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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

End-gas autoignition fraction and flame propagation rate in laser-ignited primary reference fuel mixtures at elevated temperature and pressure

Knock in spark-ignited (SI) engines is initiated by autoignition of the unburned gasses upstream of spark-ignited, propagating, turbulent premixed flames. Knock propensity of fuel/air mixtures is typically quantified using research octane number (RON), motor octane number (MON), or methane number (MN; for gaseous fuels), which are measured using single-cylinder, variable compression ratio engines. In this study, knock propensity of SI fuels was quantified via observations of end-gas autoignition (EGAI) in unburned gasses upstream of laser-ignited, premixed flames at elevated pressures and temperatures in a rapid compression machine. Stoichiometric primary reference fuel (PRF; n-heptane/isooctane) blends of varying reactivity (50 ≤ PRF ≤ 100) were ignited using an Nd:YAG laser over a range of temperatures and pressures, all in excess of 545 K and 16.1 bar. Laser ignition produced outwardly-propagating premixed flames. High-speed pressure measurements and schlieren images indicated the presence of EGAI. The fraction of the total heat release attributed to EGAI (i.e., EGAI fraction) varied with fuel reactivity (i.e., octane number) and the time-integrated temperature of the end-gas prior to ignition. Flame propagation rates, which were measured using schlieren images, were only weakly correlated with octane number but were affected by turbulence caused by variation in piston timing. Under conditions of low turbulence, measured flame propagation rates approached one-dimensional premixed laminar flame speed computations performed at the same conditions. Experiments were simulated with a three-dimensional CONVERGE™ model using reduced chemical kinetics (121 species, 538 reactions). The simulations accurately captured the measured flame propagation rates, as well as the variation in EGAI fraction with fuel reactivity and time-integrated end-gas temperature. The simulations also revealed low-temperature heat release as well as formaldehyde and hydrogen peroxide formation in the end-gas upstream of the propagating flame, which increased the temperature and degree of chain branching in the end-gas, ultimately leading to EGAI.

42 ENGINEERING↗
DOE OSTI2022

Influence of gasoline fuel formulation on lean autoignition in a mixed-mode-combustion (deflagration/autoignition) engine

We report stoichiometric spark-ignition engines suffer efficiency penalties due to throttling losses at low loads, a low specific-heat ratio of the stoichiometric working fluid, and limits on compression ratio due to end-gas autoignition leading to undesirable knocking. Mixed-Mode Combustion (MMC) mitigates these shortcomings by using a lean working fluid where a spark-initiated pilot-stabilized deflagrative flame front is followed by controlled end-gas autoignition. This MMC study investigates the effects of initial conditions (intake air temperature, intake pressure, equivalence ratio, and intake oxygen fraction) on autoignition tendency of four gasoline-range fuels with varying properties and composition. The use of fuels with varying octane sensitivity (S) allowed exploring the importance of low-temperature heat release in triggering autoignition. Fuels with high S were less reactive for conditions that promote low-temperature chemistry (operation at high intake air pressure or without N2 dilution). Conversely, an Alkylate fuel with low S showed a greater autoignition resistance at operating conditions that were unfavorable for low-temperature chemistry. Next, the effect of residual gas composition on autoignition tendency of fuels was examined with a chemical-kinetics model. Among the various molecules in the residual gas, nitric oxide (NO) enhanced the low-temperature chemistry and increased the autoignition tendency most significantly. The fuels’ autoignition response to increasing NO amount corroborates the experimental observations. Next, the sequential autoignition of the end-gas was assessed to be less impacted by thermal stratification because of lean mixtures showing relatively less low-temperature chemistry, when compared to stoichiometric mixtures. Next, the effect of changing equivalence ratio on the autoignition was found to be similar for all fuels, regardless of their S. With changing intake air temperature, the response of fuels’ autoignition tendency depended on the dilution level used. At high dilution (i.e. low intake [O2]), fuels’ reactivity increased with increasing intake air temperature. In contrast, for operation without dilution, the autoignition tendency of the low-S Alkylate fuel decreased with increasing intake air temperature, while that of high-S High Cycloalkane fuel still increased with increasing intake air temperature. In conclusion, conventional octane metrics (RON and MON) have utility in assessing the autoignition tendency under lean MMC operation. Moreover, the fuel requirements for MMC align with that of stoichiometric operation: i.e., high RON and high S fuels are desirable for stable non-knocking operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

