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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Sawtooth lattice multiferroic BeCr 2 O 4 : Noncollinear magnetic structure and multiple magnetic transitions

Noncollinear magnetic structures and multiple magnetic phase transitions in a sawtooth lattice antiferromagnet consisting of Cr 3+ are experimentally identified in this work, thereby proposing the scenario of magnetism-driven ferroelectricity in a sawtooth lattice. The title compound, BeCr 2 O 4 , displays three magnetic phase transitions at low temperatures—at T N1 ≈ 7.5 K, at T N2 ≈ 25 K, and at T N3 ≈ 26 K—revealed through magnetic susceptibility, specific heat, and neutron diffraction in this work. These magnetic phase transitions are found to be influenced by externally applied magnetic fields. Isothermal magnetization curves at low temperatures below the magnetic transitions indicate the antiferromagnetic nature of BeCr 2 O 4 with two spin-flop-like transitions occurring at H c1 ≈ 29 kOe and H c2 ≈ 47 kOe. Our high-resolution x-ray and neutron diffraction studies, performed on single crystal and powder samples, unambiguously determined the crystal structure as orthorhombic Pbnm. By performing the magnetic superspace group analysis of the neutron diffraction data at low temperatures, the magnetic structure in the temperature range T N3,N2 < T < T N1 is determined to be the polar magnetic space group P21nm. 1' (00g) 0s0s with a cycloidal magnetic propagation vector k 1 = (0, 0, 0.090 (1)) . The magnetic structure in the newly identified phase below T N1 is determined as P21/b.1' [b] (00 g) 00s with the magnetic propagation vector k 2 = (0, 0, 0.908 (1)). Finally, the cycloidal spin structure determined in our work is usually associated with electric polarization, thereby making BeCr 2 O 4 a promising multiferroic belonging to the sparsely populated family of sawtooth lattice antiferromagnets.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Incommensurate magnetic ordering and a possible structural transition in EuIn 4

Single crystals of EuIn 4 were grown and 151 Eu Mössbauer spectroscopy, combined with temperature dependent electrical resistance, specific heat and anisotropic magnetization measurements have been used to investigate the magnetic ordering of monoclinic EuIn 4 . Here, we find two closely spaced magnetic transitions (at ~10 K and ~11 K). The initial ordering leads to an incommensurate sinusoidally modulated structure that squares up rapidly on cooling through the lower (10 K) transition. Well above the magnetic transitions we observe an additional transition at ~130 K which may also involve two closely spaced events. This upper transition(s) is most likely structural and offers no suggestion of magnetic nature either in magnetization or Mössbauer data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Anomalous Landau Level Gaps Near Magnetic Transitions in Monolayer WSe 2

First-order phase transitions produce abrupt changes to the character of both ground and excited electronic states. Here we conduct electronic compressibility measurements to map the spin phase diagram and Landau level (LL) energies of monolayer WSe 2 in a magnetic field. We resolve a sequence of first-order phase transitions between completely spin-polarized LLs and states with LLs of both spins. Unexpectedly, the LL gaps are roughly constant over a wide range of magnetic fields below the transitions, which we show reflects spin-polarized ground states with opposite spin excitations. These transitions also extend into compressible regimes, with a sawtooth boundary between full and partial spin polarization. We link these observations to the important influence of LL filling on the exchange energy beyond a smooth density-dependent contribution. Our results show that WSe 2 realizes a unique hierarchy of energy scales where such effects induce reentrant magnetic phase transitions tuned by density and magnetic field. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Learnability Transitions in Monitored Quantum Dynamics via Eavesdropper’s Classical Shadows

Monitored quantum dynamics—unitary evolution interspersed with measurements—has recently emerged as a rich domain for phase structure in quantum many-body systems away from equilibrium. Here we study monitored dynamics from the point of view of an eavesdropper who has access to the classical measurement outcomes, but not to the quantum many-body system. We show that a measure of information flow from the quantum system to the classical measurement record—the —undergoes a phase transition in correspondence with the measurement-induced phase transition (MIPT). This transition determines the eavesdropper’s (in)ability to learn properties of an unknown initial quantum state of the system, given a complete classical description of the monitored dynamics and arbitrary classical computational resources. We make this learnability transition concrete by defining classical shadow protocols that the eavesdropper may apply to this problem, and show that the MIPT manifests as a transition in the sample complexity of various shadow-estimation tasks, which become harder in the low-measurement phase. We focus on three applications of interest: Pauli expectation values (where we find the MIPT appears as a point of optimal learnability for typical Pauli operators), many-body fidelity, and global charge in U ( 1 ) -symmetric dynamics. Our work unifies different manifestations of the MIPT under the umbrella of and gives this notion a general operational meaning via classical shadows. Published by the American Physical Society 2024

Physics↗
DOE OSTI2021

Synthetic Reference-based Transition Optimal Control of Inverter for Surge-Current-Free Rapid Interface to Grid

This article addresses the problem of the transition between objective functions in optimal control for power inverters when switching between standalone and grid-connected modes. The work proposes the use of a synthetic reference during the transition between the corresponding objective functions. Moreover, the authors aim to show that a transition function can be imposed and controlled externally from the main optimal control routine. In the end, the simulation results demonstrate the importance of properly applying the proposed strategy for a smooth transition between the operating modes with minimal surge current. These results contrast with the strategies found in the literature where the smooth transition comes with the price of a few grid cycles to sync.

