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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Thermalization and criticality on an analogue–digital quantum simulator

Abstract Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators 1,2 . Unlocking the full potential of such systems towards this goal requires flexible initial state preparation, precise time evolution and extensive probes for final state characterization. Here we present a quantum simulator comprising 69 superconducting qubits that supports both universal quantum gates and high-fidelity analogue evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. This hybrid platform features more versatile measurement capabilities compared with analogue-only simulators, which we leverage here to reveal a coarsening-induced breakdown of Kibble–Zurek scaling predictions 3 in theXYmodel, as well as signatures of the classical Kosterlitz–Thouless phase transition 4 . Moreover, the digital gates enable precise energy control, allowing us to study the effects of the eigenstate thermalization hypothesis 5–7 in targeted parts of the eigenspectrum. We also demonstrate digital preparation of pairwise-entangled dimer states, and image the transport of energy and vorticity during subsequent thermalization in analogue evolution. These results establish the efficacy of superconducting analogue–digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Science & Technology - Other Topics↗
DOE OSTI2023

Universal Passive Synchronization Method for Grid-Forming Inverters Without Mode Transition

Power systems are transforming with increasing levels of inverter-based resources (IBRs). This transformation requires critical roles of grid-forming (GFM) inverters replacing synchronous generators for bulk power system stabilization and ancillary services, also allowing flexible power system operation, such as microgrid that is operated by multiple GFM IBRs to achieve system resilience against contingencies. To realize the resilient power systems allowing flexible in-and-out operation of GFM IBRs potentially programmed with different primary controls, a synchronization method universally applicable, i.e., independent of control types, would be beneficial to ease the integration process, but it has not been actively studied. To fill the gap, this paper proposes a universal synchronization method that achieves a passive synchronization to enable a smooth transition in a grid with off-nominal system parameters, i.e., voltage and frequency. The logic proposed requires no modification on the primary control, thus applicable to any type of GFMs with a voltage reference input. To validate the concept, a simulation of an IEEE 13-bus benchmark system modified with 3 GFM inverters is presented. It simulates an inverter-driven black start scenario in which GFM inverters autonomously turn on and connect to the grid under heavy loading, using the synchronization logic. The case study demonstrates that GFM inverters can tune their voltage reference to smoothly synchronize without severe transients, and contribute to a seamless black start of the grid under unbalanced load conditions. Two GFM methods - Droop and dispatchable virtual oscillator control - are used for the demo to validate feasibility and interoperability of the passive synchronization.

black start↗
DOE OSTI2017

Semi-Analytical Benchmarks for MCNP6 [Slides]

This presentation begins with an Introduction and some background on semi-analytical benchmarks for MCNP6. Discussion topics include: verification & validation, MCNP history of V&V, semi-analytic benchmarks, numerical Results, comparison of semi-analytic to MCNP, and tips for proper comparison. This presentation ends with conclusions & future Work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Simulation of discrete cracks driven by nearly incompressible fluid via 2D combined finite-discrete element method

Here, we propose a novel hydraulic solver in order to simulate key mechanisms that control fluid-driven cracks in the framework of the combined finite-discrete element method (FDEM). The main innovative aspect of the present work is the independence of the fluid's critical time step size with the fracture opening. This advantage is extremely important because it means that very fine meshes can be used around areas of interest, such as boreholes, without penalizing the computational cost as fractures propagate (ie, open) and the fluid flows through them. This is a great advantage over other recently introduced approaches that exhibit a dependency of the time step in the form of Δt crit ∝ (l/a) 2 where l is the element size and a is the fracture aperture. This paper presents a series of benchmark cases for the proposed solver. The rationale adopted by the authors was to benchmark and validate the implementation of the hydraulic solver in an incremental fashion, starting from the simplest cases and building in complexity. The calculations shown in this work clearly demonstrate that the proposed approach is able to reproduce analytical results for fluid flow through a single crack. The results presented in this paper also demonstrate that the new approach is robust enough to deal with complex fracture patterns and complex geometries; the obtained fluid-driven fracture patterns in the vicinity of a borehole certainly stand to the scrutiny of human visual perception.