High-pressure ignition delay time measurements of a four-component gasoline surrogate and its high-level blends with ethanol and methyl acetate

Ignition delay times of a gasoline surrogate (iso-octane/toluene/n-heptane/1-hexene at 55/25/15/5% by liquid volume, developed in McCormick et al. 2017) with high-level bioblendstock-gasoline surrogate blends (50% and 85% biofuel by liquid volume of each ethanol and methyl acetate) were collected behind reflected shock waves. Post-reflected-shock temperatures ranged from 968 to 1361 K at pressures of about 4, 10, 25, and 50 atm. Data were collected for real fuel–air mixtures at fuel lean and stoichiometric conditions (φ = 0.5, 1.0) with focus on the more-practical, fuel-lean conditions. Ignition delay times were measured from OH* chemiluminescence around 307 nm using an endwall diagnostic. The RON and MON of the surrogate were 90.3 and 84.7, respectively. Ethanol was chosen due to its wide use in flex-fuel vehicles and availability, while also providing an increased octane rating. Methyl acetate was chosen for its especially high octane rating to investigate the effect of an extreme case. To validate the gasoline surrogate, the data are compared to real gasoline (RD387) and several gasoline surrogate experiments from the literature. Using the wide range of pressures studied, the gasoline surrogate’s pressure dependence was quantified to account for test-to-test variations using regression analysis. Similarly, a global correlation for gasoline and its surrogates was developed using all available data from the literature. Two modern chemical kinetics models targeting gasoline and its surrogates are compared to the ignition delay time measurements. These new high-pressure, high-bioblendstock concentration tests provide required chemical kinetic data for optimizing fuel and engine design.

42 ENGINEERING↗
DOE OSTI2021

Effects of knock intensity measurement technique and fuel chemical composition on the research octane number (RON) of FACE gasolines: Part 1 – Lambda and knock characterization

The Research and Motor Octane Number (RON and MON) rate the knock propensity of gasoline in the Cooperative Fuel Research (CFR) engine by comparing the knock intensity of sample fuels relative to that of primary reference fuels (PRF), a binary blend of iso-octane and n-heptane. Important differences exist between standard octane testing and automotive spark ignition (SI) engine knock testing including speed, load, air-to-fuel equivalence ratio (lambda), and knock characterization, which lead to a discrepancy between a fuel’s RON rating and its knock resistance characterized on an automotive SI engine based on knock-limited spark advance. This publication is the first of a set of three publications which modify operating parameters of the RON test method (ASTM D2699) to investigate the effects of these differences with automotive SI engine knock-limited spark advance testing. A fuel’s standard RON is evaluated at the lambda of the highest knock intensity, whereas automotive SI engines typically operate at stoichiometry. Here, we analyze the effects of a stoichiometric RON rating methodology. Additionally, the knock intensity response from the standard CFR knockmeter system is compared to a cylinder pressure oscillation-based knock intensity at several lambda settings. All experiments were performed with a set of seven Coordinating Research Council (CRC) Fuels for Advanced Combustion Engines (FACE) gasolines with approximately 95 RON. The fuel chemical composition impacted the lambda of the highest knock intensity, which resulted in fuel-specific offsets between the standard and stoichiometric RON ratings. The knock system comparison showed significant offsets between cylinder pressure-based and knockmeter-based knock intensity levels.

