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At least 253 records · Page 14

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DOE OSTI2025

CFD modeling and simulation for corrosive wear of refractory in molten slag

The development of high-wear resistant refractories having minimal production costs is facilitated by characterizing the wear mechanisms associated with their corrosive wear. Static cup testing is a commonly used method for comparing the corrosion resistance performance of two or more refractory materials. Although the static cup test conditions are not as severe as dynamic tests, this study shows that the thermal gradient present within the system during heating and cooling stages serves to generate movement of the slag leading to mechanical wear. The thermal gradient within the refractory, and between the slag and the refractory, occurs during the ramp stage of the test and lasts until the soaking stage is reached bringing the system to a thermal equilibrium. Using computational fluid dynamics (CFD) capabilities embedded within ANSYS software, this study modelled and quantified the convection currents within the slag and associated shear stresses generated on the refractory walls due to the thermal gradient. A traditional ladle furnace was employed as a case study to verify the results of the studied CFD model. The corrosion rate of the refractory lining was found to depend on the mass transfer coefficient of the refractory dissolution into the slag, and a velocity term which governs the extent of corrosion at any given location. This velocity term is a function of slag viscosity, as well as the concentration gradient and/or temperature gradient at the triple points. In this study, wall shear stress was used as a reliable proxy for identifying high-velocity regions prone to excessive corrosive wear. Elevated wall shear stress near the slag/air and slag/molten steel interfaces align with observed corrosion grooves, which reflects the intensified corrosive wear at these locations.

Ramteke, Rajat Rajat Durgesh Ramteke [University o↗

DOE OSTI2024

Review: Pre-Darcy flows in low-permeability porous media

The widely used Darcy’s law specifies a linear relation between the Darcy velocity of fluid flow and the pressure gradient that drives the flow. However, studies have shown that Darcy velocity can exhibit a nonlinear dependence on the pressure gradient in low-permeability porous media such as clay and shale when the pressure gradient is adequately low. This phenomenon is referred to as low-velocity non-Darcian flow or pre-Darcy flow. This paper provides a comprehensive review of the theories, experimental data, and modeling methods for pre-Darcy flow in low-permeability porous media. The review begins by outlining the fundamental mechanisms underlying pre-Darcy flow that regulate the unique characteristics such as nonlinear dependence of the Darcy velocity on the pressure gradient and its relevance to fluid–rock interactions. The review then proceeds to present a thorough compilation of experimental investigations performed in various low-permeability geomaterials including tight sandstones, shales, and clays. Next, empirical and theoretical models and simulation methods that have been developed to fit and interpret experimental data are reviewed. Finally, the review underscores the challenges encountered in conducting and interpreting pre-Darcy flow experiments and suggests future research directions. By analyzing previous experimental investigations, this review aims to offer a valuable resource for researchers and practitioners seeking to enhance their understanding of fluid dynamics in low-permeability geomaterials. This provides insights into the application of pre-Darcy flow in numerous natural and engineered processes such as shale oil and gas recovery, contaminant transport in low-permeability aquifers, and geological disposal of nuclear waste.

58 GEOSCIENCES↗

DOE OSTI2026

Mechanically graded granular scaffolds for osteochondral tissue engineering

Engineered scaffolds designed to approximate the mechanical microenvironment of the osteochondral unit often address this complexity using discrete, two-phase architectures that introduce mechanical discontinuities and interfacial stress concentrations rather than a contiguous stiffness transition. To address this challenge, we created a photoannealed polyethylene glycol (PEG) granular scaffold with a spatially controlled stiffness gradient within a cell-permissive, macroporous architecture. Stiffness was dictated by photoannealing microgels using a photomask. We tuned void volume and available surface area by varying microgel diameter and tested how mesenchymal stromal cells (MSCs) interpret local mechanical environments. MSCs exhibited position-dependent differences in morphology, cytoskeletal structure, matrix deposition, and lineage-specific gene expression within the gradient scaffolds. Softer regions supported rounded cell morphology and deposition of a glycosaminoglycan-rich matrix, whereas stiffer regions promoted cell elongation, increased cytoskeletal tension, and expression of mineral-associated markers. Gradients formed from smaller microgels magnified these spatial responses by increasing cellular confinement and adhesion site availability. Disruption of actomyosin contractility eliminated these regional differences, demonstrating that MSCs rely on tension-dependent mechanotransduction to interpret the gradient. These findings reveal that coupling microgel architecture with continuous stiffness transitions provides a tractable platform to study multiscale mechanobiologic regulation and spatially guide osteochondral tissue formation.

