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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Recent Improvements in the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) has significantly evolved since the first release of the point source database in 1993. The NSRDB has been widely used by the solar energy industry to provide long-term time-series solar resource data for various project phases. The NSRDB represents the state of the art in the satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that allows improvements in accuracy with the deployment of the next-generation geostationary satellites. This poster provides an overview of (1) the improved spatiotemporal resolution; (2) on-demand services and their applications; (3) future Improvements, such as a new direct normal irradiance model and new methods to gap-fill missing data using physics-guided machine learning; (4) data quality; and (5) data dissemination.

MATHEMATICS AND COMPUTING,SOLAR ENERGY↗
DOE OSTI2024

Coulomb Interaction-Driven Entanglement of Electrons on Helium

The generation and evolution of entanglement in many-body systems is an active area of research that spans multiple fields, from quantum information science to the simulation of quantum many-body systems encountered in condensed matter, subatomic physics, and quantum chemistry. Motivated by recent experiments exploring quantum information processing systems with electrons trapped above the surface of cryogenic noble gas substrates, we theoretically investigate the generation of entanglement between two electrons via their unscreened Coulomb interaction. The model system consists of two electrons confined in separate electrostatic traps that establish microwave-frequency quantized states of their motion. We compute the motional energy spectra of the electrons, as well as their entanglement, by diagonalizing the model Hamiltonian with respect to a single-particle Hartree product basis. We also compare our results with the predictions of an effective Hamiltonian. The computational procedure outlined here can be employed for device design and guidance of experimental implementations. In particular, the theoretical tools developed here can be used for fine-tuning and optimization of control parameters in future experiments with electrons trapped above the surface of superfluid helium or solid neon. Published by the American Physical Society 2024

Physics↗
DOE OSTI2023

Using limited neural networks to assess relative mechanistic influence on shock heating in granular solids

The rapid compaction of granular media results in localized heating that can induce chemical reactions, phase transformations, and melting. However, there are numerous mechanisms in play that can be dependent on a variety of microstructural features. Machine learning techniques such as neural networks offer a ubiquitous method to develop models for physical processes. Limiting what kind of microstructural information is used as an input and assessing normalized changes in network error, the relative importance of different mechanisms can be inferred. Here we utilize binned, initial density information as network inputs to predict local shock heating in a granular high explosive trained from large-scale molecular dynamics simulations. In this study, the spatial extent of the density field used in the network is altered to assess the importance and relevant length scales of the physical mechanisms in play, where different microstructural features result in different predictive capabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Application of machine learning to estimate fireball characteristics and their uncertainty from infrared spectral data

Experiments or events involving high explosives (HE) can be monitored remotely by infrared (IR) sensors to gather information about the configuration or materials involved in the device. Researchers at the Air Force Institute of Technology (AFIT) developed a phenomenological model for HE fireball spectra in the IR range that allows for parameters to be extracted from Fourier transform infrared (FTIR) data. This model includes parameters tied to physical characteristics of the fireball: temperature, size, soot, and gas concentrations. Previous works have sought to recover these parameters by the fitting of either whole spectra or select wavenumber bands to this phenomenological model. Difficulties arise due to the complex relationships between the parameters to be fit. Uncertainty quantification of the estimated fireball parameters is also problematic since HE experiments do not have any ground truth information on the parameters. It is suggested that artificial neural network (ANN) based approaches may be well suited to this problem, because of their ability to capture complex and highly nonlinear relationships. As such, this work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data. Networks are designed using the hyperparameter optimization tool Hyperopt and trained/tested on artificial data generated using the phenomenological model developed by AFIT. The results of applying the network to the artificial data set are compared to a physics-based band approach that uses a selected number of bands based on their physical properties. Information on the uncertainty of estimating parameters from remotely sensed experimental data is obtained by treating the accuracy of the DNN model on artificial data as an upper bound and by examining the impact of emissivity due to soot on parameter estimation error; the results for artificial data are likely to be optimistic as compared to recovering parameters from experimental data.

42 ENGINEERING↗
DOE OSTI2024

RODEM Jet Datasets

We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling.

FOS: Physical sciences↗
DOE OSTI2020

A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is introduced using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the further benefit of rigorously quantified uncertainty of these estimates.