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

Rapid prediction of fuel research octane number and octane sensitivity using the AFIDA constant-volume combustion chamber

Current research octane number (RON) and motor octane number (MON) gasoline performance characterization techniques use dated, complex engine testing methodology and limit researchers’ ability to easily characterize small volumes of experimental fuels. A novel methodology is presented that correlates measured ignition delay (ID) time to RON in an Advanced Fuel Ignition Delay Analyzer (AFIDA) constant-volume combustion chamber device at a single pressure/temperature condition, with an r2 of 0.99 and standard error (SE) of 1.0. The correlation of the slope of the ID time between two additional temperature points to octane sensitivity (S) produces an r2 of 0.97 and SE of 0.69; however, fuels with S>12 are indistinguishable. These results are based on methodology calibration using 31 primary and toluene reference fuels containing 0%-40% ethanol with RON values ranging from 85 to 113. Validation of these methods using a 102-sample fuel matrix spanning an array of base fuels and additive chemistry designed to test the robust applicability of the method, along with pump gasoline and high-octane surrogate blend samples, demonstrates an r2 of 0.94 and SE of 1.3 for the RON correlation over all samples, whereas the equivalent S correlation produces an r2 of 0.78 and SE of 1.2 by excluding two additives, 3-pentanone and diisobutylene, which displayed poor S correlation results. This novel AFIDA analysis method can be performed in 1 h and with 40 mL of fuel, offering significant improvements in time and volume requirements over traditional techniques.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Production, fuel properties and combustion testing of an iso-olefins blendstock for modern vehicles

With the increasing pressure to decarbonize the transportation sector, exploring strategies that can reduce emissions from light-duty vehicles (LDV) has become critical. Bioblendstocks that allow for higher engine efficiency and fuel economy could complement vehicle electrification and help reach carbon neutrality by 2050. In this context, the potential of a mixture of iso-olefins as a bioblendstock was investigated for multimode boosted spark-ignition (SI)/advanced compression ignition (ACI) engine operation designed to achieve higher overall vehicle fuel economy. By establishing the relationship between the molecular structure of iso-olefins and research octane number (RON), octane sensitivity (S) (i.e., the difference between RON and motor octane number [MON]), and phi-sensitivity a dimethyl-hexenes rich olefins mixture (DMHROM) was identified as a preferred blendstock for SI/ACI combustion engines. Here, a pathway for DMHROM production from biomass-derived ethanol was developed and scaled up. More than 1 gallon of DMHROM blendstock was produced for fuel properties assessment including engine testing. Measurements in a Cooperative Fuel Research Engine showed that the DMHROM blendstock possesses a RON of 94 and S of 13.5, and blends synergistically. Rapid compression machine tests coupled with single-cylinder gasoline direct injection engine measurements demonstrated the 20 vol.% DMHROM blend has higher phi-sensitivity than an olefin-free gasoline base fuel and a typical California Reformulated Gasoline Blendstock for Oxygenate Blending (CARBOB) gasoline fuel. These results demonstrate the potential of DMHROM for improving gasoline fuel performance and quality for operation under ACI conditions. The effectiveness of the aftertreatment system in mitigating emissions was verified and showed that the pure DMHROM blendstock and 20 vol.% blend would not increase non-methane organic gases, NO x , and carbon monoxide (CO) emissions. The DMHROM blendstock was found to slightly decrease sooting tendency when added to a gasoline-base fuel (i.e., ~6% reduction at 20 vol.% blending level). Oxidation stability and lubricant compatibility were both confirmed for the 20 vol.% blend. Overall, these results demonstrate that dimethyl-hexenes have potential for improving engine efficiency and fuel economy while meeting emissions regulations and ASTM specifications for gasoline fuel.

09 BIOMASS FUELS↗
DOE OSTI2023

Effects of knock intensity measurement technique and fuel chemical composition on the research octane number (RON) of FACE gasolines: Part 2 – Effects of spark timing

