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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Integration and validation of some modules for modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures

Three modules are integrated into the built-in OpenFOAM rhoCentralFoam solver towards accurate and efficient modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures within the OpenFOAM 10.0 framework. The first module is the mixture-averaged diffusion model. The second module is the built-in OpenFOAM Lagrangian solver coupled with optimised droplet drag coefficient and convective heat transfer coefficient sub-models. The last module is a sparse stiff chemistry solver based on dynamic adaptive hybrid integration (AHI-S). The optimised droplet sub-models are first verified in correct implementation for subsequent simulations in this work. Further, they show good accuracy against experimental and analytical data in the modelling of ammonia droplet acceleration and cooling in the flowing and/or low-temperature air. The accuracy and efficiency gains related to the mixture-averaged diffusion model and the AHI-S chemistry solver are examined by simulating 1-D detonation propagation in ammonia droplet-free/laden ammoniaoxygen mixtures. Numerical results of detonation propagation speed, gaseous temperature, density, and species distributions around the induction zone show good agreement with experimental data and analytical solutions. Compared to the built-in OpenFOAM diffusion model, the mixture-averaged diffusion model provides different numerical predictions of pulsating instabilities in detonation propagation. It shows better accuracy in depicting the detonation structure within the droplet-free section attributed to improved multi-component diffusion modelling. Compared to the built-in OpenFOAM solver EulerImplicit (backward Euler), the AHI-S chemistry solver reduces the computational cost by around 50%. It achieves satisfactory accuracy in calculating detonation propagation speed within the droplet-free section with the optimal efficiency when the safety factor, β, equals 0.5.

42 ENGINEERING↗
NASA NTRS1987

A diffuse radar scattering model from Martian surface rocks

Remote sensing of Mars has been done with a variety of instrumentation at various wavelengths. Many of these data sets can be reconciled with a surface model of bonded fines (or duricrust) which varies widely across the surface and a surface rock distribution which varies less so. A surface rock distribution map from -60 to +60 deg latitude has been generated by Christensen. Our objective is to model the diffuse component of radar reflection based on this surface distribution of rocks. The diffuse, rather than specular, scattering is modeled because the diffuse component arises due to scattering from rocks with sizes on the order of the wavelength of the radar beam. Scattering for radio waves of 12.5 cm is then indicative of the meter scale and smaller structure of the surface. The specular term is indicative of large scale surface undulations and should not be causally related to other surface physical properties. A simplified model of diffuse scattering is described along with two rock distribution models. The results of applying the models to a planet of uniform fractional rock coverage with values ranging from 5 to 20% are discussed.

Calvin, W. M.↗
NASA NTRS1973

Satellite ultraviolet measurements of nitric oxide fluorescence with a diffusive transport model.

Twilight measurements of fluorescence in the (1, 0) gamma band of nitric oxide were made from June 1967 to January 1969 by an ultraviolet scanning spectrometer on board the polar orbiting satellite Ogo 4. Nitric oxide vertical column emission rates were measured between solar zenith angles of 93 and 98 deg. Seasonal and latitudinal variations were found to be less than a factor of 1.3, the scatter and uncertainty in the data prohibiting more precise determinations from being made. Time independent chemical diffusion models for the vertical distribution of nitric oxide agree well with profiles measured from sounding rockets. The column emission rates calculated from the theoretical models are larger than the satellite measurements by a factor of 3.

Rusch, D. W.↗
NASA NTRS1996

The Implementation and Evaluation of the Emergency Response Dose Assessment System (ERDAS) at Cape Canaveral Air Station/Kennedy Space Center

The Emergency Response Dose Assessment System (ERDAS) is a system which combines the mesoscale meteorological prediction model RAMS with the diffusion models REEDM and HYPACT. Operators use a graphical user interface to run the models for emergency response and toxic hazard planning at CCAS/KCS. The Applied Meteorology Unit has been evaluating the ERDAS meteorological and diffusion models and obtained the following results: (1) RAMS adequately predicts the occurrence of the daily sea breeze during non-cloudy conditions for several cases. (2) RAMS shows a tendency to predict the sea breeze to occur slightly earlier and to move it further inland than observed. The sea breeze predictions could most likely be improved by better parameterizing the soil moisture and/or sea surface temperatures. (3) The HYPACT/REEDM/RAMS models accurately predict launch plume locations when RAMS winds are accurate and when the correct plume layer is modeled. (4) HYPACT does not adequately handle plume buoyancy for heated plumes since all plumes are presently treated as passive tracers. Enhancements should be incorporated into the ERDAS as it moves toward being a fully operational system and as computer workstations continue to increase in power and decrease in cost. These enhancements include the following: activate RAMS moisture physics; use finer RAMS grid resolution; add RAMS input parameters (e.g. soil moisture, radar, and/or satellite data); automate data quality control; implement four-dimensional data assimilation; modify HYPACT plume rise and deposition physics; and add cumulative dosage calculations in HYPACT.

