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At least 685 records · Page 38

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Techno-Economic Analysis of Repurposing Natural Gas Transmission Pipeline Networks to Accommodate Hydrogen Blends

Blending hydrogen into natural gas infrastructure could supplement natural gas supply and increase resilience for applications such as ammonia production, peaking and load-following power plants, and heating. The United States has an extensive network of natural gas pipelines, but the feasibility of employing this infrastructure to transport hydrogen is unclear. We analyze the costs associated with repurposing three distinct natural gas transmission pipelines in different locations within the United States to carry blends of hydrogen up to 100% via three different pipeline network modification methods and compare against the cost of building a new dedicated hydrogen pipeline. We conduct a sensitivity analysis on the hoop stress limit of the existing pipe, techno-economic parameters, emissions, and relative capacity. The results show that the capital costs required to upgrade a pipeline can vary from tens of millions to billions of dollars depending on the length and capacity of the existing pipeline section, whether the existing pipeline already operates at or below its maximum allowable operating pressure, whether future demand is expected to increase or decrease, the network modification method selected, and pipe material costs. The delivered cost of energy to end users is impacted less by the levelized cost of transporting hydrogen blends than by the cost of the natural gas and hydrogen fuels being transported. The emissions impact of blending hydrogen into natural gas transmission networks scales proportionally with the amount of energy displaced with low-emission hydrogen (such as from natural gas with carbon capture and sequestration or electrolysis powered by nuclear, renewable, or geothermal electricity), therefore low blend ratios (e.g., < 20% vol. hydrogen) will result in low emissions impacts. Factors such as permitting and right-of-way costs, the proximity of the pipeline to hydrogen demand and production, and the compatibility of and/or retrofitting costs of end-use gas-fueled technologies will likely be greater drivers in determining whether converting a particular natural gas pipeline to carry hydrogen makes economic sense.

08 HYDROGEN↗

NASA NTRS1987

A high-resolution local network study of the Nazca plate Wadati-Benioff zone under western Argentina

Seismic data, recorded by INPRES telemetered network located above one of the subhorizontal segments of the subducted Nazca plate Wadati-Benioff zone beneath western Argentina, were analyzed to determine the zone's fine structure. The depth of the center and the thickness of the subhorizontal Wadati-Benioff zone beneath the network were calculated to be about 107 km and about 20 km, respectively, with most of the seismogenic zone concentrated in a region about 12 km thick. The Nazca plate is interpreted to be in a state of down-dip tension and to be decoupled from the overriding South American plate by a weak zone of asthenospheric or shear-heated material. The South American plate is estimated to be 80 km thick, based on the location of the subducted Nazca plate and an inferred decoupling zone between the plates.

Smalley, Robert F., Jr.↗

DOE OSTI2022

Classifying handedness in chiral nanomaterials using label error robust deep learning

Abstract High-throughput scanning electron microscopy (SEM) coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles. Automated labeling removes the time-consuming manual labeling of training data, but introduces label error, and subsequently classification error in the trained neural network. Here, we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles. Using the mirror relationship between images of opposite handed particles, we artificially create populations of varying label error. We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset. Of the three training methods considered, we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets, where size and other morphological variables are held constant, but that a co-teaching approach performs the best in practical application.

36 MATERIALS SCIENCE↗

NASA NTRS1971

Computer-aided linear-circuit design.

Usually computer-aided design (CAD) refers to programs that analyze circuits conceived by the circuit designer. Among the services such programs should perform are direct network synthesis, analysis, optimization of network parameters, formatting, storage of miscellaneous data, and related calculations. The program should be embedded in a general-purpose conversational language such as BASIC, JOSS, or APL. Such a program is MARTHA, a general-purpose linear-circuit analyzer embedded in APL.

