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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

High-Efficiency Solar-To-Fuel Photoelectrochemistry in Disordered Photonic Glass Electrodes (Final Technical Report)

This project investigated how photonic glass (PG) photoelectrodes—disordered arrangements of dielectric scatterers—can serve as scalable, tunable platforms for light trapping in photoelectrochemical (PEC) solar-to-fuel systems. By leveraging disorder-driven optical phenomena such as multiple scattering resonances and light localization, PG structures offer an alternative to conventional photonic crystals and inverse opals that require high structural precision. The scientific goals were to twofold: (1) develop approaches to predictive models for high performance PG electrodes based on light absorption simulations, and (2) fabricate, characterize, and optimize PG-based photoelectrodes for solar-to-hydrogen and solar-to-fuel photoelectrochemical applications. To overcome the complexity of ensemble optical simulations for disordered materials, the researchers developed a machine-learning-accelerated emulation of all configurations in the design space. With this approach, PG photoelectrodes based on a TiO2 semiconductor were designed to enhance PEC currents of up to one hundred times higher than the equivalent ultra-thin film photoanodes and several times higher than the equivalent photonic crystal. The research also explored integrated systems for electrochemical hydrogen production based on replacing water oxidation with the specific glycerol oxidation electrocatalysis. Overall, the project outlined an approach to a simple-to-fabricate photoelectrode system to drive photoelectrochemical reactions relevant to solar photochemical energy conversion.

14 SOLAR ENERGY↗
DOE OSTI2024

The Dual Nature of Entrainment-Mixing Signatures Revealed through Large-Eddy Simulations of a Convection-Cloud Chamber

Abstract Entrainment of subsaturated air into a cloud can influence its optical and microphysical properties in various ways, depending on the droplet evaporation and turbulent mixing time scales. Previous experiments in the Pi convection-cloud chamber have revealed that, given a fixed entrained air property, the mixing of entrained subsaturated air results in complete evaporation of some cloud droplets, with the rest remaining unchanged. This is a signature of inhomogeneous mixing. While comparing the results of entrainment with varying air properties, the mixing signature appears as if the subsaturated air is well mixed with the cloud to evenly reduce the droplets’ size. In other words, taken together, the experiments appear to have the signature of homogeneous mixing. To explore these results in a greater depth, we conduct large-eddy simulations combined with a bin microphysics scheme. Our results reproduce the similar signatures of inhomogeneous and homogeneous mixing, implying that LES can resolve the inhomogeneous mixing when the grid spacing is smaller than the entrained air parcel. Additionally, we observe that increasing the aerosol injection rate enhances the signature of inhomogeneous mixing, while coarser grid spacing diminishes it. Finally, the change in wall fluxes in response to various entrained air properties confirms that the homogeneous signature seen in the analysis of an ensemble of simulations is the result of various equilibrium states. This further strengthens the suggestion that the homogeneous mixing signature found in aircraft observations near the cloud top may result from combining entrainment events of different intensities, possibly caused by various-sized eddies. Significance Statement Large-eddy simulation and size-resolved microphysics can resolve time scales for turbulent mixing and evaporation and, therefore, are well suited for reproducing, extending, and interpreting the entrainment experiment in the Pi convection-cloud chamber. Our simulation results confirm (i) the inhomogeneous mixing signature for an individual entrainment event and (ii) the appearance of homogeneous mixing in an ensemble of entrainment episodes. Furthermore, we demonstrate that the inhomogeneous mixing signature is more pronounced in a polluted cloud, but coarser grid spacing in simulations may compromise the accuracy of this signature. Last, the homogeneous mixing signature results from various equilibrium states established for different entrainment intensities and adjusted wall fluxes, which are challenging to measure experimentally but can be easily analyzed in the simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data-driven prediction of scaling and ignition of inertial confinement fusion experiments

Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

The Role of Subcloud Mesoscale Convergence in Sculpting Convective Updraft Width and Depth

