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At least 613 records · Page 34

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DOE OSTI2025

Magnetically Driven Neutron-rich Ejecta Unleashed: Global 3D Neutrino–General Relativistic Magnetohydrodynamic Simulations of Collapsars Probe the Conditions for r -process Nucleosynthesis

Abstract Collapsars—rapidly rotating stellar cores that form black holes—can power gamma-ray bursts and are proposed to be key contributors to the production of heavy elements in the Universe via the rapid neutron capture process ( r -process). Previous neutrino-transport collapsar simulations have been unable to unbind neutron-rich material from the disk. However, these simulations have not included sufficiently strong magnetic fields and the black hole (BH), both of which are essential for launching mass outflows. We present ν h-amr , a novel neutrino-transport general relativistic magnetohydrodynamic ( ν GRMHD) code, which we use to perform the first 3D global ν GRMHD collapsar simulations. We find a self-consistent formation of a weakly magnetized dense accretion disk, which has sufficient time to neutronize. Eventually, substantial magnetic flux accumulates near the BH, becomes dynamically important, leads to a magnetically arrested disk (MAD), and unbinds some of the neutron-rich material. However, the strong flux also hinders accretion, lowers density, and increases neutrino-cooling timescale, which prevents further disk neutronization. Typical collapsar progenitors with mass accretion rates, M ̇ ∼ 0.1 − 1 M ⊙ s - 1 , do not produce significant neutron-rich ( Y e < 0.25) ejecta. However, we find that MADs at higher mass accretion rates, M ̇ ≳ few M ⊙ s - 1 (e.g., for more centrally concentrated progenitors), can unbind M ej ≲ M ⊙ of neutron-rich ejecta. The outflows inflate a shocked cocoon that mixes with the infalling neutron-poor stellar gas and raises the final outflow Y e ; however, the final r -process yield may be determined earlier at the point of neutron capture freeze-out. Future work will explore under what conditions more typical collapsar engines become r -process factories.

Issa, Danat (ORCID:0009000524787631)↗

DOE OSTI2021

A virtual element generalization on polygonal meshes of the Scott-Vogelius finite element method for the 2-D Stokes problem

The Virtual Element Method (VEM) is a Galerkin approximation method that extends the Finite Element Method (FEM) to polytopal meshes. In this paper, we present a conforming formulation that generalizes the Scott-Vogelius finite element method for the numerical approximation of the Stokes problem to polygonal meshes in the framework of the virtual element method. In particular, we consider a straightforward application of the virtual element approximation space for scalar elliptic problems to the vector case and approximate the pressure variable through discontinuous polynomials. We assess the effectiveness of the numerical approximation by investigating the convergence on a manufactured solution problem and a set of representative polygonal meshes. Finally, we numerically show that this formulation is convergent with optimal convergence rates except for the lowest-order case on triangular meshes, where the method coincides with the $\mathbb{P}_1$ – $\mathbb{P}_0$ Scott-Vogelius scheme, and on square meshes, which are situations that are well-known to be unstable.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Can general circulation models (GCMs) represent cloud liquid water path adjustments to aerosol–cloud interactions?

Abstract. General circulation models (GCMs), unlike other lines of evidence, indicate that anthropogenic aerosols cause a global-mean increase in cloud liquid water path (ℒ) and thus a negative adjustment to radiative forcing of the climate by aerosol–cloud interactions. In part 1 of this series of papers, we showed that this is true even in models that reproduce the negative correlation observed in present-day internal variability in ℒ and cloud droplet number concentration (Nd). We studied several possible confounding mechanisms that could explain the noncausal cloud–aerosol correlations in GCMs and that possibly contaminate observational estimates of radiative adjustments. Here, we perform single-column and full-atmosphere GCM experiments to investigate the causal model-physics mechanisms underlying the model radiative adjustment estimate. We find that both aerosol–cloud interaction mechanisms thought to be operating in real clouds – precipitation suppression and entrainment evaporation enhancement – are active in GCMs and behave qualitatively in agreement with physical process understanding. However, the modeled entrainment enhancement has a negligible global-mean effect. This raises the question of whether the GCM estimate is incorrect due to parametric or base-state representation errors or whether the process understanding gleaned from a limited set of canonical cloud cases is insufficiently representative of the diversity of clouds in the real climate. Regardless, even at limited resolution, the GCM physics appears able to parameterize the small-scale microphysics–turbulence interplay responsible for the entrainment enhancement mechanism. We suggest ways to resolve tension between current and future (storm-resolving) global modeling systems and other lines of evidence in synthesis climate projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

