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DOE OSTI2025

Spatiotemporal predictions of toxic urban plumes using deep learning

Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situations, alternative methods are needed that can run quickly and adequately capture important spatiotemporal features. Here, we present a novel deep learning model called ST-GasNet inspired by the mathematical equations that govern the behavior of plumes as they disperse through the atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set of temporal sequences of ground-level toxic urban plumes generated by a high-resolution large eddy simulation model. On independent sequences, ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the early-time behavior as an input, even when a building splits a large plume into smaller plumes. By incorporating large-scale wind boundary condition information, ST-GasNet achieves a prediction accuracy of at least 90% on test data for the entire prediction period.

Civil and Environmental Engineering↗

DOE OSTI2020

Rapid growth of anthropogenic organic nanoparticles greatly alters cloud lifecycle in the Amazon rainforest

Atmospheric nanoparticles can significantly influence Earth’s climate if they grow to sizes large enough to nucleate cloud droplets. While the contribution of extremely low volatility vapors to nanoparticle growth has been discussed extensively, the role of semivolatile organics has been largely overlooked. Here we examine the growth and impacts of air pollution nanoparticles from an isolated metropolis amidst the Amazon rainforest. Although extremely low volatility organics are necessary for the initial growth of nanoparticles, model analysis suggests that dynamic gas-particle partitioning of semivolatile oxidation products of anthropogenic and natural hydrocarbons is predominantly responsible for the observed rapid growth to 50 nm. Furthermore, cloud-resolving simulations demonstrate that the grown particles appreciably modify shallow cloud droplet size distribution, suppress precipitation, and enhance the transition to deep clouds. With condensable semivolatile organics typically formed in greater proportion, similar nanoparticle growth and impacts on clouds could likely occur in heavily urbanized forested regions globally.

Zaveri, Rahul A↗

DOE OSTI2020

DATASET: Rapid growth of anthropogenic organic nanoparticles greatly alters cloud lifecycle in the Amazon rainforest

Zaveri, Rahul A↗

DOE OSTI2023

Deep learning-enhanced design for functionally graded auxetic lattices

Auxetic materials with counterintuitive negative Poisson’s ratio have been of significant interest due to potential applications across diverse engineering fields. Functionally grading such auxetics further enables customization of the structural response and harnesses the potential for multi-functional applications. However, a critical challenge in designing functionally graded lattices is to efficiently determine the spatial variation of the functional gradient and the corresponding geometric designs to achieve the desired response. In this paper, a highly efficient deep learning-based inverse design framework for functionally graded tetra-petal auxetics with spatially tailored properties is presented. This framework significantly improves the efficiency of tailoring functionally graded auxetics where many unit cells need to be tailor-designed. The graded tetra-petal auxetics obtained from the inverse design framework are additively manufactured and subjected to impact tests. The results show superior impact performance compared with uniform designs, demonstrating the effectiveness of the proposed inverse design framework, which can be inspirable to promote advanced structures/materials with enhanced impact resistance.

42 ENGINEERING↗

NASA NTRS1998

MOLA Topography of Impact Basins in the Northern Hemisphere of Mars

Coverage of the northern hemisphere of Mars by the Mars Orbiter Laser Altimeter (MOLA) during the aerobraking hiatus and the two Science Phasing Operation periods provides improved definition and characterization of large impact basins. Gridded MOLA data show the Utopia Basin has a pronounced bowl-like structure, as opposed to the interior rises suggested by the earlier USGS DEM. The elevation structure is concentric about the basin center as mapped by McGill. In particular, the proposed inner ring closely follows the -4 km contour over much of the southern, western and northwestern sides. Higher topography along portions of the dichotomy boundary aligns with the basin's outer ring. High topography in the polar region also occurs where the outer ring should lie, raising the possibility that perhaps some of the polar topography is due to basin structure as well as ice. Two MOLA passes near Phison Rupes provide evidence for a large "stealth" hole where Viking imagery show little evidence of any major structure. The 2 km deep, 600 km wide depression at 31OW, 3ON is as large as the Cassini impact basin 1000 km to the SW. While Cassini is easily recognized in image data, the "MOLA Hole" is not. If this depression is a deeply eroded and buried impact basin (as perhaps suggested by a decrease in the crater density and somewhat smoother terrain than in adjacent areas), it is not clear why it has managed to maintain its great depth. In Tempe at the dichotomy boundary a 300 km wide impact basin is revealed by pronounced bowl-like topography centered at 87W, 47N, even though only about 1/3 of the basin rim structure is obvious. The basin lies on a sloping boundary zone, with the more buried N rim up to 2 km below the rugged S rim. A similar N-S asymmetry in basin ring structure occurs for the much larger Isidis Basin, where the S rim rises 6 km but the subdued N rim rises barely 2 km above the floor. There is essentially no topographic expression of the main ring in the NE quadrant of Isidis where, if it exists, it lies below Hesperian-age plains.