The Research and Motor Octane Number (RON, MON) characterize a fuel’s knock resistance by rating the knock intensity of a sample fuel relative to that of Primary Reference Fuels (PRF) in a Cooperative Fuel Research (CFR) Engine. A fuel’s octane number is regulated to prevent damage from autoignition leading to knocking combustion in spark-ignition engines. The operational differences between the standard RON rating and modern engine operation are explored in a three-part publication series. The previous study focused on the effects of lambda and knock characterization. This second study primarily focuses on the effects of spark timing on RON determination. Following the findings from the first publication, the knock intensity was captured by the knockmeter and by the maximum amplitude of pressure oscillations (MAPO) at the lambda of peak knock intensity and stoichiometry. Knock-limited spark advance tests were conducted for a set of seven Fuels for Advanced Combustion Engines (FACE) from the Coordinating Research Council (CRC) with varying chemical composition, PRFs, and Toluene Standardization Fuels (TSFs). For retarded spark timings, pre-spark low-temperature heat release was found for low RON PRFs. Low RON PRFs also showed knocking characteristics before reaching the center of combustion suggesting that the use of knock-limited spark advance (KLSA) was preferred over the knock-limited combustion phasing. Primarily paraffinic fuels tended towards increased pressure oscillations while dominantly aromatic fuels experienced higher pressure rise rates. A MAPO-based KLSA correlated best to Octane Index at a negative K-factor suggesting beyond RON operation despite being at otherwise RON conditions. At stoichiometry, the MAPO-based KLSA did neither correlate to RON nor Octane Index. Finally, good agreement was found between KLSA-based effective RON from this study to the MAPO-based effective RON from the first study.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Combustion characteristics of diisopropoxymethane, a low-reactivity oxymethylene ether

Oxymethylene ethers (OMEs) have been studied for use as low-sooting diesel fuel additives or substitutes; very little literature discusses OMEs as spark-ignition (SI) fuels due to their typically high cetane numbers. In this work, a lower-reactivity, branched OME, diisopropoxymethane (DIPM), is evaluated to determine its effectiveness as a spark-ignition fuel, as it is the lowest-reactivity OME (as determined by Indicated Cetane Number) thus far evaluated in the literature. DIPM is synthesized in-house via acetalization from isopropanol (iPrOH) and trioxane using standard OME production practices. DIPM was then tested in a rapid compression machine (RCM) for autoignition and spark ignition characteristics, and in a modified CFR engine to determine effective octane numbers. In the RCM, an autoignition temperature sweep was performed at stoichiometric conditions from 1000/T = 1.7 - 1.0, at 5:1 inert ratio (comparable to approximately 25% EGR), where it was found that DIPM has ignition delay times 5–10x faster than isooctane and displays NTC ignition behavior. Blends with iPrOH indicate that reactivity can be matched with isooctane with low blend ratios of iPrOH in DIPM. Flame speeds were tested with a laser spark for ignition in the RCM, where the flame speed of DIPM and isooctane is determined to be comparable at engine relevant conditions. In the CFR engine, effective RON and MON based on pressure trace frequency domain measurements were determined for DIPM and a 15 vol% iPrOH in DIPM blend. Neat DIPM has (R+M)/2 = 59 and a negative sensitivity of S = -18, consistent with its NTC behavior and higher reactivity. Furthermore the DIPM/iPrOH blend has positive sensitivity and a pump-gasoline range (R+M)/2 = 89.3. DIPM on its own is unlikely to be an effective SI fuel, however, when blended with iPrOH as an ON booster, it may be a promising SI candidate fuel.

09 BIOMASS FUELS↗
DOE OSTI2020

Origin and composition of three heterolithic boulder- and cobble-bearing deposits overlying the Murray and Stimson formations, Gale Crater, Mars

Heterolithic, boulder-containing, pebble-strewn surfaces occur along the lower slopes of Aeolis Mons (“Mt. Sharp”) in Gale crater, Mars. They were observed in HiRISE images acquired from orbit prior to the landing of the Curiosity rover. The rover was used to investigate three of these units named Blackfoot, Brandberg, and Bimbe between sols 1099 and 1410. These unconsolidated units overlie the lower Murray formation that forms the base of Mt. Sharp, and consist of pebbles, cobbles and boulders. Blackfoot also overlies portions of the Stimson formation, which consists of eolian sandstone that is understood to significantly postdate the dominantly lacustrine deposition of the Murray formation. Blackfoot is elliptical in shape (62 x 26 m), while Brandberg is nearly circular (50 x 55 m), and Bimbe is irregular in shape, covering about ten times the area of the other two. The largest boulders are 1.5–2.5 m in size and are interpreted to be sandstones. As seen from orbit, some boulders are light-toned and others are dark-toned. Rover-based observations show that both have the same gray appearance from the ground and their apparently different albedos in orbital observations result from relatively flat skyfacing surfaces. Chemical observations show that two clasts of fine sandstone at Bimbe have similar compositions and morphologies to nine ChemCam targets observed early in the mission, near Yellowknife Bay, including the Bathurst Inlet outcrop, and to at least one target (Pyramid Hills, Sol 692) and possibly a cap rock unit just north of Hidden Valley, locations that are several kilometers apart in distance and tens of meters in elevation. These findings may suggest the earlier existence of draping strata, like the Stimson formation, that would have overlain the current surface from Bimbe to Yellowknife Bay. Compositionally these extinct strata could be related to the Siccar Point group to which the Stimson formation belongs.