Roig Greidanus, Mateo D.↗
DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
DOE OSTI2023

Addressing APC Data Sparsity in Predicting Occupancy and Delay of Transit Buses: A Multitask Learning Approach

Public transit is a vital mode of transportation in urban areas, and its efficiency is crucial for the daily commute of millions of people. To improve the reliability and predictability of transit systems, researchers have developed separate single-task learning models to predict the occupancy and delay of buses at the stop or route level. However, these models provide a narrow view of delay and occupancy at each stop and do not account for the correlation between the two. We propose a novel approach that leverages broader generalizable patterns governing delay and occupancy for improved prediction. We introduce a multitask learning toolchain that takes into account General Transit Feed Specification feeds, Automatic Passenger Counter data, and contextual temporal and spatial information. The toolchain predicts transit delay and occupancy at the stop level, improving the accuracy of the predictions of these two features of a trip given sparse and noisy data. We also show that our toolchain can adapt to fewer samples of new transit data once it has been trained on previous routes/trips as compared to state-of-the-art methods. Finally, we use actual data from Chattanooga, Tennessee, to validate our approach. We compare our approach against the state-of-the-art methods and we show that treating occupancy and delay as related problems improves the accuracy of the predictions. We show that our approach improves delay prediction significantly by as much as 4% in F1 scores while producing equivalent or better results for occupancy.

Zulqarnain, Ammar Bin↗
DOE OSTI2025

Superstructural phase transitions in polymer-grafted nanooctahedra

Superlattices of polyhedral nanocrystals exhibit emergent properties defined by their structural arrangements, but native nanocrystal ligands often limit their programmability. Polymeric ligands address this limitation by enabling tunable nanocrystal softness through modifications of polymer molecular weight and grafting density. Here, we investigate phase transitions in polymer-grafted nanooctahedra by varying polymer length, nanocrystal size, truncation, and ligand density. In two-dimensional superlattices, longer polymers or smaller nanooctahedra induce a transition from orientationally ordered to hexagonal rotator lattices. In three-dimensional superlattices, increasing polymer length drives transitions from Minkowski to body-centered cubic and plastic hexagonal close-packed phases, while higher grafting densities further enable transitions to simple hexagonal phases. Polymer brush and thermodynamic perturbation theories, supported by Monte Carlo simulations, uncover the entropic and enthalpic forces that govern these transitions. This work highlights the versatility of polymer-grafted anisotropic nanocrystals as building blocks for designing hierarchical superstructures and metamaterials with customizable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Revealing systematic changes in the transcriptome during the transition from exponential growth to stationary phase

ABSTRACT The composition of bacterial transcriptomes is determined by the transcriptional regulatory network (TRN). The TRN regulates the transition from one physiological state to another. Here, we use independent component analysis to monitor the composition of the transcriptome during the transition from the exponential growth phase to the stationary phase. With Escherichia coli K-12 MG1655 as a model strain, we trigger the transition using carbon, nitrogen, and sulfur starvation. We find that (i) the transition to the stationary phase accompanies common transcriptome changes, including increased stringent responses and reduced production of cellular building blocks and energy regardless of the limiting element; (ii) condition-specific changes are strongly associated with transcriptional regulators ( e.g. , Crp, NtrC, CysB, Cbl) responsible for metabolizing the limiting element; and (iii) the shortage of each limiting element differentially affects the production of amino acids and extracellular polymers. This study demonstrates how the combination of genome-scale datasets and new data analytics reveals the fundamental characteristics of a key transition in the life cycle of bacteria. IMPORTANCE Nutrient limitations are critical environmental perturbations in bacterial physiology. Despite its importance, a detailed understanding of how bacterial transcriptomes are adjusted has been limited. By utilizing independent component analysis (ICA) to decompose transcriptome data, this study reveals key regulatory events that enable bacteria to adapt to nutrient limitations. The findings not only highlight common responses, such as the stringent response, but also condition-specific regulatory shifts associated with carbon, nitrogen, and sulfur starvation. The insights gained from this work advance our knowledge of bacterial physiology, gene regulation, and metabolic adaptation.

