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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 991 records · Page 55

Microscale Mechanical‐Chemical Modeling of Granular Salt: Insights for Creep

Abstract Microscale numerical modeling is potentially an effective approach for understanding salt creep, but it is quite challenging because natural salt grain boundaries can have complex and evolving geometry, and because contacts between these deformable salt mineral grains are dynamic as a result of compaction, chemical reaction, fluid flow, and heat transfer. In this study, we have overcome these challenges and developed a new microscale mechanical‐chemical (MC) model to analyze creep of salt at the microscale, accounting for coupled deformation, dynamic contacts, and chemical reaction in granular systems with realistic geometric representations. The MC model was realized by linking a new microscale mechanical code based on the numerical manifold method (NMM) to a reactive transport code named Crunch. We simulated the processes of reorganization of the salt grains, microfracturing, and pressure solution that contribute to creep at larger scales. Based on this first quantitative microscale model, we found that sharp corners of mineral grains can dominate the contact dynamics, microfracturing, and pressure solution when salt is compacted, thus governing the structural changes and porosity loss of the system. We found that pressure solution, which preferentially dissolves sharp corners and edges, can lead to relatively high porosity loss in the system, thus playing an important role in the creep of salt. Our analysis shows that the dynamic changes of salt granular systems involving grain relocation and pressure solution can occur repeatedly and continuously, thus contributing to longer‐term creep of salt at larger scales.

58 GEOSCIENCES↗

Challenges and Capabilities in Estimating Snow Mass Intercepted in Conifer Canopies With Tree Sway Monitoring

Snowpack accumulation in forested watersheds depends on the amount of snow intercepted in the canopy and its partitioning into sublimation, unloading, and melt. A lack of canopy snow measurements limits our ability to evaluate models that simulate canopy processes and predict snowpack. We tested whether monitoring changes in wind-induced tree sway is a viable technique for detecting snow interception and quantifying canopy snow water equivalent (SWE). Over a 6 year period in Colorado, we monitored hourly sway of two conifers, each instrumented with an accelerometer sampling at 12 Hz. We developed an approach to distinguish changes in sway frequency due to thermal effects on tree rigidity versus intercepted snow mass. Over 60% of days with canopy snow had a sway signal that could not be distinguished from thermal effects. However, larger changes in tree sway could not generally be attributed to thermal effects, and canopy snow was present 93%–95% of the time, as confirmed with classified PhenoCam imagery. Using sway tests, we converted changes in sway to canopy SWE, which were correlated with total snowstorm amounts from a nearby SNOTEL site (Spearman r = 0.72 to 0.80, p < 0.001). Greater canopy SWE was associated with storm temperatures between -7°C and 0°C and wind speeds less than 4 m s -1 . Lower canopy SWE prevailed in storms with lower temperatures and higher wind speeds. Monitoring tree sway is a viable approach for quantifying canopy SWE, but challenges remain in converting changes in sway to mass and distinguishing thermal and snow mass effects on tree sway.

54 ENVIRONMENTAL SCIENCES↗

Experimental Whole-Ecosystem Warming Enables Novel Estimation of Snow Cover and Depth Sensitivities to Temperature, and Quantification of the Snow-Albedo Feedback Effect

Climate change is reducing the amount, duration, and extent of snow across high-latitude ecosystems. But, in landscapes where persistent winter snow cover develops, experimental platforms to specifically investigate interactions between warming and changes in snowpack, and impacts on ecosystem processes, have been lacking. We leveraged a whole-ecosystem warming experiment in a boreal peatland forest to quantify how snow duration, depth, and fractional cover vary with warming of up to +9°C. We found that every snow-related quantity we examined declined precipitously as the amount of warming increased. The importance of deep, continuous snow cover for moderating shallow soil temperature is highlighted by an increase in soil temperature variance and the frequency of short-duration freeze-thaw cycles in the warmer plots. We used a paired-plot approach to estimate the magnitude of the snow-albedo feedback effect, and demonstrate that albedo-driven warming linked to reduced snow cover varies between December (+0.4°C increase in maximum air temperature) and March (+1.2°C increase) because of differences in insolation. Overall, results show that even modest future warming will have profound impacts on northern winters and cold-season ecosystem processes. Plot-level data from this warming experiment, and emergent relationships between warming and quantities related to snow cover and duration, could be of enormous value for testing and improving the representation of snow processes in simulation models, especially under future climate scenarios that are outside of the range of historically observed variability.