Biological and medical sciences↗

DOE OSTI2025

Derivative-free stochastic optimization via adaptive sampling strategies

In this paper, we present a novel derivative-free framework for solving unconstrained stochastic optimization problems. Many problems in fields ranging from simulation optimization to reinforcement learning to quantum computing involve settings where only stochastic function values are obtained via a zeroth-order oracle, which has no available gradient information and necessitates the usage of derivative-free optimization methodologies. Our approach includes estimating gradients using stochastic function evaluations and integrating adaptive sampling techniques to control the accuracy in these stochastic approximations. Our framework encapsulates several gradient estimation techniques, including standard finite-difference, Gaussian smoothing, sphere smoothing, randomized coordinate finite-difference, and randomized subspace finite-difference methods. We provide theoretical convergence guarantees for our framework and analyze the worst-case iteration and sample complexities associated with each gradient estimation method. Finally, we demonstrate the empirical performance of the methods on logistic regression and nonlinear least squares problems.

Adaptive sampling↗

DOE OSTI2026

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets () according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ -aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets. However, gradient updates in FL retain structural patterns induced by non-independent and identically-distributed (non-IID) data, and these additional signals exposed by -aware aggregation create new opportunities for inference by an honest-but-curious server. In this work, we first show that a server equipped with gradient denoising and surrogate modeling can mount a Privacy Inference Attack that infers distributional attributes of clients and links updates from the same client across training rounds, measured via surrogate inference accuracy and linkage success, under realistic knowledge constraints. The Shuffle-Model has been widely studied as a defense against such inference risks by anonymizing update sources, but it is fundamentally incompatible with HDP-FL -aware aggregation. To address this challenge, we propose IntraShuffler, a middleware defense framework designed for HDP-FL systems. IntraShuffler introduces a privacy-aware shuffling mechanism that groups clients into privacy-compatible buckets and performs parameter-level shuffling within each bucket to disrupt persistent gradient structure while preserving -aware aggregation. Experiments across four different datasets show that IntraShuffler reduces gradient recoverability by over 60% and decreases surrogate inference accuracy from 0.78 to 0.33 while maintaining comparable model utility across multiple FL aggregation rules.

Riya, Farhin Farhad [ORNL]↗

DOE OSTI2024

An Empirical Quantile Estimation Approach for Chance-Constrained Nonlinear Optimization Problems

We investigate an empirical quantile estimation approach to solve chance-constrained nonlinear optimization problems. Our approach is based on the reformulation of the chance constraint as an equivalent quantile constraint to provide stronger signals on the gradient. In this approach, the value of the quantile function is estimated empirically from samples drawn from the random parameters, and the gradient of the quantile function is estimated via a finite-difference approximation on top of the quantile-function-value estimation. We establish a convergence theory of this approach within the framework of an augmented Lagrangian method for solving general nonlinear constrained optimization problems. The foundation of the convergence analysis is a concentration property of the empirical quantile process, and the analysis is divided based on whether or not the quantile function is differentiable. In contrast to the sampling-and-smoothing approach used in the literature, the method developed in this paper does not involve any smoothing function and hence the quantile-function gradient approximation is easier to implement and there are less accuracy-control parameters to tune. Furthermore, we demonstrate the effectiveness of this approach and compare it with a smoothing method for the quantile-gradient estimation. Numerical investigation shows that the two approaches are competitive for certain problem instances.