58 GEOSCIENCES↗
DOE OSTI2026

Rapid neutron and gamma-ray source localization using machine learning

Rapid localization of radiation sources is critical for applications including nuclear emergency response, safeguards, and security. However, conventional imaging systems such as neutron scatter cameras and Compton cameras depend on rare coincidence events, which often result in long acquisition times. In this work, we address the challenge of rapid source localization by developing a machine learning approach to predict the direction of a single radiation source using only count rates from an array of neutron and gamma-ray detectors. The proposed model is a fully connected neural network (FCNN) trained using Monte Carlo simulation data from a 252 Cf source. The model hyperparameters are optimized with a small set of routine 252 Cf measurements. We benchmarked the performance of the trained and optimized machine learning model using additional 252 Cf , 137 Cs , and PuBe measurements under laboratory conditions with varying source-detector configurations. For these measurements, the machine learning model achieved a mean localization error smaller than 30° with 3 x 10 3 system counts, corresponding to 8 s measurement time for the imaging system used in this work. In this low-statistics regime, the method outperformed traditional scatter-based imaging by more than 75% in localization accuracy for the evaluated measurement configurations. These results demonstrate that a machine learning-based approach can significantly reduce the time required for accurate single-source localization, providing a robust and computationally efficient alternative to traditional imaging systems in time-critical nuclear security and emergency response scenarios.

Gamma-ray imaging↗
DOE OSTI2013

Multi-core performance studies of a Monte Carlo neutron transport code

Performance results are presented for a multi-threaded version of the OpenMC Monte Carlo neutronics code using OpenMP in the context of nuclear reactor criticality calculations. Our main interest is production computing, and thus we limit our approach to threading strategies that both require reasonable levels of development effort and preserve the code features necessary for robust application to real-world reactor problems. Several approaches are developed and the results compared on several multi-core platforms using a popular reactor physics benchmark. A broad range of performance studies are distilled into a simple, consistent picture of the empirical performance characteristics of reactor Monte Carlo algorithms on current multi-core architectures.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2019

In Situ Observation of Growth Dynamics in DECLIC Directional Solidification Insert Onboard ISS: DSI-R Flight Campaign

The study of solidification microstructure formation is of utmost importance for materials design and processing, as solid-liquid interface patterns largely govern mechanical and physical properties. Pattern selection occurs under dynamic conditions of growth in which the initial morphological instability evolves nonlinearly and undergoes a reorganization process. The dynamic and nonlinear nature of this instability renders in situ observation of the interface an invaluable tool to gain knowledge on the time-evolution of the interface pattern. Transparent organic analogs, which solidify like metallic alloys, allow direct visualization of interface dynamics. Extensive ground-based studies of both metallic and organic bulk samples have established the presence of significant convection during solidification processes that alters the formation of interfacial microstructures. A reduced-gravity environment is therefore mandatory for fluid flow elimination in bulk samples. In the framework of the CNES project MISOL3D (MIcrostrutures de SOLidification 3D) and the NASA projects DSIP (Dynamical Selection of 3D Interface Patterns), SPADES (SPAtiotemporal Evolution of three-dimensional DEndritic array Structures) and CAMUS (ComputAtional Studies of MicrostrUcture Formation During Alloy Solidification in Microgravity), we participated in the development of the Directional Solidification Insert (DSI) of the DEvice for the study of Critical Liquids and Crystallization (DECLIC). The DECLIC-DSI is dedicated to in situ and real-time characterization of solid-liquid interface patterns during directional solidification of transparent alloys in diffusive transport regime. Between April 2010 and March 2011, the first ISS campaign (DSI) explored the entire range of microstructures resulting in unprecedented observations. A second campaign (DSI-R), performed between October 2017 and December 2018, in which the insert contained an alloy of higher solute concentration, allowed to complete the benchmark database. The increase of solute concentration resulted in well-developed dendritic patterns at lower velocities (lower interface curvature and larger tip radius). The microstructure resulting from dendritic growth is dominant in metallurgy so that it is fundamental to understand the mechanisms of its formation. The main aims of this experimental campaign are to understand: the mechanisms of the cell to dendrite transition, the fundamental mechanisms of sidebranching formation, the dependence of dendrite tip shapes on growth conditions, the interaction of primary array and secondary sidebranches, and the influence of subgrain boundaries on the spatiotemporal organization of the array structure. Preparation, analysis and interpretation of the experiments performed onboard ISS are considerably enhanced by experiments performed on ground using thin-samples (Pr. Trivedi’s group) and phase-field simulations of microstructure formation in a diffuse growth regime (Pr. Karma’s group). In this summary, we will present an initial assessment of the results obtained during the DSI-R campaign. Nomenclature Vp: pulling velocity G: thermal gradient L: solidified length rtip: tip curvature radius Acronyms/Abbreviations CADMOS: Centre d'Aide au Développement des activités en Micro-pesanteur et des Opérations Spatiales CNES: Centre National d’Études Spatiales DECLIC: Device for the study of Critical LIquids and Crystallization DSI: Directional Solidification Insert DSI-R: Directional Solidification Insert - Refurbish ISS: International Space Station NASA: National Aeronautics and Space Administration PF: phase-field SCN: Succinonitrile 3D: three-dimensional