Penfield, P.↗

NASA NTRS2001

Electron Heat Flux in Pressure Balance Structures at Ulysses

Pressure balance structures (PBSs) are a common feature in the high-latitude solar wind near solar minimum. Rom previous studies, PBSs are believed to be remnants of coronal plumes and be related to network activity such as magnetic reconnection in the photosphere. We investigated the magnetic structures of the PBSs, applying a minimum variance analysis to Ulysses/Magnetometer data. At 2001 AGU Spring meeting, we reported that PBSs have structures like current sheets or plasmoids, and suggested that they are associated with network activity at the base of polar plumes. In this paper, we have analyzed high-energy electron data at Ulysses/SWOOPS to see whether bi-directional electron flow exists and confirm the conclusions more precisely. As a result, although most events show a typical flux directed away from the Sun, we have obtained evidence that some PBSs show bi-directional electron flux and others show an isotropic distribution of electron pitch angles. The evidence shows that plasmoids are flowing away from the Sun, changing their flow direction dynamically in a way not caused by Alfven waves. From this, we have concluded that PBSs are generated due to network activity at the base of polar plumes and their magnetic structures axe current sheets or plasmoids.

Yamauchi, Yohei↗

DOE OSTI2023

Standardized Data to Improve Understanding and Modeling of Soil Nitrogen at Continental Scale

Abstract Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial–temporal scales. Here, we introduce a continentally distributed, publicly available data set collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. First, we detail the sampling design and methods used to collect and analyze soil inorganic N pool and net flux rate data from 47 terrestrial sites. We address methodological challenges in generating a standardized data set, even for a network using uniform protocols. Then, we evaluate sources of variation within the sampling design and compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2). We observed wide spatiotemporal variation in inorganic N pool sizes and net transformation rates. Site explained the most variation in NEON’s stratified sampling design, followed by plots within sites. Organic horizons had larger pools and net N transformation rates than mineral horizons on a sample weight basis. The majority of sites showed some degree of seasonality in N dynamics, but overall these temporal patterns were not matched by CESM2, leading to poor correspondence between observed and modeled data. Looking forward, these data can reveal new insights into controls on soil N cycling, especially in the context of other environmental data sets provided by NEON, and should be leveraged to improve predictive modeling of the soil N cycle.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1961

Radiation Observations with Satellite 1958 Alpha Explorer I

On 1 February 1958 at 0348 U.T. earth satellite 1958 Alpha (Explorer I) was launched from Cape Canaveral. This satellite contained a Geiger Muller tube for the measurement of the flux of energetic charged particles, and detectors for determining the micrometeorite flux and satellite temperature (see Figure 1), The high power transmitter and its associated instrumentation operated until partial exhaustion of the transmitter batteries on 12 February 1958. The transmitter reappeared briefly on 24 February, The low power system operated properly until about 0700 U,T, on 16 March, at which time the batteries powering the G.M. tube circuits became exhausted. During the operating time a large amount of data was recorded by a network of seventeen receiving stations. A sampling of early recordings was reduced and analyzed as they arrived from the receiving stations. These data, in conjunction with data from the first few orbits of 1958 Gamma (Explorer III), resulted in the announcement on 1 May 1958 of the region of high intensity radiation surrounding the earth (Van Allen, 1958), The detailed, complete reduction of the 1958 Alpha data has now been completed, This paper is a tabulation of all the data received from this satellite. It consists primarily of two parts. The first part is the master recording log on which are listed all recordings of the satellite signals obtained by the receiving station network. The actual data tabulation is contained in part two.

RADIATION↗

DOE OSTI2023

Interconnection Cost Analysis in ISO-New England

Electric transmission system operators (ISOs, RTOs, or utilities) require new large generators seeking to connect to the grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. Berkeley Lab has collected interconnection cost data for 194 projects in New England from interconnection studies performed between 2010 and 2021. Project-level cost summary data are available for download on this page. We find: -Interconnection costs have grown over time, especially for projects that withdraw. -Interconnection costs are highest for onshore wind, followed by solar and storage. Natural gas and offshore wind projects tend to cost less to interconnect, in comparison. -Economies of scale exist for solar and possibly storage projects, but not for other resource types. -Wind and solar projects requesting capacity network resource interconnection service have higher interconnection costs, despite being evaluated using the same interconnection standard in the analyzed studies. -Low and high interconnection costs can be found throughout the ISO-NE footprint. -Costs are split fairly evenly between investments at the point of interconnection and within the broader network for active and withdrawn projects, while complete projects incur most costs at the point of interconnection.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

