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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 OSTI2022

Methane liquefaction with an active magnetic regenerative refrigerator

This manuscript reports liquefaction of methane using an active magnetic regenerative refrigerator (AMRR). The AMRR used to cool from 285 K to 135 K has two identical regenerators fabricated with adjacent layers of four ferromagnetic refrigerants, each with sequentially lower Curie temperatures and lesser masses from hot to cold temperatures. The dual multilayer regenerators were assembled in opposition with a single cold region between them. For this set of experiments equal mass of helium gas was pumped through all layers of the regenerators during the two flow steps of the four-step AMR cycle. After successfully reaching 135 K, a compact condensing coil-fin tube heat exchanger (HEX) with a small storage vessel was mounted in the cold region of the dual regenerator assembly to be cooled during the hot-to-cold helium flow from alternatively demagnetized regenerators. The cold HEX was used to cool and liquefy a process stream of methane gas supplied at 295 K and different pressures in different runs. Further, by measuring time to liquefy a known volume of methane at three different pressures, cooling powers of the AMRR as a function of temperature were determined. The resultant data were compared to those predicted by the AMRR performance model. Conclusions from these experiments and suggestions for future work are presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A Field Guide to Corralling the Chaos: A Conceptual Framework for Using Models to Guide Opportunistic Field Studies of Natural Disturbances

Watersheds regulate biogeochemical processes and provide ecosystem services to human societies, but disturbances can fundamentally alter these processes across space and time. Determining when and where to sample to capture disturbance impacts in watersheds remains a central challenge. Manipulation studies and long-term monitoring are often constrained by scope, and opportunistic studies often lack pre-disturbance data needed to statistically determine disturbance impacts. We identify a persistent knowledge gap: the absence of a clear, transferable framework to guide opportunistic disturbance research where pre-disturbance data collection is not a feasible option. To address this gap, we present a conceptual framework that intentionally integrates modeling and empirical observation in an iterative, stepwise model–experiment workflow. We demonstrate its application through two contrasting case studies: wildfire impacts on headwater streams using a pre-disturbance preparedness approach, and saltwater flooding impacts on coastal forests using an ‘ex-post-facto’ approach. From these applications, we assess strengths, limitations, and the critical role of team science for transferability across disturbance types and study designs. Broadly, this framework offers a scalable path towards more rigorous, timely, and actionable disturbance science that can inform watershed management, hazard risk reduction, and ecosystem resilience.

Coastal Biogeochemistry↗
DOE OSTI2020

AmeriFlux US-PFr SE4 Tussock-2 CHEESEHEAD 2019

This is the AmeriFlux version of the carbon flux data for the site US-PFr SE4 Tussock-2 CHEESEHEAD 2019. Site Description - This tower (3m tripod) is located in the southeastern quadrant of the 10 x 10km study domain. It is located in a tussock (0.3 - 1m grasses). Located next to stream.

Desai, Ankur↗
DOE OSTI2026

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

Ding, Zixin [Chicago U.]↗
DOE OSTI2022

A Topological Approach for Motion Track Discrimination

Detecting small targets at range is difficult because there is not enough spatial information present in an image sub-region containing the target to use correlation-based methods to differentiate it from dynamic confusers present in the scene. Moreover, this lack of spatial information also disqualifies the use of most state-of-the-art deep learning image-based classifiers. Here, we use characteristics of target tracks extracted from video sequences as data from which to derive distinguishing topological features that help robustly differentiate targets of interest from confusers. In particular, we calculate persistent homology from time-delayed embeddings of dynamic statistics calculated from motion tracks extracted from a wide field-of-view video stream. In short, we use topological methods to extract features related to target motion dynamics that are useful for classification and disambiguation and show that small targets can be detected at range with high probability.

