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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

On the minimum core mass for giant planet formation at wide separations

In the core accretion hypothesis, giant planets form by gas accretion onto solid protoplanetary cores. The minimum (or critical) core mass to form a gas giant is typically quoted as 10 M {sub ⊕}. The actual value depends on several factors: the location in the protoplanetary disk, atmospheric opacity, and the accretion rate of solids. Motivated by ongoing direct imaging searches for giant planets, this study investigates core mass requirements in the outer disk. To determine the fastest allowed rates of gas accretion, we consider solid cores that no longer accrete planetesimals, as this would heat the gaseous envelope. Our spherical, two-layer atmospheric cooling model includes an inner convective region and an outer radiative zone that matches onto the disk. We determine the minimum core mass for a giant planet to form within a typical disk lifetime of 3 Myr. The minimum core mass declines with disk radius, from ∼8.5 M {sub ⊕} at 5 AU to ∼3.5 M {sub ⊕} at 100 AU, with standard interstellar grain opacities. Lower temperatures in the outer disk explain this trend, while variations in disk density are less influential. At all distances, a lower dust opacity or higher mean molecular weight reduces the critical core mass. Our non-self-gravitating, analytic cooling model reveals that self-gravity significantly affects early atmospheric evolution, starting when the atmosphere is only ∼10% as massive as the core.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning

Spiking Neural Networks (SNNs) are computational models inspired by the event-driven communication and connectivity patterns of biological neural circuits. They enable high energy efficiency and natural support for diverse architectures ranging from layered networks to small-world and graphstructured topologies. In this work, we introduce NeuroCoreX, an open-source, FPGA-based spiking neural network emulator that provides real-time, on-chip learning and flexible network organization. NeuroCoreX supports both feedforward sensory inputs streamed directly from sensors or PCs via UART and recurrent on-chip connectivity, enabling simultaneous processing and learning from external stimuli and internal network dynamics-capabilities rarely available in existing FPGA SNN platforms. The system implements a Leaky Integrate-and-Fire (LIF) neuron model with current-based synapses and supports pair-based STDP learning on both feedforward and recurrent synapses. A lightweight Python interface enables interactive configuration, live monitoring, weight read-back, and experiment control. Importantly, NeuroCoreX is tightly integrated with the SuperNeuroMAT simulator, allowing SNN models to be transferred seamlessly from software to hardware for hardware-in-the-loop development. By combining real-time plasticity, flexible connectivity, and an open-source VHDL implementation, NeuroCoreX provides an extensible and accessible platform for neuromorphic research, algorithm-hardware co-design, and energy-efficient edge intelligence.

Gautam, Ashish [ORNL]↗
NASA NTRS1984

Atrophy and growth failure of rat hindlimb muscles in tail-cast suspension

The primary objective of the present study is related to an evaluation of a modified tail-cast suspension model as a means of identifying metabolic factors which control or are associated with muscle atrophy and growth failure. Two different control conditions (normal and tail-casted weight bearing) were studied to determine the appropriate control for tail-cast suspension. A description is presented of a model which is most useful for studying atrophy of hindlimb muscles under certain conditions. Female Sprague-Dawley rats were employed in the experiments. Attention is given to growth rate and urinary excretion of urea and ammonia in different types of rats, the relationship between body weight and skeletal muscle weight, and the relationship between animal body weight and rates of protein synthesis and protein degradation.

Jaspers, S. R.↗
NASA NTRS1993

Work capacity and metabolic and morphologic characteristics of the human quadriceps muscle in response to unloading

The response of skeletal muscle to unweighting was studied in six healthy males who were subjected to four weeks of lowerlimb suspension. They performed three bouts of 30 consecutive maximal concentric knee extensions, before unloading and the day after (POST 1), 4 days after (POST 2) and 7 weeks after (REC) resumed weight-bearing. Peak torque of each contraction was recorded and work was calculated as the mean of the average peak torque for the three bouts and fatigability was measured as the decline in average peak torque over bouts. Needle biopsies were obtained from m. vastus lateralis of each limb before and at POST 1. Muscle fibre type composition and area, capillarity and the enzyme activities of citrate synthase (CS) and phosphofructokinase (PFK) were subsequently analysed. Mean average peak torque for the three bouts at POST1, POST2 and REC was reduced (P < 0.05) by 17, 13 and 7%, respectively. Fatigability was greater (P < 0.05) at POST2 than before unloading. Type I, IIA and IIB percentage, Type I and II area and capillaries per fibre of Type I and II did not change (P > 0.05) in response to unloading. The activity of CS, but not PFK, decreased (P < 0.05) after unloading. The weight-bearing limb showed no changes in the variables measured. The results of this study suggest that this human lowerlimb suspension model produces substantial impairments of work and oxidative capacity of skeletal muscle. The performance decrements are most likely induced by lack of weight-bearing.

