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At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Artificial Neural Network Modeling for 2-group Pin-Wise Group Constants

A High-fidelity multi-dimensional analysis is getting more attention because of the accurate prediction of local parameters for core design and safety assessment. For this, there are two possible approaches. One is a direct whole-core transport which is quite promising in terms of accuracy. However, it is very costly in terms of the computing time and memory requirements. Another option is the pin-by-pin core analysis using the diffusion theory. In the pin-by-pin core analysis, the multi-group constants can be produced by using the well-known generalized equivalence theory (GET) and these group constants can be corrected by the super homogenization method (SPH). Moreover, it is clear that both cross sections and discontinuity factors (DFs) must be corrected based on the leakage information in order to obtain an accurate solution for pin-by-pin core analysis. In a recent work by W. Kim and Y. Kim, the albedo-corrected parameterized equivalence constants (APEC) method was proposed, in which fuel assembly two-group XSs are parameterized as a function of an assembly-wise current-to-flux ratio (CFR). In the current work, we have investigated feasibility of APEC-like correction of two-group constants for homogenized pins in PWR cores, which is called APEC for Pin-cell or APECp. Unlike the APEC method, the artificial neural network (ANN) is used for functionalization of pin-wise XSs and DFs in this study. The pin-wise two-group parameters are evaluated using a 2- D method of characteristics (MOC)-based lattice code, DeCART2D.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Exploring gauge-fixing conditions with gradient-based optimization

Lattice gauge fixing is required to compute gauge-variant quantities, for example those used in RI-MOM renormalization schemes or as objects of comparison for model calculations. Recently, gauge-variant quantities have also been found to be more amenable to signal-to-noise optimization using contour deformations. These applications motivate systematic parameterization and exploration of gauge-fixing schemes. This work introduces a differentiable parameterization of gauge fixing which is broad enough to cover Landau gauge, Coulomb gauge, and maximal tree gauges. The adjoint state method allows gradient-based optimization to select gauge-fixing schemes that minimize an arbitrary target loss function.

Detmold, William↗
DOE OSTI2025

A geometric framework for momentum-based optimizers for low-rank training

Low-rank pre-training and fine-tuning have recently emerged as promising techniques for reducing the computational and storage costs of large neural networks. Training low-rank parameterizations typically relies on conventional optimizers such as heavy ball momentum methods or Adam. In this work, we identify and analyze potential difficulties that these training methods encounter when used to train low-rank parameterizations of weights. In particular, we show that classical momentum methods can struggle to converge to a local optimum due to the geometry of the underlying optimization landscape. To address this, we introduce novel training strategies derived from dynamical low-rank approximation, which explicitly account for the underlying geometric structure. Our approach leverages and combines tools from dynamical low-rank approximation and momentum-based optimization to design optimizers that respect the intrinsic geometry of the parameter space. We validate our methods through numerical experiments, demonstrating faster convergence, and stronger validation metrics at given parameter budgets.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗
DOE OSTI2026

Benchmarking quantum trial wavefunctions for phaseless auxiliary-field quantum Monte Carlo

The phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) method is a stochastic imaginary-time projection technique for computing ground-state properties of strongly correlated quantum systems, with accuracy that depends critically on the choice of trial wavefunction. Here, we investigate ph-AFQMC with trial states prepared using parameterized quantum circuits. In this work, we present a comprehensive benchmarking study of quantum trial wavefunctions spanning unitary coupled-cluster, Hamiltonian-informed, Jastrow-inspired, and adaptively constructed ansatze. The benchmarking evaluates accuracy, expressibility, and scalability of these ansatze within the QC-AFQMC framework. We test these ansatze on linear hydrogen chains under bond stretching and find that several ansatz families produce chemically accurate ph-AFQMC energies across the dissociation curve. We have performed simulations using the CUDA-Q quantum development platform on the GPU partition of the Perlmutter supercomputer. When comparing ansatze at similar numbers of variational parameters, we find that different ansatz families yield comparable ph-AFQMC results despite exhibiting substantially different variational energies, optimization costs, and circuit depths. Our results indicate that the variational energy of an ansatz is not always a reliable indicator of its quality for ph-AFQMC and reveal instances of over-parameterization. In the strongly correlated regime, trial wavefunctions obtained from adaptive ansatze, exemplified here by ADAPT-VQE with the UCCSD operator pool, can outperform their fixed-ansatz counterparts (UCCSD) in terms of projected energies while using substantially more compact circuits, providing a flexible route to optimize quantum resources within the ph-AFQMC framework.

