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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Full Scale HEPA Filter Encapsulation in Ultra-High-Performance Grout - Proof of Concept

The Hanford Tank Waste Treatment and Immobilization Plant (WTP) currently being constructed to treat radioactive waste, includes vitrification facilities for both the high-level waste (HLW) and low activity waste (LAW) fractions. Operation of the WTP will produce contaminated high-efficiency particulate air filters (HEPA), as part of the solid secondary waste (SSW) stream. The HEPA filters receive off-gas from the vessel vent header and primary off-gas treatment system in LAW Facility and remove particulate contaminants including 99 Tc and 129 I salts. The current disposal method for HEPA filters is encapsulation in metal containers using cementitious material (CM) and disposal in the Integrated Disposal Facility (IDF). Results from the 2017 IDF Performance Assessment (PA) WRPS (2018) demonstrated that while compliance is maintained for the 1000 year compliance period mandated by DOE O 435.1 and its accompanying manual, release of constituents from the HEPA filters result in exceedance of the performance objective imposed as the groundwater regulatory limit at later times. For example, at about 1500 years post-closure, solid secondary waste (SSW), including HEPA filters is predicted to become a dominant contributor to 99 Tc release and over the 10,000-year sensitivity analysis period, SSW is the dominant contributor of 129 I release to the groundwater. The estimated release could potentially be reduced if the HEPA filters are not compacted and waste containers could be distributed throughout a large space, thereby diluting the contaminant release. Additionally, a better cementitious material could be used to encapsulate the HEPA filters. One alternative method for disposal of contaminated HEPA filters is encapsulation of the filters in ultra high-performance grout (UHPG). UHPG is a variation of ultra-high-performance concrete (UHPC) is commonly used in the prestressed concrete industry for large structural members. Recent studies of UHPG show it has excellent properties for containing radionuclides such as 99 Tc and 129 I Nichols and Kaplan (2021). This report presents the results of the first attempt to encapsulate a clean, full-size HEPA filter in UHPG and evaluate the effectiveness of the immobilization process and final waste form. A full-scale proof-of-concept simulated waste form was prepared by encapsulating a HEPA filter in a 110-gallon stainless steel (SS) drum using UHPG. A change from Type I/II PC to Type 1L PLC was made after American Rock Products informed the team that they would no longer be using Type I/II by the end of 2024 and the northwest was phasing out Type I/II PC overall. Type I/II PC used in previous studies of UHPG for encapsulation (Nichols and Kaplan 2021). After the UHPG was cured both the scaled mockup and the full-scale simulated waste forms were sectioned for visual examination. UHPG completely encapsulated the filters and bonded to the external surfaces of materials comprising the filters. No cracks were observed in the sectioned waste forms.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Laboratory Study of Magnetic Reconnection in Lunar-relevant Mini-magnetospheres

Abstract Mini-magnetospheres are small ion-scale structures that are well suited to studying kinetic-scale physics of collisionless space plasmas. Such ion-scale magnetospheres can be found on local regions of the Moon, associated with the lunar crustal magnetic field. In this paper, we report on the laboratory experimental study of magnetic reconnection in laser-driven, lunar-like ion-scale magnetospheres on the Large Plasma Device at the University of California, Los Angeles. In the experiment, a high-repetition rate (1 Hz), nanosecond laser is used to drive a fast-moving, collisionless plasma that expands into the field generated by a pulsed magnetic dipole embedded into a background plasma and magnetic field. The high-repetition rate enables the acquisition of time-resolved volumetric data of the magnetic and electric fields to characterize magnetic reconnection and calculate the reconnection rate. We notably observe the formation of Hall fields associated with reconnection. Particle-in-cell simulations reproducing the experimental results were performed to study the microphysics of the interaction. By analyzing the generalized Ohm’s law terms, we find that the electron-only reconnection is driven by kinetic effects through the electron pressure anisotropy. These results are compared to recent satellite measurements that found evidence of magnetic reconnection near the lunar surface.

