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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Coherent Reaction between Molecular and Atomic Bose-Einstein Condensates: Integrable Model

Here, we solve a model that describes a stimulated conversion between ultracold bosonic atoms and molecules. The reaction is triggered by a linearly time-dependent transition throughout the Feshbach resonance. Our solution predicts a dependence, with a dynamic phase transition, of the reaction efficiency on the transition rate for both atoms-to-molecule pairing and molecular dissociation processes. We find that for the latter process with a linear energy dispersion of atomic modes, the emerging phase can have a thermalized energy distribution of noninteracting bosons with the temperature defined by the rate of the transition. This provides a simple interpretation of the phase transition in terms of the creation of equilibrium Bose-Einstein condensate.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Selective Electrocatalytic Reduction of CO 2 into CO at Small, Thiol-Capped Au/Cu Nanoparticles

The electrochemical CO 2 reduction reaction (CO 2 RR) is a promising approach for converting fossil fuel emissions into environmentally sustainable chemicals and fuels. The ability to control the surface structure of CO 2 RR nanocatalysts provides an opportunity to tune product selectivity. Bimetallic gold–copper catalysts have been identified as emerging electrocatalyst candidates, but Cu incorporation typically lowers product selectivity compared with pure Au. Here we show sustained CO selectivity and activity up to 49% Cu content in small (<2 nm), thiol-capped Au/Cu nanoparticles (NPs). Bimetallic NPs containing 49% Cu selectivity converted CO 2 into CO with 100 ± 6% CO Faradaic efficiency and average mass activity of ~500 mA/mg during a 12 h electrolysis experiment at –0.8 V vs RHE. Au/Cu NPs synthesized without thiol ligands selectively produced H 2 , whereas larger (>10 nm), thermally dethiolated Au/Cu NPs produced a wider product distribution including H 2 , CO, and C 2 H 4 . Density functional theory (DFT) modeling of CO 2 RR and H 2 evolution at realistic, thiol-capped Au/Cu NP structures indicated that copper–thiol surface structures sustained CO selectivity by stabilizing key *CO intermediates while making *H binding less favorable. Calculations also predicted that removing a significant fraction of the thiol ligands would increase *CO binding strength such that desorption of CO product molecules could become the most thermodynamically challenging step. This result, coupled with increased *H stability on dethiolated nanoclusters, points to decreased CO 2 RR selectivity for small, ligand-free catalysts, which is in line with experimental observations from our group and others. Here, our results demonstrate that thiol-ligand surface structures can sustain the CO selectivity of bimetallic Au/Cu NPs and reduce precious metal requirements for CO 2 RR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Relationship between microporous structure and light gas transport through glassy polymeric membranes revealed by molecular simulations

Microporous glassy polymers are attractive materials for gas separation membranes, due to their high permeability and tailorable selectivity, provided physical aging can be delayed. The archetypal microporous glassy polymer, PTMSP, can be blended with a hyper-crosslinked isatin–triptycene porous polymer network (PPN) to delay physical aging. However, while PPN is effective at reducing physical aging, it also affects the permeability of light gases through PTMSP. Molecular dynamics simulations were used here to shed fundamental light on the mechanisms responsible for these effects. Atomistic models are developed that satisfactorily reproduce experimental observations such as matrix density and cavity size distributions for neat PTMSP as well as for PTMSP–PPN blends. Analysis of the simulation results suggests that physical aging is delayed because the PPN inclusions slow down PTMSP relaxation while reducing the connectivity between free volume pockets. To understand how PPN inclusions affect light gas permeability, the atomistic models developed are used to probe CO 2 and CH 4 diffusion and sorption. These simulations are conducted for PTMSP matrices exhibiting varying density, towards reproducing experimental permeability data. Interrogating the simulation trajectories, it is discovered that while CH 4 travels preferentially through the free volume cavities, CO 2 preferentially interacts with the available surfaces, especially in the presence of PPN. These differences help interpret experimental observations. The transport of C 2 H 6 and H 2 S through PTMSP matrices was also investigated. These gases, and in particular H 2 S, were found to absorb within PTMSP, yielding very low diffusion coefficients. The ability to predict differences in diffusion pathways for various natural gas components unveils the possibility of engineering membranes to control permeability and selectivity towards large-scale applications.

