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.
Explore the source record for details and available documents.
An investigation was conducted in the Langley 20-inch Mach 6 tunnel to define the aerodynamic heat transfer to the radioisotope fuel cask (heat source) of the SNAP-19/Pioneer power system. The shape of the SNAP-19/Pioneer heat source is that of a hexagonal prism with flat ends; the fineness ratio, based on maximum (edge to edge) diameter, is 1.61. Phase-change-paint heat-transfer data and schlieren photographs were obtained on four possible 1/2-scale entry configurations of the SNAP-19/Pioneer heat source. Tests were conducted over a wide range of attitudes and at nominal Reynolds numbers, based on the length of the unablated configuration, of 33,000; 84,000; and 2,200,000.
Distribution storage networks are interconnected distribution-level bulk energy storage devices that function as core infrastructure elements to provide both grid resilience and defense against IoT-based cyber grid security threats. The recognition that these are core grid functions changes how storage of this type valued, financed, and operated as compared to storage for ancillary grid services. Distribution storage networks make use of fast bilateral storage to buffer the grid from edge device-based volatilities; this is the essence of both its resilience capability and its cyber defense capability. These storage networks must be core grid components because resilience is an intrinsic grid characteristic and both it and cyber defense of the distribution grid are the responsibility of the distribution utility.
A Chern insulator is a two-dimensional material that hosts chiral edge states produced by the combination of topology with time reversal symmetry breaking. Such edge states are perfect one-dimensional conductors, which may exist not only on sample edges, but on any boundary between two materials with distinct topological invariants (or Chern numbers). Engineering of such interfaces is highly desirable due to emerging opportunities of using topological edge states for energy-efficient information transmission. Here, we report a chiral edge-current divider based on Chern insulator junctions formed within the layered topological magnet MnBi 2 Te 4 . We find that in a device containing a boundary between regions of different thickness, topological domains with different Chern numbers can coexist. At the domain boundary, a Chern insulator junction forms, where we identify a chiral edge mode along the junction interface. We use this to construct topological circuits in which the chiral edge current can be split, rerouted, or switched off by controlling the Chern numbers of the individual domains. Our results demonstrate MnBi 2 Te 4 as an emerging platform for topological circuits design.
The effect of velocity gradient on stagnation region heat transfer augmentation by free stream turbulence was investigated. Heat transfer was measured in the stagnation region of four models with elliptical leading edges with ratios of major to minor axes of 1:1, 1.5:1, 2.25:1, and 3:1. Four geometrically similar, square bar, square mesh, biplane grids were used to generate free stream turbulence with different intensities and length. Heat transfer measurements were made for the following ranges of parameters: Reynolds number, based on leading edge diameter, 37,000 to 228,000; dimensionless leading edge velocity gradient, 1.20 to 1.80; turbulence intensity, 1.1 to 15.9%; and length scale to leading edge diameter ratio, 0.05 to 0.30. Stagnation point heat transfer augmentation by free stream turbulence can be predicted using a modified version of a previously developed correlation for a circular leading edge. Heat transfer augmentation was independent of body shape at the stagnation point. The heat transfer distribution down-stream from the stagnation point can be predicted using the normalized laminar heat transfer distribution.
The composition and oxidation state of aerosol iron were examined using synchrotron-based iron near-edge X-ray absorption spectroscopy. By combining synchrotron-based techniques with water leachate analysis, impacts of oxidation state and mineralogy on aerosol iron solubility were assessed for samples taken from multiple locations in the Southern and the Atlantic Oceans; and also from Noida (India), Bermuda, and the Eastern Mediterranean (Crete). These sampling locations capture iron-containing aerosols from different source regions with varying marine, mineral dust, and anthropogenic influences. Across all locations, pH had the dominating influence on aerosol iron solubility. When aerosol samples were approximately neutral pH, iron solubility was on average 3.4%; when samples were below pH 4, the iron solubility increased to 35%. This observed aerosol iron solubility profile is consistent with thermodynamic predictions for the solubility of Fe(III) oxides, the major iron containing phase in the aerosol samples. Source regions and transport paths were also important factors affecting iron solubility, as samples originating from or passing over populated regions tended to contain more soluble iron. Although the acidity appears to affect aerosol iron solubility globally, a direct relationship for all samples is further confounded by factors such as anthropogenic influence, aerosol buffer capacity, mineralogy and physical processes.
