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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Gamma-ray burst detection prospects for next generation ground-based VHE facilities

ABSTRACT Gamma-ray Bursts (GRB) were discovered by satellite-based detectors as powerful sources of transient γ-ray emission. The Fermi satellite detected an increasing number of these events with its dedicated Gamma-ray Burst Monitor (GBM), some of which were associated with high energy photons ($E \gt 10$ GeV), by the Large Area Telescope (LAT). More recently, follow-up observations by Cherenkov telescopes detected very high energy emission ($E \gt 100$ GeV) from GRBs, opening up a new observational window with implications on the interpretation of their central engines and on the propagation of very energetic photons across the Universe. Here, we use the data published in the 2nd Fermi-LAT Gamma Ray Burst Catalogue to characterize the duration, luminosity, redshift, and light curve of the high energy GRB emission. We extrapolate these properties to the very high energy domain, comparing the results with available observations and with the potential of future instruments. We use observed and simulated GRB populations to estimate the chances of detection with wide-field ground-based γ-ray instruments. Our analysis aims to evaluate the opportunities of the Southern Wide-field-of-view Gamma-ray Observatory (SWGO), to be installed in the Southern Hemisphere, to complement CTA. We show that a low-energy observing threshold ($E_{low} \lt 200$ GeV), with good point source sensitivity ($F_{lim} \approx 10^{-11} \,\mathrm{erg\, cm^{-2}\, s^{-1}}$ in $1$ yr), are optimal requirements to work as a GRB trigger facility and to probe the burst spectral properties down to time-scales as short as $10$ s, accessing a time domain that will not be available to Imaging Atmospheric Cherenkov Telescopes instruments.

La Mura, G. (ORCID:000000018553499X)↗
DOE OSTI2023

Opacities of singly and doubly ionized neodymium and uranium for kilonova emission modeling

ABSTRACT Even though the electromagnetic counterpart AT2017gfo to the binary neutron star merger GW170817 is powered by the radioactive decay of r-process nuclei, only few tentative identifications of light r-process elements have been made so far. One of the major limitations for the identification of heavy nuclei is incomplete or missing atomic data. While substantial progress has been made on lanthanide atomic data over the last few years, for actinides there has been less emphasis, with the first complete set of opacity data only recently published. We perform atomic structure calculations of neodymium (Z = 60) as well as the corresponding actinide uranium (Z = 92). Using two different codes [flexible atomic code (fac) and hartree–fock-relativistic (hfr)] for the calculation of the atomic data, we investigate the accuracy of the calculated data (energy levels and electric dipole transitions) and their effect on kilonova opacities. For the fac calculations, we optimize the local central potential and the number of included configurations and use a dedicated calibration technique to improve the agreement between theoretical and available experimental atomic energy levels (AELs). For ions with vast amounts of experimental data available, the presented opacities agree quite well with previous estimations. On the other hand, the optimization and calibration method cannot be used for ions with only few available AELs. For these cases, where no experimental nor benchmarked calculations are available, a large spread in the opacities estimated from the atomic data obtained with the various atomic structure codes is observed. We find that the opacity of uranium is almost double the neodymium opacity.

Flörs, A. (ORCID:0000000320242819)↗
DOE OSTI2023

Machine-Guided Design of Oxidation-Resistant Superconductors for Quantum Information Applications

Decoherence in superconducting qubits has long been attributed to two-level systems arising from the surfaces and interfaces present in real devices. A recent significant step in reducing decoherence was the replacement of superconducting niobium by superconducting tantalum, resulting in a tripling of transmon qubit lifetimes (T1). The identity, thickness, and quality of the native surface oxide, is thought to play a major role, as tantalum only has one oxide whereas niobium has several. Here we report the development of a thermodynamic metric to rank materials based on their potential to form a well-defined, thin, surface oxide. We first computed this metric for known binary and ternary metal alloys using data available from the Materials Project and experimentally validated the strengths and limits of this metric through the preparation and controlled oxidation of eight known metal alloys. Then we trained a convolutional neural network to predict the value of this metric from atomic composition and atomic properties. This allowed us to compute the metric for materials that are not present in the Materials Project, including a large selection of known superconductors, and, when combined with Tc, allowed us to identify new candidate superconductors for quantum information science and engineering (QISE) applications. We tested the oxidation resistance of a pair of these predictions experimentally. Our results are expected to lay the foundation for the tailored and rapid selection of improved superconductors for QISE.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices

Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current ‘state of the art’ from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of ‘soft recommendations’ about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage ‘open science’ practices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

How Should Machine Learning Be Successfully Used for Wind Speed Vertical Extrapolation?

