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NASA NTRS2015

Low-Profile Diffuser

The propellant tanks used in liquid rockets require pressurization gases in order to maintain tank pressure while the tanks are being drained during engine operation. The pressurization gas, which is typically much warmer than the relatively cold propellants in the tank, must be introduced into the empty ullage space at the top of the tank. The purpose of the diffuser is to control the flow of the gas into the tank in order to prevent direct impingement of the gas on the liquid surface and/or the tank walls. If the diffuser did not perform those tasks, the warm gas can create excess heat transfer causing an increase in the amount of pressurization mass required. Typical diffusers are long vertical cylinders that create a large exit area in order to minimize gas velocities. However, long vertical cylinders limit the amount of liquid that can be loaded into the tank in order not to have the liquid surface near the diffuser. A design goal for a pressurization diffuser is to create uniform flow in order to prevent jets that can impact the liquid surface and/or tank walls. The purpose of the task was to create a diffuser design that had a lower vertical profile (in order to be able to raise the liquid surface) while still maintaining uniform flow.

Martin, Michael A.↗

NASA NTRS2022

SKYSURF: Constraints on Zodiacal Light and Extragalactic Background Light through Panchromatic HST All-sky Surface-brightness Measurements: II. First Limits on Diffuse Light at 1.25, 1.4, and 1.6 μm

We present the first results from the HST Archival Legacy project "SKYSURF." As described in Windhorst et al., SKYSURF utilizes the large HST archive to study the diffuse UV, optical, and near-IR backgrounds and foregrounds in detail. Here, we utilize SKYSURF's first sky-surface-brightness measurements to constrain the level of near-IR diffuse Extragalactic Background Light (EBL) in three near-IR filters (F125W, F140W, and F160W). This is done by comparing our preliminary sky measurements of >30,000 images to zodiacal light models, carefully selecting the darkest images to avoid contamination from stray light. Our sky-surface-brightness measurements have been verified to an accuracy of better than 1%, which when combined with systematic errors associated with HST, results in sky-brightness uncertainties of ∼ 2%–4% ≃ 0.005 MJy sr−1 in each image. When compared to the Kelsall et al. zodiacal model, an isotropic diffuse background of ∼30 nW m−2 sr−1 remains, whereas using the Wright zodiacal model results in no discernible diffuse background. Based primarily on uncertainties in the foreground model subtraction, we present limits on the amount of diffuse EBL of 29, 40, and 29 nW m−2 sr−1, for F125W, F140W, and F160W, respectively. While this light is generally isotropic, our modeling at this point does not distinguish between a cosmological origin or a solar system origin (such as a dim, diffuse, spherical cloud of cometary dust).

Timothy Carleton↗

DOE OSTI2024

A finite difference informed random walker (FDiRW) solver for strongly inhomogeneous diffusion problems

In nature, many complex multi-physics coupling problems exhibit strong diffusivity inhomogeneity. For instance, in the context of radionuclide absorption by porous wasteform materials within a flowing waste stream, the difference of species’ diffusivity in solid and liquid phases spans by 3~8 orders of magnitude. To solve the diffusion equations with strongly inhomogeneous diffusivity, traditional discretization-based methods, such as the Finite Difference Method (FDM), require infinitesimally small time steps (<10 -10 ) as high spatial resolutions are employed in most microstructure evolution processes, leading to prohibitively high computational costs. Here, this work developed an integrated numerical approach (FDiRW: Finite Difference informed Random Walk) to tackle this challenge. The idea is that utilizing the Random Walk concept, the fast diffusion is modeled as a superposition of point source’s solution for a concentration distribution while FDM is used to obtain the point source’s solution at each node. A mesh-coarsening algorithm is developed to generate an exclusive coarse mesh for FDiRW approach to maximize its efficiency. The effectiveness of the coarse mesh-based FDiRW approach is validated by benchmarking Finite Difference solutions. Numerical results demonstrated that FDiRW achieves a remarkable 1000x computational efficiency improvement over FDM while preserving desired accuracy for a medium-sized model of 192 × 192 × 192 grids. Finally, as models scale up, a floating-point operations (PLOPs) analysis of the FDiRW algorithm reveals that its computational complexity grows quadratically in terms of the number of nodes employed in computation.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A provably stable numerical method for the anisotropic diffusion equation in confined magnetic fields