A review of technical and regulatory limits for hydrogen blending in natural gas pipelines

We report there is rising interest globally in the use of hydrogen for the provision of electricity or heat to industry, transport, and other applications in low-carbon energy systems. While there is attention to build out dedicated hydrogen infrastructure in the long-term, blending hydrogen into the existing natural gas pipeline network is also thought to be a promising strategy for incorporating hydrogen in the near-term. However, hydrogen injection into the existing gas grid poses additional challenges and considerations related to the ability of current gas infrastructure to operate with blended hydrogen levels. This review paper focuses on analyzing the current understanding of how much hydrogen can be integrated into the gas grid from an operational perspective and identifies areas where more research is needed. The review discusses the technical limits in hydrogen blending for both transmission and distribution networks; facilities in both systems are analyzed with respect to critical operational parameters, such as decrease in energy density, increased flow speed and pressure losses. Safety related challenges such as, embrittlement, leakage and combustion are also discussed. The review also summarizes current regulatory limits to hydrogen blending in different countries, including ongoing or proposed pilot hydrogen blending projects.

08 HYDROGEN↗

DOE OSTI2023

Simulating the Impact of Dynamic Rerouting on Metropolitan-scale Traffic Systems

The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this article, we describe enhancements to the Mobiliti parallel traffic simulator to model dynamic rerouting behavior with the addition of vehicle controller actors and vehicle-to-controller reroute requests. The simulator is designed to support distributed-memory parallel execution using discrete event simulation and be scalable on high-performance computing platforms. We demonstrate the potential of the simulator by analyzing the impact of varying the population penetration rate of dynamic rerouting on the San Francisco Bay Area road network. Using high-performance parallel computing, we can simulate a day in the San Francisco Bay Area with 19 million vehicle trips with 50 percent dynamic rerouting penetration over a road network with 0.5 million nodes and 1 million links in less than three minutes. We present a sensitivity study on the dynamic rerouting parameters, discuss the simulator’s parallel scalability, and analyze system-level impacts of changing the dynamic rerouting penetration. Furthermore, we examine the varying effects on different functional classes and geographical regions and present a validation of the simulation results compared to real-world data.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1987

Independent Orbiter Assessment (IOA): Analysis of the purge, vent and drain subsystem

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. This report documents the independent analysis results corresponding to the Orbiter PV and D (Purge, Vent and Drain) Subsystem hardware. The PV and D Subsystem controls the environment of unpressurized compartments and window cavities, senses hazardous gases, and purges Orbiter/ET Disconnect. The subsystem is divided into six systems: Purge System (controls the environment of unpressurized structural compartments); Vent System (controls the pressure of unpressurized compartments); Drain System (removes water from unpressurized compartments); Hazardous Gas Detection System (HGDS) (monitors hazardous gas concentrations); Window Cavity Conditioning System (WCCS) (maintains clear windows and provides pressure control of the window cavities); and External Tank/Orbiter Disconnect Purge System (prevents cryo-pumping/icing of disconnect hardware). Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Four of the sixty-two failure modes analyzed were determined as single failures which could result in the loss of crew or vehicle. A possible loss of mission could result if any of twelve single failures occurred. Two of the criticality 1/1 failures are in the Window Cavity Conditioning System (WCCS) outer window cavity, where leakage and/or restricted flow will cause failure to depressurize/repressurize the window cavity. Two criticality 1/1 failures represent leakage and/or restricted flow in the Orbiter/ET disconnect purge network which prevent cryopumping/icing of disconnect hardware. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode.