An accurate characterization of the wind resource available at hub-height is required for an efficient and bankable wind farm project. However, direct measurement of wind speed at the constantly increasing height of the hub of commercial wind turbines is oftentimes challenging and expensive, so that it is common practice to vertically extrapolate the wind resource from lower and more easily accessible levels. Conventional techniques for wind speed vertical extrapolation include the use of a power law and a logarithmic profile. While simple, the limits in accuracy of these methods have been shown in various studies. Recently, machine learning has been proposed as a new method to vertically extrapolate winds. All the published studies on the topic assess the performance of machine learning techniques in vertically extrapolating the wind resource at the same location where the algorithm has been trained. However, in real-world applications, the wind resource is measured at the instrument location, but it then needs to be extrapolated at hub height at the location of the wind turbines within the find farm. To be able to fully recommend the use of machine learning techniques over the simple power law and logarithmic law, the spatial variability of the performance improvements of the machine learning approaches needs to be assessed. Here, we propose a round-robin validation of a machine learning-based method for wind speed extrapolation. We use 20 months of observations at four locations spanning a 100 km wide region at the Southern Great Plains (SGP) atmospheric observatory, in north-central Oklahoma. At each location, we train a random forest to predict 30-min average wind speed at 143 m AGL. We use as input features lidar wind speed at 65 m AGL, time of day, sonic anemometer wind speed at 4 m AGL, turbulent kinetic energy, and Obukhov length. First, we perform a same-site comparison of the performance of the proposed random forest against the conventional techniques for wind speed extrapolation (namely power law and logarithmic profile, with widely accepted stability corrections). We find that the random forest outperforms the power law in vertically extrapolating wind speed in all the considered stability regimes, with a 33% reduction in MAE for stable conditions, and a 31% reduction in unstable conditions. Similar results are found when comparing predictions of extrapolated winds from the logarithmic profile and the random forest with the observed values. Next, we propose a round-robin validation, to use the random forest trained at each site to extrapolate wind speed at the remaining three sites. We find that the performance of the random forest approach degrades when the algorithm is tested at a site different than the training one. However, even under those circumstances, the machine learning-based approach still outperforms the conventional techniques for wind speed extrapolation, with, on average, a reduction in mean absolute error between 15 and 20% over the conventional methods, with the largest benefits obtained under stable conditions.

Monte Carlo↗
DOE OSTI2025

Impact of melt viscosity on filler dispersion in elastomeric nanocomposites

Compounding of commercial nanocomposites usually involves the addition of viscosity enhancers such as binder resins in ink jet inks, and paints. Contrary to this, plasticizers such as process oils are added to reduce the melt viscosity and ease processability of reinforced elastomers. Nanofillers such as silica and carbon black are typically added to reinforce rubber and enhance performance of automotive tire treads. Filler dispersion has traditionally been qualitatively (indirectly) assessed by measuring the properties of reinforced elastomers. While dispersion can be quantified by examining filler agglomeration through surface roughness measurements and microscopy, the size-scale dependence for these hierarchical fillers has usually been ignored. Here, we have recently devised a method to quantify nano-scale dispersion of fillers using Ultra small-angle X-ray scattering (USAXS) techniques. This method is advantageous since it directly links the controllable processing/compounding parameters such as the mixing speed, mixer geometry, residence time (or mixing duration), melt density, flow gap distance, and melt viscosity to nano-scale dispersion. While our previous studies have explored the impact of different processing parameters, this study specifically investigates the impact of melt viscosity on nano-scale filler dispersion in elastomer compounds. Commercially available polybutadienes with different Mooney viscosities were used in conjunction with different grades and amounts of process oils to modify the melt viscosity.