54 ENVIRONMENTAL SCIENCES↗

Investigation of Isobaric Mixing as a Mechanism for Boundary‐Layer Cloud Formation

This study investigates the potential role of isobaric mixing in the formation of marine boundary layer clouds. Cloud formation theory emphasizes uplift and adiabatic cooling, but recent observations support the existence of small clouds forming at various altitudes, even below the lifting condensation level. Isobaric mixing of air with different thermodynamic properties can generate localized supersaturation. A Gaussian mixing model is employed to simulate this process, considering the correlation between temperature and water vapor. Cloud droplet size distributions from aircraft measurements show a persistent and prominent mode of small droplets at 9 m, and the size of this mode compares favorably with predictions from the model. The results suggests that isobaric mixing plausibly contributes to the formation of clouds, particularly those observed at multiple altitudes with narrow droplet size distributions. This finding highlights the importance of considering isobaric mixing processes in understanding and modeling cloud formation.

54 ENVIRONMENTAL SCIENCES↗

Valorization of consolidated bioprocessing residues for bioplastics

This study demonstrates an organic solvent-free processing strategy to valorize consolidated bioprocessing (CBP) residues, from switchgrass and poplar biomass, into functional poly(butylene succinate) (PBS)-based biocomposites using high-shear homogenization (HSH). HSH transformed the switchgrass and poplar CBP residues (CBP-R) into fine, uniformly distributed particles and microfibers. The composites of PBS with homogenized switchgrass residues (H-CBP-R-SG) or homogenized poplar residues (H-CBP-R-P) at a 70/30 weight ratio exhibited improved processability and mechanical integrity, with the Young's modulus for the PBS/H-CBP-R-SG and PBS/H-CBP-R-P nearly doubling to 0.66 ± 0.07 GPa and 0.65 ± 0.04 GPa, respectively, compared to neat PBS (0.36 ± 0.02 GPa). Dynamic Mechanical Analysis (DMA) reveals a significant suppression of the tan δ peak magnitude, indicating that HSH-mediated physical activation facilitates stress transfer in composites typical of covalent chemical grafting systems. While the transition to a stiffness-dominated profile reduces ductility, the resulting composites exhibit the dimensional stability and resistance to thermal warping required for high-fidelity FDM 3D printing and injection molding. Beyond material performance, comprehensive techno-economic analysis (TEA) and life cycle assessment (LCA) confirmed that diverting CBP residues into composite products can improve the economic viability of the biorefinery without substantially increasing biorefinery global warming potential (GWP). At a 30 wt% blend ratio, incorporating residuals into PBS yielded a minimum selling price for the composite of $\$4.07$ per kg compared to the conventional bioplastic price of $\$5.00$ per kg. This approach aligns with circular bioeconomy principles by converting waste streams into value-added products. Furthermore, this innovative strategy addresses key challenges in bioplastic development, including cost, compatibility, and performance, while simultaneously advancing waste minimization strategies for sustainable manufacturing systems.