The initiation of deep moist convection is governed in part by the horizontal width of updrafts near cloud base, which limits the deleterious effects of entrainment-driven dilution on buoyant thermals ascending through the free troposphere. However, the factors controlling cloud-base updraft width, which in turn dictates cloud depth, are not well understood. We track the evolving three-dimensional structure of the mesoscale subcloud forcing for vertical motion and near-cloud thermodynamic ingredients within a high-resolution ensemble of simulations of seven realistic daytime orographic convection initiation events to determine their relative roles in controlling cloud width and depth. Statistical analysis of approximately 5000 cloudy updraft samples indicates that the most important contributors to the width of cloudy updrafts across the ensemble are the depth and magnitude of the subcloud mesoscale ascent. However, the depth achieved by clouds is more consistently predicted by the near-cloud ambient relative humidity within the lower to middle free troposphere and convective available potential energy. Therefore, although the width of cloudy updrafts may be partly set at low levels by the mesoscale vertical mass and moisture flux, the likelihood of deep moist convection is governed by the generation of positive buoyancy within cumulus thermals and entrainment-driven dilution that reduces it. The persistence of the low-level mesoscale vertical forcing locally consolidates and vertically transports boundary layer moisture, helping to reduce updraft dilution. However, these factors vary in relative impacts on cloudy updrafts across individual cases, indicating multiple pathways for deep convection initiation.

Convective storms↗
DOE OSTI2025

The DESI-Lensing Mock Challenge: large-scale cosmological analysis of 3x2-pt statistics

The current generation of large galaxy surveys will test the cosmological model by combining multiple types of observational probes. Realising the statistical promise of these new datasets requires rigorous attention to all aspects of analysis including cosmological measurements, modelling, covariance and parameter likelihood. In this paper we present the results of an end-to-end simulation study designed to test the analysis pipeline for the combination of the Dark Energy Spectroscopic Instrument (DESI) Year 1 galaxy redshift dataset and separate weak gravitational lensing information from the Kilo-Degree Survey, Dark Energy Survey and Hyper-Suprime-Cam Survey. Our analysis employs the 3x2-pt correlation functions including cosmic shear and galaxy-galaxy lensing, together with the projected correlation function of the spectroscopic DESI lenses. We build realistic simulations of these datasets including galaxy halo occupation distributions, photometric redshift errors, weights, multiplicative shear calibration biases and magnification. We calculate the analytical covariance of these correlation functions including the Gaussian, noise and super-sample contributions, and show that our covariance determination agrees with estimates based on the ensemble of simulations. We use a Bayesian inference platform to demonstrate that we can recover the fiducial cosmological parameters of the simulation within the statistical error margin of the experiment, investigating the sensitivity to scale cuts. This study is the first in a sequence of papers in which we present and validate the large-scale 3x2-pt cosmological analysis of DESI-Y1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Aerosol-Cloud Interactions and the Intersection of Climate Forcing and Feedback

The goal of this project has been to understand at the process level how the interaction of aerosols and cloud microphysics may alter the cloud response to climate change from the scale of turbulence to the global scale. Atmospheric aerosols (suspended particulates) have a unique role to play in the climate system. Anthropogenic aerosols may be an air pollution hazard and degrade human health. Their presence also results in the direct scattering or absorption of sunlight, with small net impacts on climate. But aerosols are also a key part of cloud formation, as the locations on which cloud droplets and ice crystals form. Understanding these Aerosol Cloud Interactions (ACI) is a critical uncertainty for climate models, including the Energy Exascale Earth System Model (E3SM), and affects how well models like E3SM can simulate the past and future. This work aims to understand cloud microphysics as well as the interaction of aerosol forcing and cloud feedback through improving our understanding of aerosol-cloud interactions (ACI) by looking at their uncertainty in critical environments at the process level. It will also contribute to the development of E3SM. Specific goals include: first, high level analysis of Perturbed Parameter Ensemble (PPE) simulations already available from the Community Earth System Model (CESM) and E3SM. Second, detailed analysis of recent changes to temperature and the role of aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

MCCCS-MN

The MCCCS‒MN (Monte Carlo for Complex Chemical Systems‒Minnesota) software is developed by the Siepmann research group at the University of Minnesota. MCCCS‒MN allows for the simulation of multi-component molecular systems in the canonical, isobaric-isothermal (including constant stress for solids), grand-canonical, semi-grand, and Gibbs (NVT, NPT, and more than two simulation boxes) ensembles. It uses the configurational-bias Monte Carlo method to efficiently sample phase space for linear, branched and cyclic chain molecules, the adiabatic nuclear and electronic sampling Monte Carlo method to treat many-body polarization effects, and the aggregation-volume-bias Monte Carlo algorithm to efficiently sample the spatial distribution of associating molecules. MCCCS-MN employs a molecular representation of the system where force fields contain bonded and non-bonded terms. Funding for the development of MCCCS-MN through grants from the National Science Foundation (simulation of fluid phase equilibria and chromatography) and the Department of Energy (simulation of adsorption equilibria) is gratefully acknowledged.