Frey, Herbert↗

DOE OSTI2024

A Medium‐Sized Paleo‐Tsunami Reconstruction by a Deep Neural Network Processing Sedimentary Deposits

Abstract Reconstructing the magnitude and recurrence time of tsunamis, one of the most destructive and unpredictable natural hazards impacting coastal communities, is essential. While major tsunamis are the most studied due to their disastrous impact, small/medium tsunamis (SMTs) are much more frequent and can still significantly impact the coast. Therefore, SMTs potentially provide an extensive archive of information preserved in the geological record. Analyzing the deposits of small/medium paleo‐tsunamis (SMPTs) opens a window into when their direct observation was unavailable. However, deposits of SMPTs are often degraded, traditional sediment deposition inversion models might fail. Recent research has shown that Deep Neural Networks (DNN) can effectively reconstruct the flow conditions of major tsunamis from their deposits. We evaluate the effectiveness of this approach in reconstructing the characteristics of a recent medium size tsunami (2006 Java) and of a medium paleo‐tsunami (1929 Grand Banks). We successfully reconstruct the flow characteristics of the 2006 Java event and show that an inversion of comparable quality is possible for the 1929 Grand Banks tsunami, despite greater uncertainties due to the deposit degradation. Our research shows that Machine Learning has the potential to unseal the meaning of data of thousands SMPTs.

Batubo, P.↗

DOE OSTI2021

Deep learning techniques to disentangle water use efficiency, climate change, and carbon sequestration across ecosystem scales

Focal Areas: We plan to use machine learning (ML) to disentangle the impact of climate change on plant water use efficiency (WUE) across the leaf level, structured canopy, and community ecosystem scales. Due to the complex behavior of modeling plant WUE’s connection to carbon storage (and hidden co-varying interactions) we suggest that physics-based deep learning must be utilized. The second topic we propose is, based on a better understanding of WUE response to climate change, there is a need to predict how WUE will limit or enhance the recycling of water from transpiration by vegetation back to the land surface worldwide, and thus impact water storage capacity (i.e., natural and reservoirs), using machine learning optimization techniques such as genetic algorithms.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Operando synchrotron X-ray analysis of melt pool dynamics in an Al-Sn immiscible alloy

The melt flow in an Al-50vol.% Sn immiscible alloy, produced by single-track laser melting of Al and Sn elemental powders, was studied in real time. High-speed synchrotron X-ray imaging was used to track Al and Sn liquids’ movements, and also to examine elemental distributions in the laser tracks, complimented by electron microscopy after solidification. Key aspects, including melt pool geometry, keyhole instability, and flow dynamics (flow pattern and velocity), were examined using digital image analysis. Relatively deeper melt pools formed at 400 W and 300 mm/s exhibited greater stability, with smooth surfaces, consistent outward flow, and minor vortices near the keyhole. In contrast, shallower pools produced at higher scanning speeds (>500 mm/s) demonstrated greater instability with increased surface waviness, and stronger velocity fluctuations, leading to numerous micro-vortices and increased Al-Sn heterogeneity. Velocity scale estimations, supported by experimental observations, examined the roles of vapour pressure, Marangoni effect, buoyancy, inertial, and surface tension forces in the flow. The results revealed that vapour pressure and mechanical waves dominated at high scanning speeds (shallow pools), while Marangoni forces were equally significant in deep pools at lower speeds (300 mm/s). Buoyancy was found to have minimal impact in both cases. Furthermore, the interaction between inertial and surface tension forces played a critical role in determining the degree of waviness of the pools’ surfaces. These findings offer valuable insights into melt pool dynamics during laser processing of immiscible alloys and other metallic systems using elemental powders, and provide guidance for developing high-fidelity computational fluid dynamics models.