Rofougaran, Rod [LBNL, Berkeley; Columbia U.; PNL,↗
DOE OSTI2020

Unified Entrainment and Detrainment Closures for Extended Eddy-Diffusivity Mass-Flux Schemes

The extended Eddy Diffusivity Mass Flux (EDMF) scheme is used as a unied parameterization that simulates subgrid-scale dynamics across a range of regimes.This work presents unied entrainment and detrainment closures that allow this scheme to simulate the dry convective boundary layer, shallow convection and deep convection , while adjacent works present other necessary closures for this unied scheme and test its performance for stable, stratocumulus topped and convective boundary layers. Entrainment and detrainment are modeled as a combination of turbulent and dynamical processes. The turbulent component is taken as downgradient diffusion between subdomains. The dynamical component is based on a ratio of buoyancy and vertical velocity that ensures consistent treatment of the ratio of mass-flux at vanishing area fraction. Dynamical detrainment is controlled by the difference in relative humidity between subdomains when liquid water is present. These closures are rst evaluated with entrainment and detrainment diagnosed from Large Eddy Simulations where decaying tracers are used to identify the different subdomains and mass exchanges between them. When implemented in a single column model (SCM), these closures are tested in simulations of several observationally motivated cases of dry convective boundary layer, shallow convection and deep convection. Results show good agreement between the SCM and Large Eddy Simulations in key properties for climate models and profiles of first, second and third moments. The development of diurnal cycles and the onset of and precipitation (with simplified microphysics) are well predicted by the SCM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Moist Process Biases in Simulations of the Madden–Julian Oscillation Episodes Observed during the AMIE/DYNAMO Field Campaign

Two Madden–Julian oscillation (MJO) episodes observed during the 2011 Atmospheric Radiation Measurement Program MJO Investigation Experiment (AMIE)/DYNAMO field campaign are simulated using a regional model with various cumulus parameterizations, a regional cloud-permitting model, and a global variable-resolution model with a high-resolution region centered over the tropical Indian Ocean. Model biases in relationships relevant to existing instability theories of MJO are examined and their relative contributions to the overall model errors are quantified using a linear statistical model. The model simulations capture the observed approximately log-linear relationship between moisture saturation fraction and precipitation, but precipitation associated with the given saturation fraction is overestimated especially at low saturation fraction values. This bias is a major contributor to the excessive precipitation during the suppressed phase of MJO. After accounting for this bias using a linear statistical model, the spatial and temporal structures of the model-simulated MJO episodes are much improved, and what remains of the biases is strongly correlated with biases in saturation fraction. The excess precipitation bias during the suppressed phase of the MJO episodes is accompanied by excessive column-integrated radiative forcing and surface evaporation. A large portion of the bias in evaporation is related to biases in wind speed, which are correlated with those of precipitation. Furthermore, these findings suggest that the precipitation bias sustains itself at least partly by cloud radiative feedbacks and convection–surface wind interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Climate Impacts of Convective Cloud Microphysics in NCAR CAM5

Here we improved the treatments of convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3) by 1) implementing new terminal velocity parameterizations for convective ice and snow particles, 2) adding graupel microphysics, 3) considering convective snow detrainment, and 4) enhancing rain initiation and generation rate in warm clouds. Furthermore, we evaluated the impacts of improved microphysics on simulated global climate, focusing on simulated cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation. Compared to CAM5.3 with the default convective microphysics, the too-strong cloud shortwave radiative forcing due primarily to excessive convective cloud liquid is largely alleviated over the tropics and midlatitudes after rain initiation and generation rate is enhanced, in better agreement with the CERES-EBAF estimates. Geographic distributions of graupel occurrence are reasonably simulated over continents; whereas the graupel occurrence remains highly uncertain over the oceanic storm-track regions. When evaluated against the CloudSat–CALIPSO estimates, the overestimation of convective ice mass is alleviated with the improved convective ice microphysics, among which adding graupel microphysics and the accompanying increase in hydrometeor fall speed play the most important role. The probability distribution function (PDF) of rainfall intensity is sensitive to warm rain processes in convective clouds, and enhancement in warm rain production shifts the PDF toward heavier precipitation, which agrees better with the TRMM observations. Common biases of overestimating the light rain frequency and underestimating the heavy rain frequency in GCMs are mitigated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Origins of climate model discrepancies in atmospheric shortwave absorption and global precipitation changes