EDGE bioinformatics was developed to help biologists process Next Generation Sequencing data (in the form of raw FASTQ files), even if they have little to no bioinformatics expertise. EDGE is a highly integrated and interactive web-based platform that is capable of running many of the standard analyses that biologists require for viral, bacterial/archaeal, and metagenomic samples. EDGE provides the following analytical workflows: quality trimming and host removal, assembly and annotation, comparisons against known references, taxonomy classification of reads and contigs, whole genome SNP-based phylogenetic analysis, and PCR analysis. EDGE provides an intuitive web-based interface for user input, allows users to visualize and interact with selected results (e.g. JBrowse genome browser), and generates a final detailed PDF report. Results in the form of tables, text files, graphic files, and PDFs can be downloaded. A user management system allows tracking of an individual’s EDGE runs, along with the ability to share, post publicly, delete, or archive their results.
EDGE bioinformatics was developed to help biologists process Next Generation Sequencing data (in the form of raw FASTQ files), even if they have little to no bioinformatics expertise. EDGE is a highly integrated and interactive web-based platform that is capable of running many of the standard analyses that biologists require for viral, bacterial/archaeal, and metagenomic samples. EDGE provides the following analytical workflows: quality trimming and host removal, assembly and annotation, comparisons against known references, taxonomy classification of reads and contigs, whole genome SNP-based phylogenetic analysis, and PCR analysis. EDGE provides an intuitive web-based interface for user input, allows users to visualize and interact with selected results (e.g. JBrowse genome browser), and generates a final detailed PDF report. Results in the form of tables, text files, graphic files, and PDFs can be downloaded. A user management system allows tracking of an individual’s EDGE runs, along with the ability to share, post publicly, delete, or archive their results.
Aquatic microalgae are a highly promising feedstock for the production of biocrude and tailored biofuels, with distinct advantages over traditional terrestrial crops, such as reduced land use and avoidance of food production competition. However, unlocking their full potential requires the development of biofuels with low life cycle greenhouse emissions biofuels, such as algae biofuels, which can significantly reduce the environmental impact of the transportation systems without requiring a complete overhaul of existing engine technology. In this study, we employ cutting-edge data-based AI modeling techniques to optimize the performance of heavy-duty engines, with a focus on transitioning towards biofuels with low life cycle greenhouse emissions biofuels. Our methodology offers significant advantages over traditional sweep testing, enabling efficient and accurate optimization of engine performance with minimal time and resources consumption. Our findings demonstrate the potential of utilizing this approach, with up to 55% NOx emissions reductions and up to 2% reduction in fuel consumption compared to the baseline optimized point. Moving forward, we plan to utilize a 30% blend of algae biofuels with diesel fuel, with the ultimate goal of achieving up to 60% lifecycle GHG emissions. Lastly, we plan to compare the results with 100% renewable biodiesel to add an additional dimension of investigating the impact of fuel chemistry on engine optimization. Overall, this study underscores the vital importance of biofuels for reducing the carbon footprint of the transportation sector and supporting a sustainable future. By harnessing the power of data-based AI modeling with low life cycle greenhouse emissions biofuels, we can accelerate the adoption of more environmentally friendly transportation systems and reduce their impact on the planet. Our findings contribute to this transition and offer insights for developing efficient and effective strategies for addressing global climate change.
Transition metal cation intercalation between the layers of two-dimensional (2D) metal halides is an underexplored research area. In this work we focus on the synthesis and physical property characterizations of two layered hybrid lead halides: a new compound [Cu(O 2 C–CH 2 –NH 2 ) 2 ]Pb 2 Br 4 and the previously reported [Cu(O 2 C–(CH 2 ) 3 –NH 3 ) 2 ]PbBr 4 . These compounds exhibit 2D layered crystal structures with incorporated Cu 2+ between the metal halide layers, which is achieved by combining Cu(II) and lead bromide with suitable amino acid precursors. The resultant [Cu(O 2 C–(CH 2 ) 3 –NH 3 ) 2 ]PbBr 4 adopts a 2D layered perovskite structure, whereas the new compound [Cu(O 2 C–CH 2 –NH 2 ) 2 ]Pb 2 Br 4 crystallizes with a new structure type based on edge-sharing dodecahedral PbBr5O3 building blocks. [Cu(O 2 C–CH 2 –NH 2 ) 2 ]Pb 2 Br 4 is a semiconductor with a bandgap of 3.25 eV. It shows anisotropic charge transport properties with a semiconductor resistivity of 1.44 × 10 10 Ω cm (measured along the a-axis) and 2.17 × 10 10 Ω cm (along the bc-plane), respectively. The fabricated prototype detector based on this material showed response to soft low-energy X-rays at 8 keV with a detector sensitivity of 1462.7 μCGy –1 cm –2 , indicating its potential application for ionizing radiation detection. Finally, these encouraging results are discussed together with the results from density functional theory calculations, optical, magnetic, and thermal property characterization experiments.
