or unlocked (U) states, as illustrated by Akçay et al., [Phys. Plasmas 28, 082106 (2021)]. This classification splits the control space into three regions: L̂, with only L states; Û, with only U states; and a hysteresis (hysteretic) region Ĥ, with both L and U states. In regions L̂ and Û, the cubic equation of torque balance yields one real root. Region Ĥ has three roots, allowing bifurcations between the L and U states. The classification of the ODE solutions into L/U is used to estimate the locking probability, conditional on the pair of the control parameters, using a neural network. We also explore estimating the locking probability for a sparse dataset, using a transfer learning method based on a dense model dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Comparison of the structure function F 2 as measured by charged lepton and neutrino scattering from iron targets

A comparison study of world data for the structure function F 2 for Iron, as measured by both charged lepton and neutrino scattering experiments, is presented. Consistency of results for both charged lepton and neutrino scattering is observed for the full global data set in the valence regime. Consistency is also observed at low x for the various neutrino data sets, as well as for the charged lepton data sets, independently. However, data from the two probes exhibit differences on the order of 15% in the shadowing/anti-shadowing transition region where the Bjorken scaling variable x is < 0.15. This observation is indicative that neutrino probes of nucleon structure might be sensitive to different nuclear effects than charged lepton probes. Details and results of the data comparison are here presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Addressing Load Imbalance in Bioinformatics and Biomedical Applications: Efficient Scheduling across Multiple GPUs

Computational bioinformatics and biomedical applications frequently contain heterogeneously sized units of work or tasks, for instance due to variability in the sizes of biological sequences and molecules. Variable-sized workloads lead to load imbalances in parallel implementations which detract from efficiency and performance. Many modern computing resources now have multiple graphics processing units(GPUs) per computer for acceleration. These multiple GPU resources need to be used efficiently through balancing of workloads across the GPUs. OpenMP is a portable directive-based parallel programming API used ubiquitously in bioscience applications to program CPUs; recently, the use of OpenMP directives for GPU acceleration has become possible. Here, motivated by experiences with imbalanced loads in GPU-accelerated bioinformatics applications, we address the load balancing problem using OpenMP task-to-GPU scheduling combined with OpenMP GPU offloading for multiply heterogeneous workloads – loads with both variable input sizes, and simultaneously, variable convergence rates for algorithms with a stochastic component – scheduled across multiple GPUs. We aim to develop strategies which are both easy to use and have lower overheads, and may be incorporated incrementally in existing programs which already make use of OpenMP for CPU-based threading in order to make use of multi-GPU computers. We test different combinations of input size variability and convergence rate variability, and characterize the effects of these different scenarios on the performance of scheduling strategies across multiple GPUs with OpenMP. We present several dynamic scheduling solutions for different parallel patterns, explore optimizations, and provide publicly available example computational kernels to make these strategies easy to use in programs. This work will enable application developers to efficiently and easily use multiple GPUs for imbalanced workloads found in bioinformatics and biomedical applications.

Thavappiragasam, Mathialakan↗
DOE OSTI2014

Initial Comparison of Baseline Physical and Mechanical Properties for the VHTR Candidate Graphite Grades

High-purity graphite is the core structural material of choice in the Very High Temperature Reactor (VHTR) design, a graphite-moderated, helium-cooled configuration capable of producing thermal energy for power generation as well as process heat for industrial applications that require temperatures higher than the outlet temperatures of present nuclear reactors. The Baseline Graphite Characterization Program is establishing accurate as-manufactured mechanical and physical property distributions in nuclear-grade graphites by providing comprehensive data that captures the level of variation in measured values. In addition to providing a thorough comparison between these values in different graphite grades, the program is also carefully tracking individual specimen source, position, and orientation information in order to provide comparisons both in specific properties and in the associated variability between different lots, different billets, and different positions from within a single billet. This report is a preliminary comparison between each of the grades of graphite that are considered “candidate” grades from four major international graphite producers. These particular grades (NBG-18, NBG-17, PCEA, IG-110, and 2114) are the major focus of the evaluations presently underway on irradiated graphite properties through the series of Advanced Graphite Creep (AGC) experiments. NBG-18, a medium-grain pitch coke graphite from SGL from which billets are formed via vibration molding, was the favored structural material in the pebble-bed configuration. NBG-17 graphite from SGL is essentially NBG-18 with the grain size reduced by a factor of two. PCEA, petroleum coke graphite from GrafTech with a similar grain size to NBG-17, is formed via an extrusion process and was initially considered the favored grade for the prismatic layout. IG-110 and 2114, from Toyo Tanso and Mersen (formerly Carbone Lorraine), respectively, are fine-grain grades produced via an isomolding process. An analysis of the comparison between each of these grades will include not only the differences in fundamental and statistically-significant individual strength levels, but also the differences in the overall variability in properties within each of the grades that will ultimately provide the basis for predicting in-service performance. The comparative performance of the different types of nuclear-grade graphites will naturally continue to evolve as thousands more specimens are fully characterized with regard to strength, physical properties, and thermal performance from the numerous grades of graphite being evaluated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Predictive Maturity of Multi-Scale Simulation Models for Fuel Performance

