SEARCH · Engineering Papers

Results for “Complex geometry”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Temperature distribution in a laser-heated diamond anvil cell as described by finite element analysis

Finite element analysis (FEA) is a powerful tool for numerically solving partial differential equations over complex geometries and is thus useful for analyzing heat transport in laser-heated diamond anvil cell (LHDAC) experiments. Our models expand on previously published simulations by calculating the volume-averaged temperatures of both the sample and insulation/pressure media under steady-state heating to determine the thermal pressure of the hot sample. Our goal is to produce an accurate relationship between the measured surface temperature of the absorbing sample and the temperature of the transparent insulating media, which is used to determine thermal pressure but susceptible to steep temperature gradients. We find that in doing so, our FEA models of temperature within the pressure/insulation media can differ from simplified estimates of temperature gradients by more than a factor of 2. We also explore temperature-dependent and temperature-independent thermal conductivity models and find that the volume-averaged temperatures differ by up to a factor of 1.3, forcing the predicted thermal pressures determined to also differ by up to a factor of 1.5 at a temperature of 2000 K at 50 GPa for neon. Higher temperatures exacerbate this difference. We also find that unintentional asymmetric sample insertion and sample heating, which are common in LHDAC experiments, do not have a first-order effect on volume-averaged temperatures. The FEA models, available in both Python and FlexPDE, are versatile across different sample geometries, materials, and heat source laser shapes.

Farah, Frederick↗

DOE OSTI2026

AnisONet: A deep neural operator-based anisotropic permeability upscaler from pore to Darcy scale

Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. In this work, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.

42 ENGINEERING↗

DOE OSTI2026

Low-energy ion beam sputtering of polycrystalline diamond

Ion beam milling is a highly versatile and precise technique for the microfabrication of diamond films, enabling controlled material removal at both micrometer and sub-micrometer scales while accommodating complex geometries and surface features. Comprehensive sputtering yield data are essential for process optimization. In this work, we systematically measure the sputter yield of polycrystalline diamond bombarded with $\textrm{N}_2^+$, Ne + , Ar + , Kr + , and Xe + ions over an energy range of 300–900 eV and beam incidence angles of 0°−80°, providing data critical for precision machining applications. Results show that the sputter yield increases with both ion energy and the incidence angle and decreases with increasing ion mass. For $\textrm{N}_2^+$ ions, the sputter yield is largely insensitive to ion energy and exceeds values expected for pure physical sputtering. The concurrent formation of volatile C 2 N 2 molecules during $\textrm{N}_2^+$ ion bombardment suggests that the process is dominated by chemical sputtering. The absence of a peak in the dependence of the sputter yield on the beam incidence angle expected at oblique incidence angles is attributed to the effects of surface roughness.

Materials science↗

DOE OSTI2021

Pressure drop in a prototypical 3D magnetohydrodynamic flow across contraction of a fusion blanket manifold

Predicting 3D magnetohydrodynamic (MHD) pressure losses associated with liquid metal flows in complex geometry ducts is required to design breeding blankets for fusion reactors. Of such components, manifolds exhibit major pressure losses. Recent correlations [T. Rhodeset al. Magnetohydrodynamic pressure drop and flow balancing of liquid metal flow in a prototypic fusion blanket manifold. Phys. Fluids. 2018; 30: 057101.] demonstrate promise for predicting pressure drops in electrically insulated inlet manifolds. In the present work, the extend to which these correlations can be applied to outlet manifolds, which feature sudden contractions, is investigated in both viscous-electromagnetic (VE) and inertial-electromagnetic (IE) regimes for Reynolds numbers 50 < Re < 1500, Hartmann numbers 2500 < Ha < 5475, and expansion/contraction ratio 10. Numerical computations have shown that the MHD pressure drops in the eutectic lead-lithium (PbLi) flows in contractions are almost identical to those in the flows featuring expansion with slightly higher magnitudes in the contraction cases. The small discripancy in the MHD pressure drop between contractions and expansions (<8%) suggests that the earlier obtained correlations for the 3D MHD pressure drop in a duct flow with a sudden expansion can also be applied to flows with a sudden contraction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A Structural Design Approach Tailored for the Rapid Preliminary Design of Microreactor Components