Projected increases in mean precipitation are constrained by the atmospheric energy budget through radiative-convective equilibrium. Yet, significant differences persist between climate models on the rate of increase in precipitation per unit warming, mostly arising from the clear-sky radiative response. While the intermodel spread in clear-sky longwave cooling has been explained by climate feedbacks, the sources of spread in clear-sky shortwave heating are still unclear. We focus on the latter. Since water vapor contributes most of the atmospheric shortwave absorption, both intermodel differences in its spatial distribution and in radiative transfer parameterizations are plausible hypotheses for the spread. This report reestablishes the primary contribution from water vapor relative to other shortwave-absorbing species and evaluates the validity of both hypotheses. It is found that the intermodel spread in shortwave absorption change most likely originates from the radiation schemes, possibly because of simplifications induced by their low spectral resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Single–Column Model Simulations of Subtropical Marine Boundary–Layer Cloud Transitions Under Weakening Inversions

Results are presented of the GASS/EUCLIPSE single-column model inter-comparison study on the subtropical marine low-level cloud transition. A central goal is to establish the performance of state-of-the-art boundary-layer schemes for weather and climate models for this cloud regime, using large-eddy simulations of the same scenes as a reference. A novelty is that the comparison covers four different cases instead of one, in order to broaden the covered parameter space. Three cases are situated in the North-Eastern Pacific, while one reflects conditions in the North-Eastern Atlantic. A set of variables is considered that reflects key aspects of the transition process, making use of simple metrics to establish the model performance. Using this method some longstanding problems in low level cloud representation are identified. Considerable spread exists among models concerning the cloud amount, its vertical structure and the associated impact on radiative transfer. Here, the sign and amplitude of these biases differ somewhat per case, depending on how far the transition has progressed. After cloud breakup the ensemble median exhibits the well-known “too few too bright” problem. The boundary layer deepening rate and its state of decoupling are both underestimated, while the representation of the thin capping cloud layer appears complicated by a lack of vertical resolution. Encouragingly, some models are successful in representing the full set of variables, in particular the vertical structure and diurnal cycle of the cloud layer in transition. An intriguing result is that the median of the model ensemble performs best, inspiring a new approach in subgrid parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hydration of divalent lanthanides, Sm 2+ and Eu 2+ : A molecular dynamics study with polarizable AMOEBA force field

The chemistry of divalent lanthanides, Ln 2+ , is a growing sub-field of heavy element chemistry owing to new synthetic approaches. However, some theoretical aspects of these unusual cations are currently underdeveloped, especially as they relate to their dynamic properties in solution. In this study, we address the hydration of two of the classical Ln 2+ cations, Sm 2+ and Eu 2+ , using atomic multipole optimized energetic for biomolecular applications (AMOEBA) force fields. These cations have not been parameterized to date with AMOEBA, and few studies are available because of their instability with respect to oxidation in aqueous media. Coordination numbers (CN's) of 8.2 and 8.1 respectively for Sm 2+ and Eu 2+ , and 8.8 for both Sm 3+ and Eu 3+ have been obtained and are in good agreement with the few available AIMD and X-ray absorption fine structures studies. The decreased CN of Ln 2+ compared with Ln 3+ arises from progressive water exchange events that indicates the gradual stabilization of 8-coordinate structures with respect to 9-coordinate geometries. Moreover, the effects of the chloride counter anions on the coordination of Ln 2+ cations have been studied at different chloride concentrations in this work. Lastly, water exchange times of Ln 2+ cations have been calculated to provide a comprehensive understanding of the behavior of Eu 2+ and Sm 2+ in aqueous chloride media.