09 BIOMASS FUELS↗

Reaching new peaks for the future of the CMS HTCondor Global Pool

The CMS experiment at CERN employs a distributed computing infrastructure to satisfy its data processing and simulation needs. The CMS Submission Infrastructure team manages a dynamic HTCondor pool, aggregating mainly Grid clusters worldwide, but also HPC, Cloud and opportunistic resources. This CMS Global Pool, which currently involves over 70 computing sites worldwide and peaks at 350k CPU cores, is employed to successfully manage the simultaneous execution of up to 150k tasks. While the present infrastructure is sufficient to harness the current computing power scales, CMS latest estimates predict a noticeable expansion in the amount of CPU that will be required in order to cope with the massive data increase of the High-Luminosity LHC (HL-LHC) era, planned to start in 2027. This contribution presents the latest results of the CMS Submission Infrastructure team in exploring and expanding the scalability reach of our Global Pool, in order to preventively detect and overcome any barriers in relation to the HL-LHC goals, while maintaining high effciency in our workload scheduling and resource utilization.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Using the ATLAS experiment software on heterogeneous resources

With the large dataset expected from 2030 onwards by the HL-LHC at CERN, the ATLAS experiment is reaching the limits of the current data processing model in terms of traditional CPU resources based on x86_64 architectures and an extensive program for software upgrades towards the HL-LHC has been set up. The ARM CPU architecture is becoming a competitive and energy efficient alternative. Accelerators like GPUs are available in any recent HPC. In the past years ATLAS has successfully ported its full data processing and simulation software framework Athena to ARM and has invested significant effort in porting parts of the reconstruction and simulation algorithms to GPUs. We report on the successful usage of the ATLAS experiment offline and online software framework Athena on ARM and GPUs through the PanDA workflow management system at various WLCG sites. Furthermore we report on performance optimizations of the builds for ARM CPUs and the GPU integration efforts. We will discuss performance comparisons of different ARM and x86_64 architectures on WLCG resources and Cloud compute providers like GCP and AWS using ATLAS productions workflows as used in the Hep-Score23 benchmark suite.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Imaging conical intersection dynamics during azobenzene photoisomerization by ultrafast X-ray diffraction

Significance Vibronic coherences are unique features that emerge in the decisive moments of photochemistry and photophysics. Here we demonstrate how X-ray diffraction can access fundamental information about these coherences. This is enabled by recent developments at free-electron lasers, working toward temporal resolution and single-molecule accessibility of scattering-based measurements. Time-resolved diffraction patterns of the isomerization of azobenzene are simulated. The process is monitored in time via the momentum-space signal, with a special focus on the mixed elastic/inelastic scattering from coherences. We give practical ideas on how this relatively weak contribution to the signal can potentially be retrieved. A direct connection to the real-space movie of the conical intersection dynamics is made, enabling the observation of quantum coherences that direct chemical processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Organic Vacuum Pump Fluids for the Vacuum Pumping of Fusion Power Plants

Vacuum pumps are the heart of the fusion fuel cycle, but most currently proposed pumping technologies are not capable of handling the required flow rates and vacuum pressures. Oil-containing vacuum pumps can readily meet the flow requirements, but vacuum fluids will degrade in fusion-relevant environments due to contact with tritium and exposure to high-energy radiation. Here, in this study, we describe a methodology to screen vacuum fluid candidates and purify these candidate fluids post oxidation, post exposure to deuterium, and post exposure to gamma radiation (<7.5 MGy) to simulate a process in which vacuum fluids can be recovered and regenerated during the fusion fuel cycle. A series of oils, including a highly purified mineral oil, phenyl silicone oil, and a polyphenyl ether, are shown to be suitable candidates for vacuum pumping. Additionally, we describe a simple purification methodology to remove oxidized functionalities and the associated isotopologues induced by contact with deuterium from the candidate vacuum fluids This purification methodology can also be applied to radiological damage with moderate effect. Finally, we demonstrate that the sorbents can be regenerated through electromagnetic microwave digestion.

oxidation↗

Modeling methane dynamics in three wetlands in Northeastern China by using the CLM-Microbe model

