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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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NASA NTRS1984

The study of microstrip antenna arrays and related problems

The physical layout of the array elements and the proximity of the microstrip feed network makes the input impedance and radiation pattern values dependent upon the effects of mutual coupling, feedline discontinuities and feed point location. The extent of these dependences was assessed and a number of single patch and module structures were constructed and measured at an operating frequency of approximately 4.0 GHz. The empirical results were compared with the ones which were theoretically predicted by the cavity model of thin microstrip antennas. Each element was modelled as an independent radiating patch and each microstrip feedline as an independent, quasi-TEM transmission line. The effects of the feedline discontinuities are approximated by lumped L-C circuit models.

Lo, Y. T.↗

NASA NTRS1992

An optically controlled Ka-band phased array antenna

The design and development of a small, optically controlled phased array antenna suitable for communication satellite applications are discussed. A vertical integration architecture is used which minimizes the size of the array with its associated beamforming network (BFN). The antenna features a four-element linear microstrip array that uses aperture coupling of the antenna elements to the BFN; a modified Wilkinson power divider BFN; and 32 GHz, four-bit monolithic microwave integrated circuit (MMIC) phase shifters in customized quartz packages with corresponding optoelectronic interface circuits (OEIC's) for control signal reception.

Kunath, R. R.↗

NASA NTRS1992

A vertically integrated Ka-band phased array antenna

The design, development, and experimental demonstration of a small phased array antenna suitable for applications on communications satellites are discussed. Each of the vertical layers was optimized for performance, and MMICs on custom carriers were characterized prior to insertion. A vertical integration architecture is used which minimizes the size of the array with its associated beamforming network (BFN). The antenna features a four-element linear microstrip array that uses aperture coupling of the antenna elements to the BFN; a modified Wilkinson power divider BFN; and 32 Ghz, 4-bit MMIC phase shifters on customized alumina carriers. Performance data are presented for all components, and far-field antenna radiation patterns are given.

Kunath, R. R.↗

NASA NTRS1993

Astro-D Prelaunch Mission Operation Report (MOR)

The Prelaunch Mission Operation Report for the Astro-D mission is enclosed for your information. Astro-D is a Japanese-led program involving the United States as a participating partner. The Japanese Institute of Space and Astronautical Science (ISAS) provides overall program management, the launch vehicle, the spacecraft, and two Gas Scintillation Imaging System detectors. NASA is providing four nested, thin-foil, grazing-incidence telescope mirrors and two X-ray charged-coupled device solid-state detectors. NASA also provides telemetry tracking support using Deep Space Network ground stations. In return for its scientific instrument contribution, the U.S. will receive 15 percent of the observing time and will share an additional 25 percent for collaborative U.S./Japan scientific investigations. Launch is scheduled for mid- February 1993 from the ISAS launch center near Kagoshima in southern Japan. This MOR: (a) describes the NASA objectives for the Astro-D mission; (b) provides brief descriptions of the spacecraft and its scientific instruments; (c) provides a chronology of launch and deployment; and (d) describes the ground operation elements that support the mission. .

Source record↗

NASA NTRS1991

High speed multiwire photon camera

An improved multiwire proportional counter camera having particular utility in the field of clinical nuclear medicine imaging. The detector utilizes direct coupled, low impedance, high speed delay lines, the segments of which are capacitor-inductor networks. A pile-up rejection test is provided to reject confused events otherwise caused by multiple ionization events occuring during the readout window.

Lacy, Jeffrey L.↗

NASA NTRS1989

High speed multiwire photon camera

Lacy, Jeffrey L.↗

NASA NTRS2016

Highlights from 4STAR Sky-Scanning Retrievals of Aerosol Intensive Optical Properties from Multiple Field Campaigns with Detailed Comparisons of SSA Reported During SEAC4RS

