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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Laser-driven flash x-ray radiography of a shocked metallic foil

Characterizing hydrodynamic instability evolution in millimeter-scale, high-Z foils is crucial for understanding complex phenomena in high-energy-density physics. Here, we demonstrate a proof-of-concept, laser-driven flash x-ray radiography platform tailored for two-dimensional linear density mapping in shocked high-Z foils. Using chromium (Cr) foils with internal shockwaves (∼100 μm width), our platform achieves a spatial resolution of 59.8 ± 1.4 μm by employing a broadband x-ray source extending into the hundreds of keV range. The setup combines a compound parabolic concentrator cone with a tantalum wire target, a magnetic field to deflect residual transmitted electrons, and a copper casing to shield the sides and rear of the image plate pack. By varying the delay of the short-pulse beam driving the flash x-ray source, we resolve shockwave dynamics, specifically the velocity, position, width, and density profile, within the Cr foil. Reported experimental results are consistent with the corresponding hydrodynamics and radiation transport simulations, which accurately reproduce the measured electron and x-ray source terms. These developments enable the conversion of shockwave radiographs into two-dimensional density maps, enhancing interpretability for hydrodynamic instability evolution applications and validating the simulation approach.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructurally informed synchrotron x-ray analysis revealing helium defect transitions in ultrafine grained tungsten

The formation of insoluble gaseous defects in materials due to nuclear transmutation or ion implantation involves the diffusion of impurity atoms to form atomic defect clusters that coalesce into bubbles or cavities and ultimately degrade the material properties. Transmission electron microscopy (TEM) is limited in its ability to resolve sub-nanometer gas clusters whereas X-ray diffraction (XRD) provides information pertaining to local atomic changes. Here, in this study, helium (He) implanted ultrafine grained tungsten is explored through a multimodal defect characterization campaign combining TEM-informed Small Angle X-ray Scattering (SAXS) analysis, XRD lattice parameter measurements, and nanoscale He cluster quantification from a region of reciprocal space accessible via Wide Angle X-ray Scattering (WAXS). Moderate elevated temperature implantations are shown to produce high concentrations of sub-nanoscale He clusters and small, homogeneously distributed cavities, which collectively are linked to lattice expansion and further substantiated through complementary atomistic simulations. Increased implantation temperatures encourage the diffusion of these defects to the grain boundaries (GBs), leading to lattice relaxation and the growth of large GB cavities manifesting as bimodal size distributions in the SAXS analysis. Overall, our results demonstrate the utility of multimodal synchrotron X-ray analysis in bridging the gap between microscale He cavity quantification and atomic-scale defect analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fluoride-Cooled High-Temperature Pebble-Bed Reactor Reference Plant Model Updates

This work presents the latest improvements to, and investigations performed with, the Fluoride-Cooled High-Temperature Pebble-Bed Reactor reference plant models for the United States Nuclear Regulatory Commission. These models, developed with the Comprehensive Reactor Analysis Bundle, or BlueCRAB, serve as the foundation for the future development of detailed design evaluation models based on license applications. BlueCRAB is the code suite proposed for non-light-water reactor systems safety analyses, and it incorporates various simulation tools developed by the Nuclear Energy Advance Modeling and Simulation program, including the Griffin code for reactor physics, the Pronghorn and SAM codes for core thermal fluids, the BISON code for solid conduction and fuel performance, and the SAM code for system analysis. The primary objective of this work is to assess the level of readiness of BlueCRAB for modeling fluoride-cooled high-temperature pebble-bed reactors. We first developed numerical models in BlueCRAB that include the key physics for this technology, ensuring an adequate level of fidelity for modeling the core performance during accident scenarios. This was followed by simulation of transient scenarios, two loss-of-forced-cooling events (one protected and one unprotected), and two control rod withdrawal events (one delayed and one prompt supercritical reactivity insertion). The analysis includes comparisons between the 2-D thermal fluid porous media models in Pronghorn and SAM, comparisons between coupled Pronghorn-Griffin and coupled SAM-Griffin models for two loss-of-forced cooling events and one control rod withdrawal event, and comparisons between SAM single-solve and domain-overlapping approaches for multi-scale thermal fluid coupling. In addition, we performed comparisons between 3-D, 2-D, and 0-D neutronic models for the two control rod withdrawal scenarios with Pronghorn-Griffin. The results show that the BlueCRAB models led to physically intuitive solutions for the scenarios examined. The changes in the various scalar and vector fields such as the neutron flux, power, temperatures, densities, pressures, and velocities are all within the expected ranges, and their distributions can be explained from the system response of the transients and the geometric and material variations. Several comparisons suggest that the porous media models in Pronghorn and SAM can lead to similar solutions, even though they are based on different methodologies. The simulations demonstrate that there are differences between the various levels of fidelity, and it is advisable to have flexible tools that can cover the breadth and depth of needs that may arise in future technical evaluations. We believe that BlueCRAB’s capabilities represent a significant asset for confirmatory analyses aimed at resolving important safety questions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Resolving turbulent magnetohydrodynamics: a hybrid operator-diffusion framework

