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The nucleardatapy toolkit for simple access to experimental nuclear data, astrophysical observations, and theoretical predictions
Systematic comparisons across theoretical predictions for the properties of dense matter, nuclear physics data, and astrophysical observations (also called meta-analyses) are performed. Existing predictions for symmetric nuclear and neutron matter properties are considered, and they are shown in this paper as an illustration of the present knowledge. Asymmetric matter is constructed assuming the isospin asymmetry quadratic approximation. It is employed to predict the pressure at twice saturation energy-density based only on nuclear-physics constraints, and we find it compatible with the one from the gravitational-wave community. To make our meta-analysis transparent, updated in the future, and to publicly share our results, the Python toolkit nucleardatapy is described and released here. Hence, this paper accompanies nucleardatapy, which simplifies access to nuclear-physics data, including theoretical calculations, experimental measurements, and astrophysical observations. This Python toolkit is designed to easily provide data for: (i) predictions for uniform matter (from microscopic or phenomenological approaches); (ii) correlation among nuclear properties induced by experimental and theoretical constraints; (iii) measurements for finite nuclei (nuclear chart, charge radii, neutron skins or nuclear incompressibilities, etc.) and hypernuclei (single particle energies); and (iv) astrophysical observations. This toolkit provides data in a unified format for easy comparison and provides new meta-analysis tools. It will be continuously developed, and we expect contributions from the community in our endeavor.
Path Forward: Materials Data Modernization for ASME Codes and Standards in the Artificial Intelligence Era
Development of the ASME Materials Properties Database was initiated in the early 2010s to support the ASME Codes and Standards. As information technologies advance at an accelerated pace with the artificial intelligence era on the horizon, the ASME Materials Properties Database must be further modernized from a database to a knowledgebase to ride the wave of digital information revolution and effectively support the ASME Codes and Standards in the new era. This paper is intended to provide an overview of the ASME Materials Properties Database and discuss a roadmap for its future development to facilitate understanding of and participation from different sectors of the Codes and Standards community. Further, it first reviews the basic concepts of data, information, knowledge, database, and database system as well as the pros and cons in different types of data management and then discusses the path forward for a desired evolution of the database into a self-explanatory and machine-readable knowledgebase that is consistent with human cognitive processes for the Codes and Standards development and, furthermore, provides resources for data processing and analysis to reach an eventual goal of streamlining the Codes and Standards development from the initial inquiry, throughout data submission, analysis, …, to Codes and Standards rule establishment for final publication.
The Role of Dislocations in the Anelasticity of the Upper Mantle
Dislocation‐based dissipation mechanisms potentially control the viscoelastic response of Earth's upper mantle across a variety of geodynamic contexts, including glacial isostatic adjustment, postseismic creep, and seismic‐wave attenuation. However, there is no consensus on which dislocation‐based, microphysical process controls the viscoelastic behavior of the upper mantle. Although both intergranular (plastic anisotropy) and intragranular (backstress) mechanisms have been proposed, there is currently insufficient laboratory data to discriminate between those mechanisms. Here, we present the results of forced‐oscillation experiments in a deformation‐DIA apparatus at confining pressures of 3–7 GPa and temperatures of 298–1370 K. Our experiments tested the viscoelastic response of polycrystalline olivine—the main constituent of the upper mantle—at stress amplitudes from 70 to 2,800 MPa. Mechanical data are complemented by microstructural analyses of grain size, crystallographic preferred orientation, and dislocation density. We observe amplitude‐ and frequency‐dependent attenuation and modulus relaxation and find that numerical solutions of the backstress model match our results well. Therefore, we argue that interactions among dislocations, rather than intergranular processes (e.g., plastic anisotropy or grain boundary sliding), control the viscoelastic behavior of polycrystalline olivine in our experiments. In addition, we present a linearized version of the constitutive equations of the backstress model and extrapolate it to conditions typical of seismic‐wave propagation in the upper mantle. Our extrapolation demonstrates that the backstress model can explain the magnitude of seismic‐wave attenuation in the upper mantle, although some modification is required to explain the weak frequency dependence of attenuation observed in nature and in previous experimental work.
