Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “network shape”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A numerical approach for detecting switch-like bistability in mass action chemical reaction networks with conservation laws

Theoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws. In this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves (1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, (2) minimization of the objective function to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that describes if the global minimum has been found and (4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability. We have developed a variation of an optimization-based method for the discovery of bistability, which is not limited to uniterminal chemical reaction networks. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters.

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

Beyond 4D tracking: using cluster shapes for track seeding

Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However, present and future hardware already have additional information that is largely unused by existing track seeding algorithms. The shape of pixel-clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding. We use neural networks to show that cluster shapes can reduce significantly the rate of fake combinatorical backgrounds while preserving a high efficiency. We demonstrate this using the information in cluster singlets, doublets and triplets. Numerical results are presented with simulations from the TrackML challenge.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Reconfiguring DNA Nanotube Architectures via Selective Regulation of Terminating Structures

Molecular assemblies inside cells often undergo structural reconfiguration in response to stimuli to alter their function. Adaptive reconfiguration of cytoskeletal networks, for example, enables cellular shape change, movement, and cargo transport and plays a key role in driving complex processes such as division and differentiation. The cellular cytoskeleton is a self-assembling polymer network composed of simple filaments, so reconfiguration often occurs through the rearrangement of its component filaments’ connectivities. DNA nanotubes have emerged as promising building blocks for constructing programmable synthetic analogs of cytoskeletal networks. Nucleating seeds can control when and where nanotubes grow and capping structures can bind nanotube ends to stop growth. Such seeding and capping structure, collectively called termini, can organize nanotubes into larger architectures. However, these structures cannot be selectively activated or inactivated in response to specific stimuli to rearrange nanotube architectures, a key property of cytoskeletal networks. Here we demonstrate how selective regulation of the binding affinity of DNA nanotube termini for DNA nanotube monomers or nanotube ends can direct the reconfiguration of nanotube architectures. Using DNA hybridization and strand displacement reactions that specifically activate or inactivate four orthogonal nanotube termini, we demonstrate that nanotube architectures can be reconfigured by selective addition or removal of unique termini. Lastly, we show how terminus activation could be a sensitive detector and amplifier of a DNA sequence signal. These results could enable the development of adaptive and multifunctional materials or diagnostic tools.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1981

The evolution of the spaceflight tracking and data network

The evolution of the Minitrack Network, the first worldwide satellite tracking capability (1957-1962), into the Satellite Tracking and Data Acquisition Network (1960-1966) is described. Attention is also given to the Mercury Network, which became operational in 1961, was the foundation for the Manned Space Flight Network, and supported the Gemini and Apollo missions. The development of the NASA Communications Network, which began to take shape in the early 1960s, is described. An account is also given of the way in which all these functions began playing together (1965) and were consolidated in the early 1970s as the Space Tracking and Data Network.

Hocking, W. M.↗
DOE OSTI2026

FOILPOLARS (Grassmannian Foil Shape Sweeps for Polar Generation) [SWR-26-095]

FOILPOLARS (Grassmannian Foil Shape Sweeps for Polar Generation): Multifidelity aerodynamic polar data generation for hydrofoil/tidal-turbine airfoil sections. Foilpolars ties together three pieces: *AeroSandbox supplies the baseline airfoil coordinates (UIUC database). *G2Aero parameterizes those shapes on a Grassmannian manifold (Karcher mean + PGA basis) and samples new perturbed shapes around that basis. *XFoil (panel method) and NeuralFoil (neural-network surrogate, shipped with AeroSandbox) each solve the resulting shapes for lift, drag, moment, and pressure at the swept angles of attack, Reynolds numbers, and n_crit values. Design optimization of foil shapes in a computationally efficient way requires polars data across many candidate shapes, not just a handful of baseline foils. However, high-fidelity CFD at that scale is too costly, and naive shape perturbation strays from realistic geometries. FOILPOLARS addresses this by loading baseline airfoils (via AeroSandbox) and mapping them onto a Grassmannian manifold (via G2Aero), computing a Karcher mean and principal geodesic analysis (PGA) basis. New shapes are sampled by perturbing PGA coefficients, keeping them close to the manifold of realistic foils. Each sampled shape is evaluated across a configurable sweep of angle of attack, Reynolds number, and critical amplification factor using two solvers: XFoil (panel method) and NeuralFoil (neural-network surrogate), producing a paired dataset of lift, drag, moment, pressure, convergence, and confidence, indexed alongside each shape's PGA coefficients and shared Grassmannian basis in a single xarray dataset. From this, FOILPOLARS produces convergence summaries and comparison plots per shape, Reynolds number, and n_crit. A command-line interface exposes each pipeline stage independently, supporting data-driven design, optimization, and machine-learning workflows for foils.