58 GEOSCIENCES↗
DOE OSTI2022

Replicating HCCI-like autoignition behavior: What gasoline surrogate fidelity is needed?

This work seeks to characterize the fidelity needed in a gasoline surrogate with the intent to replicate the complex autoignition behavior exhibited within advanced combustion engines, and specifically Homogeneous Charge Compression Ignition (HCCI). A low-temperature gasoline combustion (LGTC) engine operating in HCCI mode and a rapid compression machine (RCM) are utilized to experimentally quantify fuel reactivity, through autoignition and preliminary heat release characteristics. Fuels considered include a research grade E10 U.S. gasoline (RD5-87), three multi-component surrogates (PACE-1, PACE-8, PACE-20), and a binary surrogate (PRF88.4). Each fuel was studied at lean/HCCI-like conditions covering a wide range of temperatures and pressures that are representative of naturally aspirated to high boost engine operation. Detailed chemical kinetic modeling is also undertaken using a recently updated gasoline surrogate kinetic model to simulate the RCM experiments and to provide chemical insight into surrogate-to-surrogate differences. The LGTC engine experiments demonstrate nearly identical reactivity between PACE-20 and RD5-87 across studied conditions, while faster phasing is seen for both PACE-1 and PACE-8 due to their stronger intermediate- and low-temperature heat release (ITHR/LTHR) at naturally aspirated and boosted conditions, respectively. The RCM experiments reveal typical low-temperature, negative temperature coefficient (NTC) and intermediate-temperature autoignition behaviors at all pressure conditions for RD5-87, which are qualitatively reproduced by all surrogates. Quantitative discrepancies in both autoignition and preliminary heat release are observed for all surrogates, while their ability to replicate RD5-87 autoignition behavior follows the order of PACE-20 > PACE-1 > PACE-8 > PRF88.4. Excellent mapping is obtained between the LGTC engine and the RCM, where the engine pressure-time trajectories can be characterized by the regimes represented by the RCM autoignition isopleths. The kinetic model performs commendably when simulating both autoignition and preliminary heat release of PACE-20, while typically overpredicting ignition delay times for PACE-1, PACE-8 and PRF88.4 at high-pressure and low-temperature/NTC conditions. Sensitivity and rate of production (ROP) analyses highlight surrogate-to-surrogate differences in the governing chemical kinetics where n-pentane initiates rapid OH branching at a faster rate and an earlier timing for PACE-20 than iso-pentane does for PACE-1 and PACE-8, making it computationally more reactive than the other surrogates. The current study highlights the need to include non-standardized properties, such as the lean/HCCI-like autoignition characteristics, in addition to ASTM properties (e.g., RON, MON) as metrics of fuel reactivity and targets to be matched when formulating high-fidelity surrogates that fully capture gasoline advanced combustion behavior such as HCCI-like autoignition.

