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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Experimental and modeling studies of a biofuel surrogate compound: laminar burning velocities and jet-stirred reactor measurements of anisole

Lignocellulosic biomass is a promising alternative fuel source which can promote energy security, reduce greenhouse gas emissions, and minimize fuel consumption when paired with advanced combustion strategies. Pyrolysis is used to convert lignocellulosic biomass into a complex mixture of phenolic-rich species that can be used in a transportation fuel. Anisole (or methoxybenzene) can be used as a surrogate to represent these phenolic-rich species. Anisole also has attractive properties as a fuel component for use in advanced spark-ignition engines because of its high blending research octane number of 120. Presented in the current work are new measurements of laminar burning velocities, jet-stirred reactor (JSR) speciation of anisole/O 2 /N 2 mixtures, and the development and validation of a detailed chemical kinetic mechanism for anisole. Homogeneous, steady state, fixed gas temperature, perfectly stirred reactor CHEMKIN simulations were used to validate the mechanism against the current JSR measurements and published JSR experiments from CNRS-Nancy. Pyrolysis and oxidation simulations were based on the experimental reactant compositions and thermodynamic state conditions including P = 1 bar and T = 675–1275 K. The oxidation compositions studied in this work span fuel-lean (φ = 0.5), stoichiometric, and fuel rich (φ = 2.0) equivalence ratios. Laminar burning velocities were measured on a heat flux stabilized burner at an unburnt T = 358 K, P = 1 bar and simulated using the CHEMKIN premixed laminar flame speed module. Ignition delay times of anisole were then simulated at conditions relevant to advanced combustion strategies. Current laminar burning velocity measurements and predicted ignition delay times were compared to gasoline components (e.g., n-heptane, iso-octane, and toluene) and gasoline surrogates to highlight differences and similarities in behavior. Reaction path analysis and sensitivity analysis were used to explain the pathways relevant to the current studies. Under pyrolysis and oxidative conditions, unimolecular decomposition of anisole to phenoxy radicals and methyl radicals was found to be important due to the relatively low bond strength between the oxygen and methyl group, ~65 kcal/mol. Finally, reactions of these abundant phenoxy radicals with O 2 were found to be critical to accurately reproduce anisole's reactivity.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Silicon carbide fiber composite receiver tubes manufactured by PIP process

The performance of SiC fiber composites made by a polymer infiltration and pyrolysis (PIP) process was investigated. Two types of tubular composites were manufactured using Hi-Nicalon and Nicalon-CG silicon carbide fibers infiltrated with SMP10 silicon carbide matrix. The fiber content in both composites was about 25 vol.% with an average density of 2.39 g/cc. The tubular SiC composites showed stable solar absorptance and emittance of 0.97 and 0.91, respectively, at 800 °C. The composites were thermally shocked at 900, 1000, and 1100 °C with a waterjet simulating raindrops. As a result, the hoop tensile strength of the composites revealed no loss of mechanical strength due to the thermal shock experiments up to 1100 °C.

14 SOLAR ENERGY↗
DOE OSTI2022

Nitrate Controls on the Extent and Type of Metal Retention in Fine-Grained Sediments of a Simulated Aquifer

Aquifer groundwater quality is largely controlled by sediment composition and physical heterogeneity, which commonly sustain a unique redox gradient pattern. Attenuation of heavy metals within these heterogeneous aquifers is reliant on multiple factors, including redox conditions, and redox-active species that can further influence biogeochemical cycling. Here, we simulated an alluvial aquifer system using columns filled with natural coarse-grained sediments and two domains of fine-grained sediment lenses. Our goal was to examine heavy metal (Ni and Zn) attenuation within a complex aquifer network and further explore nitrate-rich groundwater conditions. The fine-grained sediment lenses sustained reducing conditions and served as a sink for Ni sequestration – in the form of Ni-silicates, Ni-organic matter, and a dominant Ni-sulfide phase. Further, the silicate clay and sulfide pools were also important retention mechanisms for Zn; however, Ni was associated more extensively with organic matter compared to Zn that formed layered double hydroxides. Nitrate-rich conditions promoted denitrification within the lenses that was coupled to the oxidation of Fe(II) and the concomitant precipitation of an Fe(III) phase with higher structural distortion. A decreased metal sulfide pool also resulted, where nitrate-rich conditions generated in an average 20% decrease in solid phase Ni, Zn, and Fe. Ultimately, nitrate plays a significant role in the aquifer’s biogeochemical cycling and capacity to retain heavy metals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