Sandhu, Rimple [National Laboratory of the Rockies↗
DOE OSTI2025

Mechanistic understanding of carbon mineralization in fracture systems using microfluidics

Carbon mineralization in mafic and ultramafic rocks presents an opportunity for permanent carbon storage in the Earth's subsurface. However, due to their lower permeability, pre-existing fracture networks are key for mineralization to occur. Therefore, to fully develop this technology, a mechanistic understanding of the mineralization behavior in fractures with the consideration of hydrodynamic components is required. We use high-pressure microfluidics to investigate key mechanisms influencing dissolution–precipitation in a fracture network. The experiments were conducted in micromodels made of natural rocks with a comb-shaped flow channel to mimic a fracture network. This enabled studying the effect of injection rate on coupled dissolution–precipitation in advection and diffusion-dominated flow paths. We used gypsum carbonation as an analog reaction to allow for realistic experimental time frames due to its rapid reaction kinetics. The experimental work is coupled with high-fidelity numerical simulations to enhance our understanding of the parameters affecting the mineralization reaction. Our results demonstrate the importance of flow rate on the rate and nature of the gypsum carbonation reaction revealing that higher flow rates enable deeper penetration of the mineral precipitation front into the dead-end channels. This is an important finding since for sustained mineralization in a fracture network, precipitation in dead-ends while still allowing for flowing fractures is critical. Detailed characterization of the precipitates showed that lower flow rates led to porous and loose precipitates in the form of aragonite while higher flow rates mimicked supersaturation behavior leading to the formation of calcite. The reactive transport simulations further demonstrated the significance of flow velocity in advection-dominated channels to influence the efficiency of carbon mineralization in diffusion-dominated channels, potentially clogging of dead-end channels. These findings highlight the need for coupling chemical, mechanical, and hydrodynamic processes to evaluate the nature and extent of carbon mineralization in fractured media critical for permanent storage in mafic and ultramafic formations. This research further highlights the need for more investigation in potential subsurface fracture generation techniques to aid carbon mineralization.

25 ENERGY STORAGE↗
NASA NTRS1990

Determination of optimum fin profile for a zero-G capillary drained condenser

This paper presents the analytical formulation and numerical results for heat transfer in a high heat flux condenser that relies on capillary flow along shaped fins (Gregorig surfaces) and a drainage network embedded in the condenser walls. Results are shown for a variety of fin profile shapes in order to show the geometric trade-offs involved in seeking a maximum effective heat transfer coefficient for the fin. Predictions of the model show excellent agreement with previously reported measurements for steam. Based on this work, a profile has been selected for a 2 kW ammonia condenser currently under development for use in space. In that design the fin half width is 0.5 mm and the model predicts a heat transfer coefficient referred to the base of the fin of 9 W/sq cm deg C for a heat flux of 10/W sq cm at the base.

Mccormick, John A.↗
NASA NTRS2012

Strut Shaping of 34m Beam Waveguide Antenna for Reductions in Near-Field RF and Noise Temperature

Strut shaping of NASA's Deep Space Network (DSN) 34m Beam Waveguide (BWG) antenna has been implemented to reduce near-field RF exposure while improving the antenna noise temperature. Strut shaping was achieved by introducing an RF shield that does not compromise the structural integrity of the existing antenna. Reduction in the RF near-field level will compensate for the planned transmit power increase of the antenna from 20 kW to 80 kW while satisfying safety requirements for RF exposure. Measured antenna noise temperature was also improved by as much as 1.5 K for the low elevation angles and 0.5 K in other areas.

near-field↗
NASA NTRS1989

Neural networks for data compression and invariant image recognition

An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

Gardner, Sheldon↗
DOE OSTI2023

Biobased Semi-Interpenetrating Polymer Networks of Poly(ε-caprolactone) and Epoxidized Soybean Oil with Nanoscale Morphology, Shape-Memory Effect, and Biocompatibility