Bynum, M. C., III↗

NASA NTRS2016

Generalized Fluid System Simulation Program, Version 6.0

The Generalized Fluid System Simulation Program (GFSSP) is a general purpose computer program for analyzing steady state and time-dependent flow rates, pressures, temperatures, and concentrations in a complex flow network. The program is capable of modeling real fluids with phase changes, compressibility, mixture thermodynamics, conjugate heat transfer between solid and fluid, fluid transients, pumps, compressors, and external body forces such as gravity and centrifugal. The thermofluid system to be analyzed is discretized into nodes, branches, and conductors. The scalar properties such as pressure, temperature, and concentrations are calculated at nodes. Mass flow rates and heat transfer rates are computed in branches and conductors. The graphical user interface allows users to build their models using the 'point, drag, and click' method; the users can also run their models and post-process the results in the same environment. Two thermodynamic property programs (GASP/WASP and GASPAK) provide required thermodynamic and thermophysical properties for 36 fluids: helium, methane, neon, nitrogen, carbon monoxide, oxygen, argon, carbon dioxide, fluorine, hydrogen, parahydrogen, water, kerosene (RP-1), isobutene, butane, deuterium, ethane, ethylene, hydrogen sulfide, krypton, propane, xenon, R-11, R-12, R-22, R-32, R-123, R-124, R-125, R-134A, R-152A, nitrogen trifluoride, ammonia, hydrogen peroxide, and air. The program also provides the options of using any incompressible fluid with constant density and viscosity or ideal gas. The users can also supply property tables for fluids that are not in the library. Twenty-four different resistance/source options are provided for modeling momentum sources or sinks in the branches. These options include pipe flow, flow through a restriction, noncircular duct, pipe flow with entrance and/or exit losses, thin sharp orifice, thick orifice, square edge reduction, square edge expansion, rotating annular duct, rotating radial duct, labyrinth seal, parallel plates, common fittings and valves, pump characteristics, pump power, valve with a given loss coefficient, Joule-Thompson device, control valve, heat exchanger core, parallel tube, and compressible orifice. The program has the provision of including additional resistance options through User Subroutines. GFSSP employs a finite volume formulation of mass, momentum, and energy conservation equations in conjunction with the thermodynamic equations of state for real fluids as well as energy conservation equations for the solid. The system of equations describing the fluid network is solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods. The application and verification of the code has been demonstrated through 30 example problems.

Majumdar, A. K.↗

DOE OSTI2024

Enhancing Power Grid Resilience with Causal Loops Diagram and Bayesian Networks

Enhancing power grid resilience through improved analysis and planning of Distributed Energy Resources is a key for power system planner. This paper explores the integration of Causal Loop Diagrams (CLDs) and Bayesian Networks (BNs) for enhancing resilience in power systems, focusing on Distributed Energy Resources (DER) planning. By automating CLD analysis in Python's matplotlib, we present a tool for rapid model validation and structural accuracy, crucial for power system planners. This hybrid approach utilizes BNs for inferential depth and CLDs for dynamic system modeling, offering a comprehensive framework for policy formulation and collaborative strategy development against disruptions. Here, we highlight the tool's capability to identify and analyze interconnected feedback loops, facilitating a deeper understanding of DER integration's impact on network resilience. This work aims to bridge quantitative analysis and qualitative insights, addressing the limitations of each method while providing a robust model for power system resilience assessment.

14 SOLAR ENERGY↗

DOE OSTI2021

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Regularization via f -Divergence: An Application to Multi-Oxide Spectroscopic Analysis

In this paper, we explore the application of convolutional neural networks (CNNs) for predicting the chemical composition of complex geologic samples in a simulated Martian atmospheric environment. Specifically, we aim to characterize oxide weight percentages (wt.%) of rock samples analyzed by remote Laser-Induced Breakdown Spectroscopy (LIBS), framing the problem as a multi-target regression task . Neural networks trained on LIBS spectra are prone to overfitting due to high spectral complexity, limited labeled data, and measurement noise. While regularization is critical for improving generalization, common methods (e.g., ℓ 2 regularization) impose constraints not directly tied to data distribution properties. We propose a novel regularization method based on a specific ƒ-divergence induced by a graph-based estimator, designed to constrain the distributional discrepancy between predictions and targets. This regularizer serves a dual purpose: (a) mitigating overfitting by enforcing a constraint on the distributional difference between predictions and noisy targets, and (b) acting as an auxiliary loss that penalizes large divergences. To enable backpropagation, we develop a differentiable approximation of this particular ƒ-divergence, making the method feasible for neural networks. Experiments on ChemCam and SuperCam LIBS calibration spectra show that mathematical equation-divergence regularization outperforms or matches standard regularization methods (ℓ 1 , ℓ 2 , dropout) and the classical baseline, partial least squares (PLS). Combining ƒ-divergence regularization with standard regularization yields further performance gains, indicating that distributional regularization is useful in this context giving a promising direction for robust model training in planetary science applications. Source code is publicly available at Klein and Li (2025), https://doi.org/10.11578/dc.20250530.7.