Lim, Hyun Gyu (ORCID:0000000204692388)↗
DOE OSTI2020

Transition energy measurements of the X-ray lines of neon-like europium

Here, we report the measurements of the n = 3 → n = 2 L-shell X-ray transitions of neon-like Eu 53+ . These include all seven allowed electric dipole (E1) transitions, five electric quadrupole (E2) transitions, and one magnetic quadrupole (M2) transition. The measurements were carried out at the EBIT-I electron beam ion trap facility at Livermore using an X-ray calorimeter. The measured values agree with many-body perturbation theory calculations within the experimental uncertainties. This includes the transitions that involve a 2s electron and that are, therefore, strongly affected by quantum electrodynamical effects.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Exploring baryon resonances with transition generalized parton distributions: status and perspectives

QCD gives rise to a rich spectrum of excited baryon states. Understanding their internal structure is important for many areas of nuclear physics, such as nuclear forces, dense matter, and neutrino-nucleus interactions. Generalized parton distributions (GPDs) are an established tool for characterizing the QCD structure of the ground-state nucleon. They are used to create 3D tomographic images of the quark/gluon structure and quantify the mechanical properties such as the distribution of mass, angular momentum, and forces in the system. Transition GPDs extend these concepts to N → N* transitions and can be used to characterize the 3D structure and mechanical properties of baryon resonances. They can be probed in high-momentum-transfer exclusive electroproduction processes with resonance transitions e + N → e' + M + N*, such as deeply-virtual Compton scattering (M = γ) or meson production (M = π, K, etc.), and in related photon/hadron-induced processes. This White Paper describes a research program aiming to explore baryon resonance structure with transition GPDs. This includes the properties and interpretation of the transition GPDs, theoretical methods for structures and processes, first experimental results from JLab 12 GeV, future measurements with existing and planned facilities (JLab detector and energy upgrades, COMPASS/AMBER, EIC, EicC, J-PARC, LHC ultraperipheral collisions), and the theoretical and experimental developments needed to realize this program.

Experimental Nuclear Physics↗
DOE OSTI2023

Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model

Abstract Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting) and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective “average event.” We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT’s capacities on a wave–mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass, which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also backward in time to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the reactive current . The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Capturing Travel Mode Adoption in Designing On-Demand Multimodal Transit Systems

This paper studies how to integrate rider mode preferences into the design of on-demand multimodal transit systems (ODMTSs). It is motivated by a common worry in transit agencies that an ODMTS may be poorly designed if the latent demand, that is, new riders adopting the system, is not captured. This paper proposes a bilevel optimization model to address this challenge, in which the leader problem determines the ODMTS design, and the follower problems identify the most cost efficient and convenient route for riders under the chosen design. The leader model contains a choice model for every potential rider that determines whether the rider adopts the ODMTS given her proposed route. To solve the bilevel optimization model, the paper proposes an exact decomposition method that includes Benders optimal cuts and no-good cuts to ensure the consistency of the rider choices in the leader and follower problems. Moreover, to improve computational efficiency, the paper proposes upper and lower bounds on trip durations for the follower problems, valid inequalities that strengthen the no-good cuts, and approaches to reduce the problem size with problem-specific preprocessing techniques. The proposed method is validated using an extensive computational study on a real data set from the Ann Arbor Area Transportation Authority, the transit agency for the broader Ann Arbor and Ypsilanti region in Michigan. The study considers the impact of a number of factors, including the price of on-demand shuttles, the number of hubs, and access to transit systems criteria. The designed ODMTSs feature high adoption rates and significantly shorter trip durations compared with the existing transit system and highlight the benefits of ensuring access for low-income riders. Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude. Funding: This research was partly supported by National Science Foundation [Leap HI Proposal NSF-1854684] and the Department of Energy [Research Award 7F-30154].

Operations Research & Management Science↗
DOE OSTI2021

Phase transition dynamics in one-dimensional halide perovskite crystals

Triiodide perovskites CsPbI 3 , CsSnI 3 , and FAPbI 3 (where FA is formamidinium) are highly promising materials for a range of optoelectronic applications in energy conversion. However, they are thermodynamically unstable at room temperature, preferring to form low-temperature (low-T) non-perovskite phases with one-dimensional anisotropic crystal structures. While such thermodynamic behavior represents a major obstacle toward realizing high-performance devices based on their high-temperature (high-T) perovskite phases, the underlying phase transition dynamics are still not well understood. In this work, we use in situ optical micro-spectroscopy to quantitatively study the transition from the low-T to high-T phases in individual CsSnI 3 and FAPbI 3 nanowires. We reveal a large blueshift in the photoluminescence (PL) peak (~38 meV) at the low-T/high-T two-phase interface of partially transitioned FAPbI 3 wire, which may result from the lattice distortion at the phase boundary. Compared to the experimentally derived activation energy of CsSnI 3 (~1.93 eV), the activation energy of FAPbI 3 is relatively small (~0.84 eV), indicating a lower kinetic energy barrier when transitioning from a face-sharing octahedral configuration to a corner-sharing one. Further, the phase propagation rate in CsSnI 3 is observed to be relatively high, which may be attributed to a high concentration of Sn vacancies. Our results could not only facilitate a deeper understanding of phase transition dynamics in halide perovskites with anisotropic crystal structures, but also enable controllable manipulation of optoelectronic properties via local phase engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The iron spin transition of deep nitrogen-bearing mineral Fe3N1.2 at high pressure

