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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Trends in electronic structures and s ± -wave pairing for the rare-earth series in bilayer nickelate superconductor R 3 Ni 2 O 7

The recent discovery of pressure-induced superconductivity in the bilayer La 3 Ni 2 O 7 (LNO) has opened a new platform for the study of unconventional superconductors. In this publication, we investigate theoretically the whole family of bilayer 327-type nickelates R 3 Ni 2 O 7 (R = rare-earth elements) under pressure. From La to Lu, the lattice constants and volume decrease, leading to enhanced in-plane and out-of-plane hoppings, resulting in an effectively reduced electronic correlation U / W. Furthermore, the Ni's t 2g states shift away from the e g states, while the crystal-field splitting between d 3z 2 -r 2 and d x 2 -y 2 is almost unchanged. In addition, six candidates were found to become stable in the Fmmm phase, with increasing values of critical pressure as the atomic number increases. Similar to the case of LNO, the s ± -wave pairing tendency dominates in all candidates, due to the nesting between the M = (π , π) and the X = (π , 0) and Y = (0 , π) points in the Brillouin zone. Then, T c is expected to decrease as the radius of rare-earth (RE) ions decreases. In conclusion, our results suggest that LNO is already the “optimal” candidate, with Ce a close competitor, among the whole of the RE bilayer nickelates, and to increase T c we suggest growing on special substrates with larger in-plane lattice spacings.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗
DOE OSTI2023

A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance road safety. The success of CV-based signal control depends on an accurate and computationally efficient model that accounts for the stochastic and nonlinear nature of the traffic flow. Without the necessity of prior knowledge of the traffic system’s model architecture, reinforcement learning (RL) is a promising tool to acquire the control policy through observing the transition of the traffic states. In this paper, we propose a novel data-driven traffic signal control method that leverages the latest in deep learning and reinforcement learning techniques. By incorporating a compressed representation of the traffic states, the proposed method overcomes the limitations of the existing methods in defining the action space to include more practical and flexible signal phases. The simulation results demonstrate the convergence and robust performance of the proposed method against several existing benchmark methods in terms of average vehicle speeds, queue length, wait time, and traffic density.

42 ENGINEERING↗
DOE OSTI2024

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Phenomenology of electroweak portal dark showers: high energy direct probes and low energy complementarity

We investigate the phenomenology of a dark QCD sector interacting with the Standard Model (SM) via the electroweak (EW) portals. The portal interactions allow SM bosons, such as Z and h, or additional bosons that mix with them, to decay into dark quarks, producing dark showers. The light dark mesons are expected to be long-lived particles (LLPs), as their decays back to the SM states through the EW-portal interactions typically have macroscopic decay lengths. We focus on dark shower events initiated by various bosons at the Large Hadron Collider (LHC). The most prominent signal is the displaced decay of GeV-scale dark pions as LLPs. Current limits on dark shower signals at LHC detectors are recast from public data to provide simplified limits insensitive to UV physics details. Future limits in the high-luminosity phase and proposed auxiliary detectors are also projected. Additionally, we study the flavor-changing neutral current (FCNC) B decays into dark pions, obtaining both current and projected constraints at the LHC and other facilities. These constraints can be combined for specific models, which are illustrated in two EW-portal benchmarks: one with the heavy doublet fermion mediation and another with the Z′ mediator including a mass mixing. The collider reach shows significant potential to probe the parameter space unconstrained by EW precision tests, highlighting the necessity of dedicated LLP search strategies and facilities.