42 ENGINEERING↗
DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

MoO x S y /Ni 3 S 2 Microspheres on Ni Foam as Highly Efficient, Durable Electrocatalysts for Hydrogen Evolution Reaction

Hydrogen energy derived from water splitting is the cleanest renewable energy source, but it is also very challenging to achieve because the hydrogen evolution reaction (HER) requires highly efficient and low-cost electrocatalysts. Here, we have fabricated a novel hierarchical system of amorphous molybdenum oxy/sulfide microspheres with crystalline Ni 3 S 2 intergrown in situ on Ni foam (MoO x S y /Ni 3 S 2 /NF) as an outstanding electrocatalyst for HER. The MoO x S y /Ni 3 S 2 /NF demonstrates an ultra-low overpotential of 58 mV at a current density of 10 mA cm -2 and extremely durable stability (>200 h), suggesting superior performance comparable to that of Pt-C/NF under acidic conditions. The X-ray absorption fine structure (XAFS) determines the average valence state of Mo to be +(5 + δ), with a coordination motif by O and S. To explain such high HER activity, a [Mo 2 O 2 (S,O) 4 ] dimer-based periodic model structure with the average composition of [Mo 4 O 8 S 4 ] interfaced with the Ni 3 S 2 (101) surface is proposed. The interactions between the Ni of Ni 3 S 2 and bridging S/O of [Mo 4 O 8 S 4 ] result in an average formal Mo charge state between +5 and +6, and significant charge transfer from Ni 3 S 2 to [Mo 4 O 8 S 4 ] activates the Mo = O bonds. The calculated |ΔG H* | of less than 50 meV suggests that the double-bonded O is the most active site. This work points to the importance of oxy/sulfides with Mon+ (+5 < n < +6) as exceptional electrochemical catalysts for HER.

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

Tribocatalytically-activated formation of protective friction and wear reducing carbon coatings from alkane environment

Minimizing the wear of the surfaces exposed to mechanical shear stresses is a critical challenge for maximizing the lifespan of rotary mechanical parts. In this study, we have discovered the anti-wear capability of a series of metal nitride-copper nanocomposite coatings tested in a liquid hydrocarbon environment. The results indicate substantial reduction of the wear in comparison to the uncoated steel substrate. Analysis of the wear tracks indicates the formation of carbon-based protective films directly at the sliding interface during the tribological tests. Raman spectroscopy mapping of the wear track suggests the amorphous carbon (a-C) nature of the formed tribofilm. Further analysis of the tribocatalytic activity of the best coating candidate, MoN-Cu, as a function of load (0.25–1 N) and temperature (25 °C and 50 °C) was performed in three alkane solutions, decane, dodecane, and hexadecane. Results indicated that elevated temperature and high contact pressure lead to different tribological characteristics of the coating tested in different environments. The elemental energy dispersive x-ray spectroscopy analysis and Raman analysis revealed formation of the amorphous carbon film that facilitates easy shearing at the contact interface thus enabling more stable friction behavior and lower wear of the tribocatalytic coating. These findings provide new insights into the tribocatalysis mechanism that enables the formation of zero-wear coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Anomaly Detection for Online Monitoring of Thermocouple Sensors in the Advanced Test Reactor

This study explores data-driven anomaly detection methods to analyze sensor fail- ures in the Advanced Gas Reactor (AGR) nuclear fuel irradiation experiments. Specifically, we examine failures of thermocouples (TCs), which are critical for mon- itoring and controlling in-reactor temperatures during operation. Failures were pri- marily observed during abrupt power transitions and manifested as sensor drop-outs, drifts, or unexplained behavior. We applied three time-series analysis techniques— rolling mean smoothing, matrix profile, and vector auto-regression (VAR)—to de- tect anomalies in TC data prior to failure events. The rolling mean method effec- tively highlighted deviations aligned with reported failures, while the matrix profile provided partial early warning but sometimes flagged normal fluctuations during power-down periods. VAR shows potential in capturing multivariate dependencies but requires further calibration. A rare case of TC drift was also documented, which did not result in failure, underscoring the challenge of building predictive models with sparse positive examples. Our findings demonstrate that traditional statistical tools can aid anomaly detection but have limited predictive power without richer training data. We propose future directions including synthetic data generation, real- time surrogate modeling, and multi-modal feature integration. This work provides a foundation for applying robust anomaly detection frameworks to mission-critical sensor systems in experimental settings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Analysis and modeling of tungsten emission and net erosion in the DIII-D divertor using updated atomic data