We present a hybrid machine learning framework that combines physics-informed neural operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temporal evolution of two-dimensional, incompressible, resistive magnetohydrodynamic turbulence across a broad range of Reynolds numbers (Re). The framework leverages the equation-constrained generalization capabilities of PINOs to predict coherent, low-frequency dynamics, while a conditional diffusion model stochastically corrects high-frequency residuals, enabling accurate modeling of fully developed turbulence. Trained on a comprehensive ensemble of high-fidelity simulations with Re ϵ {100, 250, 500, 750, 1000, 3000, 10000}, the approach achieves state-of-the-art accuracy in regimes previously inaccessible to deterministic surrogates. At Re = 1000 and 3000, the model faithfully reconstructs the full spectral energy distributions of both velocity and magnetic fields late into the simulation, capturing non-Gaussian statistics, intermittent structures, and cross-field correlations with high fidelity. At extreme turbulence levels (Re = 10 000), it remains the first surrogate capable of recovering the high-wavenumber evolution of the magnetic field, preserving large-scale morphology and enabling statistically meaningful predictions.

Diffusion-Integrated Neural Operators↗
DOE OSTI2024

The need for carbon-emissions-driven climate projections in CMIP7

Abstract. Previous phases of the Coupled Model Intercomparison Project (CMIP) have primarily focused on simulations driven by atmospheric concentrations of greenhouse gases (GHGs), for both idealized model experiments and climate projections of different emissions scenarios. We argue that although this approach was practical to allow parallel development of Earth system model simulations and detailed socioeconomic futures, carbon cycle uncertainty as represented by diverse, process-resolving Earth system models (ESMs) is not manifested in the scenario outcomes, thus omitting a dominant source of uncertainty in meeting the Paris Agreement. Mitigation policy is defined in terms of human activity (including emissions), with strategies varying in their timing of net-zero emissions, the balance of mitigation effort between short-lived and long-lived climate forcers, their reliance on land use strategy, and the extent and timing of carbon removals. To explore the response to these drivers, ESMs need to explicitly represent complete cycles of major GHGs, including natural processes and anthropogenic influences. Carbon removal and sequestration strategies, which rely on proposed human management of natural systems, are currently calculated in integrated assessment models (IAMs) during scenario development with only the net carbon emissions passed to the ESM. However, proper accounting of the coupled system impacts of and feedback on such interventions requires explicit process representation in ESMs to build self-consistent physical representations of their potential effectiveness and risks under climate change. We propose that CMIP7 efforts prioritize simulations driven by CO2 emissions from fossil fuel use and projected deployment of carbon dioxide removal technologies, as well as land use and management, using the process resolution allowed by state-of-the-art ESMs to resolve carbon–climate feedbacks. Post-CMIP7 ambitions should aim to incorporate modeling of non-CO2 GHGs (in particular, sources and sinks of methane and nitrous oxide) and process-based representation of carbon removal options. These developments will allow three primary benefits: (1) resources to be allocated to policy-relevant climate projections and better real-time information related to the detectability and verification of emissions reductions and their relationship to expected near-term climate impacts, (2) scenario modeling of the range of possible future climate states including Earth system processes and feedbacks that are increasingly well-represented in ESMs, and (3) optimal utilization of the strengths of ESMs in the wider context of climate modeling infrastructure (which includes simple climate models, machine learning approaches and kilometer-scale climate models).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Differences in cluster and internal wake effects from mesoscale and large-eddy simulations off the US East Coast

