Search NASA⌕ Search

SEARCH · Search NASA

Results for “Class generation”

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.

At least 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Entanglement Requirements for Coherent Enhancement in Detectors

Coherent enhancement is a powerful mechanism for improving the sensitivity of a wide range of detectors, but its practical use is often limited by the difficulty of preparing the required quantum states. We show that this difficulty has a fundamental origin: coherent enhancement of a signal interacting with a detector is quantitatively constrained by entanglement. We prove general bounds on how the strength of coherent effects can scale with system size, as a function of the single-mode entanglement entropy of the detector. These bounds smoothly interpolate between the incoherent and fully coherent regimes, and apply both to parameter-estimation problems and to scattering processes. We discuss these results from two complementary perspectives: First, they appear as bounds on the quantum Fisher information of many-body states, which translate directly into limits on parameter sensitivity via the quantum Cramér-Rao bound. Second, they can be interpreted as limits on a class of scattering cross sections, leading to predictions for how minimum detectable interaction strengths scale with target size. Together, these results provide a unified view of coherent enhancement in metrology and scattering experiments, and motivate the development of new techniques for generating entangled detector states.

Bogorad, Zachary [Fermilab] (ORCID:000000019913647↗
NASA NTRS1992

An improved computational technique for calculating electromagnetic forces and power absorptions generated in spherical and deformed body in levitation melting devices

An improved computational technique for calculating the electromagnetic force field, the power absorption and the deformation of an electromagnetically levitated metal sample is described. The technique is based on the volume integral method, but represents a substantial refinement; the coordinate transformation employed allows the efficient treatment of a broad class of rotationally symmetrical bodies. Computed results are presented to represent the behavior of levitation melted metal samples in a multi-coil, multi-frequency levitation unit to be used in microgravity experiments. The theoretical predictions are compared with both analytical solutions and with the results of previous computational efforts for the spherical samples and the agreement has been very good. The treatment of problems involving deformed surfaces and actually predicting the deformed shape of the specimens breaks new ground and should be the major usefulness of the proposed method.

Zong, Jin-Ho↗
NASA NTRS1992

An Improved Computational Technique for Calculating Electromagnetic Forces and Power Absorptions Generated in Spherical and Deformed Body in Levitation Melting Devices

An improved computational technique for calculating the electromagnetic force field, the power absorption and the deformation of an electromagnetically levitated metal sample is described. The technique is based on the volume integral method, but represents a substantial refinement; the coordinate transformation employed allows the efficient treatment of a broad class of rotationally symmetrical bodies. Computed results are presented to represent the behavior of levitation melted metal samples in a multi-coil, multi-frequency levitation unit to be used in microgravity experiments. The theoretical predictions are compared with both analytical solutions and with the results or previous computational efforts for the spherical samples and the agreement has been very good. The treatment of problems involving deformed surfaces and actually predicting the deformed shape of the specimens breaks new ground and should be the major usefulness of the proposed method.

Zong, Jin-Ho↗
DOE OSTI2015

A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing

Radiative heat transfer is an important mechanism in a class of challenging engineering and research problems. A direct all-to-all treatment of these problems is prohibitively expensive on large core counts due to pervasive all- to-all MPI communication. The massive heat transfer problem arising from the next generation of clean coal boilers being modeled by the Uintah framework has radiation as a dominant heat transfer mode. Reverse Monte Carlo ray tracing (RMCRT) can be used to solve for the radiative-flux divergence while accounting for the effects of participating media. The ray tracing approach used here replicates the geometry of the boiler on a multi-core node and then uses an all-to-all communication phase to distribute the results globally. The cost of this all-to-all is reduced by using an adaptive mesh approach in which a fine mesh is only used locally, and a coarse mesh is used elsewhere. Here, a model for communication and computation complexity is used to predict performance of this new method. We show this model is consistent with observed results and demonstrate excellent strong scaling to 262K cores on the DOE Titan system on problem sizes that were previously computationally intractable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