58 GEOSCIENCES↗
DOE OSTI2022

Nuclear Forces for Precision Nuclear Physics -- a collection of perspectives

This is a collection of perspective pieces contributed by the participants of the Institute of Nuclear Theory's Program on Nuclear Physics for Precision Nuclear Physics which was held virtually from April 19 to May 7, 2021. The collection represents the reflections of a vibrant and engaged community of researchers on the status of theoretical research in low-energy nuclear physics, the challenges ahead, and new ideas and strategies to make progress in nuclear structure and reaction physics, effective field theory, lattice QCD, quantum information, and quantum computing. The contributed pieces solely reflect the perspectives of the respective authors and do not represent the viewpoints of the Institute for Nuclear theory or the organizers of the program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data

The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ultimately sensitive to the nature of the order parameter in the material, including the functional form of Ginzburg-Landau-Devonshire expansion, and numerical value of the corresponding parameters. The preexisting materials knowledge naturally folds in the Bayesian framework in the form of prior distributions, with the different order parameters forming competing (or hierarchical) models. Here, we explore the physics of the ferroelectric domain walls in BiFeO 3 using this method, and derive the posterior estimates of relevant parameters. More generally, this inference approach both allows learning materials physics from experimental data with associated uncertainty quantification, and establishing guidelines for instrumental development answering questions on what resolution and information limits are necessary for reliable observation of specific physical mechanisms of interest.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Demonstration of Decentralized Physics-Driven Learning

In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain, neurons and synapses self-adjust based on local information. Contrastive learning algorithms have recently been proposed to train physical systems, such as fluidic, mechanical, or electrical networks, to perform machine-learning tasks from local evolution rules. However, to date, such systems have only been implemented in silico due to the engineering challenge of creating elements that autonomously evolve based on their own response to two sets of global boundary conditions. Here, we introduce and implement a physics-driven contrastive learning scheme for a network of variable resistors, using circuitry to locally compare the response of two identical networks subjected to the two different sets of boundary conditions. Using this method, our system effectively trains itself, optimizing its resistance values without the use of a central processor or external information storage. Once the system is trained for a specified allostery, regression, or classification task, the task is subsequently performed rapidly and automatically by the physical imperative to minimize power dissipation in response to the given voltage inputs. Here, we demonstrate that, unlike typical computers, such learning systems are robust to extreme damage (and thus manufacturing defects) due to their decentralized learning. Our twin-network approach is therefore readily scalable to extremely large or nonlinear networks, where its distributed nature will be an enormous advantage; a laboratory network of only 500 edges will already outpace its in silico counterpart.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Initial Delayed Critical Configuration of SHEBA I and Subcritical Measurements by Californium Source-Driven Noise Analysis

In September 1980, the initial delayed critical configuration of the Solution High-Energy Burst Assembly (SHEBA I) was assembled, and the US Department of Energy’s Oak Ridge National Laboratory (ORNL) staff performed near critical and subcritical measurements using the californium source-driven noise analysis (CSDNA) method at the Los Alamos National Laboratory (LANL) Critical Experiments Facility. An unreflected 56 cm outside diameter stainless-steel cylindrical tank was partially filled with uranyl fluoride solution (235U enrichment was 4.95 wt %) until delayed criticality was achieved. Then, measurements were performed for various fuel solution heights from delayed critical to 60% of the height required for delayed criticality. The stainless-steel tank had an inside diameter of 54.6 cm and a height of 105 cm and was partially (20–36.5 cm) filled with an aqueous solution of uranyl fluoride (with a density of 2.162 g/cm 3 ). The density of the uranium in solution was 1.042 g/cm 3 , and the solution had a H/U atomic ratio of 20.43. The tank had a 6.35 mm wall thickness and an axial reentrant tube with an inside diameter of 6.02 cm and wall thickness of 0.165 cm. The tank was essentially unreflected on the top and sides because it was in a thin metal shed. The reactivity of the near–delayed critical configuration was −10.4 cents, which corresponds to a k eff value of 0.99922. In addition to the CSDNA measurements, the prompt neutron decay constants were determined from break frequency noise analysis (BNFA) measurements. The subcritical neutron multiplication factors from CSDNA and BFNA compared extremely well. These data can be used as the basis of International Nuclear Criticality Safety Evaluation benchmark for the near-critical configuration, and the k eff values at various subcritical configurations can be used as subcritical benchmarks. Furthermore, the measured prompt neutron decay constants can be used as reactor physics benchmarks. This report documents the experimental information for the measurements performed so that, at a later date, researchers could perform the required uncertainty and calculational analyses and documentation to use these data for an International Criticality Safety Benchmark Evaluation Program (ICSBEP) or Nuclear Energy Agency benchmarks. The measured prompt neutron decay constants can be used as the basis of a benchmark for the International Reactor Physics Evaluation Program (IRPhEP). The data from these measurements are available from the ORNL Records Management Services Department, and the logbook is available from ICSBEP at Idaho National Laboratory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Motional Squeezing for Trapped Ion Transport and Separation