Dark Matter at Colliders↗
DOE OSTI2021

Mixed‐Phase Clouds Over the Southern Ocean as Observed From Satellite and Surface Based Lidar and Radar

Abstract This study investigates the occurrence of mixed‐phase clouds (MPC, i.e., cloud layers containing both liquid and ice water at sub‐freezing temperatures) over the Southern Ocean (SO) using space‐ and surface‐based lidar and radar observations. The occurrence of supercooled clouds is dominated by geometrically thin (<1 km) layers that rarely contain ice. We diagnose layers that are geometrically thicker than 1 km to contain ice ~65% and ~4% of the time from below by surface remote sensors and from above by orbiting remote sensors, respectively. We examine the discrepancy in MPC occurrence statistics as diagnosed from below and above the cloud layer. From above, we find that MPC occurrence has a gradient associated with the Antarctic Polar Front near 55°S with a rare occurrence of satellite‐derived MPC south of that latitude. In contrast, surface sensors find ice in 33% of supercooled liquid water layers. We infer using observing system simulation experiments and data analysis that space‐based lidar cannot identify the occurrence of MPC except when secondary ice‐forming processes operate in convection that is, sufficiently strong to loft ice crystals to cloud tops. We conclude that the CALIPSO phase statistics of MPC have a severe low bias in MPC occurrence. Based on surface‐based statistics in the SO, we present a parameterization of the frequency of MPC as a function of cloud top temperature that differs substantially from that used in recent climate model simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluation of CMIP6 GCMs Over the CONUS for Downscaling Studies

Despite the necessity of Global Climate Models (GCMs) sub-selection in downscaling studies, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we develop a weighted averaging technique to remove the redundancy in the evaluation criteria and rank 37 GCMs from the sixth phase of the Coupled Models Intercomparison Project over the contiguous United States. GCMs are rated based on their average performance across 66 evaluation measures in the historical period (1981–2014) after each metric is weighted between zero and one, depending on its uniqueness. The robustness of the outcome is tested by repeating the process with the empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. A disparity in GCMs' behavior related to their sensitivity to the size of the evaluation suite is observed, highlighting the need for comprehensive multi-variable GCMs evaluation at varying timescales for determining their skillfulness over a region. The sub-selection goal is to use a representative set of skillful models over the region of interest without substantial overlap in their future climate responses and modeling errors in representing historical climate. Additional analyses of GCMs' independence and spread in their future projections provide the necessary information to objectively select GCMs while keeping all aspects of necessity in view.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Discovering Elusive Dynamics Across Frontiers (Final Technical Report)

Profound puzzles, such as the nature of dark matter, the origins of the electroweak scale, the mechanism behind the small neutrino mass, and the strong CP problem, suggested new physics beyond the Standard Model and drove the particle physics program in search of the associated new particles. Despite extensive searches, conventional realizations of new physics have not yet provided conclusive evidence. This raised the serious possibility that new dynamics might be more elusive, perhaps due to a richer gauge and matter structure than previously considered. Notably, the existence of dark matter and advancements in understanding the naturalness problem urged exploration into sectors with complex gauge and matter structures. Through experiments like the LHC, DUNE, and small-scale experiments, the robust US HEP program played a critical role in pursuing these well-motivated but under-explored scenarios for new physics. I explored these physics opportunities in depth, focusing on novel searches and significant improvements in parameter space coverage. The elusive dynamics revealed rich information about the underlying theory and were essential in identifying observable opportunities. Understanding the observable consequences required a deep comprehension of the theory, which I also developed. The proposal included essential components aimed at coherently increasing our knowledge in well-motivated elusive dynamics models. Through research on high-quality axions, composite neutrinos, the Higgs boson as a portal to hidden strong dynamics, and new scalar potentials to generate alternative electroweak phase transitions, I focused on identifying new signatures and parameter regions in plausible elusive dynamics models. The exploration emphasized generic possibilities motivated by broad classes of elusive dynamics models. These signatures were not effectively probed previously due to various challenges such as triggering, background suppression, or experimental design. My research involved close interaction with experimental colleagues to overcome these difficulties, leveraging new theoretical and experimental possibilities. These efforts included identifying new observables such as timing information and substructure in calorimetries, new multiple-hit techniques, new scattering events, and new resonance searches in liquid argon detectors. This work created a positive feedback loop: theory and experimental work inspired each other, revealing new exciting opportunities that supported both programs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

A multiscale packed-bed reactor model for sustainable ethylene production via chemical looping oxidative coupling of methane