Evans, Randolph J.↗
DOE OSTI2026

Deep learning for time series forecasting: a survey of recent advances

Time series forecasting plays a critical role in numerous real-world applications, such as finance, healthcare, transportation, and scientific computing. In recent years, deep learning has become a powerful tool for modeling complex temporal patterns and improving forecasting accuracy. This survey provides an overview of recent deep learning approaches for time series forecasting, involving various architectures including RNNs, CNNs, GNNs, transformers, large language models, MLP-based models, and diffusion models. We first identify key challenges in the field, such as temporal dependency, efficiency, and cross-variable dependency, which drive the development of forecasting techniques. Then, the general advantages and limitations of each architecture are discussed to contextualize their adaptation in time series forecasting. Furthermore, we highlight promising design trends like multi-scale modeling, decomposition, and frequency-domain techniques, which are shaping the future of the field. This paper serves as a compact reference for researchers and practitioners seeking to understand the current landscape and future trajectory of deep learning in time series forecasting.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

The energy spectrum of electrons and cosmic-ray confinement A new measurement and its interpretation

Measurements of the cosmic ray flux and electron energy spectrum from 5 GeV to 300 GeV, with an absolute uncertainty in the flux level of + or - 10 percent at low energies and + or - 30 percent at 100 GeV, are described. The measured spectrum appears to represent the competing processes of radiative energy loss in the interstellar medium and leakage out of the Galaxy. In the framework of the leaky box model and diffusion models, the result is most consistent with the picture of cosmic ray electrons spending an average of 10 million years in the Galaxy independent of electron energy, probably propagating in a halo as well as in the galactic disk.

Tang, K.-K.↗
DOE OSTI2020

Hydrogen Enrichment for Solid Oxide Fuel Cell Gas Turbine (SOFC/GT) Hybrid Power Generation Systems Using Mechanical Gas Separation

This presentation, given at the 37 th annual Pittsburgh Coal Conference on September 8-11, 2020, includes information on the process, previous work, normal and oblique shocks model, diffuser design model, and calculation results (expansion, recompression with no losses, and recompression with mass depletion). Summary includes the concept of aerodynamic separation of water and carbon dioxide from a SYNGAS stream and that deposition was modeled by mass depletion from a compressible flow.

VanOsdol, John↗
DOE OSTI2025

Generative learning of densities on manifolds

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an Itô stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps , a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.

Double diffusion maps↗
DOE OSTI2024

Geometry-complete diffusion for 3D molecule generation and optimization

Abstract Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM’s generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub .

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

Application of spectroscopic evidence to diffuse layer model (DLM) parameter estimation for cation adsorption onto ferrihydrite in single- and bi-solute systems

The diffuse layer model (DLM) is one of the most frequently used surface complexation models (SCM) for predicting cation adsorption onto ferrihydrite. However, the DLM with its existing database sometimes fails to predict cation adsorption, especially in multi-solute systems. Notably, the failure is often attributed to inaccurate selection of the surface complexes and/or underestimation of the site density for DLM formulation. DLM simulations of batch adsorption data conducted with cadmium, copper, lead, and zinc on ferrihydrite in CO2 free systems demonstrates the ability of the DLM to fit single-solute data reasonably well but highlights its limitations with respect to predicting bi-solute data. Modifications to the DLM were made in two steps. First, spectroscopic evidence was used to guide cation complexation reactions at the ferrihydrite surface. Second, surface characteristics (reactive site density, specific surface area, and acidity constants) of ferrihydrite were updated according to recent literature. The results from this study suggest that when spectroscopic verification is employed and surface site density is increased, a simple two-site DLM can predict cation adsorption on ferrihydrite in single-solute systems for pH data above 3.5 and in most of the bi-solute systems tested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Employing a Modified Diffuser Momentum Model to Simulate Ventilation of the Orion CEV

The Ansys CFX CFD modeling tool was used to support the design efforts of the ventilation system for the Orion CEV. CFD modeling was used to establish the flow field within the cabin for several supply configurations. A mesh and turbulence model sensitivity study was performed before the design studies. Results were post-processed for comparison with performance requirements. Most configurations employed straight vaned diffusers to direct and throw the flow. To manage the size of the models, the diffuser vanes were not resolved. Instead, a momentum model was employed to account for the effect of the diffusers. The momentum model was tested against a separate, vane-resolved side study. Results are presented for a single diffuser configuration for a low supply flow case.

Straus, John↗
DOE OSTI2024

SurFE-XD (Surface curvature-driven Finite Elements model for Diffusion under eXtreme conditions)

SurFE-XD is a mesoscale finite element framework to model surface diffusion under mutliphysics environments. The code uses legacy C++ library dolphin wrapped with python in a FEniCS driven unified form language and just-in-time (JIT) compilation setting. The purpose of the release is to attract wide-ranging usage of the code along with publication supplementation to support reproducibility of scientific data. SurFE-XD has been originally conceived under the LDRD-DR funding for “High-Gradient (C-BAND) Breakdown tolerant accelerator materials project. Currently SrFE-XD support electrostatics and Thermo-elasticity driven surface diffusion kernels. Releasing the code will also enable to include contributions from other physical regimes e.g., plasticity and electrodynamics etc as well portability to GPU-based platforms.

