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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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At least 343 records · Page 19

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NASA NTRS1974

Dicarboxylic acids in the Murchison meteorite

A large suite of aliphatic dicarboxylic acids was found in extracts from the carbonaceous chondrite. This suite includes many of the branched and straight chained isomers with up to nine carbon atoms. The large number of compounds and the isomeric distribution suggest that the acids are the product of an abiotic synthesis. The dicarboxylic acids found have been identified by their gas chromatography retention values and by comparison of their mass spectra with those of authentic standards.

Lawless, J. G.↗

NASA NTRS2016

Exocometary Gas in the HD 181327 Debris Ring

An increasing number of observations have shown that gaseous debris discs are not an exception. However, until now, we only knew of cases around A stars. Here we present the first detection of 12CO (2-1) disc emission around an F star, HD 181327, obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) observations at 1.3 mm. The continuum and CO emission are resolved into an axisymmetric disc with ring-like morphology. Using a Markov chain Monte Carlo method coupled with radiative transfer calculations, we study the dust and CO mass distribution. We find the dust is distributed in a ring with a radius of 86.0 +/- 0.4 au and a radial width of 23.2 +/- 1.0 au. At this frequency, the ring radius is smaller than in the optical, revealing grain size segregation expected due to radiation pressure. We also report on the detection of low-level continuum emission beyond the main ring out to approximately 200 au. We model the CO emission in the non-local thermodynamic equilibrium regime and we find that the CO is co-located with the dust, with a total CO gas mass ranging between 1.2 x 10(exp -6) solar mass and 2.9 x 10(exp -6) solar mass, depending on the gas kinetic temperature and collisional partners densities. The CO densities and location suggest a secondary origin, i.e. released from icy planetesimals in the ring. We derive a CO+CO2 cometary composition that is consistent with Solar system comets. Due to the low gas densities, it is unlikely that the gas is shaping the dust distribution.

exocometary gas↗

NASA NTRSDate not supplied

A Framework for Sense and Follow Convoys for Collective Autonomous Mobility

Convoys are collections of vehicles that share similar velocity profiles. In the sense-and follow convoy construct, a vehicle can follow a member of an existing convoy resulting in convoy chaining. We prove conditions under which this can occur such that the resulting convoy chain is bounded and the velocities of all members converge to the leader velocity through locally distributed control algorithms. To assess the approach, we utilize a six degree-of-freedom rigid body vehicle dynamics model for each agent. A dynamic inversion based control law is used to track the desired trajectory generated by the convoy algorithms. To simulate the system, we utilize Unity, a cross-platform game engine, where 3-dimensional terrain, building, and vehicle data can be visualized. Numerous simulations are presented to illustrate the concept.

convoys↗

NASA NTRSDate not supplied

A Framework for Sense and Follow Convoys for Collective Autonomous Mobility

convoys↗

DOE OSTI2025

Discrete generative diffusion models without stochastic differential equations: A tensor network approach

Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs, this is done by learning a “score function” that reverses the effect of adding diffusive noise to the distribution of interest. Here we consider the generalisation of DMs to lattice systems with discrete degrees of freedom, and where noise is added via Markov chain jump dynamics. We show how to use tensor networks (TNs) to efficiently define and sample such “discrete diffusion models” (DDMs) without explicitly having to solve a stochastic differential equation. We show the following: (i) by parametrising the data and evolution operators as TNs, the denoising dynamics can be represented exactly; (ii) the auto-regressive nature of TNs allows to generate samples efficiently and without bias; (iii) for sampling Boltzmann-like distributions, TNs allow to construct an efficient learning scheme that integrates well with Monte Carlo. We illustrate this approach to study the equilibrium of two models with non-trivial thermodynamics, the d = 1 constrained Fredkin chain and the d = 2 Ising model. Published by the American Physical Society 2025

Causer, Luke (ORCID:0000000194243473)↗

DOE OSTI2024

Modeling Framework for the Assessment of a Sustainable Hydrogen Production and Supply Chain Network in California

