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NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗

DOE OSTI2023

Opposing Hysteresis Patterns in Flow and Outflow Macroscopic Fundamental Diagrams and Their Implications

Two key aggregated traffic models are the relationship between average network flow and density (known as the network or flow macroscopic fundamental diagram [flow-MFD]) and the relationship between trip completion and density (known as network exit function or the outflow-MFD [o-FMD]). The flow- and o-MFDs have been shown to be related by average network length and average trip distance under steady-state conditions. However, recent studies have demonstrated that these two relationships might have different patterns when traffic conditions are allowed to vary: the flow-MFD exhibits a clockwise hysteresis loop, while the o-MFD exhibits a counter-clockwise loop. One recent study attributes this behavior to the presence of bottlenecks within the network. The present paper demonstrates that this phenomenon may arise even without bottlenecks present and offers an alternative, but more general, explanation for these findings: a vehicle’s entire trip contributes to a network’s average flow, while only its end contributes to the trip completion rate. This lag can also be exaggerated by trips with different lengths, and it can lead to other patterns in the o-MFD such as figure-eight patterns. A simple arterial example is used to demonstrate this explanation and reveal the expected patterns, and they are also identified in real networks using empirical data. Then, simulations of a congestible ring network are used to unveil features that might increase or diminish the differences between the flow- and o-MFDs. Finally, more realistic simulations are used to confirm that these behaviors arise in real networks.

42 ENGINEERING↗

DOE OSTI2021

Using Complex Probability Amplitudes to Simulate Solute Transport in Composite Porous Media

Probability amplitudes are fundamental to quantum mechanics and offer robust descriptions of complicated systems, which have allowed physicists to explain behaviors inaccessible to classical physics. This article ponders how some of the same conceptual underpinnings of the mathematics used for modeling quantum systems might be applied to subsurface water resources problems and speculates how these tools could facilitate applications on quantum computers. A probability amplitude-based model for describing advective-dispersive transport in porous media using linear operators is investigated. The proposed complex valued model decomposes spreading into two “sub-continuum partial dispersion” coefficients, and this recovers classical spreading when the sum of these coefficients is the Fickian dispersion coefficient. However, the probability amplitudes have a manyto-one relationship to a probability distribution, so it embeds a level of heterogeneity into seemingly equivalent functions. Two propagators with different sub-continuum coefficients may have the same macroscopic behavior when either is considered in isolation, but when they act on the other the system’s behavior changes. Additionally, differences in the amplitudes cause a reduction in spreading as velocity correlations are disrupted, despite both propagators having identical dispersion coefficients, and this cannot be achieved using classical methods without changing the dispersion coefficient. The main point is that these amplitude-based models offer a way to embed information about the system into the propagators, instead of just “averaging it out” when making an upscaled model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗

NASA NTRS1975

Interpretation of lunar heat flow data

Lunar heat flow observations at the Apollo 15 and 17 sites can be interpreted to imply bulk U concentrations for the moon of 5 to 8 times those of normal chondrites and 2 to 4 times terrestrial values inferred from the earth's heat flow and the assumption of thermal steady state between surface heat flow and heat production. A simple model of nearsurface structure that takes into account the large difference in (highly insulating) regolith thickness between mare and highland provinces is considered. This model predicts atypically high local values of heat flow near the margins of mare regions - possibly a factor of 10 or so higher than the global average. A test of the proposed model using multifrequency microwave techniques appears possible wherein heat flow traverse measurements are made across mare-highland contacts. The theoretical considerations discussed here urge caution in attributing global significance to point heat-flow measurements on the moon.

Conel, J. E.↗

NASA NTRS1995

Recent improvements to and validation of the one dimensional NASA wave rotor model

A numerical model has been developed at the NASA Lewis Research Center which can predict both the unsteady flow quantities within a wave rotor passage and the steady averaged flows in the ports. The model is based on the assumptions of one-dimensional, unsteady, perfect gas flow. The model assesses not only the dominant wave behavior, but the loss effects of finite passage opening time, leakage from the passage ends, viscosity, and heat transfer to and from the passages. The model operates in the rotor reference frame; however, until recently no account was made for the often significant effect of the rotor circumferential velocity component. The present model accounts for this by modifying the passage boundary conditions, allowing the internal computational scheme to remain the rotor reference frame, while quantities such as inlet duct stagnation properties may be specified in the fixed or absolute reference frame. Accurate modeling of this effect is critical to successful wave rotor analysis and design, particularly in off-design predictions where the flows in the inlet ducts are mismatched with the rotor passages and significant turning may take place (i.e., work is done on the gas). The relative simplicity of the model makes it useful for design and optimization, as well as analysis, of wave rotor cycles for many applications. This report, building on several earlier papers, describes the most recent modifications to the model. These include accounting for the relative/absolute transition at the passage boundaries and refinements to the viscous source term correlation which resulted from this accounting. Comparison of model predictions with measured data is then presented and discussed.

