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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Construction and application of analytical model of wet vapor turbine blad erosion
Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.
Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO 2 ] and methane [CH 4 ]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. Furthermore, this approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO 2 and CH 4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).
Wind turbines are complex systems where component-level changes can have significant system-level effects. Effective wind turbine optimization generally requires an integrated analysis approach with a large number of design variables. Optimizing across large variable sets is orders of magnitude more efficient with gradient-based methods as compared with gradient-free method, particularly when using exact gradients. We have developed a wind turbine analysis set of over 100 components where 90% of the models provide numerically exact gradients through symbolic differentiation, automatic differentiation, and adjoint methods. This framework is applied to a specific design study focused on downwind land-based wind turbines. Downwind machines are of potential interest for large wind turbines where the blades are often constrained by the stiffness required to prevent a tower strike. The mass of these rotor blades may be reduced by utilizing a downwind configuration where the constraints on tower strike are less restrictive. The large turbines of this study range in power rating from 5-7MW and in diameter from 105m to 175m. The changes in blade mass and power production have important effects on the rest of the system, and thus the nacelle and tower systems are also optimized. For high-speed wind sites, downwind configurations do not appear advantageous. The decrease in blade mass (10%) is offset by increases in tower mass caused by the bending moment from the rotor-nacelle-assembly. For low-wind speed sites, the decrease in blade mass is more significant (25-30%) and shows potential for modest decreases in overall cost of energy (around 1-2%).
Sediment-associated natural organic matter (NOM) is a complex assemblage of organic molecules with a wide range of sizes, functional groups, and structures, which is intricately associated with mineral particles. Organic carbon (OC) concentrations in subsurface sediments are typically 10 to 200 times lower than in surface soils, posing a distinct challenge for characterization. A range of chemical extractions were evaluated for extraction of NOM, and a NOM extraction scheme was developed using a combination of sequential extraction with water (MQ) and sodium pyrophosphate at pH 10 (PP), and purification by dialysis and solid phase extraction in order to isolate different fractions of sediment-associated NOM. Analysis of these different NOM fractions was then carried out by Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible (UV-Vis) spectroscopy, and electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICR-MS). The OC extraction efficiency of the tested extractions followed the order PP>NaOH>HCl=hydroxylamine hydrochloride>dithionite. Greater concentrations of OC in PP vs NaOH extracts suggest that metal complexation and/or ligand exchange plays an important role in OC stabilization. Characterization of different pools of extraction NOM by FITR shows that the water soluble fraction has a higher fraction of aliphatic and carboxylic groups, while the PP fractions have higher C=C groups. This trend from aliphatic to more aromatic is also supported by the UV-Vis and ESI-FTICR-MS data.
Linear eigenmode analysis often fails to describe turbulence in model systems that have non-normal linear operators and thus nonorthogonal eigenmodes, which can cause fluctuations to transiently grow faster than expected from eigenmode analysis. When combined with energetically conservative nonlinear mode mixing, transient growth can lead to sustained turbulence even in the absence of eigenmode instability. Since linear operators ultimately provide the turbulent fluctuations with energy, it is useful to define a growth rate that takes into account non-modal effects, allowing for prediction of energy injection, transport levels, and possibly even turbulent onset in the subcritical regime. We define such a non-modal growth rate using a relatively simple model of the statistical effect that the nonlinearities have on cross-phases and amplitude ratios of the system state variables. In particular, we model the nonlinearities as delta-function-like, periodic forces that randomize the state variables once every eddy turnover time. Furthermore, we estimate the eddy turnover time to be the inverse of the least stable eigenmode frequency or growth rate, which allows for prediction without nonlinear numerical simulation. We test this procedure on the 2D and 3D Hasegawa-Wakatani model [A. Hasegawa and M. Wakatani, Phys. Rev. Lett. 50, 682 (1983)] and find that the non-modal growth rate is a good predictor of energy injection rates, especially in the strongly non-normal, fully developed turbulence regime.
This procedure covers the determination of the percent 14 C in biogenic gasoline, jet fuel, and diesel range hydrocarbons prepared from distillation of hydrotreated pyrolysis oils and blended with fossil derived hydrocarbons (e.g. fossil diesel, gasoline, jet, or toluene). Either an internal 14C standard or a color quench curve is used to correct for potential signal quenching effects. Recent publications provide some background of direct LSC methods used for determination of biogenic fuel content.
One of the main limitations in the use of operando scanning transmission electron microscopes to study dynamic chemical processes is the effect of the electron beam on the kinetics of the reaction being observed. Here we demonstrate that a flexible Gaussian mixture model can be used to extract quantitative information directly from sub-sampled images, i.e.~images where not all the pixels in the image are illuminated with the beam. The use of this method is demonstrated for the charge/discharge cycle of a Li-battery, where the deposition and dissolution of Li at the anode can be accurately quantified for sub-sampling levels down to 1%. Performing operando imaging using a small fraction of the pixels means that the observations can significantly reduce the effect of the beam, automatically increase the imaging speed and decrease the total data transfer rate required. Such new software capabilities offer the potential to significantly widen the application of operando hardware approaches to study nanoscale dynamics in materials.
Kesterite Cu 2 ZnSn(S,Se) 4 (CZTSSe) is a sustainable material for thin‐film photovoltaics with device efficiencies greater than 12% have been demonstrated. Despite similar crystal structure and polycrystalline film microstructures, there is widespread evidence for larger‐amplitude potential and bandgap fluctuations in CZTS than in the analogous Cu(In,Ga)Se 2 (CIGSe) chalcopyrite material. This disorder is believed to account for a sizable part of the larger open‐circuit voltage ( V OC ) deficit in CZTS devices, yet the detailed origins and length scales of these fluctuations have not been fully elucidated. Herein, we present a transmission electron microscopy study focusing on composition variation within bulk multicrystals of CZTS grown by the travelling heater method (THM). In these slow‐cooled, solution grown crystals we find direct evidence for spatial composition fluctuations of amplitude <1 at.% (∼5 × 10 20 cm −3 ) and thus, explainable by point defects. However, rather than being homogeneously‐distributed we find a characteristic 20 nm length scale for these fluctuations, which sets a definite length scale for band gap and potential fluctuations. At Σ3 grain boundaries, we find no evidence of composition variation compared to the bulk. The finding highlights such variations reported at grain boundaries in polycrystalline thin‐films are direct consequences of processing methods and not intrinsic properties of CZTS itself.
Abstract not provided.
Not provided.
Not Available
Not Available
Thermal ionization mass spectrometry (TIMS) has been evaluated for the detection of the radioactive isotopes of strontium and cesium. The instrument was investigated for the analysis of isotopic ratios of 89Sr to 90Sr and 135Cs to 137Cs in the presence of atomic isobars (Y and Zr for Sr analysis and Ba for Cs analysis). Signicant discrimination using TIMS was found for both Sr and Cs through evaluation using stable isotopes. Fusion procedures with different flux were evaluated for preparation of sample matrices. Chemical separations were developed and used after fusion to isolate the Sr and Cs from a prepared background matrix (i.e., Montana Soil). A graded approach, increasing in stable background isotopes, was performed to monitor the chemistry and instrument response. A thermal neutron irradiation was performed to produce a sample that contained the radioisotopes of Sr and Cs. Even though the irradiated sample was not a certified standard it does provide accurate expectation values via published, well understood cumulative fission yield data. The results presented in this work correlate extremely well with the published data. Furthermore, a significant decrease in uncertainty (by a factor of 5 { 10) was found for the isotopic ratios measured with TIMS compared to the published values.
Not Available