Creating biobased polymer blends with outstanding properties, nanoscale morphology, shape-memory capability, and biocompatibility is very crucial and requires a fundamental understanding of the phase behavior, macromolecular structure, and biological compatibility of the polymer blends with living cells. It is very critical to understand the complex relationships among the polymer structure, morphology, and performance of multifunctional smart materials under conditions that they are likely to encounter during use, particularly in biomedical applications. Biobased semi-interpenetrating polymer networks of poly(ε-caprolactone) and epoxidized soybean oil with nanoscale morphology have been successfully synthesized via in situ cationic polymerization and compatibilization in a homogeneous solution. Varies analytical and characterization techniques, such as Fourier transform infrared spectroscopy, differential scanning calorimetry, dynamic mechanical analysis, transmission electron microscopy, X-ray scattering, cell toxicity, and shape-memory effects (SMEs), have been employed to understand the structure–properties relationship of these smart, biobased nanostructured polymer blends. The synthesized nano blends were nontoxic or biocompatible and supported attachment of human vein endothelial cells, showing their potential use in biomedical applications. The current versatile, low-cost strategy for synthesizing the nanoscale morphology of semi-interpenetrating polymer networks with SMEs and biocompatibility should be widely applicable for polymer systems. This study is also considered as a continuation to our efforts in the area of biobased polymers to develop innovative technologies to transform natural resources into smart multifunctional materials for a wide range of applications, including coatings, adhesives, and medical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of $\sqrt{s}=13\,\text {TeV} $ at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 $\,\text {fb}^{-1}$. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to $\hbox {b}\bar{\hbox {b}}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1972

Subsonic, transonic, and supersonic nozzle flow by the inverse technique.

The inverse technique is used to obtain a mathematically and physically consistent solution of the flowfield in a nozzle from the mass generation surface through the supersonic region. The inverse method employs an assumed centerline function which is of the Cauchy type in that the values and the derivatives of the function are known. Since the Cauchy boundary conditions can give rise to numerical instabilities, the governing gasdynamic equations for rotational steady flow were transformed into a form which puts the geometry into a rectangular shape, and which spaces the network of interior points more finely in regions of the greatest gradients of the dependent variables. For arbitrarily specified centerline data, the solution of the governing flow equations may not exist, and if it does it may not depend continuously on the data.

Norton, D. J.↗
DOE OSTI2024

Host and nonhost bacteria support bacteriophage dissemination along mycelia and abiotic dispersal networks

Abstract Bacteriophages play a crucial role in shaping bacterial communities, yet the mechanisms by which nonmotile bacteriophages interact with their hosts remain poorly understood. This knowledge gap is especially pronounced in structured environments like soil, where spatial constraints and air-filled zones hinder aqueous diffusion. In soil, hyphae of filamentous microorganisms form a network of ‘fungal highways’ (FHs) that facilitate the dispersal of other microorganisms. We propose that FHs also promote bacteriophage dissemination. Viral particles can diffuse in liquid films surrounding hyphae or be transported by infectable (host) or uninfectable (nonhost) bacterial carriers coexisting on FH networks. To test this, two bacteriophages that infect Pseudomonas putida DSM291 (host) but not KT2440 (nonhost) were used. In the absence of carriers, bacteriophages showed limited diffusion on 3D-printed abiotic networks, but diffusion was significantly improved in Pythium ultimum-formed FHs when the number of connecting hyphae exceeded 20. Transport by both host and nonhost carriers enhanced bacteriophage dissemination. Host carriers were five times more effective in transporting bacteriophages, particularly in FHs with over 30 connecting hyphae. This study enhances our understanding of bacteriophage dissemination in nonsaturated environments like soils, highlighting the importance of biotic networks and bacterial hosts in facilitating this process.

Périat, Claire (ORCID:000000020825625X)↗
NASA NTRS2000

Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks

Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or clusters of genes with common activity profiles may overlook the most critical features of cellular information processing which normally determine how signal specificity is established and maintained in living cells. Copyright 2000 Academic Press.

Review↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2021

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA): Component 5: Imaging Protein Conformations, Shapes & Assemblies in Solution & Administration project (Final Scientific/Technical Report, Subcontract No. 6974584)

We set ambitious goals to examine microorganism communities and measure both their chemical input and output as a read out of specific biochemical activity. These scientific goals are driving the development of sophisticated algorithms to analyze large amounts of experimental measurements made using high throughput technologies to explain and predict how the environment influences biological function at multiple scales and how the microbial systems in tum modify the environment. By examining how bacteria communities rely on symbiotic metabolic relationships for survival and reproductive success, and how these relationships consequently affect their biochemical capabilities. This was accomplished using state-of-the-art mass spectrometry-based methods, and metabolic fingerprinting approaches with a high degree of chemical specificity and sensitivity. During this period the original effort transitioned and was consolidated into what is now ENIGMA, the efforts on technology development expanded to include more untargeted metabolomics and its application to organisms on a systems level.

59 BASIC BIOLOGICAL SCIENCES↗