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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Constant Overhead Entanglement Distillation via Scrambling

High-fidelity quantum entanglement enables key quantum networking capabilities such as secure communication and distributed quantum computing, but long-distance entanglement distribution is limited by noise and loss. Entanglement distillation protocols address this problem by extracting high-fidelity Bell pairs from multiple noisy ones. The primary objective is minimizing the resource overhead: the number of noisy input pairs needed to distill each high-fidelity output pair. While protocols achieving optimal overhead are known in theory, they often require complex decoding operations that make practical implementation challenging. We circumvent this challenge by introducing protocols that use quantum scrambling—the spreading of quantum information under chaotic dynamics—through random Clifford operations. Based on this scrambling mechanism, our protocol maintains asymptotically constant overhead, independent of the desired output error rate $\bar{𝜖}$ , and can be implemented with shallow quantum circuits of depth 𝑂⁡(poly log log⁡ $\bar{𝜖}$ −1 ) and memory 𝑂⁡(poly log⁡ $\bar{𝜖}$ −1 ). Our protocol remains effective even with noisy quantum gates. By incorporating error correction, our protocol achieves state-of-the-art performance: starting with pairs of 10% initial infidelity, we require only seven noisy inputs per output pair to distill a single Bell pair with infidelity $\bar{𝜖}$ =10 −12 , substantially outperforming existing schemes. We demonstrate the utility of our protocols for quantum repeater networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

CP‐SyNet: A tool for generating customised cyber‐power synthetic network for distribution systems with distributed energy resources

Abstract The integration of distributed energy resources and advancement in information technology has enabled the transition of traditional power distribution systems to active cyber‐physical distribution systems. A growing amount of research has been done on the modelling, analysis, and optimisation of power distribution system behaviour. However, existing publicly available distribution test feeders are limited in numbers and have minimal features. Furthermore, these test feeders do not include cyber models and are not customisable. To bridge this gap, we propose and develop Cyber‐physical synthetic distribution system network (CP‐SyNet), a tool for generating customisable cyber‐physical synthetic distribution test feeders. CP‐SyNet generates three‐phase unbalanced test feeders according to users' requirements, while simultaneously considering both the cyber side and the physical side of the network for cyber‐physical analysis. The physical test network is developed using a graph‐theoretical approach that employs information from existing test feeders. The cyber side considers an equivalent communication network by transforming the physical topology into possible and feasible simulated network. Two examples are presented to demonstrate the feasibility of the proposed framework to generate cyber‐physical test feeders.

Wang, Lusha↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Quantum Machine Learning Applications in High-Energy Physics

Some of the most significant achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that continually push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated to understand how the community can benefit from the advantages of quantum information science. Nonetheless, to unleash the full potential of quantum computing, there is a need to understand the quantum behavior and, thus, scale up current algorithms beyond what can be simulated in classical processors. In this work, we explore potential applications of quantum machine learning to data analysis tasks in HEP and how to overcome the limitations of algorithms targeted for Noisy Intermediate-Scale Quantum (NISQ) devices.

Delgado, Andrea↗
DOE OSTI2026

Electronic transport, thermal transport, thermal expansion, and magnetization in the strongly correlated metal LaNi⁢O 3

Perovskite structured LaNiO 3 is a strongly correlated metal with intriguing thermal and magnetic properties. The volume dependence of calculated and measured physical properties can add additional critical information to develop a more in-depth understanding of this strongly correlated phenomenon. Taking advantage of recent single crystal LaNiO 3 growth using the floating-zone method, we have measured the thermal expansion, the magnetostriction, and the pressure dependence of the magnetic susceptibility, which then allows derivation of the Grüneisen parameters γ e = $\frac{dlnN(E_F)}{d lnV}$, γ χ = $\frac{dlnχ}{d lnV}$, as well as of electric and thermal transport properties. We simulate the volume dependence of structural and magnetic properties using Density Functional Theory calculations at the Generalized Gradient Approximation level. A large discrepancy between experimental values and calculated ones suggests that strong correlations are likely to be dynamic in nature. This study also provides a side-by-side comparison of measurements in single crystal and polycrystalline samples of LaNiO 3 to elucidate intrinsic materials properties. A broad hump at high temperatures in the temperature dependence of magnetization found in the single crystal sample of LaNiO 3 has been rationalized by a model that includes the influence of electron correlations on the Landau diamagnetism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Magnon-magnon coupling in an all-oxide insulator garnet Y 3 ⁢Fe 5 ⁢O 12 /Tm 3 ⁢Fe 5⁢ O 12 heterostructure