A Comparative Study of the Efficacy of Local/Global and Parametric/Nonparametric Machine Learning Methods for Establishing Structure–Property Linkages in High-Contrast 3D Elastic Composites

Reduced-order structure–property (S-P) linkages play a pivotal role in the tailored design of materials for advanced engineering components. There is a critical need to distill these from the simulation datasets aggregated using sophisticated, computationally expensive, physics-based numerical tools (e.g., finite element methods). The recent emergence of materials data science approaches has opened new avenues for addressing this challenge. In this paper, we critically compare the relative merits of the application of four distinct machine learning approaches for their efficacy in extracting microstructure-property linkages from the finite element simulation data aggregated on high-contrast elastic composites with different microstructures. The machine learning approaches selected for the study have included different combinations of local/global and parametric/nonparametric approaches. Furthermore, the nonparametric approaches selected for this study are based on Gaussian Process (GP) models that allow for a formal treatment of uncertainty quantification in the predicted values. The predictive performances of these different approaches have been compared against each other using rigorous cross-validation error metrics. Furthermore, their sensitivity to both the dataset size and dimensionality has been investigated.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Effect of Operating Conditions on Cryogenic Carbon Dioxide Removal

In this investigation, a natural-gas (NG) treatment system for NG sources with high CO 2 content is simulated experimentally and theoretically using Aspen Plus. The concept uses patented heat exchangers and a process design developed as part of the cryogenic carbon capture(CCC) process to remove enough CO 2 to enable the treated gas to be handled without the modification of traditional NG equipment. Simultaneously, the process removes liquefied natural gas (LNG) very efficiently. This process provides a simpler alternative to the solvent-based CO 2 removal, absorbent drying, and cryogenic NG liquid extraction systems found in traditional NG processing. The data from an experimental bench-scale apparatus verify the results of the simulations as a function of NG composition and pressure. Costing parameters, exergy loss, and heat-exchanger efficiency are also discussed. The results confirm low exergy losses for bench-scale apparatus. The system uses heat exchangers with efficiencies of more than 90 %. High pressure and low methane content in the NG result in high CO 2 capture.

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

Composited structure of non-precipitating shallow cumulus clouds

The normalized distributions of thermodynamic and dynamical variables both within and outside shallow clouds are investigated through a composite algorithm using large-eddy simulations of oceanic and continental cases. The normalized magnitude is maximum near the cloud centre and decreases outwards. While relative humidity (RH) and cloud liquid water ( q l ) decrease smoothly to match the environment, the vertical velocity, virtual potential temperature (θ v ), and potential temperature (θ) perturbations have more complicated behaviour towards the cloud boundary. Below the inversion layer, θ' v becomes negative before the vertical velocity has turned from an updraft to a subsiding shell outside the cloud, indicating the presence of a transition zone where the updraft is negatively buoyant. Due to the downdraft outside the cloud and enhanced horizontal turbulent mixing across the edge, the normalized turbulent kinetic energy (TKE) and horizontal turbulent kinetic energy (HTKE) decrease more slowly from the cloud centre outwards than the thermodynamic variables. The distributions all present asymmetric structures in response to the vertical wind shear, with more negatively buoyant air, stronger downdrafts, and larger TKE on the downshear side. We discuss several implications of the distributions for theoretical models and parameterizations. Positive buoyancy near the cloud base is mostly due to the virtual effect of water vapour, emphasizing the role of moisture in triggering. The mean vertical velocity is found to be approximately half the maximum vertical velocity within each cloud, providing a constraint to achieve possible power-law distributions for some models. Finally, the normalized distributions for different variables are used to estimate the vertical heat and moisture fluxes within clouds. The results suggest that distributions near the cloud edge and variability of maximum perturbations need careful treatment. The fluxes are underestimated in the inversion layer because cloud-top downdrafts cannot be captured well.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Phase-Field Study on Internal to External Oxidation Transition in High-Temperature Structural Alloys