NASA Center KSC↗
DOE OSTI2026

Bayesian Optimization of Catalysis with In-Context Learning

Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning (ICL), allowing the model to observe query-relevant examples at inference time and eliminating the need for additional weight updates to generalize beyond its original training data. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-4o, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to materials discovery by representing materials as synthesis and testing procedures for use in natural language prompts. This Bayesian, design-first approach prioritizes optimization toward target material properties before detailed characterization, in contrast to conventional experimental workflows that often emphasize characterization of suboptimal materials. On benchmarks like aqueous solubility and oxidative coupling of methane (OCM), BO-ICL matches or outperforms Gaussian processes. In live experiments on the reverse water–gas shift (RWGS) reaction, BO-ICL identifies multimetallic catalysts that approach equilibrium CO yield within 6 and 10 iterations from a pool of 3,700 and 360,000 candidates, respectively. Our method redefines materials representation and accelerates discovery, with broad applications across catalysis, materials science, and AI.

Calibration↗
NASA NTRS2005

Development of the Architectural Simulation Model for Future Launch Systems and its Application to an Existing Launch Fleet

A significant portion of lifecycle costs for launch vehicles are generated during the operations phase. Research indicates that operations costs can account for a large percentage of the total life-cycle costs of reusable space transportation systems. These costs are largely determined by decisions made early during conceptual design. Therefore, operational considerations are an important part of vehicle design and concept analysis process that needs to be modeled and studied early in the design phase. However, this is a difficult and challenging task due to uncertainties of operations definitions, the dynamic and combinatorial nature of the processes, and lack of analytical models and the scarcity of historical data during the conceptual design phase. Ultimately, NASA would like to know the best mix of launch vehicle concepts that would meet the missions launch dates at the minimum cost. To answer this question, we first need to develop a model to estimate the total cost, including the operational cost, to accomplish this set of missions. In this project, we have developed and implemented a discrete-event simulation model using ARENA (a simulation modeling environment) to determine this cost assessment. Discrete-event simulation is widely used in modeling complex systems, including transportation systems, due to its flexibility, and ability to capture the dynamics of the system. The simulation model accepts manifest inputs including the set of missions that need to be accomplished over a period of time, the clients (e.g., NASA or DoD) who wish to transport the payload to space, the payload weights, and their destinations (e.g., International Space Station, LEO, or GEO). A user of the simulation model can define an architecture of reusable or expendable launch vehicles to achieve these missions. Launch vehicles may belong to different families where each family may have it own set of resources, processing times, and cost factors. The goal is to capture the required resource levels of the major launch elements and their required facilities. The model s output can show whether or not a certain architecture of vehicles can meet the launch dates, and if not, how much the delay cost would be. It will also produce aggregate figures of missions cost based on element procurement cost, processing cost, cargo integration cost, delay cost, and mission support cost. One of the most useful features of this model is that it is stochastic where it accepts statistical distributions to represent the processing times mimicking the stochastic nature of real systems.

Rabadi, Ghaith↗
DOE OSTI2019

Association of Maternal Social Relationships With Cognitive Development in Early Childhood

This study examines how different types of social network structures are associated with early cognitive development in children. Objectives To assess how social relationships and structures are associated with early cognitive development and to elucidate whether variations in the mother’s social networks alter a child’s early cognitive development patterns. Design, Setting, and Participants This cohort study used data from 1082 mother-child pairs in the University of Tennessee Health Science Center–Conditions Affecting Neurocognitive Development and Learning and Early Childhood project to examine the association between networks of different levels of complexity (triad, family, and neighborhood) and child cognitive performance after adjustment for the mother’s IQ, birth weight, and age, and the father’s educational level. The final model was adjusted for the household poverty level. Data were collected from December 2006 through January 2014 and analyzed from October through November 2018. Exposures The child-mother relationship, child-mother-father triad, family setting, child’s dwelling network, mother’s social support network, and neighborhood networks. Main Outcomes and Measures Measure of cognitive development of the child using Bayley Scales of Infant Development (BSID) at 2 years of age. Results Of 1082 participants, 544 (50.3%) were males and 703 (65.1%) were African American; the mean (SD) age was 2.08 (0.12) years. Large family size had a negative association with early cognitive development, with a mean 2.21-point decrease in BSID coefficient score (95% CI, 0.40 to 4.02; P = .01). Mother’s social support network size was positively associated early cognitive development, with a mean 0.40-point increase in BSID coefficient score (95% CI, 0.001 to 0.80; P = .05). Knowing many neighbors was not statistically significantly associated with early cognitive development, with a mean 1.39-point increase in BSID coefficient score (95% CI, -0.04 to 2.83; P = .06). Conclusions and Relevance The findings suggest that maternal social relationships are associated with cognitive development in children and that social relationships beyond the mother-child-father triad are significantly associated with children’s cognitive development. This study investigates the environmental influences on child health outcomes and, specifically, how early cognitive development is associated with social networks for the primary caregiver.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Diffusion of biomass pyrolysis products in H-ZSM-5 by molecular dynamics simulations