The Relationship Between African Easterly Waves and Tropical Cyclones in Historical and Future Climates in the HighResMIP‐PRIMAVERA Simulations
Abstract The deadly and destructive nature of tropical cyclones (TCs) makes understanding their response to future climate change of the utmost importance. TC genesis hinges on multiple factors, including an initial disturbance. African easterly waves (AEWs) have been shown to serve as such disturbances for TCs developing in the North Atlantic. It is therefore crucial to understand the relationship between AEWs and TCs and how this relationship may be affected by climate change. In this study, we examine the AEW‐TC relationship in historical and future climates using three models from the HighResMIP PRIMAVERA simulations. The AEWs and TCs were tracked in the model data using objective tracking algorithms, and AEW and TC tracks were then matched together if they were close to each other in space and time. The strength of the AEWs was measured using the eddy kinetic energy and the curvature vorticity of the waves. TC strength and intensity were measured using potential intensity and lifetime maximum 10 m windspeed. We found that future changes in the frequency of AEWs are not a good indicator of future TC activity. However, AEW strength, as well as environmental conditions conducive to strong TCs, are good indicators of AEWs that develop into TCs in both historical and future climates.
Learning how structures form in drift-wave turbulence
Drift-wave turbulence produces anomalous transport via cross-correlations between fluctuations. This transport has profound implications for confinement, structure formation, and virtually all aspects of the non-linear turbulent dynamics. Herein, we use a data-driven method based on deep learning in order to study turbulent transport in the 2D Hasegawa–Wakatani system and infer a reduced mean-field model from numerical solution. In addition to the usual turbulent diffusion, we find an effect which couples the particle flux to the local gradient of vorticity, which tends to modulate the density profile. The direct coupling to the shear is relatively weak. In addition, the deep learning method finds a model for spontaneous zonal flow generation by negative viscosity, stabilized by non-linear and hyperviscous terms. We compare these results to analytic calculations using quasilinear theory and wave kinetics, finding qualitative agreement, though the calculations miss certain higher-order effects. A simplified, 1-D model for the evolution of the profile, flow, and intensity based on the deep learning results is solved numerically and compared to previous models for staircasing based on bistability. We see that the physics uncovered by the deep learning method provided simple explanations for the formation of zonal structures in the density, flow, and turbulence fields. We highlight the important role of symmetry in the deep learning method and speculate on the portability of the method to other applications.
The EGS Collab Project – Stimulations at Two Depths
The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. We have completed Experiment 1 (of 3), which examined hydraulic fracturing in a well-characterized underground fractured phyllite test bed at a depth of approximately 1.5 km at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests using ambient temperature and chilled water. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. More detailed numerical simulation was performed to answer key experimental design questions, forecast fracture propagation trajectories and extents, and analyze and evaluate results. Data are freely available from the Geothermal Data Repository. Experiment 2 examines the potential for hydraulic shearing in amphibolite at a depth of about 1.25 km at SURF. This site has a different set of stress and fracture conditions than Experiment 1. The Experiment 2 testbed consists of nine subhorizontal boreholes configured in two fans of two boreholes which surround the testbed and contain grouted-in electrical resistance tomography, seismic sensors, active seismic sources and distributed fiber sensors. A “five-spot” set of test wells that extends from a custom mined alcove includes an injection well and four production/monitoring wells. The testbed was characterized geophysically and hydrologically, and three stimulations have been performed using the Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP) tool to measure strains, and a new strain quantifying tool (downhole robotic strain analysis tool -DORSA) was deployed in a monitoring hole during stimulation. Real-time data were broadcast during stimulations to allow real-time response to arising issues.