Carbon black↗
DOE OSTI2021

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Quartet-based inference is statistically consistent under the unified duplication-loss-coalescence model

Abstract Motivation The classic multispecies coalescent (MSC) model provides the means for theoretical justification of incomplete lineage sorting-aware species tree inference methods. This has motivated an extensive body of work on phylogenetic methods that are statistically consistent under MSC. One such particularly popular method is ASTRAL, a quartet-based species tree inference method. Novel studies suggest that ASTRAL also performs well when given multi-locus gene trees in simulation studies. Further, Legried et al. recently demonstrated that ASTRAL is statistically consistent under the gene duplication and loss model (GDL). GDL is prevalent in evolutionary histories and is the first core process in the powerful duplication-loss-coalescence evolutionary model (DLCoal) by Rasmussen and Kellis. Results In this work, we prove that ASTRAL is statistically consistent under the general DLCoal model. Therefore, our result supports the empirical evidence from the simulation-based studies. More broadly, we prove that the quartet-based inference approach is statistically consistent under DLCoal. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rapid CO2 changes cause oscillations in photosynthesis that implicate PSI acceptor-side limitations

Abstract Oscillations in CO2 assimilation rate and associated fluorescence parameters have been observed alongside the triose phosphate utilization (TPU) limitation of photosynthesis for nearly 50 years. However, the mechanics of these oscillations are poorly understood. Here we utilize the recently developed dynamic assimilation techniques (DATs) for measuring the rate of CO2 assimilation to increase our understanding of what physiological condition is required to cause oscillations. We found that TPU-limiting conditions alone were insufficient, and that plants must enter TPU limitation quickly to cause oscillations. We found that ramps of CO2 caused oscillations proportional in strength to the speed of the ramp, and that ramps induce oscillations with worse outcomes than oscillations induced by step change of CO2 concentration. An initial overshoot is caused by a temporary excess of available phosphate. During the overshoot, the plant outperforms steady-state TPU and ribulose 1,5-bisphosphate regeneration limitations of photosynthesis, but cannot exceed the rubisco limitation. We performed additional optical measurements which support the role of PSI reduction and oscillations in availability of NADP+ and ATP in supporting oscillations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Valuation and cost reduction of behind-the-meter hydrogen production in Hawaii

A 250kW hydrogen electrolysis facility was recently installed at the Natural Energy Laboratory of Hawaii Authority's (NELHA's) campus. This facility that will begin operation in 2020 to produce hydrogen for fuel cell buses on the island to demonstrate of the application of hydrogen to decarbonize transportation. Given the size of the electrolysis station, it has the potential to significantly increase electricity costs for the campus, which is subject to energy and peak demand charges from the local utility. In this paper, we analyze the cost of hydrogen production at NELHA given the rate structure options available from the utility. Production costs are estimated using optimal versus constant scheduling of the facility to meet the buses’ demand. A model of the electrolysis station is used to capture changes in production efficiency over the power range in the optimization routine. The effects of combining the station and campus load versus standalone operation and increasing solar generation are also explored. The analyses surrounding this scenario show the importance of multiple factors on the potential profitability of hydrogen production in behind-the-meter applications and show trends that could have implications for other similar installations.