Emerson, Tegan H.↗
DOE OSTI2026

Opportunities and challenges in thermochemical conversion of municipal solid waste: A comprehensive review

Recent advancements in thermochemical conversion processes have elucidated new pathways for converting municipal solid waste into valuable resources. This review explores the primary thermochemical conversion methods, including combustion, gasification, pyrolysis, torrefaction, hydrothermal carbonization, and hydrothermal liquefaction, emphasizing their potential roles in waste management and energy recovery. Key challenges including feedstock variability, ash behavior, and scale-up limitations are discussed alongside opportunities for hybrid systems and circular economy integration. A comparative analysis of research publications indicates a significant focus on thermochemical pathways within the broader context of municipal solid waste research, underscoring the growing interest in these technologies. Recent advancements in each thermochemical process, alongside their operational, technical, and economic challenges, are discussed. Comparative data reveal that torrefaction enhances the hydrophobicity and grindability of municipal solid waste components, though its energy densification benefits are more modest than those observed in biomass. Hydrothermal carbonization and liquefaction are highlighted for their ability to process high-moisture and heterogeneous waste streams. The review also synthesizes recent findings on reactor configurations, emissions control, and synergistic effects in co-processing municipal solid waste fractions. The findings underscore the importance of developing standardized protocols for municipal solid waste characterization and the need for innovative hybrid systems to improve efficiency.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Impedance-Based Detection of NO 2 Using Ni-MOF-74: Influence of Competitive Gas Adsorption

Chemically robust, low-power sensors are needed for the direct electrical detection of toxic gases. Metal–organic frameworks (MOFs) offer exceptional chemical and structural tunability to meet this challenge, though further understanding is needed regarding how coadsorbed gases influence or interfere with the electrical response. To probe the influence of competitive gases on trace NO 2 detection in a simulated flue gas stream, a combined structure–property study integrating synchrotron powder diffraction and pair distribution function analyses was undertaken, to elucidate how structural changes associated with gas binding inside Ni-MOF-74 pores correlate with the electrical response from Ni-MOF-74-based sensors. Data were evaluated for 16 gas combinations of N 2 , NO 2 , SO 2 , CO 2 , and H 2 O at 50 °C. Fourier difference maps from a rigid-body Rietveld analysis showed that additional electron density localized around the Ni-MOF-74 lattice correlated with large decreases in Ni-MOF-74 film resistance of up to a factor of 6 × 10 3 , observed only when NO 2 was present. These changes in resistance were significantly amplified by the presence of competing gases, except for CO 2 . Without NO 2 , H 2 O rapidly (<120 s) produced small (1–3×) decreases in resistance, though this effect could be differentiated from the slower adsorption of NO 2 by the evaluation of the MOF’s capacitance. Furthermore, samples exposed to H 2 O displayed a significant shift in lattice parameters toward a larger lattice and more diffuse charge density in the MOF pore. Evaluating the Ni-MOF-74 impedance in real time, NO 2 adsorption was associated with two electrically distinct processes, the faster of which was inhibited by competitive adsorption of CO 2 . Together, this work points to the unique interaction of NO 2 and other specific gases (e.g., H 2 O, SO 2 ) with the MOF’s surface, leading to orders of magnitude decrease in MOF resistance and enhanced NO2 detection. Finally, understanding and leveraging these coadsorbed gases will further improve the gas detection properties of MOF materials.

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

SmallTAL: Real-Time Egocentric Online Temporal Action Localization for the Data-Impoverished

Abstract We propose a real-time, online temporal action localization system that requires a small amount of annotated data. The main challenges we address are high intra-class variability and a large and diverse background class. We address these using a flexible frame descriptor, dynamic time warping, and a novel approach to database construction. Our solution receives egocentric RGB-D streams as input and makes predictions at regular temporal intervals. We validate our approach by localizing actions in a digital twin of an electrical substation, in which certain objects have been replaced by functional virtual replicas.

Computer Science↗
DOE OSTI2026

Predictive Model for Starlink Maritime Performance Using Multi-Horizon RandomForest