Linear Time-Invariant Models of a Large Cumulus Ensemble
Abstract Methods in system identification are used to obtain linear time-invariant state-space models that describe how horizontal averages of temperature and humidity of a large cumulus ensemble evolve with time under small forcing. The cumulus ensemble studied here is simulated with cloud-system-resolving models in radiative–convective equilibrium. The identified models extend steady-state linear response functions used in past studies and provide accurate descriptions of the transfer function, the noise model, and the behavior of cumulus convection when coupled with two-dimensional gravity waves. A novel procedure is developed to convert the state-space models into an interpretable form, which is used to elucidate and quantify memory in cumulus convection. The linear problem studied here serves as a useful reference point for more general efforts to obtain data-driven and interpretable parameterizations of cumulus convection.
Turbulence-parameter estimation for current-energy converters using surrogate model optimization
Surrogate models maximize information utility by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence and its dissipation. An OpenFOAM model was developed where actuator disc k-ε turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the k-ε equations. Additionally, parameter influences on longitudinal wake profiles were estimated using Gaussian process regression with subsequent optimization minimizing the objective function within 3.1% of those obtained using the full model representation, but for 74% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data.
Chemist: A Domain-Specific Language by Chemists for Chemists
Managing the complexity of quantum chemistry (QC) software is key to ensuring it remains accessible, maintainable, and reusable. Noticeably missing from the QC ecosystem are modules targeting bottleneck routines. Here we argue that this is likely due to the difficulty in defining interfaces for such modules. To that end, we introduce the open-source, publicly available Chemist library https://github.com/NWChemEx/Chemist. Chemist is a domain-specific language targeting the QC domain. Chemist has been developed focusing on performance and user-friendliness. Using Chemist, QC tasks are defined using familiar domain concepts such as molecules, wave functions, and operators. The domain objects are hierarchical to ensure a systematic encapsulation of information. Key features of Chemist include: extensibility, the ability to alias existing data, and the ability to succinctly define many common QC tasks. The usefulness of Chemist is demonstrated by discussing the interface of NWChemEx’s Fock build module and by showcasing a proof-of-concept self-consistent field algorithm containing uncertainty propagation.
Determination of acoustic nonlinearity parameters using thermal modulation of ultrasonic waves
This study presents a test method and its theoretical framework to determine the acoustic nonlinearity parameters (α, β, δ) of material using thermal modulation of ultrasonic waves. Temperature change-induced thermal strain excites the nonlinear response of the material and modulates the ultrasonic wave propagating in it. Experimental results showed a strong correlation between the relative wave velocity change and the temperature change. With a quadratic polynomial model, the acoustic nonlinearity parameters were obtained from the polynomial coefficients by curve fitting the experimental data. Their effects on thermal-induced velocity change were discussed. The parameters α, β, and δ govern the hysteretic gap, average slope, and curvature of the correlation curve, respectively. The proposed theory was validated on aluminum, steel, intact and damaged concrete samples. The obtained nonlinear parameters show reasonable agreement with values reported in the literature. Compared to other nonlinear acoustic methods using vibration or acoustic excitation, the thermal modulation method generates more uniform, slow changing, and larger strain field in the test sample. Employing the thermal effect as the driving force for nonlinearity instead of an undesired influencing factor, this method can measure the absolute values of α, β, and δ with good accuracy using a simple ultrasonic test setup.
𝐽/𝜓-meson–nucleon scattering length from threshold photoproduction on light nuclei
The quality of recent Short-Range Correlations/Color-Transparency (SRC/CT) Collaboration 𝐽/𝜓 photoproduction data off a 4 He target from Hall D at Jefferson Laboratory, combined with the feasibility of measuring the reaction close to the free-nucleon energy threshold, opens the door to using incoherent 𝐽/𝜓 photoproduction to explore a variety of interesting physics aspects. An example is the estimation of the 𝐽/𝜓 𝑝 scattering length |𝛼 𝐽/𝜓 𝑝 | on the bound proton, obtained using the vector meson dominance model. The new experiment E12–25–002, conducted by the SRC/CT Collaboration, was recently approved in Hall D with the GlueX spectrometer, which will significantly enhance the number of reconstructed 𝐽/𝜓 mesons. Using a plane-wave theoretical model to generate quasidata, we estimated that the experiment could achieve a result of |𝛼 𝐽/𝜓 𝑝 | = 3.08 ± 0.45 mfm. This value can be compared to an extraction based on the free proton data from the GlueX Collaboration, |𝛼 𝐽/𝜓 𝑝 | = 3.08 ± 0.55 mfm. A comparison between the two would provide an opportunity to evaluate the effects of medium modification in light nuclei.