Bao Le, Tran Thi [Univ. of Oklahoma, Norman, OK (U↗
DOE OSTI2021

Aerosol emissions from water-lean solvents for post-combustion CO 2 capture

Advanced water-lean solvents (WLS) for post-combustion CO 2 capture have been gaining interest due to their ability to reduce the parasitic penalty from energy needed for solvent regeneration. Commercial implementation of these novel CO 2 capture technologies hinges on successful control of amine emissions. RTI conducted a parametric study of fundamental and operational variables influence on overall amine aerosol and vapor emissions from our water-lean solvent eCO 2 Sol™ using our 6-kW equivalent bench-scale gas absorption system. The parametric testing used a simulated flue gas with 15 % CO 2 , 2.3–4.2 % H 2 O, and 0–6 ppm sulfite (SO 3 ) to examine the impact of the presence of aerosols to the capture performance and amine emissions from the system. The SO 3 reacts with water in the flue gas to create H 2 SO 4 , which forms liquid aerosol droplets and provide nucleation sites for growth of aerosols. Scanning Mobility Particle Sizer and Aerodynamic Particle Sizer instruments monitored the aerosol particle size distribution. Parametric testing results suggested that the presence of the aerosols in the flue gas could increase the overall amine emissions by 10X compared to the baseline emissions from WLS’s vapor pressure. Principal component analysis (PCA) and projection to latent squares (PLS) developed models to predict the aerosol-based amine emissions from process data. The predictive PLS model had a correlation coefficient (Q 2 ) of 0.92 and could predict the aerosol-based emissions from the NAS process with ±15 % accuracy (average absolute deviation, AAD). The PLS regression model also identified key variables affecting aerosol-based emissions from WLS.

42 ENGINEERING↗
DOE OSTI2019

Chance Constraints for Improving the Security of AC Optimal Power Flow

This paper introduces a scalable method for improving the solutions of ac optimal power flow (AC OPF) with respect to deviations in predicted power injections from wind and other uncertain generation resources. The aim of this paper is on providing solutions that are more robust to short-term deviations, and that optimize both the initial operating point and a parametrized response policy for control during fluctuations. We formulate this as a chance-constrained optimization problem. To obtain a tractable representation of the chance constraints, we introduce a number of modeling assumptions and leverage recent theoretical results to reformulate the problem as a convex, second-order cone program, which is efficiently solvable even for large instances. Our experiments demonstrate that the proposed procedure improves the feasibility and cost performance of the OPF solution, while the additional computation time is on the same magnitude as a single deterministic AC OPF calculation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Creating ground truth for nanocrystal morphology: a fully automated pipeline for unbiased transmission electron microscopy analysis

Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42,650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. Further, a classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr 3 nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Numerical study of proppant transport and settling processes in fractures

Reservoir stimulation by creating hydraulically conductive fractures is the key step for enabling enhanced geothermal systems (EGS). The effectiveness of stimulation is significantly influenced by the deposition of proppant inside induced fractures. The transportation and settling of proppant in a propagating fracture is controlled by a multitude of operational and physical parameters, including the fracturing fluid rheology, injection rate, proppant concentration, fracture length/aperture evolution, proppant size/density/shape, etc. A numerical tool that robustly and efficiently accounts for all important attributes can facilitate the design and optimization of reservoir stimulation. This study presents the novel computational tool ELK (ELectrical fracKing) developed for the numerical simulation of proppant-fluid mixture circulation in a fractured geothermal reservoir. We enriched the MOOSE-based PorousFlow module with a suite of equations to consider the fluid-proppant mixture with particle-particle/fluid interactions, which include gravitational settling, particle convection, particle hampering, and strong density and viscosity contrasts. The computational tool is validated by comparing the predicted proppant bed evolution against two different laboratory scale experiments of proppant transport in a fixed aperture channel. Further parameter studies were performed, and the modeling results show that the proppant deposition is determined by the mixing characteristics and settling of the particles from the slurry. Concentration-dependent density and viscosity lead to an inhomogeneous distribution of the proppant, particle collision, and enhanced settling at the bottom of the fractures. Preliminary coupling with dynamic fracture propagation shows promising results and will be further developed to simulate hydraulic stimulation at high fidelity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part II: Modeling Study

Abstract Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2018

Tuning Precursor Reactivity toward Nanometer-Size Control in Palladium Nanoparticles Studied by in Situ Small Angle X-ray Scattering

Synthesis of monodisperse nanoparticles (NPs) with precisely controlled size is critical for understanding their size-dependent properties. Although significant synthetic developments have been achieved, it is still challenging to synthesize well-defined NPs in a predictive way due to a lack of in-depth mechanistic understanding of reaction kinetics. Here we use synchrotron-based small-angle X-ray scattering (SAXS) to monitor in situ the formation of palladium (Pd) NPs through thermal decomposition of Pd–TOP (TOP: trioctylphosphine) complex via the “heat-up” method. We systematically study the effects of different ligands, including oleylamine, TOP, and oleic acid, on the formation kinetics of Pd NPs. Through quantitative analysis of the real-time SAXS data, we are able to obtain a detailed picture of the size, size distribution, and concentration of Pd NPs during the syntheses, and these results show that different ligands strongly affect the precursor reactivity. We find that oleylamine does not change the reactivity of the Pd–TOP complex but promote the formation of nuclei due to strong ligand–NP binding. On the other hand, TOP and oleic acid substantially change the precursor reactivity over more than an order of magnitude, which controls the nucleation kinetics and determines the final particle size. A theoretical model is used to demonstrate that the nucleation and growth kinetics are dependent on both precursor reactivity and ligand–NP binding affinity, thus providing a framework to explain the synthesis process and the effect of the reaction conditions. Quantitative understanding of the impacts of different ligands enables the successful synthesis of a series of monodisperse Pd NPs in the broad size range from 3 to 11 nm with nanometer-size control, which serve as a model system to study their size-dependent catalytic properties. Furthermore, the in situ SAXS probing can be readily extended to other functional NPs to greatly advance their synthetic design.