Low Earth orbit (LEO) satellite systems have become a crucial enabler of broadband access for maritime industries, where traditional networks are unavailable. However, the high mobility of LEO constellations and constantly changing weather conditions result in unpredictable link fluctuations, limiting the ability of maritime platforms to plan bandwidth usage proactively. To the best of our knowledge, no prior work has developed a short-term predictive model for maritime LEO connectivity using real experimental field measurements. This paper proposes a data-driven forecasting model that predicts future downlink throughput using multi-horizon RandomForest regression. The model is trained using real experimental coastal measurement data incorporating recent throughput history, network-layer indicators, and environmental variables. The proposed approach reduces mean absolute error by approximately 31% compared to a persistence baseline for 15-minute horizons. It maintains a measurable improvement at 30 minutes, despite increased stochasticity. These findings confirm that proactive bandwidth awareness is feasible on maritime platforms and can effectively support operational decisions such as adaptive streaming, routing, and resource scheduling. The performance gap between forecasting horizons also highlights the need for expanded offshore datasets to improve prediction robustness under harsher maritime environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Probing the Galactic halo with RR Lyrae stars – II. The substructures of the Milky Way

ABSTRACT We identify substructures of the Galactic halo using 3003 type ab RR Lyrae stars with six-dimensional position–velocity information from the Sloan Digital Sky Survey, the Large Sky Area Multi-Object Fiber Spectroscopic Telescope and the Gaia Early Data Release 3. Based on this information, we define the separation of any two of the stars in the integrals of motion space and we identify substructures by utilizing the friends-of-friends algorithm. We identify members belonging to several known substructures: the Sagittarius stream, the Gaia-Enceladus-Sausage (GES), the Sequoia and the Helmi streams. In addition to these known substructures, there are three other substructures possibly associated with globular clusters NGC 5272, 6656 and 5024, respectively. Finally, we also find three remaining unknown substructures, one of which has large angular momentum and a mean metallicity −2.13 dex, which may be a new substructure. As for the GES, we find that it accounts for a large part of substructures in the inner halo and the apocentre distance is in the range 10–34 kpc, which suggests that the GES is mainly distributed in the inner halo. The near one-third proportion of the GES and the peak value of 20 kpc of the apocentre distance suggest that the GES could account for the break in the density profile of the Galactic halo at the Galactocentric distance ~20–25 kpc. The similarity when comparing the kinematic properties of the GES with the Hercules–Aquila Cloud and Virgo Overdensity suggests that the three substructures may have similar origins.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A High-Throughput Solver for Marginalized Graph Kernels on GPU

Here, we present the design and optimization of a solver for efficient and high-throughput computation of the marginalized graph kernel on General Purpose GPUs. The graph kernel is computed using the conjugate gradient method to solve a generalized Laplacian of the tensor product between a pair of graphs. To cope with the large gap between the instruction throughput and the memory bandwidth of the GPUs, our solver forms the graph tensor product on-the-fly without storing it in memory. This is achieved by using threads in a warp cooperatively to stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse. Warps across a thread block can further share tiles via the shared memory to increase data reuse. We exploit the sparsity of the graphs hierarchically by storing only non-empty tiles using a coordinate format and nonzero elements within each tile using bitmaps. We propose a new partition-based reordering algorithm for aggregating nonzero elements of the graphs into fewer but denser tiles to further exploit sparsity. We carry out extensive theoretical analyses on the graph tensor product primitives for tiles of various density and evaluate their performance on synthetic and real-world datasets. Our solver delivers three to four orders of magnitude speedup over existing CPU-based solvers such as GraKeL and GraphKernels. The capability of the solver enables kernel-based learning tasks at unprecedented scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Chloride Salt Purification by Reaction With Thionyl Chloride Vapors to Remove Oxygen, Oxygenated Compounds, and Hydroxides

Molten chloride salts (including MgCl 2 , KCl, NaCl, and ZnCl 2 ) are being considered for heat transfer media for renewable (solar) and nuclear power generators, as fuel carrier for nuclear reactors, and as thermal energy storage media. Impurities such as oxygen, hydroxides, moisture, and sulfur are known to negatively influence the corrosion of materials in contact with the salt (e.g., structural metals). Commercially available chloride salts come with a range of impurities. Before using the chloride salts at high temperature, it is desirable to remove the impurities to increase the performance of the salt and reduce corrosion. In this study, we tested the use of thionyl chloride vaporized into a stream of argon to react with oxygenated impurities in a mixture of MgCl 2 -KCl-NaCl, removing them as HCl and SO 2 . The reagent was bubbled through the salt when both above and below the melting point. The reaction was followed using thermocouple data from the salt and by Fourier transform infrared (FTIR) spectroscopy on the exhaust of the reactor. The reaction kinetics were followed by comparing the peaks from SO 2 product to SOCl 2 reagent in the FTIR spectra. The purity of the salt was assessed at the end of the purification process by x-ray diffraction and inductively coupled plasma analysis. Although the process was effective in removing the oxygen content of the mixture, ternary compounds were formed in the process, including KNiCl 3 and KMgCl 3 . The nickel in KNiCl 3 came from the reaction between the salt and the nickel vessel. Thus, these experiments suggest that improvements to the process must be made before using SOCl 2 vapors for the purification of chloride salts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Global Methane Budget 2000–2020