Hybrid magnonic systems have recently attracted significant attention due to their intriguing physics and potential applications in coherent information processing. In this regard, we have investigated magnon-magnon coupling in the all-oxide insulator garnet Y 3⁢ Fe 5⁢ O 12 (YIG)/Tm 3 ⁢Fe 5 ⁢O 12 (TmIG) heterostructure. The presence of an avoided mode crossing region between the ferromagnetic resonance modes of YIG and TmIG indicates interfacial exchange coupling. This all-insulator system enables interfacial coupling via magnon-magnon interactions. The mode crossing occurs at 𝑓 𝑐 = 6.88 GHz, corresponding to the minimal resonance separation between the two hybrid modes. The coupling strength is determined to be 78 MHz (or 2.8 mT), providing clear evidence of magnon-magnon coupling between the two Kittel modes in the YIG/TmIG insulator system at 200 K. As a result, these findings pave the way for engineering magnonic band structures, tunable coherent magnonic interactions, and nonreciprocal spin transport, which are crucial for advancing ultralow-power magnonic devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Situational Awareness of Grid Anomalies (SAGA) for Visual Analytics—Near-Real-Time Cyber-Physical Resiliency Through Machine Learning

The Situational Awareness of Grid Anomalies (SAGA) project built upon foundational power system tools developed at the National Renewable Energy Laboratory (NREL) integrated with an ever-increasing set of Gridmetrics data extracted from the cable television (CATV) broadband network infrastructure while assimilating other time-series geospatial data and information, such as weather and cyber-physical phenomena, to demonstrate a disruptive technology for power system data analytics relying on existing infrastructure. Three research thrusts supported (1) visual analytics, (2) cyber-physical power system simulation, and (3) anomaly detection. SAGA created technology that leverages, couples, and fortifies two vastly different realms - power and broadband - to increase the resiliency of the power grid in the face of increasing cyberattacks and operational challenges related to integrating DERs. The exploration of potential synergies of broadband-enabled grids resulted in identifying a mutually beneficial symbiosis that can increase the resiliency of both power and broadband services. Broadband networks perform better with reliable power and are good at providing real-time measurements that identify where the grid is under attack, is failing, or is weak. Likewise, sensor-starved distribution grids perform better and can be more reliable when their operation is buttressed with observations of broadband-detected anomalies. Future research can explore broadband's contribution to continuing to improve grid resiliency, reliability, and cost-effective operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Crescendo beyond the horizon: More gravitational waves from domain walls bounded by inflated cosmic strings

Gravitational-wave (GW) signals offer a unique window into the dynamics of the early universe. GWs may be generated by the topological defects produced in the early universe, which contain information on the symmetry of UV physics. We consider the case in which a two-step phase transition produces a network of domain walls bounded by cosmic strings. Specifically, we focus on the case in which there is a hierarchy in the symmetry-breaking scales, and a period of inflation pushes the cosmic string generated in the first phase transition outside the horizon before the second phase transition. We show that the GW signal from the evolution and collapse of this string-wall network has a unique spectrum, and the resulting signal strength can be sizeable. In particular, depending on the model parameters, the resulting signal can show up in a broad range of frequencies and can be discovered by a multitude of future probes, including the pulsar timing arrays and space- and ground-based GW observatories. As an example that naturally gives rise to this scenario, we present a model with the first phase transition followed by a brief period of thermal inflation driven by the field responsible for the second stage of symmetry breaking. Here, the model can be embedded into a supersymmetric setup, which provides a natural realization of this scenario. In this case, the successful detection of the peak of the GW spectrum probes the soft supersymmetry breaking scale and the wall tension.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Imaging the initial condition of heavy-ion collisions and nuclear structure across the nuclide chart