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

Porosity-Permeability Evolution During Simultaneous Mineral Dissolution and Precipitation

Permeability in reactive porous media evolves in complex ways that are hard to predict. Macroscopic empirical equations are often used to estimate permeability evolution but fail to reflect the impact of pore scale reactions on permeability. This work aims to understand the evolution of permeability in systems where mineral dissolution and precipitation occur simultaneously. Pore network models are used for permeability simulation of a Paluxy sandstone using pore and pore-throat size distributions from the analysis of X-ray CT images. Pore and pore-throat radii are increased and decreased to reflect the effects of dissolution and precipitation reactions. Varying spatial distributions of reactions are simulated and the resulting porosity and permeability values compared with commonly used macroscopic equations. It is observed that when dissolution occurs at the inlet and precipitation at the outlet, porosity increases while permeability decreases. Similar results are also observed in the respective opposite scenario where precipitation occurs at the inlet and dissolution at the outlet. When dissolution and precipitation reactions are randomly distributed throughout the network, porosity increases with little change in permeability that is not captured by the empirical equations. When reactions are controlled by pore and pore-throat sizes, such as dissolution occurring in pores and pore-throats of small size and precipitation in pores and pore-throats of larger size, porosity and permeability decrease. When precipitation occurs in pores and pore-throats of smaller size and dissolution in pores and pore-throats of larger size, porosity and permeability increase and is described relatively well by the Verma-Pruess porosity-permeability equation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2018

Ignition Delay in Low Temperature Combustion

Low temperature combustion (LTC) strategies present a means of reducing soot and oxides of nitrogen (NOx) emissions while simultaneously increasing efficiency relative to conventional combustion modes. By sufficiently premixing fuel and air before combustion, LTC strategies avoid high fuel-to-air equivalence ratios that lead to soot production. Dilution of the mixture lowers the combustion temperatures to reduce NOx production and offers thermodynamic advantages for improved efficiency. However, issues such as high heat release rates (HRRs), incomplete combustion, and difficulty in controlling the timing of combustion arise with low equivalence ratios and combustion temperatures. Ignition delay (the time until the start of combustion) is a way to quantify the time available for fuel and air to mix inside the cylinder before combustion. Previous studies have used ignition delay to explain trends seen in LTC such as combustion stability and HRRs. This study provides a novel method of integrating ignition delay into the investigation of LTC to determine what insights ignition delay calculations can provide for the different challenges associated with LTC strategies. To eliminate the need for large chemical kinetic models and interpolation to predict ignition delay, an ignition delay correlation is used. The flexibility gained using the correlation allows this method to be quickly adapted to target different LTC strategies and challenges. In this work, three cases of low temperature gasoline combustion (LTGC) with varying stratification levels were evaluated with an emphasis on the non-monotonic trend of the HRRs. The described method for ignition delay calculation is used to detail the stratification and ignition delay distributions for each case and provide insight into the observed trends.

Drallmeier, Joseph↗
DOE OSTI2024

A Generation-Storage Coordination Dispatch Strategy for Power System Based on Causal Reinforcement Learning

In the backdrop of global energy transformation, power systems integrating high proportions of renewable energy sources are facing unprecedented challenges in operational stability and dispatch efficiency. To address these challenges, this study introduces a generation-storage coordination real-time dispatch strategy based on Causal Power System Dynamic Reinforcement Learning (CPSDRL). Diverging from traditional reinforcement learning approaches, CPSDRL innovatively incorporates causal inference within the state prediction model - the crux of model-based reinforcement learning - thereby establishing the Power Causal Dynamic Model (PCDM). Assisted by the prior knowledge of power systems, the model significantly enhances prediction accuracy and reliability through a two-stage training process. Utilizing PCDM, this study further applies a direct policy search algorithm to optimize the real-time dispatch strategy. Experimental results indicate that the proposed method improves the stability of generation-storage coordination real-time dispatch and exhibits competitive advantages in sample efficiency and computational speed, compared to traditional model-based and model-free reinforcement learning algorithms. This method is expected to enhance the practicality and adaptability of causal reinforcement learning techniques in power system scheduling and control.