Siepmann, J.Ilja [University of Minnesota - Twin C↗
NASA NTRS2024

Dust Model Sensitivity to Dust Source Mask, Sandblasting Efficiency, Air Density, and Land Use: Implications for Model Improvement

This study compares dust storm simulations using two commonly adopted methods for representing four important dust emission parameters. Compared to a dynamic dust source mask based on land use and vegetation cover, a static mask based solely on land use overestimates dust concentration and optical depth by a factor of 2, besides generating spurious emissions. The results reinforce that seasonal variations in vegetation cover can significantly affect dust emissions. For sandblasting efficiency, a clay-dependent semiempirical expression produces 10 times more dust than a physics-based expression. Simulations using model-predicted versus a fixed constant for air density differ by only 8%. However, this difference could range between 12 and 22% for annual simulations over global dust source regions. Simulations with updated versus old land use data, using the same dust source mask, differ twofold, indicating the significant impact of land use change on regional dust emission in central Arizona. The differences in the pairs of these simulations are generally larger than the uncertainty due to meteorology. The simulations align better with observation when using the dynamic dust source mask, the physics-based sandblasting efficiency, and the up-to-date land use data. Given the high sensitivity of dust to surface conditions, the results discussed have implications for improving the dust cycle in weather and climate models and for interpreting model intercomparisons.

Dust-emission source↗
NASA NTRS2023

Breaking Wave Field Statistics With A Multi-Layer Model

The statistics of breaking wave fields are characterised within a novel multi-layer framework, which generalises the single-layer Saint-Venant system into a multi-layer and non-hydrostatic formulation of the Navier–Stokes equations. We simulate an ensemble of phase-resolved surface wave fields in physical space, where strong nonlinearities, including directional wave breaking and the subsequent highly rotational flow motion, are modelled, without surface overturning. We extract the kinematics of wave breaking by identifying breaking fronts and their speed, for freely evolving wave fields initialised with typical wind wave spectra. The Λ(c) distribution, defined as the length of breaking fronts (per unit area) moving with speed c to c + dc following Phillips (J. Fluid Mech., vol. 156, 1985, pp. 505–531), is reported for a broad range of conditions. We recover the Λ(c) ∝ c−6 scaling without wind forcing for sufficiently steep wave fields. A scaling of Λ(c) based solely on the root-mean-square slope and peak wave phase speed is shown to describe the modelled breaking distributions well. The modelled breaking distributions are in good agreement with field measurements and the proposed scaling can be applied successfully to the observational data sets. The present work paves the way for simulations of the turbulent upper ocean directly coupled to a realistic breaking wave dynamics, including Langmuir turbulence, and other sub-mesoscale processes.

Jiarong Wu↗
NASA NTRS2012

NASA Center for Climate Simulation (NCCS) Presentation

The NASA Center for Climate Simulation (NCCS) offers integrated supercomputing, visualization, and data interaction technologies to enhance NASA's weather and climate prediction capabilities. It serves hundreds of users at NASA Goddard Space Flight Center, as well as other NASA centers, laboratories, and universities across the US. Over the past year, NCCS has continued expanding its data-centric computing environment to meet the increasingly data-intensive challenges of climate science. We doubled our Discover supercomputer's peak performance to more than 800 teraflops by adding 7,680 Intel Xeon Sandy Bridge processor-cores and most recently 240 Intel Xeon Phi Many Integrated Core (MIG) co-processors. A supercomputing-class analysis system named Dali gives users rapid access to their data on Discover and high-performance software including the Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT), with interfaces from user desktops and a 17- by 6-foot visualization wall. NCCS also is exploring highly efficient climate data services and management with a new MapReduce/Hadoop cluster while augmenting its data distribution to the science community. Using NCCS resources, NASA completed its modeling contributions to the Intergovernmental Panel on Climate Change (IPCG) Fifth Assessment Report this summer as part of the ongoing Coupled Modellntercomparison Project Phase 5 (CMIP5). Ensembles of simulations run on Discover reached back to the year 1000 to test model accuracy and projected climate change through the year 2300 based on four different scenarios of greenhouse gases, aerosols, and land use. The data resulting from several thousand IPCC/CMIP5 simulations, as well as a variety of other simulation, reanalysis, and observationdatasets, are available to scientists and decision makers through an enhanced NCCS Earth System Grid Federation Gateway. Worldwide downloads have totaled over 110 terabytes of data.