The plateau-to-plateau transition in quantum Hall effect under high magnetic fields is a celebrated quantum phase transition between two topological states. It can be achieved by either sweeping the magnetic field or tuning the carrier density. The recent realization of the quantum anomalous Hall (QAH) insulators with tunable Chern numbers introduces the channel degree of freedom to the dissipation-free chiral edge transport and makes the study of the quantum phase transition between two topological states under zero magnetic field possible. Here, we synthesized the magnetic topological insulator (TI)/TI pentalayer heterostructures with different Cr doping concentrations in the middle magnetic TI layers using molecular beam epitaxy. By performing transport measurements, we found a potential plateau phase transition between C=1 and C=2 QAH states under zero magnetic field. In tuning the transition, the Hall resistance monotonically decreases from h/e2 to h/2e 2 , concurrently, the longitudinal resistance exhibits a maximum at the critical point. Our results show that the ratio between the Hall resistance and the longitudinal resistance is greater than 1 at the critical point, which indicates that the original chiral edge channel from the C=1 QAH state coexists with the dissipative bulk conduction channels. Subsequently, these bulk conduction channels appear to self-organize and form the second chiral edge channel in completing the plateau phase transition. Our study will motivate further investigations of this novel Chern number change-induced quantum phase transition and advance the development of the QAH chiral edge current-based electronic and spintronic devices.
The development of a ground based direct detection Doppler lidar based on the recently described aerosol double edge technique is reported. A pulsed, injection seeded Nd:YAG laser operating at 1064 nm is used to make range resolved measurements of atmospheric winds in the free troposphere. The wind measurements are determined by measuring the Doppler shift of the laser signal backscattered from atmospheric aerosols. The lidar instrument and double edge method are described and initial tropospheric wind profile measurements are presented. Wind profiles are reported for both day and night operation. The measurements extend to altitudes as high as 14 km and are compared to rawinsonde wind profile data from Dulles airport in Virginia. Vertical resolution of the lidar measurements is 330 m and the rms precision of the measurements is a low as 0.6 m/s.
Purpose: To investigate the efficacy of a new family of measurements made on individual pulmonary cysts extracted from computed tomography (CT) for assessing the severity of lymphangioleiomyomatosis (LAM). Methods: CT images were analyzed using thresholding to identify a cystic region of interest from chest CT of LAM patients. Individual cysts were then extracted from the cystic region by the watershed algorithm, which separates individual cysts based on subtle edges within the cystic regions. A family of measurements were then computed, which quantify the amount, distribution, and boundary appearance of the cysts. Sequential floating feature selection was used to select a small subset of features for quantification of the severity of LAM. Adjusted R{sup 2} from multiple linear regression and R{sup 2} from linear regression against measurements from spirometry were used to compare the performance of our proposed measurements with currently used density based CT measurements in the literature, namely, the relative area measure and the D measure. Results: Volumetric CT data, performed at total lung capacity and residual volume, from a total of 49 subjects enrolled in the MILES trial were used in our study. Our proposed measures had adjusted R{sup 2} ranging from 0.42 to 0.59 when regressing against the spirometry measures, with p < 0.05. For previously used density based CT measurements in the literature, the best R{sup 2} was 0.46 (for only one instance), with the majority being lower than 0.3 or p > 0.05. Conclusions: The proposed family of CT-based cyst measurements have better correlation with spirometric measures than previously used density based CT measurements. They show potential as a sensitive tool for quantitatively assessing the severity of LAM.
Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used to reconstruct subsurface velocity images, however it is time consuming, has high computational cost and depend heavily on human interaction. Recently, deep learning has accelerated it’s use in several data-driven techniques, however most deep learning techniques suffer from overfitting and stability issues. In this work, we propose an edge computing-based data-driven inversion technique based on supervised deep convolutional neural network to accurately reconstruct the subsurface velocities. Deep learning based data-driven technique depends mostly on bulk data training. In this work, we train our deep convolutional neural network (DCN) (UNet and InversionNet) on the raw seismic data and their corresponding velocity models during the training phase to learn the non-linear mapping between the seismic data and velocity models. The trained network is then used to estimate the velocity models from new input seismic data during the prediction phase. The prediction phase is performed on a resource-constrained edge device such as Raspberry Pi. Raspberry Pi provides real-time and on-device computational power to execute the inference process. In addition, we demonstrate robustness of our models to perform inversion in the presence on noise by performing both noise-aware and no-noise training and feeding the resulting trained models with noise at different signal-to-noise (SNR) ratio values. We make great efforts to achieve very feasible inference times on the Raspberry Pi for both models. Specifically, the inference times per prediction for UNet and InversionNet models on Raspberry Pi were 22 and 4 s respectively whilst inference times for both models on the GPU were 2 and 18 s which are very comparable. Finally, we have designed a user-friendly interactive graphical user interface (GUI) to automate the model execution and inversion process on the Raspberry Pi.
A method based on integral techniques is presented to determine the turbulent separation location and separation pressure for trailing edge stall airfoils exhibiting extensive separated flow regions during prestall and poststall conditions. The method and experimental laser velocimetry data in the turbulence amplification region are used to establish a relationship between fluctuating velocity components and mean velocity in the boundary layer. The method is demonstrated for symmetrical, cambered, conventional and supercritical airfoils in the 5-to-21-percent thickness ratio range, and over a Reynolds number range of 2 to 22 million. Good agreement with experimental data is found, and the method is used in conjunction with the NASA Multi-Component Airfoil Computer Program to predict maximum L/D ratios and maximum section/lift coefficients.
Graphene holds great promise for replacing conventional Si material in field effect transistors (FETs) due to its high carrier mobility. Previously proposed graphene FETs either suffer from low ON-state current resulting from constrained channel width or require complex fabrication processes for edge-defecting or doping. Here, we propose an alternative graphene FET structure created on intrinsic metallic armchair-edged graphene nanoribbons with uniform width, where the channel region is made semiconducting by drilling a pore in the interior, and the two ends of the nanoribbon act naturally as connecting electrodes. The proposed GNP-FETs have high ON-state currents due to seamless atomic interface between the channel and electrodes and are able to be created with arbitrarily wide ribbons. In addition, the performance of GNP-FETs can be tuned by varying pore size and ribbon width. As a result, their performance and fabrication process are more predictable and controllable in comparison to schemes based on edge-defects and doping. Using first-principle transport calculations, we show that GNP-FETs can achieve competitive leakage current of ∼70 pA, subthreshold swing of ∼60 mV/decade, and significantly improved On/Off current ratios on the order of 10{sup 5} as compared with other forms of graphene FETs.
High capital expenditures associated with manufacturing thin silicon wafers make it difficult for the industry to scale up and prevent novel technologies from entering the market. Thin wafers fail largely due to breakage during solar cell processing and handling. One of the root causes for breakage is sub-mm edge cracks in the silicon wafer, and these cracks cannot be reliably detected by most commercially-available crack detection tools. In this work, we first investigate the correlation between wafer thickness and critical crack length, and explain the importance of detecting sub-500-μm edge cracks as the wafer thickness is reduced. Secondly, we extend our previous work of micro-crack detection to demonstrate an edge illumination technique using a near-infrared laser to image edge cracks less than 500 μm in length in multicrystalline silicon. Thirdly, we investigate two fundamental edge illumination mechanisms based on dark-field imaging; namely, direct and vicinal illumination. We will then compare these methods to a state-of-the-art rear illumination method. The advantages and disadvantages of both illumination methods are presented and provide an in-depth analysis of light-crack interaction. In particular, we find that the robustness of vicinal illumination is due to diffuse reflectance. The diffuse reflectance has less dependence on crack configurations, while direct illumination has more dependence on the crack configurations because it utilizes specular reflectance. Our results show that our proposed prototype can detect sub-mm edge cracks in multicrystalline silicon wafers, which is an important step in enabling thin silicon wafer manufacturing.
Means of obtaining improved dimensional measurements from neutron radiographs of nuclear fuel elements are discussed. The use of video-electronic image analysis relative to edge definition in radiographic images is described. Based on this study, an edge definition criterion is proposed for overcoming image unsharpness effects in taking accurate diametral measurements from radiographs. An electronic density slicing method for automatic edge definition is described. Results of measurements made with video micrometry are compared with scanning microdensitometer and micrometric physical measurements. An image quality indicator for estimating photographic and geometric unsharpness is described.