Mota, F.L.↗
DOE OSTI2017

ENDF/B-VIII.0 testing with AMPX and SCALE

As the new ENDF/B-VIII.0 data has continued development from its initial beta release into the final release version, it has subsequently undergone processing and testing at Oak Ridge National Laboratory with AMPX and SCALE. Each of the beta releases has been processed with AMPX to create both multigroup (MG) and continuous-energy (CE) libraries. The MG libraries were all processed using the SCALE 252-group structure. These libraries were then used with the VALID suite for benchmarking the new data library's performance against the currently distributed ENDF/B-VII.1 (VII.1)-based SCALE libraries. Here, we compare the MG and CE VALID results for the two current ENDF releases, versions VII.1 and the most recent beta release of ENDF/B-VIII.0 as of this writing (VIII.0.β4). Generally, both the MG and CE VALID aggregate results for VIII.0.β4 compare reasonably well with their VII.1-derived counterparts. As can be expected of any new ENDF release, some of the individual cases have substantially different computed neutron multiplicity (k{sub eff}) values between the two releases, although typically the differences are less than 100 percent-milli (pcm). However, changes to the plutonium cross sections have induced much larger changes in the ratio of the computed k{sub eff} to the experimental value (C/E) for thermal spectrum plutonium solution critical systems. This paper details the comparative performance of the VII.1 and VIII.0.β4 data for the broad range of test cases covered by the VALID suite. A companion paper at this conference provides details of the baseline validation of SCALE 6.2, including the first release of the VII.1 data. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Ethanol Conversion to C 4+ Olefins over Bimetallic Copper- And Lanthanum-Containing Beta Zeolite Catalysts

We report ethanol conversion to C 4+ olefins remains a critical yet nonselective process for producing renewable middle distillates. Here, Cu–La/Beta catalysts composed of copper and lanthanum incorporated onto a dealuminated Beta support are reported for ethanol conversion to C 4+ olefins (73% selectivity, ~98% ethanol conversion, 623 K,<4% C 1 –C 3 hydrocarbons) which particularly favors C 5+ olefin formation (43% selectivity) as a distinction from the benchmarking Cu–Y/Beta catalyst. Monometallic Cu/Beta or La/Beta samples are insufficient to catalyze the C 4+ olefin formation and primarily form dehydration products (e.g., ethylene and diethyl ether), indicating the necessity of both Cu and La species for butene and C 5+ olefin formation. Increasing the bulk La loading at a fixed Cu content yields higher C 5+ olefins until the La/Cu molar ratio reaches 3.6. These findings indicate Cu–La/Beta as an effective ethanol conversion catalyst that facilitates multiple C–C bond formation events required for synthesizing C 5+ olefins (i.e., hexenes and octenes).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms

Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging. This has led to wide-spread adoption of AI-powered tools, in pursuit of improving accuracy and efficiency of this process. While the unique challenges presented by each modality and clinical task demand customized tools, the cumbersome process of making problem-specific choices has triggered the critical need for a generic solution to enable rapid development of models in practice. In this spirit, we develop DDxNet, a deep architecture for time-varying clinical data, which we demonstrate to be well-suited for diagnostic tasks involving different modalities (ECG/EEG/EHR), required level of characterization (abnormality detection/phenotyping) and data fidelity (single-lead ECG/22-channel EEG). Using multiple benchmark problems, we show that DDxNet produces high-fidelity predictive models, and sometimes even provides significant performance gains over problem-specific solutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Diagonal isometric form for tensor network states in two dimensions

Isometric tensor network states (isoTNS) generalize the isometric form of the one-dimensional matrix product states to tensor networks in two and higher dimensions. Here, we introduce an alternative isometric form for isoTNS by incorporating auxiliary tensors to represent the orthogonality hypersurface. We implement the time evolving block decimation algorithm on this new isometric form and benchmark the method by computing ground states and the real time evolution of the transverse field Ising model in two dimensions on large square lattices of up to 1250 sites. Our results demonstrate that isoTNS can efficiently capture the entanglement structure of two-dimensional area law states. The short-time dynamics is also accurately reproduced even at the critical point. Our isoTNS formulation further allows for a natural extension to different lattice geometries, such as the honeycomb or kagome lattice.

2-dimensional systems↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
NASA NTRS1988

Accuracy requirements and benchmark experiments for CFD validation

The role of experiment in the development of Computation Fluid Dynamics (CFD) for aerodynamic flow prediction is discussed. The CFD verification is a concept that depends on closely coordinated planning between computational and experimental disciplines. Because code applications are becoming more complex and their potential for design more feasible, it no longer suffices to use experimental data from surface or integral measurements alone to provide the required verification. Flow physics and modeling, flow field, and boundary condition measurements are emerging as critical data. Four types of experiments are introduced and examples given that meet the challenge of validation: flow physics experiments; flow modeling experiments; calibration experiments; and verification experiments. Measurement and accuracy requirements for each of these differ and are discussed. A comprehensive program of validation is described, some examples given, and it is concluded that the future prospects are encouraging.

Marvin, Joseph G.↗
NASA NTRS1988

Accuracy requirements and benchmark experiments for CFD validation

The role of experiment in the development of Computational Fluid Dynamics (CFD) for aerodynamic flow prediction is discussed. The CFD verification is a concept that depends on closely coordinated planning between computational and experimental disciplines. Because code applications are becoming more complex and their potential for design more feasible, it no longer suffices to use experimental data from surface or integral measurements alone to provide the required verification. Flow physics and modeling, flow field, and boundary condition measurements are emerging as critical data. Four types of experiments are introduced and examples are given that meet the challenge of validation: flow physics experiments; flow modeling experiments; calibration experiments; and verification experiments. Measurement and accuracy requirements for each of these differ and are discussed. A comprehensive program of validation is described, some examples given, and it is concluded that the future prospects are encouraging.

Marvin, Joseph G.↗
DOE OSTI2022

Sensitivity Coefficients Calculated for the Prompt Neutron Decay Constant at or Near Delayed Critical [Abstract]

Experimenters at Los Alamos National Laboratory (LANL) measure the prompt neutron decay constant for many experiments at the National Criticality Experiments Research Center (NCERC) to infer reactivity and the effective neutron multiplication factor. These quantities are very important for nuclear criticality safety and validating nuclear data. Uncertainty in measures of criticality of an experimental configuration can be determined prior to physically performing the experiment by applying first order perturbation theory to Monte Carlo codes, such as MCNP®. The first order perturbation theory produces first derivatives of some nuclear parameter to nuclear data (e.g., cross section data). This first derivative is commonly referred to as a sensitivity coefficient. Currently, the MCNP® software has the capability of computing effective neutron multiplication factor sensitivity coefficients to cross section data. This work builds off of this MCNP® capability and the first order perturbation theory to provide a method of calculating sensitivity coefficients for the prompt neutron decay constant at or near delayed critical to cross section data. The prompt neutron decay constant sensitivity coefficient calculated in this work does not depend on any modification of the MCNP® source code. Prompt neutron decay constant sensitivity coefficient calculations can be used to infer reactivity and effective neutron multiplication factor sensitivity coefficient values as well. By investigating the trends of prompt neutron decay constant sensitivity coefficients for nuclide-reaction pairs across energy spectra, experiments can be designed to maximize or minimize the uncertainty in the prompt neutron decay constant in a particular energy region, which can lead to further optimization studies. Prompt neutron decay constant sensitivity coefficients will be calculated for the Jezebel benchmark. Subsequently, these sensitivity coefficients will be used in a data assimilation process to determine if there is or are optimal experiments that can be performed to provide insight into adjustments of uncertain/inaccurate cross section data. Specifically, the effect of the prompt neutron decay constant sensitivity coefficients on the nuclear data-induced uncertainty in the effective neutron multiplication factor will be examined