Structural alloys applied at high temperatures rely on an external dense layer of oxide scale for protection. As some reactive alloy components are selectively oxidized internally, understanding how these dispersed metal oxide particles reach the surface to form a protective scale—the so-called internal to external oxidation transition—is crucial for designing these alloys. While the literature is replete with experimental studies on oxidation of alloys, there is a lack of computational studies in this realm due to the complex nature of coupled reaction and diffusion processes in multicomponent multi-phase alloy systems. In this work, we apply a recently developed phase-field model to simulate the oxidation processes under different compositions and nucleation scenarios to gain insights into how a continuous oxide scale can be established. The results show that while alloy composition is critical for internal to external oxidation transition, the oxide nuclei size, shape and distribution also have significant impact on the transition kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO 2 ) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO 2 . Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO 2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO 2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Reactive transport modelling of a cement backfill for radioactive waste disposal

Cementitious materials are included in many geological disposal facilities for radioactive wastes, including the UK candidate backfill material Nirex Reference Vault Backfill (NRVB). As part of an ongoing programme of work to assess NRVB performance, 1-D reactive-transport models have been constructed to simulate reaction with different illustrative groundwater compositions. Variant cases were also produced to explore the effect of model assumptions on the predicted behaviour of the backfill. Depending on groundwater composition, cement alteration pathways included: carbonation; external sulphate attack; chloride attack; the formation of magnesium-rich solids; and the precipitation of secondary aluminosilicate minerals. In general, the models suggest that the pore space in backfill associated with radioactive waste disposal systems is likely to become clogged (to some degree) over time close to backfill-rock interfaces resulting in a reduction in capacity for solute transport. However, the models do not include all relevant process couplings which is potentially, an area for further work.

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

An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture

Meshfree discretizations of state-based peridynamic models are attractive due to their ability to naturally describe fracture of general materials. However, two factors conspire to prevent meshfree discretizations of state-based peridynamics from converging to corresponding local solutions as resolution is increased: quadrature error prevents an accurate prediction of bulk mechanics, and the lack of an explicit boundary representation presents challenges when applying traction loads. Herein, we develop a reformulation of the linear peridynamic solid (LPS) model to address these shortcomings, using improved meshfree quadrature, a reformulation of the nonlocal dilatation, and a consistent handling of the nonlocal traction condition to construct a model with rigorous accuracy guarantees. In particular, these improvements are designed to enforce discrete consistency in the presence of evolving fractures, whose a priori unknown location render consistent treatment difficult. In the absence of fracture, when a corresponding classical continuum mechanics model exists, our improvements provide asymptotically compatible convergence to corresponding local solutions, eliminating surface effects and issues with traction loading which have historically plagued peridynamic discretizations. When fracture occurs, our formulation automatically provides a sharp representation of the fracture surface by breaking bonds, avoiding the loss of mass. We provide rigorous error analysis and demonstrate convergence for a number of benchmarks, including manufactured solutions, free-surface, nonhomogeneous traction loading, and composite material problems. Finally, we validate simulations of brittle fracture against a recent experiment of dynamic crack branching in soda-lime glass, providing evidence that the scheme yields accurate predictions for practical engineering problems.