immiscible alloys↗

DOE OSTI2024

Target mass corrections in lepton–nucleus DIS: Theory and applications to nuclear PDFs

Motivated by the wide range of kinematics covered by current and planned deep-inelastic scattering (DIS) facilities, we revisit the formalism, practical implementation, and numerical impact of target mass corrections (TMCs) for DIS on unpolarized nuclear targets. An important aspect is that we only use nuclear and later partonic degrees of freedom, carefully avoiding a picture of the nucleus in terms of nucleons. After establishing that formulae used for individual nucleon targets (p,n), derived in the Operator Product Expansion (OPE) formalism, are indeed applicable to nuclear targets, here we rewrite expressions for nuclear TMCs in terms of re-scaled (or averaged) kinematic variables. As a consequence, we find a representation for nuclear TMCs that is approximately independent of the nuclear target. We go on to construct a single-parameter fit for all nuclear targets that is in good numerical agreement with full computations of TMCs. We discuss in detail qualitative and quantitative differences between nuclear TMCs built in the OPE and the parton model formalisms, as well as give numerical predictions for current and future facilities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry

Since their advent over 2 decades ago, autonomous Argo floats have revolutionized the field of oceanography, and, more recently, the addition of biogeochemical and biological sensors to these floats has greatly improved our understanding of carbon, nutrient, and oxygen cycling in the ocean. While Argo floats offer unprecedented horizontal, vertical, and temporal coverage of the global ocean, uncertainties remain about whether Argo sampling frequency and density capture the true spatiotemporal variability in physical, biogeochemical, and biological properties. As the true distributions of, e.g., temperature or oxygen are unknown, these uncertainties remain difficult to address with Argo floats alone. Numerical models with synthetic observing systems offer one potential avenue to address these uncertainties. Here, we implement synthetic biogeochemical Argo floats into the Energy Exascale Earth System Model version 2 (E3SMv2), which build on the Lagrangian In Situ Global High-Performance Particle Tracking (LIGHT) module in E3SMv2 (E3SMv2-LIGHT-bgcArgo-1.0). Since the synthetic floats sample the model fields at model run time, the end user defines the sampling protocol ahead of any model simulation, including the number and distribution of synthetic floats to be deployed, their sampling frequency, and the prognostic or diagnostic model fields to be sampled. Using a 6-year proof-of-concept simulation, we illustrate the utility of the synthetic floats in different case studies. In particular, we quantify the impact of (i) sampling density on the float-derived detection of deep-ocean change in temperature or oxygen and on float-derived estimates of phytoplankton phenology, (ii) sampling frequency and sea-ice cover on float trajectory lengths and hence float-derived estimates of current velocities, and (iii) short-term variability in ecosystem stressors on estimates of their seasonal variability.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1973

Preliminary geologic investigation of the Apollo 17 landing site

A geological investigation of the Apollo 17 lunar landing site was conducted. The Taurus-Littrow valley is interpreted as a deep graben formed by structural adjustment of lunar crustal material to the Serenitatis impact. Materials of the valley fill were sampled at many stations. Ejecta around many craters on the valley floor consist of basalt, showing that the graben was partly filled by lava flows. The geological objectives of the Apollo 17 mission are divided into orbital and lunar surface data collection. The data obtained for both types of investigation are presented in tables, photographs, and drawings.

Muehlberger, W. R.↗

NASA NTRS1973

Geologic exploration of Taurus-Littrow - Apollo 17 landing site

Apollo 17 landed in a deep graben valley embaying the mountainous highlands southeast of the Serenitatis basin. Impact-generated breccias underlie the massifs adjacent to the valley, and basalt has flooded and leveled the valley floor. The dark mantle inferred from orbital photographs was not recognized as a discrete unit; the unusually thick regolith of the valley floor contains a unique high concentration of dark glass beads that may cause the low albedo of much of the surface.

