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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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The basic types of diffusion models available for aerospace air quality analyses are reviewed in terms of meteorological input differences. The NASA/MSFC rocket exhaust effluent diffusion (REED) code is summarized and discussed as a tool for aerospace air quality analyses. The generic approach to a climatological air quality analysis using the NASA/MSFC REED code is considered.
We describe here diffusion models apt to construct a multipole-based, cable braid time domain model for conducting wires. Implementation details of both a ladder network valid for time-domain signals with all frequency content and an approximate single-stage circuit valid for low-frequency dominated time signals (such as electromagnetic pulses) are reported. This time domain model can be leveraged to treat system-generated electromagnetic pulse events, as well as used to further confirm the correctness of the multipole-based, cable braid frequency domain model.
Diffusion models for chemically reacting air Couette flow with hydrogen injection
Terahertz (THz) metamaterials with high‐figure‐of‐merit (high‐FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low‐asymmetry geometric structures, leaving high‐asymmetry designs largely unexplored due to the inefficiency of trial‐and‐error‐based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data‐driven nature. Here, a novel prior knowledge‐guided generative model augmented by a physics‐constrained active learning mechanism to design high‐asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high‐FoM THz resonance and generates new high‐asymmetry structures. To mitigate the limited number of classical structures, the generated high‐asymmetry structures are actively selected and integrated into the initial training dataset based on their physical characteristics. Experimental results demonstrate the superior resonance performance of the generated high‐asymmetry metamaterials over classical designs, exhibiting improvements exceeding 30% in key resonance metrics. Remarkably, this performance is attained using only 68 classical structures as the initial training dataset, significantly reducing the data requirements for deep learning‐based metamaterial design. The proposed scheme for generating high‐asymmetry structures provides a new effective and efficient solution for high‐FoM resonance, expanding applications in high‐sensitivity THz metadevices.
Interplanetary diffusion model for time dependency of intensity of solar cosmic ray event
A model for radial diffusion caused by electromagnetic disturbances was published by Falthammar (1965) using a two-parameter model of the disturbance perturbing a background dipole magnetic field. Schulz and Lanzerotti (1974) extended this model by recognizing the two parameter perturbation as the leading (non--dipole) terms of the Mead Williams magnetic field model. They emphasized that the magnetic perturbation in such a model induces an electric ield that can be calculated from the motion of field lines on which the particles are ‘frozen’. Roederer and Zhang (2014) describe how the field lines on which the particles are frozen can be calculated by tracing the unperturbed field lines from the minimum-B location to the ionospheric footpoint, and then tracing the perturbed field (which shares the same ionospheric footpoint due to the frozen -in condition) from the ionospheric footpoint back to a perturbed minimum B location. The instantaneous change n Roederer L*, dL*/dt, can then be computed as the product (dL*/dphi)*(dphi/dt). dL*/Dphi is linearly dependent on the perturbation parameters (to first order) and is obtained by computing the drift across L*-labeled perturbed field lines, while dphi/dt is related to the bounce-averaged gradient-curvature drift velocity. The advantage of assuming a dipole background magnetic field, as in these previous studies, is that the instantaneous dL*/dt can be computed analytically (with some approximations), as can the DLL that results from integrating dL*/dt over time and computing the expected value of (dL*)^2. The approach can also be applied to complex background magnetic field models like T89 or TS04, on top of which the small perturbations are added, but an analytical solution is not possible and so a numerical solution must be implemented. In this talk, I discuss our progress in implementing a numerical solution to the calculation of DL*L* using arbitrary background field models with simple electromagnetic perturbations.
A simulation model, based on neutron diffusion, has been developed to predict prompt burst excursions in fissile solutions. This model combines neutron diffusion with simple expressions for radiolytic gas generation and solution temperature change through a neutron cross-section model that accounts for reactivity changes. The simulation model tracks neutron flux as a function of time and position during transients. The model is intended to serve as a general survey tool for the behavior of unreflected aqueous fissile solutions commonly encountered at a variety of nuclear materials processing facilities. The model has been tested against experimental data from the KEWB and Silene research reactors.