We present a novel numerical method for solving the anisotropic diffusion equation in magnetic fields confined to a periodic box which is accurate and provably stable. We derive energy estimates of the solution of the continuous initial boundary value problem. A discrete formulation is presented using operator splitting in time with the summation by parts finite difference approximation of spatial derivatives for the perpendicular diffusion operator. Weak penalty procedures are derived for implementing both boundary conditions and parallel diffusion operator obtained by field line tracing. We prove that the fully-discrete approximation is unconditionally stable. Discrete energy estimates are shown to match the continuous energy estimate given the correct choice of penalty parameters. A nonlinear penalty parameter is shown to provide an effective method for tuning the parallel diffusion penalty and significantly minimises rounding errors. Several numerical experiments, using manufactured solutions, the “NIMROD benchmark” problem and a single island problem, are presented to verify numerical accuracy, convergence, and asymptotic preserving properties of the method. Finally, we present a magnetic field with chaotic regions and islands and show the contours of the anisotropic diffusion equation reproduce key features in the field.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Multi-Objective design of interlocking metasurfaces using conditional diffusion models

Unit cell design remains a major challenge for interlocking metasurfaces, a promising joining technology for dissimilar materials, due to the complex, competing, multivariate design space and the need for rapid adaptation to varying performance requirements. This study explores Conditional Diffusion Models as a design optimization tool for interlocking metasurfaces. Given the complex, competing, multivariate design space for interlocking metasurfaces, unit cell design remains a major challenge for this joining technology. We trained a conditional diffusion model on 25,000 finite element analysis-simulated interlocking metasurface unit cells to generate designs with tailored thermo-mechanical properties (tensile strength, shear strength, and thermal conductivity) based on specified performance criteria. The model demonstrated a success rate of approximately 72 % in producing designs that met specified property bounds. The conditional diffusion model generated both thermally resistive and conductive designs, revealing clear trends in design characteristics: taller, dendritic structures were advantageous for tensile loads, while shorter, robust designs excelled in shear applications. Our findings indicate that the model's performance is more influenced by the breadth of the design space than by the quantity of training data, highlighting the importance of expansive design domains for generating innovative solutions. This work establishes conditional diffusion models as a highly efficient and adaptable tool for rapid interlocking metasurface unit cell design, paving the way for advancements in multi-material joining technologies, as well as highlighting the justification to leverage conditional diffusion models as design tools across complex design domains.

Conditional diffusion models↗

DOE OSTI2026

Enabling probabilistic learning on manifolds through double diffusion maps

Here, we present a generative learning framework for probabilistic sampling that extends Probabilistic Learning on Manifolds (PLoM), which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a (representative) set of observations. In its original form, PLoM constructs a reduced-order probabilistic model by combining three main components: (a) kernel density estimation to approximate the underlying probability measure, (b) Diffusion Maps to characterize the manifold of the data, and (c) a reduced-order Itô Stochastic Differential Equation (ISDE) to sample from the learned distribution. However, its sampling dynamics are posed in the ambient space and the retained number of reduced coordinates is chosen by projection-reconstruction error. In practice, this often (i) requires more coordinates than the data’s intrinsic dimension to achieve stable sampling and (ii) lacks a smooth, basis-independent lifting back to the data domain; moreover, standard Diffusion Maps emphasize harmonic eigenfunctions and can miss non-harmonic latent structure. We address these limitations by decoupling geometry learning from sampling: a first Diffusion Maps pass identifies non-harmonic coordinates on which we formulate a full-order ISDE directly in the latent space, while Double Diffusion Maps captures multiscale geometric features and Geometric Harmonics (GH) learns a smooth lifting map to the ambient variables that is independent of the particular diffusion basis. This hybrid design preserves the system’s dynamical richness with a compact geometric representation and enables principled out-of-sample inference. The effectiveness and robustness of the proposed method are illustrated through two numerical studies: one based on data generated from two-dimensional Hermite polynomial functions and another based on high-fidelity simulations of a detonation wave in a reactive flow.