High-temperature microreactors can play a role in developing reliable, portable energy sources for off-grid remote locations, microgrid concepts, and industrial process heat. Portability and passive safety criteria tend to skew microreactor structural component designs toward complex geometries, high thermal stresses, and design bases with large numbers of startup/shutdown cycles. Current design rules, as typified by Section III of the American Society of Mechanical Engineers (ASME) Boiler & Pressure Vessel Code, are less than optimal for these conditions, particularly for preliminary component designs where developers need to rapidly consider a large number of potential component configurations. This paper presents a design method targeted toward rapid, efficient evaluation of preliminary component designs using modern finite element analysis. The new method retains key connections with the ASME Code rules and design data while streamlining the design approach. This paper presents the design method, several verification examples illustrating the similarities and differences between the new method and the current ASME rules, and the application of the new approach to the evaluation of a test article mimicking key features of a heat pipe–cooled microreactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

A New 1D Model for Thermal Mixing and Stratification in Advanced Reactor Transients

Thermal mixing and stratification in large pools and enclosures play a critical role in the safety and performance of pool-type nuclear reactors, particularly during transient scenarios involving significant temperature differences between incoming and bulk coolant. Accurate modeling of these phenomena is essential for predicting system behavior and supporting passive safety features such as natural circulation. Here, this paper presents a new 1D model for thermal mixing and stratification, developed and implemented in the SAM code. The model represents a large pool as 1D coolant jet channels and zero-dimensional bulk pool volumes, enabling the simulation of a wide range of flow configurations, including hot and cold jet interactions, stratified layers, and the influence of complex geometries such as ceilings, free surfaces, and internal obstacles. Heat exchange between jet and pool regions is governed by closure relations calibrated against 3D computational fluid dynamics (CFD) simulations. The model improves upon earlier approaches by incorporating time-dependent jet characteristics and capturing the associated delay effects more accurately. Code-to-code comparisons and validation against experimental data from the Thermal Stratification Test Facility demonstrate the model’s accuracy and flexibility. This work offers two key contributions: (1) an efficient and robust method for simulating thermal mixing and stratification at the system level, eliminating the need for external coupling between system analysis codes and CFD, and (2) a significant enhancement of SAM’s capabilities to analyze thermal stratification phenomena in advanced reactor systems.

SAM↗

DOE OSTI2020

Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach

Reynolds-Averaged Navier-Stoke (RANS) models offer an alternative avenue in predicting flow characteristics when the corresponding experiments are difficult to achieve due to geometry complexity, limited budget, or knowledge. RANS models require the knowledge of subgrid scale physics to solve conservation equations for mass, energy, and momentum. Mechanistic turbulence models, such as k-epsilon, are generally evaluated and calibrated for specific flow conditions with various degrees of uncertainty. These models have limited capability to assimilate a substantial amount of data due to model form constraints. Meanwhile, deep learning (DL) has been proven to be universal approximators with the potential to assimilate available, relevant, and adequately evaluated data. Moreover, deep neural networks (DNNs) can create surrogate models without knowing function forms. Such a data-driven approach can be used in updating fluid models based on observations as opposed to hard-wiring models with precalibrated correlations. The paper presents progress in applying DNNs to model Reynolds stress using two machine learning (ML) frameworks. A novel flow feature coverage mapping is proposed to quantify the physics coverage of DL-based closures. It can be used to examine the sufficiency of training data and input flow features for data-driven turbulence models. The case of a backward-facing step is formulated to demonstrate that not only can DNNs discover underlying correlation behind fluid data but also they can be implemented in RANS to predict flow characteristics without numerical stability issues. Finally, the presented research is a crucial stepping-stone toward the data-driven turbulence modeling, which potentially benefits the design of data-driven experiments that can be used to validate fluid models with ML-based fluid closures.