Abstract. Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget. We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling Streamflow and Sediment Transport Under Current and Future Climates at the West Valley Site - 20231

Performance Assessment models often require inputs representing specific hydrologic processes such as streamflow and sediment flux. This paper details the development of a process-level hydrologic model and its use in a Probabilistic Performance Assessment (PPA) system-level model for the West Valley Site in western New York. For this PPA model, a watershed model was developed using SWAT (Soil and Water Assessment Tool) and used to evaluate water and sediment fluxes in nearby streams draining the Site. A particular challenge of probabilistic modeling over long periods of time is scaling parameters appropriately so that outcomes are not grossly over- or underestimated. This challenge is especially important when the spatial and temporal scales of process and system level models differ. In this instance, the SWAT watershed model and GoldSim PPA model have similar spatial scales of stream reaches and sub-catchments within a single watershed (approximately hundreds to tens of thousands of square meters, but the temporal scales differ considerably. The SWAT model calculates streamflow and sediment transport rates at the daily scale, while the PPA model requires long-term average values that are applied over hundreds to thousands of year-long periods. As such, distributions of daily climatic parameters such as average precipitation rate, temperature, wind speed, and relative humidity were developed to force a transient SWAT model that captures the dynamic behavior of the watershed in response to daily changes in the weather. Output from the model, which includes stream flow (m{sup 3}/y) and sediment transport rate (Mg/y) is then averaged over a 100-year period as an approximation for the long-term average values which can be applied in GoldSim. A single SWAT model was run 5000 times with varying climatic inputs and deterministic soil, land use, and elevation data, and the range of outputs were compiled into a distribution which can be implemented stochastically into the PPA Model. Additionally, this paper includes a high-level discussion of how changes in future climate affect weather input distributions, and a summary of how those changes in weather affect the outcomes from the SWAT model and the inputs into the PPA model. In this analysis, future climate distributions were developed based on climate research documenting the expected changes to precipitation and temperature in western New York. The 5,000 SWAT simulations were then repeated with these future climate distributions, and the resulting output was implemented into the PPA Model as hydrologic conditions under a future climate. In the current version of the PPA Model, hydrologic parameters change linearly from the 'current climate' conditions to the 'future climate' conditions over a period of 100 years, after which they remain constant. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Advancing Ethanol-to-Jet cost Effectiveness via direct conversion to n -Butene-Rich olefins and Co-Product Valorization

Ethanol is a promising feedstock for sustainable aviation fuel production; however, conventional routes face significant energy and cost challenges, particularly due to the ethanol dehydration step to ethylene. Here, this study leverages breakthrough experimental data to perform comprehensive techno-economic and life-cycle assessments of an innovative ethanol-to-jet process. The process employs a single-step catalytic conversion, enabled by multifunctional Cu-ZrO 2 /SBA-16 catalyst, to directly upgrade ethanol into a mixed olefin stream rich in n-butene. The single-step conversion eliminates the costly ethanol dehydration step in the conventional process. High selectivity toward n-butene offers key advantages: it simplifies downstream oligomerization into jet-range hydrocarbons and enables the co-production of renewable n-butene alongside sustainable aviation fuel. The analysis estimates a minimum fuel selling price as low as $\$$2.50 per gallon, whether using corn ethanol or cellulosic ethanol from corn stover. Life cycle CO 2 equivalent emissions are projected to be as low as 10.6 g CO 2 eq/MJ sustainable aviation fuel, representing over 70% reduction compared to conventional petroleum-based jet fuel. This one-step ethanol upgrading approach not only facilitates SAF and n-butene co-production but also provides operational flexibility. The ability to tailor product outputs allows the ethanol-to-jet process to adapt to varying feedstocks, incentive programs, and market dynamics, ultimately enhancing the economic viability of sustainable aviation fuel production.