Applied Probability↗

DOE OSTI2025

Differentiable lagrangian shock hydrodynamics with application to stable shock acceleration of density interfaces

We develop a gradient based optimization approach for the equations of compressible, Lagrangian hydrodynamics and demonstrate how it can be employed to automatically uncover strategies to control hydrodynamic instabilities arising from shock acceleration of density interfaces. Strategies for controlling the Richtmyer-Meshkov instability (RMI) are of great benefit for inertial confinement fusion (ICF) where shock interactions with many small imperfections in the density interface lead to instabilities which rapidly grow over time. These instabilities lead to mixing which, in the case of laser driven ICF, quenches the runaway fusion process ruining the potential for positive energy return. Here, we demonstrate that control of these instabilities can be achieved by optimization of initial conditions with ( > 100) parameters. Optimizing over a large parameter space like this is not possible with gradient-free optimization strategies. This requires computation of the gradient of the outputs of a numerical solution to the equations of Lagrangian hydrodynamics with respect to the inputs. We show that the efficient computation of these gradients is made possible via a judicious application of (i) adjoint methods, the exact formal representation of sensitivities involving partial differential equations, and (ii) automatic differentiation (AD), the algorithmic calculation of derivatives of functions. Careful regularization of multiple operators including artificial viscosity and timestep control is required. We perform design optimization of > 100 parameter energy field driving the Richtmyer Meshkov instability showing significant suppression while simultaneously enhancing the acceleration of the interface relative to a nominal baseline case.

Hydrophysics↗

DOE OSTI2025

Directed energy deposition of radially functionally graded nickel based superalloys IN625 and IN738: Microstructure and mechanical properties

This study investigates the microstructure and mechanical properties of radially functionally graded (RFG) materials fabricated from nickel-based superalloys IN625 and IN738 using directed energy deposition (DED). RFG cylindrical deposits with radial gradients of IN625-IN738 and IN738-IN625 were successfully fabricated and confirmed to be crack-free through tailored composition grading, scan strategy and process parameters that minimized thermal gradients and solidification velocities. Microstructural analysis revealed distinct variations across the gradient, characterized by differences in dendrite arm spacing, microsegregation, and the formation of distinct γ' precipitates and carbide/boride phases (MC, M 6 C, M 2 C, and MB 2 ) in as-deposited (AD) conditions, which evolved into M 23 C 6 and M 2 B upon subsequent heat treatment (HT). EBSD showed predominant columnar grains with a [100] texture, while microhardness profiles demonstrated consistent variation across the gradients. Mechanical testing highlighted the synergy of the graded structure, combining IN738's strength with IN625's ductility, resulting in enhanced properties such as a yield strength of 1131 MPa, ultimate tensile strength of 1382 MPa, and elongation of 12 % for HT IN625-IN738 grading. Numerical simulations employing CALPHAD thermodynamic calculations and finite element thermal modeling are used to establish phase evolution and microstructural feature correlation with thermal profiles, aiding in the optimization of process-structure-property relationship.

Additive manufacturing↗

DOE OSTI2025

Influence of Soil Moisture on the Development of Organized Convective Systems in South America

Abstract This study investigates the role of soil moisture (SM) on the initiation and organization of convective systems using the convection‐permitting ICOsahedral Non‐hydrostatic (ICON) model. We conduct two sets of experiments: a Control experiment with interactive SM and a fixed SM experiment (FixedSM) with invariable SM conditions. We focus on two regions in South America: the Amazon and southeastern South America (SESA). Larger organized convective systems are associated with greater SM heterogeneity in both regions, though other large‐scale synoptic influences affect the robustness of this relationship in SESA. These results remain largely unaffected by disabling the effects of precipitation on SM in the FixedSM experiment, and complementary analyses using satellite‐based estimates of SM and precipitation support these findings. Spatial compositing of mesoscale environments in the Amazon shows the presence of well‐defined SM gradients, at a length scale of a few hundred kilometers, many hours before convective system detection. Larger SM gradients correspond to larger gradients in thermodynamic variables, particularly surface temperature and sensible heat flux, and are associated with larger convective systems. Overall, our findings suggest that surface heterogeneities such as SM gradients not only affect deep convection initiation, as previously suggested, but they can also encourage the growth and organization of convective systems into larger clusters, particularly in the absence of significant synoptic influences.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Using the Metropolis algorithm to explore the loss surface of a recurrent neural network