An Activity-Based Sensing Approach to Monitor Nanomaterial-Promoted Changes in Labile Metal Pools in Living Systems

Metal-based nanoparticles are a promising class of materials for diagnosis and treatment of cancer and other diseases. However, mechanisms of action of these nanomedicines remain insufficiently understood due in large part to our limited understanding of the dynamic equilibria between solid metal nanoparticles and labile metal ions generated from these nanoparticles within complex biological milieus. Here, we apply activitybased sensing to directly identify and investigate the fate of labile copper pools with metal and oxidation state-specificity generated by anticancer copper nanomedicines. We found that treatment of cells with copper-releasing nanoparticles alter labile Cu(I)/Cu(II) ratios through an increase in labile Cu(II), while overall labile copper levels decrease. Labile copper release triggers compensatory responses in two major antioxidant pathways, glutathione (GSH) and nuclear factor erythroid 2-related factor 2 (NRF2), as well as in metal homeostasis to limit copper availability via regulation of copper export (ATP7B) and copper import (CTR1) proteins. These findings establish the value of activity-based sensing as a generalizable approach for labile metal imaging to help decipher molecular mechanisms of bioactive metal nanoparticles and guide the development of more effective nanomedicine diagnostics and therapies to target metal-dependent disease vulnerabilities.

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

Data-driven design of metal–organic frameworks for wet flue gas CO 2 capture

Limiting the increase of CO 2 in the atmosphere is one of the largest challenges of our generation. Because carbon capture and storage is one of the few viable technologies that can mitigate current CO 2 emissions, much effort is focused on developing solid adsorbents that can efficiently capture CO 2 from flue gases emitted from anthropogenic sources. One class of materials that has attracted considerable interest in this context is metal-organic frameworks (MOFs), in which the careful combination of organic ligands with metal-ion nodes can, in principle, give rise to innumerable structurally and chemically distinct nanoporous MOFs. However, many MOFs that are optimized for the separation of CO 2 from nitrogen do not perform well when using realistic flue gas that contains water, because water competes with CO 2 for the same adsorption sites and thereby causes the materials to lose their selectivity. Although flue gases can be dried, this renders the capture process prohibitively expensive. Here we show that data mining of a computational screening library of over 300,000 MOFs can identify different classes of strong CO 2 -binding sites-which we term 'adsorbaphores'-that endow MOFs with CO 2 /N 2 selectivity that persists in wet flue gases. We subsequently synthesized two water-stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials. Testing the performance of these MOFs in an industrial setting and consideration of the full capture process-including the targeted CO 2 sink, such as geological storage or serving as a carbon source for the chemical industry-will be necessary to identify the optimal separation material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Luminescent MOFs (LMOFs): recent advancement towards a greener WLED technology

The replacement of traditional incandescent, halogen and fluorescent lamps by white light emitting diodes (WLEDs) is expected to reduce the global electricity consumption by one-third by 2030, according to the US Department of Energy. The current WLED technology uses rare-earth element (REE) based phosphor materials, which, not only is cost-intensive but also constitutes an environmental concern. Hence, researchers are in a quest for a new-generation of opto-electronic materials that can replace the conventional phosphors in WLEDs and thus aim towards a cleaner and more energy efficient lighting technology for the future. Luminescent metal–organic frameworks (LMOFs) have recently emerged as a new sub-class of MOFs which have demonstrated enormous potential for applications in sensing, imaging, optoelectronics and in solid-state lighting (SSL) technology. LMOFs could be game changers as lighting phosphors due to advantages such as high luminescence quantum yield, tunable excitation and emission which can be achieved by rational design and optimization of metal centers, linkers, and the guest molecules, facile fabrication into devices, and structural robustness. These clear advantageous features of LMOFs make them score over other contemporary materials, and enable them to be futuristic phosphor materials for WLED technology. Here, in this feature article, we will provide an overview of the most recent developments of LMOF-based phosphor materials for SSL with a special focus on WLED technology. The emphasis will be centered around REE-free LMOFs, as the aim is to direct the attention of the readers towards a more viable and greener lighting technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Equilibrium ultrastable glasses produced by random pinning