Astronomy & Astrophysics↗
DOE OSTI2021

Comparative Performance of Low Global Warming Potential (GWP) Refrigerants as Replacement for R-410A in a Regular 2-Speed Heat Pump for Sustainable Cooling

Heat pumps are currently being developed to reduce the energy footprint for residential and commercial building space conditioning and water heating loads in cold climates. To mitigate the nocuous impact of greenhouse gas emissions on the environment, and to create a carbon-neutral building stock, alternate lower-GWP refrigerants must also replace the predominant use of R-410A, without re-engineering the mechanical hardware. In this paper, we analyze the performance of lower-GWP alternative refrigerants (R-32, R-452B, R-454B, and R-466A) relative to the conventional R-410A and draw conclusions on the relative performances to meet cooling loads. The simulations are accomplished using the heat pump design model, a well-known, public-domain design tool with a free web interface and downloadable desktop version to support public use and the HVAC R&D community. The contributions contain detailed, hardware-based heat exchanger and system analyses to provide a comprehensive assessment. The results of the simulation are scrutinized from the first (capacity and energy efficiency) and second laws (exergy analysis) to identify sources of systemic inefficiency, the root cause of lost work. This rigorous approach provides an exhaustive analysis of alternate lower-GWP refrigerants to replace R-410A using the same hardware. The results have practical value in engineering heat pumps in an economy that is compelled to alter by the consequences and uncertainties of climate change, to reduce its anthropogenic carbon footprint.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Near-field models and simulations of pellet ablation in tokamaks

Here, detailed numerical studies of the ablation of a single neon pellet in the plasma disruption mitigation parameter space have been performed. Simulations were carried out using FronTier, a hydrodynamic/low magnetic Reynolds number MHD code with explicit tracking of material interfaces. FronTier's physics models resolve the pellet surface ablation and the formation of a dense, cold cloud of ablated material, the deposition of energy from hot plasma electrons, the expansion of the ablation cloud along magnetic field lines, and the radiation losses. A local thermodynamic equilibrium model based on Saha equations has been used to resolve atomic processes in the cloud and Redlich–Kwong corrections to the ideal gas equation of state for cold and dense gases, which have been used near the pellet surface. The FronTier pellet code is a next generation of the code described. It has been validated against the semi-analytic improved neutral gas shielding model in the 1D spherically symmetric approximation. Main results include quantification of the influence of atomic processes and Redlich–Kwong corrections on the pellet ablation in spherically symmetric approximation and verification of analytic scaling laws in a broad range of pellet and plasma parameters. Using axially symmetric MHD simulations, the properties of ablation channels and the reduction of pellet ablation rates in magnetic fields of increasing strength have been studied. While the main emphasis has been given to neon pellets for the plasma disruption mitigation, selected results on deuterium fueling pellets have also been presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Plug & play directed evolution of proteins with gradient-based discrete MCMC

Abstract A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Hidden deep in the halo: selection of a reduced proper motion halo catalogue and mining retrograde streams in the velocity space

ABSTRACT The Milky Way halo is one of the few galactic haloes that provides a unique insight into galaxy formation by resolved stellar populations. Here, we present a catalogue of ∼47 million halo stars selected independent of parallax and line-of-sight velocities, using a combination of Gaia DR3 proper motion and photometry by means of their reduced proper motion. We select high tangential velocity (halo) main sequence stars and fit distances to them using their simple colour-absolute-magnitude relation. This sample reaches out to ∼21 kpc with a median distance of 6.6 kpc thereby probing much further out than would be possible using reliable Gaia parallaxes. The typical uncertainty in their distances is $0.57_{-0.26}^{+0.56}$ kpc. Using the colour range 0.45 < (G0 − GRP, 0) < 0.715, where the main sequence is narrower, gives an even better accuracy down to $0.39_{-0.12}^{+0.18}$ kpc in distance. The median velocity uncertainty for stars within this colour range is 15.5 km s−1. The distribution of these sources in the sky, together with their tangential component velocities, are very well-suited to study retrograde substructures. We explore the selection of two complex retrograde streams: GD-1 and Jhelum. For these streams, we resolve the gaps, wiggles and density breaks reported in the literature more clearly. We also illustrate the effect of the kinematic selection bias towards high proper motion stars and incompleteness at larger distances due to Gaia’s scanning law. These examples showcase how the full RPM catalogue made available here can help us paint a more detailed picture of the build-up of the Milky Way halo.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Validation of Thermal Scattering Laws for Light Water at Elevated Temperatures with Diffusion Experiments