Bagchi, Soumendu↗
NASA NTRS1999

Diffusion Barriers to Increase the Oxidative Life of Overlay Coatings

Currently, most blades and vanes in the hottest section of aero gas turbine engines require some type of coating for oxidation protection. Newly developed single crystal superalloys have the mechanical potential to operate at increasingly higher component temperatures. However, at these elevated temperatures, coating/substrate interdiffusion can shorten the protective life of the coating. Diffusion barriers between overlay coatings and substrates are being examined to extend the protective life of the coating. A previously- developed finite-difference diffusion model has been modified to predict the oxidative life enhancement due to use of a diffusion barrier. The original diffusion model, designated COSIM, simulates Al diffusion in the coating to the growing oxide scale as well as Al diffusion into the substrate. The COSIM model incorporates an oxide growth and spalling model to provide the rate of Al consumption during cyclic oxidation. Coating failure is predicted when the Al concentration at the coating surface drops to a defined critical level. The modified COSIM model predicts the oxidative life of an overlay coating when a diffusion barrier is present eliminating diffusion of Al from the coating into the substrate. Both the original and the modified diffusion models have been used to predict the effectiveness of a diffusion barrier in extending the protective life of a NiCrAl overlay coating undergoing cyclic oxidation at 1100 C.

Nesbitt, James A.↗
NASA NTRS1998

Modeling of Diffusion in Liquid Ge and Its Alloys

This report summarizes progress made on NASA Grant NAG3-1437, Modeling of diffusion in Liquid Ge and Its Alloys, which was in effect from January 15, 1993 through July 10, 1997. It briefly describes the purpose of the grant, and the work accomplished in simulations and other studies of thermophysical properties of liquid semiconductors and related materials. A list of publications completed with the support of the grant is also given.

Stroud, David G.↗
DOE OSTI2025

Diffusion Model-Guided Inverse Design of Bimetallic Catalysts for Ammonia Decomposition

In the past decade, artificial intelligence and deep learning have played increasingly prominent roles in materials design and discovery. Among these, generative AI models, known for their ability to create unique and complex structures, have emerged as state-of-the-art tools for materials screening due to their high efficiency and low computational cost. In catalysis, one of the major challenges is identifying promising material candidates within an immense chemical space. This challenge can be addressed using generative approaches, such as diffusion-based inverse design models. In this study, we present a machine learning-guided workflow that employed a diffusion model for the inverse design of bimetallic alloy catalysts for low-carbon ammonia decomposition, a key reaction for ammonia emission control and sustainable hydrogen production. Catalyst candidates were evaluated using nitrogen adsorption energy as the key descriptor, inspired by multiscale modeling. The proposed workflow identified low-cost, environmentally friendly catalysts with excellent catalytic performance, which have been validated theoretically and experimentally. Our framework decoupled the generative and property-prediction components, enhancing both flexibility and accuracy in the catalytic material design process.

Adsorption↗
DOE OSTI2025

Mitigating spectral bias in neural operators via high-frequency scaling for physical systems

Neural operators have emerged as powerful surrogates for modeling complex physical problems. However, they suffer from spectral bias making them oblivious to high-frequency modes, which are present in multiscale physical systems. Therefore, they tend to produce over-smoothed solutions, which is particularly problematic in modeling turbulence and for systems with intricate patterns and sharp gradients such as multi-phase flow systems. In this work, we introduce a new approach named high-frequency scaling (HFS) to mitigate spectral bias in convolutional-based neural operators. By integrating HFS with proper variants of UNet, we demonstrate a higher prediction accuracy by mitigating spectral bias in single and two-phase flow problems. Unlike Fourierbased techniques, HFS is directly applied to the latent space, thus eliminating the computational cost associated with the Fourier transform. Additionally, we investigate alternative spectral bias mitigation through a diffusion model conditioned on neural operators. While the diffusion model integrated with the standard neural operator may still suffer from significant errors, these errors are substantially reduced when the diffusion model is integrated with a HFS-enhanced neural operator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Bridging Scales to Model Reactive Diffusive Transport in Porous Media

Published by ECS. Two novel scale-bridging algorithms to model reaction-diffusion transport in porous media are presented. The algorithms are based on direct numerical simulations and couple the information of a micro-scale model, which accounts for the large field of view provided by micro X-ray computed tomography (X-ray CT), and a nano-scale model, which locally resolves transport in the fine structure extracted from nano X-ray CT. The micro-scale model is discretized in the through-plane direction into a 1D grid, where effective properties and internal boundaries are determined based on the results from the nano-scale model. The validated algorithms are used to examine transport of oxygen in precious group metal-free electrodes considering both zero- and first-order kinetics. Unlike conventional methods, the results show that the effective diffusivity is not a passive property but increases in regions where the reaction-rate coefficient is large. The proposed algorithms account for the multiscale coupling of reaction-diffusion transport and material microstructure, thus improving the predictions compared to conventional methods.

42 ENGINEERING↗