Wetlands account for up to 70% of the natural source of methane (CH 4 ) in terrestrial ecosystems on a global scale. Soil microbes are the ultimate producers and biological consumers of CH 4 in wetlands. Therefore, simulating microbial mechanisms of CH 4 production and consumptionwould improve the predictability of CH 4 flux in wetland ecosystems. In this study, we applied a microbial-explicit model, the CLM-Microbe, to simulate CH 4 flux in three major natural wetlands in northeastern China. The CLM-Microbe model was able to capture the seasonal variation of gross primary productivity (GPP), dissolved organic carbon (DOC), and CH 4 flux. The CLM-Microbe model explained more than 40% of the variation in GPP and CH 4 flux across sites. Marsh wetlands had higher CH 4 flux than mountain peatlands. Ebullition dominated the CH 4 transport pathway in all three wetlands. The methanogenesis dominates while methanotroph makes a minor contribution to the CH 4 flux, making all wetlands a CH 4 source. Sensitivity analysis indicated that microbial growth and death rates are the key factors governing CH 4 emission and vegetation physiological properties (flnr) and maintenance respiration predominate GPP variation. Explicitly simulating microbial processes allows genomic information to be incorporated, laying a foundation for better predicting CH 4 dynamics under the changing environment.

54 ENVIRONMENTAL SCIENCES↗

Quantum simulation of bosons with the contracted quantum eigensolver

Abstract Quantum computers are promising tools for simulating many-body quantum systems due to their potential scaling advantage over classical computers. While significant effort has been expended on many-fermion systems, here we simulate a model entangled many-boson system with the contracted quantum eigensolver (CQE). We generalize the CQE to many-boson systems by encoding the bosonic wavefunction on qubits. The CQE provides a compact ansatz for the bosonic wave function whose gradient is proportional to the residual of a contracted Schrödinger equation. We apply the CQE to a bosonic system, where N quantum harmonic oscillators are coupled through a pairwise quadratic repulsion. The model is relevant to the study of coupled vibrations in molecular systems on quantum devices. Results demonstrate the potential efficiency of the CQE in simulating bosonic processes such as molecular vibrations with good accuracy and convergence even in the presence of noise.

Physics↗

Characterization of W production during ICRF operations: experiments and modeling

For successfully heating plasma with waves in the ion cyclotron range of frequencies (ICRFs), mitigating impurity production is just as crucial as maximizing power coupling, especially in high-Z environments. ICRF can effectively deposit energy on ions, modify turbulence-driven transport, and enhance fusion reaction efficiency, but only when its power coupling has minimal impact on impurity production. To do so, one must rely on a toroidal array of at least three active elements excited with appropriate phasing and power ratio to reduce the currents induced on the antenna frame below levels critical for physical sputtering. In contrast to classic two-strap antennas, which are optimized for dipole phasing with equal power on both straps, three-strap antennas in ASDEX Upgrade (AUG)—but also four-strap antennas in JET, Alcator C-Mod, SPARC and ITER—offer the possibility to act also on the power ratio between the central and outer straps. With optimal settings, impurity production can be reduced substantially, making the ICRF compatible with the high-Z wall. This paper explores the characteristics of the AUG three-strap antennas in terms of impurity production, as well as the key role of plasma composition in this process. Numerical simulations were performed using SSWICH and Petra-M (finite element codes) to quantify impurity production and compare with experimental results. Energies of ions falling on antenna limiters (measured with probes) are well predicted by both codes. These tools are then used to further describe the source of the impurity, namely the gross erosion of tungsten from an ICRF antenna, for different plasma mixtures. Results are also compared to spectroscopy data. Ultimately, we show that deleterious effects of the ICRF on plasma surface interactions will be weaker in plasmas containing larger fractions of highly ionized heavier low-Z impurity, which is typically relevant for experiments relying on impurity seeding.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Changes in soil water content and lateral flow exert large effects on soil thermal dynamics across Alaskan landscapes

Both lateral surface and subsurface water flow affect soil moisture dynamics, yet most land surface models only solve subsurface water movement vertically. Here, we use a 3D ecosystem model that considers both land surface and subsurface hydrologic processes to simulate soil moisture, which is then used to drive a 1-D vertical soil thermal model to simulate the soil moisture effects on soil thermal dynamics in central Alaska. Our coupled model improves soil temperature (ST) estimates by 43.5% in comparison with observational data. Soil moisture has little effect on ST during the wet season (-1.5%) and a substantial influence during the dry season (60%). Spatially, water lateral flow has significant impacts on both soil moisture and ST, causing model estimates for thawed areas in the transition season to increase by ~10% in the study area. Our results highlight the importance of considering dynamical soil moisture, as well as lateral flow effects, on soil thermal dynamics in permafrost regions.