The rising global warming concerns and shale gas discovery have prompted research in the direction of greenhouse gas (GHG), such as methane, reduction and conversion. Oxidative coupling of methane (OCM) offers a pathway to low carbon-intense valorization of methane while producing ethylene, a chemical regarded as central to the petrochemical industry. Even after decades of OCM discovery, researchers keep understanding the process and underlying chemical reactions in a pursuit to achieve industrial viability for OCM. Here, in general, OCM suffers from low C 2 selectivity, yield and reactor temperature runaways due to highly exothermic nature of its reactions. Computational Fluid Dynamics (CFD) tools help analyze spatial gradients within the reactor to deeply understand the diffusion of species, mass and heat transfer phenomena. Furthermore, challenges associated with scaling up such as hot spot formation and parametric sensitivity can be addressed without having to expend on costly experiments. The current paper presents a multiscale packed-bed reactor CFD model coupled with a chemical kinetic model for the chemical looping OCM. The CFD model includes two scales i.e., macroscale for catalyst bed and microscale for individual pellets. Moreover, a chemical kinetic model based on 10 gas-phase reactions is integrated with the CFD model. An additional surface reaction for the formation of gas-phase oxygen from catalyst surface is added to account for the absence of feed oxygen. The model is calibrated against experimental results. The calibrated model captures trends in CH 4 conversion, C 2 selectivity and C 2 yield within a ± 4.35 % range across a temperature range of 700-900 °C. Moreover, model fidelity is evaluated by varying key computational parameters such as mesh resolution and time step size. The model is also verified by varying the inlet methane concentration and the gas hourly space velocity (GHSV) and comparing the results with literature. A sensitivity analysis and scale-up of the current model is undergoing.

Chemical looping↗
DOE OSTI2026

Optimizing fluvial flood mitigation strategies: A multi-objective approach for cost-effective and socially-aware infrastructure feasibility analysis

Effective levee planning must balance capital cost, risk reduction, and community priorities. These objectives are rarely optimized together. This study presents a feasibility phase, simulationin-the-loop framework that couples terrain-based flood modeling with a socially aware multiobjective optimizer. Flood risk is measured as Expected Annual Exposed Population (EAEP), obtained by integrating exposure over Annual Exceedance Probability (AEP) nodes, mirroring the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA) expected-annual formulation but with people rather than dollars. Exposure per scenario is computed by overlaying binary inundation masks with a population surface at the tract level. Distributional fairness is encoded through a Group Benefit Share (GBS) constraint that requires high-SVI tracts to receive at least a baseline share of annualized benefits. Capital cost is represented by a height-dependent unit-cost model suitable for screening. This study addresses the two-objective problem, minimize cost and expected annual exposure subject to the GBS constraint, using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and leveraging Pareto front for feasibility phase decision making. Implemented with terrain-based flood modeling, GeoFlood, for rapid scenario evaluation, the framework is demonstrated in Southeast Texas. The results reveal clear trade-offs among cost, risk, and social benefits and identify non-dominated levee height configurations that satisfy the benefit-share floor. The contributions are a scalable decision support method that operationalizes expected annual population-based risk, embeds enforceable benefit-sharing guarantees, and uses lightweight simulation to explore large design spaces before higher fidelity design stages.

Flood mitigation↗
DOE OSTI2021

Skyrmions Near Defects

We study the impact of an exchange-reducing defect on a skyrmion for a thin film of finite thickness. Attraction of the skyrmion to a defect is demonstrated in a lattice model by computing the micromagnetic energy accounting for the exchange, Dzyaloshinskii-Moriya interaction, magnetic anisotropy, and dipole-dipole coupling. The spiraling dynamics of the skyrmion towards the defect is illustrated by solving numerically the full Landau-Lifshitz-Gilbert equations on a lattice and, independently, the Thiele equation, with the two methods in agreement with each other. We find that the skyrmion deforms when approaching the defect. Here, deformation is small in the parameter space far from the phase boundary that determines stability of skyrmions. It increases dramatically near the phase boundary, leading to the transformation of the skyrmion by the defect into a snake-like magnetic domain.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Twinned nanostructure of VO 2 thin films grown on r-cut sapphire