Mesoscale simulations are increasingly used to estimate wake effects within and between large wind farms, despite limited validation for large-scale wake effects. This study evaluates the capabilities and limitations of mesoscale simulations in capturing wake-induced impacts on wind turbine power production through a direct comparison with large-domain large-eddy simulations (LESs) for three planned offshore wind farms under realistic atmospheric conditions and a range of atmospheric stabilities. We assess mesoscale performance in replicating wake characteristics behind single and multiple turbine clusters and quantify the resulting variability in mean turbine power. Results show that mesoscale Weather Research and Forecasting simulations with the Fitch wind farm parameterization capture key features of the velocity deficit downstream of both single and multiple wind farms, with mean root-mean-square errors near 5 % and good agreement with stability-driven wake behavior. However, in these simulations, the mesoscale Fitch parameterization underestimates power losses from internal wake effects, particularly when turbines align with the prevailing wind direction or under stable stratification. In these conditions, individual wakes persist and dominate downstream power deficits. The coarse resolution of the mesoscale simulations limits their ability to resolve individual wind turbine wakes that drive power fluctuations within wind farms. Nonetheless, mesoscale simulations can yield accurate estimates of combined wake losses from internal and cluster effects across some wind direction sectors, where errors in wake representation may cancel each other out. These findings underscore the strengths of mesoscale simulations for capturing broader wake patterns while highlighting their limitations for modeling turbine-level power losses. Future work should explore hybrid modeling approaches to capture both long-range cluster wake propagation and localized internal wake dynamics.

17 WIND ENERGY↗
DOE OSTI2026

Cross-slip and easy-glide CRSS in titanium: Theoretical predictions and in-situ TEM measurements

This study investigates the mechanics of prismatic and first-order pyramidal $\langle$a$\rangle$ slip in titanium (Ti), elucidating the physics of easy-glide and cross-slip through a combination of theory and experiments. Screw-character prismatic (Pr) dislocations in Ti are of particular interest because their complex cores can be stable or unstable, leading to activation by either cross-slip or planar glide. To investigate these mechanisms, site-specific micro tensile samples were prepared using focused ion beam (FIB) milling and mounted on a push-to-pull (PTP) device for in-situ transmission electron microscopy (TEM) tensile testing. The in-situ experiments provide direct observations of the onset of dislocation motion and the precise determination of the critical resolved shear stress (CRSS) for the activated mechanisms, and its evolution with load cycling. A comprehensive theory has been developed to predict the CRSS values for easy glide, cross-slip, and multiplication of dislocations. Predicted critical stresses for pyramidal (π)-to-Pr and reverse cross-slip agree closely with the experimental measurements. The latter cross-slip stress is a factor of two higher than that of unobstructed planar slip. The model accounts for overlapping dislocation cores and employs a Wigner-Seitz based cell to evaluate misfit energies. By combining ab initio density functional theory (DFT) with anisotropic elasticity, the framework identifies minimum energy pathways for dislocation glide, which can be intermittent and zig-zag. A simplified expression utilizing (π) and Pr Schmid factor ratios is proposed for critical stress corresponding to (π)-to-Pr cross-slip transition. The results are strongly dependent on crystal orientation, underscoring non-Schmid behavior. Overall, this study explores key critical stress parameters essential for informing higher-scale simulations of plasticity in Ti.

Critical stress↗
DOE OSTI2025

Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential

Zn Cl 2 solutions are promising electrolytes for aqueous zinc-ion batteries. Here, we report a joint computational and experimental study of the structural and dynamic properties of aqueous Zn Cl 2 electrolytes with concentrations ranging from salt-in-water to water-in-salt (WIS). By developing a neural network potential (NNP) model, we perform molecular dynamics (MD) simulations with accuracy but at much larger lengths and longer timescales. The NNP predicted structures are validated by the structure factors measured by X-ray total scattering experiments. The MD trajectories provide a comprehensive and quantitative picture of the Zn 2 + solvation shell structures. Additionally, we find that the O − H covalent bonds in water are strengthened with increasing salt concentration, thus expanding the electrochemical stability window of aqueous electrolytes. In terms of dynamic properties, the calculated and experimentally measured conductivities are in good agreement. Through the analysis of the calculated cation transference number, we propose a three-stage charge carrier transport mechanism with increasing concentration: independent ion transport, strongly correlated ion transport, and small positive charge carrier diffusion through negatively charged polymeric clusters. Our study provides fundamental atomic scale insights into the structure and transport properties of the Zn Cl 2 electrolyte that can aid the optimization and development of WIS electrolytes. Published by the American Physical Society 2025