54 ENVIRONMENTAL SCIENCES↗

Wasserstein normalized autoencoder for anomaly detection

A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution—a Boltzmann distribution where the energy is the reconstruction error of the autoencoder (AE)—and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets—conical sprays of visible standard model (SM) particles and invisible dark matter states—with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated SM processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model exhibits stable, convergent training and recovers strong classification performance for a wide range of signals against the selected background process, for which a standard AE fails because of outlier reconstruction. In addition, the model improves upon standard normalized autoencoders while remaining fully agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗

The impact of spectroscopic incompleteness in direct calibration of redshift distributions for weak lensing surveys

ABSTRACT Obtaining accurate distributions of galaxy redshifts is a critical aspect of weak lensing cosmology experiments. One of the methods used to estimate and validate redshift distributions is to apply weights to a spectroscopic sample, so that their weighted photometry distribution matches the target sample. In this work, we estimate the selection bias in redshift that is introduced in this procedure. We do so by simulating the process of assembling a spectroscopic sample (including observer-assigned confidence flags) and highlight the impacts of spectroscopic target selection and redshift failures. We use the first year (Y1) weak lensing analysis in Dark Energy Survey (DES) as an example data set but the implications generalize to all similar weak lensing surveys. We find that using colour cuts that are not available to the weak lensing galaxies can introduce biases of up to Δz ∼ 0.04 in the weighted mean redshift of different redshift intervals (Δz ∼ 0.015 in the case most relevant to DES). To assess the impact of incompleteness in spectroscopic samples, we select only objects with high observer-defined confidence flags and compare the weighted mean redshift with the true mean. We find that the mean redshift of the DES Y1 weak lensing sample is typically biased at the Δz = 0.005−0.05 level after the weighting is applied. The bias we uncover can have either sign, depending on the samples and redshift interval considered. For the highest redshift bin, the bias is larger than the uncertainties in the other DES Y1 redshift calibration methods, justifying the decision of not using this method for the redshift estimations. We discuss several methods to mitigate this bias.

79 ASTRONOMY AND ASTROPHYSICS↗

Changes in polarization dictate necessary approximations for modeling electronic deexcitation intensity: Application to x-ray emission

Accurate simulation of electronic excitations and deexcitations are critical for complementing complex spectroscopic experiments and can provide validation to theoretical approaches. Here, using a generalized framework, we contrast the accuracy and validity of orbital-constrained and linear-response approaches that build upon Kohn-Sham density functional theory (DFT) to simulate emission spectra of electronic origin and propose an efficient approximation, named many-body x-ray emission spectroscopy or MBXES, for simulating such processes. We show analytically as well as with computed examples that for electronic (de)excitation leading to an appreciable change in polarization (i.e., density rearrangement), the adiabatic approximation in a response-based formalism will be inadequate for the calculation of oscillator strength. Thus, such a change (e.g., in the net electrostatic dipole moment of a finite system) can be used as a metric for evaluating the applicability of the adiabatic response-based approach and can be particularly valuable in x-ray emission spectroscopy. On the other hand, MBXES, the flexible method introduced in this paper, can compute oscillator strengths accurately at a much lower computational expense on the basis of two DFT-based self-consistent field calculations. Using illustrative examples of emission spectra, the efficacy of the MBXES method is demonstrated by comparison with its parent theory, orbital-optimized DFT, and with experiments.

74 ATOMIC AND MOLECULAR PHYSICS↗