Double diffusion maps↗

DOE OSTI2025

Precise Linker Length and Dynamic Bond Exchange Control Penetrant Diffusion in Dense Vitrimers

Polymer networks with dynamic covalent bonds have been investigated for their self-healing ability, recyclability, and potential as more sustainable materials. Recent results have indicated that in some cases, bond exchange can enhance the transport of penetrants in dense networks, pointing to their potential for separations of membranes. Here, imine dynamic bonds in ethylene oxide (EO) networks with precise linker lengths were synthesized to investigate the transport of N,N′-bis(2,5-di-tert-butylphenyl)-3,4,9,10-perylenedicarboximide (BTBP), a large, anisotropic dye molecule. Networks with mesh sizes smaller than, comparable to, and greater than the size of the penetrant axes were investigated to probe the effects of bond exchange and network confinement on transport. Mesh sizes, which ranged from 0.5 to 1.62 nm, were determined from shear rheology, glass transitions by calorimetry, and probe diffusion coefficients by fluorescence recovery after photobleaching. Permanent networks with identical EO chain lengths were prepared as control samples, and up to a 3 orders of magnitude increase in diffusion coefficient is observed in the dynamic systems for short linkers containing 13 backbone atoms. The longest linkers with 71 backbone atoms show no difference between the permanent and dynamic networks. Linkers shorter than 11 backbone atoms, corresponding to a mesh size smaller than the penetrant small axis, diffusion is no longer observable on the experimental time scale, indicating a sharp cutoff attributed to the precise linkers and narrow mesh size distribution. The dynamic imine exchange time scales were compared to the diffusive hopping times of penetrants and indicate that exchange can occur during a diffusive displacement. Furthermore, these findings provide insights into the factors affecting penetrant transport in dense polymers and inspire the development of next-generation selective polymer membranes.

Diffusion↗

DOE OSTI2026

Influence of Rigidity–Hydration Coupling on Size-Dependent Diffusion in Hydrated Polymer Membranes

Selective ion transport in polymer membranes depends critically on how penetrant motion couples to polymer dynamics and hydration. Yet, the mechanistic interplay between polymer rigidity, water content, and penetrant size remains poorly understood, especially in the regime where the penetrant diameter, polymer Kuhn length, and correlation length are comparable. Here, we employ coarse-grained molecular dynamics simulations to systematically investigate penetrant diffusion in hydrated polymer networks across a broad range of water volume fractions, chain rigidities, and penetrant sizes. The results reveal a transition from a decoupled regime, where small penetrants diffuse nearly independently of polymer relaxation, to a coupled regime in which large penetrants require cooperative polymer motion for transport. Increasing polymer rigidity amplifies the sensitivity of diffusivity to hydration, particularly at low water content, leading to pronounced deviations from Stokes−Einstein scaling. Comparison with scaling theories and free-volume models shows that classical nanoparticle-based frameworks fail to capture this intermediate regime. To address this gap, we extend the Yasuda model to incorporate polymer rigidity through a single parameter that quantifies the dynamic contribution of chain stiffness to free-volume fluctuations. The resulting model collapses diffusivity data across all sizes, water contents, and rigidities, providing a unified description of penetrant transport in hydrated polymer matrices. Furthermore, these findings establish polymer rigidity as a key, tunable determinant of diffusion and offer a framework for interpreting size-dependent transport in ion-selective membranes.

diffusion↗

DOE OSTI2024

Surface Self-Diffusion Induced Sintering of Nanoparticles

Despite the critical role of sintering phenomena in constraining the long-term durability of nanosized particles, a clear understanding of nanoparticle sintering has remained elusive due to the challenges in atomically tracking the neck initiation and discerning different mechanisms. Through the integration of in situ transmission electron microscopy and atomistic modeling, this study uncovers the atomic dynamics governing the neck initiation of Pt–Fe nanoparticles via a surface self-diffusion process, allowing for coalescence without significant particle movement. Real-time imaging reveals that thermally activated surface morphology changes in individual nanoparticles induce significant surface self-diffusion. Further, the kinetic entrapment of self-diffusing atoms in the gaps between closely spaced nanoparticles leads to the nucleation and growth of atomic layers for neck formation. This surface self-diffusion-driven sintering process is activated at a relatively lower temperature compared to the classic Ostwald ripening and particle migration and coalescence processes. The fundamental insights have practical implications for manipulating the morphology, size distribution, and stability of nanostructures by leveraging surface self-diffusion processes.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Charge Carrier and Spin Diffusion in a Polycrystalline and Single Crystal Lead Halide Perovskite Semiconductor

The directed movement of spin-bearing excitations in semiconductors can enable many potential schemes for spintronic applications. Understanding the mechanism of spin motion, as opposed to charge carrier or exciton motion, may require specialized techniques and sample conditions. Here we employ the noncontact and time-resolved technique of light-induced transient grating spectroscopy (LITG) to measure both carrier and spin motion in a perovskite semiconductor with varying crystallinity. The carrier motion aligns with expectations from past studies undertaken at low fluence, revealing an ambipolar diffusion coefficient on the order of 1 cm 2 /sec and diffusion length of roughly 1.5 μm for single crystals that is strongly attenuated as crystallite size decreases. The spin diffusion is measured with cross-polarized excitation beams and uncovers an intensity-dependent coefficient rising above 100 cm 2 /sec. The fast room-temperature spin relaxation limits the spin diffusion length, but the remarkable speedup of spin over carrier diffusion suggests a mechanism involving a combination of exchange-mediated and doping effects that enhance spin transport.