The cost-effective and sustainable deployment of hydrogen supply and demand networks, especially in large economic regions like California, can be challenging considering the spatial-temporal availability and variability of the different actors across the network such as production processes, distribution modes, and end-users. In this presentation, we will provide an overview and demonstration of a modeling framework used to assess the environmental, economic, and human health impacts of plausible hydrogen production and supply chain networks in California. Scenarios focus on green hydrogen production pathways using water electrolysis and biomass gasification. End-use applications included in the model are transit, medium and heavy-duty trucking, port authorities, and power and aviation companies that currently consume natural gas, diesel, and aviation fuel for their day-to-day operation. Representative locations for hydrogen production and end-use are based on recent projections of the hydrogen economy in California. All mass and energy flows, as well as estimated emissions, are based on H2A process model designs and projections of technology performance, literature review, and LBNL process, economic and life cycle modeling, and not on company data for the sake of this presentation. Human health impacts are included following methodologies developed for the University of California Irvine HyDeal project. Life cycle phases associated with hydrogen production include feedstock preparation (water and biomass), energy production and consumption (renewable, grid, and combination of renewable and grid electricity), maintenance (chemical utilization in electrolysis and natural gas combustion in gasification), carbon sequestration, hydrogen storage (compression and liquefaction), and distribution (truck and pipeline). We apply the framework utilizing California specific emission factors, financial data, and human health damages and explore the impact of network characteristics on results. Example variations include: the inclusion of policy incentives or not, different representations of the electricity grid and source, electrolysis versus gasification versus combinations of both for production, liquefaction versus compression based on producer capacity cutoffs, transportation truck versus pipeline based on existing infrastructure, and ultimate end use. Comparison of these different scenarios can help inform future projects by demonstrating the trade-offs among environmental, economic, and human health impacts. This model, automated in R, is a starting platform upon which new analysis, modeling capabilities, locations, and emission factors can be rapidly tested and integrated.

Zaki, Mohammed Tamim↗

DOE OSTI2025

Porous and Amorphous Mn x Mo 3 S 13 Chalcogel Electrode for High-Capacity Conversion-Based Lithium-Ion Batteries

While Li-ion batteries (LIBs) are a leading energy storage technology, their energy densities are limited by the low capacity of conventional intercalation cathodes, driving interest in high energy-density Li–S batteries that make use of conversion chemistry. Achieving high capacity, reversibility, and cycle stability, and controlling volume changes in conversion batteries during the charge–discharge process, however, remains challenging. Here, in this study, we present a porous, amorphous, sulfide-based Mn x Mo 3 S 13 chalcogel, which concurrently offers high capacity and cycle stability. The solution-processable room temperature synthesized Mn x Mo 3 S 13 (x = 0.25) chalcogel exhibits a local structure that resembles the Mo 3 S 13 cluster with Mn 2+ distributed across the Mo 3 S 13 matrix, as determined by synchrotron X-ray pair distribution function (PDF) and extended X-ray absorption fine structure (EXAFS). Ab initio molecular dynamics (AIMD) simulations reveal that Mn 2+ incorporation shortens the polysulfide chain in the gel matrix compared to the Mo 3 S 13 chalcogel, while forming a coordination environment with disulfide groups, analogous to the experimental findings. A Li/Mn 0.25 Mo 3 S 13 half-cell delivers 897 mAh g –1 capacity during the first discharge and retains 571 mAh g –1 capacity after 100 cycles at a C/3 rate. Distribution of relaxation time (DRT) unveils a stable solid–electrolyte interphase (SEI) formation upon cycling that enables charge–discharge reversibility. Here, the enhanced capacity retention and cycle stability compared to those of the Li/Mo 3 S 13 cell are attributed to the reduced dissolution of active mass into the electrolyte, facilitated by the formation of shorter polysulfide chains within the Mn 0.25 Mo 3 S 13 structure and the strong affinity of Lewis-acidic Mn 2+ for polysulfide anions generated during the charge–discharge process of the Li/Mn 0.25 Mo 3 S 13 cell. Thus, this work illustrates a design principle of material for high-capacity and cycle-stable Li-metal sulfide batteries.

