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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Rate coefficients for rotational state-to-state transitions in H 2 O + H 2 O collisions for cometary and planetary applications, as predicted by mixed quantum-classical theory

We present new calculations of collision cross sections for state-to-state transitions between the rotational states in an H 2 O + H 2 O system, which are used to generate a new database of collisional rate coefficients for cometary and planetary applications. Calculations were carried out using a mixed quantum-classical theory approach that is implemented in the code MQCT. The large basis set of rotational states used in these calculations permits us to predict thermally averaged cross sections for 441 transitions in para- and ortho-H 2 O in a broad range of temperatures. It is found that all state-to-state transitions in the H 2 O + H 2 O system split into two well-defined groups, one with higher cross-section values and lower energy transfer, which corresponds to the dipole-dipole driven processes. The other group has smaller cross sections and higher energy transfer, driven by higher-order interaction terms. We present a detailed analysis of the theoretical error bars, and we symmetrized the state-to-state transition matrixes to ensure that excitation and quenching processes for each transition satisfy the principle of microscopic reversibility. We also compare our results with other data available from the literature for H 2 O + H 2 O collisions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Seismic monitoring of EGS fracture stimulations at Utah FORGE (Part 1): Time-lapse variations of b-values and Shear-wave Splitting Rates of induced microearthquakes

Characterizing geologic formations near geothermal wells is crucial for efficient extraction of subsurface geothermal energy resources. To monitor effective fracture stimulation of enhanced geothermal systems (EGS), we analyze a catalog of induced microearthquakes to characterize seismicity induced by Phase 2C fracture stimulations of the Utah FORGE project and derive the b-values for 424 induced microearthquakes in three stages of fracture stimulation. Here, we find the time-lapse variations of these b-values. We apply eigenvector rotation and waveform correlation to 3C geophone data to separate fast S 1 and slow S 2 waves, determine their directions, measure their lag time, and calculate the S-wave splitting rate (SSR) for six induced microearthquakes in Stage 1 and 21 in Stage 3. Both SSR values and b-values increase with time. The increase in these values may be related to heavily fractured zones created by fracture stimulations during Stage 2 injections. Large and highly variable SSR values in Stage 3 may reflect heterogeneities in the fracture zones. Time-lapse variations of SSR values and b-values may be one of the most useful approaches to monitoring and characterizing the effectiveness and efficiency of various types of fracture stimulations.

58 GEOSCIENCES↗
DOE OSTI2023

SAIL Field Campaign X-Band Precipitation Radar Surface Quantitative Precipitation Estimation (SQUIRE) Value-Added Product Report

In 2010, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility procured 3- and 5-cm wavelength radars for documenting the macrophysical, microphysical, and dynamical structure of precipitating systems. In order to maximize the scientific impact, ARM supported the development of an application chain to correct for various phenomena in order to retrieve the lowest retrieved value on a Cartesian grid. This report details the motivation, science, and progress to date, as well as charting a path forward.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Powering the Blue Economy: Progress Exploring Marine Renewable Energy Integration With Ocean Observations

The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and inperson interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.

marine renewable energy, powering the blue economy↗
DOE OSTI2024

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗
DOE OSTI2022

Autoregressive Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation

The theory of open quantum systems lays the foundation for a substantial part of modern research in quantum science and engineering. Rooted in the dimensionality of their extended Hilbert spaces, the high computational complexity of simulating open quantum systems calls for the development of strategies to approximate their dynamics. In this Letter, we present an approach for tackling open quantum system dynamics. Using an exact probabilistic formulation of quantum physics based on positive operator-valued measure, we compactly represent quantum states with autoregressive neural networks; such networks bring significant algorithmic flexibility due to efficient exact sampling and tractable density. We further introduce the concept of string states to partially restore the symmetry of the autoregressive neural network and improve the description of local correlations. Efficient algorithms have been developed to simulate the dynamics of the Liouvillian superoperator using a forward-backward trapezoid method and find the steady state via a variational formulation. Our approach is benchmarked on prototypical one-dimensional and two-dimensional systems, finding results which closely track the exact solution and achieve higher accuracy than alternative approaches based on using Markov chain Monte Carlo method to sample restricted Boltzmann machines. Our Letter provides general methods for understanding quantum dynamics in various contexts, as well as techniques for solving high-dimensional probabilistic differential equations in classical setups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Applying a Multisector Scenario Framework to Evaluate Past and Future Public Surface Water Supply Infrastructure Strategies in Texas