The project proposed to provide a Predictive Maturity Framework with its companion metrics that (1) introduce a formalized, quantitative means to communicate information between interested parties, (2) provide scientifically dependable means to claim completion of Validation and Uncertainty Quantification (VU) activities, and (3) guide the decision makers in the allocation of Nuclear Energy’s resources for code development and physical experiments. The project team proposed to develop this framework based on two complimentary criteria: (1) the extent of experimental evidence available for the calibration of simulation models and (2) the sophistication of the physics incorporated in simulation models. The proposed framework is capable of quantifying the interaction between the required number of physical experiments and degree of physics sophistication. The project team has developed this framework and implemented it with a multi-scale model for simulating creep of a core reactor cladding. The multi-scale model is composed of the viscoplastic self-consistent (VPSC) code at the meso-scale, which represents the visco-plastic behavior and changing properties of a highly anisotropic material and a Finite Element (FE) code at the macro-scale to represent the elastic behavior and apply the loading. The framework developed takes advantage of the transparency provided by partitioned analysis, where independent constituent codes are coupled in an iterative manner. This transparency allows model developers to better understand and remedy the source of biases and uncertainties, whether they stem from the constituents or the coupling interface by exploiting separate-effect experiments conducted within the constituent domain and integral-effect experiments conducted within the full-system domain. The project team has implemented this procedure with the multi- scale VPSC-FE model and demonstrated its ability to improve the predictive capability of the model. Within this framework, the project team has focused on optimizing resource allocation for improving numerical models through further code development and experimentation. Related to further code development, we have developed a code prioritization index (CPI) for coupled numerical models. CPI is implemented to effectively improve the predictive capability of the coupled model by increasing the sophistication of constituent codes. In relation to designing new experiments, we investigated the information gained by the addition of each new experiment used for calibration and bias correction of a simulation model. Additionally, the variability of ‘information gain’ through the design domain has been investigated in order to identify the experiment settings where maximum information gain occurs and thus guide the experimenters in the selection of the experiment settings. This idea was extended to evaluate the information gain from each experiment can be improved by intelligently selecting the experiments, leading to the development of the Batch Sequential Design (BSD) technique. Additionally, we evaluated the importance of sufficiently exploring the domain of applicability in experiment-based validation of high-consequence modeling and simulation by developing a new metric to quantify coverage. This metric has also been incorporated into the design of new experiments. Finally, we have proposed a data-aware calibration approach for the calibration of numerical models. This new method considers the complexity of a numerical model (the number of parameters to be calibrated, parameter uncertainty, and form of the model) and seeks to identify the number of experiments necessary to calibrate the model based on the level of sophistication of the physics. The final component in the project team’s work to improve model calibration and validation methods is the incorporation of robustness to non-probabilistic uncertainty in the input parameters. This is an improvement to model validation and uncertainty quantification stemming beyond the originally proposed scope of the project. We have introduced a new metric for incorporating the concept of robustness into experiment-based validation of numerical models. This project has accounted for the graduation of two Ph.D. students (Kendra Van Buren and Josh Hegenderfer) and two M.S. students (Matthew Egeberg and Parker Shields). One of the doctoral students is now working in the nuclear engineering field and the other one is a post-doctoral fellow at the Los Alamos National Laboratory. Additionally, two more Ph.D. students (Garrison Stevens and Tunc Kulaksiz) who are working towards graduation have been supported by this project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Climate Modeling: Ocean Cavities below Ice Shelves