Paxson, Daniel E.↗

NASA NTRS2000

An Investigation of Polar Ozone Recovery in the 1997 Southern Hemisphere Spring

A chemical transport model is used to investigate the processes that control the depth and duration of the ozone 'hole' in the lower stratosphere through comparisons of model output with measurements from the Total Ozone Mapping Spectrometer (TOMS) and from the Microwave Limb Sounder (MLS) and Halogen Occultation Experiment (HALOE), both on the Upper Atmosphere Research Satellite (UARS). This study extends previous model comparisons with observations into October and November and examine levels in (greater than 31 hPa) and above (less than 31 hPa) the chemical loss region. Averages of column ozone in the model decrease through mid-October below 31 hPa but begin to increase in mid-September above 31 hPa. An investigation of model-tracer data comparisons and other meteorological parameters indicate that the model presents a consistent picture of top-down recovery and tracer transport. An O03budget study at 500 K (below 31 hPa) and 840 K (above 31 hPa) is carried out to investigate the processes that control the timing of the transition of ozone from a chemical to dynamically driven regime. The model ozone decrease at 500 K is due to chemical loss in August and September but is due to upward motion in October. The ozone increase at 840 K is primarily due to photochemical production, with a smaller contribution from transport. These results show that chemistry and dynamics can play different roles in polar vortex ozone recovery at different levels.

Pierson, J. M.↗

NASA NTRSDate not supplied

Atmospheric Variability and Measurement Uncertainty: Pitfalls in Averaging in situ Data

Satellite measurements and atmospheric models, two essential components of the integrated global observing system, provide crucial tools for monitoring and predicting regional and global foci, spanning numerous Earth Science fields. Unfortunately, models can lack the spatial and temporal resolution needed to resolve finer scale structure. Satellite measurements also have similar tempo-spatial restriction issues, but additionally can only measure certain species and can have biases that must be evaluated. Ground measurement networks are critical components of this system, by providing both independent inputs of species needed by models (including those that satellites do not provide) and assisting with investigation of biases in satellite products. Similarly, aircraft measurements play a vital role in closing the gaps between satellite measurements, model products, and ground monitoring networks by providing high accuracy, high-resolution data on local to regional spatial scales. Therefore, both ground-based and airborne observations are widely used to assess model predictions and satellite observations. One of the great challenges in using both ground and aircraft data in this fashion is matching the data temporally and spatially to the model/satellite data. A meaningful comparison with model or satellite requires a solid assessment of the variability of the in-situ measurements, which include both the instrument uncertainty and the statistical uncertainty due to atmospheric variability. While instrument uncertainty is generally more straightforwardly characterized, it can be challenging to accurately capture this variability uncertainty as it often presents in a non-Gaussian manner (e.g. emission plumes, frontal passages). We will present results examining spatial and temporal variability over a selection of scales relevant to satellite measurements and models of several in situ measurement species spanning both airborne and ground measurements. The extent of non-Gaussian variability will be quantified, and we will discuss additional statistical parameters that help assess the fitness of gaussian variability assumption when temporally or spatially averaging.

satellite validation↗

DOE OSTI2020

Importance learning estimator for the site-averaged turnover frequency of a disordered solid catalyst

For disordered catalysts such as atomically dispersed “single-atom” metals on amorphous silica, the active sites inherit different properties from their quenched-disordered local environments. The observed kinetics are site-averages, typically dominated by a small fraction of highly active sites. Standard sampling methods require expensive ab initio calculations at an intractable number of sites to converge on the siteaveraged kinetics. We present a new method that efficiently estimates the site-averaged turnover frequency (TOF). The new estimator uses the same importance learning algorithm [Vandervelden et al., React. Chem. Eng. 5, 77 (2020)] that we previously used to compute the siteaveraged activation energy. We demonstrate the method by computing the site-averaged TOF for a simple disordered lattice model of an amorphous catalyst. The results show that with the importance learning algorithm, the site-averaged TOF and activation energy can now be obtained concurrently with orders of magnitude reduction in required ab initio calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Delving into the depths of NGC 3783 with XRISM

