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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Characterization of Tri-lab β-Tin (Sn)

This report documents details of the microstructure and mechanical properties of -tin (Sn), that is used in the Tri-lab (Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratories (SNL)) collaboration project on Multi-phase Tin Strength. We report microstructural features detailing the crystallographic texture and grain morphology of as-received -tin from electron back scatter diffraction (EBSD). Temperature and strain rate dependent mechanical behavior was investigated by multiple compression tests at temperatures of 200K to 400K and strain rates of 0.0001 /s to 100 /s. Tri-lab tin showed significant temperature and strain rate dependent strength with no significant plastic anisotropy. A sample to sample material variation was observed from duplicate compression tests and texture measurements. Compression data was used to calibrate model parameters for temperature and rate dependent strength models, Johnson-Cook (JC), Zerilli-Armstrong (ZA) and Preston-Tonks-Wallace (PTW) strength models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

DECOVALEX-2023: Task D Final Report

Task D of DECOVALEX-2023 is focused on the simulation of the coupled thermal hydraulic-mechanical (THM) behaviour in the full-scale engineered barrier system (EBS). The Horonobe EBS experiment is the demonstration of the full-scale EBS in the underground research laboratory (URL) (performed by JAEA in the Horonobe URL in Japan). Task D consisted of the three steps, a preliminary step (Step 0), simulation of the laboratory tests (Step 1) and simulation of the in-situ full-scale EBS experiment (Step 2). Since the Horonobe EBS experiment demonstrates the vertical emplacement option of the EBS, the experiment gallery is also backfilled with the backfill material. Therefore, interaction between the EBS and the backfill material can also be demonstrated, such as deformation (change of density) of the buffer material. The underground water in the Horonobe URL is saline. This fact adds chemical processes to THM behaviour. For example, mechanical properties (such as swelling pressure of the buffer material and backfill material) and hydraulic properties (such as permeability of the buffer material and backfill material) change depending on the water chemistry. Task D was therefore a challenging Task focused on not only the relatively simple THM behaviour but also complex THM behaviour including chemical processes. Six research teams (BGR, CAS, JAEA, KAERI, SNL and Taipower) participated the Task D. BGR, CAS, JAEA, KAERI and Taipower research teams selected a THM approach, while the SNL research team selected a TH approach. Step 1 involved the simulation of laboratory test results and was important to check the numerical codes developed by the research teams. Step 1 was divided into four sub steps. The simulation results through the Step 1 identified the parameters for simulation of the Step 2. Basic parameters of the materials (buffer material, backfill material, rock mass, concrete, sand) were provided by JAEA. Special parameters which research team needed were identified by back analysis of Step 1. Most notably the mechanical behaviour of swelling and displacement depended on the applied model (elastic model or elastoplastic model). Parameters such as Young’s modulus were found to need smaller values than characterised in the fundamental laboratory test results (Step 1-1, 1-2) for the elastic model. Although laboratory experiments are usually simple, test results contained some error. For example, if the saturation level is 100 % or higher, it should be considered an error. This situation was presented in the Step 1-3. A possible reason is that the buffer material is a mixture of bentonite and silica sand. When a specimen is cut to measure volume or weight, sand grains will affect the measurement data. In Step 2, boundary conditions such as temperature on the surface of the simulated overpack, heater power of the electrical heaters installed in the simulated overpack, injection pressure and inflow rate of the test water, were applied. The outer boundary conditions can be selected using measured data (injection pressure and inflow rate of the test water that is controlled by the injection systems installed in the sand layer around the buffer material and in the boundary between backfill material and concrete support). Since such measured data has some noise, research teams developed their own simplified boundary conditions. Inner boundary conditions can be selected using measured data as heater power and temperature on the surface of the simulated overpack. These data also contain some noise, so research teams developed their own simplified developed boundary conditions. Task D validated various approaches thorough the simulation of the in-situ full scale EBS system including backfill of the gallery: variations in the coupling processes (THM or THC), analysis codes, and boundary conditions. Temperature distribution in the buffer material was simulated well by all research teams. This means thermal behaviour is not sensitive to the simulation approaches. Although the water content distribution on the outside of the buffer material was well simulated by all research teams, the simulation results differ from the measured values inside the buffer material (at the centre and inside, near the simulated overpack). The buffer material is made from tap water, but in the in-situ experiment, saline groundwater infiltrates the buffer material. Therefore, the selection of the hydraulic parameters of the buffer material greatly affects the simulation results of the re saturation behaviour of the buffer material. In the Horonobe EBS experiment, measured values suitable for validating the simulation results were not obtained near the simulated overpack. When simulating the pressure and deformation of the buffer material, the measurement data is easily affected by the installation conditions of the measurement sensors, so verifying the measurement data itself remains an issue. Mechanical simulation results differ depending on whether they are considered as elastic or elastoplastic phenomena. The accuracy of measured in-situ data can be assessed by detailed analysis comparing sampling specimen analysis and measured data. The Horonobe EBS experiment is scheduled to be dismantled in the future (FY2026 and 2027). This detailed dismantling investigation will finally confirm the measured data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Self‐Trapped Hole Migration and Defect‐Mediated Thermal Quenching of Luminescence in α‐ and β‐Ga 2 O 3