In the limit of small trial moves the Metropolis Monte Carlo algorithm is equivalent to gradient descent on the energy function in the presence of Gaussian white noise. This observation was originally used to demonstrate a correspondence between Metropolis Monte Carlo moves of model molecules and overdamped Langevin dynamics, but it also applies in the context of training a neural network: making small random changes to the weights of a neural network, accepted with the Metropolis probability, with the loss function playing the role of energy, has the same effect as training by explicit gradient descent in the presence of Gaussian white noise. We explore this correspondence in the context of a simple recurrent neural network. We also explore regimes in which this correspondence breaks down, where the gradient of the loss function becomes very large or small. In these regimes the Metropolis algorithm can still effect training, and so can be used as a probe of the loss function of a neural network in regimes in which gradient descent struggles. We also show that training can be accelerated by making purposely-designed Monte Carlo trial moves of neural-network weights.

Casert, Corneel↗

DOE OSTI2026

Non-classical ion-indiffusion processes in quasi-one-dimensional ionic conductor KTiOPO4

Developments in coercive field engineering and waveguide writing via ion-exchange in the ferroelectric ionic-conductor KTiOPO4 (KTP) have led to breakthroughs within the field of nonlinear optics and quantum photonics. However, the microscopic diffusion dynamics of ion-exchange in KTP are poorly understood and show signs of non-classical diffusion dynamics. The precise control of ion-exchange gradients is critical for developing KTP into a mature, integrated nonlinear optical material but requires a level of control that, in turn, demands a deeper understanding of the ion-exchange process. We employ direct ToF-SIMS mapping to untangle the complex diffusion dynamics of the commonly used ion-exchange dopant ions: Rb, Ba, and K. We map periodic diffusion gradients of two exchanges, one dominated by Rb-ions and the other by Ba-ions. For the first time, we show the interdependent nature of the Ba- and Rb ion in the ion-exchange diffusion gradient. The low incorporation of the Ba ion is mapped and quantified, and dopant–crystal interactions are identified. This work highlights the complex diffusion dynamics of dopants in the KTP structure and lays the foundation for deterministic control of diffusion gradients for the next generation of integrated nonlinear and quantum photonic devices.

Barrett, Laura [ORNL]↗

DOE OSTI2026

Neoclassical transport and profile prediction in transport barriers

Strong gradient regions in tokamaks, such as the pedestal or internal transport barriers, are regions of reduced turbulence where neoclassical transport can play a dominant role. However, standard neoclassical transport theory assumes that the gradient length scales of density, temperature, and potential are of the order of the system size. In the pedestal, gradient length scales are much shorter and are measured to be of the order of the ion poloidal gyroradius. We present an extension of neoclassical theory that is applicable in transport barriers of large aspect ratio tokamaks. We show that particle and momentum transport are connected in such a way that a source of parallel momentum can drive a significant neoclassical ion particle flux. In strong gradient regions, density, electric potential, mean parallel flow, and ion temperature are shown to no longer be flux functions. Instead, they have a small but important poloidally varying piece that modifies the transport equations to lowest order. This introduces a nonlinearity in the transport problem through the coupling with quasineutrality that yields multiple co-existing solutions when solving for the plasma profiles. The different solutions could be connected to low and high transport states and jumps between solutions could provide a new neoclassical explanation for H-L back-transitions.