General circulation models simulate negative liquid water path–droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path

General circulation models' (GCMs) estimates of the liquid water path adjustment to anthropogenic aerosol emissions differ in sign from other lines of evidence. This reduces confidence in estimates of the effective radiative forcing of the climate by aerosol–cloud interactions (ERFaci). The discrepancy is thought to stem in part from GCMs' inability to represent the turbulence–microphysics interactions in cloud-top entrainment, a mechanism that leads to a reduction in liquid water in response to an anthropogenic increase in aerosols. In the real atmosphere, enhanced cloud-top entrainment is thought to be the dominant adjustment mechanism for liquid water path, weakening the overall ERFaci. We show that the latest generation of GCMs includes models that produce a negative correlation between the present-day cloud droplet number and liquid water path, a key piece of observational evidence supporting liquid water path reduction by anthropogenic aerosols and one that earlier-generation GCMs could not reproduce. However, even in GCMs with this negative correlation, the increase in anthropogenic aerosols from preindustrial to present-day values still leads to an increase in the simulated liquid water path due to the parameterized precipitation suppression mechanism. This adds to the evidence that correlations in the present-day climate are not necessarily causal. We investigate sources of confounding to explain the noncausal correlation between liquid water path and droplet number. These results are a reminder that assessments of climate parameters based on multiple lines of evidence must carefully consider the complementary strengths of different lines when the lines disagree.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: general mode, corrected

The KAZRCFRCOR Value Added Product (VAP) performs several corrections to the ingested KAZR data and also creates a significant detection mask for each radar mode. The VAP computes gaseous attenuation as a function of time and radial distance from the radar antenna, based on ambient relative humidity, and corrects observed reflectivities for that effect. KAZR reflectivities are also corrected in the antenna ‘nearfield’ range to compensate for the near-field antenna pattern and gain. KAZRCFRCOR also dealiases mean Doppler velocities, to correct velocities whose magnitudes exceed the radar’s Nyquist velocity. This dataset contains the 'kazrge' (general) mode output from this VAP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Validation of a Generalized Formulation for Load-Sharing Behavior in Epicyclic Gears for Wind Turbines: Preprint

In an ideal epicyclic gear set every parallel gear path transmits the same amount of torque. However, it is well known that certain manufacturing variations result in unequal load sharing between the parallel gear paths. Previous works have developed and validated a general closed-form analytical model of this phenomenon that describes the load sharing characteristics of epicyclic gear sets from three to six planets at any torque level. More recently, this analytical model has been reformulated to include the effects of gravity, carrier bearing clearance, and external applied moments, all of which are relevant to most gearboxes and their mounting configuration in horizontal-axis wind turbines. In this paper, the reformulated model is compared to load measurements collected from two similar wind turbine gearboxes with three-planet epicyclic gear sets. The resulting load sharing values are also compared to the mesh load factor requirements in the AGMA 6006 and IEC 61400-4 wind turbine gearbox design standards. Load sharing factors as high as 1.16 at extreme rotor moments and 1.08 with no moment were measured for the gearbox with cylindrical roller bearings, but the load sharing factor remained below 1.10 for the gearbox with tapered roller bearings The results show that in the wind turbine application, load sharing is not equal - even for three-planet systems with a floating central member because of the effects of gravity, rotor moments, and the resulting relative motions among the epicyclic gear components within the carrier bearing clearance.

