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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Numerical analysis of a time discretized method for nonlinear filtering problem with Lévy process observations

Abstract In this paper, we consider a nonlinear filtering model with observations driven by correlated Wiener processes and point processes. We first derive a Zakai equation whose solution is an unnormalized probability density function of the filter solution. Then, we apply a splitting-up technique to decompose the Zakai equation into three stochastic differential equations, based on which we construct a splitting-up approximate solution and prove its half-order convergence. Furthermore, we apply a finite difference method to construct a time semi-discrete approximate solution to the splitting-up system and prove its half-order convergence to the exact solution of the Zakai equation. Finally, we present some numerical experiments to demonstrate the theoretical analysis.

Mathematics↗
DOE OSTI2025

Asymptotic-preserving dynamical low-rank method for the stiff nonlinear Boltzmann equation

In kinetic theory, numerically solving the full Boltzmann equation is extremely expensive. This is because the Boltzmann collision operator involves a high-dimensional, nonlinear integral that must be evaluated at each spatial grid point and every time step. The challenge becomes even more pronounced in the fluid (strong collisionality) regime, where the collision operator exhibits strong stiffness, causing explicit time integrators to impose severe stability restrictions. In this paper, we propose addressing this problem through a dynamical low-rank (DLR) approximation. The resulting algorithm requires evaluating the Boltzmann collision operator only r 2 times, where r, the rank of the approximation, is much smaller than the number of spatial grid points. We propose a novel DLR integrator, called the XL integrator, which reduces the number of steps compared to the available alternatives (such as the projector splitting or basis update & Galerkin (BUG) integrator). For a class of problems including the Boltzmann collision operator which enjoys a separation property between physical and velocity space, we further propose a specialized version of the XL integrator, called the sXL integrator. This version requires solving only one differential equation to update the low-rank factors. Furthermore, the proposed low-rank schemes are asymptotic-preserving, meaning they can capture the asymptotic fluid limit in the case of strong collisionality. Our numerical experiments demonstrate the efficiency and accuracy of the proposed methods across a wide range of regimes, from non-stiff (kinetic) to stiff (fluid).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Topographical shifts in fine root lifespan in a mixed, mesic temperate forest

Root lifespan, often is estimated in landscape- and ecosystem-level carbon models using linear approximations. In water manipulation experiments, fine root lifespan can vary with soil water content. Soil water content is generally structured by complex topography, which is largely unaccounted for in landscape- and ecosystem-scale carbon models. Topography governs the range of soil water content experienced by roots which may impact their lifespan. We hypothesized that root lifespan varied nonlinearly across a temperate, mesic, forested catchment due to differences in soil water content associated with topographic position. We expected regions of the landscape that were too wet or too dry would have soils that were not optimal for roots and thus result in shorter root lifespans. Specifically, we hypothesized that root lifespan would be longest in areas that consistently had soil water content in the middle of the soil water content spectrum, while in soils at either very low or very high soil water content, root lifespan would be relatively short. We tested this hypothesis by collecting and analyzing two years of minirhizotron and soil moisture data in plots widely distributed in the Shale Hills catchment of the Susquehanna-Shale Hills Critical Zone Observatory in Pennsylvania. We found that fine root lifespans were longer in traditionally wetter topographic regions, but detected no short term (biweekly) effect of soil moisture on root lifespan. Additionally, depth in soil, soil series, slope face orientation, and season of birth strongly affected root lifespans across the catchment. In contrast, lifespan was unaffected by root diameter or mycorrhizal association. Failure to account for these variables could result in erroneous estimates of fine root lifespan and, consequentially, carbon flux in temperate forested regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Nonlinear sparse Bayesian learning for physics-based models