Chemist
Chemist is a domain-specific language targeting the QC domain. Chemist has been developed focusing on performance and user-friendliness. Using Chemist, QC tasks are defined using familiar domain concepts such as molecules, wave functions, and operators. The domain objects are hierarchical to ensure a systematic encapsulation of information. Key features of Chemist include: extensibility, the ability to alias existing data, and the ability to succinctly define many common QC tasks.
Triton Initiative: FY22 Communications, Outreach, and Engagement End-of-Year Report
The Department of Energy (DOE) Water Power Technologies Office (WPTO) Triton Initiative supports the advancement of the marine energy (ME) industry through environmental monitoring research and technology development. This report presents the results and analysis of the Triton Initiative’s communications, outreach, and engagement (TCOE) efforts in fiscal year 2022. The primary TCOE goals were to: (1) educate and raise awareness of ME and the role of Triton's environmental monitoring research in supporting the industry; (2) build trust with audiences through transparent communications and outreach; and (3) evaluate and refine TCOE tactics based on feedback and metrics. To support these goals, the TCOE team used multiple platforms and approaches. Notable achievements include: (1) 12 newsletter issues sent to 185 subscribers with an average open rate of 56.4%. (2) 10 Triton Stories, resulting in 2,871 collective views contributing to 46% of all Triton website views. (3) 78 Triton-specific social media posts, which generated a total of 81,896 impressions, 1,037 post-clicks, and 7,814 video views. (4) 6,320 Triton website views with increased search engine optimization ranking in several categories. (5) 3 Triton researchers interviewed as guests on two different podcasts, Water Women and Big Deep: An Ocean Podcast. (6) A Triton special issue of the Journal of Marine Science and Engineering (JMSE) featuring 10 peer reviewed publications. All articles ranked in the top 25%, and two in the top 5%, for digital attention of all research outputs scored by Altmetric. (7) A seven-part webinar series called Triton Talks to share and discuss research and results from the research published in the JMSE special issue. Triton leveraged unique opportunities, particularly the JMSE special issue, to disseminate research results to end users, educate stakeholders, and gain valuable feedback. These concerted communication efforts increased exposure across platforms ultimately resulting in greater reach and access across audiences. Based on audience analyses of webinar attendees and newsletter subscribers, the TCOE team was able to successfully engage with general audiences, research partners, and ME stakeholders, including subject matter experts from government agencies, research organizations, and the regulatory community. The TCOE task established channels to gather input and create opportunities for two-way communication with people engaged with Triton’s outreach efforts. The feedback received will enable the TCOE team to support its goals for ongoing evaluation of outreach and engagement tools while continuing to build trust in the ME community and educate diverse audiences about the impactful research conducted by the Triton Initiative. This report is a Triton Initiative Fiscal Year 2022 Quarter 4 Milestone Deliverable due to the DOE WPTO Sponsor on 9/30/2022. An updated version to include data through 9/30/2022 will be delivered in October 2022.
Impact of Systematic Modeling Uncertainties on Kilonova Property Estimation
The precise atomic structure and therefore the wavelength-dependent opacities of lanthanides are highly uncertain. This uncertainty introduces systematic errors in modeling transients like kilonovae and estimating key properties such as mass, characteristic velocity, and heavy metal content. Here, we quantify how atomic data from across the literature as well as choices of thermalization efficiency of r-process radioactive decay heating impact the light curve and spectra of kilonovae. Specifically, we analyze the spectra of a grid of models produced by the radiative transfer code Sedona that span the expected range of kilonova properties to identify regions with the highest systematic uncertainty. Our findings indicate that differences in atomic data have a substantial impact on estimates of lanthanide mass fraction, spanning approximately 1 order of magnitude for lanthanide-rich ejecta, and demonstrate the difficulty in precisely measuring the lanthanide fraction in lanthanide-poor ejecta. Mass estimates vary typically by 25%–40% for differing atomic data. Similarly, the choice of thermalization efficiency can affect mass estimates by 20%–50%. Observational properties such as color and decay rate are highly model dependent. Velocity estimation, when fitting solely based on the light curve, can have a typical error of ∼100%. Atomic data of light r-process elements can strongly affect blue emission. Even for well-observed events like GW170817, the total lanthanide production estimated using different atomic data sets can vary by a factor of ∼6.