The 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) instrument combines airborne sun tracking capabilities of the Ames Airborne Tracking Sun Photometer (AATS-14) with AERONET (Aerosol Robotic Network)-like sky-scanning capability and adds state-of-the-art fiber-coupled grating spectrometry to yield hyperspectral measurements of direct solar irradiance and angularly resolved sky radiance. The combination of sun-tracking and sky-scanning capability enables retrievals of wavelength-dependent aerosol optical depth (AOD), mode-resolved aerosol size distribution (SD), asphericity, and complex refractive index, and thus also the scattering phase function, asymmetry parameter, single-scattering albedo (SSA), and absorption aerosol optical thickness (AAOT). From 2012 to 2014 4STAR participated in four major field campaigns: the U.S. Dept. of Energy's TCAP (Two-Column Aerosol Project) I & II campaigns, and NASA's SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) and ARISE (Arctic Radiation - IceBridge Sea & Ice Experiment) campaigns. Establishing a strong performance record, 4STAR operated successfully on all flights conducted during each of these campaigns. Sky radiance spectra from scans in either constant azimuth (principal plane) or constant zenith angle (almucantar) were interspersed with direct beam measurements during level legs. During SEAC4RS and ARISE, 4STAR airborne measurements were augmented with flight-level albedo from the collocated Shortwave Spectral Flux Radiometer (SSFR) providing improved specification of below-aircraft radiative conditions for the retrieval. Calibrated radiances and retrieved products will be presented with particular emphasis on detailed comparisons of ambient SSA retrievals and measurements during SEAC4RS from 4STAR, AERONET, HSRL2 (High Spectral Resolution Lidar), and from in situ measurements.

Radiance↗

NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗

DOE OSTI2021

Routing flits in a network-on-chip based on operating states of routers

A system is described that includes an integrated circuit chip having a network-on-chip. The network-on-chip includes multiple routers arranged in a topology and a separate communication link coupled between each router and each of one or more neighboring routers of that router among the multiple routers in the topology. The integrated circuit chip also includes multiple nodes, each node coupled to a router of the multiple routers. When operating, a given router of the multiple routers keeps a record of operating states of some or all of the multiple routers and corresponding communication links. The given router then routes flits to destination nodes via one or more other routers of the multiple routers based at least in part on the operating states of the some or all of the multiple routers and the corresponding communication links.

Bharadwaj, Srikant↗

DOE OSTI2022

Modeling Integrated Electricity and Natural Gas Systems Using Co-Simulation

One approach for modeling natural gas and electricity networks as integrated systems is co-simulation, which involves separating a single model into subproblems that are solved iteratively, with coupling information passed between then. Although a fully integrated problem might be more direct, co-simulation offers advantages in that it can sometimes be more computationally tractable, and can also leverage existing simulation tools to represent the subproblems. Here we present a framework for co-simulation of natural gas and electricity networks using the HELICS tool. We provide validation of the co-simulation approach by comparing results with a fully integrated approach that uses the same simulation tool, finding that the two approaches provide identical results for a range of test cases. We then present results from natural gas and electricity network co-simulation case study of the Colorado Front Range, with an evaluation of the present-day system and also a future system with higher shares of renewable power. The presentation will close with a discussion on future avenues of work for co-simulation of gas and power networks.

co-simulation↗

DOE OSTI2025

Normalizing flows for high-dimensional detector simulations

Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimeter shower simulations with increasing phase space dimension. We use fast and expressive coupling spline transformations applied to the CaloChallenge datasets. In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. We evaluate our networks on several metrics, including high-level features, classifiers, and generation timing. Our findings demonstrate that invertible neural networks have competitive performance when compared to autoregressive flows, while being substantially faster during generation.

Ernst, Florian↗

DOE OSTI2025

Animal movement estimation and network-based epidemic modeling: Illustration for the swine industry in Iowa (US)

Animal movement plays a critical role in disease transmission between farms. However, in the United States, the lack of available animal shipment data, sometimes coupled with a lack of detailed information about farm demographics and characteristics, presents great challenges for epidemic modeling and prediction. In this study, we proposed a new method based on the maximum entropy to generate “synthetic” animal movement networks, considering available statistics about the premises operation type, operation size, and the distance between premises. We illustrated our method for the swine movement networks in Iowa and performed network analyses to gain insights into the swine industry. We then applied the generated networks to a network-based epidemic model to identify potential system vulnerabilities in terms of disease transmission. The model was parameterized for African Swine Fever (ASF) as the US swine industry is quite concerned about this disease. Results show that premises with a central role in the network are more vulnerable to disease outbreaks and play an important role in disease spread. Simulations with outbreaks starting from random farms reveal no significant large outbreaks, indicating the system’s relative robustness against arbitrary disease introductions. However, outbreaks originating from high out-degree farms can lead to large epidemic sizes. This underscores the importance for stakeholders and policymakers to continue improving animal movement records and traceability programs in the US and the value of making that data available to epidemiologists and modelers to better understand risk and inform strategies aimed to cost-effectively prevent and control disease transmission. Our approach could be easily adapted to estimate movement networks in other animal production systems and to inform disease spread models for various infectious diseases.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