closed-form analytical model↗

DOE OSTI2020

Validation of a Generalized Formulation for Load Sharing Behavior in Epicyclic Gears for Wind Turbines

closed-form analytical model↗

DOE OSTI2022

MELCOR Model Development of the General Atomics Fast Modular Reactor

General Atomics Electromagnetic Systems (GA-EMS) is currently developing a fast modular reactor (FMR) capable of providing safe, carbon-free electricity by 2035. As part of the development process, researchers at the University of Wisconsin-Madison (UWM) use MELCOR 2.2 to develop a simulation model of the proposed design to perform accident analyses. This paper summarizes the efforts of researchers at UWM in the creation of the FMR model. This model was built based on initial FMR design parameters. The design parameters will change as the design evolves and is refined.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Bearing Fault Detection on Wind Turbine Gearbox Vibrations Using Generalized Likelihood Ratio-Based Indicators: Preprint

Studies in condition monitoring literature often aim to detect rolling element bearing faults because they have one of the biggest shares among defects in turbo machinery. Accordingly, several prognosis and diagnosis methods have been devised to identify fault signatures from vibration signals. The underlying idea behind traditional indicators often revolves around tracking both cyclostationarity and abnormal impulses in the vibration signals without distinguishing the two. A recently proposed method to capture the rolling element bearing degradation lays out the groundwork for new indicator families utilizing generalized likelihood ratio test. This novel approach exploits the cyclostationarity and the impulsiveness of vibration signals independently in order to estimate the most suitable indicators for a given fault. However, the method has yet to be tested on complex experimental vibration signals such as those of a wind turbine gearbox. In this study, the approach is applied to the NREL Wind Turbine Gearbox Condition Monitoring Round Robin Study data set for bearing fault detection purposes. The data set is measured on an experimental test rig of a wind turbine gearbox, hence the complexity of the vibration signals is similar to a real case. Furthermore, the new indicators are also tested with signals that carry multiple fault signatures. The outcome demonstrates that the proposed method is capable of distinguishing between healthy and damaged vibration signals measured on a complex wind turbine gearbox.

condition monitoring↗

DOE OSTI2022

A Lyapunov-Based Generalized Dc-Side Controller Design for PV-Connected Systems: Preprint

The objective of this paper is to realize a universal dc-side controller for photovoltaic (PV) systems where the control is agnostic to the downstream converter configuration. To achieve this, the downstream power converter and its controls are manipulated into an effective power control loop that is then cast into a generalized multi-loop design framework. On the dc side, a nonlinear small-signal model of the PV input is realized exclusively in terms of PV datasheet parameters (i.e., open-circuit voltage, short-circuit current, and maximum power point). Finally, a linear controller is used to modulate the dc-side PV system with the generic downstream power controller. A Lyapunov candidate is proposed to analyze the stability of the interconnected system and provide a streamlined approach for the controller design. The proposed design is validated on a 1-kVA experimental setup that interfaces a PV module to the grid.

dc-dc control↗

DOE OSTI2023

Beyond Generalized Eigenvalues in Lattice Quantum Field Theory

Two analysis techniques, the generalized eigenvalue method (GEM) or Prony's (or related) method (PM), are commonly used to analyze statistical estimates of correlation functions produced in lattice quantum field theory calculations. GEM takes full advantage of the matrix structure of correlation functions but only considers individual pairs of time separations when much more data exists. PM can be applied to many time separations and many individual matrix elements simultaneously but does not fully exploit the matrix structure of the correlation function. We combine both these methods into a single framework based on matrix polynomials. As these algebraic methods are well known for producing extensive spectral information about statistically-noisy data, the method should be paired with some information criteria, like the recently proposed Bayesean model averaging.