Trinczek, Silvia [Princeton Plasma Physics Laborat↗

DOE OSTI2024

Sprain energy consequences for damage localization and fracture mechanics

The 2023 smooth Lagrangian Crack-Band Model (slCBM), inspired by the 2020 invention of the gap test, prevented spurious damage localization during fracture growth by introducing the second gradient of the displacement field vector, named the “sprain,” as the localization limiter. The key idea was that, in the finite element implementation, the displacement vector and its gradient should be treated as independent fields with the lowest ( C 0 ) continuity, constrained by a second-order Lagrange multiplier tensor. Coupled with a realistic constitutive law for triaxial softening damage, such as microplane model M7, the known limitations of the classical Crack Band Model were eliminated. Here, we show that the slCBM closely reproduces the size effect revealed by the gap test at various crack-parallel stresses. To describe it, we present an approximate corrective formula, although a strong loading-path dependence limits its applicability. Except for the rare case of zero crack-parallel stresses, the fracture predictions of the line crack models (linear elastic fracture mechanics, phase-field, extended finite element method (XFEM), cohesive crack models) can be as much as 100% in error. We argue that the localization limiter concept must be extended by including the resistance to material rotation gradients. We also show that, without this resistance, the existing strain-gradient damage theories may predict a wrong fracture pattern and have, for Mode II and III fractures, a load capacity error as much as 55%. Finally, we argue that the crack-parallel stress effect must occur in all materials, ranging from concrete to atomistically sharp cracks in crystals.

Science & Technology - Other Topics↗

DOE OSTI2024

The role of ion-scale micro-turbulence in pedestal width of the DIII-D wide-pedestal QH mode

The low-edge rotation, intrinsically ELM-free, and improved confinement wide-pedestal quiescent H-mode (QH-mode), discovered in DIII-D tokamak, has pedestal widths exceeding the EPED-kinetic-ballooning mode (KBM) model scaling typically by at least 25%. Ion-scale ($k_yp_s$ < 1) microturbulence and its role in setting the pedestal structure is investigated using the radially local δ$f$ gyrokinetic code CGYRO. The electromagnetic trapped electron mode (TEM) is unstable at the pedestal top, while plasma beta (β$_e$) is ∼60% below the KBM onset threshold and the electron temperature gradient mode is found to be unstable in the peak gradient region. Nonlinear simulation reveals that the ion-scale turbulence could produce electron energy flux consistent with the flux inferred from power balance at the pedestal top, with a reasonable variation of the local shearing rate; and the local neoclassical transport from NEO is dominant over the simulated turbulent transport in the ion energy flux channel. The simulated ion-scale turbulence produces much lower electron energy flux than inferred from experiment in the pedestal peak gradient region. A correction to the EPED-KBM pedestal width scaling is obtained based on the two-dimensional scan of pedestal top plasma beta (β$_e$) and normalized electron density and temperature scale lengths,${a}$/${L_n}_e$, ${a}$/${L_T}_n$using CGYRO linear simulations. Mode transitions among TEM, micro-tearing mode, ion-temperature gradient mode and KBM, are observed in the 2D scan at the pedestal top. A fixed normalized growth rate for these drift-type modes is taken to determine the pedestal width scaling, which shows good consistency with the QH experimental database on pedestal heights and widths. The onset of KBM instabilities and the local E × B shear suppression criterion set the lower and upper limit for the pedestal width of standard QH-mode, wide-pedestal QH-mode and type-I ELMy H mode. A potentially higher and wider pedestal is expected from the new scaling of pedestal width. This work presents an improved understanding of the ion-scale micro-turbulence of wide-pedestal QH-mode and sheds light on a promising scenario for future reactors, including ITER and beyond.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Importance of $\delta B_{\|}$ on ETG stability, turbulence, and transport in NSTX