Light water is the most important neutron moderator in many reactor applications. Thermal neutron scattering kernels for H bound in H 2 O are represented by ENDF-format thermal scattering laws (TSLs) evaluated at discrete temperatures T . Nuclear data evaluations are conventionally validated utilizing a combination of critical benchmarks and experimental cross section data. Existing public critical benchmarks may be inadequate for testing water TSLs over the full range of T of interest in reactor applications, and experimental thermal scattering cross section data for water is sparse at elevated T . In this work, MC21 is used to simulate the decay of the equilibrium thermal neutron flux in light-water spheres of several radii. The fundamental-mode time eigenvalue $\mathcal{a}$ is calculated at 22 °C and 227 °C (at saturation pressure) for each sphere using three different H-H 2 O TSLs. Fitting $\mathcal{a}$ to a polynomial function of geometric buckling allows calculation of the thermal neutron diffusion length L . Extrapolation lengths are treated as a function of the transport mean free path and geometry. Experimental L data from 25 publications at 49 temperatures (from 10 °C to 295 °C), computed by several different time-dependent and space-dependent decay methods, is used to develop an empirical fit of L vs. T . The MC21-calculated L for the TSLs tested are within ±1% of the predicted values at 22 °C and 227 °C. This validation approach, which may be repeated at arbitrary T , constitutes an integral benchmark specific to and characterizing the detailed physics of the thermal scattering kernel applied.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

The Study on the Shock Wave Propagation Rule of a Gas Explosion in a Gas Compartment

Combined with the k-ε turbulence model of general application, a refined finite element model of a utility tunnel’s gas compartment filled with the methane/air mixture is developed. A series of analyses are made by using the powerful industry-leading computational fluid dynamics (CFD) software flame acceleration simulator (FLACS) to study the shock wave propagation rule in the gas compartment. The longitudinal and transversal distribution laws of the explosion shock wave are gained taking into consideration the spatial characteristics of the gas compartment. The influences of a few parameters, such as initial conditions and section size of the gas compartment, on the shock wave propagation rule are further discussed. The basic procedure for predicting the peak pressure of the blast wave is provided by considering the initial conditions and the gas compartment, and the corresponding injury effect of the explosion wave on the living beings is assessed. The investigation demonstrates that the peak pressure by the coupled effect between the initial conditions is significantly influenced, especially at the upper and lower gas explosion limits. The peak pressure increases gradually as the width or height increases, and both basically meet the linear relation. The proposed method can forecast the peak pressure of the explosion shock wave in the gas compartment accurately. According to the peak pressure longitudinal and transversal distributions of the blast wave, the peak pressure is far greater than the killing pressure threshold in the underground and closed space; consequently, it is not safe for the living beings in the gas compartment.

Lin, Shu-Chao↗
DOE OSTI2023

Holographic Nano‐Imaging of Terahertz Dirac Plasmon Polaritons in Topological Insulator Antenna Resonators

Abstract Excitation of Dirac plasmon polaritons (DPPs) in bi‐dimensional materials have attracted considerable interest in recent years, both from perspectives of understanding their physics and exploring their transformative potential for nanophotonic devices, including ultra‐sensitive plasmonic sensors, ultrafast saturable absorbers, modulators, and switches. Topological insulators (TIs) represent an ideal technological platform in this respect because they can support plasmon polaritons formed by Dirac carriers in the topological surface states. Tracing propagation of DPPs is a very challenging task, particularly at terahertz (THz) frequencies, where the DPP wavelength becomes over one order of magnitude shorter than the free space photon wavelength. Furthermore, severe attenuation hinders the comprehensive analysis of their characteristics. Here, the properties of DPPs in real TI‐based devices are revealed. Bi 2 Se 3 rectangular antennas can efficiently confine the propagation of DPPs to a single dimension and, as a result, enhance the DPPs visibility despite the strong intrinsic attenuation. The plasmon dispersion and loss properties from plasmon profiles are experimentally determined, along the antennas, obtained using holographic near‐field nano‐imaging in a wide range of THz frequencies, from 2.05 to 4.3 THz. The detailed investigation of the unveiled DPP properties can guide the design of novel topological quantum devices exploiting their directional propagation.