42 ENGINEERING↗
DOE OSTI2018

Agro-waste shaped porous Al2O3/Ni composites: Corrosion resistance performance and artificial neural network modelling

Highlights: • Porous Al{sub 2}O{sub 3}/Ni composites were developed with agro-waste pore-forming agents. • The RH and SCB graded composites showed Ni{sub 3}Al{sub 2}SiO{sub 8} and NiAl{sub 2}O{sub 4} spinels respectively. • Superior chemical stability was demonstrated by the Al{sub 2}O{sub 3}-2Ni-RH composite. • The ANN model demonstrated a good performance with R{sup 2} value of approx. 0.99. - Abstract: In the present study, an analysis on the combined effect of nickel (Ni) reinforcement and pore former type in characterizing the corrosion behavior of composite porous alumina ceramics was performed. In order to showcase the potential of the new porous ceramics, pore-forming agents (PFAs) from rice husk (RH) and sugarcane bagasse (SCB) were used in shaping the plain and composite porous alumina samples having sample formulation of Al{sub 2}O{sub 3}-xNi-PFA; x = 0, 2, 4, 6 and 8 wt%. Results showed that the emergence of a highly stable Ni{sub 3}Al{sub 2}SiO{sub 8} spinelloid phase in the RH-graded composites enhanced their chemical stability in the corrosive mediums (10 wt% NaOH and 20 wt% H{sub 2}SO{sub 4}) relative to the plain and the corresponding SCB-graded counterparts. An artificial neural network (ANN) model has been developed for predicting the corrosion behavior of the plain and composite porous alumina ceramics based on the experimental data. The developed ANN model satisfactorily predicted the percent mass losses of the porous ceramics in strong alkali and strong acid solutions with coefficient of determination (R{sup 2}) of approximately 0.99.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. Here, in this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Scalable Screening of Soft Matter: A Case Study of Mixtures of Ionic Liquids and Organic Solvents

Room-temperature ionic liquids (RTILs) are a class of organic salts that are liquid at room temperature. Their physiochemical properties, including low vapor pressure and wide electrochemical stability window, have expedited their use as electrolytes in many electrochemical applications; yet, the slow transport properties of many RTILs have limited their utility in some applications. This issue is often mitigated by solvating ionic liquids in neutral organic solvents. To date, however, solvent interactions have only been explored for a small number of solvents, particularly acetonitrile and propylene carbonate, at only a few compositions. Here, we use molecular dynamics simulations in the context of a computational screening approach to study mixtures of ionic liquids in many different solvents at a range of concentrations. Building on prior work, we again find that ionic liquid diffusivity increases monotonically with greater solvent concentration. In contrast to prior work, we find that pure solvent diffusivity, not polarity, is the most influential solvent property on mixture behavior. We also explore the concentration dependence of ionic conductivity and find maxima at intermediate concentrations. Experimental conductivity measurements, inspired by the computational screening study, support this observation with qualitatively consistent results. These results can further guide the selection of solvents for electrochemical applications of RTILs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Influence of Nanostructure on the Exciton Dynamics of Multichromophore Donor–Acceptor Block Copolymers

Here, we explore the synthesis and photophysics of nanostructured block copolymers that mimic light-harvesting complexes. We find that the combination of a polar and electron-rich boron dipyrromethene (BODIPY) block with a nonpolar electron-poor perylene diimide (PDI) block yields a polymer that self-assembles into ordered “nanoworms”. Numerical simulations are used to determine optimal compositions to achieve robust self-assembly. Photoluminescence spectroscopy is used to probe the rich exciton dynamics in these systems. Using controls, such as homopolymers and random copolymers, we analyze the mechanisms of the photoluminescence from these polymers. With this understanding it allows us to probe in detail the photophysics of the block copolymers, including the effects of their self-assembly into nanostructures on their excited-state properties. Similar to natural systems, ordered nanostructures result in properties that are starkly different than the properties of free polymers in solution, such as enhanced rates of electronic energy transfer and elimination of excitonic emission from disordered PDI trap states.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Elucidating the structure of the magnesium aluminum chloride complex electrolyte for magnesium-ion batteries