25 ENERGY STORAGE↗

DOE OSTI2020

Variations of the physical parameters of the blazar Mrk 421 based on analysis of the spectral energy distributions

Abstract We report on the variations of the physical parameters of the jet observed in the blazar Mrk 421, and discuss the origin of X-ray flares in the jet, based on analysis of several spectral energy distributions (SEDs). The SEDs are modeled using the one-zone synchrotron self-Compton model, its parameters determined using a Markov chain Monte Carlo method. The lack of data at TeV energies means many of the parameters cannot be uniquely determined and are correlated. These are studied in detail. We find that the optimal solution can be uniquely determined only when we apply a constraint to one of four parameters: the magnetic field (B), the Doppler factor, the size of the emitting region, and the normalization factor of the electron energy distribution. We used 31 sets of SEDs from 2009 to 2014 with optical–UV data observed with UVOT/Swift and the Kanata telescope, X-ray data with XRT/Swift, and γ-ray data with the Fermi Large Area Telescope. The result of our SED analysis suggests that, in the X-ray faint state, the emission occurs in a relatively small area (∼1016 cm) with a relatively strong magnetic field (B ∼ 10−1 G). The X-ray bright state shows a tendency opposite to that of the faint state, that is, a large emitting area (∼1018 cm), probably downstream of the jet, and a weak magnetic field (B ∼ 10−3 G). The high X-ray flux was due to an increase in the maximum energy of electrons. On the other hand, the presence of two kinds of emitting areas implies that the one-zone model is unsuitable for reproducing at least part of the observed SEDs.

Yamada, Yurika↗

NASA NTRS1993

Interaction of various mechanical activity models in regulation of myosin heavy chain isoform expression

The purpose of this study was to determine the effects of a novel combination of mechanical activity paradigms on the isomyosin distribution in rat hindlimb muscles. Thirty female Sprague-Dawley rats were divided into five experimental groups as follows: normal control, functional overload (OV) of the plantaris, OV in conjunction with hindlimb suspension (OV-S), and a combination of OV-S and either static standing weight-bearing activity (OV-SS) or high-incline treadmill exercise (OV-SE). OV of the plantaris resulted in significant hypertrophy and significant fast-to-slow isomyosin shifts. These changes were completely inhibited by the addition of hindlimb suspension (OV-S). Also, neither of the two weight-bearing regimes (OV-SS and OV-SE) was able to attenuate the suspension-induced atrophy. In the vastus intermedius and vastus lateralis, however, OV-SS was able to partially retard the atrophy associated with suspension. In both the plantaris and vastus intermedius, only OV-SS was able to partially reverse the slow-to-fast isomyosin transitions associated with suspension. These results suggest that the type of mechanical activity is important in determining adaptation to altered loading conditions, with OV-SS appearing more effective than OV-SE in reversing the effects of unweighting.

Diffee, Gary M.↗

DOE OSTI2024

First measurement in a magnetic confinement fusion experiment of the H 3 + H 3 → He 5 + n intermediate two-body resonant reaction

We report on the first experimental measurements made at a magnetic confinement fusion device of the tritium(T)-tritium(T) reaction T + T → He 4 + 2 n indicating the presence of the intermediate two-body resonant reaction T + T → He 5 + n . During the second deuterium-tritium campaign (DTE2) at the Joint European Torus, measurements of fusion plasmas with high tritium concentrations, n T / ( n T + n D ) ≈ 0.99 , heated with tritium neutral beam injection, were performed using the neutron time-of-flight (TOF) spectrometer TOFOR. We detect a peak in the neutron emission TOF spectrum consistent with the two-body resonant reaction. The TT neutron emission energy spectrum is modeled using an R -matrix framework where the distributions of the most likely model parameters given our experimental TOF data are determined utilizing a Markov chain Monte Carlo approach. We compare our best estimate of the T + T neutron emission energy spectrum with results obtained at inertial confinement fusion experiments at the OMEGA facility and find a spectral shape that is consistent with the energy dependency in the neutron spectrum observed at OMEGA. Published by the American Physical Society 2024

Physics↗

DOE OSTI2024

Online MCMC Thinning with Kernelized Stein Discrepancy

A fundamental challenge in Bayesian inference is efficient representation of a target distribution. Many nonparametric approaches do so by sampling a large number of points using variants of Markov chain Monte Carlo (MCMC). Here, we propose an MCMC variant that retains only those posterior samples which exceed a kernelized Stein discrepancy (KSD) threshold, which we call KSD thinning. We establish the convergence and complexity trade-offs for several settings of KSD thinning as a function of the KSD threshold parameter, sample size, and other problem parameters. We provide experimental comparisons against other online nonparametric Bayesian methods that generate low-complexity posterior representations. We observe superior consistency/complexity trade-offs across a range of settings including MCMC sampling on two Bayesian inference problems from the biological sciences, and 10 × inference speedup and storage reduction for Bayesian neural networks with no loss of accuracy and no increase in training time. Our code is available at https://github.com/colehawkins/KSD-Thinning.