Due to strong electron-phonon interactions, strain engineering is a powerful tool to control quantum critical phenomena in strongly correlated oxides. Exploring this possibility requires understanding the nanoscale structure of quantum materials and the role it plays in forming the strain landscape. In this work, we used a combination of x-ray nanoimaging and reciprocal space mapping to study the nanostructure of the archetypal Mott insulator VO 2 featuring an insulator-to-metal and structural phase transitions. We found that VO 2 thin films grown on r-cut sapphire consist of two intertwined crystal lattices, permanently inclined with respect to each other. This persistent pattern of twin domains stands out from the symmetry breaking induced by the structural phase transition and conceivably originates from the post-growth strain relaxation process. We propose a model explaining the formation of twin domains and the emergence of anisotropy in the film nanostructure. Our work suggests using miscut substrates to suppress either one or the other twin that can serve as a new tool to control strain in VO 2 films.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach

Abstract. Atmospheric aerosols have important impacts on air quality and the Earth–atmospheric energy balance. However, as computing power is limited, Earth system models generally use coarse spatial grids and parameterize finer-scale atmospheric processes. These parameterizations and the simulation of atmospheric aerosols are often sensitive to model horizontal resolutions. Understanding the sensitivities is necessary for the development of Earth system models at higher resolutions with the deployment of more powerful supercomputers. Using the Energy Exascale Earth System Model (E3SM) version 1, this study investigates the impact of horizontal grid spacing on the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols (ERFaer) over the contiguous United States. We examine the resolution sensitivity by comparing the nudged simulation results for 2016 from the low-resolution model (LR) and the regional refinement model (RRM). As expected, the simulated emissions of natural dust, sea salt, and marine organic matter are substantially higher in the RRM than in the LR. In addition, RRM simulates stronger aqueous-phase production of sulfate through the enhanced oxidation of sulfur dioxide by hydrogen peroxide due to increased cloud liquid water content. In contrast, the gas-phase chemical production of sulfate is slightly suppressed. The RRM resolves more large-scale precipitation and produces less convective precipitation than the LR, leading to increased (decreased) aerosol wet scavenging by large-scale (convective) precipitation. Regarding aerosol effects on clouds, RRM produces larger temporal variabilities in the large-scale liquid cloud fractions than LR, resulting in increased microphysical cloud processing of aerosols (more interstitial aerosols are converted to cloud-borne aerosols via aerosol activation) in RRM. Water vapor condensation is also enhanced in RRM compared to LR. Consequently, the RRM simulation produces more cloud droplets, a larger cloud droplet radius, a higher liquid water path, and a larger cloud optical depth than the LR simulation. A comparison of the present-day and pre-industrial simulations indicates that, for this contiguous United States domain, the higher-resolution increases ERFaer at the top of the model by about 12 %, which is mainly attributed to the strengthened indirect effect associated with aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Latent heat thermal energy storage performance maps enabling fast & accurate building energy simulations

Thermal energy storage (TES) using phase change materials (PCMs) has gained attention as an effective approach to manage energy demand fluctuations and shift peak building loads. PCM embedded heat exchangers (PCM-HXs) offer high energy storage density and low temperature variation during phase change, being suitable for load-shifting applications. However, this component is typically evaluated using computationally expensive methods, which present significant challenges when the ultimate goal is to assess the performance of PCM-HX integrated thermal energy storage systems in the full building context. In this paper, we present a methodology to generate highly accurate and computationally efficient PCM-HX performance maps which can be easily integrated into building energy simulation tools to analyze the feasibility of space conditioning systems with latent heat PCM-based TES. The performance maps are generated using a computationally efficient PCM-HX simulation tool based on a Generalized Resistance-Capacitance Model (GRCM) which can simulate arbitrary PCM-HXs with high accuracy and significantly less computational effort compared to full CFD simulations. The methodology was verified for a case study considering a 5-ton (~17.5 kW) air-to-water heat pump-thermal energy storage system (HP-TES), which was co-simulated in Modelica for a DOE prototype small-office building in Vienna, Austria, using Spawn of EnergyPlus™. The TES performance maps provided accurate predictions of PCM-HX behavior when used as Modelica component, with deviations within 2-4% while also achieving at least 103 computational time reduction. Leveraging this faster prediction capability, four PCMs with different melting temperatures for cooling (12°C, 16°C) and heating (31°C, 36°C) were assessed to investigate their impact on system performance. This work highlights the importance of robust PCM-HX models for efficient and high-fidelity building-level simulations, presenting new opportunities for advanced control strategy development and parametric analysis of TES configurations in a computationally efficient manner