14 SOLAR ENERGY↗

DOE OSTI2025

Superionic-like diffusion in yttrium dihydride

For the next-generation high temperature microreactors, yttrium dihydride (YH 2 ) is an attractive solid state neutron moderator. Despite a number of recent investigations, the mechanism of hydrogen transport remains poorly understood. Experimental evaluations of diffusivity are inconclusive with large variations in diffusivities and activation energies. In this work, we perform ab initio molecular dynamics (AIMD) simulations on YH 2 for temperatures spanning 300 K to 1200 K. Our main finding is that YH 2 shows a superionic-like behavior with hydrogen atoms hopping from one native site to another above a characteristic temperature of 800 K. This correlated motion results in quasi-one-dimensional string-like displacements that enable the hydrogen atoms to diffuse rapidly. We confirm that the octahedral sites are mostly unoccupied, although channeling through them is the most favored pathway between lattice hops above 800 K. At the highest temperature of 1200 K, the string relaxation time is merely of the order of a few picoseconds, which indicates a liquid-like diffusive behavior. Based on the formation of spontaneous thermal vacancies, an order-disorder crossover temperature T α ~ 800 K is established for YH 2 with an activation energy of 0.83 eV for hydrogen diffusion in the superionic-like state.

Superionic-like Diffusion↗

NASA NTRS1954

Heat transfer, diffusion, and evaporation

Although it has long been known that the differential equations of the heat-transfer and diffusion processes are identical, application to technical problems has only recently been made. In 1916 it was shown that the speed of oxidation of the carbon in iron ore depends upon the speed with which the oxygen of the combustion air diffuses through the core of gas surrounding the carbon surface. The identity previously referred to was then used to calculate the amount of oxygen diffusing to the carbon surface on the basis of the heat transfer between the gas stream and the carbon surface. Then in 1921, H. Thoma reversed that procedure; he used diffusion experiments to determine heat-transfer coefficients. Recently Lohrisch has extended this work by experiment. A technically very important application of the identity of heat transfer and diffusion is that of the cooling tower, since in this case both processes occur simultaneously.

GASES - DIFFUSION↗

NASA NTRS2003

Mathematical model of diffusion-limited evolution of multiple gas bubbles in tissue

Models of gas bubble dynamics employed in probabilistic analyses of decompression sickness incidence in man must be theoretically consistent and simple, if they are to yield useful results without requiring excessive computations. They are generally formulated in terms of ordinary differential equations that describe diffusion-limited gas exchange between a gas bubble and the extravascular tissue surrounding it. In our previous model (Ann. Biomed. Eng. 30: 232-246, 2002), we showed that with appropriate representation of sink pressures to account for gas loss or gain due to heterogeneous blood perfusion in the unstirred diffusion region around the bubble, diffusion-limited bubble growth in a tissue of finite volume can be simulated without postulating a boundary layer across which gas flux is discontinuous. However, interactions between two or more bubbles caused by competition for available gas cannot be considered in this model, because the diffusion region has a fixed volume with zero gas flux at its outer boundary. The present work extends the previous model to accommodate interactions among multiple bubbles by allowing the diffusion region volume of each bubble to vary during bubble evolution. For given decompression and tissue volume, bubble growth is sustained only if the bubble number density is below a certain maximum.