Webster, William P.↗
DOE OSTI2024

Numerical investigation of multiphase flow through self-affine rough fractures

Multiphase flow through fractures has great significance in subsurface energy recovery and gas storage applications. Different fracture and flow properties affect flow through a fracture which is difficult to control in laboratory experiments. Here, we perform lattice Boltzmann simulations in an ensemble of synthetically generated fractures. Drainage simulations are performed at different capillary numbers, wettability, and viscosity ratios. We track the invading front and quantify breakthrough saturations and show that roughness and wettability have a strong effect on fluid invasion through a complex fracture. Invading a more viscous fluid results in more stable displacement regardless of the capillary number while at very low capillary numbers, fluid migration is dependent on the inherent structure of the fracture. We develop a fluid displacement phase diagram in a single rough fracture and compare our results from that in the literature. Finally, we extend the phase diagrams across multiple fractures and demonstrate the importance of natural fracture features of roughness and wettability in identifying stable versus unstable displacement regimes during multiphase flow through rough fractures. Our work presents an end-to-end numerical pathway for testing on experimental data and expanding numerical data sets for testing combinations of different physical phenomenon and make valuable predictions on fluid flow through rough fractures.

02 PETROLEUM↗
DOE OSTI2025

Bottom-Up Simulation, Reconstruction, and Quantification of Macromolecule Sequences from Experimental Polymerizations

Motivated by the canonical sequence–structure–function paradigm, tools to characterize chemical patterning in natural biomacromolecules, from proteins to nucleic acids, have grown exponentially in recent years. However, analogous strategies for synthetic macromolecules remain in nascent stages, complicated by sequence polydispersity and analytical limitations. To address this, we have developed a comprehensive and open-source Python package, PRISM (polymer rate insights and sequence modeling), an end-to-end workflow that provides a path from experimental kinetics measurements to quantitative and qualitative metrics for describing chemical patterning in stochastic polymers. First, a numerical integration strategy was constructed to simulate and fit experimental data from reversible addition–fragmentation chain transfer (RAFT) polymerization kinetics, enabling the facile estimation of relevant reactivity ratios. These ratios were then used in a mechanism-specific stochastic kinetic simulation strategy to simulate sequence ensembles corresponding to model systems spanning experimental copolymers, classes of statistical polymers (e.g., alternating, block, and gradient), and multiblock copolymers. Lastly, inspired by sequence homology metrics from bioinformatics, we introduce visualization strategies and quantitative metrics to facilitate comparisons of different sequence ensembles. As the sequence–structure–function paradigm becomes increasingly central in de novo design of synthetic macromolecules, this toolkit provides a first step toward accurate and representative sequence description and featurization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unique Temperature Trend Pattern Associated With Internally Driven Global Cooling and Arctic Warming During 1980–2022

Abstract Diagnosing the role of internal variability over recent decades is critically important for both model validation and projections of future warming. Recent research suggests that for 1980–2022 internal variability manifested as Global Cooling and Arctic Warming (i‐GCAW), leading to enhanced Arctic Amplification (AA), and suppressed global warming over this period. Here we show that such an i‐GCAW is rare in CMIP6 large ensembles, but simulations that do produce similar i‐GCAW exhibit a unique and robust internally driven global surface air temperature (SAT) trend pattern. This unique SAT trend pattern features enhanced warming in the Barents and Kara Sea and cooling in the Tropical Eastern Pacific and Southern Ocean. Given that these features are imprinted in the observed record over recent decades, this work suggests that internal variability makes a crucial contribution to the discrepancy between observations and model‐simulated forced SAT trend patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Sea Breezes and Associated Convective Cloud Evolution in the Model Gray Zone

