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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Modeling the diffusion of heat energy within composites of homogeneous materials using the uncertainty principle

The goal of this paper is to develop a highly accurate and efficient numerical method for the solution of a time-dependent partial differential equation with a piecewise constant coefficient, on a finite interval with periodic boundary conditions. The resulting algorithm can be used, for example, to model the diffusion of heat energy in one space dimension, in the case where the spatial domain represents a medium consisting of two homogeneous materials. The resulting model has, to our knowledge, not yet been solved in closed form through analytical methods, and is difficult to solve using existing numerical methods, thus suggesting an alternative approach. The approach presented in this paper is to represent the solution as a linear combination of wave functions that change frequencies at the interfaces between different materials. It is demonstrated through numerical experiments that using the Uncertainty Principle to construct a basis of such functions, in conjunction with a spectral method, a mathematical model for heat diffusion through different materials can be solved much more efficiently than with conventional time-stepping methods.

97 MATHEMATICS AND COMPUTING↗
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↗
NASA NTRS1983

Effects of drift on the transport of cosmic rays. VI - A three-dimensional model including diffusion

The first results are presented from a series of computer simulations of the solar modulation of galactic cosmic rays using a full three-dimensional model which incorporates all known important effects on particle transport, particle drifts, convection with the solar wind, energy loss, and anisotropic diffusion. The model is time-independent in the coordinate frame rotating with the sun, so corotating effects can be studied. Modulation in an interplanetary magnetic field model is considered in which the current sheet separating the northern and southern solar hemispheres is warped and corotating with the sun. The amplitude of the warp is varied to simulate possible solar cycle variation of the magnetic field. Substantial effects due to the warp of the current sheet are found. Comparison of the model results with various data is presented. Among other things, it is shown that the intensity may decrease away from the current sheet for both signs of the magnetic field, as suggested by recent observations, and in contrast with inferences from earlier, more approximate calculations.

Kota, J.↗
DOE OSTI2025

Phase-field modeling of diffusion bonding in 316H stainless steel: Impact of processing conditions on grain morphology and bonding quality

A novel multi-phase, multi-component phase‐field model is presented to study the diffusion bonding of 316H stainless steel. Combined with targeted experimental investigations, this model simulates the bond-growth process and predicts the bonding quality. Unlike previous models, our approach captures the simultaneous evolution of voids and grain structures, while quantifying bonding quality using defined bonding ratio. A comprehensive analysis of bond process control is performed by changing temperature, pressure and surface roughness observing the resulting bond structure, which is consistent with experimental observations and analytical predictions. Temperature is determined to be the dominant factor, with the transition from a flat to a robust bond occurring between 1000 °C and 1050 °C. At the ideal bonding temperature of 1050 °C, a surface roughness exceeding 0.6 μm or an applied stress below 4 MPa results in poor bonding quality. Beyond this, higher pressures and smoother surfaces reduce void size, accelerate void shrinkage, and lead to improved bond integrity. This diffuse-interface model can be extended to other material systems if supplied with appropriate thermodynamic and kinetic data. In conclusion, this makes it an effective modeling platform for optimizing high-temperature diffusion bonding and developing reliable bonded components such as compact heat exchangers.

Diffusion bonding↗
NASA NTRS1973

Prediction and evaluation of eddy-viscosity models for free mixing

Analysis for the turbulent mixing of free jets is presented in this paper and compared to recent experimental results. A turbulent mass diffusion model is presented and is based on the concentration potential core. The model yielded good results when compared with the experimental results except for low-speed flows where few experimental data are available. A review of recent experimental results verifies again that the three diffusion processes in turbulent mixing are interrelated; however, no single diffusion model may be used for all three processes. This is especially true when pressure gradients are present in the flow field. It is shown that even though momentum diffusion is significantly affected by pressure gradients, mass diffusion is not. It is further indicated that the mass diffusion model has been derived and is based on the accurate correlations of experimental results obtained for the concentration potential core. Similar techniques may be used in deriving an expression for the momentum and thermal diffusion coefficients. These expressions would be more complicated since they would have to take care of the boundary layer at the start of the mixing region. Finally, a comparison of the analyses, using this particular model and Ferri's model, with available experimental results is made.

Zakkay, V.↗
DOE OSTI2023

Digital polycrystalline microstructure generation using diffusion probabilistic models

Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. Here, this work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Simulating the CMS High Granularity Calorimeter with ML

Detector simulation is a key component of physics analysis and related activities in CMS. In the upcoming High Luminosity LHC era, simulation will be required to use a smaller fraction of computing in order to satisfy resource constraints. At the same time, CMS will be upgraded with the new High Granularity Calorimeter (HGCal), which requires significantly more resources to simulate than the existing CMS calorimeters. This computing challenge motivates the use of generative machine learning models as surrogates to replace full physics-based simulation. We study the application of state-of-the-art diffusion models to simulate particle showers in the CMS HGCal. We will discuss methods to overcome the challenges posed by the high-dimensional, irregular geometry of the HGCal. The quality of the showers produced by the diffusion model will be assessed by comparison to the full GEANT4-based simulation. The increase in simulation throughput will be quantified and methods to accelerate the diffusion model inference will also be discussed.

