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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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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Dynamical ejecta from binary neutron star mergers: Impact of a small residual eccentricity and of the equation of state implementation

Predicting the properties of the matter ejected during and after a neutron star merger is crucial to our ability to use electromagnetic observations of these mergers to constrain the masses of the neutron stars, the equation of state of dense matter, and the role of neutron star mergers in the enrichment of the Universe in heavy elements. Furthermore, our ability to reliably provide such predictions is however limited by a broad range of factors, including the finite resolution of numerical simulations, their treatment of magnetic fields, neutrinos, and neutrino-matter interactions, and the approximate modeling of the equation of state of dense matter. In this manuscript, we study specifically the role that a small residual eccentricity and different implementations of the same equation of state have on the matter ejected during the merger of a 1.3M ⊙ –1.4M ⊙ binary neutron star system. We find that a residual eccentricity e ~ 0.01, as measured ~ 4–6 orbits before merger, causes O(25%–30%) changes in the amount of ejected mass, mainly due to changes in the amount of matter ejected as a result of core bounces during merger. We note that O(1%) residual eccentricities have regularly been used in binary neutron star merger simulations as proxy for circular binaries, potentially creating an additional source of error in predictions for the mass of the dynamical ejecta.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. Here, to address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A phase-field diffraction model for thermo-hydro-mechanical propagating fractures

This paper introduces a novel diffraction based thermo-hydraulic–mechanical (THM) model for fracture propagation using a phase-field fracture (PFF) approach. The key innovation of the THM-PFF model lies in its integrated treatment of four solution variables—displacements, phase-field, pressure, and temperature—each governed by a combination of conservation of momentum (mechanics problem), a variational inequality (constrained minimization problem), mass conservation (pressure problem), and energy conservation (temperature problem). This leads to a new formulation of a coupled variational inequality system. A major advancement is the development of an extended fixed-stress algorithm, where displacements, phase-field, pressures, and temperatures are solved in a staggered sequence. An important aspect of this work is the global coupling of pressures and temperatures across the domain using diffraction systems, with diffraction coefficients defined by material parameters weighted by the diffusive phase-field variable. To ensure robust local mass conservation, we employ enriched Galerkin finite elements (EG) for both pressure and temperature diffraction equations. By enriching the continuous Galerkin basis functions with discontinuous piecewise constants, EG accurately represents solution and parameter discontinuities while preserving local mass and energy conservation—crucial aspects for THM problems and realistic behavior. Moreover, the use of a predictor–corrector local mesh adaptivity scheme is employed, allowing the model to handle small phase-field length-scale parameters while maintaining high numerical accuracy and reasonable computational cost. Furthermore, these new model and algorithmic developments represent significant advances in the field and have been substantiated through rigorous numerical tests.

Diffraction systems↗
DOE OSTI2025

Comprehensive model for evaluating voltage losses and performance improvements in thin-film photovoltaic devices

Progress of state-of-the-art and next-generation thin-film photovoltaic devices is often stymied by open-circuit voltage (𝑉 oc ) that is significantly lower than theoretical and practical limits. Yet, effectively diagnosing the primary sources of voltage loss remains challenging. Herein, a sequence of device-level characterization techniques and simulations are employed to identify and rank loss mechanisms. For the research-based Cd⁡(Se,Te) device under study, most of the loss was at the front semiconductor heterointerface due to a clifflike conduction-band offset that lowered the recombination activation energy. Additional losses due to band tails were quantified by photoluminescence analysis. The latter provided the absorption coefficient and activation energy reduction associated with band tails as inputs to device models. Simulations showed that alleviating front-interface issues would improve 𝑉 oc , but it would then be limited by bulk recombination. Further improvement of the bulk would then lead to back-contact limitations. Reducing band tails is beneficial in any circumstance. In conclusion, this analysis provides guidance for reaching toward the radiative 𝑉 oc limit.

14 SOLAR ENERGY↗
DOE OSTI2024

Bifurcation of equilibrium positions for ellipsoidal particles in inertial shear flows between two walls

We conducted a systematic numerical investigation of spherical, prolate and oblate particles in an inertial shear flow between two parallel walls, using smoothed particle hydrodynamics (SPH). It was previously shown that above a critical Reynolds number, spherical particles experience a supercritical pitchfork bifurcation of the equilibrium position in shear flow between two parallel walls, namely that the central equilibrium position becomes unstable, leading to the emergence of two new off-centre stable positions (Foxet al.,J. Fluid Mech., vol. 915, 2021). This phenomenon was unexpected given the symmetry of the system. In addition to confirming this finding, we found, surprisingly, that ellipsoidal particles can also return to the centre position from the off-centre positions when the particle Reynolds number is further increased, while spherical particles become unstable under this increased Reynolds number. By utilizing both SPH and the finite element method for flow visualization, we explained the underlining mechanism of this reverse of bifurcation by altered streamwise vorticity and symmetry breaking of pressure. Furthermore, we expanded our investigation to include asymmetric particles, a novel aspect that had not been previously modelled, and we observed similar trends in particle dynamics for both symmetric and asymmetric ellipsoidal particles. While further validation through laboratory experiments is necessary, our research paves the road for development of new focusing and separation methods for shaped particles.