Non-aqueous Mg-ion batteries offer a promising way to overcome safety, costs, and energy density limitations of state-of-the-art Li-ion battery technology. Here, we present a rigorous analysis of the magnesium aluminum chloride complex (MACC) in tetrahydrofuran (THF), one of the few electrolytes that can reversibly plate and strip Mg. We use ab initio calculations and classical molecular dynamics simulations to interrogate the MACC electrolyte composition with the goal of addressing two urgent questions that have puzzled battery researchers: (i) the functional species of the electrolyte, and (ii) the complex equilibria regulating the MACC speciation after prolonged electrochemical cycling, a process termed as conditioning, and after prolonged inactivity, a process called aging. A general computational strategy to untangle the complex structure of electrolytes, ionic liquids and other liquid media is presented. The analysis of formation energies and grand-potential phase diagrams of Mg–Al–Cl–THF suggests that the MACC electrolyte bears a simple chemical structure with few simple constituents, namely the electro-active species MgCl + and AlCl 4 - in equilibrium with MgCl 2 and AlCl 3 . Knowledge of the stable species of the MACC electrolyte allows us to determine the most important equilibria occurring during electrochemical cycling. We observe that Al deposition is always preferred to Mg deposition, explaining why freshly synthesized MACC cannot operate and needs to undergo preparatory conditioning. Similarly, we suggest that aluminum displacement and depletion from the solution upon electrolyte resting (along with continuous MgCl 2 regeneration) represents one of the causes of electrolyte aging. Finally, we compute the NMR shifts from shielding tensors of selected molecules and ions providing fingerprints to guide future experimental investigations.

25 ENERGY STORAGE↗
DOE OSTI2014

Efficient modeling of photonic crystals with local Hermite polynomials

Developing compact algorithms for accurate electrodynamic calculations with minimal computational cost is an active area of research given the increasing complexity in the design of electromagnetic composite structures such as photonic crystals, metamaterials, optical interconnects, and on-chip routing. We show that electric and magnetic (EM) fields can be calculated using scalar Hermite interpolation polynomials as the numerical basis functions without having to invoke edge-based vector finite elements to suppress spurious solutions or to satisfy boundary conditions. This approach offers several fundamental advantages as evidenced through band structure solutions for periodic systems and through waveguide analysis. Compared with reciprocal space (plane wave expansion) methods for periodic systems, advantages are shown in computational costs, the ability to capture spatial complexity in the dielectric distributions, the demonstration of numerical convergence with scaling, and variational eigenfunctions free of numerical artifacts that arise from mixed-order real space basis sets or the inherent aberrations from transforming reciprocal space solutions of finite expansions. The photonic band structure of a simple crystal is used as a benchmark comparison and the ability to capture the effects of spatially complex dielectric distributions is treated using a complex pattern with highly irregular features that would stress spatial transform limits. This general method is applicable to a broad class of physical systems, e.g., to semiconducting lasers which require simultaneous modeling of transitions in quantum wells or dots together with EM cavity calculations, to modeling plasmonic structures in the presence of EM field emissions, and to on-chip propagation within monolithic integrated circuits.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Neutron interaction and their transport with bulk materials

In the current paper an attempt was made to study and provide fundamental information about neutron interactions that are important to nuclear material measurements. The application of this study is explained about macroscopic interactions with bulk compound materials through a program in DEV C++ language which is done by enabling interaction of neutrons in nature. The output of the entire process depends upon the random number (i.e., incident neutron number), thickness of the material and mean free path as input parameters. Further the current study emphasizes on the usage of materials in shielding.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Impact behaviour of Napier/polyester composites under different energy levels

The effects of different energy levels on the impact behaviour of Napier fibre/polyester reinforced composites were investigated. Napier fibre was extracted using traditional water retting process to be utilized as reinforcing materials in polyester composite laminates. 25% fibre loading composite laminates were prepared and impacted at three different energy levels; 2.5,5 and 7.5 J using an instrumented drop weight impact testing machine (IMATEK IM10). The outcomes show that peak force and contact time increase with increased impact load. The energy absorption was then calculated from the force displacement curve. The results indicated that the energy absorption decreases with increasing energy levels of the impact. Impacted specimens were observed visually for fragmentation fracture using an optical camera to identify the failure mechanisms. Fracture fragmentation pattern from permanent dent to perforation with radial and circumferential was observed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