Transport, separation, and merging of trapped ion crystals are essential operations for most large-scale quantum computing architectures. In this Letter, we develop a theoretical framework that describes the dynamics of ions in time-varying potentials with a motional squeeze operator, followed by a motional displacement operator. Using this framework, we develop a new, general protocol for trapped ion transport, separation, and merging. We show that motional squeezing can prepare an ion wave packet to enable transfer from the ground state of one trapping potential to another. Here, the framework and protocol are applicable if the potential is harmonic over the extent of the ion wave packets at all times. As illustrations, we discuss two specific operations: changing the strength of the confining potential for a single ion and separating same-species ions with their mutual Coulomb force. Both of these operations are, ideally, free of residual motional excitation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Polariton-Based Quantum Memristors

Information processing and storage by the same physical system is emerging as a promising alternative to traditional computing platforms. In turn, this requires the realization of elementary units the memory content of which can be easily tuned and controlled. In this work, we introduce a polariton-based quantum memristor where the memristive nature arises from the intercavity polariton exchange and is controlled by a time-varying atom-cavity detuning. A dynamical hysteresis is characterized by the fluctuations in the instantaneous polariton number, where the history information is encoded into a dynamical phase. Using a Lindblad master-equation approach, we find that features of the quantum memristor dynamics, such as the area and circulation of the hysteresis loop, showcase a kind of “plasticity” controlled by quantum state initialization. This makes this quantum memristor very versatile for a wide range of applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Potential applications of microbial genomics in nuclear non-proliferation

As nuclear technology evolves in response to increased demand for diversification and decarbonization of the energy sector, new and innovative approaches are needed to effectively identify and deter the proliferation of nuclear arms, while ensuring safe development of global nuclear energy resources. Preventing the use of nuclear material and technology for unsanctioned development of nuclear weapons has been a long-standing challenge for the International Atomic Energy Agency and signatories of the Treaty on the Non-Proliferation of Nuclear Weapons. Environmental swipe sampling has proven to be an effective technique for characterizing clandestine proliferation activities within and around known locations of nuclear facilities and sites. However, limited tools and techniques exist for detecting nuclear proliferation in unknown locations beyond the boundaries of declared nuclear fuel cycle facilities, representing a critical gap in non-proliferation safeguards. Microbiomes, defined as “characteristic communities of microorganisms” found in specific habitats with distinct physical and chemical properties, can provide valuable information about the conditions and activities occurring in the surrounding environment. Microorganisms are known to inhabit radionuclide-contaminated sites, spent nuclear fuel storage pools, and cooling systems of water-cooled nuclear reactors, where they can cause radionuclide migration and corrosion of critical structures. Microbial transformation of radionuclides is a well-established process that has been documented in numerous field and laboratory studies. These studies helped to identify key bacterial taxa and microbially-mediated processes that directly and indirectly control the transformation, mobility, and fate of radionuclides in the environment. Expanding on this work, other studies have used microbial genomics integrated with machine learning models to successfully monitor and predict the occurrence of heavy metals, radionuclides, and other process wastes in the environment, indicating the potential role of nuclear activities in shaping microbial community structure and function. Results of this previous body of work suggest fundamental geochemical-microbial interactions occurring at nuclear fuel cycle facilities could give rise to microbiomes that are characteristic of nuclear activities. These microbiomes could provide valuable information for monitoring nuclear fuel cycle facilities, planning environmental sampling campaigns, and developing biosensor technology for the detection of undisclosed fuel cycle activities and proliferation concerns.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Anatomy of $tthh$ physics at the HL-LHC