Datasets supporting the index model and scenario analysis used in evaluating surface water supply strategies across different water system types in Texas. These data underpin the scenario development and application of five key indicators: Water Availability Index (WAI), Water Quality Index (WQI), Energy Requirement Index (ERI), Water Treatment Cost (WTC), and Water Infrastructure Cost (WIC). The datasets are organized by system type—stream reaches (flowlines), waterbodies, and reservoirs—and include both raw and standardized index values. The integrated datasets also provide scenario classifications (original and adjusted) based on infrastructure and planning priorities, enabling comparison across Shared Socioeconomic Pathways (SSPs). Additional strategy-level data are included to support evaluation of state-level new reservoir projects in relation to cost and availability tradeoffs. Please refer to the README file provided in Files for more details. Descriptions of the datasets are provided below. Dataset(s) Descriptions Folder: Index_model_database.zip Subfolder: Stream_reach.zip Fl_wf.csv, Fl_wq.csv, Fl_er.csv, Fl_wf_wtcUV.csv, Fl_wf_wtcnoUV.csv, Fl_allfac_wic1.csv, Fl_allfac_wic2.csvDatasets for computing WAI, WQI, ERI, WTC, and WIC for surface water systems classified as stream reaches (flowlines). Subfolder: Waterbody.zip Wb_wf.csv, Wb_wq.csv, Wb_er.csv, Wb_wf_wtcUV.csv, Wb_wf_wtcnoUV.csv, Wb_allfac_wic1.csv, Wb_allfac_wic2.csvEquivalent index model datasets for waterbodies, reflecting hydrologic and infrastructure attributes specific to impounded natural systems. Subfolder: Reservoir.zip Rs_wf.csv, Rs_wq.csv, Rs_er.csv, Rs_wf_wtcUV.csv, Rs_wf_wtcnoUV.csv, Rs_allfac_wic1.csv, Rs_allfac_wic2.csvIndex model datasets specific to regulated reservoir systems, incorporating both resource indicators and cost parameters. Folder: Integrated data.zip combined_merged_data.csv, combined_merged_data_scenario.csvDatasets integrating index model indicators (both raw and scaled) with scenario classifications, including adjustments reflecting SSP-aligned transitions and planning shifts. Folder: Additional data.zip wai_supplystrat_wic_merged.csvCurated dataset capturing proposed major reservoir-based municipal water supply strategies in Texas. Integrates site-level planning data with estimated capital infrastructure costs and water availability scores for comparative assessment.

geospatial↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2021

Open-Loop Control of Adjustable Tuned Mass Dampers for Floating Wind Turbine Platforms: Preprint

Floating offshore wind turbines have several advantages over their land-based counterparts, including access to stronger, more consistent winds and proximity to population centers. However, increased costs from the platform have been a challenge inhibiting their widespread adoption. New damping technologies with tunable target frequencies and damping ratios promise a greater degree of control over platform motions, allowing hulls to be designed smaller and reducing the overall cost of the turbines. In this work, a control strategy is proposed for these adjustable tuned mass dampers (TMDs). A frequency-domain model of the system is developed, from which response amplitude operators (RAOs) for the platform's rigid-body modes can be obtained for different natural frequencies and damping ratios of the dampers. Using these RAOs, and JONSWAP spectrums of operational and extreme sea states, the performance of the various damper settings are compared by evaluating the standard deviation of a rigid-body motion of interest (platform pitch or heave). By finding the optimal damper setting for a range of representative design load cases (DLCs), a lookup function is made to return the damper target frequency given the peak period of the sea state. To determine the current sea state, we propose a simple estimator that uses wave measurements (e.g., from a measurement buoy) to determine the peak period and significant wave height. The significant wave height is determined using the statistics of the past 100 seconds of wave elevation information and the peak period is computed using a frequency locked loop. Initial results show that we can use these estimated values to control the TMD natural frequency with an open loop controller. The controlled system is tested in a range of environmental conditions determined by the International Electrotechnical Commission design load cases (DLCs), which include normal and extreme wind and wave models. In these tests, we compare the performance of 4 cases: (1) no TMD, (2) a constant TMD based on the worst case DLC, (3) a controlled TMD based on known wind and wave environments, and (4) a real-time controlled TMD using estimated wind and wave environments. The effect of hull-based TMD control is also compared to changes in traditional wind turbine control via blade pitch.

control↗
DOE OSTI2021

Unravelling chloride transport/microstructure relationships for blended-cement pastes with the mini-migration method