Mechanics↗
DOE OSTI2025

Niobium hydride formation in superconducting qubit thin films

The formation of nonsuperconducting hydrides in 160–170-nm-thick films of niobium is examined. We identify six elastically distinct orientation relationships ɛ−Nb 4 ⁢H 3 takes within the matrix, solid-solution 𝛼−NbH 𝑥 phase. We employ a phase field model to assess the impact of elastic energy induced by the strain of phase transformation on the morphology and transformation dynamics of ɛ precipitates within a thin film. We consider the dimensions of the thin film, crystallographic growth direction, and diffusion rates to predict the timescale of hydride evolution. Leveraging the finite element method, we predict two-dimensional and three-dimensional equilibrium shapes of ɛ-hydrides within a bulk sample and in a thin film that has a traction-free surface. Our results suggest that niobium hydrides migrate to the free surface of the film. Precipitates which reach the free surface coarsen, while precipitates within the film dissolve. Precipitates in both two dimensions and three dimensions experience a repulsive interaction force at the free surface, that is attractive in the bulk, shown in experiment and theory of previous studies.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Non-conformal interface-cohesive modeling with the shifted boundary method

The accurate simulation of boundary- and interface-dominated problems on complex geometries remains challenging when boundary- or interface-fitted meshes are difficult to generate, particularly for curved boundaries, polycrystalline microstructures, and dense interface networks. The Shifted Boundary Method (SBM) alleviates this meshing burden by shifting the enforcement of boundary conditions from the true boundary to a nearby surrogate boundary and recovering the effect of the true boundary through geometric correction terms, thereby enabling standard finite element spaces on non-boundary-fitted meshes. In this report, we develop a general shiftedboundary and shifted-interface framework within the open-source MOOSE framework. We first present a general SBM implementation for complex geometries on non-boundary-fitted meshes. We then adopt the Shifted Interface Method (SIM) for internal interfaces and develop a unified shifted-interface treatment in which the interface law is enforced on a surrogate interface and the effect of the true interface is recovered through shifted jumps, fluxes, and tractions. This perspective brings scalar thermal-contact and vector-valued cohesive-zone mechanics into a single framework, the latter realized as the Shifted Cohesive Zone Method (SCZM) and coupled with history-dependent constitutive models from NEML2. We further extend the MOOSE mesh infrastructure to support cohesive-zone calculations on distributed meshes. The framework is verified and demonstrated through three progressive studies: Poisson’s equation on a smoothed starshaped domain, a manufactured thermal-contact problem on a non-interface-fitted mesh, and a two-dimensional polycrystalline representative volume element combining crystal plasticity with cohesive grain-boundary interfaces. Across these studies, the shifted formulations reproduce boundary- and interface-fitted reference solutions with high fidelity, indicating that the proposed framework provides an accurate and efficient route to boundary- and interface-dominated simulations on arbitrary geometries without requiring fitted meshes.

Yang, Cheng-Hau↗
DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗
DOE OSTI2024

Accelerating Thermochemical Equilibrium Calculations for Nuclear Reactor Applications

Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Momentum-Resolved Direct Observation of Chiral Phonons in Elemental Tellurium

Chiral phonons carry finite phonon angular momentum, yet their momentum-resolved behavior in acoustic branches remains largely unexplored. In this work, we report the direct detection of chiral acoustic phonons in elemental Te. Through a combination of high-energy-resolution inelastic x-ray scattering experiments and atomistic modeling, we confirm that longitudinal acoustic modes develop pronounced circular atomic motion by mixing with transverse modes. Our results establish a direct link between unexpected scattering intensity in nominally forbidden geometries and circular phonons, offering a framework for characterizing momentum-resolved phonon chirality and highlighting the emergence of intrinsic phonon angular momentum in acoustic branches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Kinetic Plasma Simulation Capabilities in the MOOSE Framework: Verification of Particle-Particle Collisions