08 HYDROGEN↗
DOE OSTI2021

The stable isotopes of natural waters at the Marcell Experimental Forest

Here, we present a new data set from the Marcell Experimental Forest (MEF) that compiles water isotope measurements from multiple research catchments, some of which have been studied since the 1960s. The MEF is located in northern Minnesota, USA, and is home to heavily studied and monitored forests, streams, bogs, and fens. Peat-forming systems (bogs and fens) are an important component of the MEF landscape and have a profound impact on the water cycle in these catchments. Within the last decade, analysis of stable isotopes of water (expressed as δD and δ 18 O) has been implemented to characterize the different components of the water budget, and to allow researchers to look at catchment and peatland-specific hydrologic effects in the watershed. This δD and δ 18 O data set of natural waters from MEF catchments is primarily composed of measurements from three peatlands (S1, S2, S6) during an 11-year period. More recently collection and analysis were expanded to also include samples from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) project in the S1 bog, peatlands S3, S4, S5, as well as nearby lakes. We establish a local meteoric water line by analyzing the isotopic composition of precipitation, which fills a void in regional meteoric water lines for Minnesota. Furthermore, we establish baseline isotopic composition for bog outlet streams, bog porewater, aquifer groundwater, overland flow, subsurface stormflow, and snowpack, as well as runoff from the SPRUCE experimental chambers. These data are publicly available and will be expanded upon in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Lattice dynamics of high-pressure hydrides studied by inelastic neutron scattering

Due to the small mass and anomalously large neutron scattering cross-section of proton (about 80 barns compared to a few barns for other nuclei), inelastic neutron scattering is considered as one of the most effective tools in studying optical vibrations of hydrogen atoms in metal hydrides. The current review is focused on the binary hydrides of 3d- and 4d-metals of groups VI–VIII, which were produced at high hydrogen pressures of several gigapascals in relatively large quantities of hundreds of mg, quenched to low temperature and studied by INS ex situ at ambient pressure with high statistical accuracy. One of the unusual effects revealed by INS is a strong increase in the strength of the metal-hydrogen interactions with decreasing atomic number of the d-metal accompanied by an increase in the Me-H distance. Based on the available experimental results, the spectra g(E) of the phonon density of states and temperature dependencies CV(T) of the heat capacity at constant volume at T up to 1000 K have been derived in this paper and presented both in the figures and in digital form. This provides the reference data for the theoretical investigations of the crystal structures and compositions of new practically important hydrides giving the opportunity to validate calculation methods by comparing the calculated g(E) and CV(T) with the accurate experimental dependencies for the binary hydrides. Recent INS studies showed [R.A. Klein et al., J. Alloy. Compd. 894 (2022) 162381] that the fingerprints of anomalously short H-H separations of 1.6 Å violating the “2 Å rule” can be easily and unambiguously identified in the complex INS spectra of quaternary hydrides (La,Ce)NiInH 1+x . This makes neutron spectroscopy an attractive means for obtaining valuable data in the search for novel hydrides with a record high hydrogen capacity.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Calibrating Microscopic Car-Following Models for Adaptive Cruise Control Vehicles: Multiobjective Approach

Adaptive cruise control (ACC) vehicles are the first step toward comprehensive vehicle automation. However, the impacts of such vehicles on the underlying traffic flow are not yet clear. Therefore, it is of interest to accurately model vehicle-level dynamics of commercially available ACC vehicles so that they may be used in further modeling efforts to quantify the impact of commercially available ACC vehicles on traffic flow. Importantly, not only model selection but also the calibration approach and error metric used for calibration are critical to accurately model ACC vehicle behavior. In this work, we explore the question of how to calibrate car following models to describe ACC vehicle dynamics. Specifically, we apply a multi-objective calibration approach to understand the tradeoff between calibrating model parameters to minimize speed error vs. spacing error. Three different car-following models are calibrated for data from six vehicles. The results are in line with recent literature and verify that targeting a low spacing error does not compromise the speed accuracy whether the opposite is not true for modeling ACC vehicle dynamics.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Synthesis of hydroxycinnamoyl shikimates and their role in monolignol biosynthesis