Diffusion of biomass pyrolysis vapors and their upgraded products is an essential catalytic property of zeolites during catalytic fast pyrolysis and likely plays a critical role in the selectivity of these catalysts. Characterizing the diffusivities of representative biofuel molecules is critical to understand shape selectivity and interpret product distribution. Yet, experimental measurements on the diffusivities of oxygenated biofuel molecules at pyrolysis temperatures are very limited in the literature. As an alternative approach, we conducted MD simulations to measure the diffusion coefficients of several selected molecules that are representative of biomass pyrolysis vapors, namely water, methanol, glycolaldehyde, and toluene in H-ZSM-5 zeolite. The results show the diffusion coefficients calculated via MD simulations are consistent with available NMR measurements at room temperature. The effect of molecular weight and molecular critical diameter on the diffusivity among the chosen model compounds is also examined. Furthermore, we have characterized the diffusivities of representative biofuel molecules, namely xylene isomers, in H-ZSM-5. Our calculations determined that the ratio of the diffusion coefficients for xylene isomers is p -xylene: o -xylene: m -xylene ≈ 83:3:1 at 700 K. Furthermore, our results also demonstrate the different diffusivity between p-xylene and toluene is due to the molecular orientations when the molecules diffuse along the channels in H-ZSM-5 and provide deep insight into the effect of molecular orientation on its diffusivity.

09 BIOMASS FUELS↗
DOE OSTI2011

In-plane and growth direction electron cyclotron effective mass in short period InAs/GaSb semiconductor superlattices

In plane and growth direction electron effective mass in short period InAs/GaSb semiconductor superlattices (SL) was measured using cyclotron resonance at different orientations of magnetic field with respect to SL growth direction. It was demonstrated that the electron spectrum near the bottom of the SL subband has 3D character, with the in-plane effective masses ranging from 0.023 m{sub 0} to 0.028 m{sub 0} and growth direction effective masses of 0.03-0.034 m{sub 0} depending on the SL period and growth conditions. The measured effective masses are close to those calculated in the weak coupling limit of the Kronig-Penney model. In this limit the SL electron effective mass is a weighted average of the electron effective masses of corresponding bulk materials. Correlation between the magnitude of cyclotron mobility, amplitude of negative magnetoresistance, and steepness of the long wavelength side of the photoluminescence spectrum indicate that the crystalline structure disorder is a major factor contributing to the momentum relaxation time of the electrons.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Spatial coupling of gyrokinetic simulations, a generalized scheme based on first-principles

In this paper, we present a scheme that spatially couples two gyrokinetic codes using first-principles. Coupled equations are presented and a necessary and sufficient condition for ensuring accuracy is derived. This new scheme couples both the field and the particle distribution function. The coupling of the distribution function is only performed once every few time-steps, using a five-dimensional (5D) grid to communicate the distribution function between the two codes. This 5D grid interface enables the coupling of different types of codes and models, such as particle and continuum codes, or delta-f and total-f models. Transferring information from the 5D grid to the marker particle weights is achieved using a new resampling technique. Demonstration of the coupling scheme is shown using two XGC gyrokinetic simulations for both the core and edge. We also apply the coupling scheme to two continuum simulations for a one-dimensional advection–diffusion problem.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Reversed dynamics of bottlebrush polymers with stiff backbone and flexible side chains

The segmental dynamics of bottlebrush polymers with a stiff backbone and flexible side chains has been studied. The segmental relaxation time of side chains attached to a flexible backbone follows the same trend as linear polymers, an increase with the increasing molecular weight, but is slowed down compared to their linear counterparts. Theoretical work predicts a reversal of the molecular weight dependence of the relaxation time for stiff backbones. As a model for a stiff-g-flexible system, bottlebrushes with poly(norbornene) backbone and poly(propylene oxide) side chains, PNB-g-PPO, at a uniform grafting density have been synthesized and characterized with quasi-elastic neutron scattering. Indeed, the anticipated reversed dynamics was found. Increasing the side chain length decreases the segmental relaxation time. This indicates the importance of the characteristics of the grafting site beyond a simplified picture of an attached side chain. The mean square displacement shows a similar trend with longer side chains exhibiting a larger displacement.