High-energy nuclear collisions encompass three key stages: the structure of the colliding nuclei, informed by low-energy nuclear physics, the initial condition , leading to the formation of quark–gluon plasma (QGP), and the hydrodynamic expansion and hadronization of the QGP, leading to final-state hadron distributions that are observed experimentally. Recent advances in both experimental and theoretical methods have ushered in a precision era of heavy-ion collisions, enabling an increasingly accurate understanding of these stages. However, most approaches involve simultaneously determining both QGP properties and initial conditions from a single collision system, creating complexity due to the coupled contributions of these stages to the final-state observables. To avoid this, we propose leveraging established knowledge of low-energy nuclear structures and hydrodynamic observables to independently constrain the QGP’s initial condition. By conducting comparative studies of collisions involving isobar-like nuclei—species with similar mass numbers but different ground-state geometries—we can disentangle the initial condition’s impacts from the QGP properties. This approach not only refines our understanding of the initial stages of the collisions but also turns high-energy nuclear experiments into a precision tool for imaging nuclear structures, offering insights that complement traditional low-energy approaches. Opportunities for carrying out such comparative experiments at the Large Hadron Collider and other facilities could significantly advance both high-energy and low-energy nuclear physics. Additionally, this approach has implications for the future electron-ion collider. While the possibilities are extensive, we focus on selected proposals that could benefit both the high-energy and low-energy nuclear physics communities. Originally prepared as input for the long-range plan of U.S. nuclear physics, this white paper reflects the status as of September 2022, with a brief update on developments since then.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Chapter Four—MSW characterization and preprocessing for biofuels and bioproducts

The organic fraction of MSW (OFMSW) has a high carbohydrate and lipid content, indicating its good potential as a feedstock for bioethanol and biodiesel production. However, variability in feedstock composition for MSW is a critical challenge for downstream conversion processes such as pyrolysis, gasification, incineration, and biotechnological conversion. MSW characterization is critical to inform and optimize the design of physical handling processes to minimize costs and environmental impacts, and improve the downstream conversion performance of MSW materials. Here, this book chapter focuses on current MSW characterization and preprocessing technologies and discusses the costs, benefits, and environmental impacts associated with MSW preprocessing. Also, the book chapter outlines the potential strategies to increase total revenue and mitigate negative environmental impacts for MSW recycling systems.

09 BIOMASS FUELS↗
DOE OSTI2023

Up up down down left right left right B A Start for the catalytic hackers of programmable materials

Catalysts have advanced over the last century to accelerate and control reactions based on static active sites. More effective catalysis can be achieved using catalysts that change with time over the course of a reaction, providing a dynamic free energy landscape that is tailored to each step in the reaction sequence. Here, the catalyst is modulated via an input program that directs the surface to change physically or electronically with time, providing information regarding the extent and duration of change optimized for specific combinations of chemistry and programmable catalyst surfaces. While in its infancy, programmable catalysis is advancing with parallel efforts to establish fundamental principles of dynamic catalysts, design of programmable materials, and strategies to design input programs that will control catalysis for faster and more selective reactions.

catalyst↗
DOE OSTI2022

BISON fuel performance modeling optimization for experiment X447 and X447A using axial swelling and cladding strain measurements

With the recent need to qualify new reactor designs such as the Versatile Test Reactor (VTR), fuel performance calculations need to be performed to determine safety criteria of the proposed designs. In order to validate the fuel performance results obtained by a fuel performance code, BISON, for new reactor designs, legacy fuel from EBR-II and FFTF MFF with Post -Irradiation Examination (PIE) data need to be used as validation cases to benchmark models. Here in this work, BISON has been paired with the Fuels Irradiation & Physics Database (FIPD) and IFR Materials Information System (IMIS) to supply PIE data for comparison with simulations of EBR-II experiments X447/X447A. X447/X447A were assessed by implementing models for Fuel Cladding Chemical Interaction (FCCI) within BISON and optimizing the friction coefficient between the fuel surface and the cladding, the anisotropic swelling factor, and the HT9 first thermal creep scalar (which scales the first term in the HT9 creep equation) to best match the PIE axial fuel swelling height and cladding profilometry for all pins in X447/X447A. The optimal values were found using a generic algorithm developed to select different values for the three parameters until end criteria was met and error couldn’t be reduced further. The BISON-simulated cladding profilometry was evaluated using Standard Error of the Estimate (SEE) to account for the profile shape of the cladding profilometry. Optimal values for the friction coefficient, anisotropic fuel swelling factor, and HT9 first thermal creep scalar were found to best fit the BISON simulation results to the PIE measurements found in IMIS and FIPD. Improvements to current models are suggested to account for the underprediction of fuel swelling at low burnups and the overprediction of fuel swelling at higher burnups observed for the axial fuel swelling height. Although two pins in EBR-II X447/X447A (DP70 and DP75) were known to fail due to FCCI, none of the pins simulated in BISON reached a cumulative damage fraction (CDF) above 0.008 with FCCI correlations coupled in the BISON simulations. The error estimate generated for all pins in X447/X447A using optimal values was 209 µm, which is deemed acceptable.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Turning Outsiders into Insiders: A Call for Papers to Expand the Reach of National Laboratories’ Safety Successes, Challenges, Research, and Approaches