Chemistry↗
DOE OSTI2022

Quantum efficiency of InGaN–GaN multi-quantum well solar cells: Experimental characterization and modeling

InGaN-based multi-quantum well (MQW) solar cells are promising devices for photovoltaics (e.g., for tandem solar cells and concentrator systems), space applications, and wireless power transfer. In order to improve the efficiency of these devices, the factors limiting their efficiency and stability must be investigated in detail. Due to the complexity of a MQW structure, compared with a simple pn junction, modeling the spectral response of these solar cells is not straightforward, and ad hoc methodologies must be implemented. In this paper, we propose a model, based on material parameters and closed-formula equations, that describes the shape of the quantum efficiency of InGaN/GaN MQW solar cells, by taking into account the layer thickness, the temperature dependence of the absorption coefficient, and quantum confinement effects. We demonstrate (i) that the proposed model can effectively reproduce the spectral response of the cells; in addition, (ii) we prove that the bulk p-GaN layer absorbs radiation, but the carriers photogenerated in this region do not significantly contribute to device current. Finally, we show that (iii) by increasing the temperature, there is a redshift of the absorption edge due to bandgap narrowing, which can be described by Varshni law and is taken into account by the model, and a lowering in the extraction efficiency due to the increase in recombination (mostly Shockley–Read–Hall) inside the quantum wells, which is also visible by decreasing light intensity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Proton Acceleration in Low- β Magnetic Reconnection with Energetic Particle Feedback

Magnetic reconnection regions in space and astrophysics are known as active particle acceleration sites. There is ample evidence showing that energetic particles can take a substantial amount of converted energy during magnetic reconnection. However, there has been a lack of studies understanding the backreaction of energetic particles at magnetohydrodynamical scales in magnetic reconnection. To address this, we have developed a new computational method to explore the feedback by nonthermal energetic particles. This approach considers the backreaction from these energetic particles by incorporating their pressure into magnetohydrodynamics (MHD) equations. The pressure of the energetic particles is evaluated from their distribution evolved through Parker's transport equation, solved using stochastic differential equations (SDEs), so we coin the name MHD-SDE. Applying this method to low-β magnetic reconnection simulations, we find that reconnection is capable of accelerating a large fraction of energetic particles that contain a substantial amount of energy. When the feedback from these particles is included, their pressure suppresses the compression structures generated by magnetic reconnection, thereby mediating particle energization. Consequently, the feedback from energetic particles results in a steeper power-law energy spectrum. These findings suggest that feedback from nonthermal energetic particles plays a crucial role in magnetic reconnection and particle acceleration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Distributions of Particles Accelerated by Strong Alfvénic Turbulence

This work presents a model for generating nonthermal power-law tails of particles’ energy probability density functions in turbulent collisionless plasmas, applicable to both nonrelativistic and relativistic scenarios. We propose that strong Alfvénic turbulence energizes plasma particles through curvature acceleration, particularly for particles with Larmor radii comparable to the scales of turbulence. When the energy density of the energized particles increases, the efficiency of the energy exchange process diminishes. As a result, the acceleration process saturates, leading to power-law distributions of particle momentum and energy. In the nonrelativistic case, the momentum probability density function scales as f(p)dp ∝ p −3 dp, while in the ultrarelativistic case, the energy probability density function scales as f(γ)dγ ∝ γ −3 dγ, where γ is the Lorentz factor. This model provides a unified framework for understanding particle acceleration in both energy regimes, complementing existing analytical approaches. The predicted scalings are consistent with available observations of energetic ion distributions in the heliosphere and with the findings from numerical simulations of ultrarelativistic particle acceleration in magnetically dominated plasma turbulence.