This paper extends our recent work on the Physics-Informed Neural Networks (PINN) approach for the fixed source diffusion models and applies it to the diffusion theory based k-eigenvalue problems. To make the PINN equitable for the eigenvalue problems, we introduce a novel integral regularization term to the loss function in the framework, and allow the direct inference of the principal eigenvalue and the associated eigenfunction. The regularization term enforces a pre-defined value on the integration of the model predictions, and this value can be directly related to a physical property of the system. We also introduce an additional learnable parameter to approximate the principal eigenvalue. As a proof of principle, we solve the one-group two-dimensional k-eigenvalue neutron diffusion equation in this work. We then provide two numerical examples to demonstrate the applicability of the PINN approach. In each example, we solve the k-eigenvalue diffusion equation in a multi-region configuration constrained with a set of Robin boundary conditions for generality. We use a FEM solution based on the power-iteration method to verify the results of the PINN solution. The results showed relative percentage error in the predicted eigenvalue of about 0.77% and about 1.2% for example 1 and example 2, respectively. The mean absolute error in the predicted flux for example 1 is ∼ 0.002 and for example 2 is ∼ 0.0024. These results indicate some preliminary successes of the PINN application to k-eigenvalue problems. (authors)
We prove a sufficient stochastic maximum principle for the optimal control of a regime-switching diffusion model. We show the connection to dynamic programming and we apply the result to a quadratic loss minimization problem, which can be used to solve a mean-variance portfolio selection problem.
Here, the rational design of ion exchange membranes (IEMs) is becoming more pertinent as their usage becomes broader and as their staple applications (i.e., electrodialysis, flow batteries, and fuel cells) improve in commercial viability. Such efforts would be catalyzed by an improved fundamental understanding of ion transport in IEMs. This review discusses recent progress in modeling ion partitioning and diffusion in IEMs in an effort to relate IEM performance metrics to fundamental membrane properties over which researchers and membrane manufacturers possess direct and sometimes precise control. Central focus is given to the Donnan-Manning model for ion partitioning and the Manning-Meares model for ion diffusion in IEMs. These two frameworks, which are derived from Manning's counter-ion condensation theory for polyelectrolyte solutions, have been widely used within the IEM literature since their recent introduction. To explore this topic, the mathematical derivation of both models is revisited, followed by a survey of experimental and computational discussions of counter-ion condensation in IEMs. Alternative models which fulfill similar roles in predicting IEM transport properties are compared. This review concludes by highlighting the uniquely favorable positions of the Donnan-Manning and Manning-Meares models and discussing their prospects as leading predictors of IEM partitioning and diffusive properties.
Unified interplanetary diffusion model for time behavior of intensity of several solar cosmic ray events, accounting for decay phases
Chemically reacting Couette flow with variable transport properties and hydrogen injection for two diffusion models
Two-dimensional time dependent diffusion model for phase delay between thermospheric density and temperature
The assimilation of argon injected into post-disruption runaway electron (RE) plateaus is studied and compared to the vertical loss rate for vertically unstable RE plateaus. A 1D diffusion model is developed to include neutral diffusion and ionization and is used to help in data interpretation. It is found that the radial mixing time scale of argon ions (~0.05 s) is comparable to the vertical loss timescale. Neutral argon becomes the dominant Ar species in the RE plateau for large Ar numbers (> 3 x 10 21 ); at the same time the neutral Ar diffusivity decreases due to plasma cooling, causing a saturation in the assimilation of injected Ar on the vertical loss time scale. Injection of Ar into vertically unstable RE plateaus in DIII-D does increase the vertical loss rate, as predicted by previous modeling of ITER. Furthermore, there is a decreasing trend in RE current at the wall strike as the Ar quantity is turned up or as the Ar is injected earlier, indicating a decrease in RE energy deposited to the wall.
Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.
Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.
Distributed wind market demand is driven by current and future wind price and performance, along with several non-price market factors like financing terms, retail electricity rates and rate structures, future wind incentives, and others. We developed a new distributed wind technology diffusion model for the contiguous United States that combines hourly wind speed data at 200m resolution with high resolution electricity load data for various consumer segments (e.g., residential, commercial, industrial), electricity rates and rate structures for utility service territories, incentive data, and high resolution tree cover. The model first calculates the economics of distributed wind at high spatial resolution for each market segment, and then uses a Bass diffusion framework to estimate the evolution of market demand over time. The model provides a fundamental new tool for characterizing how distributed wind market potential could be impacted by a range of future conditions, such as electricity price escalations, improvements in wind generator performance and installed cost, and new financing structures. This paper describes model methodology and presents sample results for distributed wind market potential in the contiguous U.S. through 2050.
Spin-accumulation and spin-current profiles are calculated for a disordered Pt film subjected to an in-plane electric current within the nonequilibrium Green's function approach. In the bulklike region of the sample, this approach captures the intrinsic spin Hall effect found in other calculations. Near the surfaces, the results reveal qualitative differences with the results of the widely used spin-diffusion model, even when the boundary conditions are modified to try to account for them. One difference is that the effective spin-diffusion length for transverse spin transport is significantly different from its longitudinal counterpart and is instead similar to the mean-free path. Furthermore, this feature may be generic for spin currents generated via the intrinsic spin Hall mechanism because of the differences in transport mechanisms compared to longitudinal spin transport. Orbital accumulation in the Pt film is only significant in the immediate vicinity of the surfaces and has a small component penetrating into the bulk only in the presence of spin-orbit coupling, as a secondary effect induced by the spin accumulation.