The Accelerated Climate Model for Energy (ACME), a new initiative by the U.S. Department of Energy, includes unstructured-mesh ocean, land-ice, and sea-ice components using the Model for Prediction Across Scales (MPAS) framework. The ability to run coupled high-resolution global simulations efficiently on large, high-performance computers is a priority for ACME. Sub-ice shelf ocean cavities are a significant new capability in ACME, and will be used to better understand how changing ocean temperature and currents influence glacial melting and retreat. These simulations take advantage of the horizontal variable-resolution mesh and adaptive vertical coordinate in MPAS-Ocean, in order to place high resolution below ice shelves and near grounding lines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Automated Blazar Light Curves Using Machine Learning

This presentation describes a problem and methodology pertaining to automated blazar light curves. Namely, optical variability patterns for blazars require the construction of light curves and in order to generate the light curves, data must be filtered before processing to ensure quality.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Economy and Emissions Impacts from Solazyme Fuel in UPS Delivery Vehicles

To improve understanding of the potential fuel economy and emissions impacts from switching a fleet of vehicles from conventional petroleum diesel to synthetic renewable diesel, the National Renewable Energy Laboratory (NREL) conducted fuel economy and emissions analyses at NREL's Renewable Fuels and Lubricants (ReFUEL) Laboratory. Representative test cycles were developed based on real-world data from six package delivery vehicles and six class 8 day-cab tractors operated by UPS in the Dallas, Texas, area. A three-week in-field data collection period yielded 170 days of real-world vehicle operations data that NREL used to select representative standard drive cycles for testing. Fuel economy and emissions tests at the ReFUEL Laboratory showed that, in general, when switching from conventional diesel to renewable diesel observed changes in tailpipe carbon dioxide (CO2), fuel consumption, and fuel economy are primarily driven by changes in fuel properties such as the hydrogen-to-carbon ratio, density, and lower heating value (LHV). The vehicles tested with the renewable diesel showed a consistent 4.2% reduction in tailpipe CO2 emissions, but a 3.5%-4.8% reduction in fuel economy compared with local pump diesel. This is consistent with the 4.2% lower volumetric LHV of the sourced renewable diesel compared to the pump diesel. The UPS package car tested on renewable diesel also demonstrated a 4.1% oxides of nitrogen (NOx) reduction. NOx emissions from the UPS selective-catalyst-reduction-equipped tractor were an order of magnitude lower than the package car but showed relatively higher variability in results from cycle to cycle.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Improving the luminosity for Beam Energy Scan II at RHIC

The QCD (Quantum Chromodynamics) phase diagram [1], one of the most im-portant graphs in nuclear physics, has many uncharted territories. In particular,the nature of the transformation from Quark-Gluon plasma (QGP) to the stateof Hadronic gas is totally unknown [2]. A beam energy scan [3,4], scanning thephase diagram with variable collision energy, has been conducted at RHIC toexplore the first-order phase transition and determine the location of a possible critical point.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

ARM shortwave spectrometers to study the clear-cloud transition zone and mixing processes