25 ENERGY STORAGE↗
DOE OSTI2026

Fully generalized, turbulent trace impurity transport with Gkeyll and Flan in the DIII-D far-SOL

The Monte Carlo trace impurity turbulent transport code Flan is introduced for the first time. Flan follows impurities in a turbulent background plasma from Gkeyll using the Lorentz force to resolve the full particle gyro orbit. Collisions are handled using the Nanbu collision algorithm (Nanbu 1997 Phys. Rev. E 55 4642–52), and ionization/recombination is handled via ADAS coupling. The far-SOL of a generic DIII-D L-mode is simulated with and without the collision model to show how collisions affect radial tungsten transport. Anomalous diffusion coefficient (D r ) and pinch velocity (v p ) profiles are extracted from fits to the results. With collisions, D r and v p are between 0–1.0 m 2 s −1 and −100–100 m s −1 , respectively. Without collisions, D r and v p are between 0–0.3 m 2 s −1 and −50–50 m s −1 , respectively. Exponential fits to the radial W density profiles and experimental data from W deposition along a collector probe are in good agreement, demonstrating Flan as a useful interpretive modeling tool. Additional simulations show that impurity transport away from the wall increases with atomic number, though it is not clear why. Flan has the potential to better interpret existing data and improve reactor scale predictions of core contamination because the underlying physics model is very general and does not rely on arbitrary user-defined transport coefficients.

DIII-D↗
DOE OSTI2026

Wetted-foam target stability for laser-direct-drive inertial confinement fusion

Using highly resolved three-dimensional radiation-hydrodynamic simulations, the effects of wetted-foam (WF) ablators in direct-drive inertial confinement fusion cryogenic implosions on the OMEGA laser were investigated. The performance of these implosions was compared with that of implosions using targets with solid-plastic ablators. Simulations considering WF ablators as a homogeneous material show no mitigation of the effects of laser imprint, where the absence of this mitigation can be attributed to the use of picket pulses. Heterogeneous random-structure WF ablators introduce large short-wavelength perturbations, which are seeded by WF inhomogeneities. These perturbations exceed the perturbations seeded by the imprint and cause an additional reduction in implosion performance when the average pore size in the WF exceeds ~0.2 μm. Foam-seeded perturbations are expected to be less damaging in more stable high-adiabat (⁠ ≳ 5⁠) implosions. Here, the perturbations introduced by the WF ablators can be substantially reduced by using a foam matrix with a layered structure instead of a random one. Further study of the effects of WFs in ignition designs of the MJ laser energy scale is required in the perspective of future inertial fusion energy mass production.

Igumenshchev, I. V. [University of Rochester, NY (↗
DOE OSTI2025

Forecasting Dark Matter Subhalo Constraints from Stellar Streams using Implicit Likelihood Inference

The evidence for dark matter (DM) remains compelling, although attempts to understand its particle nature remain inconclusive. One promising method to study DM is detecting DM subhalos through their gravitational interactions with stellar streams. In this study, we apply Neural Posterior Estimation (NPE) to constrain subhalo interaction parameters, including mass, scale radius, velocity, and encounter geometry, from stellar stream kinematics. We generate particle spray simulations based on the Lagrange Cloud stripping technique, focusing on the ATLAS-Aliqa Uma stream as a test case. We train multiple NPE models across multiple observational scenarios, quantifying how kinematic completeness affects inference and forecasting constraints from upcoming surveys including LSST, 4MOST, and 10-year Gaia data. Our results demonstrate that NPE can produce accurate and well-calibrated posteriors. In the idealized case with full 6D coordinates, we achieve subhalo mass uncertainties of 15-20% for a $10^7 \, \mathrm{M_\odot}$ subhalo, with 5D coordinates (excluding radial velocities) achieving similar performance. Under realistic observational conditions, mass uncertainties range from 50% (present-day) to 20-40% (future scenarios), with comparable performance between the photometric-only LSST sample and a smaller sample that includes Gaia proper motions and 4MOST radial velocities. Most notably, we find that velocity bimodality emerges when phase space is poorly sampled, whether due to missing kinematic information or limited stellar tracers. Combining large photometric samples with targeted spectroscopic follow-up can effectively resolves this degeneracy. These results demonstrate the power of implicit likelihood inference for optimizing stellar stream observational strategies and forecasting DM subhalo constraints from upcoming surveys.