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

MCNP ® Code V.6.3.0 Release Notes

The Monte Carlo N-Particle ® (MCNP ® ) code is a general-purpose, continuous-energy, generalized geometry, time-dependent, radiation transport code developed by the MCNP development team. The MCNP calculations provide predictive capabilities that can replace expensive or impossible-to perform experiments. Specific application problems include simulations of experimental diagnostics, intrinsic radiation, radiation detection and measurement, criticality safety, nuclear threat reduction and response, radiation health protection, nuclear weapons effects, and nuclear forensics. This MCNP code, version 6.3.0, follows the MCNP6.2.0 version. Since the release of MCNP6.2.0, many changes have been made to the MCNP code. These changes include new or improved features, a new build system, code enhancement and modernization, and bug fixes. The MCNP code, version 6.3.0, theory and user input information is documented in MCNP ® Code Version 6.3.0 Theory & User Manual, the build guidance for various platforms is documented in MCNP ® Code Version 6.3.0 Build Guide, and the verification and validation testing for various application benchmark test suites is documented in MCNP ® Code Version 6.3.0 Verification & Validation Testing.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Initial Validation of the Neutron Physics Codes in COSINE Using the MIT BEAVRS Benchmark

A software package for nuclear power plant design and analysis, known as COSINE, has been developed by State Nuclear Power Software Development Center (SNPSDC). A detailed benchmark called BEAVRS which includes measured reactor data is presented by MIT. The BEAVRS benchmark is based on a commercial power plant and provides a highly-detailed Pressurized Water Reactor (PWR) specification with measured operational data that has been used to validate the neutron physics codes in COSINE. In this paper, a COSINE core model is prepared to calculate the BEAVRS benchmark. The first stage validation is focused on the depletion of two cycles and initial physics test of cycle 1. The calculation results agree well with the measured data. It could be confirmed that the COSINE codes package has the ability to design and analyze a commercial PWR core, and the precision of the calculation results can meet the requirements of industrial design and analysis. A calculation of MIT BEAVRS benchmark using COSINE has been performed in this paper. A lot of measurements are given in the benchmark specification which can be used for code validation. The calculation includes a depletion calculation of both two cycles and a calculation of the initial physics test of cycle 1. COSINE neutron physics codes use the mature industrial calculation method which can achieve reasonable results with acceptable accuracy and speed. Through the calculation, it can be seen that the difference of critical boron concentration calculated from cos Nu is less than 25 pcm compared with measured data. And the relative difference of rod worth is less than 10%. The isothermal temperature coefficient difference between measurement and calculation is less than 1 pcm/deg. F. All results meet the criteria of ANS. The validation for measurements after initial physics test following a more complex power history and the validation for in-core detector signals are underway. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

IER 501: Pulsed Neutron Die-Away Experiments at LLNL [Slides]

This presentation notes the purpose of Pulsed Neutron Die Away (PNDA) for Thermal Neutron Scattering Law (TSL) validation. PNDA for TSL validation does not require fissile material. It consists of simple target shapes and compositions and is only sensitive to absorption and scattering of target medium. Well conducted experiments have uncertainties of 0.1% - 0.5%. This presentation also covers Pulsed Neutron Die Away Experiments, while noting previous experiments such as the FY22 PNDA Experiments. The presentation concludes with FY23 benchmarking efforts and a timeline to FY24.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