Fleming, George T.↗

DOE OSTI2023

Generalizing mkFit and its Application to HL-LHC

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Analytic Sensitivity Coefficients for General Multigroup Infinite Medium k-Eigenvalue Problems

The general multigroup infinite medium k-eigenvalue neutron transport equation is used to derive analytic expressions for the infinite medium k-eigenvalue, the scalar neutron flux and adjoint, and the sensitivity of $k$ ∞ to perturbations in the multigroup nuclear data of a single species isotropic elastic scattering material. In the appendix, we present the multigroup nuclear data for U-235 and U-238 along with the corresponding k-eigenvalue, flux, adjoint, and sensitivity profiles, which include the sensitivity of $k$ ∞ to the total, fission, capture, and scattering macroscopic cross sections as well as to the group-to-group scattering cross section matrix, group neutron production, and the unconstrained and constrained fission neutron energy distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning

To assess the reliability of critical technologies like nuclear plants and infrastructure systems and improve the robustness of design, engineers have to quantify the uncertainties surrounding the system behavior accurately. However, the complexity of the problem can make standard reliability analysis algorithms prohibitively expensive, primarily due to the high computational cost of estimating the system response at each iteration. This cost can be greatly reduced by using multi-fidelity modeling and machine learning to build a surrogate model to replace the expensive response function. We propose a general and robust method for building surrogates from multiple Low Fidelity (LF) models coupled with machine learning to retain accuracy. Our framework first constructs “Corrected Low Fidelity models” (CLFs) by coupling a High Fidelity (HF) model inferred Gaussian Process correction term with each of the LF models. It then uses the correction terms to assign model probabilities to each of these CLFs in an explainable way before using them to assemble the final surrogate. No assumptions are made about the type of the LF models or their correlation with the HF model. The proposed surrogate modeling framework is used within the subset simulation algorithm (a variance-reduced MCMC-based reliability analysis algorithm) for enhanced efficiency. Additionally, an active learning step is added to the algorithm to adaptively decide when the surrogate is not sufficiently accurate, at which point the HF model is called and used to refine the surrogate. Through a frame buckling example, our method is shown to be highly efficient at reducing the expensive HF model calls while accurately estimating the failure probability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Neural Image Compression: Generalization, Robustness, and Spectral Biases

Recent advances in neural image compression (NIC) have resulted in models which are starting to outperform traditional codecs. While this has led to growing excitement about using these methods in real-world applications, the successful adoption of any machine learning system (including NIC) in the wild requires it to generalize (and be robust) to unseen distribution shifts at deployment time. Unfortunately, current research lacks comprehensive datasets and informative tools to evaluate and understand compression performance in real-world settings. To bridge this crucial gap, first, this paper presents a comprehensive benchmark suite to evaluate the out-of-distribution (OOD) performance of image compression methods. Specifically, we design CLIC-C and Kodak-C by introducing 15 common corruptions to popular CLIC and Kodak benchmarks. Next, we propose spectrally inspired introspection tools to gain a deeper understanding of errors introduced by image compression methods as well as their OOD performance. To this end, we carry out a detailed performance comparison of the classical codec with various variants of NIC (e.g., original, variable rate, pruned), revealing intriguing findings that challenge our current understanding of the strengths and limitations of NIC. Finally, we corroborate our empirical findings with theoretical analysis, providing an in-depth view of the OOD performance of NIC. Our benchmarks, spectral introspection tools, and findings provide a crucial bridge to the real-world adoption of NIC. We hope that our work will propel future efforts in designing more robust and generalizable NIC methods.

neural networks, Variational Autoencoder, robustne↗

DOE OSTI2024

Development & Experimental Validation of a Generalized Resistance-Capacitance Model for Numerical Simulation of Phase-Change Material Embedded Heat Exchangers