This study employs electron-scale gyrokinetic simulations to investigate the electron temperature gradient (ETG) driven instabilities, turbulence, and transport in the pedestal region of the National Spherical Torus Experiment, comparing non-lithiated (narrow pedestal) and lithiated (wide pedestal) scenarios. Our findings reveal that, in the non-lithiated case, a branch of strongly unstable ETG modes exhibiting finite parallel magnetic field fluctuations ($\delta B_{\parallel} \neq 0$) emerges at the pedestal top and upper density pedestal region. This branch is uncovered only when $\delta B_{\parallel}$ is retained in the simulations and is associated with substantial electrostatic electron heat flux. This region of strong ETG transport corresponds to the only region in the plasma where the pressure gradient is far below the critical gradient for kinetic ballooning modes. We investigated the origin of this finite $\delta B_{\parallel}$ ETG branch by analyzing the gyrokinetic field equations. Nonlinear saturation is also analyzed and contrasted for simulations with and without $\delta B_{\parallel}$. In contrast with the nonlithiated case, ETG modes in the lithiated case produce substantial transport in the steep gradient region, but are negligible at the pedestal top.

ETG↗

DOE OSTI2024

Robust wind farm layout optimization

Wake interactions in wind farms cause losses in annual energy production (AEP) on the order of 10%. Wind farm designers optimize the layout of the farm to mitigate wake losses, especially in the dominant site-specific wind directions. As wind turbines and wind farms grow in scale, optimization becomes more complex. Offshore wind farms regularly comprise more than 100 wind turbines and are characterized by complex boundaries due to shipping lanes, neighboring wind farms, and other constraints. Layout optimization methods are broadly split between gradient-based and gradient-free approaches. Gradient-based approaches can converge quickly and perform well for smaller, academic problems but are often sensitive to initial conditions and tuning parameters and require expert knowledge to use. On the other hand, gradient-free approaches can be more robust to problem complexities. We present a robust layout optimization approach based on a random search algorithm. The algorithm is intended for those who are not optimization experts and has few tuning parameters that need specification to achieve satisfactory results. Unlike off-the-shelf methods, which use generally available, non-domain-specific optimization routines that accept as inputs an optimization function and constraint definitions, this approach takes advantage of the relative computational costs of the different evaluations by evaluating cheaper computations first (boundary and minimum distance constraints) and running expensive AEP evaluations only if all other checks pass. Moreover, an outer genetic algorithm allows multiple solutions to evolve in parallel, enabling rapid solution development on high-performance computers. We discuss the relative ease of selecting necessary tuning parameters and demonstrate the efficacy of the genetic random search on a complex layout problem consisting of placing 70 turbines in a nonconvex and unconnected boundary region.

17 WIND ENERGY↗

DOE OSTI2024

MISIP: a data standard for the reuse and reproducibility of any stable isotope probing-derived nucleic acid sequence and experiment

DNA/RNA-stable isotope probing (SIP) is a powerful tool to link in situ microbial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, process rates, and population dynamics, offering valuable insights for a wide range of research questions. Data reuse maximizes the information derived from the labor and resource-intensive SIP approaches. Yet, a review of publicly available SIP sequencing metadata showed that critical information necessary for reproducibility and reuse was often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) framework and include examples of MISIP reporting for common SIP experiments. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires 5 metadata fields—isotope, isotopolog, isotopolog label, labeling approach, and gradient position—and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (e.g., gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will improve the reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding of in situ biogeochemical processes and microbial ecology.

Simpson, Abigayle↗

DOE OSTI2025

Chiral edge state control of thermoelectric effects

Thermoelectric responses in two-dimensional electron gases subjected to magnetic fields have the potential to provide unique information about quasiparticle statistics. In this study, we show that chiral edge states play a key role in thermoelectric Hall bar measurements by completely controlling the direction of the internal thermal gradient. To this end, we perform measurements of the magnetothermoelectric responses of cadmium arsenide quantum wells. The magnetothermoelectric responses in the quantum Hall regime agree with theoretical predictions if one considers the role of chiral edge states, which flow in opposite directions on either side of the Hall bar and establish an internal temperature gradient that is perpendicular to the externally applied thermal gradient. We show that the results are self-consistent within this picture under different measurement conditions. We discuss potential applications of the findings, such as in nanoscale control of local temperature gradients and thermoelectric effects along with the characterization of other topological systems with chiral edges states.

Science & Technology - Other Topics↗