High-fidelity simulations of complex plasma systems allow researchers to gain key insights into and understanding of these systems. To facilitate massively parallel high-fidelity plasma simulations, finite-element-based particle-in-cell capabilities are being developed within the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) based framework called Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER). While SALAMANDER’s primary objective is modeling edge plasmas and plasma-facing components in fusion devices, the particle-in-cell capabilities being developed are general and will support modeling low-temperature plasmas as well. Previously, collisionless magnetostatic simulation capabilities have been verified with the two-stream and Dorey-Guest-Harris instabilities, and single particle motion. Collisions were implemented using the direct simulation Monte Carlo method, and verification of this capability will be presented here several verification problems: relaxation of a randomly initialized gas to a Maxwellian distribution, Fourier heat flow, and comparison of reaction rates to both analytic calculations and those calculated using a multi-term Boltzmann solver.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Prototype Modeling for a Light-Trapping Planar-Cavity Enclosed Particle Solar Receiver

Concentrating solar thermal (CST) systems present a promising avenue for affordable and reliable energy production. Solar receivers are key components that determine the efficiency and longevity of these systems. Particle-based solar receivers have emerged as a compelling alternative to traditional technologies, offering several advantages that address limitations in current CST systems. This is especially true as next-generation CST technologies target applications including electricity generation, thermochemical processes, and industrial process heat, many of which necessitate higher operating temperatures than current commercial molten salt systems. Molten-salt thermal energy storage (TES) systems, commonly used in CSP, face challenges related to freezing and corrosion. Particle-based TES systems, in contrast, do not experience these issues, as particles are stable at high temperatures, exceeding 1000 degrees Celsius. This capability allows for a wider range of applications, including those requiring higher temperatures for industrial processes and efficient electricity generation. A novel innovation in particle-based solar receiver technology is the light-trapping planar cavity receiver (LTPCR) configuration developed by NREL. The LTPCR design consists of small cavity-like structures using opaque planar surfaces, enabling efficient capture and absorption of solar energy. A high incident flux concentration at the cavity aperture is absorbed on the receiver walls, and subsequently transferred to particles on the inside of cavities. The particles flow through the system, forming a fluidized bed inside of the receiver panels, effectively capturing the absorbed solar heat. Air is used as a fluidizing medium in this process to enhance particle heat transfer and mixing. The effectiveness of this design lies in its ability to manage solar flux conditions and ensure high solar-to-thermal receiver efficiency. A 100-kW prototype is currently being tested at the King Saud University in Saudi Arabia to assess the receiver performance. A range of modeling analyses for the optical, thermal, and mechanical effects were conducted to assess the performance of the receiver under on-sun conditions. The solar flux resulting from the KSU heliostat field was modeled using NREL SolTrace software and produced up to 600 kW/m2 at the receiver aperture. The solar flux absorbed on the receiver walls was then used within a computational fluid dynamics (CFD) model to predict wall temperature distributions along with radiation and convection loss. A two-phase CFD model was developed for the fluidized bed of silica sand inside the receiver panels to predict local wall-to-particle heat transfer coefficients, particle temperature distributions, and outlet temperature of the particles. We have also conducted analyses to understand the thermomechanical behavior of these innovative enclosed light-trapping solar receivers optimized for particle heating. We used finite element analysis (FEA) to predict the receiver's performance using temperature distributions obtained from CFD and based on the resulting stress profiles, evaluated creep-fatigue damage with a goal of achieving a 30-year service life. Analysis showed a significant impact of the particle-to-wall heat transfer coefficients (HTCs) on receiver performance, with higher HTCs resulting in reduced stress and increased lifespan. For instance, when using Inconel 740H, increasing the HTC from 800 W/m2 K to 1400 W/m2 K increased the creep life from 4,000 hours to over 100,000 hours. This highlights the importance of understanding and optimizing heat transfer in the design of high-efficiency receivers.

14 SOLAR ENERGY↗
DOE OSTI2026

Computationally inexpensive part-scale thermal history of additive friction-stir deposition

This study presents an analytical model for steady-state power generation and tool heat loss in additive friction-stir deposition (AFSD), developed to enable part-scale thermal simulation while remaining computationally inexpensive. The model predicts total generated power, yielding 3.7–4.7 kW across deposition temperature setpoints of 400–460 °C for the deposition of AA6061 with a Be-Cu tool. This corresponds to 90–95% of the reported spindle power. Tool heat loss is experimentally determined by calibrating a steady-state energy balance between the generated power, the substrate-deposition thermal gradient, and a temperature dependent tool heat loss term: q tool (T) = a + b (T - 400°C) with a = 2.7 x 10 6 Wm -2 and b = 9.5 x 10 3 Wm -2 K -1 . The calibration indicates that about 69% of the generated heat is conducted into the tool for this configuration, which is much higher than previously reported. The calibrated heat-source is implemented in finite element software (Adamantine) to simulate the transient thermal history of a 100 cm 3 representative build in 8 min on a standard desktop (at 0.635 mm build-height resolution). For the first three layers, the substrate temperatures between simulation and experiment are within 10% mean absolute percentage error. Sensitivity analysis indicates that uncertainties in average deposition temperature and deformation localization (stir-zone geometry, depth, and spatial dependance of strain-rate and flow stress) dominate model variance, motivating additional experimental verification.