Hydroxycinnamoyl shikimates were reported in 2005 to be intermediates in monolignol biosynthesis. 3-Hydroxylation of p-coumarate, originally thought to occur via coumarate 3-hydroxylase (C 3 H) from p-coumaric acid or its CoA thioester, was revealed to be via the action of coumaroyl shikimate 3'-hydroxylase (C 3 'H) utilizing p-coumaroyl shikimate as the substrate, itself derived from p-coumaroyl-CoA via hydroxycinnamoyl-CoA: shikimate hydroxycinnamoyltransferase (HCT). The same HCT was conjectured to convert the product, caffeoyl shikimate, to caffeoyl-CoA to continue on the pathway starting with its 3-O-methylation. At least in some plants, however, a more recently discovered caffeoyl shikimate esterase (CSE) enzyme hydrolyzes caffeoyl shikimate to caffeic acid from which it must again produce its CoA thioester to continue on the monolignol biosynthetic pathway. HCT and CSE are therefore monolignol biosynthetic pathway enzymes that have provided new opportunities to misregulate lignification. To facilitate studies into the action and substrate specificity of C 3 H/C 3 'H, HCT, and CSE enzymes, as well as for metabolite authentication and for enzyme characterization, including kinetics, a source of authentic substrates and products was required. A synthetic scheme starting from commercially available shikimic acid and the four key hydroxycinnamic acids (p-coumaric, caffeic, ferulic, and sinapic acid) has been developed to provide this set of hydroxycinnamoyl shikimates for researchers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Search for Exotic Hadrons in eta (') pi at GlueX

The theoretical description of the strong interaction between quarks and gluons that form hadrons is provided by Quantum Chromodynamics. However, the impact of gluonic excitations on the characteristics of hadrons and their role in hadronic structure is yet to be determined. Recent discoveries of several possibly exotic hadrons highlight the significance of spectroscopic measurements in comprehending the nature of the strong interaction. These proceedings focus on the status of the hunt for exotic contributions in photoproduction data obtained with the GlueX experiment at Jefferson Lab in ?(?)? systems. Specifically, we discuss the investigation of the a2(1320) meson production in these key channels, which is an initial step towards identifying exotic quantum number hybrid mesons. Furthermore, the discussion will cover the application of an amplitude analysis that exploits the polarization of the photon beam available to the GlueX experiment and its implications for identifying the lightest hybrid meson.

Albrecht, Malte↗
DOE OSTI2025

Exploring Geologic Hydrogen: A New Frontier for Affordable, Reliable Energy Security

Recent successes in the exploration, drilling, and discovery of geologic hydrogen have generated notable excitement. This new energy resource has the potential to make an important contribution to our nation’s energy supply, resiliency, and security. Contemporary studies of geologic hydrogen have a common theme of suggesting places where it might be found or even more specifically, what rocks in what geologic formations may contribute to its formation — either naturally or via artificially induced means. This vital ongoing body of work sets the stage for imagining what may be possible with vast available quantities of naturally occurring hydrogen in the subsurface. While acknowledging current approaches to characterizing geologic hydrogen, this report advances the discussion by suggesting next steps, including the critical science and engineering necessary to make geologic hydrogen an affordable and reliable part of the U.S. energy portfolio.

08 HYDROGEN↗
DOE OSTI2026

Feasibility and strategic implications of deploying nuclear power reactors in Africa