58 GEOSCIENCES↗

DOE OSTI2021

Training Spiking Neural Networks with Synaptic Plasticity under Integer Representation

Neuromorphic computing is emerging as a promising Beyond Moore computing paradigm that employs event-triggered computation and non-von Neumann hardware. Spike Timing Dependent Plasticity (STDP) is a well-known bio-inspired learning rule that relies on activities of locally connected neurons to adjust the weights of their respective synapses. In this work, we analyze a basic STDP rule and its sensitivity on the different hyperparameters for training spiking neural networks (SNNs) with supervision, customized for a neuromorphic hardware implementation with integer weights. We compare the classification performance on four UCI datasets (iris, wine, breast cancer and digits) that depict varying levels of complexity. We perform a search for optimal set of hyperparameters using both grid search and Bayesian optimization. Through the use of Bayesian optimization, we show the general trends in hyperparameter sensitivity in SNN classification problem. With the best sets of hyperparameters, we achieve accuracies comparable to some of the best performing SNNs on these four datasets. With a highly optimized supervised STDP rule we show that these accuracies can be achieved with just 20 epochs of training.

Kulkarni, Shruti↗

NASA NTRS1981

The small community solar thermal power experiment. Parabolic dish technology for industrial process heat application

Aspects of incorporating a thermal energy transport system (ETS) into a field of parabolic dish collectors for industrial process heat (IPH) applications were investigated. Specific objectives are to: (1) verify the mathematical optimization of pipe diameters and insulation thicknesses calculated by a computer code; (2) verify the cost model for pipe network costs using conventional pipe network construction; (3) develop a design and the associated production costs for incorporating risers and downcomers on a low cost concentrator (LCC); (4) investigate the cost reduction of using unconventional pipe construction technology. The pipe network design and costs for a particular IPH application, specifically solar thermally enhanced oil recovery (STEOR) are analyzed. The application involves the hybrid operation of a solar powered steam generator in conjunction with a steam generator using fossil fuels to generate STEOR steam for wells. It is concluded that the STEOR application provides a baseline pipe network geometry used for optimization studies of pipe diameter and insulation thickness, and for development of comparative cost data, and operating parameters for the design of riser/downcomer modifications to the low cost concentrator.

Polzien, R. E.↗

NASA NTRSDate not supplied

Evaluating a Cognitive Extension for the Licklider Transmission Protocol in a Spacecraft Emulation Testbed

In space communications, particularly when involving regions beyond cislunar space, the development of advanced networking solutions is essential to address the challenges posed by limited connectivity, substantial propagation delays, and radio signal variations. This study explores a data-driven approach to the Licklider Transmission Protocol (LTP), specifically focusing on dynamically adjusting the maximum payload size of segments. Prior research has emphasized the potential benefits of dynamically adjusting this parameter, introducing the concept of Cognitive LTP. This paper presents a software implementation of Cognitive LTP (CLTP) within an open-source Delay Tolerant Networking (DTN) framework, specifically the High-rate Delay Tolerant Networking (HDTN), and experimentally evaluates its performance under realistic space conditions. Leveraging the Cognitive Ground Testbed (CGT), developed by NASA GRC for spacecraft communication emulation, this study effectively bridges the gap between theoretical advancements and practical applications. By thoroughly analyzing CLTP’s functionality within the CGT, this research offers insights into the practical implications of adaptive networking strategies, emphasizing the importance of conducting tests in relevant environments for the maturation of space communication technologies.

Delay Tolerant Networking↗