Ultrastable glasses have risen to prominence due to their potentially useful material properties and the tantalizing possibility of a general method of preparation via vapor deposition. Despite the importance of this novel class of amorphous materials, numerical studies have been scarce because achieving ultrastability in atomistic simulations is an enormous challenge. Here, we bypass this difficulty and establish that randomly pinning the position of a small fraction of particles inside an equilibrated supercooled liquid generates ultrastable configurations at essentially no numerical cost, while avoiding undesired structural changes due to the preparation protocol. Building on the analogy with vapor-deposited ultrastable glasses, we study the melting kinetics of these configurations following a sudden temperature jump into the liquid phase. In homogeneous geometries, we find that enhanced kinetic stability is accompanied by large scale dynamic heterogeneity, while a competition between homogeneous and heterogeneous melting is observed when a liquid boundary invades the glass at constant velocity. Our work demonstrates the feasibility of large-scale, atomistically resolved, and experimentally relevant simulations of the kinetics of ultrastable glasses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Coordinating Garbage Collectionfor Arrays of Solid-State Drives

The primary goal of the extreme-scale plasma turbulence studies described in this article is to gain new insights on confinement scaling in magnetic fusion systems by using powerful, world-class supercomputers to carry out simulations with unprecedented resolution and temporal duration. New insights have been gained on the key question of how the turbulent transport of thermal energy and particles in the plasma and the associated confinement scale from present-generation devices to much larger ITER-size plasmas. In particular, new results from large-scale simulation studies have demonstrated that improvement in confinement as devices grow larger is far more gradual, with significantly lower loss rates than less-powerful computer simulations have indicated in research carried out over the past decade.

Computer Science↗
DOE OSTI2023

Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock

Computationally designed multi-subunit assemblies have shown considerable promise for a variety of applications, including a new generation of potent vaccines. One of the major routes to such materials is rigid body sequence-independent docking of cyclic oligomers into architectures with point group or lattice symmetries. Current methods for docking and designing such assemblies are tailored to specific classes of symmetry and are difficult to modify for novel applications. Here we describe RPXDock, a fast, flexible, and modular software package for sequence-independent rigid-body protein docking across a wide range of symmetric architectures that is easily customizable for further development. RPXDock uses an efficient hierarchical search and a residue-pair transform ( RPX ) scoring method to rapidly search through multidimensional docking space. We describe the structure of the software, provide practical guidelines for its use, and describe the available functionalities including a variety of score functions and filtering tools that can be used to guide and refine docking results towards desired configurations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Application of Next-Generation Sequencing to Engineering mRNA Turnover in Cyanobacteria

This early career project began on July 15th, 2013, and concluded on July 14th, 2018. The project was staffed by three post-doctoral fellows and two graduate students. Three rotation students, one undergraduate, one high school student, and two summer REU have also worked on the project. The chief objective of the project was to develop a deeper understanding of how messenger RNA molecules are recycled in two bacterial species 1.) Escherichia coli and 2.) cyanobacteria Synechococcus sp. PCC7002 by applying next generation nucleic acid sequencing technologies. We succeeded in developing tools and approaches for performing these experiments and developed a better understanding of RNA turnover in each organism. We now have a better understanding of RNA molecules cut by RNase III, a common class of endonuclease, in both organisms and a global map of RNA turnover in cyanobacteria – the first of its kind for this class of microbe. To disseminate our findings, we published six primary research manuscripts as of the deposition of this report and expect to publish two others in 2019. We have deposited two sets of RNA sequencing data into a public database in support of our manuscripts. We have also published three reviews and commentaries of related topics. Dr. Gina Gordon, a funded graduate student defended her thesis in 2018 based on work performed in the course of the project. Dr. Travis Korosh defended his thesis in 2018 based in part on work performed in the course of the project. Dr. Jeffrey Cameron, now an Assistant Professor at CU-Boulder, Dr. Andrew Markley, and Dr. Michael Engstrom also worked on the project as post-doctoral researchers. In summary, the project has discovered new molecular biology information, trained five full-time researchers, deposited novel datasets in a public repository, and published research articles and presentations to disseminate our findings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Extreme-scale stochastic optimization and simulation via learning-enhanced decomposition and parallelization (Final Technical Report)