Gallium oxide (Ga 2 O 3 ) is a promising ultrawide bandgap semiconductor for next-generation power electronics and optoelectronic devices. Here, temperature-dependent and polarization-resolved photoluminescence excitation spectroscopy data, complemented by hybrid-functional first-principles calculations, are presented, and a microscopic model is derived that explains the interplay of hole migration, defect trapping, and carrier recombination at defects underlying thermal quenching phenomena in α- and β-Ga 2 O 3 . In α-Ga 2 O 3 , the UV emission is attributed to self-trapped holes, while the blue luminescence arises from defect-related processes, including gallium split vacancies and their defect complexes. Calculations reveal an energy barrier of 88 meV for self-trapped hole migration in α-Ga 2 O 3 , consistent with activation energies from temperature-dependent photoluminescence. This enables efficient trapping by defects, enhancing blue luminescence and quenching UV emission. In β-Ga 2 O 3 , a higher migration barrier of 0.36 eV reduces the defect trapping, allowing the UV self-trapped hole emission to remain intense, with blue luminescence emerging only at elevated temperatures. These results establish a direct link between self-trapped hole migration, defect trapping, and thermal quenching of emission in both phases. The insights advance the understanding of carrier dynamics in ultrawide bandgap oxides and may guide defect engineering for high-performance functional devices.

Hajizadeh, Nima [Leibniz-Institut im Forschungsver↗
DOE OSTI2026

Taylor approximation variance reduction for approximation errors in PDE-constrained Bayesian inverse problems

In numerous applications, surrogate models are used as a replacement for accurate parameter-to-observable mappings when solving large-scale inverse problems governed by partial differential equations (PDEs). The surrogate model may be a computationally cheaper alternative to the accurate parameter-to-observable mappings and/or may ignore additional unknowns or sources of uncertainty. The Bayesian approximation error (BAE) approach provides a means to account for the induced uncertainties and approximation errors, i.e. the errors between the accurate parameter-to-observable mapping and the surrogate. The statistics of these errors are, however, in general unknown a priori, and are thus calculated using Monte Carlo sampling. Although the sampling is typically carried out offline, i.e. before considering the data, the process can still represent a computational bottleneck. In this work, we develop a scalable computational approach for reducing the costs associated with the sampling stage of the BAE approach. Specifically, we consider the Taylor expansion of the accurate and surrogate forward models with respect to the uncertain parameter fields either as a control variate for variance reduction or as a means to directly and efficiently approximate the mean and covariance of the approximation errors. We propose efficient methods for evaluating the expressions for the mean and covariance of the Taylor approximations based on linear(-ized) PDE solves. Furthermore, the proposed approach is independent of the dimension of the uncertain parameter, depending instead on the intrinsic dimension of the data, ensuring scalability to high-dimensional problems. The potential benefits of the proposed approach are demonstrated for two high-dimensional inverse problems governed by PDE examples, namely for the estimation of a distributed Robin boundary coefficient in a linear diffusion problem, and for a coefficient estimation problem governed by a nonlinear diffusion problem.