Chemistry↗
DOE OSTI2012

Fast patient-specific Monte Carlo brachytherapy dose calculations via the correlated sampling variance reduction technique

Purpose: To demonstrate potential of correlated sampling Monte Carlo (CMC) simulation to improve the calculation efficiency for permanent seed brachytherapy (PSB) implants without loss of accuracy. Methods: CMC was implemented within an in-house MC code family (PTRAN) and used to compute 3D dose distributions for two patient cases: a clinical PSB postimplant prostate CT imaging study and a simulated post lumpectomy breast PSB implant planned on a screening dedicated breast cone-beam CT patient exam. CMC tallies the dose difference, {Delta}D, between highly correlated histories in homogeneous and heterogeneous geometries. The heterogeneous geometry histories were derived from photon collisions sampled in a geometrically identical but purely homogeneous medium geometry, by altering their particle weights to correct for bias. The prostate case consisted of 78 Model-6711 {sup 125}I seeds. The breast case consisted of 87 Model-200 {sup 103}Pd seeds embedded around a simulated lumpectomy cavity. Systematic and random errors in CMC were unfolded using low-uncertainty uncorrelated MC (UMC) as the benchmark. CMC efficiency gains, relative to UMC, were computed for all voxels, and the mean was classified in regions that received minimum doses greater than 20%, 50%, and 90% of D{sub 90}, as well as for various anatomical regions. Results: Systematic errors in CMC relative to UMC were less than 0.6% for 99% of the voxels and 0.04% for 100% of the voxels for the prostate and breast cases, respectively. For a 1 x 1 x 1 mm{sup 3} dose grid, efficiency gains were realized in all structures with 38.1- and 59.8-fold average gains within the prostate and breast clinical target volumes (CTVs), respectively. Greater than 99% of the voxels within the prostate and breast CTVs experienced an efficiency gain. Additionally, it was shown that efficiency losses were confined to low dose regions while the largest gains were located where little difference exists between the homogeneous and heterogeneous doses. On an AMD 1090T processor, computing times of 38 and 21 sec were required to achieve an average statistical uncertainty of 2% within the prostate (1 x 1 x 1 mm{sup 3}) and breast (0.67 x 0.67 x 0.8 mm{sup 3}) CTVs, respectively. Conclusions: CMC supports an additional average 38-60 fold improvement in average efficiency relative to conventional uncorrelated MC techniques, although some voxels experience no gain or even efficiency losses. However, for the two investigated case studies, the maximum variance within clinically significant structures was always reduced (on average by a factor of 6) in the therapeutic dose range generally. CMC takes only seconds to produce an accurate, high-resolution, low-uncertainly dose distribution for the low-energy PSB implants investigated in this study.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Synthetic method of analogues for emerging infectious disease forecasting

The Method of Analogues (MOA) has gained popularity in the past decade for infectious disease forecasting due to its non-parametric nature. In MOA, the local behavior observed in a time series is matched to the local behaviors of several historical time series. The known values that directly follow the historical time series that best match the observed time series are used to calculate a forecast. This non-parametric approach leverages historical trends to produce forecasts without extensive parameterization, making it highly adaptable. However, MOA is limited in scenarios where historical data is sparse. This limitation was particularly evident during the early stages of the COVID-19 pandemic, where the emerging global epidemic had little-to-no historical data. In this work, we propose a new method inspired by MOA, called the Synthetic Method of Analogues (sMOA). sMOA replaces historical disease data with a library of synthetic data that describe a broad range of possible disease trends. This model circumvents the need to estimate explicit parameter values by instead matching segments of ongoing time series data to a comprehensive library of synthetically generated segments of time series data. We demonstrate that sMOA has competitive performance with state-of-the-art infectious disease forecasting models, out-performing 78% of models from the COVID-19 Forecasting Hub in terms of averaged Mean Absolute Error and 76% of models from the COVID-19 Forecasting Hub in terms of averaged Weighted Interval Score. Additionally, we introduce a novel uncertainty quantification methodology designed for the onset of emerging epidemics. Developing versatile approaches that do not rely on historical data and can maintain high accuracy in the face of novel pandemics is critical for enhancing public health decision-making and strengthening preparedness for future outbreaks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ILAMBv2.7 benchmarking results comparing E3SMv2.1 land-atmosphere coupled (BGCv2LNDATM) and stand alone land (ELM) simulations with CMIP6 emission driven historical simulations