Latent heat thermal energy storage (LHTES) using phase change material (PCM) has attracted increased attention as a viable solution for overcoming the mismatch between energy supply and demand for renewable energy-based systems. PCM-embedded heat exchangers (PCM-HX) have the potential to significantly improve thermal performance due to high storage capacity and low temperature variation during the phase change process. Most models for simulating LHTES heat transfer use Computational Fluid Dynamics (CFD) simulations, which have high computational costs resulting from considering the complex and time-dependent physics relevant to PCM-HXs. In this paper, a Generalized Resistance Capacitance-based Model (GRCM) was developed to predict the thermal performance of arbitrary PCM-HXs in a computationally efficient manner without compromising modeling accuracy. The GRCM is exercised for three case studies: (i) verification for a single-slabbed finned PCM-HX, (ii) verification and validation for a copper foam/paraffin composite PCM-HX, and (iii) validation for a straight tube annular finned PCM-HX. The copper foam PCM-HX uses an electric heater at the top of HX, while the other two configurations utilize water as heat transfer fluid. For the single-slabbed finned PCM-HX melting case, the mean deviation in average PCM temperature predicted by the GRCM compared to the CFD model was between 0.56 – 0.73 K, with maximum temperature deviation of 2.68 K. For the HTF outlet temperature, the validation results showed that GRCM prediction matches very well with experimental data, with mean temperature deviation of 0.24 K during melting case, while for solidification case was 0.34 K. These results showcase the GRCM’s capability for accurately reproducing the thermal characteristics of PCM-HXs with considerably lower computational effort.

42 ENGINEERING↗

DOE OSTI2024

General Testing Setup for Hyperelastic Transducers-DEEC-Tec & Marine Renewable Energy: Preprint

Distributed embedded energy conversion technologies (DEEC-Tec), an emerging domain for ocean wave energy conversion technology, is showing promise for a range of applications. Research is being conducted at the National Renewable Energy Laboratory that leverages this domain to investigate the potential of ocean wave energy converters (WECs) constructed from hyperelastic forms of distributable and embeddable energy transducers. These transducers are available in forms such as disks, rectangles, or hexagons and can be combined in various ways to form energy-producing metamaterials and flexible WECs. DEEC-Tec, therefore, could open doors that enhance ocean wave energy conversion in ways not previously thought possible by allowing for many WEC topologies and morphologies. However, the same diversity and adaptability pose challenges for the development of these DEEC-Tec-oriented hyperelastic transducers. A commercial tensile testing setup was not found that was adaptable enough to accommodate the varied transducers while providing precise force control, range of motion, and noncontact data collection. Because of this lack, a comprehensive test rig was designed in house to be used with a 3D laser scanning device-providing contactless measurements while also allowing for different geometries and various uniaxial loadings. This paper and presentation will discuss these unique challenges and the processes for overcoming them to provide a robust and general testing setup for hyperelastic transducers, of any form, for the DEEC-Tec marine renewable energy domain.

DEEC-Tec↗

DOE OSTI2026

The General Antiparticle Spectrometer (GAPS) Antarctic Balloon Payload

The General Antiparticle Spectrometer (GAPS) is an Antarctic stratospheric balloon mission designed to provide unmatched sensitivity to low-energy (<0.25 GeV/n) cosmic-ray antiprotons, antideuterons, and antihelium nuclei as signatures of dark matter. The distinctive GAPS particle identification technique relies on measuring the energy loss along the track of an incoming antinucleus as it slows down and is captured into an exotic atom, and then detecting the de-excitation X-rays and the nuclear annihilation products. This measurement is realized using a Tracker composed of more than 1000 custom silicon strip detectors and a plastic scintillator time-of-flight (TOF) system instrumenting more than 40m$^2$. Together, these subsystems provide the velocity and energy resolution, stopping power, particle tracking, and X-ray identification necessary to distinguish rare antinucleus signals from the abundant positive-nucleus backgrounds, all within the constraints of a high-altitude mission. A multi-loop capillary heat pipe system has been developed to maintain the tracker operating temperature with significant mass and power savings over a conventional pump-based system. The first GAPS science payload flew for 25 days during the 2025/26 NASA Antarctic balloon campaign. We detail the design, integration, and commissioning of the payload prior to flight.

Aoyama, Kazutaka [JAXA, Sagamihara]↗