Design and Testing of a Water-Cooled Rotating Detonation Combustor at Elevated Operating Pressures - Abstract
This is an extended abstract being submitted for consideration that briefly describes the material that will be included in the final paper. This study will detail the design and testing of a water-cooled Rotating Detonation Combustor (RDC) that permits operation for extended periods of time ensuring that the device has reach a stable operating temperature. Extended run times also provides an opportunity to consider transient behaviors that occur as a result of altering the operating conditions such as equivalence ratio. The experimental setup is also unique in that it consists of a ducted exhaust with a downstream high-temperature valve that can control the pre-combustion pressure in the RDC independent of the combustor annulus and exit geometry. This paper will examine the results from the extended operation of the water-cooled RDC over a range of both transient and steady state equivalence ratios (0.5 - 1.0), pre-combustion operating pressures (0 - 207 kPa), mass flow rates (0.42 – 0.5 kg/sec) and air inlet throat area to combustor channel area ratios (0.09 – 0.32). All tests were performed while operating on hydrogen in air at a nominal air inlet temperature of 340 – 350 K. Limited data obtained at an elevated inlet air temperature of 480 K will also be discussed. Results suggest that under certain conditions the operating mode of the detonation wave can vary as it approaches a stable operating temperature. For the RDC tested, a stable operating temperature is not achieved until approximately 8-10 seconds of run time.
Failure prediction and estimation of failure parameters
Machine-learning methods and apparatus are disclosed to determine frictional state or other parameters in an earthquake zone or other failing medium, using acoustic emission, seismic waves, or other detectable indicators of microscopic processes. Predictions of future failures are demonstrated in different regimes. A classifier is trained using time series of acoustic emission data along with historic data of frictional state or failure events. In disclosed examples, random forests and gradient boost trees are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to testing or live data in order to assess a frictional state, assess seismic hazard, or make predictions regarding a future failure event. The technology has been developed in a double direct shear apparatus, but can be widely applied to seismic faults, other terrestrial failures, or failures in man-made structures. Variations are disclosed.
Simulation studies of the interaction of laser radiation with additively manufactured foams
The interaction of laser radiation with foams of various porosities and low densities has been the subject of several numerical and experimental studies. In all cases, the modeling of low-Z under-dense foams as uniform gases of equivalent average density using standard radiation-hydrodynamics codes has resulted in heat-front velocities that are considerably faster than those observed experimentally. It has been theoretically conjectured that this difference may be attributed to the breakdown of the foam’s morphology, leading to a dynamics of filament expansion where the ion and electron energy partitions are significantly different from those calculated using the uniform gas model. We found that 3D computer simulations employing a disconnected representation of the foam’s microstructure which allowed for the dynamics of foam element heating, expansion, and stagnation largely supported the theoretical picture. Simulations using this model for laser experiments on under-dense 2 mg cc -1 SiO 2 aerogel foams reproduced the experimental data fairly well. We used the validated model in simulations of low-density structured foam-like materials (produced via additive manufacturing) with a variety of morphologies. We found that the log-pile configurations were consistent with the analytical propagation model of Gus’kov et al. Further validation of the model was obtained by simulating experiments performed at the Jupiter Laser Facility using the log-pile and octet-truss foam morphologies. Simulations of the foam-laser interaction using a wave propagation code showed that the microstructure was able to enhance stimulated Brillouin scattering (SBS) by concentrating the light energy into density holes. In turn, this promotes laser filamentation, reducing SBS and bringing the predicted values closer to the experimental data.