Additive Friction-Stir Deposition↗
DOE OSTI2025

Non-Hermitian quantum mechanics approach for extracting and emulating continuum physics based on bound-state-like calculations: Detailed description

Here, this work applies a reduced basis method to study the continuum physics of a finite quantum system—either few or many-body. Specifically, I develop reduced-order models, or emulators, for the underlying inhomogeneous Schrödinger equation and train the emulators against the equation's bound-state-like solutions at complex energies. The emulators rapidly and accurately interpolate and extrapolate the matrix elements of the Hamiltonian resolvent operator (Green's function) across a parameter space that includes both complex energy and other real-valued physical inputs in the Schrödinger equation. The spectra, discretized and compressed as the result of emulation, and the associated resolvent matrix elements (or amplitudes), have the defining characteristics of non-Hermitian quantum mechanics calculations, featuring complex eigenenergies with negative imaginary parts and branch cuts moved below the real axis in the complex energy plane. Therefore, one now has a method that extracts continuum physics from bound-state-like calculations and emulates those extractions in the input parameter space. Building on a prior Letter [Zhang, Phys. Rev. Lett. 135, 242501 (2025)], this article provides the full theoretical details, a comprehensive analysis of the method's performance, and a brief discussion of how it can be coupled with existing continuum approaches to perform emulations in their input parameter spaces.

ab initio calculations↗
DOE OSTI2026

Multitask graph neural networks for elastoplastic response prediction in dual-phase polycrystals

Microstructure-sensitive prediction of elastoplastic response remains a recurring bottleneck in multiscale damage and fatigue modeling, where large ensembles of statistically distinct polycrystals are required to quantify variability and extreme-value behavior. In this work, we develop a multitask graph neural network (GNN) surrogate that maps dual-phase ferrite–martensite polycrystal microstructures to Statistical Volume Element (SVE)-level elastoplastic Quantities of Interest (QoIs). Each SVE is represented as a grain-adjacency graph, with node features encoding phase, geometry, and crystallographic orientation, and edge features encoding relative misorientation. A message-passing graph convolution generates node embeddings, which are pooled into a graph representation and passed to a multitask regression head that jointly predicts 10 scalar QoIs and vector-valued stress–strain responses in orthogonal loading directions across multiple martensite volume fractions and SVE sizes. Results show high accuracy for scalar QoIs and strong agreement for full stress–strain trajectories, with population envelopes reproducing both median behavior and finite-SVE variability across compositions and partition scales. A unified model trained on pooled volume-fraction data preserves most within-regime accuracy relative to regime-specific models while also capturing the broader cross-regime variation reflected in the pooled test set. Distributional comparisons further demonstrate that the surrogate preserves heterogeneity under SVE partitioning, enabling statistically consistent block-wise random-field construction for mesoscale analyses. Overall, the proposed grain-graph surrogate provides a practical pathway to accelerate ensemble-based studies of SVE-level constitutive variability in dual-phase polycrystals.

Crystal plasticity↗
DOE OSTI2024

An active learning framework for the rapid assessment of galvanic corrosion

The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhanced Collisional Losses from a Magnetic Mirror Using the Lenard-Bernstein Collision Operator

Collisions are crucial in governing particle and energy transport in plasmas confined in a magnetic mirror trap. Modern gyrokinetic codes model transport in magnetic mirrors, but some utilize approximate model collision operators. This study focuses on a Pastukhov-style method of images calculation of particle and energy confinement times using a Lenard-Bernstein model collision operator. Prior work on parallel particle and energy balances used a different Fokker-Planck plasma collision operator. The method must be extended in non-trivial ways to study the Lenard-Bernstein operator. To assess the effectiveness of our approach, we compare our results with a modern finite element solver. Our findings reveal that the particle confinement time scales like a exp( a 2 ) using the Lenard-Bernstein operator, in contrast to the more accurate scaling that the Coulomb collision operator would yield a 2 exp( a 2 ), where a 2 is approximately proportional to the ambipolar potential. We propose that codes solving for collisional losses in magnetic mirrors utilizing the Lenard-Bernstein or Dougherty collision operator scale their collision frequency of any electrostatically confined species. This study illuminates the collision operator’s intricate role in the Pastukhov-style method of images calculation of collisional confinement.

fusion plasma↗