Bayesian inference↗

DOE OSTI2020

A software package for modeling and simulating fault graphs

This report describes a novel fault graph modeling language and a simulation tool for executing models specified in the language. The modeling language has three primary features that distinguish it from similar reliability analysis tools. These are (1) a random variable modeling several distinct outcomes of a single fault; (2) chains of faults in which one fault triggers another; and (3) time to fail sampled from probability distributions including positive normal, exponential, Weibull with a minimum, or immediate. These features are motivated by their use in a historical analysis of centrifuge reliability.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2016

Developing Atmospheric Retrieval Methods for Direct Imaging Spectroscopy of Gas Giants in Reflected Light I: Methane Abundances and Basic Cloud Properties

Reflected light spectroscopy and photometry of cool, directly imaged extrasolar giant planets are expected to be performed in the next decade by space-based telescopes equipped with optical wavelength coronagraphs and integral field spectrographs, such as the Wide-Field Infrared Survey Telescope (WFIRST). We are developing a new atmospheric retrieval methodology to help assess the science return and inform the instrument design for such future missions, and ultimately interpret the resulting observations. Our retrieval technique employs an albedo model coupled with both a Markov chain Monte Carlo Ensemble Sampler (emcee) and a multimodal nested sampling algorithm (MultiNest) to map the posterior distribution. This combination makes the global evidence calculation more robust for any given model, and highlights possible discrepancies in the likelihood maps. Here we apply this methodology to simulated spectra of cool giant planets. As a proof-of-concept, our current atmospheric model contains 1 or 2 cloud layers, methane as a major absorber, and a H2-He background gas. This 6-to-9 parameter model is appropriate for Jupiter-like planets and can be easily expanded in the future. In addition to deriving the marginal likelihood distribution and confidence intervals for the model parameters, we perform model selection to determine the significance of methane and cloud detection as a function of expected signal-to-noise, in the presence of spectral noise correlations. After internal validation, the method is applied to realistic reflected-light spectra of Jupiter, Saturn, and HD 99492 c, a likely observing target. We find that the presence or absence of clouds and methane can be determined with high accuracy, while parameters uncertainties are model-dependent.

spectroscopy↗

NASA NTRS2017

Reconfigurable Model Execution in the OpenMDAO Framework

NASA's OpenMDAO framework facilitates constructing complex models and computing their derivatives for multidisciplinary design optimization. Decomposing a model into components that follow a prescribed interface enables OpenMDAO to assemble multidisciplinary derivatives from the component derivatives using what amounts to the adjoint method, direct method, chain rule, global sensitivity equations, or any combination thereof, using the MAUD architecture. OpenMDAO also handles the distribution of processors among the disciplines by hierarchically grouping the components, and it automates the data transfer between components that are on different processors. These features have made OpenMDAO useful for applications in aircraft design, satellite design, wind turbine design, and aircraft engine design, among others. This paper presents new algorithms for OpenMDAO that enable reconfigurable model execution. This concept refers to dynamically changing, during execution, one or more of: the variable sizes, solution algorithm, parallel load balancing, or set of variables-i.e., adding and removing components, perhaps to switch to a higher-fidelity sub-model. Any component can reconfigure at any point, even when running in parallel with other components, and the reconfiguration algorithm presented here performs the synchronized updates to all other components that are affected. A reconfigurable software framework for multidisciplinary design optimization enables new adaptive solvers, adaptive parallelization, and new applications such as gradient-based optimization with overset flow solvers and adaptive mesh refinement. Benchmarking results demonstrate the time savings for reconfiguration compared to setting up the model again from scratch, which can be significant in large-scale problems. Additionally, the new reconfigurability feature is applied to a mission profile optimization problem for commercial aircraft where both the parametrization of the mission profile and the time discretization are adaptively refined, resulting in computational savings of roughly 10% and the elimination of oscillations in the optimized altitude profile.