The proposed research is a collaborative effort between NASA/Goddard Space Flight Center and the Hebrew University of Jerusalem. While the NASA team focused on analyzing ground-based hyper-spectral radiance observations to understand cloud edge properties and their connection to mixing processes, the Hebrew University team tackled the problem through cloud modeling activities. By approximating the shortwave spectra in the cloud-clear transition zone as a linear combination of purely clear and purely cloudy spectra we can characterize the variations of cloud optical thickness and cloud droplet effective radius in the transition zone. When applying this method to the measurements of a ground-based shortwave spectroradiometer at the ARM’s SGP site, representing continental conditions, and MAGIC field campaign between Log Angeles, California and Honolulu, Hawaii, representing maritime scenarios, we found that cloud optical depth consistently decreases in both cases, but droplet size decreases much more substantially for the continental regime, suggesting different mixing processes for the continental and maritime conditions. The investigation and measurements of radiation clouds were coupled with a unique cloud modeling. A novel spectral bin microphysics was developed and implemented to the System of Atmospheric Modeling (SAM). In order to resolve cloud transition zones with high spatial gradients of microphysical variables a unique high resolution (10 m) was used in simulations. The model calculates droplet size distributions in each grid point. The model output was transferred to the NASA/GSFC team for utilization in radiative calculations and testing of both radiative algorithm and model representation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Useability and Optimization Improvements in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) framework is a foundational capability used by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to create over 15 different simulation tools for advanced nuclear reactors. Due to this broad use, improvements to the framework in support of modeling and simulation goals are critical to the program. These improvements can take many forms, including optimization, improved user experience, streamlined application programming interfaces (APIs), parallelism, and new capabilities. The work transcribed in this report was conducted in direct support of the simulation tools and has already been deployed or will be deployed in the coming months. The capabilities were implemented in the same order as they are covered in this report: increased support of face variables, arbitrary spatial and temporal evaluation of material properties, and the addition of a triangular meshing library in libMesh.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude–longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. Linear regressions of DMS against the environmental parameters show that on a global-scale mixed layer depth and solar radiation are the strongest predictors of DMS. These parameters capture ~9 % and ~7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (~39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures ~66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (20.12±0.43 Tg S yr –1 ) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Runoff evaluation in an Earth System Land Model for permafrost regions in Alaska

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce. In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-based integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM's parameterized representation of total runoff. Results show that (1) ELM's total runoff was the same order of magnitude as the ATS simulations, and both models were similarly variable over time; (2) minor adjustments to coefficients in ELM's runoff parameterization improved the match between the ATS simulation and ELM's parameterized representation of annual and seasonal total runoff; (3) overall, runoff responses in ATS and ELM are more similar in flat hillslope environments compared to steep hillslopes; and (4) shallower active layer thicknesses and higher precipitation simulations resulted in lower correlations between the two models due to greater total runoff. By incorporating the optimized runoff coefficients from the Sagavanirktok River basin into ELM, the simulated total runoff better matched the streamflow observations at a small watershed located on the Seward Peninsula of Alaska. Our findings revealed important insights into the effectiveness of runoff parameterizations in land surface models and pathways for improving runoff coefficients in typical Arctic regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Synthesis of porphyrin nanostructures

The present disclosure generally relates to self-assembly methods for generating porphyrin nanostructures. For example, in one embodiment a method is provided that includes preparing a porphyrin solution and a surfactant solution. The porphyrin solution is then mixed with the surfactant solution at a concentration sufficient for confinement of the porphyrin molecules by the surfactant molecules. In some embodiments, the concentration of the surfactant is at or above its critical micelle concentration (CMC), which allows the surfactant to template the growth of the nanostructure over time. The size and morphology of the nanostructures may be affected by the type of porphyrin molecules used, the type of surfactant used, the concentration of the porphyrin and surfactant the pH of the mixture of the solutions, and the order of adding the reagents to the mixture, to name a few variables.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms: Preprint

This paper presents a distributed approach to perform real-time optimization of large wind farms. Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. This paper optimizes the overall power produced by a wind farm by formulating and solving a nonconvex optimization problem where the yaw angles are optimized to allow some turbines to operate in misaligned conditions and shape the aerodynamic interactions in a favorable way. The solution of the nonconvex smooth problem is tackled using a proximal primal-dual gradient method, which provably identifies a first-order stationary solution in a global sublinear manner. By adding auxiliary optimization variables for every pair of turbines that are coupled aerodynamically and properly adding consensus constraints into the underlying problem, a distributed algorithm with turbine-to-turbine message passing is obtained; this allows for turbines to be optimized in parallel using local information rather than information from the whole wind farm. This algorithm is computationally light, as it involves closed-form updates. This approach is demonstrated on a large wind farm with 60 turbines. The results indicate that similar performance can be achieved as with finite-difference gradient-based optimization at a fraction of the computational time and thus approach real-time control/optimization.