42 ENGINEERING↗

DOE OSTI2026

Mathematical Models and Numerical Methods for High-Fidelity Simulation of Ignition of Reactive Mixtures by Nanosecond Plasma Discharges in Realistic Configurations

We present a newly developed framework for the numerical simulation of ignition of reactive mixtures using single or repeated nanosecond discharge pulses. The framework builds upon the AMReX library, using the existing compressible solver PeleC and low-Mach solver PeleLMeX and allowing for adaptive mesh refinement, complex geometries, and execution on next-generation high-performance computing (HPC) systems. High-fidelity elementary models are adopted for weakly-ionised plasma discharges with significant energy deposition, consistent with nanosecond discharge pulses, and then implemented in the solver. The treatment of non-thermal electrons and charged species, thermodynamics of non-equilbrium species, plasma kinetics, limiting time scales, and boundary conditions for charged species are discussed and addressed for computational efficiency. The framework is demonstrated for three relevant applications: single and multi-pulse discharges in air, single pulse ignition of an ethylene/air mixture, and a three-dimensional plasma discharge in air with temperature stratification. The successful application of the framework demonstrates the feasibility of high-fidelity simulation of ignition of air/hydrocarbon mixtures in three-dimensions with multiple discharge pulses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Fully Additively Manufactured Wetted Foam Capsules for Inertial Confinement Fusion

In the pursuit of realizing reliable clean energy generation via inertial confinement fusion (ICF), wetted foam (WF) capsule targets have long been coveted due to their potential to simplify the target fielding process and suppress hydrodynamic instabilities and material mixing that limit achievable energy output, yet producing and deploying a WF target has proven challenging. Here, in this work, we demonstrate the design, fabrication, metrology, and testing of fully additively manufactured (AM) foam-lined capsules using two-photon polymerization (2PP) for ICF. We successfully fielded an AM polymeric foam capsule with a 3-mm outer diameter, a nominally 15-µm-thick solid outer layer, a 120-µm-thick inner foam layer, and a 250-µm outer diameter copper fill tube on the National Ignition Facility for a polar direct-drive shot, and we showcase deuterium wetting of the capsule foam layer inside an ignition target proofing station. Our exploration showed that 2PP can produce fieldable targets with complex geometries and potentially shorten the design iteration turnaround time and the overall target fabrication time.

Target↗

DOE OSTI2026

Additive Manufacturing of Cryogenic Austenitic Steel JK2LB via Wire-Fed Directed Energy Deposition (DED) for Fusion Energy Applications

This study explores the feasibility of fabricating cryogenic austenitic steel JK2LB via both laser-based directed energy deposition (laser-DED) and arc-based directed energy deposition (arc-DED) additive manufacturing processes for potential application in fusion reactors. JK2LB, a low-nickel, high-manganese stainless steel developed for ITER, offers excellent cryogenic toughness, radiation resistance, and decay-to-clearance characteristics. Although JK2LB was originally designed to endure cyclic stresses at cryogenic temperatures in tokamaks, its low-temperature mechanical integrity and radiation tolerance also make it a promising candidate for structural components, such as the coil case/support structure in nonplanar high-temperature superconducting magnet assemblies in stellarators. Directed energy deposition (DED) additive manufacturing was selected for this study due to its capability to fabricate large structures with complex geometries. Here, to address the long lead time and high cost associated with acquiring conventional JK2LB solid wire, JK2LB powder-cored wire was developed as the feedstock material. Testing blocks were then fabricated using both wire-fed laser-DED and arc-DED processes. Microstructural and compositional analyses revealed that both DED approaches yield fully austenitic phase and columnar grain structures. Mechanical testing at room temperature revealed that both DED routes achieved yield strength and elongation comparable to those of conventionally processed JK2LB via vacuum melting, electroslag remelting, extrusion, and drawing, though ultimate tensile strength was reduced due to Mn loss and large columnar grains. As a study mainly focusing on the additive manufacturing process, this work demonstrates the potential of additive manufacturing for fusion energy applications and provides a basis for optimization and future cryogenic mechanical evaluation.

Cryogenic steel↗

DOE OSTI2024

Simulating hindered grain boundary diffusion using the smoothed boundary method

Abstract Grain boundaries can greatly affect the transport properties of polycrystalline materials, particularly when the grain size approaches the nanoscale. While grain boundaries often enhance diffusion by providing a fast pathway for chemical transport, some material systems, such as those of solid oxide fuel cells and battery cathode particles, exhibit the opposite behavior, where grain boundaries act to hinder diffusion. To facilitate the study of systems with hindered grain boundary diffusion, we propose a model that utilizes the smoothed boundary method to simulate the dynamic concentration evolution in polycrystalline systems. The model employs domain parameters with diffuse interfaces to describe the grains, thereby enabling solutions with explicit consideration of their complex geometries. The intrinsic error arising from the diffuse interface approach employed in our proposed model is explored by comparing the results against a sharp interface model for a variety of parameter sets. Finally, two case studies are considered to demonstrate potential applications of the model. First, a nanocrystalline yttria-stabilized zirconia solid oxide fuel cell system is investigated, and the effective diffusivities are extracted from the simulation results and are compared to the values obtained through mean-field approximations. Second, the concentration evolution during lithiation of a polycrystalline battery cathode particle is simulated to demonstrate the method’s capability.