We characterize convective clouds associated with sea‐breeze circulations (SBC) using multi‐agency observations and multi‐case ensemble model simulations. The focus is on assessing convective cloud lifecycle properties and their merging behavior, as well as the environmental conditions they are embedded in, particularly SBC features. In total, 46 SBC days over the Houston‐Galveston region are selected and simulated using the Weather Research and Forecasting (WRF) model at a gray zone scale with a forecast‐like parameterization setup. Advanced techniques, including change‐point detection, a Lagrangian cloud tracking method, and a newly developed cell merging and splitting detection algorithm, are applied and/or developed for this study. Our findings indicate that the WRF model at 1 km grid spacing well represents the thermodynamic conditions over the region, as well as SBC timing and intensity. However, for the associated convective cells, WRF overestimates the 30‐dBZ echo top height, cell area, and maximum radar reflectivity compared to radar observations. This overestimation is potentially due to under‐resolved entrainment processes, an overestimated merging frequency, and the overestimation of updraft intensity. Furthermore, the model exhibits a deficiency in simulating congestus clouds, showing a more rapid transition from shallow to deep convection compared to observed behavior. Moreover, observations indicate stronger, deeper, and wider clouds when merging happens. Conversely, in simulations, the merging process does not necessarily lead to higher or longer‐lived cells, as many cases experience rapid and frequent merging and splitting which may result in more variance in convective updraft velocity during the convection lifetime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

RCEMIP‐ACI: Aerosol‐Cloud Interactions in a Multimodel Ensemble of Radiative‐Convective Equilibrium Simulations

Aerosol‐cloud interactions are a persistent source of uncertainty in climate research. This study presents findings from a model intercomparison project examining the impact of aerosols on clouds and climate in convection‐permitting radiative‐convective equilibrium (RCE) simulations. Specifically, 11 different modeling teams conducted RCE simulations under varying aerosol concentrations, domain configurations, and sea surface temperatures (SSTs). We analyze the response of domain‐mean cloud and radiative properties to imposed aerosol concentrations across different SSTs. Additionally, we explore the potential impact of aerosols on convective aggregation and large‐scale circulation in large‐domain simulations. The results reveal that the cloud and radiative responses to aerosols vary substantially across models. However, a common trend across models, SSTs, and domain configurations is that increased aerosol loading tends to suppress warm rain formation, enhance cloud water content in the mid‐troposphere, and consequently increase mid‐tropospheric humidity and upper‐tropospheric temperature, thereby impacting static stability. The warming of the upper troposphere can be attributed to reduced lateral entrainment effects due to the higher environmental humidity in the mid‐troposphere. However, models do not agree on aerosol impacts on convective updraft velocity based on the preliminary examination of high‐percentiles of vertical velocity at a single mid‐troposheric layer (500 hPa). In large‐domain simulations, where convection tends to self‐organize, aerosol loading does not consistently influence self‐organization but tends to reduce the intensity of large‐scale circulation forming between convective clusters and dry regions. This reduction in circulation intensity can be explained by the increase in static stability due to the upper tropospheric warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

From Points to Planes: A Workflow for Converting Three‐Dimensional Point Cloud Data Into Discrete Fracture Network Flow and Transport Models

We present the Point cLoud Algorithm for NEtwork Extraction of Discrete Fracture Networks (PLANE-DFN), a point cloud–based algorithm for automatic fracture network extraction designed to support discrete fracture network (DFN) modeling workflows. PLANE-DFN segments three-dimensional fracture planes from raw point cloud data using RANdom SAmple Consensus coupled with statistical outlier removal and density-based clustering to isolate individual fracture features. Each candidate plane is constrained against site-specific structural constraints based on strike and dip. After segmentation, each fracture is converted into a 2-D convex polygon suitable for meshing and simulation. The PLANE-DFN algorithm is validated by comparing geometric and flow and transport data against data from dfnWorks simulations with ensembles of plane-fit networks. We find that the flow and transport in plane-fit networks are comparable to dfnWorks-generated networks when realistic network geometry is maintained. The PLANE-DFN algorithm provides an automated and streamlined workflow to transform point clouds of data into DFN network geometry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Monte Carlo Simulations of Crystal Defects in Open Ensembles

Zero- and two-dimensional crystal defects form in open statistical ensembles, such as the grand canonical, that are usually inaccessible with conventional simulation techniques. This longstanding challenge is overcome with a new Hamiltonian Monte Carlo method that samples energy-biased gradual transformations. In conclusion, the method enables free energy calculations for nonideal point defects and the direct prediction of finite-temperature interface structures.

Grain boundaries↗