We present our study of the X-Ray Imaging and Spectroscopy Mission (XRISM) observation of the Seyfert-1 galaxy NGC 3783. XRISM’s Resolve microcalorimeter has enabled, for the first time, a detailed characterization of the highly ionized outflows in this active galactic nucleus. Our analysis constrains their outflow and turbulent velocities, along with their ionization parameter (ξ) and column density (N H ). The high-resolution Resolve spectrum reveals a distinct series of Fe absorption lines between 6.4 and 7.8 keV, ranging from Fe XVIII to Fe XXVI. At lower energies (1.8−3.3 keV), absorption features from Si, S, and Ar are also detected. Our spectroscopy and photoionization modeling of the time-averaged Resolve spectrum uncovers six outflow components, five of which exhibit relatively narrow absorption lines with outflow velocities ranging from 560 to 1170 km s −1 . In addition, a broad absorption feature is detected, which is consistent with Fe XXVI outflowing at 14 300 km s −1 (0.05 c). The kinetic luminosity of this component is 0.8−3% of the bolometric luminosity. Our analysis of the Resolve spectrum shows that more highly ionized absorption lines are intrinsically broader than those of lower-ionization species, indicating that the turbulent velocity of the six outflow components (ranging from 0 to 3500 km s −1 ) increases with ξ. Furthermore, we find that the column density (N H ) of the outflows generally declines with the ionization parameter up to log ξ = 3.2 but rises beyond this point, suggesting a complex ionization structure. The absorption profile of the Fe XXV resonance line is intriguingly similar to UV absorption lines (Lyα and C IV) observed by the Hubble Space Telescope, from which we infer that the outflows are clumpy in nature. Our XRISM/Resolve results from lower- and higher-ionization regimes support a “hybrid wind” scenario in which the observed outflows have multiple origins and driving mechanisms. We explore various interpretations of our findings within active galactic nucleus wind models.

X-rays: galaxies↗

NASA NTRS2001

Quantitative Assessment of the Integrated Response in Global Heat and Moisture Budgets to Changing Solar Irradiance

Earlier, we found time sequences of basin- and global-average upper ocean temperature (that is, diabatic heat storage above the main pycnocline) for 40 years from 1955-1994 and of sea surface temperature for 95 years from 1900-1994 associated with changes in the Sun's radiative forcing on decadal and interdecadal timescales, lagging by 10 deg.- 30 deg. of phase and confined to the upper 60-120 m. Yet, the observed changes in upper ocean temperature (approx. 0.1 K) were approximately twice those expected from the Stefan-Boltzmann black-body radiation law for the Earth's surface, with phase lags (0 deg. to 30 deg. of phase) much shorter than the 90 deg. phase shift expected as well. Moreover, White et al. (1997, 1998) found the Earth's global decadal mode in covarying SST and SLP anomalies phase locked to the decadal signal in the Sun's irradiance. Yet, Allan (2000) found this decadal signal also characterized by patterns similar to those observed on biennial and interannual time scales; that is, the Troposphere Biennial Oscillation (TBO) and the El Nino and the Southern Oscillation (ENSO). This suggested that small changes in the Sun's total irradiance could excite this global decadal mode in the Earth's ocean-atmosphere-terrestrial system similar to those excited internally on biennial and interannual period scales. This is a significant finding, proving that energy budget models (that is, models based on globally-averaged radiation balances) yield unrealistic responses. Thus, the true response must include positive and negative feedbacks in the Earth's ocean-atmosphere-terrestrial system as its internal mode (that is, the natural mode of the system) respond in damped resonance to quasi-periodic decadal changes in the Sun's irradiance. Moreover, these responses are not much different from those occurring internally on biennial and interannual period scales.

White, Warren B.↗

NASA NTRS1980

Earth radiation budget measurements from satellites and their interpretation for climate modeling and studies

The annual and seasonal averaged Earth atmosphere radiation budgets derived from the most complete set of satellite observations available are presented. The budgets were derived from a composite of 48 monthly mean radiation budget maps. Annually and seasonally averaged radiation budgets are presented as global averages and zonal averages. The geographic distribution of the various radiation budget quantities is described. The annual cycle of the radiation budget was analyzed and the annual variability of net flux was shown to be largely dominated by the regular semi and annual cycles forced by external Earth-Sun geometry variations. Radiative transfer calculations were compared to the observed budget quantities and surface budgets were additionally computed with particular emphasis on discrepancies that exist between the present computations and previous surface budget estimates.