Materials Science↗

DOE OSTI2022

Artificial spin ice phase-change memory resistors

Abstract We present a proposal for realization of an electrical memory reminiscent of a memristor in connected Kagome artificial spin ice. We show that current flowing through the system alters the magnetic ensemble, which in turns controls the overall resistance thus leaving memory of current passage in the system. This introduces a current-dependent effect for a dynamic resistive state. We simulate a spin-induced thermal phase-change mechanism, and an athermal domain-wall spin inversion. In both cases we observe electrical memory behavior with an I – V hysteretic pinched loop, typical of memristors. These results can be extended to the more complex geometries in which artificial spin ice can be designed to engineer the hysteresis curve.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Numerical investigation of liquid wall ablation in inertial fusion energy chambers

This paper presents a novel approach for modeling liquid wall ablation in liquid wall-protected inertial fusion energy (IFE) chambers. These systems are promising candidates for the implementation of fusion technology, yet significant gaps remain in understanding the underlying physical processes and their implications for design. Following target ignition, a portion of the fusion energy is released as x-rays, which deposit their energy into an array of liquid jets, leading to partial vaporization. Accurately modeling this heat deposition and vaporization process remains challenging due to the complex geometries typical of (pre-conceptual) IFE chamber designs. Furthermore, the subsequent expansion of vaporized material into the chamber’s vacuum environment poses difficulties for conventional CFD methods based on continuum assumptions, which can lead to significant inaccuracies. To address some aspects of these challenges, this work introduces a ray-tracing-based methodology to map the spatial distribution of ablated material in liquid wall-protected systems. In addition, a vacuum-tracking scheme is developed to extend the applicability of an OpenFOAM-based solver to gas dynamics in rarefied environments. The proposed approach has been verified through numerical benchmarks and applied to a practical case involving the HYLIFE-II (High Yield Lithium Injection Fusion Energy) chamber. The methodology advances the modeling capabilities for liquid wall-protected IFE systems and provides valuable tools to support their design and optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

An efficient numerical model for predicting residual stress and strain in parts manufactured by laser powder bed fusion

Abstract Computational modeling of additively manufactured structures plays an increasingly important role in product design and optimization. For laser powder bed fusion processes, the accurate modeling of stress and distortion requires large amount of computational cost due to very localized heat input and evolving complex geometries. The current study takes advantage of a graphics processing unit accelerated explicit finite element analysis code and approximated heat conduction analysis to predict the macroscopic thermo-mechanical behavior in laser selective melting. Adjacent layers and tracks were lumped to reduce the number of time steps and elements in the finite element model. The effects of track and layer grouping on prediction accuracy and solution efficiency are investigated to provide a guidance for a cost-effective simulation. Thin-wall builds from Inconel alloy 625 (IN625) powders were simulated by applying the developed modeling approach to get the detailed residual stress and distortion at a computational speed 50 times higher than conventional approach. Under repeated heating and cooling cycles, a high tensile stress was produced near surfaces of a build due to a larger shrinkage on surface than that in central area. It is also shown that horizontal stresses concentrate near the root and top layers of the IN625 build. The predicted residual elastic strain distribution was validated by the experimental measurement using x-ray synchrotron diffraction.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A Comparison of Solidification Structures and Submicroscale Cellular Segregation in Rapidly Solidified Stainless Steels Produced via Two-Piston Splat Quenching and Laser Powder Bed Fusion