AMOEBA force field parameterization↗
DOE OSTI2018

Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

Dynamical downscaling with high-resolution regional climate models may offer the possibility of realistically reproducing precipitation and weather events in climate simulations. As resolutions fall to order kilometers, the use of explicit rather than parametrized convection may offer even greater fidelity. However, these increased resolutions both allow and require increasingly complex diagnostics for evaluating model fidelity. Here we focus on precipitation evaluation and analyze five 2-month-long dynamically downscaled model runs over the continental United States that employ different convective and microphysics parameterizations, including one high-resolution convection-permitting simulation. All model runs use the Weather Research and Forecasting Model driven by National Center for Environmental Prediction reanalysis data. We show that employing a novel rainstorm identification and tracking algorithm that allocates essentially all rainfall to individual precipitation events allows new insights into model biases. Results include that, at least in these runs, model wet bias is driven by excessive areal extent of individual precipitating events, and that the effect is time-dependent, producing excessive diurnal cycle amplitude. This amplified cycle is driven not by new production of events but by excessive daytime enlargement of long-lived precipitation events. Here, we further show that in the domain average, precipitation biases appear best represented as additive offsets. Of all model configurations evaluated, convection-permitting simulations most consistently reduced biases in precipitation event characteristics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Influence of sun zenith angle on canopy clumping and the resulting impacts on photosynthesis

Addressing the impact of vegetation architecture on shortwave radiation transfer in land surface models is important for accurate weather forecasting, carbon budget estimates, and climate predictions. This paper investigates to what extent it is possible to retrieve structural parameters of two different parameterization schemes from direct transmittance derived from digital hemispherical photography and 3D radiative transfer modeling for two study sites with different vegetation canopy architectures. Neglecting the representation of 3D canopy structure in radiative transfer schemes leads to significant errors in shortwave radiation partitioning (up to 3.5 times more direct transmittance in the 3D model). Structural parameters, referred to as whole canopy ‘clumping indices’, were obtained in order to evaluate the impact of angular variation in clumping on shortwave radiation transfer. Impacts on photosynthesis were evaluated at site level with the UKESM land surface model, JULES. A comparison between flux tower derived and modeled photosynthesis indicates that considering zenith angular variations of structural parameters in the radiative transfer scheme of the UKESM land surface model significantly improves photosynthesis prediction in light limited ecosystems (from RMSE = 2.91 μmol CO 2 .m -2 .s -1 to RMSE = 1.51 μmol CO 2 .m -2 .s -1 , 48% smaller), typically with enhanced photosynthesis from bottom layers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Development of a Reactive Force Field for Simulating Photoinitiated Acrylate Polymerization

Light-driven and photo-curable polymer based additive manufacturing (AM) has enormous potential due to its excellent resolution and precision. Acrylated radical chain-growth polymerized resins are widely used in photopolymer AM due to their fast kinetics, and often serve as a departure point for developing other resin materials for photopolymer-based AM technologies. For successful control of the photopolymer resins, the molecular basis of the acrylate free-radical polymerization has to be understood in detail. We present an optimized reactive force field (ReaxFF) for molecular dynamics (MD) simulations of acrylate polymer resins that captures radical polymerization thermodynamics and kinetics. The force field is trained against an extensive training set including density functional theory (DFT) calculations of reaction pathways along the radical polymerization from methyl acrylate to methyl butyrate, bond dissociation energies, and structures and partial charges of several molecules and radicals. We also found that it was critical to train the force field against an incorrect, nonphysical reaction pathway observed in simulations that used parameters not optimized for acrylate polymerization. As a result, the parameterization process utilizes a parallelized search algorithm, and the resulting model can describe polymer resin formation, crosslinking density, conversion rate, and residual monomers of the complex acrylate mixtures.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Constraining Aging Processes of Black Carbon in the Community Atmosphere Model Using Environmental Chamber Measurements