This paper addresses the issue of overfitting while calibrating unknown parameters of over-parameterized physics-based models with noisy and incomplete observations. Here, a semi-analytical Bayesian framework of nonlinear sparse Bayesian learning (NSBL) is proposed to identify sparsity among model parameters during Bayesian inversion. NSBL offers significant advantages over machine learning algorithm of sparse Bayesian learning (SBL) for physics-based models, such as 1) the likelihood function or the posterior parameter distribution is not required to be Gaussian, and 2) prior parameter knowledge is incorporated into sparse learning (i.e. not all parameters are treated as questionable). NSBL employs the concept of automatic relevance determination (ARD) to facilitate sparsity among questionable parameters through parameterized prior distributions. The analytical tractability of NSBL is enabled by employing Gaussian ARD priors and by building a Gaussian mixture-model approximation of the posterior parameter distribution that excludes the contribution of ARD priors. Subsequently, type-II maximum likelihood is executed using Newton's method whereby the evidence and its gradient and Hessian information are computed in a semi-analytical fashion. We show numerically and analytically that SBL is a special case of NSBL for linear regression models. Subsequently, a linear regression example involving multimodality in both parameter posterior pdf and model evidence is considered to demonstrate the performance of NSBL in cases where SBL is inapplicable. Next, NSBL is applied to identify sparsity among the damping coefficients of a mass-spring-damper model of a shear building frame. These numerical studies demonstrate the robustness and efficiency of NSBL in alleviating overfitting during Bayesian inversion of nonlinear physics-based models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

An energy-stable time-integrator for phase-field models

Here, we introduce a provably energy-stable time-integration method for general classes of phase-field models with polynomial potentials. We demonstrate how Taylor series expansions of the nonlinear terms present in the partial differential equations of these models can lead to expressions that guarantee energy-stability implicitly, which are second-order accurate in time. The spatial discretization relies on a mixed finite element formulation and isogeometric analysis. We also propose an adaptive time-stepping discretization that relies on a first-order backward approximation to give an error-estimator. This error estimator is accurate, robust, and does not require the computation of extra solutions to estimate the error. This methodology can be applied to any second-order accurate time-integration scheme. We present numerical examples in two and three spatial dimensions, which confirm the stability and robustness of the method. The implementation of the numerical schemes is done in PetIGA, a high-performance isogeometric analysis framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Bayesian Spectral Moment Estimation and Uncertainty Quantification

We present a Bayesian spectral fitting method developed for spectroscopic data analysis, particularly (but not solely) in the context of fusion energy research. The presented techniques are particularly valuable to estimating moments and corresponding uncertainties whenever the spectra result from line-integrated measurements in nonuniform plasmas, for which the approximation of atomic line shapes being ideal Gaussians gives poor estimates. We decompose multiple, potentially overlapping spectral lines into a sum of Gauss-Hermite polynomials, whose properties allow efficient truncation and uncertainty quantification, often with only three free parameters per atomic emission line. Tests with both synthetic and experimental data demonstrate the effectiveness and robustness where more standard nonlinear fitting routines may experience difficulties. A parallelized version of our implementation is publicly released under an open source license.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Ensuring Transient Stability With Guaranteed Region of Attraction in DC Microgrids

DC microgrids have promising applications in renewable integration due to their better energy efficiency when connecting DC components. However, they might be unstable since many loads in a DC microgrid are regulated as constant power loads (CPLs) that have a destabilizing negative impedance effect. As a result, the state trajectory displacement caused by abrupt load changes or contingencies can easily lead to instability. Many existing works have been devoted to studying the region of attraction (ROA) of a DC microgrid, in which the system is guaranteed to be asymptotically stable. Nevertheless, existing work either focuses on using numerical methods for ROA approximations that generally have no performance guarantees or cannot ensure a desired ROA for a general DC microgrid. To close this gap, this paper develops an innovative control synthesis algorithm to make a general DC microgrid have a theoretically guaranteed ROA, for example, to cover the entirety of its operating range regarding state trajectories. Here, we first study the nonlinear dynamics of a DC microgrid to derive a novel transient stability condition to rigorously certify whether a given operating range is a subset of the ROA; then, we formulate a control synthesis optimization problem to guarantee the condition’s satisfaction. This condition is a linear constraint, and the optimization problem resembles an optimal power flow problem and has a good computational behavior. Simulation case studies verify the validity of the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

On the influence of over-parameterization in manifold based surrogates and deep neural operators