Fusion-based additive manufacturing techniques leverage rapid solidification (RS) conditions to create parts with complex geometries, unique microscale/nanoscale morphological features, and elemental segregation. Three custom composition stainless steel alloys with varying chromium equivalence to nickel equivalence ratio (Cr eq /Ni eq ) between 1.53 and 1.95 were processed using laser powder bed fusion (LPBF) and/or two-piston splat quenching (SQ) to produce solidification rates estimated between 0.4 and 0.8 m/s. Both scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were utilized to collect high-resolution images, electron backscatter diffraction (EBSD) phase identification, and measure cellular segregation. Similar features were observed in both LPBF and SQ samples including phase and microstructure, nanoscale oxide particles, cell size, and segregation behavior. However, dislocation pileup was observed along the cell boundaries only in the LPBF austenite solidified microstructure. Targeted adjustment of the SQ feedstock Cr and Ni concentrations, within the ASTM A240 specification for 316L resulted in no observable impact on the cell size, oxide particle size, or magnitude of segregation. Also, the amount of Ni segregation in the ferrite solidified microstructures did not significantly differ, regardless of Cr/Ni eq or processing technique. Here, SQ is demonstrated as capable of simulating RS rates and microstructures similar to LPBF for use as an alternative screening tool for new RS alloy compositions.

42 ENGINEERING↗

DOE OSTI2025

Semiclassical Wormholes toward Typical Entangled States

What do the typical entangled states of two black holes look like? Do they contain semiclassical interiors? We approach these questions constructively, providing ensembles of states that densely explore the black hole Hilbert space. The states contain very long Einstein-Rosen caterpillars : semiclassical wormholes with large numbers of matter inhomogeneities. Distinguishing these ensembles from the typical entangled states of the black holes is hard. We quantify this by deriving the correspondence between a microscopic notion of quantum randomness and the geometric length of the wormhole. This formalizes a “complexity = geometry” relation.

quantum aspects of black holes↗

DOE OSTI2022

Effect of emitters on quantum state transfer in coupled cavity arrays

Over the last decade, conditions for perfect state transfer in quantum spin chains have been discovered and their experimental realizations addressed, as have their extensions to more complex geometries of coupled cavity-emitter arrays. In this paper we further consider such studies and situations in which quantum state transfer can occur with high fidelity, even when the cavity-cavity coupling rates and cavity-emitter interaction rates are comparable. This is accomplished through the development and use of a Monte Carlo approach to the inverse eigenvalue problem, which allows the determination of coupling rates which optimize quantum state transfer fidelity and subsequent time evolution of the polariton wave function through exact diagonalization of the resulting Jaynes-Cummings-Hubbard Hamiltonian. The effect of inhomogeneous emitter locations is also evaluated. Furthermore, our key results include the demonstration that our methodology can be used successfully to establish Hamiltonian parameters for high-fidelity state transfer in more general lattice geometries and excitation number sectors, and also a determination of the effects of fluctuations in those parameters about their optimal values.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

tomoCAM : fast model-based iterative reconstruction via GPU acceleration and non-uniform fast Fourier transforms

X-ray-based computed tomography is a well established technique for determining the three-dimensional structure of an object from its two-dimensional projections. In the past few decades, there have been significant advancements in the brightness and detector technology of tomography instruments at synchrotron sources. These advancements have led to the emergence of new observations and discoveries, with improved capabilities such as faster frame rates, larger fields of view, higher resolution and higher dimensionality. These advancements have enabled the material science community to expand the scope of tomographic measurements towards increasingly in situ and in operando measurements. In these new experiments, samples can be rapidly evolving, have complex geometries and restrictions on the field of view, limiting the number of projections that can be collected. In such cases, standard filtered back-projection often results in poor quality reconstructions. Iterative reconstruction algorithms, such as model-based iterative reconstructions (MBIR), have demonstrated considerable success in producing high-quality reconstructions under such restrictions, but typically require high-performance computing resources with hundreds of compute nodes to solve the problem in a reasonable time. Here, tomoCAM , is introduced, a new GPU-accelerated implementation of model-based iterative reconstruction that leverages non-uniform fast Fourier transforms to efficiently compute Radon and back-projection operators and asynchronous memory transfers to maximize the throughput to the GPU memory. The resulting code is significantly faster than traditional MBIR codes and delivers the reconstructive improvement offered by MBIR with affordable computing time and resources. tomoCAM has a Python front-end, allowing access from Jupyter -based frameworks, providing straightforward integration into existing workflows at synchrotron facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