Nguyen, Tri [Northwestern U. (main); SkAI, Chicago↗
DOE OSTI2025

Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

17 WIND ENERGY↗
DOE OSTI2025

Unraveling Hydrogen Induced Geochemical Reaction Mechanisms through Coupled Geochemical Modeling and Machine Learning

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H 2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H 2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. Furthermore, this study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone.

08 HYDROGEN↗
DOE OSTI2024

Electronic 1/f noise as a probe of dimensional effects on spin-glass dynamics

Over the past decade, spin-glass simulations have improved to the point that they now access time- and length-scales comparable to experiments at the mesoscale. A recent series of thin-film field-cooled/zero-field-cooled magnetization (FC/ZFC) experiments demonstrated activated spin dynamics, with a temperature-independent activation energy proportional to the logarithm of the film thickness and with coefficients in remarkable agreement with the simulation. These measurements require the application of small magnetic fields, which has been shown to affect the spin-glass energy landscape. Measurements of the 1/f noise in metallic spin-glasses have been previously shown to be a sensitive probe of the spin dynamics, and the measurements can be made without applying a magnetic field. In this mini-review, we review these techniques and discuss how transport measurements can fit into the current landscape of spin-glass measurements. We compare previous measurements to more recent measurements on similar films, made with ostensibly different cooling protocols, and compare both the previous and recent measurements to the magnetometry. The transport measurements—taken over a wider range of temperature than magnetometry—suggest that the maximum spin-glass energy barrier height is temperature-dependent, not fixed, possibly due to two-dimensional dynamics. We discuss this possibility, along with future measurements, which may be able to resolve this mystery.

1/f noise↗
DOE OSTI2025

Q2 Report for FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

This report describes the work and activities carried out towards the completion of each of the following milestones in FY25 Q2: 1. Demonstrate workflow for generating self-consistent CESOL plasma profiles + first wall and divertor loading prediction and generate the CAT plasma and neutron loading needed for further engineering analysis: $\circ$ Run CESOL with BOUT++/Hermes-3 and immersed boundary condition to directly map to wall: • Run BOUT++/Hermes-3 through the IPS workflow to find radial particle and energy diffusivities to match either the Eich or the physics-based scaling of the SOL heat flux width, and • Expand source of first wall heat flux to include charged particles, neutrals, and radiation from the core+edge. 2. Generate medium fidelity parametrized CAD: $\circ$ Develop the TRACER tool to read an existing CAD, regenerate the geometry based on vertex location and connectivity information, define vertex translation and parameters needed for scaling the CAD, and $\circ$ Utilize the FreeGS code to determine CAT PF coil placement, including minimizing the number of coils, coil current, and electromechanical stresses. 3. Utilize plasma loading for engineering analysis: $\circ$ Couple the plasma loading to input for OpenFOAM and demonstrate initial test of thermal analysis of CAT first wall loading with typical DCLL blanket component cooling boundary conditions. 4. Demonstrate nuclear analysis: $\circ$ Apply initial analysis of tritium transport in DCLL blanket by evaluating spatially resolved tritium generation rates, tritium diffusion and convection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Mapping the Perseus galaxy cluster with XRISM: Gas kinematic features and their implications for turbulence