Tungsten (W) is one of the leading candidate materials for plasma-facing components. However, its main drawback is its high radiative efficiency; if W penetrates the plasma, it can lead to core degradation or even collapse. Since eroded tungsten tends to ionize in the sheath and redeposit promptly, the net erosion flux that escapes prompt redeposition can differ significantly from the gross erosion. This work presents a modeling framework to estimate net erosion and photon emission from W coatings exposed to the lower divertor of DIII-D using the DiMES material exposure probe. The approach couples RustBCA for sputtering yields with a Monte Carlo transport code (LPTMC) that models redeposition and W emission. Computation is carried out with new atomic data, based on R-matrix and Mons calculations, leading to lower ionization probabilities and a twofold increase in net erosion estimates compared to calculations done with OPEN-ADAS atomic data. The model results are benchmarked against experimental measurements, showing quantitative agreement for erosion, although the trends in W emission are reproduced only qualitatively. The model is also used to assess whether W II emission can serve as a direct measurement of the net erosion of W in the lower divertor of DIII-D. Simulations show that this is not valid if the electron pressure is above ~120 Pa or if the toroidal length of the eroded material is smaller than the parallel-to-B distance traveled by impurity ions before steady-state conditions are reached. Finally, simulations suggest that when W is sputtered by carbon ions with high impact energies (≳300 eV) in DIII-D, W net erosion scales with W gross erosion and can be numerically approximated using W I flux alone as input.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Full calibration of the tomographic redshift distribution from the HSC PDR3 Shape Catalog with DESI

The calibration of tomographic redshift distributionsis essential for cosmological analysis of weak lensing data.In this work, we calibrate all four tomographic bins of the Hyper Suprime Camera (HSC) weak lensing catalog with the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 and 2 using the clustering redshifts technique. We include z > 1.2 redshift sources such as emission line galaxies (ELG) and quasars (QSO) sources in our calibration, which were not available in the previous HSC calibration (Rau et al. (2022), Mon. Not. Roy. Astron. Soc. 524 (2023) 5109), allowing a complete calibration of all the redshift bins. We find the first tomographic bin exhibits a small shift towards low redshifts. The second bin is in good agreement with the photometric calibration, while third and fourth bin exhibit a shift towards higher redshifts. However, these shifts are considerably smaller than the shifts obtained in the HSC Year 3 cosmic shear analyses. We evaluate the impact of galaxy bias and magnification effects from all the samples on the measurements, finding them to be small, and we propose corrections to reduce them further. Specifically, we relax the assumption of linear bias and only assume no redshift evolution of the cross-correlation coefficient, allowing us to leverage smaller clustering scales. We model the redshift distributions with splines and compare our results to previous analyses as well as to other parameterizations found in literature. For the two high-redshift tomographic bins, we find the shifts to higher redshifts with respect to the measurements performed in Rau+2022 to be Δz$_{3}$ =-0.039$^{+0.020}$$_{-0.021}$ and Δz$_{4}$ = -0.048$^{+0.012}$$_{-0.012}$.

Choppin de Janvry, J. [LBL, Berkeley; UC, Berkeley↗
DOE OSTI2026

Density profile of dynamical halos

Among the most fundamental properties of a dark matter halo is its density profile. Motivated by the recent proposal by García et al . [Mon. Not. R. Astron. Soc. 521 , 2464 (2023)] to define a dynamical halo as the collection of orbiting particles in a gravitationally bound structure, we characterize the mean and scatter of the orbiting profile of dynamical halos as a function of their orbiting mass. Here, we demonstrate that the orbiting profile of individual halos at fixed mass depends on a single dynamical variable, the halo radius 𝑟 h , which characterizes the spatial extent of the profile. The scatter in halo radius at fixed orbiting mass is ≈16%. Only a small fraction of this scatter arises due to differences in halo formation time, with late-forming halos being more compact (smaller halo radii). Accounting for this additional correlation results in an ≈11% scatter in halo radius at fixed mass and halo formation time.

79 ASTRONOMY AND ASTROPHYSICS↗