Amram, Oz↗
NASA NTRS1991

Energy Models for One-Carrier Transport in Semiconductor Devices

Moment models of carrier transport, derived from the Boltzmann equation, made possible the simulation of certain key effects through such realistic assumptions as energy dependent mobility functions. This type of global dependence permits the observation of velocity overshoot in the vicinity of device junctions, not discerned via classical drift-diffusion models, which are primarily local in nature. It was found that a critical role is played in the hydrodynamic model by the heat conduction term. When ignored, the overshoot is inappropriately damped. When the standard choice of the Wiedemann-Franz law is made for the conductivity, spurious overshoot is observed. Agreement with Monte-Carlo simulation in this regime required empirical modification of this law, or nonstandard choices. Simulations of the hydrodynamic model in one and two dimensions, as well as simulations of a newly developed energy model, the RT model, are presented. The RT model, intermediate between the hydrodynamic and drift-diffusion model, was developed to eliminate the parabolic energy band and Maxwellian distribution assumptions, and to reduce the spurious overshoot with physically consistent assumptions. The algorithms employed for both models are the essentially non-oscillatory shock capturing algorithms. Some mathematical results are presented and contrasted with the highly developed state of the drift-diffusion model.

Jerome, Joseph W.↗
DOE OSTI2019

Comprehensive evaluation and parametric sensitivity of interatomic potential models for diffusion kinetics of Cr 2 O 3 in molecular dynamics

While molecular dynamics (MD) has proven to be a promising approach to investigate the diffusion properties, the grand challenge resides in evaluating potential model parameters to accurately replicate experimentally measured properties. The Buckingham potential model with Columbic interaction is widely employed in MD simulations of chromia (Cr 2 O 3 ) systems, as it allows for reasonable computational cost and accuracy. However, considering the well-known limitation of classical potential models in simultaneous reproduction of various physical phenomena, further comprehensive evaluation of the potential is required for calculation of diffusion properties. In this study, we benchmark the performance of three different Buckingham models with the experimental data by calculating structural, thermodynamic, and mechanical properties of defect-free Cr 2 O 3 , and diffusion properties of Cr 2 O 3 with vacancy defects. Available Buckingham models display limited accuracies, consolidating the necessity of retraining the potential parameters for all properties impacting the diffusion dynamics. Oversimplification in parameterization procedures is suggested to impede the universal performance in property reproduction. Furthermore, this research also demonstrates effective guidelines for choosing a proper parameter set of current Buckingham potential for MD simulation with Cr 2 O 3 depending on properties and for potential reparameterization.

36 MATERIALS SCIENCE↗
NASA NTRS1971

Solutions for Reacting and Nonreacting Viscous Shock Layers with Multicomponent Diffusion and Mass Injection

Numerical solutions are presented for the viscous shocklayer equations where the chemistry is treated as being either frozen, equilibrium, or nonequilibrium. Also the effects of the diffusion model, surface catalyticity, and mass injection on surface transport and flow parameters are considered. The equilibrium calculations for air species using multicomponent: diffusion provide solutions previously unavailable. The viscous shock-layer equations are solved by using an implicit finite-difference scheme. The flow is treated as a mixture of inert and thermally perfect species. Also the flow is assumed to be in vibrational equilibrium. All calculations are for a 45 deg hyperboloid. The flight conditions are those for various altitudes and velocities in the earth's atmosphere. Data are presented showing the effects of the chemical models; diffusion models; surface catalyticity; and mass injection of air, water, and ablation products on heat transfer; skin friction; shock stand-off distance; wall pressure distribution; and tangential velocity, temperature, and species profiles.

Moss, J. N.↗
NASA NTRS2003

Evaluation of Models for Diffuse Continuum Gamma Rays in EGRET Range

The GALPROP model for cosmic-ray propagation produces explicit predictions for the angular distribution of gamma-rays. We compare our current models with EGRET spectra for various regions of the sky. This allows a critical test of alternative hypotheses for the observed GeV excess. We show that a population of hard-spectrum sources cannot be solely responsible for the excess since it also appears at high latitudes; on the other hand the 'hard electron' spectrum model cannot explain the gamma-ray excess in the inner Galaxy. Hence some combination of these explanations is required.

Strong, A.↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Predicting the oxidative lifetime of beta NiAl-Zr alloys

Nickel aluminides containing 40 to 50 at. pct Al and 0.1 at. pct Zr were studied following cyclic oxidation at 1400 C. The selective oxidation of Al resulted in the formation of protective Al2O3 scales on each alloy composition. However, repeated cycling eventually resulted in the gradual formation of less-protective NiAl2O4, first appearing on the 40Al alloys followed at longer times on the 45Al alloys. The appearance of the NiAl2O4, signaling the end of the protective scale-forming capability of the alloy, was related to the presence of gamma-prime (Ni3Al) which formed as a result of the loss of Al from the sample. A diffusion model, based on finite-difference techniques, was developed to predict the protective life of beta Ni-Al alloys. This model predicts Ni and Al concentration profiles after various oxidation exposures. The model can predict the oxidative lifetime due to Al depletion when the Al concentration decreases to a critical concentration. Measured Al concentration profiles on two alloys after various oxidation exposures are compared to those predicted by the diffusion model. The time to the appearance of the NiAl2O4 and that predicted by the diffusion model are compared and discussed.

Nesbitt, J. A.↗