causal reinforcement learning↗
DOE OSTI2019

Entropy and Enthalpy Mediated Segregation of Bottlebrush Copolymers to Interfaces

The composition of polymer blends near interfaces can differ from the average blend composition because the attraction of each polymer toward surfaces is controlled by its chemistry, size, and architecture. In this work, we studied thin film blends of bottlebrush copolymers and linear homopolymers to understand the enthalpic and entropic effects that drive preferential segregation of one constituent to film interfaces. Bottlebrush copolymers containing polystyrene (PS) and poly(methyl methacrylate) (PMMA) side chains were blended with either linear PS or linear PMMA, and time-of-flight secondary ion mass spectroscopy was used to quantify the distribution of bottlebrushes through the film thickness as a function of homopolymer type, homopolymer molecular weight, and processing conditions. We found that the bottlebrush copolymers segregated to air and substrate interfaces above a critical molecular weight of the linear homopolymer, consistent with an entropic preference for chain ends and shorter chains toward the interfaces. This segregation was used to tailor the surface wettability of blend films using bottlebrush additives as a minority component. Modeling using self-consistent field theory highlighted effects of conformational entropy and enthalpic interactions in driving almost complete segregation from the interior of the films toward interfaces. Furthermore, enthalpic interactions were predicted to cause lateral phase segregation in cases where the homopolymer is preferred over the bottlebrush copolymer at the substrate, an effect that was also observed in experiments. Finally, this study demonstrates that bottlebrush copolymer additives can be designed to spontaneously segregate to surfaces in thermal blends, providing a possible route to decouple surface properties from bulk properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Application of the Software as a Service Model to the Control of Complex Building Systems

In an effort to create broad access to its optimization software, Lawrence Berkeley National Laboratory (LBNL), in collaboration with the University of California at Davis (UC Davis) and OSISoft, has recently developed a Software as a Service (SaaS) Model for reducing energy costs, cutting peak power demand, and reducing carbon emissions for multipurpose buildings. UC Davis currently collects and stores energy usage data from buildings on its campus. Researchers at LBNL sought to demonstrate that a SaaS application architecture could be built on top of this data system to optimize the scheduling of electricity and heat delivery in the building. The SaaS interface, known as WebOpt, consists of two major parts: a) the investment& planning and b) the operations module, which builds on the investment& planning module. The operational scheduling and load shifting optimization models within the operations module use data from load prediction and electrical grid emissions models to create an optimal operating schedule for the next week, reducing peak electricity consumption while maintaining quality of energy services. LBNL's application also provides facility managers with suggested energy infrastructure investments for achieving their energy cost and emission goals based on historical data collected with OSISoft's system. This paper describes these models as well as the SaaS architecture employed by LBNL researchers to provide asset scheduling services to UC Davis. The peak demand, emissions, and cost implications of the asset operation schedule and investments suggested by this optimization model are analysed.

25 ENERGY STORAGE↗
DOE OSTI2017

Managing time-substitutable electricity usage using dynamic controls

A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

High-Resolution Near-Surface Imaging at the Basin Scale Using Dark Fiber and Distributed Acoustic Sensing: Toward Site Effect Estimation in Urban Environments

Near-surface seismic structure, particularly the shear wave velocity (V s ), can strongly affect local site response, and should be accurately estimated for ground motion prediction during seismic hazard assessment. The Imperial Valley (California), occupying the southern end of the Salton Trough, is a seismically active basin with thick surficial lacustrine sedimentary deposits. In this study, we utilize ambient noise records and local earthquake events for high-resolution near-surface characterization and site effect estimation with an unlit fiber-optic telecommunication infrastructure (dark fiber) in Imperial Valley by using the distributed acoustic sensing (DAS) technique. We apply ambient noise interferometry to retrieve coherent surface waves from DAS records, and evaluate performances of three different surface wave methods on DAS ambient noise dispersion imaging. We develop a quality control workflow to improve the dispersion measurement of noisy portions of the DAS data set by using a data selection strategy. Using the joint inversion of both the fundamental mode and higher overtones of Rayleigh waves, a high resolution two-dimensional (2D) V s structure down to 70 m depth is obtained. We successfully achieve an improved V s 30 (the time-averaged shear-wave velocity in the top 30 m) model with higher spatial-resolution and reliability compared to the existing community model for the area. We also explore the potential for utilizing DAS earthquake events for site amplification estimation. The preliminary results reveal a clear anti-correlation between the approximated site response and the V s 30 profile. In conclusion, our results indicate the potential utility of DAS deployed on dark fiber for near-surface characterization in appropriate contexts.

58 GEOSCIENCES↗