Bayesian approximation error↗
DOE OSTI2026

Nonmonotonic-potential description of polarization effects, fusion, and nuclear rainbows in elastic scattering of 6 Li + 12 C at 4.5–600 MeV

The experimental differential cross-section (CS) and analyzing power (AP) data of the 6 Li + 12 C elastic scattering over a wide laboratory energy scale (4.5MeV≤𝐸 lab ≤600MeV) are analyzed within the framework of the optical model (OPM) using nonmonotonic (NM) nucleus–nucleus potentials. The real part of the NM potentials is derived from the Pauli-embodied energy density-functional (EDF) formalism with the sudden approximation. The real part of the noncentral spin-orbit and tensor terms, as well as the imaginary parts, are treated phenomenologically. The effect of the radius of sensitivity on the CS and AP data is found to be more important at lower energies. The diffractive and refractive scattering with Airy structures in the whole angular region of the elastic scattering across the studied energy range is successfully described within the OPM using the NM 6 Li + 12 C potential. The near- and far-side (N and F) decomposition of the total elastic-scattering amplitudes has also been studied using our NM potentials. The evolution of the Airy minima in the angular distributions, coupled with the fitting of the AP data, provides an accurate description of Airy minima of different orders. The OPM calculations with the NM potentials describe exceptionally well the CS, vector analyzing power (VAP), and tensor analyzing power data at 𝐸 lab =9.0,19.24,20,30, and 50MeV. In agreement with our past successful descriptions of CS and the opposite signs of the VAP data for the 6 Li and 7 Li elastic scattering using NM potentials in OPM, the present results appear to provide a better fit, so far, than those obtained from the coupled-channels method. The fusion cross sections of 6 Li + 12 C have been predicted in the energy range (4.5MeV≤𝐸 lab ≤20MeV), fitting the experimental data well in the range 𝐸 lab =2.97–11.77MeV. The EDF potential without any energy dependence and renormalization is also found to describe satisfactorily the experimental CS and AP data at energies up to several hundreds of MeV.

6 ≤ A ≤ 19↗
DOE OSTI2024

Anomalous frequency and temperature dependent scattering in the dilute metallic phase in lightly doped-SrTiO 3

The mechanism of superconductivity in materials with aborted ferroelectricity and its emergence out of a dilute metallic phase in systems like doped-SrTiO 3 is an outstanding issue in condensed matter physics. This dilute metal has anomalous properties that are both similar and different to those found in the normal state of other unconventional superconductors. For instance, T 2 resistivity can be found at densities that are too small to allow current decay through electronelectron scattering. We have investigated the optical properties of the dilute metallic phase in doped-SrTiO 3 using THz time-domain spectroscopy. At low frequencies the THz response exhibits a Drude-like form as expected for typical metal-like conductivity. We observed the frequency and temperature dependencies to the low energy scattering rate Γ(ω, T) ∝ (¯hω) 2 + (pπk B T) 2 expected in a conventional Fermi liquid. However we find the lowest known p values of 0.39-0.72. As p is 2 in a canonical Fermi liquid and existing models based on energy dependent elastic scattering bound p from below to 1, our observation lies outside current explanation. Our data also gives insight into the high temperature regime and shows that the temperature dependence of the resistivity derives in part from strong T dependent mass renormalizations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Analytically Derived Piezoelectric Ejecta Data Transformations, with Implications for Claims of Experimentally Observed Self-Similarity

Building upon a previously documented analytic formalism, we derive a set of data transformations that relate piezoelectrically inferred time-dependent ejecta masses, dynamical ejecta cloud densities, and "radiographic" ejecta cloud density distributions. These transformation rules are derived rigorously, with careful attention to the assumption of instant ejecta production which is inherent in the piezoelectric mass inference procedure. Some of these relationships have not appeared previously in the literature, including solutions for the unique ejecta velocity distribution and radiographic ejecta cloud density distribution consistent with the instant-production assumption for a given piezoelectrically inferred mass dataset. In deriving the latter, we prove that—again subject to the ubiquitous instant-creation assumption—measurements at a single sensor are sufficient to determine properties of the ejecta cloud everywhere at all times. Other data transformation relationships have appeared in the literature without a rigorous derivation, or have been stated in forms that obscure the key mathematical relationships. We apply these results to arguments for self-similar ejecta cloud evolution, thereby deriving mathematical expressions by which claims of self-similarity may be examined. We prove that one analysis used in the literature to claim experimental evidence of a short ejecta production timescale—and consequently of self-similar cloud evolution—in fact places no constraint the ejecta production interval and therefore does not support the associated conclusion. We also provide a potentially useful uniqueness proof.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Second ARM Aerosol Chemical Speciation Monitor Users’ Meeting Report