A chloride mini-migration method is proposed to estimate effective diffusion coefficients at the scale of the cement paste and to investigate mechanisms with complementary microstructure analyses on the same material. Blended-cement pastes with a wide range of properties were investigated in this study: systems at w/b = 0.3–0.5 including Portland cement, white Portland cement, slag-Portland cement, fly ash, glass powder, and/or limestone and calcined clay. Comparisons showed the relative and combined importance of three main parameters on the effective diffusion coefficient: the porosity, the pore connectivity parameter and the conductivity of the pore solution (low values of the latter two are generally key aspects of the high resistance of blended-cement systems against chloride ingress). Notably, a general correlation for all the investigated systems was established between the effective diffusion coefficient and the bulk conductivity (whereas no simple correlation was observed between the effective diffusion coefficient and the formation factor or the pore connectivity parameter).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Computational Experiment Design for Operations Model Simulation

Computer simulations that demonstrate the value of novel approaches are crucial to developing more flexible and robust power systems operations with high penetrations of renewable energy at multiple geographic and temporal scales. However, optimization-based simulations that depend on forecast data often face challenges in evaluating performance, reproducing results, and testing under realistic simulation scenarios. In this paper, we develop scientific computing best-practices for the validation and reproduction of power systems operational models. We then employ two case studies to demonstrate the proposed validation and reproduction framework.

MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D↗
DOE OSTI2024

Enhancing the accuracy and generality of the Debye–Grüneisen Model: Optimizing the volume dependence for accurate predictions across varied compositions

In this work, we have introduced an optimized Debye-Grüneisen model that revolutionizes the determination of the Debye temperature and Grüneisen parameters. Unlike conventional methods, our model requires only the 0 K energy volume data for a material as input, eliminating the need to determine the bulk modulus and its pressure derivative, which often pose challenges due to numerical uncertainties. This unique feature sets our model apart from existing approaches and streamlines the process, enabling accurate predictions of thermal expansion behavior across various materials. To demonstrate its effectiveness, we showcase its excellent agreement with measured coefficients of thermal expansion (CTE) for the nickel-cobalt-chromium-aluminum-yttrium (Ni-Co-Cr-Al-Y) bond-coating system. Additionally, we apply our approach by conducting a high-throughput search for potential bond-coating materials among 90,000 compositions within the aluminum-cobalt-chromium-iron-nickel (Al-Co-Cr-Fe-Ni) system. From this extensive search, four compositions are synthesized, and the measured CTE values agree very well with theoretical predictions, hence validating our approach. In conclusion, the current optimized Debye-Grüneisen model combined with Density Functional Theory (DFT)-based thermodynamic database enables reliable and efficient high-throughput calculations of CTE of of a material without expensive phonon calculations.

Bond coating materials↗
DOE OSTI2024

Next-Generation Reverse Logistics Networks of Photovoltaic Recycling: Perspectives and Challenges

With the growing adoption of solar energy as a key component of the global energy transition and its new industrial policy (the Inflation Reduction Act and others), the United States is witnessing a significant increase in solar investments. This surge in installations and domestic and reshored manufacturing of solar photovoltaic (PV) panels brings with it a pressing issue: the proper management of end-of-life (EoL) PV panels. As these panels are decommissioned, either due to reaching the end of their lifespans or due to breakage across the various stages of the forward supply chain, it becomes crucial to establish efficient reverse supply chain logistics systems to address the challenges associated with their disposal, while also unlocking the value of the inherent materials that are of critical value for other forward supply chains. This perspective article examines the challenges regarding EoL PV panels and relevant reverse supply chain and logistics networks, and proposes future research directions based on the gaps observed among academic research, industry, and policy-making challenges. We identify the main bottlenecks and hurdles including, among others, the lack of supportive regulations and absence of structured, optimized recycling infrastructure. To this end, it is proposed that the key to achieving a sustainable reverse supply chain network for solar PV panels lies in relentless end-to-end supply chain cost optimization efforts supported by enabling policies. Moreover, it is proposed that designing systematic decision-making modeling frameworks is vital for examining different possibilities and scenarios for state, region or nation-wide optimization of solar PV reverse supply chain networks. Indicative to this effect, we discuss the development of a Resource-Task-Network (RTN)-based model and demonstrate its application and benefits through a case study. We wrap up with conclusions and future research directions.

circular economy↗
DOE OSTI2020

Measurement of the mass difference and the binding energy of the hypertriton and antihypertriton