Alfven waves↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Development of Property Composition Models For RPP-WTP LAW Glasses (Final Report)

This report describes the development of property-composition models for low-activity waste (LAW) glasses for the River Protection Project Waste Treatment Plant (RPP-WTP) at the Hanford site. The RPP-WTP will separate Hanford tank wastes into LAW and high-level waste (HLW) streams and each stream will be vitrified separately. The development of LAW and HLW glass formulations for the RPP-WTP has been reported previously and is an ongoing activity. Acceptable formulations must meet a variety of processability, product quality, and waste loading requirements that are dictated either by the RPP-WTP contract or by the characteristics of the particular treatment processes that have been selected. These requirements amount to constraints on the acceptable ranges of certain glass properties. These properties are determined first and foremost by the composition of the glass or glass melt. Thus, while there is no direct way of controlling the glass properties of interest during production, there are simple and extremely effective methods of achieving the same result by instead controlling the glass composition. This basic principle is no different from that used to produce enormous volumes of commercial glass to meet exacting product specifications. An essential difference in waste vitrification, however, is that one of the raw materials (the waste itself) can be subject to considerable compositional uncertainty and variability. Thus, waste vitrification facilities and associated process control systems (of which, the operating envelope in glass formulation space is a key part) must, of course, be designed to be robust with respect to such variations. The determination of quantitative relationships between the glass properties that must be controlled and the glass composition can play an important role in the development of such facilities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Interactions of particles with “continuous spin” fields

Powerful general arguments allow only a few families of long-range interactions, exemplified by gauge field theories of electromagnetism and gravity. However, all of these arguments presuppose that massless fields have zero spin scale (Casimir invariant) and hence exactly boost invariant helicity. This misses the most general behavior compatible with Lorentz symmetry. We present a Lagrangian formalism describing interactions of matter particles with bosonic “continuous spin” fields with arbitrary spin scale ρ. Remarkably, physical observables are well approximated by familiar theories at frequencies larger than ρ, with calculable deviations at low frequencies and long distances. For example, we predict specific ρ-dependent modifications to the Lorentz force law and the Larmor formula, which lay the foundation for experimental tests of the photon’s spin scale. We also reproduce known soft radiation emission amplitudes for nonzero ρ. The particles’ effective matter currents are not fully localized to their worldlines when ρ ≠ 0, which motivates investigation of manifestly local completions of our theory. Our results also motivate the development of continuous spin analogues of gravity and non-Abelian gauge theories. Given the correspondence with familiar gauge theory in the small ρ limit, we conjecture that continuous spin particles may in fact mediate known long-range forces, with testable consequences.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Conservative DG method for the micro-macro decomposition of the Vlasov–Poisson–Lenard–Bernstein model

The micro-macro (mM) decomposition approach is considered for the numerical solution of the Vlasov– Poisson–Lenard–Bernstein (VPLB) system, which is relevant for plasma physics applications. In the mM approach, the kinetic distribution function is decomposed as $f$ = $\mathscr{E}$[$ρ_f$] + g , where $\mathscr{E}$ is a local equilibrium distribution, depending on the macroscopic moments $ρ_f$ = $∫_{\mathbb{R}}$ $efdv$ = $\langle$$ef$$\rangle$$_{\mathbb{R}}$, where e = (1, $v$, $\frac{1}{2}$ $v^2$) T , and $g$, the microscopic distribution, is defined such that $\langle$$eg$$\rangle$$_{\mathbb{R}}$ = 0. We aim to design numerical methods for the mM decomposition of the VPLB system, which consists of coupled equations for $ρ_f$ and $g$. To this end, we use the discontinuous Galerkin (DG) method for phase-space discretization, and implicit-explicit (IMEX) time integration, where the phase-space advection terms are integrated explicitly and the collision operator is integrated implicitly. We give special consideration to ensure that the resulting mM method maintains the $\langle$$eg$$\rangle$$_{\mathbb{R}}$ = 0 constraint, which may be necessary for obtaining (i) satisfactory results in the collision dominated regime with coarse velocity resolution, and (ii) unambiguous conservation properties. The constraint-preserving property is achieved through a consistent discretization of the equations governing the micro and macro components. Here, we present numerical results that demonstrate the performance of the mM method. The mM method is also compared against a corresponding DG-IMEX method solving directly for $f$.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

Abstract For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.

54 ENVIRONMENTAL SCIENCES↗