Constructing accurate and generalizable approximators (surrogate models) for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. The main question is what type of surrogate models we should construct and should these models be under-parameterized or over-parameterized. In this work, we propose new developments and perform comparisons for two promising approaches: manifold-based polynomial chaos expansion (m-PCE) and the deep neural operator (DeepONet), and we examine the effect of over-parameterization on generalization. While m-PCE enables the construction of a mapping by first identifying low-dimensional embeddings of the input functions, parameters, and quantities of interest (QoIs), a neural operator learns the nonlinear mapping via the use of deep neural networks. Here, we demonstrate the performance of these methods in terms of generalization accuracy by solving the 2D time-dependent Brusselator reaction-diffusion system with uncertainty sources, modeling an autocatalytic chemical reaction between two species. We first propose an extension of the m-PCE by constructing a mapping between latent spaces formed by two separate embeddings of the input functions and the output QoIs. To further enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in the loss function. We demonstrate that the performance of m-PCE and DeepONet is comparable for cases of relatively smooth input-output mappings. However, when highly non-smooth dynamics is considered, DeepONet shows higher approximation accuracy. We also find that for m-PCE, modest over-parameterization leads to better generalization, both within and outside of distribution, whereas aggressive over-parameterization leads to over-fitting. In contrast, an even highly over-parameterized DeepONet leads to better generalization for both smooth and non-smooth dynamics. Furthermore, we compare the performance of the above models with another recently proposed operator learning model, the Fourier Neural Operator, and show that its over-parameterization also leads to better generalization. Taken together, our studies show that m-PCE can provide very good accuracy at very low training cost, whereas a highly over-parameterized DeepONet can provide better accuracy and robustness to noise but at higher training cost. In both methods, the inference cost is negligible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Probability Density Function Method for Langevin Equations with Colored Noise

We present a novel method to derive closed-form, computable PDF equations for Langevin systems with colored noise. The derived equations govern the dynamics of joint or marginal probability density functions (PDFs) of state variables, and rely on a so-called Large-Eddy-Diffusivity (LED) closure. We demonstrate the accuracy of the proposed PDF method for linear and nonlinear Langevin equations, describing the classical Brownian displacement and dispersion in porous media.

PDF method, uncertainty quantification, Langevin e↗
DOE OSTI2013

Fuel Assembly Shaker Test Simulation

This report describes the modeling of a PWR fuel assembly under dynamic shock loading in support of the Sandia National Laboratories (SNL) shaker test campaign. The focus of the test campaign is on evaluating the response of used fuel to shock and vibration loads that a can occur during highway transport. Modeling began in 2012 using an LS-DYNA fuel assembly model that was first created for modeling impact scenarios. SNL’s proposed test scenario was simulated through analysis and the calculated results helped guide the instrumentation and other aspects of the testing. During FY 2013, the fuel assembly model was refined to better represent the test surrogate. Analysis of the proposed loads suggested the frequency band needed to be lowered to attempt to excite the lower natural frequencies of the fuel assembly. Despite SNL’s expansion of lower frequency components in their five shock realizations, pretest predictions suggested a very mild dynamic response to the test loading. After testing was completed, one specific shock case was modeled, using recorded accelerometer data to excite the model. Direct comparison of predicted strain in the cladding was made to the recorded strain gauge data. The magnitude of both sets of strain (calculated and recorded) are very low, compared to the expected yield strength of the Zircaloy-4 material. The model was accurate enough to predict that no yielding of the cladding was expected, but its precision at predicting micro strains is questionable. The SNL test data offers some opportunity for validation of the finite element model, but the specific loading conditions of the testing only excite the fuel assembly to respond in a limited manner. For example, the test accelerations were not strong enough to substantially drive the fuel assembly out of contact with the basket. Under this test scenario, the fuel assembly model does a reasonable job of approximating actual fuel assembly response, a claim that can be verified through direct comparison of model results to recorded test results. This does not offer validation for the fuel assembly model in all conceivable cases, such as high kinetic energy shock cases where the fuel assembly might lift off the basket floor to strike to basket ceiling. This type of nonlinear behavior was not witnessed in testing, so the model does not have test data to be validated against.a basis for validation in cases that substantially alter the fuel assembly response range. This leads to a gap in knowledge that is identified through this modeling study. The SNL shaker testing loaded a surrogate fuel assembly with a certain set of artificially-generated time histories. One thing all the shock cases had in common was an elimination of low frequency components, which reduces the rigid body dynamic response of the system. It is not known if the SNL test cases effectively bound all highway transportation scenarios, or if significantly greater rigid body motion than was tested is credible. This knowledge gap could be filled through modeling the vehicle dynamics of a used fuel conveyance, or by collecting acceleration time history data from an actual conveyance under highway conditions.