Vonderhaar, T. H.↗

DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Temperature distribution in a laser-heated diamond anvil cell as described by finite element analysis

Finite element analysis (FEA) is a powerful tool for numerically solving partial differential equations over complex geometries and is thus useful for analyzing heat transport in laser-heated diamond anvil cell (LHDAC) experiments. Our models expand on previously published simulations by calculating the volume-averaged temperatures of both the sample and insulation/pressure media under steady-state heating to determine the thermal pressure of the hot sample. Our goal is to produce an accurate relationship between the measured surface temperature of the absorbing sample and the temperature of the transparent insulating media, which is used to determine thermal pressure but susceptible to steep temperature gradients. We find that in doing so, our FEA models of temperature within the pressure/insulation media can differ from simplified estimates of temperature gradients by more than a factor of 2. We also explore temperature-dependent and temperature-independent thermal conductivity models and find that the volume-averaged temperatures differ by up to a factor of 1.3, forcing the predicted thermal pressures determined to also differ by up to a factor of 1.5 at a temperature of 2000 K at 50 GPa for neon. Higher temperatures exacerbate this difference. We also find that unintentional asymmetric sample insertion and sample heating, which are common in LHDAC experiments, do not have a first-order effect on volume-averaged temperatures. The FEA models, available in both Python and FlexPDE, are versatile across different sample geometries, materials, and heat source laser shapes.

Farah, Frederick↗

NASA NTRS2013

Does Aspartic Acid Racemization Constrain the Depth Limit of the Subsurface Biosphere?

Previous studies of the subsurface biosphere have deduced average cellular doubling times of hundreds to thousands of years based upon geochemical models. We have directly constrained the in situ average cellular protein turnover or doubling times for metabolically active micro-organisms based on cellular amino acid abundances, D/L values of cellular aspartic acid, and the in vivo aspartic acid racemization rate. Application of this method to planktonic microbial communities collected from deep fractures in South Africa yielded maximum cellular amino acid turnover times of approximately 89 years for 1 km depth and 27 C and 1-2 years for 3 km depth and 54 C. The latter turnover times are much shorter than previously estimated cellular turnover times based upon geochemical arguments. The aspartic acid racemization rate at higher temperatures yields cellular protein doubling times that are consistent with the survival times of hyperthermophilic strains and predicts that at temperatures of 85 C, cells must replace proteins every couple of days to maintain enzymatic activity. Such a high maintenance requirement may be the principal limit on the abundance of living micro-organisms in the deep, hot subsurface biosphere, as well as a potential limit on their activity. The measurement of the D/L of aspartic acid in biological samples is a potentially powerful tool for deep, fractured continental and oceanic crustal settings where geochemical models of carbon turnover times are poorly constrained. Experimental observations on the racemization rates of aspartic acid in living thermophiles and hyperthermophiles could test this hypothesis. The development of corrections for cell wall peptides and spores will be required, however, to improve the accuracy of these estimates for environmental samples.

cellular↗

NASA NTRS2008

Gaseous Surrogate Hydrocarbons for a Hifire Scramjet that Mimic Opposed Jet Extinction Limits for Cracked JP Fuels

