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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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This public dataset contains openly-documented, machine readable digital research data corresponding to figures published in C.E. Schaefer et al., “Modeling of Injected Current Stream-Induced 3D Perturbations in Local Helicity Injection Plasmas,” accepted for publication in Physics of Plasmas.
Carbon Capture and Storage (CCS) represents a technology aimed to reduce atmospheric loading of CO 2 from power plants and heavy industries by injecting it into deep geological formations, such as saline aquifers. A number of trapping mechanisms contribute to effective and secure storage of the injected CO 2 in supercritical fluid phase (scCO 2 ) in the formation over the long term. The primary trapping mechanisms are structural, residual, dissolution and mineralization. Knowledge gaps exist on how the heterogeneity of the formation manifested at all scales from the pore to the site scales affects trapping and parameterization of contributing mechanisms in models. An experimental and modeling study was conducted to fill these knowledge gaps. Experimental investigation of fundamental processes and mechanisms in field settings is not possible as it is not feasible to fully characterize the geologic heterogeneity at all relevant scales and gathering data on migration, trapping and dissolution of scCO 2 . Laboratory experiments using scCO 2 under ambient conditions are also not feasible as it is technically challenging and cost prohibitive to develop large, two- or three-dimensional test systems with controlled high pressures to keep the scCO 2 as a liquid. Hence, an innovative approach that used surrogate fluids in place of scCO 2 and formation brine in multi-scale, synthetic aquifers test systems ranging in scales from centimeter to meter scale developed used. New modeling algorithms were developed to capture the processes controlled by the formation heterogeneity, and they were tested using the data from the laboratory test systems. The results and findings are expected to contribute toward better conceptual models, future improvements to DOE numerical codes, more accurate assessment of storage capacities, and optimized placement strategies. This report presents the experimental and modeling methods and research results.
Graph signal processing (GSP) has emerged as a powerful tool for practical network applications, including power system monitoring. By representing power system voltages as smooth graph signals, recent research has focused on developing GSP-based methods for state estimation, attack detection, and topology identification. Included, efficient methods have been developed for detecting false data injection (FDI) attacks, which until now were perceived as non-smooth with respect to the graph Laplacian matrix. Consequently, these methods may not be effective against smooth FDI attacks. In this paper, we propose a graph FDI (GFDI) attack that minimizes the Laplacian-based graph total variation (TV) under practical constraints. In addition, we develop a low-complexity algorithm that solves the non-convex GDFI attack optimization problem using ell_1-norm relaxation, the projected gradient descent (PGD) algorithm, and the alternating direction method of multipliers (ADMM). We then propose a protection scheme that identifies the minimal set of measurements necessary to constrain the GFDI output to high graph TV, thereby enabling its detection by existing GSP-based detectors. Our numerical simulations on the IEEE-57 bus test case reveal the potential threat posed by well-designed GSP-based FDI attacks. Moreover, we demonstrate that integrating the proposed protection design with GSP-based detection can lead to significant hardware cost savings compared to previous designs of protection methods against FDI attacks.
Laser wakefield acceleration of electron bunches possessing various initial injection energies in capillary waveguides is studied in the conditions of an asymmetric input of laser radiation into a waveguide (the propagation direction of laser radiation deviates from the capillary axis or the laser spot is not symmetric). The factors determining the critical angle of the laser radiation input into the capillary, within which the wake acceleration of electrons is close to optimal, have been found. It is shown that in acceleration stages where electron energies are high, the requirements to angular concentricity of the capillary axis and laser radiation focusing are substantially weaker due to the relativistic 'weighting' of the electron mass. (extreme laser radiation: physics and fundamental applications)
Engine knock remains one of the major barriers to further improvement in thermal efficiency of Direct-Injection Spark-Ignition (DISI) engines. While Research Octane Number and Motor Octane Number are often used as standard rating methods for knock resistance of fuels, the impacts of other fuel properties on knock propensity in modern engines such as heat of vaporization (HoV) and laminar flame speed (LFS) require better understanding in order to co-optimize fuels and engine designs to achieve higher thermal efficiency and lower CO2 emission. In the present study, computational fluid dynamics (CFD) is used to model a boosted DISI engine with a focus on knock prediction and fuel property effects. A level-set G-equation model is employed to capture turbulent premixed combustion, and is coupled with a transported Livengood-Wu (L-W) integral approach to predict autoignition in the unburnt region. A criterion associated with the L-W integral is developed to accurately predict knock onset and knock-limited spark-advance. This model is then applied to a sensitivity analysis of HoV and LFS on knock tendency and thermal efficiency. The pressure-temperature trajectory framework is applied and extended to study the fuel effects on auto-ignition process in the engine. An existing efficiency-based merit function, which is derived from experiments for boosted SI engines, is evaluated and improved based on the current CFD results.