Klymyshyn, Nicholas A.↗
DOE OSTI2026

Second-order wave excitation forces in WEC-Sim/MOST: Implementation, experimental validation, and code-to-code comparison

Accurate prediction of second-order hydrodynamic loads is essential for floating bodies, including floating offshore wind turbines, wave energy converters, and hybrid wind–wave platforms. These nonlinear effects, arising from both sum- and difference-frequency forcing, are critical for capturing key response characteristics but remain challenging to model efficiently. In this work, we extend the open-source Wave Energy Converter Simulator / MATLAB for Offshore Simulation Tool by implementing second-order wave excitation forces, supporting both the full Quadratic Transfer Function formulation and the Newman approximation. The full Quadratic Transfer Function method is used for all code-to-code comparisons and experimental validation, while the Newman approximation is provided as a computationally lighter alternative. To benchmark the new capability, we perform a code-to-code comparison with OpenFAST and OrcaFlex. We then validate the enhanced model using wave-tank measurements of a 1:96 scale DeepCwind semi-submersible, showing that second-order effects are required to reproduce platform motions. The implementation employs a computationally efficient pre-computation strategy for second-order wave excitation forces, reducing simulation cost while maintaining engineering accuracy. Overall, this work advances the tool as an open-source and versatile tool for modelling floating offshore renewable-energy systems requiring second-order hydrodynamic fidelity.

17 WIND ENERGY↗
DOE OSTI2014

Discrete models of fluids: spatial averaging, closure and model reduction

We consider semidiscrete ODE models of single-phase fluids and two-fluid mixtures. In the presence of multiple fine-scale heterogeneities, the size of these ODE systems can be very large. Spatial averaging is then a useful tool for reducing computational complexity of the problem. The averages satisfy exact balance equations of mass, momentum, and energy. These equations do not form a satisfactory continuum model because evaluation of stress and heat flux requires solving the underlying ODEs. To produce continuum equations that can be simulated without resolving microscale dynamics, we recently proposed a closure method based on the use of regularized deconvolution. Here we continue the investigation of deconvolution closure with the long term objective of developing consistent computational upscaling for multiphase particle methods. The structure of the fine-scale particle solvers is reminiscent of molecular dynamics. For this reason we use nonlinear averaging introduced for atomistic systems by Noll, Hardy, and Murdoch-Bedeaux. We also consider a simpler linear averaging originally developed in large eddy simulation of turbulence. We present several simple but representative examples of spatially averaged ODEs, where the closure error can be analyzed. Based on this analysis we suggest a general strategy for reducing the relative error of approximate closure. For problems with periodic highly oscillatory material parameters we propose a spectral boosting technique that augments the standard deconvolution and helps to correctly account for dispersion effects. We also conduct several numerical experiments, one of which is a complete mesoscale simulation of a stratified two-fluid flow in a channel. In this simulation, the operation count per coarse time step scales sublinearly with the number of particles.

Multiscale modeling, discrete models, fluid flow↗
DOE OSTI2016

ROM-based Subset Selection Algorithm for Efficient Surrogate Modeling

This summary discusses a new methodology to support the construction of surrogate models for computationally expensive models with many input parameters and output responses. In realistic engineering calculations, such as thermal-hydraulics simulation, the cost of the simulation is typically expensive, especially when detailed models are employed to describe system behavior, thereby justifying the need for surrogate models. The cost of surrogate model construction is typically a function of the number of model parameters and the degree of model nonlinearity. For sufficiently complex models, the cost of constructing the surrogate model could be by itself overwhelmingly expensive, thereby diminishing its value. One key requirement of surrogate model construction is the ability to preselect the best parametric form to describe the model behavior over the expected range of parameter variations. This summary presents a new algorithm to help select the best approximation out of a template of functions using recent advances in reduced order modeling (ROM) techniques. The idea of selecting functions out of a template is typically referred to as subset selection in the statistical and applied mathematics community. Different from existing subset selection algorithms, we rely on ROM to help identify the most influential functions for the surrogate model. Previous work has shown that ROM can help reduce the effective dimensionality of the parameter space, which can help reduce the cost of surrogate model construction. In this summary, we adopt a different philosophy, where the fitting functions are treated as pseudo parameters and ROM is applied on the pseudo parameter space, with the result now being a ranked list of the pseudo parameters along with their contribution to the quality of the surrogate model fit. This approach allows the analyst to discard all terms that have negligible impact on the quantity of interest. A relap5 model is employed to demonstrate the application of the proposed methodology. (authors)