Abstract The direct radiative forcing of black carbon aerosol (BC) on the Earth system remains unsettled, largely due to the uncertainty with physical properties of BC throughout their lifecycle. Here we show that ambient chamber measurements of BC properties provide a novel constraint on the crude BC aging representation in climate models. Observational evidence for significant absorption enhancement of BC can be reproduced when the aging processes in the four‐mode version of the Modal Aerosol Module (MAM4) aerosol scheme in the Community Atmosphere Model version 5 are calibrated by the recent in situ chamber measurements. An observation‐based scaling method is developed in the aging timescale calculation to alleviate the influence of biases in the simulated model chemical composition. Model sensitivity simulations suggest that the different monolayer settings in the BC aging parameterization of MAM4 can cause as large as 26% and 24% differences in BC burden and radiative forcing, respectively. We also find that an increase in coating materials (e.g., sulfate and secondary organic aerosols) reduces BC lifetime by increasing the hygroscopicity of the mixture but enhances its absorption, resulting in a net increase in BC direct radiative forcing. Our results suggest that accurate simulations of BC aging processes as well as other aerosol species are equally important in reducing the uncertainty of BC forcing estimation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework

Ice microphysics parameterizations in models must make major simplifications relative to observations, typically employing empirical relationships to represent average functional properties of particles. However, previous studies have established that ice particle properties vary even in similar cloud types and thermodynamic environments, and it remains unclear how this so-called “natural variability” impacts simulated deep convection. This uncertainty is addressed by implementing a stochastic framework into the Predicted Particle Properties microphysics scheme in the Weather Research and Forecasting model. The approach stochastically varies the coefficients of the mass-size (m-D) relationship (m=aD b ) for unrimed and partially rimed ice. Using guidance from aircraft in situ measurements obtained during the Midlatitude Continental Convective Clouds Experiment (MC3E), the scheme samples from distributions of the prefactor (a) and the exponent (b) of the m-D relationship. Simulations of two MC3E deep convective cases indicate that the stochastic m-D scheme produces considerable variability of anvil cirrus cloud optical depth (τ) distributions, even for the same ice water path (IWP). Thus, the stochastic scheme produces variable cloud radiative forcing that is independent of IWP. This τ-IWP relationship variability is nonexistent using the deterministic m-D ensemble. Additional sensitivity tests are performed in which the fallspeed-size relationship (V=cD d ) is stochastically varied, resulting in variable precipitation amounts and rain rate distributions. Results are presented in the context of satellite and precipitation observations and include comparison with other ensemble configurations using perturbed initial and lateral boundary conditions and small-amplitude noise added to the potential temperature field.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Comparing Ocean Surface Boundary Vertical Mixing Schemes Including Langmuir Turbulence

Six recent Langmuir turbulence parameterization schemes and five traditional schemes are implemented in a common single-column modeling framework and consistently compared. These schemes are tested in scenarios versus matched large eddy simulations, across the globe with realistic forcing (JRA55-do,WAVEWATCH-III simulated waves) and initial conditions (Argo), and under realistic conditions as observed at ocean moorings. Traditional non-Langmuir schemes systematically underpredict large eddy simulation vertical mixing under weak convective forcing, while Langmuir schemes vary in accuracy. Under global, realistic forcing Langmuir schemes produce 6% (–1% to 14% for 90% confidence) or 5.2 m (–0.2 m to 17.4 m for 90% confidence) deeper monthly mean mixed layer depths than their non-Langmuir counterparts, with the greatest differences in extratropical regions, especially the Southern Ocean in austral summer. Discrepancies among Langmuir schemes are large (15% in mixed layer depth standard deviation over the mean): largest under wave-driven turbulence with stabilizing buoyancy forcing, next largest under strongly wave-driven conditions with weak buoyancy forcing, and agreeing during strong convective forcing. Non-Langmuir schemes disagree with each other to a lesser extent, with a similar ordering. Langmuir discrepancies obscure a cross-scheme estimate of the Langmuir effect magnitude under realistic forcing, highlighting limited understanding and numerical deficiencies. Maps of the regions and seasons where the greatest discrepancies occur are provided to guide further studies and observations.

58 GEOSCIENCES↗