Photoinduced electron transfer from a molecular chromophore into mesoporous metal oxide substrates occurs efficiently from both singlet and triplet excited states for traditional metal-to-ligand charge transfer (MLCT) dyes. All organic molecules that undergo singlet fission (SF), the process of generating two triplet excitons with photoexcitation of one singlet exciton, offers a promising method of generating two charges per one absorbed photon. However, the singlet-triplet energy balance required to drive SF often places the driving force for triplet electron injection into metal oxides too low. As such, the dynamics of singlet vs. triplet electron transfer for SF in dye-sensitized solar cell configurations has not been elucidated. Here, we use applied bias transient absorption spectroscopy to modulate the driving force for triplet electron transfer from an anthradithiophene-based SF chromophore (ADT-COOH) adsorbed to mesoporous indium tin oxide (nanoITO).
Diesel engines are an important technology for transportation of both people and goods. However, historically they have suffered a significant downside of high soot and nitrogen oxides (NO x ) emissions. Recently, ducted fuel injection (DFI) has been demonstrated to attenuate soot formation in compression-ignition engines and combustion vessels by 50% to 100%. This allows for diesel engines to be run at low-NO x emissions that would have otherwise produced significantly more soot due to the soot/NO x tradeoff. Currently the root causes of this soot attenuation are not well understood. To be able to better optimize DFI for use across a variety of engines and conditions, it is important to understand clearly how it works. This study expands on the current understanding of DFI by using numerical modeling under nonreacting conditions to provide insights about the roles of entrainment and mixing that would have been much more challenging to obtain experimentally. We report this study found that DFI enhances charge gas entrainment upstream of the duct and blocks entrainment inside of the duct. Mixing is enhanced by the duct, which results in lower peak equivalence ratios at the exit of the duct.
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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.
Converting biomass to a useful fuel commonly incorporates the pyrolysis of the biomass feed stock. The base liquid fraction usually contains high concentrations of ketones, aldehydes and carboxylic acids, of which each can cause detrimental issues related to the storage and upgrading process. Knowing the carbonyl species and the concentration of each will provide value information to the pyrolysis researchers, specifically as that community branches into more targeted end-products such as jet fuel or biogenic-derived oxygenate-containing fuel products. The analysis of aldehydes, ketones and small alkyl carboxylic acids using 2,4-dinitrophenylhydrazine (DNPH) derivation method has been well documented and the method is commonly used the analytical community. By using liquid chromatograph coupled to tandem mass spectrometry, biomass sample analysis can be complete with identification of most carbonyl species. The issue of identifying isobaric ketone and aldehyde compounds can be resolved by utilizing differences in retention time or characteristic fragment ions of ketones and aldehydes. One issue which could not resolved using published methods was identifying aromatic or large non-aromatic carboxylic acids from their corresponding hydroxyl aldehyde or ketone analogs. By modifying the current method for determining carbonyls in biomass samples, carboxylic and hydroxyl-carbonyl can be determined. A careful adjustment of the pH during the extraction procedure and extended heating time of the DNPH solution allowed for the successful derivation of aromatic carboxylic acids. Like other dinitrophenylhydrazones, carboxylic acid derivatives also produce a unique secondary ion pattern, which was useful to distinguish these species from the non-acid analogs.
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