42 ENGINEERING↗
DOE OSTI2017

Effect of Delayed Energy Release on Power Normalization in Reactor Depletion Calculations

Reactor depletion calculations typically assume that the thermal power of the reactor is directly proportional to the instantaneous fission rate within the core. However, approximately 7% of the sensible energy released per fission comes from the radioactive decay of fission products, which occurs alter the fission event. Thus, the assumed direct relationship between reactor power and fission rate implies that all fission products have reached secular equilibrium within the core. In situations where fission product concentrations have not reached equilibrium (e.g., shortly after reactor startup or a change in reactor power during operation), the delayed energy release due to fission product decay creates a nonlinear relationship between reactor power and fission rate. This non-linear relationship, in turn, affects the neutron flux levels and fuel consumption rate required to maintain the desired thermal power output for the reactor. For example, during initial startup of a commercial light water reactor the apparent energy release per fission is ∼7{sup %} lower than the typical value of ∼200 MeV/fission due to the potential energy stored in radioactive fission products. As a consequence, the flux level (and, therefore, uranium consumption rate) in the reactor must be correspondingly higher to preserve the desired reactor power. As the reactor operates and fission product inventories increase, the decay heat produced by the reactor will rise, producing a corresponding increase in the apparent energy release per fission. The characteristic time-dependent behavior of energy release due to the decay of fission products (i.e., decay heat) following reactor startup, shutdown, or changes in reactor power is well-known and widely-used in reactor safety analyses. However, to our knowledge, the effects of time-dependent decay heating rates on reactor depletion calculations have not been thoroughly investigated. In this paper, we present a simple methodology for explicitly accounting for delayed energy release due to the decay of fission products in reactor depletion calculations. In the proposed method, the balance equations for fission product decay energy are incorporated into the depletion coefficient matrix, using a strategy similar to that described by Isotalo in 2016. The modified system of differential equations can be solved via a matrix exponential approximation (or other suitable numerical method) to yield solutions for both nuclide densities and decay energy emission densities as a function of time. Numerical results from the method are presented for simplified (one-group point (0-D) and 1-D) representations of a commercial pressurized water reactor (PWR), in order to estimate the effect of delayed energy release on fuel consumption and flux level immediately following reactor startup.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

EFFECT OF BACKGROUND MAGNETIC FIELD ON TURBULENCE DRIVEN BY MAGNETOROTATIONAL INSTABILITY IN ACCRETION DISKS

We investigate the background magnetic field dependence of the saturated state of a magnetorotational instability (MRI) in an accretion disk by performing three-dimensional magnetohydrodynamic simulations. We assume an unstratified disk by employing the local shearing box approximation. Three different uniform background magnetic field configurations are treated for a wide range of field intensities. These simulations indicate that the time variations of the turbulent stress and the magnetic energy are altered by the presence of a poloidal component of the background field. We find that the saturation amplitude of the turbulent stress and the magnetic energy are determined by both the poloidal and azimuthal components of the field. In particular, when the poloidal component has the same intensity, the obtained turbulent stress for {beta}{sub y0} Almost-Equal-To 200 becomes smaller than those for a purely poloidal field case. Despite the fact that the background field affects the MRI turbulence, the correlation between the obtained turbulent stress and the magnetic energy in the nonlinear stage is independent of the field topology. Our results indicate that the saturated turbulent stress has a stronger correlation with the power of the perturbed component of the magnetic field than with the power of the total magnetic field. These results suggest that both the intensity and the direction of the background magnetic field significantly affect the turbulent motion of the MRI in accretion disks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation

This study addresses the problem of calibrating utility-maximizing nested logit activity-based travel demand model-systems. After estimation, it is common practice to use aggregate measurements to calibrate the estimated model-system’s parameters prior to their application in transportation planning, policy making, and operations. However, calibration of activity-based model-systems has received much less attention. Existing calibration approaches are myopic heuristics in the sense that they do not consider the fundamental inter-dependencies among choice-models and do not have a systematic way to adjust model parameters. Also, other purely simulation-based approaches do not perform well in large-scale applications. In this study, we focus on utility-maximizing nested logit activity-based model-systems and calibrating aggregate statistics such as activity shares, mode shares, time-dependent & mode-specific OD flows, and time-dependent & mode-specific sensor counts. We formulate the calibration problem as a simulation-based optimization problem and propose a stochastic gradient-based solution procedure to solve it. The solution procedure relies on microsimulation to calculate expectations of the aggregate statistics of interest to the calibration problem. Additionally, we derive approximate analytical expressions for the gradient of the objective function —that are evaluated through microsimulation on mini-batches of the population. The proposed solution procedure is sensitive to the fundamental structure of the activity-based model-system and is non-myopic in considering the dependencies across its model components. The formulated optimization problem is non-convex, highly nonlinear, and potentially has multiple-minima. Lastly, we show —through a real-world application— that the proposed solution procedure outperforms other state-of-the-art purely simulation-based optimization approaches in terms of computational efficiency, stability, and convergence. We also compare various gradient-based solution algorithms to determine the best algorithm to update the parameters. This work has the potential to facilitate wider and easier application of activity-based model-systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Highly parallelisable simulations of time-dependent viscoplastic fluid flow with structured adaptive mesh refinement

We present the extension of an efficient and highly parallelisable framework for incompressible fluid flow simulations to viscoplastic fluids. The system is governed by incompressible conservation of mass, the Cauchy momentum equation, and a generalised Newtonian constitutive law. In order to simulate a wide range of viscoplastic fluids, we employ the Herschel-Bulkley model for yield-stress fluids with nonlinear stress-strain dependency above the yield limit. We utilise Papanastasiou regularisation in our algorithm to deal with the singularity in apparent viscosity. The resulting system of partial differential equations is solved using the IAMR (Incompressible Adaptive Mesh Refinement) code, which uses second-order Godunov methodology for the advective terms and semi-implicit diffusion in the context of an approximate projection method to solve adaptively refined meshes. By augmenting the IAMR code with the ability to simulate regularised Herschel-Bulkley fluids, we obtain efficient numerical software for time-dependent viscoplastic flow in three dimensions, which can be used to investigate systems not considered previously due to computational expense. We validate results from simulations using this new capability against previously published data for Bingham plastics and power-law fluids in the two-dimensional lid-driven cavity. In doing so, we expand the range of Bingham and Reynolds numbers which have been considered in the benchmark tests. Moreover, extensions to time-dependent flow of Herschel-Bulkley fluids and three spatial dimensions offer new insights into the flow of viscoplastic fluids in this test case, and we provide missing benchmark results for these extensions.

42 ENGINEERING↗
DOE OSTI2020

The Large-Scale Vorticity Balance of the Antarctic Continental Margin in a Fine-Resolution Global Simulation

The depth-integrated vorticity budget of a global, eddy-permitting ocean/sea ice simulation over the Antarctic continental margin (ACM) is diagnosed to understand the physical mechanisms implicated in meridional transport. The leading-order balance is between the torques due to lateral friction, nonlinear effects, and bottom vortex stretching, although details vary regionally. Maps of the time-averaged depth-integrated vorticity budget terms and time series of the spatially averaged, depth-integrated vorticity budget terms reveal that the flow in the Amundsen, Bellingshausen, and Weddell Seas and, to a lesser extent, in the western portion of East Antarctica, is closer to an approximate topographic Sverdrup balance (TSB) compared to other segments of the ACM. Correlation and coherence analyses further support these findings, and also show that inclusion of the vorticity tendency term in the response (the planetary vorticity advection and the bottom vortex stretching term) increases the correlation with the forcing (the vertical net stress curl), and also increases the coherence between forcing and response at high frequencies across the ACM, except for the West Antarctic Peninsula. Finally, these findings suggest that the surface stress curl, imparted by the wind and the sea ice, has the potential to contribute to the meridional, approximately cross-slope, transport to a greater extent in the Amundsen, Bellingshausen, Weddell, and part of the East Antarctic continental margin than elsewhere in the ACM.

58 GEOSCIENCES↗