Source record↗

NASA NTRS1991

Martian seismicity through time from surface faulting

An objective of future Mars missions involves emplacing a seismic network on Mars to determine the internal structure of the planet. An argument based on the relative geologic histories of the terrestrial planets suggests that Mars should be seismically more active than the Moon, but less active than the Earth. The seismicity is estimated which is expected on Mars through time from slip on faults visible on the planets surface. These estimates of martian seismicity must be considered a lower limit as only structures produced by shear faulting visible at the surface today are included (i.e., no provision is made for buried structures or non-shear structures); in addition, the estimate does not include seismic events that do not produce surface displacement (e.g., activity associated with hidden faults, deep lithospheric processes or volcanism) or events produced by tidal triggering or meteorite impacts. Calibration of these estimates suggests that Mars may be many times more seismically active than the Moon.

Golombek, M. P.↗

NASA NTRS2017

The Importance of Lake Overflow Floods for Early Martian Landscape Evolution: Insights From Licus Vallis

Open-basin lake outlet valleys are incised when water breaches the basin-confining topography and overflows. Outlet valleys record this flooding event and provide insight into how the lake and surrounding terrain evolved over time. Here we present a study of the paleolake outlet Licus Vallis, a >350 km long, >2 km wide, >100 m deep valley that heads at the outlet breach of an approx.30 km diameter impact crater. Multiple geomorphic features of this valley system suggest it records a more complex evolution than formation from a single lake overflow flood. This provides unique insight into the paleohydrology of lakes on early Mars, as we can make inferences beyond the most recent phase of activity..

Goudge, T. A.↗

NASA NTRSDate not supplied

Fiber-Detector Subsystem Loss Comparison for A Ground-Based Photon-Counting Optical Receiver

In this paper we present a comparative study of the losses associated with fiber-coupled single-photon detectors in two different configurations, each with the goal of receiving a pulsed-position modulated signal with a maximum data rate of 267 Mbps. First, we consider a 7x1 few-mode-fiber (FMF) photonic lantern coupled to seven individual superconducting nanowire single photon detectors (SNSPDs). In the second configuration we assess a single FMF coupled to a 16-channel monolithic SNSPD array. In each case we measure and compare combined fiber coupling, SNSPD blocking, and system efficiency losses under emulated atmospheric turbulence conditions. We address subsystem impact on link performance and analyze feasibility for future NASA lunar and deep-space optical communications missions.

Fiber optics↗

NASA NTRSDate not supplied

Fiber-Detector Subsystem Loss Comparison for A Ground-Based Photon-Counting Optical Receiver

A comparative study of the losses associated with fiber-coupled single-photon detectors in two different configurations, each with the goal of receiving a pulsed-position modulated signal with a maximum data rate of 267 Mbps. First, we consider a 7x1 few-mode-fiber (FMF) photonic lantern coupled to seven individual superconducting nanowire single photon detectors (SNSPDs). In the second configuration we assess a single FMF coupled to a 16-channel monolithic SNSPD array. In each case we measure and compare combined fiber coupling, SNSPD blocking, and system efficiency losses under emulated atmospheric turbulence conditions. We address subsystem impact on link performance and analyze feasibility for future NASA lunar and deep-space optical communications missions.

Fiber optics↗

DOE OSTI2023

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Research & Experimental Medicine↗

DOE OSTI2021

Impact of improved energy resolution on DUNE sensitivity to neutrino non-standard interactions

The full physics potential of the next-generation Deep Underground Neutrino Experiment (DUNE) is still being explored. In particular, there have been some recent studies on the possibility of improving DUNE’s neutrino energy reconstruction. The main motivation is that a better determination of the neutrino energy in an event-by-event basis will translate into an improved measurement of the Dirac C P phase and other neutrino oscillation parameters. To further motivate studies and improvements on the neutrino energy reconstruction, we evaluate the impact of energy resolution at DUNE on an illustrative new physics scenario, viz. non-standard interactions (NSI) of neutrinos with matter. We show that a better energy resolution in comparison to the ones given in the DUNE conceptual and technical design reports may significantly enhance the experimental sensitivity to NSI, particularly when degeneracies are present. While a better reconstruction of the first oscillation peak helps disentangling standard C P effects from those coming from NSIs, we find that the second oscillation peak also plays a nontrivial role in improving DUNE’s sensitivity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