Modelica Building Simulations↗
DOE OSTI2025

Crystallographic stability of the BCC phase during quenching of metastable beta titanium alloy Ti-5553 and comparison of structural criteria for the predictive capability of α” martensite

This work evaluates the compositional standard currently developed for Ti-5553 powder and explores the metastable β region for sensitivity to martensitic formation during rapid quenching from the melt. Ti-5553 is a beta-stabilized titanium alloy that is increasingly being used in additive manufacturing applications. A series of alloys within the Ti-5553 compositional space was processed using two-piston spat quenching to perform rapid solidification and quenching for each of these alloy compositions. Typical empirical models such as molybdenum equivalency are not able to fully separate the retained BCC and martensitic compositions. Further, the use of thermodynamic data to estimate the transformation energy needed to form martensite can differentiate the alloys and provide a metric to further develop compositional limits for metastable beta titanium alloy development.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Arp 58 and Arp 68: two M 51-type systems

ABSTRACT We study two M 51-type systems Arp 68 and Arp 58, which strongly differ by their stellar masses, gas content, and environment. Long-slit spectral observations obtained at the Russian 6-m telescope were used to trace the distributions of a line-of-sight (LOS) velocity and a gas-phase oxygen abundance along the spectral cuts. Two systems are compared by their observed properties. We found a very strong large-scale non-circular motion of gas in both systems and a kpc-size saw-edged velocity profile along the tidal spiral arm of Arp 68, probably caused by the gas outflow due to the stellar feedback. A deep decrease of LOS velocity is also found in the ‘hinge’ region in Arp 58, where the inner spiral arm transforms into the tidal spiral arm, which was predicted earlier for M 51-type galaxies. Local sites of star formation and the satellites are compared with the evolutionary models at the colour–colour diagrams. Unlike the spiral galaxy Arp 58, the main galaxy in Arp 68 system is experiencing an ongoing burst of star formation. Gas-phase metallicity estimates show that Arp 58 has a higher metal abundance and reveals a shallow negative radial gradient of the gas-phase oxygen abundance. The emission gas in Arp 68 has noticeably lower metallicity than it is expected for a given luminosity of this galaxy, which may be connected with its space position in the local void.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Coherence-Induced Deep Thermalization Transition in Random Permutation Quantum Dynamics

We report a phase transition in the projected ensemble—the collection of postmeasurement wave functions of a local subsystem obtained by measuring its complement. The transition emerges in systems undergoing random permutation dynamics, a type of quantum time evolution wherein computational basis states are shuffled without creating superpositions. It separates a phase exhibiting deep thermalization, where the projected ensemble is distributed over Hilbert space in a maximally entropic fashion (Haar random), from a phase where it is minimally entropic (“classical bit-string ensemble”). Crucially, this deep thermalization transition is invisible to the subsystem’s density matrix, which always exhibits thermalization to infinite temperature across the phase diagram. Through a combination of analytical arguments and numerical simulations, we show that the transition is tuned by the total amount of injected by the input state and the measurement basis, and is exhibited robustly across different microscopic models. Our findings represent a novel form of ergodicity-breaking universality in quantum many-body dynamics, characterized not by a failure of regular thermalization, but rather by a failure of deep thermalization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