We present extended gas kinematic maps of the Perseus cluster based on a combination of five new XRISM/Resolve pointings observed in 2025 with four performance verification datasets from 2024, totaling a net exposure of 745 ks. To date, Perseus remains the only cluster that has been extensively mapped out to ≃0.7 r 2500 by XRISM/Resolve, while simultaneously offering sufficient spatial resolution to resolve gaseous substructures driven by mergers and active galactic nucleus (AGN) feedback. Our observations cover multiple radial directions and a broad range of dynamical scales, enabling us to characterize the kinematic properties of the intracluster medium up to a scale of ∼500 kpc. In the measurements, we detected high-velocity dispersions (≃300km s −1 ) in the eastern region of the cluster that are spatially coincident with the extended X-ray surface brightness excess and correspond to a nonthermal pressure fraction of ≃7 − 13%. The velocity field outside the AGN-dominant region can be effectively described by a single, large-scale kinematic driver based on the velocity structure function, which statistically favors an energy injection scale of at least a few hundred kpc. The estimated turbulent dissipation energy is comparable to the gravitational potential energy released by a recent merger, implying a significant role of turbulent cascade in the merger energy conversion. In the bulk velocity field, we observed a dipole-like pattern along the east-west direction with an amplitude of ≃ ± 200 − 300 km s −1 , indicating rotational motions induced by the recent merger event. This feature constrains the viewing direction to ≃30° −50° relative to the normal of the merger plane. Our hydrodynamic simulations suggest that Perseus has experienced at least two energetic mergers since redshift z ∼ 1, the most recent of which is associated with the radio galaxy IC310, in agreement with recent SRG/eROSITA findings. This study showcases exciting scientific opportunities for future missions with high-resolution spectroscopic capabilities (e.g., HUBS, LEM, and NewAthena).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Responses of summer mesoscale convective systems to irrigation over the North China Plain based on convection-permitting model simulations

Extensive irrigation activities in the North China Plain (NCP) significantly influence regional weather and climate. However, previous studies focusing on the NCP were primarily based on coarse-resolution models, which are unable to explicitly resolve convection systems, causing large uncertainty in precipitation simulations. In this study, a convection-permitting model coupled with a dynamic irrigation scheme is utilized to investigate the impacts of irrigation on summertime mesoscale convective systems (MCSs) over the NCP. Sensitivity experiments with irrigation off and on are conducted for 5 summers and an MCS identification and tracking algorithm is applied to both satellite observations and model simulations. We find that incorporating irrigation in the model increases MCS precipitation, which agrees more with observations. The probability distributions of MCS lifetime, area, propagation speed, and intensity are all better simulated with irrigation. Irrigation increases the occurrence frequency of MCSs throughout the entire day. The nighttime increase is partly because of more frequent local initiation of MCS developed from isolated deep convection, while the daytime increase is mainly attributed to the changes in MCSs initiating elsewhere and then propagating to the NCP. On average, irrigation induces additional moisture that is more thermodynamically favorable for precipitation, but this effect is partially offset by the weakened ascending air motion primarily caused by irrigation surface cooling. Compared to weak MCS precipitation events, strong MCS precipitation events experience greater enhancement in precipitation intensity when including irrigation because the offset effect from the change in large-scale ascending air motion is insignificant. In addition, irrigation makes the variation of MCS precipitation intensity more correlated with the variation in ascending motion but less correlated with that in atmospheric moisture. Our results suggest the pronounced impacts of irrigation on MCSs over the NCP which should be included in numerical models to improve regional precipitation simulation and prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optomechanical self-organization in a mesoscopic atom array

Increasing the number of particles in a system often leads to qualitative changes in its properties, such as breaking of symmetries and the appearance of phase transitions. This renders a macroscopic system fundamentally different from its individual microscopic constituents. Lying between these extremes, mesoscopic systems exhibit microscopic fluctuations that influence behaviour on longer length scales, leading to critical phenomena and dynamics. Therefore, tracing the properties of well-controlled mesoscopic systems can help bridge the gap between an exact description of few-body microscopic systems and the emergent description of many-body systems. Here we explore the mesoscopic signatures of an optomechanical self-organization phase transition using arrays of cold atoms inside an optical cavity. By precisely engineering atom–cavity interactions, we reveal how critical behaviour depends on the atom number, identify characteristic dynamical behaviours in the self-organized regime and observe a finite optomechanical susceptibility at the critical point. These findings advance our understanding of particle-number- and time-resolved properties of phase transitions in mesoscopic systems.

Atomic and molecular interactions with photons↗