The aerosol chemical speciation monitor (ACSM) was developed to adapt the technology of the aerosol mass spectrometer (AMS) to routine, long-term, standalone monitoring. The calculation of particulate mass concentration from ACSM data requires the measurement of the response of the instrument to aerosol of specific size and composition as well as assumptions about the instrument response based on laboratory measurements and field experience acquired over more than two decades of operation of AMS and a decade of operation of the ACSM. Three parameters in the concentration calculations that are particularly important are the NO3 response factor (RFNO3), relative ionization efficiency (RIE), and the collection efficiency (CE). The values of RFNO3 and RIEs are determined from calibration, however the jump calibration method previously used in calibration can result in errors in the RIE for sulfate. This has been corrected by implementing a continuous calibration method. The default collection efficiency is 0.5. This has been shown to result in mass loadings that do not agree with mass determinations from other instruments because of effects of composition on the vaporization of the particles. The previous work of investigators addressing this issue is discussed. After preliminary work on ACSM and scanning mobility particle sizer (SMPS) data from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) observatory collected in late 2016 and 2017 produced a parameterization of composition dependent collection efficiency very different from the results of previous studies, SMPS data were examined and we determined that there was significant mass that the instrument did not capture because the particles with diameters larger than 465 nm are not counted by this instrument. Data for the ultra-high-sensitivity aerosol spectrometer (UHSAS), SMPS, and ACSM are available for nearly all of 2019. The data from UHSAS and SMPS collected in 2019 were compared. We found that the UHSAS data has particle counts and total volumes significantly less than measured by the condensation particle counter (CPC) and SMPS. The SMPS data were extended by fitting the average volume distribution with a log normal curve and using this relationship to estimate a mass value for the SMPS over extended diameter range. The extended SMPS mass values result in a CDCE parameterization that is in better agreement with the results of other investigators, but is still different from other formulations. The working group recommends that the ACSM data be processed with a collective efficiency (CE)=1, that this be documented clearly in the metadata, and the use of the default CE of 0.5 or a formulation of composition-dependent collection efficiencies (CDCE) chosen by the user should be implemented based on the ammonium nitrate mass fraction. This is clearly necessary for the wintertime SGP ACSM data because of the high nitrate concentrations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Estimation of the time for steam generator trip due to cyber intrusions

The time required to trip a pressurized water reactor (PWR) by inserting malicious signals into its steam generator (SG) control system has been studied using the Generic PWR (GPWR) Simulator. A semi-analytical model is developed to approximately reproduce the simulator response and understand the dynamics of the control unit. A series of two proportional-integral controllers determines control action according to preset constants, the readings from the feedwater level sensor, and those from feedwater and steam flowrate transmitters. It is observed that the most important factor that determines whether a trip will occur is how much additional water is added to or withheld from the SG over time compared to normal operating conditions. In order to determine the effects of control action on the SG, changes in mass inventory are considered. This approach models the SG water level as a function of mass inventory and has a backward temporal memory. A Python interface is developed for the GPWR framework to automatically simulate different spoofing scenarios and post-process the related data. We observe that the trip times predominantly depend on flow mismatch and/or level errors. Controller parameters, including the integral time and gain constants, either speed up or slow down the rate of progression to a trip setpoint but do not cause a trip by themselves. The reactor can trip on a high-level signal when the reading crosses above 78%, increased from its reference level of 57%, or a low-level reading when it is below 25%. The present results show roughly how long the operators would have to respond to an attack, given a specific set of spoofing signals within the issue space analyzed. Furthermore, we have generated a simple surface by fitting a combination of exponential functions to the data obtained from the GPWR Simulator. In general, trips on a low level have been observed to occur faster than those on a high level.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Data for "Understanding multiscale disorder in superconducting nanowire single photon detectors"

This dataset addresses DC transport, dark-count measurements, and bias-dependent microwave transmission spectroscopy of superconducting nanowire. This publication includes the raw and processed data to produce figures in the paper "Understanding multiscale disorder in superconducting nanowire single photon detectors" (Sarkar et al., 2026).