According to the CPT theorem, which states that the combined operation of charge conjugation, parity transformation and time reversal must be conserved, particles and their antiparticles should have the same mass and lifetime but opposite charge and magnetic moment. In this work, we test CPT symmetry in a nucleus containing a strange quark, more specifically in the hypertriton. This hypernucleus is the lightest one yet discovered and consists of a proton, a neutron and a Λ hyperon. With data recorded by the STAR detector at the Relativistic Heavy Ion Collider, we measure the Λ hyperon binding energy B Λ for the hypertriton, and find that it differs from the widely used value and from predictions, where the hypertriton is treated as a weakly bound system. Our results place stringent constraints on the hyperon–nucleon interaction and have implications for understanding neutron star interiors, where strange matter may be present. A precise comparison of the masses of the hypertriton and the antihypertriton allows us to test CPT symmetry in a nucleus with strangeness, and we observe no deviation from the expected exact symmetry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Indium and thallium extraction into betainium bis(trifluoromethylsulfonyl)imide ionic liquid from aqueous hydrochloric acid media

The partitioning behavior of indium and thallium ions between aqueous hydrochloric acid solutions and hydrophobic betainium bis(trifluoromethylsulfonyl)imide ionic liquid was studied using a standard liquid–liquid extraction technique. The experiments were mainly carried out by means of a radiochemical method, using 111 In and 201 Tl medical radionuclides at an ultra-trace concentration. Both mono- and trivalent thallium were utilized and the highest oxidation state (3+) was produced by adding either chlorine or bromine water to the aqueous phase prior to extraction. The metal ion transfer was optimized by varying the aqueous concentration of hydrochloric acid and by introducing zwitterionic betaine as an extracting agent into the aqueous phase. It was found that the highest D values of In(III), Tl(III) and Tl(I) in the investigated chemical systems were up to 200, 70, and 1, respectively. The influence of the addition of lithium salt, having common with the ionic liquid counterion, and the variation of mixing time on the distribution ratio of the metal ions were also studied. Finally, to understand the underlying extraction mechanism of the metal ions and to propose the predominant extracted species, mathematical models based on ion pair formation and ion exchange were developed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Data Assimilation using Non-invasive Monte Carlo Sensitivity Analysis of Reactor Kinetics Parameters

Accurately predicting the criticality of an experiment before interacting with the experimental components is very important for criticality safety. Radiation transport software can be utilized to calculate the effective neutron multiplication factor of a nuclear system. Because of the integral nature of the effective neutron multiplication factor, the value calculated contains various sources of nuclear-data induced uncertainty. The sensitivity analysis and data assimilation technique presented in this paper exhibit one possible method of identifying and reducing the effective neutron multiplication factor nuclear-data induced uncertainty. The results presented in this work show that it is possible to use relative sensitivity coefficients of the prompt neutron decay constant and the effective delayed neutron fraction to 239 Pu nuclear data to reduce nuclear-data induced uncertainties in the effective neutron multiplication factor. This work has been utilized by members of the Los Alamos National Laboratory project EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) for optimally designing a new experiment, which will be used to reduce compensating errors in 239 Pu nuclear data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Intermediate-temperature topological Uhlmann phase on IBM quantum computers

A spin-1 system can exhibit an intermediate-temperature topological regime with a quantized Uhlmann phase sandwiched by topologically trivial low- and high-temperature regimes. We present a quantum circuit consisting of system and ancilla qubits plus a probe qubit which prepares an initial state corresponding to the purified state of a spin-1 system at finite temperature, evolves the system according to the Uhlmann process, and measures the Uhlmann phase via expectation values of the probe qubit. Although classical simulations suggest the quantized Uhlmann phase is observable on International Business Machines (IBM’s) noisy intermediate-scale quantum (NISQ) computers, an implementation of the circuit without any optimization exceeds the gate count for the error budget and results in unresolved signals. Through a series of optimization with Qiskit and BQSKit, the gate count can be substantially reduced, making the jumps of the Uhlmann phase more visible. A recent hardware upgrade of IBM quantum computers further improves the signals and leads to a clearer demonstration of interesting finite-temperature topological phenomena on NISQ hardware.

Mastandrea, Christopher [Univ. of California, Merc↗
DOE OSTI2026

Compact representation and long-time extrapolation of real-time data for quantum systems using the ESPRIT algorithm

Representing real-time data as a sum of complex exponentials provides a compact form that enables both denoising and extrapolation. As a fully data-driven method, the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is agnostic to the underlying physical equations, making it broadly applicable to various observables and experimental or numerical setups. In this work, we consider applications of the ESPRIT algorithm primarily to extend real-time dynamical data from simulations of quantum systems. We evaluate ESPRIT's performance in the presence of noise and compare it to other extrapolation methods. We demonstrate its ability to extract information from short-time dynamics to reliably predict long-time behavior and determine the minimum time interval required for accurate results. We discuss how this insight can be leveraged in numerical methods that propagate quantum systems in time, and we show how ESPRIT can predict infinite-time values of dynamical observables, offering a purely data-driven approach to characterizing quantum phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