A Direct Probe of the Hydrogen Bond Network in Aqueous Glycerol Aerosols

The properties of aerosols are of paramount importance in atmospheric chemistry and human health. The hydrogen bond network of glycerol-water aerosols generated from an aqueous solution with different mixing ratios is probed directly with X-ray photoelectron spectroscopy. The carbon and oxygen X-ray spectra reveal contributions from gas and condensed phase components of the aerosol. It is shown that water suppresses glycerol evaporation up to a critical mixing ratio. A dielectric analysis using terahertz spectroscopy coupled with infrared spectroscopy of the bulk solutions provides a picture of the microscopic heterogeneity prevalent in the hydrogen bond network when combined with the photoelectron spectroscopy analysis. The hydrogen bond network is composed of three intertwined regions. At low concentrations, glycerol molecules are surrounded by water forming a solvated water network. Adding more glycerol leads to a confined water network, maximizing at 22 mol %, beyond which the aerosol resembles bulk glycerol. This microscopic view of hydrogen bonding networks holds promise in probing evaporation, diffusion dynamics, and reactivity in aqueous aerosols.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling

The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. As a result, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1983

Proceedings of the 1st Space Plasma Computer Analysis Network (SCAN) Workshop

The purpose of the workshop was to identify specific cooperative scientific study topics within the discipline of Ionosphere Magnetosphere Coupling processes and to develop methods and procedures to accomplish this cooperative research using SCAN facilities. Cooperative scientific research was initiated in the areas of polar cusp composition, O+ polar outflow, and magnetospheric boundary morphology studies and an approach using a common metafile structure was adopted to facilitate the exchange of data and plots between the various workshop participants. The advantages of in person versus remote workshops were discussed also.

Green, J. L.↗

DOE OSTI2026

Hybrid Symbolic-Numerical Modeling and Parametric Stability Analysis of DC–AC Power Systems

Hybrid DC-AC power systems integrating diverse inverter-based resources (IBRs) and multi-terminal high-voltage direct current (MTDC) networks represent a promising architecture for future power grids, while introducing challenges for modeling, stability analysis, and control design. This paper develops a hybrid symbolic-numerical modeling framework and tool to characterize the parametric small-signal stability of DC-AC coupled power systems. The proposed approach constructs parametric state-space models to enable efficient representation of system dynamics under varying control parameters and network configurations, with target parameters retained as symbolic variables and the remainder treated numerically. The stability analysis framework covers eigenvalue, sensitivity, and stability boundary and region characterization. Enhanced linear matrix inequality (LMI) techniques are proposed to directly certify small-signal stability over regions of parameter space while also reducing the conservativeness and computational burden. The resulting tools and frameworks enable rapid parametric model construction across diverse grid conditions, thereby facilitating stability-informed control and operation in complex DC–AC power systems.

DC–AC power systems↗

DOE OSTI2021

Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics

Coupling particle and reactor scale models is as essential as reactor fluid dynamics and particle motion for accurate Computational Fluid Dynamic (CFD) simulations of biomass fast pyrolysis reactors due to intraparticle heat transfer and chemical reactions controlling conversion time and product distributions. Direct online coupling of a particle model with a reactor model is computationally expensive, while offline coupling is case-dependent. In this research, solutions from a series of particle pyrolysis simulations were regressed with Artificial Neural Network (ANN). Furthermoer, this machine learning-derived model predicted the same temperature and conversion profiles compared with particle resolved simulation while the isothermal approach overpredicted the temperature by 130 K and underpredicted the conversion time by 30 s. The ANN model was then integrated into CFD simulations of fluidized bed biomass fast pyrolysis with varied feedstocks via coupling PyTorch and MFiX. The averaged error of simulation predicted bio-oil yields with four feedstocks is 6.4%. This multi-scale approach provides an efficient tool for the coupled particle and reactor scale simulations of biomass pyrolysis.

09 BIOMASS FUELS↗

DOE OSTI2023

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li x MnO 2 , the finite temperature phase diagram for Li x FePO 4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs.

36 MATERIALS SCIENCE↗