Sarkar, Nirjhar [Oak Ridge National Laboratory] (O↗
DOE OSTI2023

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers

Abstract Machine learning (ML) offers promising tools to develop surrogate models for polymers' structure–property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accuracy of such ML models appears to depend on the feature space representation of polymers, the range of training data, and learning algorithms. Here, we establish connections between these factors for predicting the glass transition temperature (T g ) of polymers. Our analysis suggests linear models with fewer fitting parameters are as accurate as nonlinear models with many hidden and unexplainable parameters. Also, the performance of a monomer topology‐based ML model is found to be qualitatively identical to that of a physicochemical descriptor‐based ML model. We find that the ML models's performance in the extrapolative region is enhanced as the property range of the training data increases. Moreover, we establish newT g – polymer chemistry correlations via ML. Our work illustrates how ML can advance the fundamental understanding of polymer structure–property correlations and its efficacy for extrapolation problems.

Polymer Science↗
DOE OSTI2024

Data Acquisition and Control for Marine Energy Devices: Cost Considerations

This document discusses the process involved with developing a data acquisition system specifically in the context of applications for Marine Renewable Energy (MRE) technologies however, much of what is presented is applicable to applications requiring data acquisition in general. The detail on the process is provided to highlight the critical steps and needs for a successful measurement campaign and to understand what can impact the overall outcome, cost, and schedule. The process presented is an amalgamation of best practices, lessons learned, recommendations, and prudent technical project planning and management. Data acquisition systems may be tightly integrated with or into the device under measurement and it often has its own dependencies that must be met. Therefore, early consideration and planning for the data acquisition system are stressed throughout this document.

13 HYDRO ENERGY↗
DOE OSTI2020

Modeling Nondestructive Defect Detection in Additively Manufactured Metallic Structures for Nuclear Applications

The future of quickly, economically produced metallic nuclear reactor parts with minimal supply-chain dependence lies in Laser Powder Bed Fusion (LPBF) Additive Manufacturing (AM): a 3D printing method involving laser melting and net shaping stainless steel and Inconel metallic powder into a solid structure. However, intrinsic features in LPBF frequently leads to the formation of materials defects, such as pores, within 3D printed structures. As safe long-term use in energy applications requires knowledge of all relevant defects before deployment in a reactor, we must develop methods for nondestructive detection of these defects. We are investigating Pulsed Thermal Tomography (PTT), which is a non-contact nondestructive imaging method scalable to arbitrary structure size. Thermal tomography (TT) is a computational method for 3D spatial reconstruction of material thermal effusivity from flash or pulsed thermography temperature data cube. Thermography data cube consists of 2D surface temperature measurements at different times. The objective of the present work is to investigate limits on defect detection in AM metallic structures with PTT. To this effect, we modeled PTT with COMSOL heat transfer computer simulations. We developed a layered media COMSOL simulation consisting of a Stainless Steel 316 (SS316) plate with an internal layer of un-sintered SS316 powder. Thermophysical properties of the powder layer were modeled with equivalent volume mixing model. To account for partial sintering at the boundary of the defect, the transition between solid and powder layers was modeled as a Gaussian. Using data from COMSOL simulations, we reconstructed depth-dependent thermal effusivity, which allowed defect visibility estimation. A series of parametric studies determined that at 1mm depth, 50µm is the smallest detectable defect. In addition, classification of the defects which can lead to early fatigue of the metallic structure in a reactor is briefly discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗
DOE OSTI2023

Measurements of J/ ψ Production vs. Event Multiplicity in Forward Rapidity in p + p Collisions in the PHENIX Experiment