This report assesses the feasibility and strategic implications of deploying nuclear power reactors, including large-scale plants, advanced small modular reactors (SMRs), and microreactors, in African countries. Case studies focus on South Africa, Egypt, Kenya, Ghana, and Nigeria, examining nuclear energy’s role in Africa’s rapidly evolving energy landscape, marked by fast-growing demand, significant electricity access gaps, increasing renewable penetration, and strong policy commitments to industrialization and energy security. Several U.S. reactor technologies and designs are considered based on their development status and readiness for deployment. The analysis finds that nuclear power can provide reliable, clean baseload and flexible generation, as well as high-temperature process heat for desalination, hydrogen production, and industrial applications. However, suitability is highly country-specific, depending on grid size and stability, transmission capacity, cooling water availability, regulatory readiness, and fuel supply chains. Near-term deployment opportunities are strongest for light-water reactors (such as NuScale, BWRX-300, AP300, and SMR-300) that use low-enriched uranium and build on proven technology. More advanced concepts, including gas-cooled, sodium-cooled, molten-salt cooled reactors, and microreactors, will likely be relevant for African deployment in the 2030s or later, contingent on demonstration projects, high-assay low-enriched uranium (HALEU) fuel availability, and mature international licensing frameworks. Economic analysis shows that SMRs are capital-intensive, with projected overnight costs for 300 MWe units in 2025 ranging from approximately 1.4 to 2.6 billion USD per module. The levelized cost of electricity (LCOE) is highly sensitive to the weighted average cost of capital (WACC). Given typically higher financing costs and utility balance-sheet weaknesses in many African countries, bankable project structures will require sovereign guarantees, robust offtake arrangements, and layered financing from export credit agencies, development finance institutions, and vendor nations. Comparisons with recent large nuclear projects in the United Arab Emirates (UAE) and Egypt underscore the central role of state-backed loans, long tenors, and concessional terms. Country case studies illustrate a spectrum of readiness and opportunity. South Africa operates two 920 MWe pressurized light water reactors (totaling 1,840 MWe) at Koeberg and has the most mature regulatory and industrial base, positioning it as a prime candidate for both large reactors and SMRs to replace coal, support desalination, and anchor industrial hubs. Egypt is constructing four VVER-1200 units at El Dabaa with strong state leadership and could later complement this fleet with SMRs for coastal and industrial applications. Kenya and Ghana are advancing through IAEA Milestones with growing institutional capacity and clear interest in SMRs that match their smaller grids and industrialization plans. Nigeria has the largest demand potential but faces acute constraints in grid reliability, project bankability, and regulatory capacity; targeted deployments of large reactors and SMRs near coastal or industrial sites could have high impact if accompanied by major grid upgrades and institutional reforms. The report identifies cross-cutting challenges such as financing, political continuity, public acceptance, nonproliferation and security, waste and back-end management, regulatory capacity, grid adequacy, and long deployment timelines for first-of-a-kind designs, and ANL/NSE-26/3 ii proposes broad directions for resolution. These include stronger multifaceted financing for nuclear, long-term national energy strategies that transcend electoral cycles, proactive stakeholder engagement, strengthened regional and national regulators, and systematic workforce development through centers of excellence and expanded training. The United States should develop partnerships with African countries and offer end-to-end nuclear package similar to those used effectively by competitors: coordinated project development, state-backed financing, long-term fuel services, and durable in-country support through regional offices and sustained workforce/regulatory training. With timely planning, sustained political commitment, and appropriate financing and institutional support, nuclear energy, both large reactors and advanced SMRs, can become a meaningful, though not dominant, pillar of Africa’s future power mix, enhancing energy security, enabling industrial growth, and supporting climate goals.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Spatial Mapping of Riverbed Grain-Size Distribution Using Machine Learning

Recent alluvial sediments in riverbeds play a significant role in controlling hydrologic exchange flows (HEFs) in river systems. The alluvial layer is usually associated with strong heterogeneity in physical properties (e.g., permeability and hydraulic conductivity), which affects local HEFs and therefore biogeochemical processes. The spatial distribution of these physical properties needs to be determined to inform the numerical models used to reveal the realistic hydro-biogeochemical behaviors. Such information can be obtained based on the intrinsic link between sediment grain-size distribution and hydraulic properties where sediment texture information is available. However, grain-size measurements are usually spatially sparse and do not have adequate coverage and resolution, particularly for a relatively large domain such as the Hanford Reach of the Columbia River. In this paper, we adopted machine learning (ML) approaches for categorizing and mapping the spatial distributions of riverbed substrate grain size and filling in missing areas of substrate data using the ML models along the reach. Such ML models for substrate size mapping were trained at 13,372 locations using measured substrate sizes along with observed and simulated attributes, including bathymetric attributes (e.g., elevation, slope, and aspect ratio) from LIDAR and bathymetric surveys, and hydrodynamic properties (e.g., water depth, velocity, shear stress, and their statistical moments). An ensemble bagging-based ML technique, Random Forest, was adopted to identify the most influential factors as predictors to develop the predictive models with over-fitting issues addressed. The models were evaluated with respect to each individual substrate size class and the lumped group, and then used to generate the final substrate size maps covering all the grid cells in the numerical modeling domain.

54 ENVIRONMENTAL SCIENCES↗