Hwang, John T.↗

NASA NTRSDate not supplied

A New Architecture for Parallelization of Complex Spacecraft Trajectory Optimization Scans

This paper describes CopScanner, a new component of the Copernicus ecosystem for spacecraft trajectory design and optimization. CopScanner is a Python library being developed at the NASA JSC which enables easy parallelization of Copernicus scans. CopScanner is currently being developed and implemented for production of Copernicus trajectory scans for upcoming Artemis Missions (Artemis II and beyond). On the backend, CopScanner utilizes Dask, an open-source Python library for parallel computing which enables parallelization over both multi-core local machines and large-scale distributed computing clusters. CopScanner abstracts the trajectory scanning process into a DAG which is constructed using a chain of individual subscans. Each node in the DAG executes a python module, called the callable, for which there are built-in defaults, or users may specify their own. Support for custom callables makes CopScanner a versatile trajectory optimization software. All output files and associated metadata from a CopScanner scan are compressed and stored in a two-file output, collectively called the FileStore, consisting of a SQLite database and a compressed JSON MessagePack file, for which CopScanner provides a Python class for interaction.

Quentin Moore↗

DOE OSTI2023

Efficient Subset Simulation using Hamiltonian Neural Network enhanced Markov Chain Monte Carlo Methods

The Monte Carlo method delivers an unbiased estimate of the probability of failure. However, the variance of the estimate depends on the number of evaluated samples. This number must be very large for estimations of a low probability of failure. If the evaluation of each sample is computationally expensive, the crude Monte Carlo simulation strategy is impracticable. Therefore, subset simulations are used to reduce the required number of evaluations. Subset simulations require a Markov Chain Monte Carlo sampler, such as the random walk Metropolis-Hastings algorithm. The algorithm, however, struggles with sampling in low-probability regions, especially if they are narrow. As a consequence, advanced Markov Chain Monte Carlo simulations have been developed. In particular, the Hamiltonian Monte Carlo method explores the target distribution rapidly. Driven by the idea of Hamiltonian dynamics, this sampler provides a non-random walk through the target distribution. The incorporation of subset simulation and Hamiltonian Monte Carlo methods has shown promising results for reliability analysis. One downside of the Hamiltonian Monte Carlo method is that gradient evaluations are computationally expensive, especially when dealing with high-dimensional problems and evaluating long trajectories. We show that integrating Hamiltonian neural networks in Hamiltonian Monte Carlo simulations significantly speeds up the sampling task. Furthermore, the enhancement of adaptive trajectory length within the Hamiltonian Monte Carlo results in the efficient proposal of the following states. Based on this recent enhancement, we provide a fast sampling strategy for subset simulations using Hamiltonian neural networks to replace the evaluation of the gradient and significantly speed up the Hamiltonian Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Fault Graph (fg)

This software package offers a novel fault graph modeling language and a simulation tool for executing models specified in the language. The modeling language has three primary features that distinguish it from similar reliability analysis tools. These are (1) a random variable modeling several distinct outcomes of a single fault; (2) chains of faults in which one fault triggers another; and (3) time to fail sampled from probability distributions including positive normal, exponential, Weibull with a minimum, or immediate.

Nutaro, James↗

DOE OSTI2021

Influence of functional groups on low-temperature combustion chemistry of biofuels

Ongoing progress in synthetic biology, metabolic engineering, and catalysis continues to produce a diverse array of advanced biofuels with complex molecular structure and functional groups. In order to integrate biofuels into existing combustion systems, and to optimize the design of next-generation combustion systems, understanding connections between molecular structure and ignition at low-temperature conditions (< 1000 K) remains a priority that is addressed in part using chemical kinetics modeling. The development of predictive models relies on detailed information, derived from experimental and theoretical studies, on molecular structure and chemical reactivity, both of which influence the balance of chain reactions that occur during combustion – propagation, termination, and branching. In broad context, three main categories of reactions affect ignition behavior: (i) initiation reactions that generate a distribution of organic radicals, ; (ii) competing unimolecular decomposition of and bimolecular reaction of with O2; (iii) decomposition mechanisms of peroxy radical adducts (RO), including isomerization via RO ⇌ OOH. Furthermore, all three categories are influenced by functional groups in different ways, which causes a shift in the balance of chain reactions that unfold over complex temperature- and pressure-dependent mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