Stochastic optimization and simulation models ubiquitously arise in designing and operating complex service/engineering systems. They can be extreme in scale due to high-dimensional data and decisions, and can also involve decisions made sequentially in response to newly revealed data, both causing significant computational challenge. The objective of this research is to explore a unified framework that integrates machine learning with discrete optimization and risk-averse modeling, to improve the efficiency of decomposition paradigms for stochastic optimization and simulations at extreme scale. The models we consider represent a broad class of complex decision-making problems, where 0-1 or continuous decisions are made before and/or after knowing multiple sources of uncertainties that could be correlated. We will employ machine learning methods to dynamically decide and prioritize computational procedures, including cut generation, branching, and bounding of the optimal objective. Furthermore, the research will shed new lights on the traditional decomposition algorithms for extreme-scale computing. Deliverables of the research include new modeling and computational methods for advancing the state-of-the-art research in optimization and simulation, bringing many relevant risk-averse, data-driven optimization problems in practice within the range of tractability. Examples include distributed computing server scheduling and sensor deployment for monitoring critical infrastructures. Success in this effort will enable progress in solving multiple extreme-scale problems in the complex system design and operations arising from DoE missions in energy, environment, and national security.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

RU Net for Automatic Characterization of TRISO Fuel Cross Sections

TRistructural ISOtropic (TRISO) particle fuel is a type of nuclear fuel known for its high-temperature and high-burnup performance. Each sub-millimeter diameter TRISO particle consists of uranium-oxycarbide (UCO) or UO2 fuel kernel, coated with buffer, inner pyrolytic carbon (IPyC), silicon carbide (SiC), and outer pyrolytic carbon (OPyC) layers. The SiC layer acts as the main containment barrier for the TRISO particle to retain the fission products, while the IPyC and OPyC layers provide additional barriers to the release of fission products, especially fission gases. During irradiation, phenomena like kernel swelling, buffer densification, and IPyC fracture may impact fuel performance. Post-irradiation microscopy on entire compact cross sections or samples of individual particles deconsolidated from compacts is often used to identify these irradiation-induced changes in morphology. However, each fuel compact generally contains thousands of TRISO particles. To get statistical information on these phenomena, it is cumbersome work if done manually. For example, to get information about swelling/densification behaviors of different layers or kernels after irradiation, researchers previously manually measured the perimeter of each TRISO layer in hundreds of particles after four rounds of iterative grinding and polishing encompassing more than 2000 cross-section images for a total of four fuel compacts. To attempt to reduce the subjectivity inherent in that process and accelerate data analysis, we conducted a study on the automatic TRISO layer segmentation on cross-sectional microscopic images using Convolutional Neural Networks (CNNs). CNNs are a class of machine learning algorithms specifically designed for processing structured grid data that have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we have generated the large irradiated TRISO layer dataset with more than 2000 cross-section TRISO microscopic images and the corresponding annotated images. Based on these annotated images, we have employed different CNNs for automatic segmentation of different TRISO layers. These include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net has the best performance in terms of intersection-over-union (IoU). Through the aid of these CNN models, we can expedite the analysis of TRISO particle cross-sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

Convolutional Neural Networks↗
NASA NTRS1972

Decision surface estimate of nonlinear system stability domain by Lie series method.