This dataset contains land model benchmarking results for the Energy Exascale Earth System Model version 2.1 (E3SMv2.1), including outputs from both coupled biogeochemistry simulations and stand-alone land model simulations. These results are compared against several emission-driven historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Benchmarking was conducted using the International Land Model Benchmarking (ILAMB) package, version 2.7 (ILAMBv2.7). CMIP6 model outputs were sourced from the Earth System Grid Federation (ESGF), while the E3SMv2.1 results were derived from raw model outputs. These outputs underwent processing steps such as time serialization, conservative regridding, and data standardization to ensure comparability. For spatial interpolation, the Earth System Modeling Framework (ESMF) tool, ESMF_RegridWeightGen, was employed to generate regridding weights, enabling the transformation of E3SM’s native cubed-sphere grid to a regular latitude-longitude grid.

Feng, Sha [PNNL]↗
DOE OSTI2017

Demixing by a Nematic Mean Field: Coarse-Grained Simulations of Liquid Crystalline Polymers

Liquid crystalline polymers exhibit a particular richness of behaviors that stems from their rigidity and their macromolecular nature. On the one hand, the orientational interaction between liquid-crystalline motifs promotes their alignment, thereby leading to the emergence of nematic phases. On the other hand, the large number of configurations associated with polymer chains favors formation of isotropic phases, with chain stiffness becoming the factor that tips the balance. In this work, a soft coarse-grained model is introduced to explore the interplay of chain stiffness, molecular weight and orientational coupling, and their role on the isotropic-nematic transition in homopolymer melts. We also study the structure of polymer mixtures composed of stiff and flexible polymeric molecules. We consider the effects of blend composition, persistence length, molecular weight and orientational coupling strength on the melt structure at the nano-and mesoscopic levels. Conditions are found where the systems separate into two phases, one isotropic and the other nematic. We confirm the existence of non-equilibrium states that exhibit sought-after percolating nematic domains, which are of interest for applications in organic photovoltaic and electronic devices.

Ramírez-Hernández, Abelardo↗
DOE OSTI2023

Optimizing Long Term Hydrogen Fueling Infrastructure Plans on Freight Corridors for Heavy Duty Fuel Cell Electric Vehicles

The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This paper develops a long-term plan for refueling infrastructure deployment using the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy duty (HD) fuel cell electric commercial vehicles (FCEV). This constraint-based optimization model considers existing fueling locations, regional specific vehicle fuel economy and weight, vehicle origin and destination (OD), vehicle volume by class and infrastructure costs to characterize in-mission refueling requirements for a given freight corridor. The authors applied this framework to determine the ideal long term public access locations for hydrogen refueling (constrained by existing fueling stations and dispensing technology), the minimal viable cost to deploy sufficient hydrogen fuel dispensers, and associated equipment, to accommodate a growing population of hydrogen fuel cell trucks. So the framework discussed in this paper can be expanded and applied to additional electrified powertrains as well as a larger interstate system, expanded regional corridor, or other transportation networks.

08 HYDROGEN↗
NASA NTRS1974

The prebiotic synthesis of oligonucleotides

This paper is primarily a review of recent developments in the abiotic synthesis of nucleotides, short chain oligonucleotides, and their mode of replication in solution. It also presents preliminary results from this laboratory on the prebiotic synthesis of thymidine oligodeoxynucleotides. A discussion, based on the physicochemical properties of RNA and DNA oligomers, relevant to the molecular evolution of these compounds leads to the tentative hypothesis that oligodeoxyribonucleotides of about 12 units may have been of sufficient length to initiate a self replicating coding system. Two models are suggested to account for the synthesis of high molecular weight oligomers using short chain templates and primers.

Oro, J.↗
NASA NTRS1974

Estimation of nonlinear aerodynamic derivatives of a variable geometry fighter aircraft from flight data

This paper is concerned with the estimation of stability and control parameters of a high performance fighter aircraft from data obtained from high angle of attack flight. The estimation process utilizes a maximum likelihood algorithm derived for the case of a nonlinear aerodynamic force and moment model. The aircraft used was a high speed variable sweep heavy weight fighter with twin vertical tails. Comparisons of results from the nonlinear analysis are made with linear theory and wind tunnel results when available.

Ramachandran, S.↗