Evaluation Studies↗

NASA NTRS2015

Kinetic Monte Carlo Simulation of Oxygen Diffusion in Ytterbium Disilicate

Ytterbium disilicate is of interest as a potential environmental barrier coating for aerospace applications, notably for use in next generation jet turbine engines. In such applications, the transport of oxygen and water vapor through these coatings to the ceramic substrate is undesirable if high temperature oxidation is to be avoided. In an effort to understand the diffusion process in these materials, we have performed kinetic Monte Carlo simulations of vacancy-mediated and interstitial oxygen diffusion in Ytterbium disilicate. Oxygen vacancy and interstitial site energies, vacancy and interstitial formation energies, and migration barrier energies were computed using Density Functional Theory. We have found that, in the case of vacancy-mediated diffusion, many potential diffusion paths involve large barrier energies, but some paths have barrier energies smaller than one electron volt. However, computed vacancy formation energies suggest that the intrinsic vacancy concentration is small. In the case of interstitial diffusion, migration barrier energies are typically around one electron volt, but the interstitial defect formation energies are positive, with the result that the disilicate is unlikely to exhibit experience significant oxygen permeability except at very high temperature.

Coatings↗

NASA NTRSDate not supplied

Review of IRT CFD & Diffuser Study Results - February 2024 Test Entries

In February 2024, multiple test entries were conducted in the NASA Glenn Research Center Icing Research Tunnel (IRT) to collect CFD validation data, particularly near the facility heat exchanger inlet and outlet planes, measure the boundary layer characteristics in the test section, and survey the diffuser flow field. The CFD validation testing utilized ultrasonic anemometers to acquire 3-components of flow velocity at the inlet and outlet planes across a row of heat exchanger blocks. The test section boundary layer surveys used 18-inch boundary layer rakes centered on each of the four test section surfaces with pitot tubes at turntable centerline. The diffuser study included surveys at three streamwise positions within the diffuser with a 5-hole flow angularity probe at between 3 and 7 lateral locations at a given streamwise position. All IRT diffuser data were acquired with the flow angularity probe at the centerline of the test section. An ultrasonic anemometer was also used in the diffuser to survey near-wall velocity profiles.

Icing↗

NASA NTRS2024

Ion Diffusive Transport Across the Separatrix Between the Low-Latitude Mantle and the Plasma Sheet By Kinetic Alfvén Waves: MMS Observation

To understand the entry of the cool low‐latitude mantle ions into the tail plasma sheet near the flanks under persistent interplanetary magnetic field B y , we evaluate the role of the cross‐field diffusive transport by kinetic Alfvén waves (KAWs) by investigating two events observed by multiscale (MMS) spacecraft. Around the separatrix between the open and closed field‐line regions, a two‐component mixing of hot plasma sheet ions of a few keV with cool mantle ions of a few hundred eV was observed, indicating transport across the separatrix. The waves observed between 0.01 and 10 Hz around the separatrix had characteristics consistent with those of KAWs. The consistency allowed us to estimate the wave vectors as a function of frequency by fitting KAW dispersion to the observations. Using the observed wave powers, plasma moments, and the estimated wave vectors, we computed the cross‐field diffusion rates associated with KAWs. The diffusion rates were found to be comparable to or larger than the Bohm diffusion rates during the intervals when the two‐component mixing was observed, indicating that the KAW diffusive transport can play a role in the entry of low‐latitude mantle ions into the plasma sheet.

Kinetic Alfvén waves↗

DOE OSTI2024

Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution

A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution. (Model and code can be found at imageomics.github.io/phylo-diffusion)

Khurana, Mridul↗

DOE OSTI2025

Ab-initio informed cluster dynamics simulation of self- and Xe diffusivity in uranium mononitride under irradiation

Uranium mononitride (UN) is one of the ceramic nuclear fuel alternatives to oxide fuels considered for light water reactor and advanced reactor designs, as it presents significant advantages such as high uranium density (better economics) and high thermal conductivity and melting point (increased safety). Self- and fission gas diffusivities need to be better understood, given that they influence key fuel performance phenomena like swelling and fission gas release. Recently, radiation enhanced diffusivity was investigated in UN by means of cluster dynamics simulations relying on empirical potential-based parameterizations, the reliability of which highly depends on the interatomic potential accuracy. Here, in this work, we refine this approach by determining, using ab-initio calculations, the properties of defect clusters containing vacancies, self-interstitials and Xe impurities. We also consider larger clusters than previous studies. The obtained dataset (formation enthalpies, entropies, and migration barriers) is used to parameterize a cluster dynamics model of mobile clusters, and to calculate the defect cluster concentrations under irradiation. This gives us access to the radiation enhanced self- and fission gas diffusivities. Although the resulting diffusivities are close to the values reported in the literature, we find important qualitative differences in the diffusion mechanisms. Capturing the correct mechanisms is crucial to properly describe the chemistry and fission rate dependence of the model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