The t t h h production at colliders contains rich information on the nature of the Higgs boson. In this article, we systematically studied its physics at the high-luminosity Large Hadron Collider (HL-LHC), using exclusive channels with multiple ( ≥ 5 ) b -jets and one lepton ( 5 b 1 ℓ ), multiple ( ≥ 5 ) b -jets and opposite-sign dilepton ( 5 b 2 ℓ ), same-sign dilepton ( SS 2 ℓ ), multiple leptons (multi- ℓ ), and ditau resonance ( τ τ ). The scenarios analyzed include: (1) the t t h h production in Standard Model; (2) the t t h h production mediated by anomalous cubic Higgs self-coupling and t t h h contact interaction; (3) heavy Higgs ( H ) production with t t H → t t h h ; and (4) pair production of fermionic top partners ( T ) with T T → t t h h . To address the complication of event topologies and the mess of combinatorial backgrounds, a tool of boosted-decision-tree was applied in the analyses. The 5 b 1 ℓ and SS 2 ℓ analyses define the two most promising channels. For the nonresonant t t h h production, a combination of these exclusive analyses allows for its measurement in the SM with a statistical significance ~ 0.9 σ (with S / B > 1 % ), and may partially break the sensitivity degeneracy with respect to a varying cubic Higgs self-coupling, a difficulty usually thought to exist in gluon fusion di-Higgs analysis at HL-LHC. These sensitivities were also projected to future hadron colliders at 27 TeV and 100 TeV. For the resonant t t h h productions, the heavy Higgs boson in type II two-Higgs-doublet-model could be efficiently searched for between the mass thresholds 2 m h < m H < 2 m t and even beyond that, for relatively small tan β (vacuum alignment), while the fermionic top partners in composite Higgs models could be probed up to ~ 1.5 TeV and ~ 1.7 TeV , for Br ( T → t h ) = 25 % and 50%, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Quantum speed limit for the out-of-time-ordered correlator from an open-system perspective

Scrambling, the delocalization of initially localized quantum information, is commonly characterized by the out-of-time-ordered correlator (OTOC). Employing the OTOC–Renyi-2 entropy theorem, we derive a quantum speed limit for the OTOC, which sets a lower bound for the rate with which information can be scrambled. This bound becomes particularly tractable by describing the scrambling of information in a closed quantum system as an effective decoherence process of an open system interacting with an environment. We prove that decay of the OTOC can be bounded by the strength of the system-environment coupling and two-point environmental correlation functions. We validate our analytic bound numerically using the nonintegrable transverse field Ising model. Furthermore, our results provide a universal and model-agnostic quantitative framework for understanding the dynamical limits of information spreading across quantum many-body physics, condensed matter systems, and engineered quantum platforms.

Fermions↗
DOE OSTI2023

Cosmic birefringence tomography and calibration independence with reionization signals in the CMB

ABSTRACT The search for cosmic polarization rotation or birefringence in the cosmic microwave background (CMB) is well motivated because it can provide powerful constraints on parity-violating new physics, such as axion-like particles. In this paper, we point out that since the CMB polarization is produced at two very different redshifts – it is generated at both reionization and recombination – new parity-violating physics can generically rotate the polarization signals from these different sources by different amounts. We explore two implications of this. First, measurements of CMB birefringence are challenging because the effect is degenerate with a miscalibration of CMB polarization angles; however, by taking the difference of the reionization and recombination birefringence angles (measured from different CMB angular scales), we can obtain a cosmological signal that is immune to instrumental angle miscalibration. Secondly, we note that the combination with other methods for probing birefringence can give tomographic information, constraining the redshift origin of any physics producing birefringence. We forecast that the difference of the reionization and recombination birefringence angles can be competitively determined to within ∼0.05 deg for future CMB satellites such as LiteBIRD. Although much further work is needed, we argue that foreground mitigation for this measurement should be less challenging than for inflationary B-mode searches on similar scales due to larger signals and lower foregrounds.

Astronomy & Astrophysics↗
DOE OSTI2022

Cyber-Resilient Design Methodology for Microgrids

Recent advancement in tools has helped with microgrid design, development, planning and operation. Microgrids offer a unique application based on users with different requirements for tools. The process of designing, constructing, commissioning, and assessing a microgrid is not always straightforward due to these distinct requirements. Additionally, metrics are needed for performance evaluation. This panel will offer an overview and description of tools that helps with microgrid design, construction, planning, operation, cyber security, and metrics-driven performance assessment driven by multiple diverse applications and use cases.

CCE↗