This paper describes, first, the top-down methodology used to define simple gaseous surrogate hydrocarbon (HC) fuel mixtures for a hypersonic scramjet combustion subtask of the HiFIRE program. It then presents new and updated Opposed Jet Burner (OJB) extinction-limit Flame Strength (FS) data obtained from laminar non-premixed HC vs. air counterflow diffusion flames at 1-atm, which follow from earlier investigations. FS represents a strain-induced extinction limit based on cross-section-average air jet velocity, U(sub air), that sustains combustion of a counter jet of gaseous fuel just before extinction. FS uniquely characterizes a kinetically limited fuel combustion rate. More generally, Applied Stress Rates (ASRs) at extinction (U(sub air) normalized by nozzle or tube diameter, D(sub n or t) can directly be compared with extinction limits determined numerically using either a 1-D or (preferably) a 2-D Navier Stokes simulation with detailed transport and finite rate chemistry. The FS results help to characterize and define three candidate surrogate HC fuel mixtures that exhibit a common FS 70% greater than for vaporized JP-7 fuel. These include a binary fuel mixture of 64% ethylene + 36% methane, which is our primary recommendation. It is intended to mimic the critical flameholding limit of a thermally- or catalytically-cracked JP-7 like fuel in HiFIRE scramjet combustion tests. Our supporting experimental results include: (1) An idealized kinetically-limited ASR reactivity scale, which represents maximum strength non-premixed flames for several gaseous and vaporized liquid HCs; (2) FS characterizations of Colket and Spadaccini s suggested ternary surrogate, of 60% ethylene + 30% methane + 10% n-heptane, which matches the ignition delay of a typical cracked JP fuel; (3) Data showing how our recommended binary surrogate, of 64% ethylene + 36% methane, has an identical FS; (4) Data that characterize an alternate surrogate of 44% ethylene + 56% ethane with identical FS and nearly equal molecular weights; this could be useful when systematically varying the fuel composition. However, the mixture liquefies at much lower pressure, which limits on-board storage of gaseous fuel; (5) Dynamic Flame Weakening results that show how oscillations in OJB input flow (and composition) can weaken (extinguish) surrogate flames up to 200 Hz, but the weakening is 2.5x smaller compared to pure methane; and finally, (6) FS limits at 1-atm that compare with three published 1-D numerical OJB extinction results using four chemical kinetic models. The methane kinetics generally agree closely at 1-atm, whereas, the various ethylene models predict extinction limits that average ~ 45% high, which represents a significant problem for numerical simulation of surrogate-based flameholding in a scramjet cavity. Finally, we continue advocating the FS approach as more direct and fundamental for assessing idealized scramjet flameholding potentials than measurements of "unstrained" premixed laminar burning velocity or blowout in a Perfectly Stirred Reactor.

Pellett, Gerald L.↗

DOE OSTI2021

Multiscale Concurrent Atomistic-Continuum (CAC) modeling of multicomponent alloys

We report strengthening in complex multicomponent systems such as solid solution alloys is controlled primarily by the dynamic interactions between dislocation lines and heterogeneously distributed solute species. Modeling of extended defect length scales in such multicomponent systems becomes prohibitively expensive, motivating the development of reduced order approaches. This work explores the application of the Concurrent Atomistic-Continuum (CAC) method to model dislocation mobility in random alloys at extended length scales. By employing recently developed average-atom interatomic potentials, the average “bulk” material response in coarse-grained regions interacts with true random solute species in the atomistic-scale domain. We demonstrate that spurious stresses in domain resolution transition regions are eliminated entirely due to the CAC formulation. Simultaneously, the key details of local stress fluctuation due to randomness in the dislocation core region are captured, and fluctuating stress smoothly decays to the long-range dislocation stress field response. Dislocation mobility calculations, for line lengths over 400 nm, are computed as a function of alloy composition in the model FeNiCr system and compared to full molecular dynamics (MD). The results capture the composition-dependent trends, while reducing degrees of freedom by nearly 40%. This approach can be readily extended to any system described by an EAM potential and facilitates the study of large-scale defect dynamics in complex solute environments to support computational alloy design.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A multi-model assessment of global freshwater temperature and thermoelectric power supply under climate change

Water temperature is a key abiotic factor influencing aquatic ecosystem health and the services provided to both nature and humans. Global water temperature models offer possibilities to improve our understanding of water temperature regimes, which is increasingly important against the backdrop of climate change. Yet, existing studies have predominantly relied on a single model, which can lead to an incomplete representation of uncertainty and potential biases, in addition to limited insight into the range of possible future conditions, which ultimately reduces the robustness of climate impact assessments. Here, we provide a comprehensive assessment of surface freshwater temperature changes from various river and lake models for both past conditions and under future scenarios of climate change. Global models consistently simulate that surface water temperatures are currently 0.5 °C–0.8 °C higher than at the turn of the century (i.e. 1981–2000), and that warming will extend and intensify with future global change throughout the 21st century. While the strength of warming is highly sensitive to the different water temperature models, emissions scenarios and global climate models, our multi-model ensemble shows a global average annual water temperature rise of between +1.3 °C and +4.1 °C by the end of the century. To illustrate a potential societal impact of our results, we evaluate how future changes in discharge and water temperature may affect existing thermoelectric power plants, estimating average annual reductions of 1.5%–6% in global usable capacity by the end of the century. However, with river water temperatures projected to exhibit more pronounced seasonal patterns in the future—especially under the more extreme climate change scenarios and during summer months in the Northern Hemisphere—intra-annual reductions in usable capacity can be much more severe. Given the challenges associated with (large-scale) adaptation to control water temperature regimes, strong climate change mitigation is crucial for minimising water temperature rises and its associated negative impacts on humankind and ecosystems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