J / Ψ, a charmonium bound state made of a charm and an anti-charm quark, was discovered in the 1970s and confirmed the quark model. Because the mass of charm quarks is significantly above the quantum chromodynamics (QCD) scale Λ QCD , charmonia are considered excellent probes to test perturbative quantum chromodynamics (pQCD) calculations. In recent decades, they have been studied extensively at different high-energy colliders. However, their production mechanisms, which involve multiple scales, are still not very well understood. Recently, in high-multiplicity p + p collisions at RHIC and at the LHC, a significant enhancement of J / Ψ production yield has been observed, which suggests a strong contribution of multi-parton interaction (MPI). This is different from the traditional pQCD picture, where charm quark pairs are produced from a single hard scattering between partons in p + p collisions. In this work, we will report the J / Ψ normalized production yield as a function of normalized charged particle multiplicity over a board range of rapidity and event multiplicity in the J / Ψ → μ + μ – channel with PHENIX Run 15 p + p data at $\sqrt s$ =200 GeV. The results are compared with PYTHIA 8 simulations with the MPI option turned on and off. Finally, the outlooks of J / Ψ in p + Au and Au + p collisions, along with color glass condensate (CGC) predictions and the multiplicity-dependent Ψ(2S) /J / Ψ ratio in p + p data, will be briefly discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Inclusive J /$\psi$ production at midrapidity in pp collisions at $\sqrt{s}$ = 13 TeV

We report on the inclusive J/ψ production cross section measured at the CERN Large Hadron Collider in proton–proton collisions at a center-of-mass energy $\sqrt{s}$ = 13 TeV. The J/ψ mesons are reconstructed in the e + e - decay channel and the measurements are performed at midrapidity (|y|<0.9) in the transverse-momentum interval 0 < p T < 40 GeV/c, using a minimum-bias data sample corresponding to an integrated luminosity L int =32.2 nb -1 and an Electromagnetic Calorimeter triggered data sample with L int =8.3 pb -1 . The p T -integrated J/ψ production cross section at midrapidity, computed using the minimum-bias data sample, is dσ/dy| y = 0 = 8.97±0.24 (stat)±0.48 (syst)±0.15 (lumi) μb. An approximate logarithmic dependence with the collision energy is suggested by these results and available world data, in agreement with model predictions. The integrated and p T -differential measurements are compared with measurements in pp collisions at lower energies and with several recent phenomenological calculations based on the non-relativistic QCD and Color Evaporation models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Data-driven models of nonautonomous systems

Nonautonomous dynamical systems are characterized by time-dependent inputs, which complicates the discovery of predictive models describing the spatiotemporal evolution of the state variables of quantities of interest from their temporal snapshots. When dynamic mode decomposition (DMD) is used to infer a linear model, this difficulty manifests itself in the need to approximate the time-dependent Koopman operators. Our approach is to approximate the original nonautonomous system with a modified system derived via a local parameterization of the time-dependent inputs. The modified system comprises a sequence of local parametric systems, which are subsequently approximated by a parametric surrogate model using the DRIPS (dimension reduction and interpolation in parameter space) framework. The offline step of DRIPS relies on DMD to build a linear surrogate model, endowed with reduced-order bases for the observables mapped from training data. The online step interpolates on suitable manifolds to construct a sequence of iterative parametric surrogate models; the target/test parameter points on these manifolds are specified by a local parameterization of the test time-dependent inputs. Here, we use numerical experimentation to demonstrate the robustness of our method and compare its performance with that of deep neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Simulating transient heat transfer in graphene at finite Knudsen number via the Boltzmann transport equation and molecular dynamics

The phonon Boltzmann transport equation (BTE) with the relaxation time approximation (RTA) scattering model is used to calculate transient temperature profiles in graphene, and the results are compared to analogous molecular dynamics (MD) simulations. For the BTE calculations, the phonon dispersion relation and frequency-dependent scattering rates are obtained from a combination of MD data and semi-empirical power-law expressions for the normal and Umklapp phonon lifetimes. The dimensions and initial temperature conditions of graphene are varied to study the size and temperature dependence of thermal transport physics at the mesoscopic scale. Good quantitative agreement to within 5% is found between the BTE and MD results, over a wide range of temperatures and lengthscales of the temperature variation in the graphene sheet. Small differences are attributed to the inaccuracy of the RTA as applied to graphene, and to neglecting four-phonon scattering in the BTE simulations. The present results may further understanding in applications such as the transient heating of nanoelectronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