The Lie series recursive algorithm for Zubov's partial differential equation is used to generate two sets of points, where one represents the exact asymptotic stability boundary of an equilibrium state of the nonlinear system under consideration and the other is interior to it. Based on these two sets of data as training samples of two classes, a decision hypersurface can be determined such that it is a close approximation of the asymptotic stability boundary.

Kormanik, J.↗
NASA NTRS1972

Nonlinear and Digital Man-machine Control Systems Modeling

An adaptive modeling technique is examined by which controllers can be synthesized to provide corrective dynamics to a human operator's mathematical model in closed loop control systems. The technique utilizes a class of Liapunov functions formulated for this purpose, Liapunov's stability criterion and a model-reference system configuration. The Liapunov function is formulated to posses variable characteristics to take into consideration the identification dynamics. The time derivative of the Liapunov function generate the identification and control laws for the mathematical model system. These laws permit the realization of a controller which updates the human operator's mathematical model parameters so that model and human operator produce the same response when subjected to the same stimulus. A very useful feature is the development of a digital computer program which is easily implemented and modified concurrent with experimentation. The program permits the modeling process to interact with the experimentation process in a mutually beneficial way.

Mekel, R.↗
NASA NTRS1992

RGB-NDVI colour composites for visualizing forest change dynamics

The study presents a simple and logical technique to display and quantify forest change using three dates of satellite imagery. The normalized difference vegetation index (NDVI) was computed for each date of imagery to define high and low vegetation biomass. Color composites were generated by combining each date of NDVI with either the red, green, or blue (RGB) image planes in an image display monitor. Harvest and regeneration areas were quantified by applying a modified parallelepiped classification creating an RGB-NDVI image with 27 classes that were grouped into nine major forest change categories. Aerial photographs and stand history maps are compared with the forest changes indicated by the RGB-NDVI image. The utility of the RGB-NDVI technique for supporting forest inventories and updating forest resource information systems are presented and discussed.

Sader, S. A.↗
NASA NTRS2008

Compact Dielectric-Rod White-Light Delay Lines

Optical delay lines of a proposed type would be made from rods of such dielectric materials as calcium fluoride, fused silica, or sapphire. These would offer advantages over prior optical delay lines, as summarized below. Optical delay lines are key components of opto-electronic microwave oscillators, narrow-band opto-electronic microwave filters, evanescent-field optical biochemical detectors, and some Fourier-Transform spectrum analyzers. Heretofore, optical delay lines used in such applications have been of two types: resonators and coiled long optical fibers, both of which have disadvantages: Resonators are compact, but excitation must be provided by narrow-band lasers. Wide-band (including noisy) laser light cannot be coupled efficiently to narrow-band resonators. When light is coupled into a narrowband resonator from a source of reasonably high power, a significant amount of optical energy circulates within the resonator, causing nonlinear loss and significant noise. Typically, a coil-type optical delay line is made of fused-silica fiber, which exhibits fundamental loss. To overcome the limit imposed by the optical loss in fused silica, it would be necessary to use fibers having crystalline cores. Although space is saved by winding fibers into coils, fiber-coil delay lines are still inconveniently bulky. The proposed compact dielectric-rod delay lines would exploit the special class of non-diffracting light beams that are denoted Bessel beams because their amplitudes are proportional to Bessel functions of the radii from their central axes. High-order Bessel beams can have large values of angular momentum. They can be generated with the help of whispering-gallery-mode optical resonators, as described, for example, in "Simplified Generation of High-Angular-Momentum Light Beams" (NPO-42965) NASA Tech Briefs, Vol. 31, No. 3 (March 2007), page 8a. In a delay line according to the proposal, the dielectric rod would be dimensioned to function as a multimode waveguide. Suitably chosen high-angular-momentum modes in such a waveguide exhibit low group velocity (hence, long delay) and no resonance. Such a delay line could perform well at any wavelength or range of wavelengths within the transparency wavelength band of the dielectric material, and the maximum possible group delay achievable through suitable design would be limited only by the optical loss in the rod material.

Maleki, Lute↗