17 WIND ENERGY↗
DOE OSTI2019

Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms

This paper presents a distributed approach to performing real-time optimization of large wind farms. Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. This paper optimizes the overall power produced by a wind farm by formulating and solving a nonconvex optimization problem where the yaw angles are optimized to allow some turbines to operate in misaligned conditions and shape the aerodynamic interactions in a favorable way. The solution of the nonconvex smooth problem is tackled using a proximal primal-dual gradient method, which provably identifies a first-order stationary solution in a global sublinear manner. By adding auxiliary optimization variables for every pair of turbines that are coupled aerodynamically, and properly adding consensus constraints into the underlying problem, a distributed algorithm with turbine-to-turbine message passing is obtained; this allows for turbines to be optimized in parallel using local information rather than information from the whole wind farm. This algorithm is computationally light, as it involves closed-form updates. This approach is demonstrated on a large wind farm with 60 turbines. The results indicate that similar performance can be achieved as with finite-difference gradient-based optimization at a fraction of the computational time and thus approaching real-time control/optimization.

17 WIND ENERGY↗
DOE OSTI2023

Electronic transport mechanisms in a thin crystal of the Kitaev candidate α -RuCl 3 probed through guarded high impedance measurements

α-RuCl 3 is considered to be the top candidate material for the experimental realization of the celebrated Kitaev model, where ground states are quantum spin liquids with interesting fractionalized excitations. It is, however, known that additional interactions beyond the Kitaev model trigger in α-RuCl 3 a long-range zigzag antiferromagnetic ground state. In this work, we investigate a nanoflake of α-RuCl 3 through guarded high impedance measurements aimed at reaching the regime where the system turns into a zigzag antiferromagnet. We investigated a variety of temperatures (1.45–175 K) and out-of-plane magnetic fields (up to 11 T), finding a clear signature of a structural phase transition at ≈160 K as reported for thin crystals of α-RuCl 3 , as well as a thermally activated behavior at temperatures above ≈30 K, with a characteristic activation energy significantly smaller than the energy gap that we observe for α-RuCl 3 bulk crystals through our angle resolved photoemission spectroscopy (ARPES) experiments. Additionally, we found that below ≈30 K, transport is ruled by Efros–Shklovskii variable range hopping (VRH). Most importantly, our data show that below the magnetic ordering transition known for bulk α-RuCl 3 in the frame of the Kitaev–Heisenberg model (≈7 K), there is a clear deviation from VRH or thermal activation transport mechanisms. Finally, our work demonstrates the possibility of reaching, through specialized high impedance measurements, the thrilling ground states predicted for α-RuCl 3 at low temperatures in the frame of the Kitaev–Heisenberg model and informs about the transport mechanisms in this material in a wide temperature range.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Epoxy elastomers reinforced with functionalized multi-walled carbon nanotubes as stimuli-responsive shape memory materials

In this work, the incorporation of multiwalled carbon nanotubes (MWCNT) into epoxy-based elastomers was carried out in order to obtain nanocomposite systems with shape memory effect. For the preparation of elastomeric matrices, p-bis(2,3-epoxypropoxy)-α-methylstilbene (DOMS) was cured with sebacic acid. DOMS was synthesized in our laboratory and it is characterized by a rigid-rod, potentially liquid crystalline structure. A lightly cross-linked liquid crystalline elastomer was obtained. As for nanocomposites, variable amounts (0.75, 1.50, 3.0, 6.0, 12.0 wt.%) of COOH-MWCNTs were employed. In order to improve the nanotubes dispersibility and the interfacial adhesion with the epoxy matrix, an optimized two-step procedure was developed, which consisted in grafting the epoxy monomer onto the nanotube surface and then curing it in presence of crosslinking agent. DOMS-functionalized MWCNT were characterized through solvent dispersion experiments, FTIR spectroscopy and TGA analysis, which demonstrated the occurred covalent functionalization of the nanotubes with the epoxy monomers. The morphological analysis through electron microscopy demonstrated that this was an efficient strategy to improve the dispersion of nanotubes within the matrix. The second part of the work was devoted to the structural, thermal, mechanical and electric characterization of elastomeric nanocomposites. The results indicated a general improvement of properties of nanocomposites. Also, independently of the nanotube content, a smectic phase formed. Shape memory features of LC systems were also evaluated. It was demonstrated the shape could be recovered through heating, solvent immersion, as well as upon the application of an electrical field.

36 MATERIALS SCIENCE↗