The most complex challenges facing the world today comprise the work of [Department of Energy’s] (DOE’s) 17 National Laboratories: [...] From furthering U.S. energy independence and leadership in clean technologies; to promoting innovation that advances U.S. economic competitiveness; to conducting research of the highest caliber in the physical, chemical, biological, materials, computational, and information sciences to advance understanding of the world around us-the Laboratories’ purview is expansive and further their contributions are indispensable.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Studying phonon coherence with a quantum sensor

Nanomechanical oscillators offer numerous advantages for quantum technologies. Their integration with superconducting qubits shows promise for hardware-efficient quantum error-correction protocols involving superpositions of mechanical coherent states. Limitations of this approach include mechanical decoherence processes, particularly two-level system (TLS) defects, which have been widely studied using classical fields and detectors. In this manuscript, we use a superconducting qubit as a quantum sensor to perform phonon number-resolved measurements on a piezoelectrically coupled phononic crystal cavity. This enables a high-resolution study of mechanical dissipation and dephasing in coherent states of variable size ($\overline{n}$ ≃ 1 – 10 phonons). We observe nonexponential relaxation and state size-dependent reduction of the dephasing rate, which we attribute to TLS. Using a numerical model, we reproduce the dissipation signatures (and to a lesser extent, the dephasing signatures) via emission into a small ensemble (N = 5) of rapidly dephasing TLS. Our findings comprise a detailed examination of TLS-induced phonon decoherence in the quantum regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Overlapping qubits from non-isometric maps and de Sitter tensor networks

The emergence of a local effective theory from a more fundamental theory of quantum gravity with seemingly fewer degrees of freedom is a major puzzle of theoretical physics. A recent approach to this problem is to consider general features of the Hilbert space maps relating these theories. In this work, we construct approximately local observables, or overlapping qubits, from such non-isometric maps. We show that local processes in effective theories can be spoofed with a quantum system with fewer degrees of freedom, with deviations from actual locality identifiable as features of quantum gravity. For a concrete example, we construct two tensor network models of de Sitter space-time, demonstrating how exponential expansion and local physics can be spoofed for a long period before breaking down. Our results highlight the connection between overlapping qubits, Hilbert space dimension verification, degree-of-freedom counting in black holes, holography, and approximate locality in quantum gravity.

Quantum information↗
DOE OSTI2025

Fault-tolerant operation and materials science with neutral atom logical qubits

We report on the fault-tolerant operation of logical qubits on a neutral atom quantum computer, with logical performance surpassing physical performance for multiple circuits including Bell state preparation (12x error reduction), random circuits (15x), and a prototype Anderson Impurity Model ground state solver for materials science applications (up to 6x, non-fault-tolerantly). The logical qubits are implemented via the [[4, 2, 2]] code (C 4 ). Our work constitutes the first complete realization of the benchmarking protocol proposed by Gottesman 2016 demonstrating results consistent with fault tolerance. In light of recent advances on applying concatenated C 4 /C 6 detection codes to achieve error correction with high code rates and thresholds, our work can be regarded as a building block towards a practical scheme for fault tolerant quantum computation. Our demonstration of a materials science application with logical qubits particularly demonstrates the immediate value of these techniques on current experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reduction of the molecular hamiltonian matrix using quantum community detection

Abstract Quantum chemistry is interested in calculating ground and excited states of molecular systems by solving the electronic Schrödinger equation. The exact numerical solution of this equation, frequently represented as an eigenvalue problem, remains unfeasible for most molecules and requires approximate methods. In this paper we introduce the use of Quantum Community Detection performed using the D-Wave quantum annealer to reduce the molecular Hamiltonian matrix in Slater determinant basis without chemical knowledge. Given a molecule represented by a matrix of Slater determinants, the connectivity between Slater determinants (as off-diagonal elements) is viewed as a graph adjacency matrix for determining multiple communities based on modularity maximization. A gauge metric based on perturbation theory is used to determine the lowest energy cluster. This cluster or sub-matrix of Slater determinants is used to calculate approximate ground state and excited state energies within chemical accuracy. The details of this method are described along with demonstrating its performance across multiple molecules of interest and bond dissociation cases. These examples provide proof-of-principle results for approximate solution of the electronic structure problem using quantum computing. This approach is general and shows potential to reduce the computational complexity of post-Hartree–Fock methods as future advances in quantum hardware become available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