Xu, Yiling [Pacific Northwest National Laboratory ↗
DOE OSTI2020

Multidimensional simulations of Mckenna-driven flow tube configuration: Investigating non-ideality in NO x formation flow tube experiments

Multidimensional simulations have been conducted to simulate atmospheric pressure, flat-flame/McKenna-burner-driven-flow tube experiments targeted to obtain NO x speciation data for predicting/analyzing syngas combustion emissions. In a prior work, we demonstrated the impacts of multidimensional transport on post flame region prediction departures from those assuming unidimensional flow/transport conditions. In this work, we develop and utilize a multidimensional laminar reacting flow solver to simulate the fully coupled flame and post flame regions to further elucidate the impacts of the earlier unidimensional modeling assumptions on interpreting post flame NO x experimental data. The model is used to simulate a lean, premixed syngas/air flame and its associated post flame regions within a cylindrical flow-tube-like arrangement. The combustion process takes place under atmospheric condition with trace amount of NO x seeding fed into the inlet gas stream. The spatial evolution of NO x species (NO and NO 2 ) in the flame and in the post-combustion zone suggests two distinct regions: 1) a region encompassing the flame structure itself; and 2) a post flame region in which the temperature decays due to both axial and radial transport processes. The predictions show that for the conditions studied, a pulsatile flow field exists due to the formation of an expanding and contracting recirculation zone in the outer periphery of the flow tube. By resolving the nature of the flow, the resulting time-averaged temperature and species concentrations show improved agreement with existing experimental measurements. The flow-field interaction results in radial inhomogeneities in the NO 2 profiles with the maximum concentration offset from the flow centerline. The location of the peak in NO 2 is coupled with radial temperature gradients from wall cooling effects and their significant influence on NO/NO 2 interconversion kinetics, producing notable NO 2 accumulation in regions near the wall. Geometrical configurations capable of suppressing/minimizing the pulsatile nature are also investigated and the results are compared. Other experimental configurations could be considered in parametric simulations to determine the optimal configuration that would minimize non-idealities in the observations. The work shows the value in performing such computations in advance of settling on a particular design for flow tube/flow reactor experiments.

42 ENGINEERING↗
DOE OSTI2022

Cosmological constraints on light but massive relics

Many scenarios of physics beyond the standard model predict new light, weakly coupled degrees of freedom, populated in the early universe and remaining as cosmic relics today. Due to their high abundances, these relics can significantly affect the evolution of the universe. For instance, massless relics produce a shift ΔN eff to the cosmic expectation of the effective number of active neutrinos. Massive relics, on the other hand, additionally become part of the cosmological dark matter in the later universe, though their light nature allows them to freely stream out of potential wells. This produces novel signatures in the large-scale structure (LSS) of the universe, suppressing matter fluctuations at small scales. Here, we present the first general search for such light (but massive) relics (LiMRs) with cosmic microwave background (CMB) and LSS data, scanning the 2D parameter space of their masses m X and temperatures $T^{(0)}_{X}$ today. In the conservative minimum-temperature ($T^{(0)}_{X}$ = 0.91 K) scenario, we rule out Weyl (and higher-spin) fermions -- such as the gravitino -- with m X ≥ 2.26 eV at 95% C.L., and set analogous limits of m X ≤ 11.2,1.06,1.56 eV for scalar, vector, and Dirac-fermion relics. This is the first search for LiMRs with joint CMB, weak-lensing, and full-shape galaxy data; we demonstrate that weak-lensing data is critical for breaking parameter degeneracies, while full-shape information presents a significant boost in constraining power relative to analyses with only baryon acoustic oscillation parameters. Under the combined strength of these datasets, our constraints are the tightest and most comprehensive to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion

In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets.

36 MATERIALS SCIENCE↗