Abstract Nitrogen is an essential element for life, one of the most abundant volatiles in the atmosphere, and an important component in the Earth’s interior, where iron nitride is an essential host of deep nitrogen. Here, we investigate the pressure-induced electronic spin-pairing transition of iron in siderazot (Fe3N1.2) at pressures up to 45.8 GPa at room temperature, using diamond-anvil cell techniques coupled with synchrotron X-ray emission spectroscopy. The integrated intensity of the satellite emission peak (K′β) decreases upon compression but remains unchanged at pressures greater than 30.5 GPa. In other words, the high-spin to low-spin transition of iron in Fe3N1.2 starts immediately at very low pressures and completes at ~30.5 GPa. The iron spin transition completion pressures increase with the nitrogen concentration of hexagonal close-packed iron nitrides (i.e., Fe3N1.2, Fe7N3, and Fe2N). Moreover, the identity and concentration of light elements in binary iron-rich compounds such as Fe3N, Fe3C, Fe3P, Fe3S, Fe7C3, and Fe7N3, together with their crystal structure, could affect the iron spin transition pressures. The spin transition of iron-rich alloys could alter the bonding nature and the physical properties, including the thermal and electrical conductivity, thereby influencing the thermal state and evolution of planetary interiors.

Geochemistry & Geophysics↗
DOE OSTI2020

Coupling of Laminar-Turbulent Transition with RANS Computational Fluid Dynamics

This project combines several new concepts to create a boundary layer transition prediction capability that is suitable for analyzing modern hypersonic flight vehicles. The first new concept is the use of ''optimization'' methods to detect the hydrodynamic instabilities that cause boundary layer transition; the use of this method removes the need for many limiting assumptions of other methods and enables quantification of the interactions between boundary layer instabilities and the flow field imperfections that generate them. The second new concept is the execution of transition analysis within a conventional hypersonics CFD code, using the same mesh and numerical schemes for the transition analysis and the laminar flow simulation. This feature enables rapid execution of transition analysis with less user oversight required and no interpolation steps needed.

42 ENGINEERING↗
DOE OSTI2026

Quantum enabled precision measurements of the 229Th nuclear isomer transition (final report)

The existence of the nuclear isomer transition in thorium-229 was first inferred from keV lines in the gamma spectrum of uranium-233 decay more than 40 years ago. Over the years, the value of the transition energy has been refined with indirect measurements using nuclear physics techniques, and the current evidence points to transition energy in the laser-accessible vacuum ultra-violet region of the spectrum. At the start of this project, the two best measurements of the transition energy were 7.8±0.5 eV and 8.28±0.17 eV from high precision gamma ray spectroscopy and kinetic energy of internal conversion electrons, respectively. This project aimed to reduce the uncertainty in the transition energy to 10 meV using direct calorimetric measurements of the decay energy with superconducting nanowire single photon detectors (SNSPDs). Specifically, the method that was pursued was to generate thorium-229 in the excited isomer state by the alpha decay of uranium-233, embed the excited state thorium into an SNSPD, then detect the energy released when the isomer deexcites.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

HATS-71b: A Giant Planet Transiting an M3 Dwarf Star in TESS Sector 1

We report the discovery of HATS-71b, a transiting gas giant planet on a P=3.7955 day orbit around a G=15.35 mag M3 dwarf star. HATS-71 is the coolest M dwarf star known to host a hot Jupiter. The loss of light during transits is 4.7%, more than in any other confirmed transiting planet system. The planet was identified as a candidate by the ground-based HATSouth transit survey. It was confirmed using ground-based photometry, spectroscopy, and imaging, as well as space-based photometry from the NASA Transiting Exoplanet Survey Satellite mission (TIC 234523599). Combining all of these data, and utilizing Gaia DR2, we find that the planet has a radius of 1.024±0.018 R{sub J} and mass of 0.37 ±0.24 M{sub J} (95% confidence upper limit of <0.80 M{sub J}), while the star has a mass of 0.4861±0.0060 M{sub ⊙} and a radius of 0.4783±0.0060 R{sub ⊙}.

79 ASTRONOMY AND ASTROPHYSICS↗